Keino Hutchinson

Dina Buitrago Silva

Joshua Bohlke

Chase Clausen

Allen A Thomas

Massimiliano Bonomi
email: massimiliano.bonomi@gmail.com

Avner Schlessinger
email: avner.schlessinger@mssm.edu

Dina Buitrago

Affiliations

Describing Inhibitor Specificity for the Amino Acid Transporter LAT1 from Metainference Simulations

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Statement of Significance

LAT1 is a membrane transporter of amino acids, thyroid hormones, and therapeutic drugs, that is primarily found in the BBB and placenta, as well as in tumor cells of several cancer types. We combine metainference MD simulations, molecular docking, and experimental testing, to characterize LAT1inhibitor interactions. Our computational analysis predicts S66, G67, F252, G255, Y259, W405 are critical residues for inhibitor binding and druggable sub-pockets in the outward-occluded conformation that are ideal for LAT1 inhibitor discovery. Using virtual screening and functional testing, we discovered multiple LAT1 inhibitors with diverse scaffolds and binding modes. Our results improve our understanding of LAT1's structure and function, providing a framework for development of future therapeutics targeting LAT1 and other SLC transporters.

INTRODUCTION

The L-type amino acid transporter 1 (LAT1; SLC7A5) mediates the import of large neutral amino acids such as phenylalanine and tyrosine, in a 1:1 exchange for intracellular amino acids (e.g., glutamine) [START_REF] Kanai | Expression cloning and characterization of a transporter for large neutral amino acids activated by the heavy chain of 4F2 antigen (CD98)[END_REF] .

LAT1 is a member of the Amino acid, polyamine, and organocation (APC) superfamily that includes Na + independent secondary transporters and plays an important role in a broad range of biological processes [START_REF] Wong | The amino acid-polyamine-organocation superfamily[END_REF] . LAT1 is among the most abundant transporters in the blood-brain-barrier (BBB), with 100fold greater expression levels in BBB endothelial cells relative to other tissues [START_REF] Boado | Selective expression of the large neutral amino acid transporter at the blood-brain barrier[END_REF] . LAT1 can also be found in the placenta [START_REF] Ohtsuki | Reduction of L-type amino acid transporter 1 mRNA expression in brain capillaries in a mouse model of Parkinson's disease[END_REF] , where it delivers essential neutral amino acids and thyroid hormones, making it a key protein involved in cell growth and development. For example, leucine is imported by LAT1 to activate the mechanistic target of rapamycin complex 1 (mTORC1), a known regulator of cell growth and metabolism [START_REF] Nicklin | Bidirectional transport of amino acids regulates mTOR and autophagy[END_REF][START_REF] Nagamori | Structure-activity relations of leucine derivatives reveal critical moieties for cellular uptake and activation of mTORC1-mediated signaling[END_REF] .

Genetic variations of LAT1 have been associated with multiple diseases and disorders. LAT1 has recently been associated with epileptic seizures due to its role in negatively regulating the ion channel Kv1.245 [START_REF] Baronas | Slc7a5 regulates Kv1.2 channels and modifies functional outcomes of epilepsy-linked channel mutations[END_REF] . Additionally, rare mutations in coding regions of the SLC7A5 gene (A246V and P375L), have been associated with autism spectrum disorder (ASD) [START_REF] Tarlungeanu | Impaired Amino Acid Transport at the Blood Brain Barrier Is a Cause of Autism Spectrum Disorder[END_REF] . These mutations decrease the activation of the amino acid response (AAR) pathway with a corresponding reduction in mRNA translation, thereby leading to an adverse outcome of neuronal activity, microcephaly and other neurobehavioral problems related to ASD [START_REF] Tarlungeanu | Impaired Amino Acid Transport at the Blood Brain Barrier Is a Cause of Autism Spectrum Disorder[END_REF] . LAT1 overexpression has been linked to disease in numerous cancer types [20][START_REF] Yanagisawa | L-amino acid transporter 1 may be a prognostic marker for local progression of prostatic cancer under expectant management[END_REF][START_REF] Ichinoe | High expression of L-type amino-acid transporter 1 (LAT1) in gastric carcinomas: comparison with non-cancerous lesions[END_REF][START_REF] Ebara | L-type amino-acid transporter 1 expression predicts the response to preoperative hyperthermo-chemoradiotherapy for advanced rectal cancer[END_REF][START_REF] Kobayashi | Expression of L-type amino transporter 1 (LAT1) in esophageal carcinoma[END_REF][START_REF] Takeuchi | LAT1 expression in non-small-cell lung carcinomas: analyses by semiquantitative reverse transcription-PCR (237 cases) and immunohistochemistry (295 cases)[END_REF][START_REF] Soares-Da-Silva | High-and low-affinity transport of L-leucine and L-DOPA by the hetero amino acid exchangers LAT1 and LAT2 in LLC-PK1 renal cells[END_REF][START_REF] Imai | L-type amino acid transporter 1 expression is a prognostic marker in patients with surgically resected stage I non-small cell lung cancer[END_REF][START_REF] Yanagisawa | High expression of L-type amino acid transporter 1 as a prognostic marker in bile duct adenocarcinomas[END_REF][START_REF] Hayashi | L-type amino acid transporter 1 as a target for inflammatory disease and cancer immunotherapy[END_REF] , particularly in prostate [START_REF] Yanagisawa | L-amino acid transporter 1 may be a prognostic marker for local progression of prostatic cancer under expectant management[END_REF] , gastric [START_REF] Ichinoe | High expression of L-type amino-acid transporter 1 (LAT1) in gastric carcinomas: comparison with non-cancerous lesions[END_REF] , and pancreatic [START_REF] Ebara | L-type amino-acid transporter 1 expression predicts the response to preoperative hyperthermo-chemoradiotherapy for advanced rectal cancer[END_REF] cancers. This upregulation of LAT1 increases the activation of the mTOR pathway, leading to the hyperproliferation of cells in the immune system and cancer [START_REF] Hayashi | L-type amino acid transporter 1 as a target for inflammatory disease and cancer immunotherapy[END_REF] .

In addition, LAT1 is highly important pharmacologically. It transports drug [START_REF] Dickens | Transport of gabapentin by LAT1 (SLC7A5)[END_REF][START_REF] Takahashi | Transport of pregabalin via L-type amino acid transporter 1 (SLC7A5) in human brain capillary endothelial cell line[END_REF][START_REF] Cornford | Melphalan penetration of the blood-brain barrier via the neutral amino acid transporter in tumor-bearing brain[END_REF][START_REF] Yun | JPH203, an L-Type Amino Acid Transporter 1-Selective Compound, Induces Apoptosis of YD-38 Human Oral Cancer Cells[END_REF][START_REF] Imai | Inhibition of L-type amino acid transporter 1 has antitumor activity in non-small cell lung cancer[END_REF] and prodrug [START_REF] Gynther | Large neutral amino acid transporter enables brain drug delivery via prodrugs[END_REF][START_REF] Killian | Targeting the cerebrovascular large neutral amino acid transporter (LAT1) isoform using a novel disulfide-based brain drug delivery system[END_REF][START_REF] Peura | Synthesis and Brain Uptake of LAT1-Targeted Amino Acid Prodrugs of Dopamine[END_REF][START_REF] Puris | L-type amino acid transporter 1 utilizing prodrugs: How to achieve effective brain delivery and low systemic exposure of drugs[END_REF][40][START_REF] Huttunen | L-Type amino acid transporter 1 (lat1)-mediated targeted delivery of perforin inhibitors[END_REF] substrates that are delivered specifically into cells or tissues expressing this transporter. For example, LAT1 mediates the transport of the CNS drugs gabapentin [START_REF] Dickens | Transport of gabapentin by LAT1 (SLC7A5)[END_REF] , L-DOPA [START_REF] Peura | Synthesis and Brain Uptake of LAT1-Targeted Amino Acid Prodrugs of Dopamine[END_REF] , pregabalin [START_REF] Takahashi | Transport of pregabalin via L-type amino acid transporter 1 (SLC7A5) in human brain capillary endothelial cell line[END_REF] , and melphalan [START_REF] Cornford | Melphalan penetration of the blood-brain barrier via the neutral amino acid transporter in tumor-bearing brain[END_REF] across the BBB. LAT1 is also an emerging therapeutic target for cancer, where LAT1 inhibition deprives tumor cells from nutrients that fuel cancer cell proliferation 20 . For instance, the LAT1 inhibitor JPH-203 is currently under clinical investigation for treating Cholangiocarcinoma [START_REF] Yun | JPH203, an L-Type Amino Acid Transporter 1-Selective Compound, Induces Apoptosis of YD-38 Human Oral Cancer Cells[END_REF] . Furthermore, due to its increased expression of LAT1 in T-cells, it can also be targeted to augment cancer immunotherapy treatment as well as for treating auto immune diseases [START_REF] Hayashi | L-type amino acid transporter 1 as a target for inflammatory disease and cancer immunotherapy[END_REF] . Other LAT1 inhibitors that have been reported are KMH-233 [START_REF] Huttunen | L-Type amino acid transporter 1 (lat1)-mediated targeted delivery of perforin inhibitors[END_REF] (a phenylalanine derivative with IC50=18 μM), 3-iodo-L-tyrosine [START_REF] Geier | Structure-based ligand discovery for the Large-neutral Amino Acid Transporter 1, LAT-1[END_REF] (a tyrosine derivative with IC50= 7.9 μM) and meta-substituted phenylalanine derivatives [START_REF] Augustyn | LAT-1 activity of meta-substituted phenylalanine and tyrosine analogs[END_REF][START_REF] Venteicher | The Effects of Prodrug Size and a Carbonyl Linker on l-Type Amino Acid Transporter 1-Targeted Cellular and Brain Uptake[END_REF] (IC50 = 5-10 μM) than contain large lipophilic moieties.

Despite its biological importance, our understanding of LAT1 substrate and inhibitor specificity is still lacking [START_REF] Chien | Reevaluating the Substrate Specificity of the L-Type Amino Acid Transporter (LAT1)[END_REF] . We have previously developed structure-activity relationship (SAR) models that allowed us to predict whether compounds are more likely to be a substrate or an inhibitor, which is highly relevant to drug delivery applications [START_REF] Zur | LAT1 activity of carboxylic acid bioisosteres: Evaluation of hydroxamic acids as substrates[END_REF][START_REF] Augustyn | LAT-1 activity of meta-substituted phenylalanine and tyrosine analogs[END_REF][START_REF] Napolitano | Novel insights into the transport mechanism of the human amino acid transporter LAT1 (SLC7A5). Probing critical residues for substrate translocation[END_REF][START_REF] Napolitano | Potent inhibitors of human LAT1 (SLC7A5) transporter based on dithiazole and dithiazine compounds for development of anticancer drugs[END_REF][START_REF] Geier | Structure-based ligand discovery for the Large-neutral Amino Acid Transporter 1, LAT-1[END_REF] . For example, we discovered that LAT1 is considerably more tolerant of structural changes to its ligands than was previously thought [START_REF] Geier | Structure-based ligand discovery for the Large-neutral Amino Acid Transporter 1, LAT-1[END_REF] . LAT1 does not require a carboxylic acid to be present in the substrate [START_REF] Zur | LAT1 activity of carboxylic acid bioisosteres: Evaluation of hydroxamic acids as substrates[END_REF] , and substitution with various polar functional groups that could be used in linking amino acids to drug molecules is viable [START_REF] Augustyn | LAT-1 activity of meta-substituted phenylalanine and tyrosine analogs[END_REF] . In contrast, substitution with larger groups led to potent inhibitors [START_REF] Venteicher | The Effects of Prodrug Size and a Carbonyl Linker on l-Type Amino Acid Transporter 1-Targeted Cellular and Brain Uptake[END_REF] .

LAT1 forms a heterodimer complex with type-II membrane glycoprotein 4F2 cell-surface antigen heavy chain (4F2hc; SLC3A2) [START_REF] Fotiadis | The SLC3 and SLC7 families of amino acid transporters[END_REF] . While SLC3A2 does not play a specific role in transport, it aids in localization to the plasma membrane and improves the stability of transport by LAT1 [START_REF] Rosell | Structural bases for the interaction and stabilization of the human amino acid transporter LAT2 with its ancillary protein 4F2hc[END_REF] . Recently, two independent groups determined complex LAT1/4F2hc structures in both outward-occluded and inward-open conformations [START_REF] Yan | Structure of the human LAT1-4F2hc heteromeric amino acid transporter complex[END_REF][START_REF] Lee | Cryo-EM structure of the human L-type amino acid transporter 1 in complex with glycoprotein CD98hc[END_REF][START_REF] Yan | Mechanism of substrate transport and inhibition of the human LAT1-4F2hc amino acid transporter[END_REF] . These structures revealed that LAT1 has 12 transmembrane helices (TMs), with 10 core TMs that follow a LeuT-like fold as seen in other APC members, where the TMs are arranged in a 5+5 inverted repeats topology. The structures also confirmed the putative substrate-binding site and modes of substrate recognition proposed by homology models based on prokaryotic homologs [START_REF] Chien | Reevaluating the Substrate Specificity of the L-Type Amino Acid Transporter (LAT1)[END_REF][START_REF] Colas | SLC Transporters: Structure, Function, and Drug Discovery[END_REF] .

Similar to related transporters [START_REF] Hediger | The ABCs of solute carriers: physiological, pathological and therapeutic implications of human membrane transport proteinsIntroduction[END_REF] , LAT1 uses a gated-pore mechanism, adopting different conformations during transport. Structures of homologs of LAT1, including the arginine/agmatine antiporter (AdiC) [START_REF] Ilgu | Effects of Mutations and Ligands on the Thermostability of the l-Arginine/Agmatine Antiporter AdiC and Deduced Insights into Ligand-Binding of Human l-Type Amino Acid Transporters[END_REF][START_REF] Ilgu | Insights into the molecular basis for substrate binding and specificity of the wildtype L-arginine/agmatine antiporter AdiC[END_REF] from Escherichia coli and Amino acid, polyamine and organocation transporter [START_REF] Shaffer | Structure and mechanism of a Na+-independent amino acid transporter[END_REF][START_REF] Singh | Insights into the structure, function, and ligand discovery of the large neutral amino acid transporter 1, LAT1[END_REF] (GkApcT) from Geobacillus kaustophilus have revealed potential conformations, providing templates to characterize LAT1 in various stages of the transport cycle.

In this study, we used various computational and experimental approaches to improve our understanding of the structural basis for LAT1 interaction with inhibitors. We first performed molecular docking to determine the most relevant interactions between the LAT1 and its inhibitors. Next, we conducted metainference MD simulations, which allowed for evaluating the inherent heterogeneity in the recently published LAT1 cryo-EM structures and potential ligand binding modes. The proposed specificity determinants were then tested by the prediction of putative LAT1 inhibitors and experimental testing of top scoring hits using a cell-based assay. Finally, we discuss these results and how they can be used to describe mechanisms of ligand binding and transport by LAT1, as well as the relevance of our approach to study other Solute Carrier (SLC) transporters.

MATERIALS AND METHODS

Molecular docking

Molecular Docking was performed using Glide 47 from the Schrödinger suite. All ligands were docked to the recently solved cryo-EM structures of LAT1 (PDB: 6IRT 8 , 7DSL [START_REF] Yan | Mechanism of substrate transport and inhibition of the human LAT1-4F2hc amino acid transporter[END_REF] ). These structures were prepared for docking with the Glide Protein Preparation under default parameters. The ligand binding site was defined based on the coordinates of the respective ligand in each published structure. The receptor grid for docking was generated via Glide Receptor Grid Generation panel. The small molecules used in molecular docking were prepared for docking using LigPrep of the Schrödinger suite using default parameters. The docking results were visualized via PyMOL 48 .

Enrichment analysis

Docking and enrichment calculations were performed with Glide as described above. 8 known ligands of LAT1 were selected from the literature, using available IC50 data and ability to dock into the inwardopen conformation as selection criteria. 635 decoys were then generated with the DUD-E server 49 using these ligands. This set of 643 molecules was then screened against both inward-open (6IRT) and outward-occluded (7DSL) structurers, at the substrate binding site for LAT1. The results were evaluated by calculating the AUC (Area Under the Curve) and LogAUC, representing the ability of the virtual screening to discriminate known ligands among the set of decoys [START_REF] Schlessinger | Structure-based discovery of prescription drugs that interact with the norepinephrine transporter[END_REF][START_REF] Evers | Ligand-supported homology modelling of protein binding-sites using knowledge-based potentials[END_REF] . The corresponding plots (Fig. 1)

show the percentage of known ligands correctly predicted (y-axis) within the top ranked subset of all database compounds (x-axis on logarithmic scale).

Virtual Ligand Screening

We used Glide to virtually screen the ZINC20 [START_REF] Irwin | ZINC20-a free ultralarge-scale chemical database for ligand discovery[END_REF] Lead-Like library ('In Stock') and Enamine amino acid library against the two LAT1 conformations. We then selected compounds that contained an amino acid group from the 1,000 top-scoring compounds of the Lead-Like library screen and did the same for the Enamine screen, as it contained many amino acid derivatives. We prioritized compounds predicted to interact with the binding site through hydrogen bonding with important residues (Fig. 1) and removed compounds with an unlikely pose or strained conformation [START_REF] Shoichet | Virtual screening of chemical libraries[END_REF][START_REF] Carlsson | Ligand discovery from a dopamine D3 receptor homology model and crystal structure[END_REF][START_REF] Ung | Inhibitor discovery for the human GLUT1 from homology modeling and virtual screening[END_REF] .

Pocket Volume Measurement

POVME3 [START_REF] Wagner | POVME 3.0: software for mapping binding pocket flexibility[END_REF] (Pocket Volume Measurer 3) was used to calculate binding site volumes. We used default parameters for Ligand-defined inclusion region, using BCH (PDB:6IRT) and JX-078 (PDB:7DSL) as the reference ligands, respectively.

Metainference MD simulations

The coordinates of LAT1 (chain B) were extracted from the PDB structures 6IRT, 7DSL cryo-EM structures, including the respective ligands (BCH, JX-078). CHIMERA [START_REF] Pettersen | UCSF Chimera -A Visualization System for Exploratory Research and Analysis[END_REF] was used to select the cryo-EM density within 5 Å of the structure [START_REF] Goddard | Visualizing density maps with UCSF Chimera[END_REF] , which was then used as input of the Metainference simulation of LAT1-ligand binding. The starting model was prepared using CHARMM-GUI [START_REF] Jo | CHARMM-GUI: A Web-based Graphical User Interface for CHARMM[END_REF] 92 POPC lipids were added to the system along with 13,297 water molecules in a triclinic periodic box of volume equal to 726 nm [START_REF] Ohtsuki | Reduction of L-type amino acid transporter 1 mRNA expression in brain capillaries in a mouse model of Parkinson's disease[END_REF] . 31 potassium and 30 chloride ions were added to ensure charge neutrality and a salt concentration of 0.15 M. The CHARMM36m force field [START_REF] Huang | CHARMM36m: an improved force field for folded and intrinsically disordered proteins[END_REF] was used for the protein, lipids, and ions; the CHARMM General Force Field and the TIP3P model were used for the ligand and water molecules, respectively. A 30 ns-long equilibration was performed using GROMACS [START_REF] Abraham | GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers[END_REF] following the standard CHARMM-GUI protocol consisting in multiple consecutive simulations in the NVT and NPT ensembles.

During these equilibration steps, harmonic restraints on the positions of the lipids, ligand, and protein atoms were gradually switched off.

In the metainference simulation, a Gaussian noise model with one error parameter for each voxel of the cryo-EM map was used. 16 replicas of the system were used, and their initial configurations were randomly selected from the last 10ns-long step of the equilibration protocol. The metainference run was conducted for a total aggregated time of 10.8 μs for 6IRT, 30 μs for the 7DSL and 20.5 μs for 7DSK.

All simulations were carried out using GROMACS 2020.1 and the ISDB module [START_REF] Bonomi | Integrative structural and dynamical biology with PLUMED-ISDB[END_REF] of the open-source, community-developed PLUMED library [START_REF] Bonomi | Promoting transparency and reproducibility in enhanced molecular simulations[END_REF] [GitHub ISDB branch;

https://github.com/plumed/plumed2/tree/isdb]. For the analysis, the initial frames of the trajectory of each replica, corresponding to 20% of the total simulation time, were considered as additional equilibration steps under the cryo-EM restraint and discarded. The remaining conformations from all replicas were merged and clustered using: i) the Root Mean Square Deviation (RMSD) calculated on all the heavy atoms of the ligand and of the protein residues within 5 Å of the ligand in at least one member of the ensemble; ii) the gromos algorithm [START_REF] Daura | Peptide folding: when simulation meets experiment[END_REF] with a cutoff equal to 1.0, 1.5 and 2.0 Å.

Metainference MD Simulation Trajectory Analysis

Root mean square deviation (RMSD) and root mean square fluctuation (RMSF) were calculated using MDAnalysis [START_REF] Gowers | MDAnalysis: a Python package for the rapid analysis of molecular dynamics simulations[END_REF] . RMSF was calculated using the initial frame as the reference frame and all frames were aligned to the receptor backbone. The LAT1-JX-078 interaction map was generated using ProLif [START_REF] Bouysset | ProLIF: a library to encode molecular interactions as fingerprints[END_REF] .

Cell Culture Preparation of hLAT1 Stable Cell Line

TREx HEK-hLAT1 (XenoPort Inc., Santa Clara, CA) [START_REF] Venteicher | The Effects of Prodrug Size and a Carbonyl Linker on l-Type Amino Acid Transporter 1-Targeted Cellular and Brain Uptake[END_REF][START_REF] Hall | l-Type amino acid transporter 1 activity of 1, 2, 3-triazolyl analogs of l-histidine and ltryptophan[END_REF] is an inducible cell line that is under the control of a tetracycline inducible promoter for LAT1 and a secondary tetracycline inducible plasmid for 4F2hc (SLC3A2). Both LAT1 and 4F2hc are important components for uptake activity, as they form a heterodimer to promote native transport of their substrates [START_REF] Fotiadis | The SLC3 and SLC7 families of amino acid transporters[END_REF] . Doxycycline (Dox) and sodium butyrate, a histone deacetylase (HDAC) inhibitor, are supplied in the cell media to promote expression of LAT1 and 4F2hc 24 hours before experimentation occurs. The creation of HEK-hLAT1 stable cells is previously described in further details [START_REF] Chien | Reevaluating the Substrate Specificity of the L-Type Amino Acid Transporter (LAT1)[END_REF] . Cell culture preparation of these cells requires that they are grown and maintained in Dulbecco's Modified Eagle Medium (DMEM) media with 100 units/mL penicillin, 100 units/mL streptomycin, 2 mM L-glutamine, 1 μg/mL Fungizone, 10% fetal bovine serum, and 3 μg/mL blasticidin. Cells were grown in a humidified incubator with 5% CO2 at a stable 37 °C.

cis-Inhibition Assay and IC50 Computations

TREx HEK-hLAT1 cells were plated at a seeding density of 0.2 x 10 6 cells per well in poly-D-lysine coated 24-well plates in DMEM medium described above. Cells were grown to at least 90% confluency 48 hours post seeding and 24 hours before the assay, the cells were induced with 2 mM sodium butyrate and 1 μg/mL doxycycline for proper LAT1 expression [START_REF] Chien | Reevaluating the Substrate Specificity of the L-Type Amino Acid Transporter (LAT1)[END_REF] . On the day of the assay, cells are washed twice with warm sodium-free choline buffer (140 mM choline chloride, 2 mM potassium chloride, 1 mM magnesium chloride, 1 mM calcium chloride, 1 M Tris) and then incubated with the same solution for 10 minutes. For the cis-inhibition assay, the choline buffer was removed and replaced with radioactive uptake buffer (sodium-free choline buffer containing 6 nM of [ 3 H]-gabapentin) and each compound at a final concentration of 200μM. For the IC50 determinations, the choline buffer was replaced with radioactive uptake buffer mixed with varying concentrations of each inhibitor ranging from 0.1 μM to 200 μM. For each assay, the uptake was performed in an incubator at 37 °C for 3 minutes and the reaction was terminated by washing the cells twice with ice-cold choline buffer. The cells were then lysed with 600 μL of lysis buffer (0.1 N NaOH, 0.1% SDS) at room temperature overnight or on a shaker for 1 hour. Only 150 μL of lysis is used to measure intracellular radioactivity which is determined by scintillation counting on a LS6500 scintillation counter (Beckman Coulter Inc., Bea, CA). IC50 calculations were analyzed using GraphPad Prism version 9.3 (San Diego, CA) and % [ 3 H]-gabapentin uptake is normalized relative to % inhibition by L-phenylalanine at 200 μM.

Chemistry: general information

Flash chromatography purification was performed using a Teledyne ISCO Flash Chromatography System. Preparative HPLC was performed using a Gilson PLC 2020. Column: Synergi 4 Fusion-RP by Phenomenex, 150 x 21.2 mm. The following preparative HPLC method employed elution at 20 mL/min flow rate: A: gradient elution over 40 min of 5-40% CH3CN with water, both of which contained 0.1% TFA. LC-MS analysis was performed using an Agilent G6125 single quad ESI source and a 1260

Infinity HPLC system (G7112B Binary Pump and G7114A Dual  Absorbance Detector). Column:

Synergi 4 Fusion-RP by Phenomenex, 150 x 4.6 mm. The following LC-MS method was performed using a 1.0 mL/min flow rate: A: gradient elution over 10 min of 10-80% CH3CN with water, both of which contained 0.1% formic acid. [START_REF] Kanai | Expression cloning and characterization of a transporter for large neutral amino acids activated by the heavy chain of 4F2 antigen (CD98)[END_REF] H NMR and [START_REF] Hediger | The ABCs of solute carriers: physiological, pathological and therapeutic implications of human membrane transport proteinsIntroduction[END_REF] C NMR were recorded on an Avance III HD Bruker instrument operating at 400 MHz and 100 MHz, respectively.

Chemistry: synthesis

Preparation of benzyloxy-substituted tryptophan analogs (AT182, AT183, AT184).

(±)-2-Amino-3-(4-(benzyloxy)-1H-indol-3-yl)propanoic acid hydrochloride (AT182).

The title compound was synthesized in two steps using a modification of the procedure described by Blaser [START_REF] Blaser | The facile synthesis of a series of tryptophan derivatives[END_REF] , as follows:

Step A: Preparation of (±)-acetamido-3-( 4

-(benzyloxy)-1H-indol-3-yl)propanoic acid

To a stirred mixture of acetic anhydride (1.3 mL, 13 mmol) in acetic acid (5 mL) was added 4benzyloxyindole (1.0 g, 4.5 mmol) and L-serine (0.47 g, 4.5 mmol; note: the L isomer was used because it was on hand, but the D isomer or racemic serine would also give the same racemic product as demonstrated by Blaser). The mixture was heated to 80 °C and stirring continued overnight. The reaction was concentrated in vacuo at 80 °C and carried forward into base-mediated acetyl deprotection without purification at this step.

Step B: Preparation of AT182 Preparation of benzylcarbamoyl-substituted tryptophan analogs (AT185, AT186).

(±)-2-Amino-3-(5-(benzylcarbamoyl)-1H-indol-3-yl)propanoic acid hydrochloride (AT185).

The title compound was prepared in five steps (A-E) as follows.

Step A: Preparation of (±)-3

-(2-acetamido-2-carboxyethyl)-1H-indole-5-carboxylic acid

Prepared from 1H-indole-5-carboxylic acid (3.0 g, 19 mmol) using the same procedure as in AT182,

Step A. Crude product was carried directly to the next step without purification.

Step B: Preparation of (±)-3-( 2 Purified crude by trituration with hot EtOAc, followed by cooling to rt and filtration. Yield: 0.50 g (34%). water (20 mL). Separated phases and re-extracted aqueous with EtOAc (20 mL). Combined organic phases were washed with water (2 x 20 mL), brine (20 mL), then dried (MgSO4), filtered and concentrated. Crude was purified by ISCO flash chromatography, eluting with a gradient of 30% EtOAc in hexanes to 100% EtOAc over 8 min. Yield: 0.40 g (64%). [START_REF] Kanai | Expression cloning and characterization of a transporter for large neutral amino acids activated by the heavy chain of 4F2 antigen (CD98)[END_REF] Step E: Preparation of AT185

Using a modification of a method originally described by Poulie [START_REF] Poulie | Tetrabutylammonium Fluoride as a Mild and Versatile Reagent for Cleaving Boroxazolidones to Their Corresponding Free α-Amino Acids[END_REF] , the BBN protecting group was Converted product to the HCl salt by adding 1M aq. HCl (2 mL) to the purified product and freeze drying to remove excess water and HCl. Yield: 97 mg (30%). >95% purity by LC-MS (220 nm, Method A).

(±)-2-Amino-3-(6-(benzylcarbamoyl)-1H-indol-3-yl)propanoic acid hydrochloride (AT186).

The title compound was prepared according to the five-step procedure for AT185, except replacing 1Hindole-5-carboxylic acid with 1H-indole-6-carboxylic acid in Step A. >95% purity by LC-MS (220 nm, Method A). [START_REF] Kanai | Expression cloning and characterization of a transporter for large neutral amino acids activated by the heavy chain of 4F2 antigen (CD98)[END_REF]

RESULTS

Docking of known ligands to LAT1 structures

We applied molecular docking and ligand enrichment calculations to examine the pharmacological relevance of different conformations of LAT1's binding site and ligands, as well as key interactions between them (Fig. 1). This analysis was also conducted to evaluate the optimal parameters for rational design of new LAT1 ligands. Specifically, we analyzed the ability of molecular docking and the cryo-EM structure to distinguish between known ligands of LAT1 and decoy compounds, which have similar physical properties to the known ligands but possess different topological properties, to reduce likelihood of binding [START_REF] Geier | Structure-based ligand discovery for the Large-neutral Amino Acid Transporter 1, LAT-1[END_REF] . Compounds were docked against two different conformations of LAT1: an inward-open conformation bound to the inhibitor BCH (PDB identifier: 6IRT) [START_REF] Yan | Structure of the human LAT1-4F2hc heteromeric amino acid transporter complex[END_REF] and an outward-occluded conformation bound to the inhibitor JX-078 (PDB identifier: 7DSL) [START_REF] Yan | Mechanism of substrate transport and inhibition of the human LAT1-4F2hc amino acid transporter[END_REF] .

We docked this set of known LAT1 ligands and decoy compounds against the inward-open conformation using default parameters and obtained a logAUC value of 13.88 (Fig. 1C), which corresponds to a random selection of ligands. Conversely, similar docking parameters used for docking against the outward-occluded structure yielded logAUC value of 76.06, suggesting that this conformation can capture LAT1-ligand complementarity and may yield productive virtual screening.

We then used different parameters enforcing different LAT1-ligand interactions to specific residues that have been shown [START_REF] Yan | Structure of the human LAT1-4F2hc heteromeric amino acid transporter complex[END_REF][START_REF] Yan | Mechanism of substrate transport and inhibition of the human LAT1-4F2hc amino acid transporter[END_REF][START_REF] Chien | Reevaluating the Substrate Specificity of the L-Type Amino Acid Transporter (LAT1)[END_REF][START_REF] Singh | Rigorous sampling of docking poses unveils binding hypothesis for the halogenated ligands of L-type Amino acid Transporter 1 (LAT1)[END_REF] to be important for ligand binding and/or transport (i.e., S66, G67, F252, G255) (Fig. 1). Interestingly for the inward-open conformation, constraining ligands to interact with F252, which is thought to serve as a "gate" for LAT1 ligands [START_REF] Yan | Structure of the human LAT1-4F2hc heteromeric amino acid transporter complex[END_REF][START_REF] Napolitano | Novel insights into the transport mechanism of the human amino acid transporter LAT1 (SLC7A5). Probing critical residues for substrate translocation[END_REF] yielded significantly improved enrichment scores, further validating the importance of this residue for LAT1 binding. For the outwardoccluded structure, constrained docking to S66, G67, F252, and G255 yielded comparable enrichment scores to the scores derived from unconstrained docking. Notably, it is likely that the ability of the outward-occluded conformations to enrich for inhibitors is due to its increased size as compared to the inward-open conformation, i.e., 275 Å 3 vs. 118 Å 3 , respectively (Fig. 2), which results from the movement of the sidechain of Y259. Taken together, these results suggest that the outward-occluded conformation better captures protein-ligand complementarity and is potentially more useful for the discovery of new ligands with structure-based virtual screening.

Dynamics of LAT1 binding site and inhibitor interaction

In parallel, we investigated the heterogeneity in the LAT1-inhibitor complex structure, using the metainference integrative approach [START_REF] Bonomi | Metainference: A Bayesian inference method for heterogeneous systems[END_REF][START_REF] Bonomi | Promoting transparency and reproducibility in enhanced molecular simulations[END_REF] . In metainference, the molecular mechanics force field used in standard MD simulations is augmented by spatial restraints that enforce the agreement of an ensemble of replicas of the system with the cryo-EM density map [START_REF] Bonomi | Simultaneous determination of protein structure and dynamics using cryo-electron microscopy[END_REF] . Using a Bayesian inference framework, this approach accounts for the simultaneous presence of structural heterogeneity, data ensembleaveraging, and variable level of noise in different areas of the experimental map. We performed metainference simulations on the outward-occluded structure of LAT1 bound to JX-078 (7DSL). In particular, we generated clusters of conformations of the protein-ligand complex and obtained a representative model for each cluster (Fig. 3A). We assessed the stereochemical quality of each model using MolProbity [START_REF] Williams | MolProbity: More and better reference data for improved allatom structure validation[END_REF] , which considers the number of steric clashes, rotamer outliers, and percentage of backbone Ramachandran conformations outside favored regions. Notably, all models scored better (lower) than the cryo-EM structure, with Model 1 scoring the best at 1.55 compared to the cryo-EM structure at 1.92 (Fig. S1).

We analyzed the differences and similarities among the models, focusing on the interactions between JX-078 and LAT1 (Fig. 3B). The ligand conformations remained consistent among the representative models, with the amino and carboxy group of JX-078 making sustained hydrogen bonds with the same residues highlighted from our enrichment study, i.e., S66, G67, F252, G255, maintaining hydrogen bonding for over 90% of the simulation (Fig. S2). We also observed that these interactions were further stabilized by Y259 making π-stacking interactions with the biphenyl ring that connects to the amino group (Fig. S2). Additionally, we observed that the hydrophobic tail of JX-078 consistently binds deeper into the binding pocket but explores regions of the binding site, revealing two sub-pockets, C and D (Fig. 5A), Interestingly, we observed that inhibitor showed some preference for sub-pocket C (Fig. 5A), as seen in models 4, 5 and 6, with residue W405 playing a key role in inhibitor binding by making an additional π-stacking interaction with the benzyloxy ring of the inhibitor (Fig. S2). Most of the simulation displayed a binding pose targeting sub-pocket D, in the ensemble simulation, representing over 90% of all the conformations found (Fig. 3B: Models 1 & 6; Fig. S1). We hypothesized that these sub-pockets can be exploited with rational ligand design (Fig. 5E).

We also assessed the stability and movement of the LAT1-JX-078 structure throughout the simulation.

We observed that the overall structure and binding site residues remained stable, which is reflected in the low RMSD of all backbone atoms throughout the simulation and particularly in the binding site residues (Fig. 4A). Closer inspection of the individual residues of LAT1 by Root Mean Square Fluctuation (RMSF) analysis, revealed that most of structural variability compared with the cryo-EM structure occurs away from the binding site, with movement less than 1 Å (Fig. 4B). This analysis supports the notion that the size and shape of the LAT1 binding pocket in the outward-occluded conformation remains mostly stable, with most of the flexibility occurring at TM10. The inhibitor displayed some flexibility (Fig. 4C) showing some movement as great as 5 Å. Most of this flexibility stems from the hydrophobic tail of the inhibitor, which is captured by the representative models 1-6 (Fig. 3B). Notedly, the stability of ligand mirrors the increase in contacts between the residues: I63, S65, S66, G67, F252, G255 and Y259. We speculate that these residues are essential to JX-078 binding to LAT1.

Virtual screening and functional testing of novel compounds for LAT1

Next, we computationally screened a library of purchasable compounds from the ZINC20 lead-like database (3.8 million compounds) and amino-acid like compounds from Enamine (8,137 compounds), against the outward-occluded conformation. The top scoring compounds from each screen were prioritized for further analysis (Methods). Specifically, we prioritized compounds for experimental testing based on the following considerations:

(i) Compounds contain both carboxy and amino group that make critical interactions with binding site residues.

(ii) Compounds are predicted to make hydrogen bonds with S66, G67, G255 and F255.

(iii) Compounds have a hydrophobic tail that extends deeper into the binding site, preferably to sub-pocket C or D (Fig. 5A).

Overall, 24 compounds were initially selected for experimental testing using a cis-inhibition assay, which is based on an inhibitor's capacity to reduce intracellular accumulation of the probe substrate

[ 3 H]-gabapentin using stable TREx HEK-hLAT1 cell line (see materials and methods). Eleven compounds were identified as LAT1 inhibitors by reducing cellular uptake by > 50% at inhibitor concentration of μM 200 (Table S1). The most potent compound was KH13 (Fig. 5B), which exhibited an IC50 value of 11.8 µM. KH13 is a benzyloxy-substituted tryptophan, and its predicted binding pose is nearly identical to that of tryptophan (Figs. 5B and5C). the benzyloxy group of KH13 interacts with the newly discovered sub-pocket C and W405 and likely contributes to its potency. We designed and tested analogs of KH13, three benzyloxy regioisomers and two benzyl amides (AT182-186), that were targeted to the same sub-pockets (e.g., AT185, Fig. 5D). These analogs exhibited comparable potency to that of KH13 (Table S1). These results increased our confidence in the predicted binding mode of

DISCUSSION

LAT1 is physiologically important protein and a key drug target for a range of diseases and disorders.

Despite significant improvement in our understanding of LAT1 structure and function over the past decade, the description of LAT1-ligand interactions at the molecular level is still inadequate. Structural description of LAT1 conformations and mode of binding with ligands is needed for understanding LAT1 transport, as well as for the development of novel small molecule modulators of its function. The goal of this study was to characterize LAT1-inhibitor interactions by combining computational and experimental approaches. The following three key results emerge from this study.

Characterizing LAT1's binding site and ligand binding

First, we identified key binding site residues and propose the binding modes of known and newly discovered inhibitors. Ligand enrichment (Fig. 1) in combination with metainference analysis (Figs. 3,4), highlighted S66, G67, F252 and G255 as critical residues for inhibitor binding in agreement with previous studies [START_REF] Zur | LAT1 activity of carboxylic acid bioisosteres: Evaluation of hydroxamic acids as substrates[END_REF][START_REF] Augustyn | LAT-1 activity of meta-substituted phenylalanine and tyrosine analogs[END_REF][START_REF] Napolitano | Novel insights into the transport mechanism of the human amino acid transporter LAT1 (SLC7A5). Probing critical residues for substrate translocation[END_REF][START_REF] Napolitano | Potent inhibitors of human LAT1 (SLC7A5) transporter based on dithiazole and dithiazine compounds for development of anticancer drugs[END_REF][START_REF] Geier | Structure-based ligand discovery for the Large-neutral Amino Acid Transporter 1, LAT-1[END_REF][START_REF] Singh | Rigorous sampling of docking poses unveils binding hypothesis for the halogenated ligands of L-type Amino acid Transporter 1 (LAT1)[END_REF] . We also propose the importance of an additional two residues: Y259 and W405.

The backbone atoms of S66 and G67 form hydrogen bonds with the JX-078 amino group, and those of F252 and G255 form hydrogen bonds with the inhibitor's carboxy group (Fig. 1,3). Furthermore, analysis of cryo-EM structures of LAT1 in two different conformations (i.e., outward-occluded and inward-open) in complex with different inhibitors (i.e., BCH and JX-078, respectively) revealed that the interactions between the amino and carboxy moieties of the ligands and LAT1 remain mostly consistent, with only a subtle rearrangement of the backbone residues of the ligand binding site (Fig. 3A). Of note, the binding site in the outward-occluded structure is significantly larger (Fig. 2) and possesses potential sub-pockets that are not seen in the inward-open conformation (Fig. 3,5), in part due to the orientation of the Y259 sidechain (Fig. 2). Our metainference analysis revealed two potential druggable subpockets that the hydrophobic tail of JX-078 fits into (Figs. 3,5), with W405 playing a key role in subpocket C binding. Overall, our results suggest that the outward-occluded structure is a more relevant conformation for rational drug design than the inward-open conformation.

Identifying unique LAT1 inhibitors

Second, we identified previously unknown pharmacophores of LAT1 inhibitors. Currently, there is a limited number of small molecule tool compounds targeting LAT1, representing few chemical scaffolds [START_REF] Chien | Reevaluating the Substrate Specificity of the L-Type Amino Acid Transporter (LAT1)[END_REF][START_REF] Singh | Rigorous sampling of docking poses unveils binding hypothesis for the halogenated ligands of L-type Amino acid Transporter 1 (LAT1)[END_REF] . Structure-based virtual screening allows the exploration of novel scaffolds by predicting binding of large compound libraries with minimal bias. Although the newly experimentally validated inhibitors from the virtual screens, including those in this study, are not optimized, and are thus, not sufficiently potent to be lead compounds, some of them possess chemical moieties that can serve as starting points for the exploration of unique chemical spaces. For example, KH14 contains a distinctive pyrrolidine-pyrazole moiety that offers opportunities to substitute with different groups off the pyrazole ring nitrogen; and KH1 shows that a carbamate moiety can be used to project a 9H-fluorene ring into both sub-pockets C and D. EN14 contains an uncommon aryl azo group and extends deep into subpocket C and provides an opportunity for additional modification by replacing and/or functionalizing the phenyl ring.

While other newly identified compounds are derived from known scaffolds, these compounds exhibited unique modes of binding. For example, substituted tryptophan KH13 and substituted tyrosine EN2 both extend deep into the previously uncharacterized sub-pockets C and D, respectively. While extensive SAR work has been done on investigating phenylalanine [START_REF] Chien | Reevaluating the Substrate Specificity of the L-Type Amino Acid Transporter (LAT1)[END_REF][START_REF] Augustyn | LAT-1 activity of meta-substituted phenylalanine and tyrosine analogs[END_REF][START_REF] Ylikangas | Structure-activity relationship study of compounds binding to large amino acid transporter 1 (LAT1) based on pharmacophore modeling and in situ rat brain perfusion[END_REF] and tyrosine [START_REF] Augustyn | LAT-1 activity of meta-substituted phenylalanine and tyrosine analogs[END_REF] derivatives, until now, tryptophan has been largely overlooked as a starting point for inhibitor or prodrug design. Thus, this work provides a framework for developing future tryptophan analogs, by building upon the results of KH13 and AT182-186, all of which are potent and novel LAT1 inhibitors.

Demonstrating the utility of metainference MD simulations

Third, we demonstrated an application of emerging integrative modeling approach to characterize the transporter binding site and rationally design small molecule ligands. Cryo-EM is emerging as the primary method for structure determination of membrane proteins including SLC transporters [START_REF] Bai | How cryo-EM is revolutionizing structural biology[END_REF][START_REF] Choy | A 10-year meta-analysis of membrane protein structural biology: detergents, membrane mimetics, and structure determination techniques[END_REF] .

However, cryo-EM structures are often not determined in a resolution sufficient for structure-based compound design. Further, this challenge is amplified by the inherent flexibility of membrane transporters, including LAT1. Metainference can account for the heterogeneity in cryo-EM maps as a result from the intrinsic dynamics of these molecules, as well as the errors in the measurement [START_REF] Bonomi | Metainference: A Bayesian inference method for heterogeneous systems[END_REF][START_REF] Bonomi | Simultaneous determination of protein structure and dynamics using cryo-electron microscopy[END_REF] . We have previously shown that metainference can identify pharmacologically relevant conformations of an unrelated amino acid transporter and guide the optimizations of inhibitors [START_REF] Garibsingh | Rational design of ASCT2 inhibitors using an integrated experimentalcomputational approach[END_REF] . Here, we apply metainference to identify putative sub-pockets in the binding site, as well as to estimate the biophysical features of the binding site such as flexibility and accessible volume for ligand binding. Remarkably, metainference-generated structures exhibited more favorable stereochemical quality over the experimentally determined structure (Fig. S1), as well as explored ligand and transporter conformations that guided the identification of unique LAT1 inhibitors. As more structures of LAT1 and other SLCs are solved with cryo-EM, metainference and other computational approaches such as the machine learning based approach AlphaFold2 82,83 , can be applied to identify novel ligands for effective therapy and study of human diseases.

  -amino-2-carboxyethyl)-1H-indole-5-carboxylic acid With stirring, heated crude (±)-3-(2-acetamido-2-carboxyethyl)-1H-indole-5-carboxylic acid from Step A with 6M aq. HCl (20 mL, 120 mmol) at 100 °C overnight. Concentrated in vacuo using CH3CN to azeotrope water (5 cycles). Dried solids under high vacuum overnight. Carried crude product into Step C without purification at this step. -4'-((5-carboxy-1H-indol-3-yl)methyl)-5'-oxo-9l4boraspiro[bicyclo[3.3.1]nonane-9,2'-[1,3,2]oxazaborolidin]-3'-ium Using a similar procedure as described by Venteicher 74 , we protected crude (±)-3-(2-amino-2carboxyethyl)-1H-indole-5-carboxylic acid from Step B (1.0 g, 4.0 mmol) by stirring with pyridine (1.3 mL, 16 mmol) and 9-borabicyclo(3.3.1)nonane (9-BBN, 0.5 M in THF, 16 mL, 8.1 mmol) in DMF (5 mL) overnight at rt. Reaction mixture was partitioned between EtOAc (15 mL) and 1M aq. KHSO4 (35 mL).Separated phases, and reextracted aqueous phase with EtOAc (35 mL). Combined organic phases were washed with water (2 x 35 mL), brine (50 mL), dried (MgSO4), filtered and concentrated in vacuo.

1 H

 1 NMR ((CD3)2SO)  12.4 (br s, 1H), 11.3 (s, 1H), 8.27 (s, 1H), 7.74 (m, 1H), 7.43 (m, 2H), 6.52 (m, 1H), 5.68 (m, 1H), 3.84 (m, 1H), 3.29 (m, 1H), 3.17 (m, 1H), 1.30-1.81 (m, 12H), 0.45 (m, 2H).Step D: Preparation of (±)-4'-((5-(benzylcarbamoyl)-1H-indol-3-yl)methyl)-5'-oxo-9l4boraspiro[bicyclo[3.3.1]nonane-9,2'-[1,3,2]oxazaborolidin]-3'-ium 9-BBN-protected (±)-3-(2-amino-2-carboxyethyl)-1H-indole-5-carboxylic acid from Step C (0.50 g, 1.4 mmol) was stirred overnight at rt with benzylamine (0.15 mL, 1.4 mmol), (i-Pr)2NEt (0.71 mL, 4.1 mmol), and 1-[bis(dimethylamino)methylene]-1H-1,2,3-triazolo[4,5-b]pyridinium 3-oxide hexafluorophosphate (HATU, 0.77 g, 2.0 mmol) in DMF (5 mL). Partitioned reaction mixture between EtOAc (20 mL) and

  -4'-((5-(Benzylcarbamoyl)-1H-indol-3-yl)methyl)-5'-oxo-9l4boraspiro[bicyclo[3.3.1]nonane-9,2'-[1,3,2]oxazaborolidin]-3'-ium from Step D (0.40 g, 0.87 mmol) was stirred overnight at rt with TBAF (1M in THF, 3.5 mL, 3.5 mmol) and THF (3 mL). Water (3 mL) was added and it was stirred for an additional 5 min. Acidified with formic acid (1-2 mL) and concentrated in vacuo at 60 °C to remove most of the THF. Dissolved the resulting residue in warmed 20% CH3CN in water containing 0.1% TFA (15 mL). Syringe filtered the mixture through a 0.45 micron filter to remove undissolved particles. Purified product by two successive preparative HPLC runs (Method A).

  our initial inhibitors. Other new inhibitors included the phenylalanine analog EN14 (IC50 = 16 µM) and tyrosine analog EN2 (IC50 = 63.3 µM). EN14 contains a unique diazo linkage, and like KH13, it places a hydrophobic phenyl ring into sub-pocket C and is also a potent inhibitor. Interestingly, the benzyloxy group in EN2 docks into sub-pocket D, and the decrease in potency relative to KH13 suggests this is a less optimal position for the benzyloxy group. Finally, KH1 (IC50 = 44.4 µM) contains a bulky fluorenylmethoxycarbonyl (Fmoc) sidechain that interacts with both sub-pockets C and D. Taken together, these newly discovered LAT1 inhibitors represent diverse scaffolds and/or novel binding modes.

Figure 1 :

 1 Figure 1: Ligand enrichment for LAT1 structures. Key binding site residues are shown as sticks where oxygen, nitrogen, and carbon atoms are represented in red, blue, and white (A) or white (B-C), respectively. Hydrogen bonds are displayed as a dashed black line. (A) Docking pose of BCH to LAT1 guided by enrichment. BCH is predicted to form hydrogen bonds with G65, S66, and G67, key residues for LAT1 amino acid recognition. (B) The logarithmic area under the curve (logAUC) derived from constrained docking at residue G67 (red line; logAUC=64.4), as compared to random selection of ligands from a set of ligands and decoys (dashed blue line); (C) Table showing logAUC values for constrained docking to selected residues of LAT1 substrate binding site in the inward-open conformation (D) Binding pose of JX-078 to LAT1 found in published outward-occluded structure (E) logAUC values of constrained docking to residue G67 (F) logAUC values for constrained docking to selected residues of LAT1 substrate binding site in the outward-occluded conformation.

Figure 2 :

 2 Figure 2: LAT1 binding site in different conformations. Visualization of LAT1 binding site and accessible regions for the (A) Inward-open (orange) and (B) Outward-occluded conformation of LAT1 (blue). (C) Volume and surface area of the binding site in each conformation.

FIGURE 3 Figure 3 :

 33 FIGURE 3

Figure 4 :

 4 Figure 4: Quantitative analysis of binding site movements. (A) Root Mean Square Deviation (RMSD) analysis of metainference simulation focusing on the backbone α-carbon atoms of all residues (B) Root Mean Square Fluctuation (RMSF) analysis of individual residues. Binding site residues are broken down into four regions: I, II, II IV in yellow, green, purple, and red, respectively. (C) RMSD analysis of JX-078's atoms. (D) Fraction of contacts made between residues (I63, S65, S66, G67, F252, G255, Y259) in the LAT1 substrate binding site and JX-078 throughout the simulation. (E) Binding site of 7DSL with each region from (B) identified.

5 Figure 5 :

 55 FIGURE 5

Figure 6 :

 6 Figure 6: IC50 values of newly LAT1 inhibitors. 2-D representation of (A) Compound KH13 and (B) Compound KH1 (C) Compound EN2 (D) Compound EN14 and (E) L-Phenylalanine, along with each representative dose-dependent inhibition curve. For IC50 determination, varying concentrations of each compound were tested ranging from 0.1 µM to 200 µM. GraphPad Prism version 9.3 was used to calculate IC50 values, and each concentration point (n=2) was normalized to L-Phenylalanine at 200 µM.

  residual solvent peak).[START_REF] Hediger | The ABCs of solute carriers: physiological, pathological and therapeutic implications of human membrane transport proteinsIntroduction[END_REF] C NMR (CD3OD)  171.8, 156.9, 139.1, 139.0, 129.4, 128.7, 128.5, 124.5, 122.9, 119.7, 111.3, 107.9, 97.4, 71.5, 54.6, 27.7. m/z (ESI-pos) M + 1 = 311.1.Prepared by the same two step procedure as AT182, except replacing 4-(benzyloxy)-1H-indole with 7-(benzyloxy)-1H-indole in Step A. >95% purity by LC-MS (254 nm, Method A).[START_REF] Kanai | Expression cloning and characterization of a transporter for large neutral amino acids activated by the heavy chain of 4F2 antigen (CD98)[END_REF] H NMR ((CD3)2SO) 

	(±)-2-Amino-3-(7-(benzyloxy)-1H-indol-3-yl)propanoic acid hydrochloride (AT184).
	11.2 (s, 1H), 8.41 (br s, 2H), 7.55 (d, J = 8 Hz, 2H), 7.41 (m, 2H), 7.34, (m, 1H), 7.18, (m, 2H), 6.90 (m,
	1H), 6.74 (d, J = 8 Hz, 1H), 5.25 (s, 2H), 4.07 (m, 1H), 3.27 (m, 2H). 13 C NMR ((CD3)2SO)  170.6,
	145.10, 145.05, 137.4, 128.8, 128.4, 127.7, 127.5, 126.5, 126.3, 124.7, 124.5, 119.1, 111.4, 107.3,
	103.0, 69.1, 52.5, 26.1. m/z (ESI-pos) M + 1 = 311.1.
	7.52 (d, J = 7 Hz, 2H), 7.38 (m, 2H), 7.32 (m, 1H), 7.04 (m, 3H), 6.64 (d, J = 7 Hz, 1H), 5.24 (s, 2H),
	4.36 (dd, J = 4, 11 Hz, 1H), 3.79 (dd, J = 4, 14 Hz, 1H), 3.02 (dd, J = 11, 14 Hz, 1H). 13 C NMR (CD3OD)
	 171.8, 154.4, 140.6, 138.6, 129.6, 129.0, 124.9, 123.9, 118.0, 108.4, 106.4, 101.8, 71.2, 55.2, 29.6.
	m/z (ESI-pos) M + 1 = 311.1.
	(±)-2-Amino-3-(6-(benzyloxy)-1H-indol-3-yl)propanoic acid hydrochloride (AT183).
	Prepared by the same two step procedure as AT182, except replacing 4-(benzyloxy)-1H-indole with 6-

(±)-Acetamido-3-(4-(benzyloxy)-1H-indol-3-yl)propanoic acid from Step A (0.50 g, 1.4 mmol) was heated with an aqueous 15% solution of NaOH (8.1 mL, 36 mmol) in a teflon-capped glass pressure tube at 110 °C for 2 d. After cooling to rt, carefully added conc. HCl (3 mL) and filtered the resulting tan solids, rinsing with water (2 x 3 mL). Heated solids with 1:1 CH3CN /water (20 mL). Filtered the warm suspension to remove undissolved impurities. Chilled the mixture in a -10 °C freezer and then saturated the aqueous phase with NaCl. Sonicated the mixture to dissolve the NaCl. Separated the phases and re-extracted the aqueous with CH3CN (2 x 5 mL). Concentrated the CH3CN phases and redissolved the residue in 1:1 CH3CN /water (15 mL), and then syringe filtered through a 0.45 micron filter to remove smaller, undissolved particles. Purified the crude by preparative HPLC (Method A). Converted product to the HCl salt by adding 1M aq. HCl (2 mL) to the purified product and freeze drying to remove excess water and HCl. Yield: 81 mg (18%). >95% purity by LC-MS (254 nm, Method A).

[START_REF] Kanai | Expression cloning and characterization of a transporter for large neutral amino acids activated by the heavy chain of 4F2 antigen (CD98)[END_REF]

H NMR (CD3OD)  (benzyloxy)-1H-indole in Step A. >95% purity by LC-MS (254 nm, Method A). 1 H NMR (CD3OD)  7.49 (d, J = 9 Hz, 1H), 7.44 (m, 2H), 7.35 (m, 2H), 7.29 (m, 1H), 7.10 (s, 1H), 6.99 (d, J = 2 Hz, 1H), 6.82 (dd, J = 2, 9 Hz, 1H), 5.08 (s, 2H), 4.22 (dd, J = 5, 8 Hz, 1H), 3.46 (dd, J = 5, 15 Hz, 1H), 3.29 (m, 1H, partially overlaps

Acknowledgments

Funding for this work was provided by the following grants: R01 GM108911 (A.S.), T32 GM062754 (K.H.), and R15 NS099981 (A.A.T.)

This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. M.B. acknowledges the support of the French Agence Nationale de la Recherche (ANR), under grant ANR-20-CE45-0002 (project EMMI).

Data availability.

The GROMACS topology and input files as well as PLUMED input files used are available on PLUMED-NEST (https://www.plumed-nest.org/) under accession ID plumID:22.018.

Scripts used for post metainference trajectory analysis are available at: https://github.com/schlessinger-lab/LAT1-metainference

Author Contributions

Declarations of Interests

The authors declare no competing interests.


Z Faidon Brotzakis

Thomas Löhr

Steven Truong

Samuel E Hoff

Massimiliano Bonomi

Michele Vendruscolo

Determination of the structure and dynamics of the fuzzy coat of an amyloid fibril of IAPP using cryo-electron microscopy

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

In the last several years, cryo-electron microscopy (cryo-EM) has been pushing the boundaries of structural biology in terms of structure resolution, system complexity, and macromolecular size [START_REF] Kühlbrandt | The resolution revolution[END_REF][START_REF] Bai | How cryo-EM is revolutionizing structural biology[END_REF] . Imaging single particles by rapid cryo-cooling and vitrification [START_REF] Dubochet | Vitrification of pure water for electron microscopy[END_REF][START_REF] Dubochet | Electron microscopy of frozen water[END_REF] enables structural studies under near-native environmental conditions while offering sample protection from beam radiation. Technical advances in electron detectors, computational algorithms accounting for beam-induced motion, and automation of data collection and image analysis [START_REF] Renaud | Cryo-EM in drug discovery: Achievements, limitations and prospects[END_REF] have paved the way to a spectacular increase of high-resolution cryo-EM density maps. The Electron Microscopy Data Bank (EMDB) currently holds 15,800 single-particle cryo-EM density maps, which offer exquisitely detailed structural information about macromolecular systems of central importance in cell biology.

In standard cryo-EM structure determination, two-dimensional (2D) images of single particles are first classified in conformationally homogenous classes and then averaged in a computational image processing step, thereby leading to a substantial increase in the signal-tonoise ratio, and thus in structure resolution [START_REF] Van Heel | Use of multivariate statistics in analysing the images of biological macromolecules[END_REF][START_REF] Frank | Spider-A modular software system for elec tron image processing[END_REF] . However, the continuous dynamics of flexible regions are difficult to detect, therefore complicating the generation of homogenous classes of structures. The resulting low densities cannot be readily used to determine atomistic structures, and are thus often excluded from the final structural model. While methods such as the manifold embedding approach 8 can determine multiple structures from cryo-EM density maps, to account for the conformational dynamics one should quantitatively and atomistically interpret the cryo-EM density maps as an envelope that corresponds to an averaged conformational ensemble of states with certain populations that interconvert with a characteristic timescale [START_REF] Bonomi | Determination of protein structural ensembles using cryo-electron microscopy[END_REF] . Such a viewpoint moves away from a single structure interpretation of the data and links the data to the statistical mechanics concept of free energy landscapes of conformational ensembles [START_REF] Bonomi | Principles of protein structural ensemble determination[END_REF][START_REF] Fraser | What will computational modeling approaches have to say in the era of atomistic cryo-EM data[END_REF] . Integrative structural ensemble-modelling methods incorporate experimental information into molecular simulations and enable the determination of structural ensembles that maximally conform to the experimental data with atomistic resolution [START_REF] Pittera | On the use of experimental observations to bias simulated ensembles[END_REF][START_REF] Cavalli | Molecular dynamics simulations with replica-averaged structural restraints generate structural ensembles according to the maximum entropy principle[END_REF][START_REF] Roux | On the statistical equivalence of restrained-ensemble simulations with the maximum entropy method[END_REF][START_REF] Boomsma | Combining experiments and simulations using the maximum entropy principle[END_REF][START_REF] Hummer | Bayesian ensemble refinement by replica simulations and reweighting[END_REF][START_REF] Bonomi | Metainference: A Bayesian inference method for heterogenous systems[END_REF][START_REF] Marrinelli | A molecular simulation method to sample experimental distributions[END_REF][START_REF] Olsson | Combining experimental and simulation data of molecular processes via augmented Markov models[END_REF][START_REF] Cesari | Using the maximum entropy principle to combine simulations and solution experiments[END_REF][START_REF] Bottaro | Integrating molecular simulation and experimental data. A Bayesian/maximum entropy reweighting approach[END_REF][START_REF] Brotzakis | A method of incorporating rate constants in molecular dynamics simulations[END_REF][START_REF] Bolhuis | A maximum caliber approach for path ensembles[END_REF] . This technique has been applied using nuclear magnetic resonance (NMR) spectroscopy [START_REF] Bonomi | Metainference: A Bayesian inference method for heterogenous systems[END_REF][START_REF] Olsson | Combining experimental and simulation data of molecular processes via augmented Markov models[END_REF][START_REF] Cesari | Using the maximum entropy principle to combine simulations and solution experiments[END_REF][START_REF] Camilloni | A tensor free method for the structural and dynamical refinement of proteins using residual dipolar couplings[END_REF][START_REF] Bottaro | Biophysical experiments and molecular simulations[END_REF][START_REF] Heller | Small-molecule sequestration of amyloid-β as a drug discovery strategy for Alzheimer's disease[END_REF][START_REF] Löhr | A kinetic ensemble of Alzheimer's Aβ peptide[END_REF][START_REF] Löhr | Metadynamic metainference: Convergence towards force field independent structural ensembles of a disordered peptide[END_REF] , fluorescence resonance energy transfer (FRET) microscopy [START_REF] Bonomi | Determining protein complex structures based on a Bayesian model of in vivo Förster Resonance Energy Transfer (FRET) data[END_REF] , small-angle scattering techniques (SAXS/SANS) [30][START_REF] Hermann | SAXS restrained ensemble simulations of intrinsically disordered proteins with commitment to the principle of maximum entropy[END_REF][START_REF] Bengtsen | Structure and dynamics of a nanodisc by integrating NMR, SAXS and SANS experiments with molecular dynamics simulations[END_REF][START_REF] Paissoni | Determination of protein structural ensembles by hybrid resolution SAXS restrained molecular dynamics[END_REF][START_REF] Jussupow | The dynamics of linear polyubiquitin[END_REF] , transition rate constants [START_REF] Brotzakis | A method of incorporating rate constants in molecular dynamics simulations[END_REF][START_REF] Bolhuis | A maximum caliber approach for path ensembles[END_REF] and cryo-EM data [START_REF] Bonomi | Simultaneous determination of protein structure and dynamics using cryo-electron microscopy[END_REF] .

One of such methods, cryo-EM metainference (EMMI) [START_REF] Bonomi | Simultaneous determination of protein structure and dynamics using cryo-electron microscopy[END_REF] , can accurately model a thermodynamic ensemble by combining prior information on the system, such as physicochemical knowledge (e.g. a force field), with noisy (i.e. subject to systematic and random errors) and heterogeneous (i.e. encoding a conformational ensemble) experimental data, using cryo-EM density maps. EMMI has already been used in a series of complex macromolecular systems, including a CLP protease 36 , microtubules [START_REF] Eshun-Wilson | Effects of alpha-tubulin acetylation on microtubule structure and stability[END_REF] , microtubule-tau complexes [START_REF] Brotzakis | A structural ensemble of a tau-microtubule complex reveals regulatory tau phosphorylation and acetylation mechanisms[END_REF] , ASCT2 transporter [START_REF] Garibsingh | Rational Design of ASCT2 inhibitors using an integrated experimental-computational approach[END_REF] , and SARS-CoV-2 membrane receptor proteins [START_REF] Brotzakis | Determination of intermediate state structures in the opening pathway of SARS-CoV-2 spike using cryo-electron microscopy[END_REF] , allowing access to the continuous dynamics of biomolecules with atomistic resolution. The quality of the EMMI structural ensembles, however, is closely related to the exhaustiveness of the conformational sampling, which requires a computational time that scales exponentially with the barriers delimiting individual structural states 9 .

To tackle this rare event problem, several enhanced sampling methods have been developed. Enhanced sampling molecular simulation methods can be classified as trajectory based [START_REF] Dellago | Transition path sampling and the calculation of rate constants[END_REF][START_REF] Van Erp | A novel path sampling method for the calculation of rate constants[END_REF][START_REF] Allen | Sampling rare switching events in biochemical networks[END_REF][START_REF] Faradjian | Computing timescales from reaction coordinates by milestoning[END_REF][START_REF] Brotzakis | Approximating free energy and committor landscapes in standard transition path sampling using virtual interface exchange[END_REF][START_REF] Chong | Path sampling strategies for simulating rare events in biomolecular systems[END_REF] and collective-variable (CV) based [START_REF] Valleau | Non physical sampling distribution in Monte Carlo freeenergy estimation: Umbrella sampling[END_REF][START_REF] Grubmuller | Predicting slow structural transitions in macromolecular systems: Conformational flooding[END_REF][START_REF] Landau | Efficient, multiple-range random walk algorithm to calculate the density of states[END_REF][START_REF] Darve | Calculating free energies using average force[END_REF][START_REF] Laio | Escaping free energy minima[END_REF] . For detailed reviews, readers can refer to the recent literature [START_REF] Valsson | Enhancing rare fluctuations: rare events and metadynamics from a conceptual viewpoint[END_REF][START_REF] Bolhuis | Transition path sampling as Markov chain Monte Carlo of trajectories: Recent algorithms, software, applications, and future outlook[END_REF] . A particularly powerful CV-based enhanced sampling method, which is very efficient once appropriate CVs are chosen, is metadynamics [START_REF] Laio | Escaping free energy minima[END_REF][START_REF] Bonomi | Promoting transparency and reproducibility in enhanced molecular simulations[END_REF] . Metadynamics adds a historydependent bias to the system as a function of microscopic degrees of freedom of the system known as collective variables. With this bias, the simulations can escape deep free energy minima and sample transitions between different states. The choice of the CVs is critical to achieve the desired speed-up of convergence [START_REF] Valsson | Enhancing rare fluctuations: rare events and metadynamics from a conceptual viewpoint[END_REF] . Recent developments in identifying and automating the search for appropriate CVs have increased the efficiency of this method, thus providing a remedy to the conformational sampling problem [START_REF] Pfaendtner | Efficient sampling of high dimensional free energy landscapes with parallel bias metadynamics[END_REF][START_REF] Tiwary | Spectral gap optimization of order parameters form sampling complex molecular systems[END_REF][START_REF] Sultan | Accelerating metadynamics by using kinetically selected collective variables[END_REF][START_REF] Marcelo | Reweighted autoencoder variational bayes for enhanced sampling (RAVE)[END_REF][START_REF] Brotzakis | Enhanced sampling of protein conformational transition via dynamically optimized collective variables[END_REF] . While metainference has been combined already with metadynamics 60 , this was not the case for EMMI (Figure 1A). Combining EMMI with enhanced sampling methods can lead to accurate and efficient determination of structural ensembles using the vast dataset of cryo-EM database, which can in turn provide atomistic insight to a range of biomolecular systems and processes.

In this work, we present the MEMMI method, which incorporates metadynamics in EMMI in order to accelerate the ability of EMMI to sample structural ensembles with slowly interconverting states. We illustrate the application of this approach to determine the structural ensemble of an amyloid fibril formed by the full-length (residues 1-37) islet amyloid polypeptide (IAPP), an aberrant assembly associated with the degeneration of pancreatic bcells in type-2 diabetes (T2D). When functioning correctly, IAPP, together with insulin, contributes to glycaemic control. IAPP and insulin are synthesized and stored together in pancreatic b-cells, but when IAPP aggregates in the extracellular space of the islets of Langerhans, amyloid-induced apoptosis of b-cells may occur [START_REF] Pilkington | Effects of protein corona on IAPP amyloid aggregation, fibril remodeling and toxicity[END_REF] . In 95% of T2D patients, IAPP is found as extracellular amyloid deposits [START_REF] Zhang | Acidic environment significantly alters aggregation pathway of human islet amyloid polypeptide at negative lipid membrane[END_REF][START_REF] Santos | pH-Dependent Aggregation in intrinsically disordered proteins is determined by charge and lipophilicity[END_REF][START_REF] Nguyen | Amyloid oligomers: A joint experimental/computational perspective on Alzheimer's disease, Parkinson's disease, type II diabetes, and amyotrophic lateral sclerosis[END_REF] , which form through surface-catalysed secondary nucleation [START_REF] Rodriguez Camargo | Surface-catalyzed secondary nucleation dominates the generation of toxic IAPP aggregates[END_REF] . IAPP fibrils represent a challenging system for structural biology studies, since no unique structure can be resolved in the low-density regions of the 12-residue long Nterminal tails, due to conformational heterogeneity and associated errors in the measurement. For this reason, although recent cryo-EM experiments determined various amyloid fibril structures of IAPP [START_REF] Cao | Cryo-EM structure and inhibitor design of human IAPP (amylin) fibrils[END_REF][START_REF] Cao | Cryo-EM structures of hIAPP fibrils seeded by patient-extracted fibrils reveal new polymorphs and conserved fibril cores[END_REF][START_REF] Röder | Cryo-EM structure of islet amyloid polypeptide fibrils reveals similarities with amyloid-β fibrils[END_REF] , the structural heterogeneity in the disordered flanking regions, known as fuzzy coat, has so far proved impossible to resolve accurately. It would be desirable to acquire a better understanding of the conformational properties of the fuzzy coat, since this region is thought to play a central role in the interactions of amyloid fibrils with other cellular components such as RNA molecules and molecular chaperones [START_REF] Ulamec | Looking beyond the vore: The role of flanking regions in the aggregation of amyloidogenic peptides and proteins[END_REF][START_REF] Wegmann | The fuzzy coat of pathological human tau fibrils is a two-layered polyelectrolyte brush[END_REF] . Moreover, the fuzzy coat is likely to be involved in cell membrane binding, potentially promoting the catalysis of aggregation and capturing amyloid precursors [START_REF] Ulamec | Looking beyond the vore: The role of flanking regions in the aggregation of amyloidogenic peptides and proteins[END_REF][START_REF] Wegmann | The fuzzy coat of pathological human tau fibrils is a two-layered polyelectrolyte brush[END_REF] . Recent studies on the tau protein, which is implicated in numerous neurodegenerative diseases known as tauopathies, show that depending on pH conditions, the thick fuzzy coat changes the fibril properties, including mechanically stiffness, and repulsive and adhesive behaviours [START_REF] Ulamec | Looking beyond the vore: The role of flanking regions in the aggregation of amyloidogenic peptides and proteins[END_REF][START_REF] Wegmann | The fuzzy coat of pathological human tau fibrils is a two-layered polyelectrolyte brush[END_REF] . Here, we detail the continuous dynamics of the fuzzy coat of an IAPP fibril and utilise a thermodynamical theory of melting to characterize the different regions of the fibril to gain insight into its mechanical properties.

The MEMMI method

The cryo-EM forward model. A cryo-EM density map resulting from class-averaging and 3D

reconstruction is typically encoded as voxels on a grid, and the map is generally distributed in this form. For computational efficiency, and to enable differentiability and analysis of correlations between data points, the map can be converted to a Gaussian mixture model 𝜙 ! (𝒙) (GMM) consisting of 𝑁 D Gaussian components

𝜙 D (𝒙) = ∑ 𝜙 D,$ (𝒙) % D $&' = ∑ 𝜔 D,$ 𝐺(𝒙|𝒙 D,$ , 𝜮 D,$ ) % D $&' (1)
where 𝒙 is a vector in Cartesian space, 𝜔 !,$ is the scaling factor of the i-th component of the data GMM and 𝐺 is a normalized Gaussian function centered at 𝒙 D,$ with covariance matrix 𝜮 D,𝒊 . The agreement between models generated by molecular dynamics (MD) and the data GMM is calculated by the following overlap function 𝑜𝑣 MD,$

𝑜𝑣 MD,$ = ∫ 𝜙 M (𝒙)𝜙 D,$ (𝒙) d𝒙 (2)
where 𝜙 M (𝒙) corresponds to the model GMM obtained from molecular dynamics. To deal with the heterogeneity of the system, EMMI simulates many replicas, r, of the system. The overlap between model GMM and data GMM is estimated over the ensemble of replicas as an average overlap per GMM component 𝑜𝑣 MD,$ . This forward model overlap can then be compared to the data GMM self-overlap 𝑜𝑣 DD,$ = ∫ 𝜙 D (𝒙)𝜙 D,$ (𝒙) d𝒙 Metainference. Metainference is a Bayesian approach for modelling statistical ensembles by combining prior information on a system with experimental data subject to noise or systematic errors [START_REF] Bonomi | Metainference: A Bayesian inference method for heterogenous systems[END_REF] . This framework is particularly well suited to structural ensemble determination through molecular dynamics simulations, in which the prior (i.e. the force field) is updated with information from experimental methods, such as NMR spectroscopy, SAXS, or cryo-EM data. Metainference is designed to handle systematic errors (such as biases in the force field or forward model), random errors (due to noise in experimental data), and errors due to the limited sample size of the ensemble [START_REF] Bonomi | Metainference: A Bayesian inference method for heterogenous systems[END_REF] . The model generation is governed by the metainference energy function, defined as 𝐸 MI = -𝑘 B 𝑇 log(𝑝 MI ), in which 𝑘 B is the Boltzmann constant, T is the temperature, and 𝑝 MI is the metainference posterior probability

𝑝 MI (𝑿, 𝜎 SEM , 𝜎 B < 𝑫) = ∏ 𝑝(𝑋 . ) ∏ 𝑝@ 𝑑 $ | | 𝑿, 𝜎 $ SEM , 𝜎 .,$ B C 𝑝@𝜎 .,$ C % D $&' % R .&' (3)
Here, X is a vector representing the atomic coordinates of the full ensemble, consisting of individual replicas 𝑋 . ; σ SEM is the error incurred by the limited number of replicas in the ensemble; σ B encodes the random and systematic errors in the prior, forward model, and experiment; and 𝑫 = [𝑑 $ ] is the experimental data. Note 𝜎 SEM is calculated per data point (𝜎 $ SEM ), while 𝜎 B is computed per datapoint i and replica r as σ $,. B . In the present case, the likelihood 𝑝@ 𝑑 $ | | 𝑋, 𝜎 $ SEM , 𝜎 .,$ B C takes the form of a Gaussian function

𝑝@𝑑 $ <𝑿, 𝜎 $ SEM , 𝜎 .,$ B C = 1 √2𝜋 K @𝜎 .,$ B C / + @𝜎 $ SEM C / exp P- 1 2 Q𝑜𝑣 DD,$ -𝑜𝑣 MD,$ R / @𝜎 .,$ B C / + @𝜎 $ SEM C / S (4)
where 𝑜𝑣 MD,$ is the ensemble average of the overlap. The metainference energy function for multiple replicas then becomes

𝐸 MI (𝑿, σ) = 𝐸 MD (𝑿) + 𝑘 B 𝑇 2 U Q𝑜𝑣 DD,$ -𝑜𝑣 MD,$ R / @𝜎 .,$ B C / + @𝜎 $ SEM C / % R ,% D .,$ + 𝐸 0 ( 5
)
where 𝐸 0 represents the energy associated with the error 𝜎 = (𝜎 B , 𝜎 SEM )

𝐸 0 = 𝑘 B 𝑇 U -log@𝜎 .,$ B C + 1 2 log W@𝜎 .,$ B C / + @𝜎 $ SEM C / X % R ,% D .,$ (6)
.

𝐸 MD represents the molecular dynamics force field. While the space of conformations 𝑋 . is sampled by multi-replica molecular dynamics simulations, the error parameters for each datapoint 𝜎 .,$ B are sampled by a Monte Carlo sampling scheme at each time step. The error parameter related to the limited number of replicas used to estimate the forward model (𝜎 SEM ) can be chosen as a constant or estimated on the fly by using a windowed average [START_REF] Bengtsen | Structure and dynamics of a nanodisc by integrating NMR, SAXS and SANS experiments with molecular dynamics simulations[END_REF] .

Metadynamic cryo-EM metainference (MEMMI).

To accelerate the sampling of the metainference ensemble, one can utilise an enhanced-sampling scheme such as metadynamics [START_REF] Laio | Escaping free energy minima[END_REF][START_REF] Bonomi | Metadynamic metainference: enhanced sampling of the metainference ensemble using metadynamics[END_REF] . In this case, we use parallel-bias metadynamics (PBMetaD) [START_REF] Pfaendtner | Efficient sampling of high dimensional free energy landscapes with parallel bias metadynamics[END_REF] with the multiple walkers scheme [START_REF] Raiteri | Efficient reconstruction of complex free energy landscapes by multiple walkers metadynamics[END_REF] . Here, 𝑉 PB is a time-dependent biasing potential acting on a set of 𝑁 CV collective variables 𝑠(𝑿), which in turn are functions of the system coordinates

𝑉 PB @𝑠 4 (𝑿), 𝑡C = -𝑘 B 𝑇 log ^∑ 𝑒𝑥𝑝 a 5 G 67 $ (𝑿),;< = B > b % CV 4&' c (7)
In contrast to conventional metadynamics, in PBMetaD multiple one-dimensional bias potentials 𝑉 G are deposited rather than a single high-dimensional one. This alleviates the curse of dimensionality while still allowing an efficient exploration of phase space [START_REF] Pfaendtner | Efficient sampling of high dimensional free energy landscapes with parallel bias metadynamics[END_REF] . Additionally, the use of multiple replicas through the multiple walkers scheme 71 allows the sharing of the bias potential to drastically improve the sampling performance, while at the same time being a natural fit for the replica averaging approach of EMMI. Analogously to well-tempered metadynamics, these bias potentials 𝑉 G @𝑠 4 (𝑿), 𝑡C eventually converge to the free energy 𝐹@𝑠 4 (𝑿)C. The MEMMI energy function then becomes 7

𝐸 MEMMI (𝑿, 𝜎, 𝑡) = 𝐸 EMMI (𝑿, 𝜎) + U 𝑉 PB [𝒔(𝑿 . ), 𝑡] % R .&' (8)
While the PBMetaD bias potential is shared among replicas, each replica may still experience a varying potential depending on its location in phase space. Thus, the arithmetic average over the forward models (i.e., the overlap) no longer presents an unbiased estimate of the ensemble average. It therefore needs to be replaced with a weighted average, utilising the bias potential of each replica r to unbias the ensemble at time t

𝑓 $ (𝑿, 𝑡) = ∑ 𝑤(𝑿 . , 𝑡)𝑓 $ (𝑿 . , 𝑡) % R .&' (9)
where the unbiasing weight 𝑤(𝑿 . , 𝑡) is defined as

𝑤(𝑿 . , 𝑡) = exp i 5 PB [𝒔(𝑿 ) ),;] = B > j a∑ exp ^5PB C𝒔6𝑿 $ <,;D = B > c % R 4&' b E' (10).
The unbiasing procedure used here is analogous to the standard umbrella-sampling technique by Torrie and Valleau 47 . Now the ensemble average 𝑜𝑣 F!,$ (𝑿) is given by

𝑜𝑣 F!,$ (𝑿) = ∑ 𝑤(𝑿 . , 𝑡) 𝑜𝑣 F!,$ (𝑿 . ) % * .&' (12)
The MEMMI energy 𝐸 MEMMI is thus equal to Eq. 5.

Initial fibril structure. We build the initial structure of the full fibril by starting from a deposited fibril structure (PDB:6Y1A), which only contains the core of the fibril, and extending each of the 16 peptide chains by adding the missing 12-residue N-terminal sequence with guidance from the cryo-EM density map EMD-10669. We use the macromolecular modelbuilding program coot [START_REF] Emsley | Features and development of coot[END_REF] .

Molecular dynamics setup and equilibration.

We continue by creating a 12.34 ´ 12.34 ´ 12.34 nm cubic simulation box, solvating with 58291 water molecules and neutralizing the net charge by adding 48 Cl -ions. We use the CHARMM22* 73 force field and TIP3P [START_REF] Jorgensen | Comparison of simple potential functions for simulating liquid water[END_REF] water models. We continue with an energy minimization followed by a 500 ps NPT equilibration at a temperature of 310 K and pressure of 1 atm, followed by an additional 2 ns NVT equilibration at 310 K. The molecular dynamics parameters are the same used previously [START_REF] Brotzakis | Determination of intermediate state structures in the opening pathway of SARS-CoV-2 spike using cryo-electron microscopy[END_REF] .

MEMMI simulations.

We first express the experimental voxel map data as a data GMM containing 10,000 Gaussians in total, resulting in a 0.975 correlation to the original voxel map EMD-10669 61 , using the gmmconvert utility [START_REF] Kawabata | Multiple Subunit Fitting into a low-resolution density map of a macromolecular complex using a gaussian mixture model[END_REF] . We continue by extracting 32 configurations from the previous NVT equilibration and initiate a MEMMI simulation, consisting of 32 replicas, resulting in an aggregate runtime of 5.49 μs, using PLUMED.2.6.0-dev [START_REF] Tribello | PLUMED 2: New feathers for an old bird[END_REF] and gromacs-2020.6 [START_REF] Abraham | GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers[END_REF] . The simulation is performed in the NVT ensemble at 310 K using the same MD parameters as in the equilibration step. Configurations are saved every 10 ps for postprocessing. The cryo-EM restraint is updated every 2 MD steps, using neighbour lists to compute the overlaps between model and data GMMs, with a neighbour list cut-off of 0.01 and update frequency stride of 100 steps. The biasing collective variables 𝑠 = [𝑠 $ ] in the simulation are shown in Figure S1A and the biasing scheme is PBMetaD 55 with the well-tempered [START_REF] Barducci | Well-tempered metadynamics: A smoothly converging and tunable free-energy method[END_REF] and multiple-walkers 71 protocols. The hill height is set to 0.3 kJ/mol, with a deposition frequency of 200 steps and an adaptive Gaussians diffusion scheme [START_REF] Branduardi | Metadynamics with adaptive Gaussians[END_REF] . The biasing collective variables correspond to degrees of freedom of the left hand-side N-terminal. The respective degrees of freedom of the right hand-side N-terminal do not feel a metadynamics potential and therefore in the remaining text will be referred to as EMMI degrees of freedom and are also listed in Figure S1. As a post-processing step, we generate the final structural ensemble by resampling the generated configurations based on the converged unbiasing weights for each structure after an equilibration of 7 ns shown in Figure . 1A. To establish convergence, we perform a clustering analysis on the structural ensemble based separately on the first and second half of each replica (taking into accounts the weights) using the GROMOS method [START_REF] Daura | Peptide folding: When simulation meets experiment[END_REF] , and with metric the root mean square deviation (RMSD) calculated on the Ca (CA) atoms (Figure S1B). Timetraces of CVs as well as their time-dependent free energy profile are shown in Figs. S2-S4. For molecular visualizations and calculating the local correlation of the final structural-ensemblegenerated cryo-EM map with the experimental cryo-EM map, we use Chimera and gmconvert [START_REF] Pettersen | UCSF Chimera-a visualization system for exploratory research and analysis[END_REF][START_REF] Kawabata | Multiple Subunit Fitting into a low-resolution density map of a macromolecular complex using a gaussian mixture model[END_REF] . Except otherwise mentioned, all the structural analysis is performed on degrees of freedom of left side tail N-tail and chains 3 and 4. We do so in order to avoid the finite size effects on the outermost N-tails.

Structure and dynamics of IAPP amyloid fibrils

Acceleration of the conformational sampling. MEMMI accelerates the conformational sampling by biasing a set of microscopic degrees of freedom of the system, also known as collective variables (CVs, Figure S1). To demonstrate the performance of this approach, we visualize the time-trace of the collective variable χ3 4 which corresponds to the disulfide bond dihedral of the 4 th peptide in the eight-layer stack of the fibril. This peptide is thus representative of a buried monomer with little interaction with the fibril ends. Note that, due to C2 helical symmetry, there are two of these dihedrals, one corresponding to the one side and one to the other sides N-terminal tail (Figure 1B). The sampling of one side is accelerated by a biasing potential of Eq. 7 (MEMMI), while the other is not (EMMI). Compared to the dihedral, the biased disulfide χ3 4 (MEMMI) shows an increased transition rate, and thus more efficient conformational sampling (Figure 1C). While both methods characterise the wells of the stable states (L, R), MEMMI is able to provide access to higher free energy transition state (TS) regions (Figure 1D). Monitoring a non-periodic CV, such as the number of contacts between N-tail three and four, shows diffusion along high-low contact regions in both MEMMI and EMMI cases but somewhat more frequent in the MEMMI case. The combination of diffusion shown in the traces of CVs and the free energy profiles as a function of simulation time of all collective variables biased by EMMI and MEMMI are shown in Figures S2-S4, and indicate that our simulations are well converged. Taken together, these results show that both the MEMMI and EMMI simulations are converged in the low free energy regions, while MEMMI enables visiting high free energy regions.

Conformational heterogeneity of the fuzzy coat.

A structure of the IAPP fibril core (residues 13-37) has been previously published (PDB:6Y1A) using the cryo-EM data used also in the present work (EMD-10669). Here, we determine a structural ensemble of the whole IAPP fibril (residues 1-37). We model the system as a stack of eight peptides per side (Figure 2A). While the core of the fibril maintains a parallel b-sheet structure, the flanking region (residues 1-12) exhibits a large conformational heterogeneity (Figure 2B). While we find the cores residues 12-37 to be largely in a b-sheet conformation, we also note significant heterogeneity for residues 23-24, 32-34 and 37. As shown in Figure 2B, residues 23-24 interact with TYR37 and maintain mostly a coil structure, while residues 32-34 interact with the fuzzy coat and maintain mostly a coiled structure. We also detected a small population of a-helical conformations in the region of residues 5-9.

Correlation between experimental and calculated cryo-EM maps. We estimate the correlation of the MEMMI structural ensemble with the experimental cryo-EM map (Figure 3). We find that using a structural ensemble IAPP (residues 1-37) correlates better with the experimental cryo-EM map than a single structure (PDB:6Y1A) (Figure 3A,B). The coefficient of correlation of the structural ensemble to the experimental electron density map is on average 0.92. Furthermore, an important feature of MEMMI is its ability to estimate the error in the experimental electron density map (Figures 3C andS5). We find that the relative error per Gaussian data point is on average 0.09, where the relative error is the error of each Gaussian data point with respect to the total overlap between all data GMM and the i th component of the data GMM [START_REF] Bonomi | Simultaneous determination of protein structure and dynamics using cryo-electron microscopy[END_REF] . The low-density, high-error volume around residues S34, N35, and T36 can likely be attributed to the lack of MES/NaOH buffer in our MEMMI simulations, which is present in the experimental setup. Both MEMMI and EMMI exhibit good corelation (about 0.83) in the respective N-tail region with the cryo-EM map (Figure 3D).

Comparison of the dynamical properties of the fibril core and fuzzy coat. The conformational properties of the fuzzy coat and core region have been shown to be relevant modulating the properties of amyloid fibrils, including their ability to interact with various cellular components [START_REF] Ulamec | Looking beyond the vore: The role of flanking regions in the aggregation of amyloidogenic peptides and proteins[END_REF][START_REF] Wegmann | The fuzzy coat of pathological human tau fibrils is a two-layered polyelectrolyte brush[END_REF] . To investigate this phenomenon in the case of the present IAPP amyloid fibril, we take inspiration from a thermodynamic theory of melting and characterize the residue-dependent Lindemann parameter DL (Figure 4), which encodes information on solid-like and liquid-like behaviour [START_REF] Lindorff-Larsen | Simultaneous determination of protein structure and dynamics[END_REF] . At the backbone-level, we find that the fibril core (23-30) is solid-like (DL < 0.15), while the flanking region (1-12) is on the verge of a liquid-like behaviour (DL ³ 0.15). The Lindemann parameters of the side-chains indicate more mobility and are liquid-like outside the region of residues 20-32. These results reveal that about half of the structural core of this amyloid fibril remains rather disordered at the side-chain level, a phenomenon observed also for folded native states [START_REF] Lindorff-Larsen | Simultaneous determination of protein structure and dynamics[END_REF] . This is in contrast to monomeric disordered proteins such as amyloid-β, which generally exhibit Lindemann parameters DL ³ 1.0 (Figure S7).

Residue-specific solubility of the amyloid fibril. To investigate whether or not the surface of the amyloid fibril is soluble, we calculate the solubility per residue, using the structurecorrected CamSol solubility score [START_REF] Sormanni | The CamSol method of rational design of protein mutants with enhanced solubility[END_REF] (Figure 5A,B). We find three insoluble regions: (i) residues 5-7 (ATC) in the fuzzy coat, (ii) residues 26-29 (ILSS) at the ends of the fibril fragment, and (iii) residues 14-18 (NFLVH) on the fibril surface, stretched along the helical axis. The latter result is consistent with experimental evidence of residues N14, H18, and S20 being implicated in IAPP aggregation [START_REF] Santos | pH-Dependent Aggregation in intrinsically disordered proteins is determined by charge and lipophilicity[END_REF][START_REF] Cao | Sensitivity of amyloid formation by human islet amyloid polypeptide to mutations at residue[END_REF] via secondary nucleation. We note that the solventexposed and aggregation-related residues 5-7 and 18-20 might present attractive targets for structure-based drug discovery.

Conclusions

We have presented the MEMMI method for the simultaneous determination of the structure and dynamics of large and conformationally heterogeneous biomolecular structures from cryo-EM measurements. To illustrate the type of information that can be extracted from this type of approach, we have reported a structural ensemble of an amyloid fibril formed by IAPP. The analysis of the structural ensembles has revealed the conformational and the aggregationrelated solubility properties of the fuzzy coat of the amyloid fibril, and that many of the sidechains in the structural core of the amyloid fibril exhibit liquid-like behaviour. Since this phenomenon has also been observed for native states of proteins, these results reveal a similarity in the structural behaviour of proteins upon folding in their native and amyloid states.

Figure 1 .Figure 2 .Figure 3 .Figure 4 .

 1234 Figure 1. Acceleration of the conformational sampling using MEMMI. (A) Summary of the capability of the different approaches discussed in this work (molecular dynamics, metadynamics, EMMI and MEMMI) to include continuous dynamics, enhanced sampling and cryo-EM restraints. (B) A representative configuration of an IAPP amyloid fibril with a biased disulfide bond (red) and an unbiased one (blue). (C) Time trace of the χ3 4 and N3-4 CVs with EMMI and MEMMI. (D) Free energy profile as a function of χ3 4 with the corresponding structural states of the disulfide bond: MEMMI (red) EMMI (blue).

Figure 5 .Figure S2 .

 5S2 Figure 5. Analysis of the solubility of the fibril surface from the MEMMI ensemble. (A) Representative structure with CamSol solubility scores indicated on the surface (right). (B) Per-residue CamSol solubility score. (C) Per-residue solvent-accessible surface area. Error bars are calculated as standard deviation between the first and second simulation half.

Figure S3 .

 S3 Figure S3. Assessment of the convergence of the MEMMI simulation. Free energy profiles for all biased collective variables for subsequent 1 µs increments of simulated time.

Figure S4 .

 S4 Figure S4. Assessment of the convergence of the EMMI simulation. Free energy profiles for all unbiased collective variables for subsequent 1 µs increments of simulated time.

Figure S5 .Figure S6 .

 S5S6 Figure S5. Assessment of the local correlation between experimental and calculated cryo-EM map. Local correlation of the cryo-EM map (EMD-EM-10669) with a map generated from the MEMMI ensemble of an IAPP amyloid fibril as a function of increasing strength of density (decreasing electron density thresholds): 1σ (A), 2σ (B), 3σ (C), and 4σ (D).

Figure S7. Lindemann parameters for the disordered monomeric amyloid-β 42 peptide computed from a previously published ensemble 27 .

 27 Figure S7. Lindemann parameters for the disordered monomeric amyloid-β 42 peptide computed from a previously published ensemble 27 . Lindemann parameters calculated for the backbone (A) and side chains (B) with the liquid-solid transition and residue-mean indicated. Error bars indicate the 95th percentile of the mean of a bootstrap sample over all 5119 trajectories in the ensemble.

Acknowledgements. Z.F.B. would like to acknowledge the Federation of European Biochemical Societies (FEBS) for financial support (LTF). M.B. and S.E.H. acknowledge the support of the French Agence Nationale de la Recherche (ANR), under grant ANR-20-CE45-0002 (project EMMI).

Data availability.

Input files for the simulations can be found at PLUMED-NEST (plumID:22.023) The ensemble can be found at https://zenodo.org/record/6518554#.YnLwby8Rqhw

Supplementary Information