Marc F Lensink
email: marc.lensink@univ-lille.fr

Nurul Nadzirin

Sameer Velankar

Shoshana J Wodak

Modeling protein-protein, protein-peptide and protein-oligosaccharide complexes: CAPRI 7 th edition

Keywords: CAPRI, protein docking, protein-protein interaction, protein-peptide interaction, protein-polysaccharide interaction, protein complexes, protein assemblies, blind prediction, docking 2

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INTRODUCTION

Proteins carry out their function by interacting with other proteins, and with macromolecular components such as DNA or RNA [START_REF] Alberts | The cell as a collection of protein machines: preparing the next generation of molecular biologists[END_REF] . The disruption or deregulation of these interactions often leads to disease [START_REF] Ideker | Protein networks in disease[END_REF][START_REF] Barabasi | Network medicine: a network-based approach to human disease[END_REF] . Charting these interactions and characterizing their biochemical, biophysical, and structural properties therefore remains an important goal in molecular biology and medicine.

Experimental and computational techniques have played a decisive role in pursuing this goal.

Proteomics techniques have been providing increasingly more comprehensive descriptions of the protein interaction landscape of specific cellular processes, of cell types, and entire organisms [START_REF] Hein | A human interactome in three quantitative dimensions organized by stoichiometries and abundances[END_REF][START_REF] Babu | Global landscape of cell envelope protein complexes in Escherichia coli[END_REF] . These descriptions yield information on the identities of the interacting proteins, and the compositions of multi-protein assemblies, or complexes, but offer little information on the stoichiometry of the interactions, or their molecular details.

Over the last 5 decades, structural biology techniques such as X-ray crystallography and Nuclear Magnetic Resonance (NMR) have been very effective in charting the 3D structure repertoire of individual proteins, with the data deposited in the PDB (Protein DataBank) [START_REF] Ww | Protein Data Bank: the single global archive for 3D macromolecular structure data[END_REF] . But so far these techniques have been slow in providing information on the 3D structures of macromolecular complexes, in particular those of larger assemblies that are active in the cell and can be detected by modern proteomics and other methods [START_REF] Berman | Trendspotting in the Protein Data Bank[END_REF][START_REF] Dutta | Large macromolecular complexes in the Protein Data Bank: a status report[END_REF] . But this is changing rapidly thanks to recent spectacular advances in single molecule cryo-EM techniques, specifically geared at determining the structure of large macromolecular assemblies to atomic resolution [START_REF] Bai | How cryo-EM is revolutionizing structural biology[END_REF][START_REF] Cheng | How Cryo-EM Became so Hot[END_REF] .

Computational methods have been playing an important role in efforts to populate the uncharted landscape of protein assemblies, by exploiting the rapidly increasing body of data on protein sequences and three-dimensional structures of individual proteins and their complexes stored in public databases [START_REF] Kundrotas | Templates are available to model nearly all complexes of structurally characterized proteins[END_REF] . In recent years these methods are often cast into a wider framework of the so-called 'hybrid' or 'integrative' modeling techniques [START_REF] Karaca | Advances in integrative modeling of biomolecular complexes[END_REF][START_REF] Smith | Structural biology. Beyond blob-ology[END_REF] , whereby larger macromolecular assemblies are modeled by integrating sequence information and structural data at various levels of resolution, with various other types of data.

An important role in these recent developments is played by methods for modeling the atomic structure of macromolecular assemblies, starting from the structures of the individual components of these assemblies. These are the so-called ''docking'' algorithms and the associated scoring schemes, the energetic criteria for singling out stable binding modes [START_REF] Wodak | Structural basis of macromolecular recognition[END_REF][START_REF] Ritchie | Recent progress and future directions in protein-protein docking[END_REF][START_REF] Vajda | Convergence and combination of methods in protein-protein docking[END_REF] . A key factor fueling progress in docking algorithm and more generally in methods for modeling protein assemblies has been the Critical Assessment of PRedicted Interactions (CAPRI) (http://pdbe.org/capri/; http://www.capri-docking.org/). CAPRI is a community-wide initiative established in 2001, offering computational biologists the opportunity to test the performance of their computational procedures in blind predictions of experimentally determined 3D structures of protein complexes, the 'targets', provided to CAPRI prior to publication. CAPRI initially focused on targets representing protein-protein complexes. Over the years its focus broadened by including targets representing complexes of proteins with other molecules (including peptides, nucleic acids and sugars) [START_REF] Lensink | Docking, scoring, and affinity prediction in CAPRI[END_REF] , tackling the problem of predicting protein binding affinities [START_REF] Fleishman | Community-wide assessment of proteininterface modeling suggests improvements to design methodology[END_REF][START_REF] Moretti | Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions[END_REF] and modeling the positions of water molecules in protein-protein interfaces [START_REF] Lensink | Blind prediction of interfacial water positions in CAPRI[END_REF] . Since CAPRI's inception the task of modeling the atomic structure of protein complexes has likewise evolved. Initially, this task involved mainly classical docking procedures, which sample and score putative binding poses of two or more proteins using as input the known unbound structures of the individual interacting components [see ref [START_REF] Wodak | Structural basis of macromolecular recognition[END_REF] ]. With the growing ease with which structural templates can be found in the PDB, docking calculations now routinely accept as input homology-built models of individual protein components of an assembly, with a growing rate of success [START_REF] Lensink | Modeling protein-protein and protein-peptide complexes: CAPRI 6th edition[END_REF][START_REF] Lensink | The challenge of modeling protein assemblies: the CASP12-CAPRI experiment[END_REF] . Furthermore, it is not uncommon to find templates for the full protein assembly. This is most often the case for assemblies of identical subunits (homo-dimers, or higher order homo-oligomers), because closely related proteins tend to adopt the same assembly mode (or oligomeric state) [START_REF] Levy | Structural, evolutionary, and assembly principles of protein oligomerization[END_REF][START_REF] Xu | Statistical analysis of interface similarity in crystals of homologous proteins[END_REF] . In such instances, classical docking calculations may no longer be required because the protein assembly can be modeled directly from the template, a task also called 'template-based docking' [START_REF] Kundrotas | Templates are available to model nearly all complexes of structurally characterized proteins[END_REF][START_REF] Negroni | Assessing the applicability of template-based protein docking in the twilight zone[END_REF][START_REF] Szilagyi | Template-based structure modeling of protein-protein interactions[END_REF] .

Methods for predicting the 3D structures of proteins ab-initio -from sequence information alone and in absence of available templates -have also come a long way. By leveraging the information on protein sequences, it is now possible to predict residues that make contacts in the protein 3D structure and therefore facilitate its prediction [27][START_REF] Ovchinnikov | Improved de novo structure prediction in CASP11 by incorporating coevolution information into Rosetta[END_REF][START_REF] Jones | MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins[END_REF] . Also, information on structure and sequence features of proteins (see for examples [START_REF] Kocher | Factors influencing the ability of knowledge-based potentials to identify native sequence-structure matches[END_REF][START_REF] Rooman | Prediction of protein backbone conformation based on seven structure assignments. Influence of local interactions[END_REF][START_REF] Rooman | Extracting information on folding from the amino acid sequence: accurate predictions for protein regions with preferred conformation in the absence of tertiary interactions[END_REF] ) is being exploited much more efficiently, thanks to new developments in Artificial Intelligence Deep Learning techniques [START_REF] Min | Deep learning in bioinformatics[END_REF][START_REF] Wang | Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model[END_REF] , enabling the prediction of the 3D structure of proteins from sequence information alone, as demonstrated recently in the ab-initio structure prediction challenge in CASP (Critical Assessment of protein Structure Prediction) [START_REF] Senior | Protein structure prediction using multiple deep neural networks in CASP13[END_REF] . As these methods progress, one would hope that they will ultimately be successfully integrated with assembly prediction procedures, and efforts in this direction have been undertaken by a close collaboration between CAPRI and CASP [START_REF] Lensink | The challenge of modeling protein assemblies: the CASP12-CAPRI experiment[END_REF][START_REF] Lensink | Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment[END_REF][START_REF] Lafita | Assessment of protein assembly prediction in CASP12[END_REF][START_REF] Lensink | Blind prediction of homo-and hetero-protein complexes: The CASP13-CAPRI experiment[END_REF] .

Here, we present the prediction results for the 16 targets of CAPRI Rounds 38-45 that took place during the years 2016-2018. These targets comprised protein-protein, protein-peptide and protein-oligosaccharide complexes. Protein-peptide complexes are a growing focus of CAPRI given the important role the recognition of short peptide motifs plays notably in regulatory processes [START_REF] Van Roey | Short linear motifs: ubiquitous and functionally diverse protein interaction modules directing cell regulation[END_REF][START_REF] Tompa | A million peptide motifs for the molecular biologist[END_REF] , whereas protein-sugar complexes represent an important category of complexes where method developments are needed. Prediction results for these targets were presented at the 7th CAPRI evaluation meeting held in April 2019 at the EBI (the European Bioinformatics Institute) (see https://bioexcel.eu/7th-capri-evaluation-meeting/). An additional prediction Round (Round 46) was run in collaboration with CASP during the CASP13 prediction challenge in the summer of 2018, with results described in a separate publication [START_REF] Lensink | Blind prediction of homo-and hetero-protein complexes: The CASP13-CAPRI experiment[END_REF] .

The results reported here were analyzed on the basis of a total of 36114 evaluated models, submitted by 57 participating groups including 13 automatic servers. The challenges posed by the different targets are discussed and the extent to which current modeling procedures were able to address them is evaluated. This evaluation is furthermore used to assess emerging trends in modeling protein complexes, and to evaluate progress in the field.

THE CAPRI TARGETS OF ROUNDS 38-45

Information on the 16 CAPRI targets of Rounds 38-45 for which predictions are evaluated here is summarized in Table I. These targets comprise 8 protein-protein complexes, 3 proteinpeptide complexes and 5 protein-oligosaccharide complexes. For all these complexes the prediction task involved modeling the 3D conformation of the different components (protein subunits, bound peptides or oligosaccharides) as well as the detailed binding mode between the components. The level of difficulty of these targets hence depended on the availability of templates for the individual subunits, or in some cases, for the hetero-complex as a whole, and on the ability to identify the correct binding mode and optimize the corresponding interface.

Protein-protein complexes (T122-125, T131-T133, T136)

This category of targets comprised 7 hetero-complexes, including 1 heterotrimer, 5 heterodimers and one homo-decamer (depicted in Figure 1). In the following they will be described in the order they were offered in CAPRI Rounds.

The first 3 targets T122, T123, T124 were offered in Round 39. T122 was the x-ray structure of the human cytokine-receptor heterotrimer complex (IL23/IL23R) determined at 2.8 Å resolution, contributed by Savvas Savvides, Ghent University, Belgium [START_REF] Bloch | Structural Activation of Pro-inflammatory Human Cytokine IL-23 by Cognate IL-23 Receptor Enables Recruitment of the Shared Receptor IL-12Rbeta1[END_REF] . The stoichiometry of the complex was experimentally verified to consist of 1 receptor monomer (IL23R) comprising 330 residues bound to 1 cytokine heterodimer (IL23A/IL12B) (comprising respectively 198 and 328 residues). In the 3D structure of the complex the receptor binds both chains of IL-23, forming interfaces that bury a total of 2330 Å 2 . This was in principle a medium difficulty target. Templates adopting somewhat different conformations were available for the receptor, (e.g. PDB code 2d9q, displaying 3.78 Å backbone rmsd relative to the bound receptor), and an unbound structure for the IL23 cytokine heterodimer was provided by the authors (later released as PDB code 5mxa; with a 3.59 Å backbone rmsd relative to the bound structure).

Nevertheless, the inter domain flexibility of the IL12B moiety and the IL23 receptor represented a challenge for the docking procedures.

T123 and T124, offered by Alain Roussel, Université Aix Marseille, France, were X-ray structures of the complexes between, respectively, the N-terminal (174 residues) and Cterminal (202 residues) domains of PorM, a component of the type 9 secretion system (T9SS), from bacteria (Porphyromonas gingivalis), and Camelid nanobodies: nb-02 (121 residues) and nb-130 (141 residues). These complexes, determined at the resolution of 2.5 Å (PorM-Nt/nb-02) and 2.15 Å (PorM-Ct/nb-130) [START_REF] Leone | Type IX secretion system PorM and gliding machinery GldM form arches spanning the periplasmic space[END_REF] , featured reasonable size interfaces of respectively 1240 Å 2 and 1120 Å 2 . These were difficult targets because although several good templates were available for the nanobodies, none were available for either of the PorM domains. The PorM-Nt domain is helical and adopts an up-down fold, but was missing the N-terminal transmembrane helix, whereas the PorM-Ct domain is a domain-swapped dimer, containing predominantly -strands [START_REF] Leone | Type IX secretion system PorM and gliding machinery GldM form arches spanning the periplasmic space[END_REF] . T124 was offered as two challenges, separated into the PorM-Ct dimeric structure, and the complex between a PorM-Ct monomer with the nanobody molecule.

To predict the structure of these complexes the PorM domains had to be modeled ab-initio, a challenging task with which CAPRI participant have very limited expertise. The PorM-Ct dimer was offered as target T0907 in the CASP12 structure prediction Round [START_REF] Kryshtafovych | Critical assessment of methods of protein structure prediction (CASP)-Round XIII[END_REF][START_REF] Moult | Critical assessment of methods of protein structure prediction (CASP)-Round XII[END_REF] , but no acceptable structures were produced then. T125, offered in Round 40 was the multi-protein assembly of the extracellular domain of the Lectin-like transcript 1 (LLT1) (protein A, 135 residues) with the extracellular domain of its inhibitor NKR-P1 (protein B, 146 residues). The structure was determined at 1.9 Å resolution by Ondřej Vaněk, Charles University, Prague, Czech Republic (PDB code 5mgt). According to the authors, the stoichiometry of the complex is A2B4, e.g. a homodimer of protein A, bound to 2 homodimers of protein B, forming a total of 5 distinct interfaces sized between 440 and 775 Å 2 of buried area (Table I). Two interfaces are those of the A2 and B2 homodimers, respectively, two other interfaces are formed between LLT1 and NKR-P1 proteins and a fifth small contact is formed between the NKR-P1 proteins (see Figure 1). This stoichiometry was however questioned by predictor groups, based on an analysis of various interfaces of the target structure (PDB code 5mgt) (see Dapkunas et al. this issue). A good quality template was available for the LLT1 homodimer, and a more distantly related template was available for the LLT1-NKR-P1 hetero-dimer. The main difficulty of this target likely resided in building the full assembly, given the uncertainty concerning the announced stoichiometry of this target.

The main interfaces of this target, i.e. the A2, B2, and AB interfaces, were therefore assessed separately.

T131 and T132, the next two protein-protein complexes offered in Round 42, were x-ray structures of host-pathogen complexes of the cell adhesion protein CEACAM1 from human (hCEACAM1 N-terminal domain; 108 residues), bound to the cell adhesion proteins of Helicobacter pylori, HopQ Type I (HopQ-I; 404 residues), and HopQ Type II (HopQ-II; 418 residues), respectively. These structures were solved to 2.8 Å and 2.59 Å resolution by Han Remaut, Free University of Brussels, and the Flemish Institute for Biotechnology, Brussels Belgium [START_REF] Moonens | Helicobacter pylori adhesin HopQ disrupts trans dimerization in human CEACAMs[END_REF] . The HopQ-I and HopQ-II are rather closely related (sequence ID ~60%), and engage in similar binding modes with the CEACAM1 protein. However HopQ-I features four loops, which contribute to the binding interface with the host protein, without altering the binding location. Excellent templates were available for the HopQ components (displaying backbone rmsd values as low as 1.0 Å, relative to HopQ-I). But none include the extra loop of HopQ-I. Excellent templates were also available for the host protein domain (rmsd below 1 Å). T131 (HopQ-I/CEACAM complex) was a more challenging target than T132, since the missing loops had to be modeled. T133, offered in Round 43, was a particularly interesting case. It represents a redesigned version of the wt Colicin E2/DNase-Im2 complex from E.coli (PDB code 3u43), which was previously a CAPRI target (T47) [START_REF] Lensink | Docking, scoring, and affinity prediction in CAPRI[END_REF] . The redesigned version, contributed by Sarel Fleishman and Ravit Netzer from the Weizmann Institute, Israel, is denoted as E des3 /Im des3 (referring to a specific version of the designed complex, where both components have been redesigned). Its structure was determined at high resolution [START_REF] Netzer | Ultrahigh specificity in a network of computationally designed protein-interaction pairs[END_REF] and shown to contain two complexes per asymmetric unit, which displayed only marginal differences. However, residues at the interface, and residue-residue contacts of both versions differed more relative to those of the wt complex (possibly as a result of a small rigid body rotation). The authors also indicated that the affinity of the redesigned complex was 73 nM (Kd), and that the latter complex displays high specificity relative to 'chimeric' versions, which contain one wt component, e.g. E des3 /Im2 and E2/Im des3 , which display 3 orders of magnitude lower affinity, and could therefore be used as controls for 'non-binders'.

Considering the relatively small differences between the designed and wt complexes, this was not a classical CAPRI prediction problem, because the challenge here was not to identify the correct association mode, which was well known, but to correctly identify the structural perturbation relative to the wt complex, which resulted in the gain in affinity. T136, offered in Round 45, was the lysine decarboxylase LdcA (751 residues), from Pseudomonas aeruginosa, a homo-decamer adopting D5 symmetry, determined by cryo-EM to 4.5 Å resolution. The target was contributed by Irina Gutsche, Institut de Biologie Structurale, Grenoble, France (personal communication), and was the first CAPRI target representing a cryo-EM multi-protein complex. The homo-decamer is composed of 2 rings, comprising 5 subunits each. These rings are packed on top of each other, such that subunits interact between rings, as well as within rings. This arrangement displays 3 distinct interfaces: a very large interface (5600 Å 2 of buried area) involving interactions between the subunits within and between rings, and two smaller interfaces of 1100 Å 2 and 800 Å, respectively, between subunits mainly within rings. This was in principle as easy target, since a 2.4 Å resolution xray structure of a related enzyme, the arginine decarboxylase AdiA from E. coli (PDB code 2vyc; backbone rmsd 2.33 Å; sequence identity, 40%) was available, and could therefore be used as template for building the structure of the full target complex.

Protein-peptide complexes (T121, T134, T135)

The protein-peptide complexes of targets T134 and T135 are depicted in Figure 2; T121 cannot be displayed at this time due to confidentiality reasons. The amino acid sequences of the peptides ligands of all 3 targets are provided in Figures 2 and7.

With 8 protein-peptide complexes offered as targets in previous CAPRI Rounds [START_REF] Lensink | Modeling protein-protein and protein-peptide complexes: CAPRI 6th edition[END_REF] , and the important role these complexes play in regulating many cellular processes, the CAPRI community has been working hard to meet the particular challenge of accurately predicting the 3D structure of such complexes. Protein-peptide complexes are indeed significantly different from protein-protein complexes. Their binding interface is usually much smaller, not exceeding 1000 Å 2 , reflecting the lower affinity of the corresponding associations. In addition, the bound peptides tend to retain a significant degree of conformational flexibility, such that often only a portion of the peptide becomes structured upon binding and the remaining residues remain too flexible to be visible in the X-ray structure. Since the protein moieties in these complexes generally undergo small conformational changes upon peptide binding [START_REF] Raveh | Sub-angstrom modeling of complexes between flexible peptides and globular proteins[END_REF] , the main challenge for predictors is to accurately model the bound conformation of the peptide and the corresponding protein-peptide interactions, including the side-chain conformations at the interface. This in turn required tightening the parameters used by the assessors to define model accuracy levels, as described below.

Target T121, offered in Round 38, was the complex between the TolAIII domain, a small Cterminal domain from the periplasmic protein TolA of P. aeruginosa (115 residues), and the 13 residue N-terminal peptide of P. aeruginosa TolB. The solution structure, solved by NMR, was contributed by Colin Kleanthous, University of Oxford UK (personal communication). The peptide used for the structure determination had an unblocked N-terminus and the Cterminus blocked with an amide group. This was the most difficult target of this category.

While an unbound x-ray structure was available for the TolAIII domain (PDB code 1lr0, backbone rmsd 1.7 Å relative to the target NMR average conformation), the buried interface in this solution structure was quite extensive for this type of targets (1400 Å 2 ). Correctly predicting the conformation of the otherwise flexible peptide and its interactions with the globular domain was therefore challenging. Regrettably, not much else can be revealed about this target at this point, since the target structure is still on-hold at the PDB, and cannot be described in any detail at this time.

Targets T134, T135, the two other protein-peptide complexes, were offered as targets in Round 44. These targets are complexes of the Dynein Light Chain subunit 8 (DLC8) from human (a homodimer) with segments from the cognate L-MAG protein (Figure 2), and were contributed by Petri Kursula, University of Bergen Norway [START_REF] Myllykoski | High-affinity heterotetramer formation between the large myelin-associated glycoprotein and the dynein light chain DYNLL1[END_REF] . The two complexes were crystallized starting from, respectively, a 50-residue segment of the cognate protein, and a 12residue peptide from the same segment, with the resulting complexes adopting very similar structures, burying respectively 1540 Å 2 and 1640 Å 2 in the protein-peptide interface. For T134 the challenge was two-fold. Predictors were given the sequence of the full 50-residue segment and asked to predict the 12-residue peptide of this segment that actually binds DLC8, and to model the resulting complex. For Target 135, predictors were given the sequence of the 12-residue peptide whose DLC8-bound structure was determined. The main challenge for the latter target was to produce an accurate model for the protein-peptide complex. Overall these were easy targets, given the conserved binding mode of diverse peptides to the DLC8 homodimer [START_REF] Fan | Structural basis of diverse sequence-dependent target recognition by the 8 kDa dynein light chain[END_REF] in structures available in the PDB. Furthermore, the DLC8 component of the target was the same as that of the PDB entry 1f3c (100% sequence identity).

Protein-oligosaccharide complexes (T126-130)

CAPRI Round 41 offered 5 targets representing protein-oligosaccharide complexes, which were contributed by Shifra Lansky and Gil Shoham, Hebrew University of Jerusalem. Four of these targets were complexes of the same protein the arabino-oligosaccharide-binding protein AbnE (an ABC transporter-bound periplasmic protein from Geobacillus stearothermophilus, involved in arabinose import), bound to arabino-oligosaccharide chains of decreasing lengths. These chains were A6 (arabinohexaose, 6 sugar units) for T126 through A3 (arabinotriose, 3 sugar units) for T129. The fifth target, T130, was the complex of a mutant (E201A) of the intracellular arabinanase AbnB, an enzyme involved in the degradation of intra-cellular arabino-oligosaccharides, bound to A5 (arabinopentaose, 5 sugar units). This was the second time protein-oligosaccharide complexes were offered as targets in CAPRI. The first such target was T57, a complex between the bacterial surface protein Bt4661 and a fragment of heparin comprising 6 sugar units, offered in CAPRI Round 27, over 6 years ago [START_REF] Lensink | Docking, scoring, and affinity prediction in CAPRI[END_REF] .

A number of templates were available for all 5 targets. Templates representing both the apo (3CU9) and holo forms of the AbnB enzyme were available for T130. The holo forms included two mutant enzymes, one (D147A) bound to arabinobiose (PDB code 3d61) and another (E201A) mutant bound to arabinotriose, but none were found to bind longer oligosaccharides, such as the arabinopentaose ligand of T130. For T126-T129, structures of AbnE homologs bound to various ligands with 6-membered sugar rings were available. These ligands included trigalacturonid acid, galactose, mannitol, tagatopyranose, glucose, but none featured the 5membered sugar rings of arabinose.

These templates were clearly helpful in guiding the challenging task of docking and modeling the association modes of these flexible sugar ligands, but only to a point, given that they bind different sugar ring moieties. On the other hand, valuable insights into the binding modes of arabinose ligands of different sizes were ultimately obtained from analyzing the experimental structures of T126-T129, which cannot be displayed at this time due to confidentiality reasons. Examining these structures revealed that arabinohexaose occupies binding sites 1-6 of the enzyme, arabinopentaose occupies sites 1-5 and arabinotetraose binds to sites 1-4.

Furthermore, the A4-A6 sugars in T126-T128 were seen to occupy the same 4 binding pockets (sites 1-4), whereas the A5 and A6 sugars occupied in addition the same pocket at site 5. This in turn suggests that the binding of any additional arabinose ring can be readily modeled by attaching it to the bound structure of the shorter sugar, or vice versa, namely, that the binding mode of a shorter sugar can be modeled by deleting the extra sugar ring (s) from the bound structure of the longer version. But the latter didn't seems to apply to T129, as shortening the sugar ligand below A4 seems to brake this 'rule' by leading to a shift within the enzyme binding pocket. Indeed the arabinotriose (A3) ligand of T129 occupies binding sites 2-4 and not 1-3. Interestingly, the sugar rings bound to sites 2-4 feature lower atomic fluctuations (lower crystallographic B-factors) than the arabinose rings bound to the other sugar binding sites on the enzyme, which may be indicating tighter binding at this site. Details of the 5 protein-oligosaccharide complexes can unfortunately not be displayed, since the structures of the 5 complexes have not been released at this time.

TARGET AND PARTICIPATION STATISTICS

A total of 57 groups participated in the 8 CAPRI Rounds evaluated here (Rounds 38-45).

The overall participation statistics for the 8 prediction rounds, including scorer and server participation are summarized in Table II. The target participation statistics and the number of models submitted for the 8 prediction rounds are summarized in Table S1 of the Supplementary Material. It is noteworthy that 13 automatic servers, a larger number than in previous assessments [START_REF] Lensink | Modeling protein-protein and protein-peptide complexes: CAPRI 6th edition[END_REF][START_REF] Lensink | The challenge of modeling protein assemblies: the CASP12-CAPRI experiment[END_REF] , participated in Rounds 38-45. Nearly half of these servers submitted predictions for all targets -including the protein-peptide and protein sugar complexes -and 5 servers also submitting scoring predictions for certain targets (see Table S2 of the Supplementary Material).

Starting in Round 39 (target T122 onwards), CAPRI participants were requested to submit up to 100 models by the prediction deadline directly to the CAPRI-EBI website. This eliminated the extra step of uploading 100 models to the Lille site, as done previously. Predictors and servers were nevertheless asked to rank the first 10 models of their set, which were assessed as usual, whereas all the 100 models were pooled with those submitted by other participants for each target and offered for the scoring experiment, after anonymizing their origins, as usual [START_REF] Lensink | Docking, scoring, and affinity prediction in CAPRI[END_REF] .

Overall, a total of 36114 models were evaluated for the 8 CAPRI Rounds. Depending on the target, 28-35 predictor groups submitted models. As previously, a smaller number of groups (16-20) submitted models for the scoring predictions. Overall 4835 and 2830 models were assessed for predictors and scorers, respectively. Scoring Rounds were offered for all targets, including the 3 protein-peptide and 5 protein-oligosaccharide complexes. This is the first time scoring Rounds have been offered for these categories of targets.

ASSESSMENT CRITERIA AND PERFORMANCE RANKING

Distinct assessment criteria were applied for the different categories of targets.

The classical CAPRI evaluation and ranking protocol [START_REF] Mendez | Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures[END_REF][START_REF] Lensink | Docking and scoring protein complexes: CAPRI 3rd Edition[END_REF] was applied to models submitted for targets of hetero-and homo-protein-protein complexes. This protocol was complemented with the DockQ score [START_REF] Basu | DockQ: A Quality Measure for Protein-Protein Docking Models[END_REF] , a continuous quality metric that integrates the main quality measures of the standard CAPRI protocol, as detailed below. Modified protocols were used to evaluate the protein-peptide and protein-sugar complexes.

The quality of the modeled 3D structure of individual protein subunits was also evaluated by computing the 'molecular' root mean square deviation, M-rms, of backbone atoms of the model versus the target. It was used mainly to gauge the influence of the quality of subunit models on the predicted structure of the assembly.

Assessment of protein-protein complexes

The classical CAPRI evaluation and ranking protocol uses three main parameters, L_rms, i_rms and f(nat), to derived the quality score of a predicted model [START_REF] Mendez | Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures[END_REF][START_REF] Lensink | Docking and scoring protein complexes: CAPRI 3rd Edition[END_REF] . f(nat) represents the fraction of native contacts in the target that is recalled in the model. Atomic contacts below 3 Å are considered clashes and predictions with too many clashes are disqualified (for the definition of native contacts, and the threshold for clashes see ref [START_REF] Mendez | Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures[END_REF] . L_rms represents the backbone rmsd (root means square deviation) over the common set of ligand residues after the receptor proteins have been superimposed, and i_rms represents the backbone rmsd calculated over the common set of interface residues after the structural superposition of these residues. An interface residue is defined as such, when any of its atoms (hydrogen atoms excluded) are located within 10 Å of any of the atoms of the binding partner. On the basis of the values of these 3 parameters models are ranked into 4 categories of quality scores: high quality, medium quality, acceptable quality and incorrect (as previously described [START_REF] Lensink | Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment[END_REF] and listed in

Table III).

Considering the uncertainty associated with the stoichiometry of the hetero-hexamer target T125, prediction results for the three main interfaces of this target, the homodimeric LLT1 and NKR-P1, T125.2 and T125.4, respectively, and the two heterodimeric LLT1/NKR-P1 interfaces, T125.1 and T125.3, were evaluated independently. For T124, the PorM-Ct homodimer interface and the binding interface of the PorM-Ct monomer with the nanobody were considered as representing separate modeling problems and were therefore evaluated separately. For the homo-decamer target (T136) with 3 distinct interfaces, the quality score (or CAPRI category) for the assembly as a whole was taken as the score of the best-predicted individual interface for the assembly. This criterion was used in evaluating models in the recent CASP13-CAPRI challenge [START_REF] Lensink | Blind prediction of homo-and hetero-protein complexes: The CASP13-CAPRI experiment[END_REF] and is more lenient than criteria applied in earlier Rounds, where the score for the entire assembly was taken as that of the worst predicted interface.

Schemes of intermediate leniency, representing linear combinations of weighted scores for

individual interfaces of the assembly were also tested, but found not to affect the performance ranking for these 3 targets.

Assessment of protein-peptide complexes

In protein-peptide complexes the interfaces are usually small. Only a portion of the bound peptide makes specific interactions with the protein, whereas the remainder of the peptide often remains very flexible. Therefore, for the predicted interface to be sufficiently informative, its accuracy needs to be higher than that of a protein-protein interface. To meet this requirement we used the modified evaluation and ranking criteria approved by the CAPRI Management Committee and successfully applied to models of protein-peptide targets in previous CAPRI Rounds [START_REF] Lensink | Modeling protein-protein and protein-peptide complexes: CAPRI 6th edition[END_REF] . The modifications were as follows: 1) an 8 Å distance cut-off on Cβ atoms is used to define interface residues; 2) a 4 Å distance atom-atom cut-off is used to define native contacts; 3) at least 90% of the sequence of the peptide has to be recovered in the model (compared to at least 70% for the protein). Moreover, parameter values for classifying model quality as 'incorrect', 'acceptable', 'medium' and 'high', have been tightened (see Table III for details).

For target T134, the ability of predictors to correctly identify the correct 12-peptide that actually binds the DLC8 domain from the full 50-residue segment of the MAG protein was also evaluated. For this target, models lacking the 10 central residues of the segment that forms the protein-peptide interface were considered as incorrect. This is slightly more lenient than the 90% sequence coverage for the peptides, mentioned above and applied to T135.

Assessment of protein-oligosaccharide complexes

The protein-oligosaccharide complexes, although not an entirely new category of targets in CAPRI, remain a challenging modeling problem for predictors and assessors alike, owing to the particularities of the sugar moieties. In an attempt to standardize the format of the submitted models, participants were invited to use the following conventions: 1) use the PDB HETATM records for the atomic coordinates of the arabinose oligosaccharide moieties; 2) the chain identifier for the sugar moieties should be the same as for the protein chain; 3) sugar residue numbering should start at 1. Participants were also provided with examples of oligosaccharide structures in the PDB, which listed the correct atomic numbering conventions.

While the requested format conventions were more or less followed, departures from the correct residue and atom number conventions were all too common. These included reversing the sugar residue numbering; wrong atom names, e.g. using O1' instead of O5' and attaching this oxygen to the wrong (previous) residue; using the same residue numbering for all sugar residues; using two different residue numbers for a single monoarabinose; first listing all carbons, then all oxygens, and numbering them sequentially; and finally, submitting models to the wrong target (e.g. AbnE/A6 submitted to T129, the AbnE/A3 complex, instead of to

T126).

With a few exceptions, these problems could fortunately all be corrected through visual inspection followed by rule-based customized scripts, applied to all the models submitted by individual predictor groups. However models submitted by scorer groups required a special treatment. Since these groups used pooled models from multiple predictors, and adopted either the source (predictor) format or their own, two transformations were applied to each Scorer submission in order to carry out the assessment. One transformation was applied to convert the scorer submission to the correct standard representation. In addition, thanks to the CAPRI tracking code, the provenance (source) of models in the uploaded set was identified and a second transformation was applied to models contributed by the same predictor group to the set. Both transformations were applied separately and the resulting models were evaluated. The more favorable evaluation was then assigned to the scorer submission. This convoluted protocol finally led to the rejection of only a handful of models over all five sugar targets.

We assessed only models where all the required sugar residues were present. The sugar atoms C1', O4', C4' and C5' were defined as backbone atoms. These atoms were used to perform the structure superimpositions. In addition, light of the small size of the protein-sugar interaction interface, which resembles the situation in protein-peptide interfaces, the following changes with respect to the assessment of protein-protein complexes were implemented: the threshold distance to determine interface residues was reduced to 6 Å, and the threshold distance for ligand-receptor contacts was reduced to 4 Å (Table III). However, the standard protocol (e.g. the standard definitions and thresholds for f(nat), L-rms and I-rms) was used for classification into the by now well-known categories of 'incorrect', 'acceptable', 'medium' and 'high' quality, models. Figure 2b illustrates model versus target fits of high, medium, and acceptable quality arabinopentaose models submitted for target T127 (AbnE/A5 complex).

Additional assessment measures

To enable a higher-level analysis of the performance across targets, we used a continuous quality metric, as formulated by the DockQ score, to evaluate each modeled interface [START_REF] Basu | DockQ: A Quality Measure for Protein-Protein Docking Models[END_REF] : with where f(nat), i_rms, and L_rms are as defined above. The rmsscaled represents the scaled rms deviations corresponding to either L_rms or i_rm, s and di is a scaling factor, d1 for L_rms and d2 for i_rms, which were optimized to fit the CAPRI model quality criteria, yielding d1 = 8.5 Å and d2 = 1.5 Å (see ref [START_REF] Basu | DockQ: A Quality Measure for Protein-Protein Docking Models[END_REF] ).

Performance ranking

The performance of predictors scorers and servers for individual targets was ranked on the basis of a per-target score (Score_T). This score was computed as the weighted sum of the number of models in each of the 3 CAPRI categories (acceptable, medium and high) submitted by a given group as part of their top 5 ranking models, as follows:

where NACC, NMED and NHIGH are the number of models of acceptable-, medium-and highquality, respectively, submitted for a given target (among the top 5 models); the values of the weights ' ' were taken as =1, =2 and =3.

The performance of predictors, scorers and servers across targets was ranked on the basis of their best quality model in the 5-model submission for each target. The global score assigned to a group or a server, denoted as Score_G, has the same form as the score for individual targets shown above. Only this time, NACC, NMED and NHIGH are the number of targets for which the best model (among the top 5) was of acceptable, medium and high quality, respectively.

For both the per-target and across target ranking we also discuss the rankings for the top 1 and top 10 submitted models. This ranking protocol, introduced recently for the CASP13-CAPRI challenge [START_REF] Lensink | Blind prediction of homo-and hetero-protein complexes: The CASP13-CAPRI experiment[END_REF] , differs from previous protocols, where priority was given to the number of targets for which medium or high quality models were submitted, and then to the number of targets with acceptable models. The new protocol leads to fairer ranking of groups, as it also takes into account the number of acceptable models that they submitted, especially for the more challenging targets.

PREDICTION RESULTS

In the following we summarize the prediction results for the different categories of targets, and present the global performance ranking across groups and automatic servers. The performance of predictors, scorers and servers was ranked on the basis of the top 5 submissions for each target, but results for the top 1 and top 10 submitted models are also discussed, whenever appropriate. The ranked group performance for individual interfaces of the targets of Round 38-45 are provided in the supplementary Tables S3-S18, and the full account of all the results for these targets are available at the official CAPRI website (https://www.ebi.ac.uk/msd-srv/capri/).

Results per target category

Protein-protein complexes (Targets T122-T125; T131-T133, T136) T122, the x-ray structure of the human cytokine-receptor complex, comprised the IL23 hetero dimers (IL23A/IL12B) bound to its receptor protein IL23R. This turned out to be a medium difficulty target, since templates were available for both the IL23 hetero dimer and the IL23R receptor protein. But the flexibility of the inter domain arrangements in IL12B and the IL23R receptor differed sufficiently to affect the prediction performance. Probably as a result only a small number of groups were able to submit medium quality models for this target. A total of three predictor groups (Venclovas, Seok, Kozakov/Vajda) submitted such models among their top 5 ranking ones. The best of these models is illustrated in Figure 3. An additional 7 groups (including the CLUSPRO server) submitted only acceptable models. The scorer performance was lower, with some scorer groups either ranking lower (e.g. among their top 10 submission) or missing altogether the medium quality models they themselves ranked higher (top 5) as predictors. The scorer performance improves when considering the top 10 ranking submissions.

The prediction results for T123 and T124, the PorM-Nt/nb-130 and PorM-Ct/nb-02 complex respectively, were very poor, due to the absence of templates for the two PorM domains. With predictors attempting and failing to model the PorM protein ab-initio -as during the CASP12 round for T0907 -only incorrect models were submitted for these targets, by the ~30 participating predictor groups and servers. However, one acceptable model was produced for T123 by the group of Andreani/Guerois. This model was ranked too low to be considered a predictor submission, but was available in the scoring set, where it was unfortunately not recognized by any of the scorer groups. Mitigated results were obtained for T125, the LLT1/NKR-P1 multi-protein hetero assembly, where the announced stoichiometry consisted of 1 homodimer of LLT1 binding 2 homodimers of NKR-P1, forming a hexamer with a total of 5 interfaces. Prediction results were evaluated only for 4 interfaces, which buried areas between 540 and 775 Å 2 . These interfaces, numbered from 1-4, include the LLT1-NKR-P1 main hetero-dimer interface (interface 1), the LLT1 homodimer interface (interface 2), the NKR-P1 homodimer interface (Interface 4) and an alternate binding mode of the hetero-dimer (Interface 3), see Table I andFigure 4. Models of acceptable quality or higher were submitted only for the 2 largest interfaces, for which templates were available. The very small size of the remaining interfaces, and the fact that consistent models featuring these interfaces were difficult to generate, are reasons for doubting the complex stoichiometry defined by the author, as mentioned above.

Eleven groups including 2 servers submitted at least 1 models of medium quality or better among their 5 top ranking models for interface 1. The two best performing groups for this interface were those of Pierce and Venclovas, with respectively 5 and 3 high quality models among their top 5 submissions. Moreover, the Pierce group had 8 high quality models among their 10 best predictions, whereas Venclovas had 5 high quality and 4 medium quality models among their top 10 predictions. It is also noteworthy that CLUSPRO and MDOCKPP, the 2 servers with correct predictions for this interface, each submitted 5 medium quality models. Superb performance was achieved for the LLT1 homodimer (interface 2), with the majority of the predictor groups obtaining at least 1 high quality model among their top 5 submissions, and as many as 12 groups generating 5 high quality models among the top 5 submissions. The latter set of best performing groups comprised those of Bates, Fernandez-Recio, Gray, Moal, Shen, Weng, Zacharias, Huang and Grudinin, and included in addition the HADDOCK, PYDOCKWEB and SWARMDOCK servers. The vast majority of predictor groups also had a high quality model as their single top prediction for this homodimer, and 10 groups including the 3 servers had high quality models for all their top 10 submissions.

Echoing the prediction performance for the different interfaces of T125, scorer groups submitted models of acceptable quality or higher only for interfaces 1 and 2. The scorer performance was good for interface 1, and excellent for interface 2, but overall lower than that of predictors (human & servers), for both interfaces. Whereas 14 scorer groups submitted at least 2 medium quality models for interface 1, only the group of Gray submitted high quality models for this interface (1 among their top 5 predictions and 2 among their top 10). The best scoring performance for this interface was by the LZERD server and the Bates group, each submitting 5 medium quality models, followed by the groups of Kihara (the authors of LZERD), Weng and Bonvin, with 4 medium quality models.

For the well predicted interface 2 of T125 as many as 16 scorer groups submitted at least 1 high quality model among their top 5 predictions. Among these groups, those of Laine, Moal and Carbone submitted 4 such models, whereas Zou, Gray, and Bonvin submitted 3 high quality models among their top 5 predictions.

The prediction performance was rather weak for targets T131 and T132, the heterocomplexes of human CEACAM1 N-terminal domain with, respectively, the related bacterial cell adhesion proteins HopQ-I and HopQ-II, despite the availability of templates for both the bacterial HopQ components and the human CEACAM1 domain. For T131 (CEACAM1/HopQ-I complex), the lower performance may be explained by the presence of extra loops in the pylori HopQ-I protein, which contributes to the binding interface, but were absent in the available HopQ templates. Of the 30 predictors and servers groups submitting models for this target, only the group of Kozakov/Vajda submitted 1 medium quality model among their 5 top (and top 10) ranking ones. The results were only marginally better for T132, the complex with HopQ-II that did not contain the extra loops, and therefore should have been easier to model.

Here, only the LZERD server and the group of Moal submitted 1 medium quality, and 1 acceptable quality model, respectively. The best model submitted for T131 is shown in Figure 5a)

The scorer performance for T131 and T132 was only somewhat better than that of predictors, with the group of Carbone submitting a medium quality model as their top submission, in addition to one acceptable model among their top 5 submissions.

T133, the redesigned version of the Colicin E2/DNase-Im2 complex (E des3 /Im des3 ), was a nonclassical CAPRI target, where predictors had to accurately model the structural perturbations produced by redesigned residues in the wt complex. In order to gauge the effect of these perturbations on the CAPRI model quality criteria, we assessed the wt complex (PDB 3u43) used as input for the design calculations, against T133. This yielded a medium-quality model (f(nat) 0.698; L-rms 2.043; i-rms 1.207), just like those submitted here by a good number of groups. Therefore, the fact that no high quality models were submitted for T133 indicates that predictors, servers and scorer groups were all unable to reproduce the finer details of interface of the redesigned complex. Ten predictor groups, those of Venclovas, Kozakov/Vajda, Birini, Bonvin, Seok, Takeda-Shitaka, and Kihara, and 3 servers, INTERPRED, HADDOCK, LZERD, each submitted at least 2 medium quality models among their top 5 submissions.

Whereas, 3 groups (Venclovas, Kozakov/Vajda and Brini, a recent CAPRI participant, submitted 5 such models. The best model obtained for this target, and the corresponding CAPRI quality measures, is illustrated in Figure 5b. Overall however, as many as 28 groups, of which 9 were servers, submitted at least 1 acceptable, or medium quality model among their top 5 predictions.

The scorer performance for this target was likewise good, with 7 scorer groups submitting at least 2 models of medium quality of better among their top 5 predictions, and 2 groups, the LZERD server and Bates, submitting 5 acceptable models of which 3 were of medium quality.

The 8th protein-protein target, T136, was the 4.5 Å resolution cryo-EM structure of the lysine decarboxylase (LdcA) homo-decamer, determined by cryo-EM to 4.5 Å resolution. This decamer comprises 3 distinct interfaces. A very large interface, involving interactions between the subunits within and between rings (Interface 1), and 2 smaller, yet sizable, interfaces: interface 2 between subunits within the ring, and interface 3, again between subunits of different rings. This was in principle an easy target that could be predicted using 'templatebased modeling' using as template the high-resolution x-ray structure of a related arginine decarboxylase (PDB code 2vyc) as template. It is therefore not surprising that good prediction results were obtained for all three interfaces, with the best results obtained for interface 1, followed by that for interfaces 2 and 3. Indeed, 18 groups, including 5 servers, submitted 5 medium quality models as their top prediction for interface 1, with another 6 groups (including 1 server) submitted at least 2 medium quality models for this interfaces. For interface 2, only 6 groups (including 1 server) submitted medium quality models for all their top 5 predictions, whereas for interface 3, the best performance was by the GALAXYPPDOCK server and the Vakser group, each submitting 4 medium quality models among their top 5 predictions. The best overall model obtained for this target is illustrated in Figure 6.

Not too surprisingly, the performance of scorer groups (and servers) reflected that of predictors but was in general lower, with a very good performance by groups such as Venclovas, Seok, and the LZERD server.

The low resolution of the target cryo-EM structure, or the genuine differences between the target structure and the available template(s), or both factors, may have contributed to the absence of high-quality models for any of these interfaces, and therefore for the homo decamer as a whole.

Protein-peptide complexes (T121, T134, T135)

Target T121 was the solution structure of the complex between the TolAIII domain and the 13-residue N-terminal peptide of P. aeruginosa TolB. This turned out to be a difficult target, although, as already mentioned, a 1.94 Å resolution x-ray unbound structure was available for the TolAIII domain. The prediction performance for this target was very poor for predictor and server groups, and consequently also in the scoring challenge. In total, only 3 groups submitted 4 acceptable models among their 5 top ranking predictions: Seok submitted 2 such models, with Bates and Zou submitting 1 acceptable model each. The groups of Schueler-Furman and Kozakov/Vajda did however submit a medium quality model among their 10 top predictions.

As to the scorer performance, the LZERD server and 5 additional scorer groups submitted acceptable models for this complex, with LZERD, Kihara and Seok each submitting 1 acceptable model as their top prediction. However, 1 medium quality model and an additional 15 acceptable models were among the 10 best models of as many as 8 scorer groups.

The only 3 medium quality models were submitted for T121 by the groups of Schueler-Furman, Kozakov/Vajda (predictors) and Moal (scorer) among their top 10 best models. These models displayed i-rms values of 2.5 Å, 2.8 Å, and 3.7 Å, respectively. Interestingly, the uploaded models (100 models submitted per participating group), displayed larger i-rms values than the predictor set (only from T122 onward were all predictor submissions automatically included in the uploaded set), with none below 3 Å, and the single medium quality model submitted by Moal scorer group represented an improvement upon its source model from the uploaded set. Among the possible reasons for the generally poor performance may have been the flexibility of the peptide, of which only a small segment was predicted to adopt a -strand conformation.

The latter possibility may have been compounded by a conformational change of the TolAIII domain relative to the available template. With the target structures still on-hold, as this report is being written, we cannot comment on which of these two possibilities played any role.

Not too surprisingly, very good results were obtained for Targets T134 and T135, the complexes of the DLC8 homodimer with a 12-residue peptide from the cognate L-MAG protein.

The additional task of having to identify the 12-residue segment that binds to the DLC8 dimer in T134 led to a generally lower prediction performance for this target, compared to T135 where the sequence of the 12-residue peptide was provided. In order to be evaluated, models submitted for T134 had to feature a sufficient overlap (of at least 10 residues, with only one residue out of phase) between the bound peptide segment and the 12-residue segment of the target complex. Only 10 out of the 31 predictors groups (including servers) achieved this goal, including 6 groups that were able to identify exactly the 12-residues segment of the target complex (Figure 7). Among the predictor and server groups whose models for T134 were evaluated, 7 groups (but no servers) had at least 1 model of medium or higher quality among their top 5 submissions. Of these groups the best performance was obtained by the group of Schueler-Furman (3 high quality and two medium quality model) followed by 4 other groups (Andreani-Guerois, Venclovas and Moal) each submitting 1 high quality model. For T135, as many as 14 predictor groups including 3 servers (HDOCK, CLUSPRO, and GALAXYPPDOCK) produced at least 1 model of medium or higher quality for this target among their top-5 submission, with several of these groups submitting 3 or 4 such models. The 5 best performers for this target were those of Schueler-Furman, with high quality models for all their top-5 submission, and the groups of Huang, Zacharias, Andreani/Guerois and the HDOCK sever, with only high and medium quality models among their top-5 submissions.

The scorer performance for these targets was quite good as well. As many as 11 groups, including the HDOCK and LZERD servers, submitted at least 1 medium quality model among their top-5 submissions for T134, with the groups of Venclovas, Oliva and Kihara submitting in addition between 1 and 5 high quality models. For T135, at least 1 medium quality model or better was submitted by as many as 14 groups, with the 3 best performing groups also submitting higher quality models: 4 such models were submitted by the group of Venclovas, 2 by Fernandez-Recio and 1 by the HDOCK server.

The most accurate models submitted for the DLC8-peptide complex (by both predictor and scorer groups) is depicted in Figure 8.

Protein-oligosaccharide complexes (T126-T130)

The prediction results for the 5 protein-oligosaccharide complexes were quite interesting because they provide insight into the challenges that CAPRI groups faced in modeling these complexes. For Targets 126 and 127, the 2 complexes of AnbE with the longer-chain arabinohexaose (A6) and arabinopentaose (A5) ligands, respectively, only the HDOCK server submitted a medium quality model (for T127), whereas all other predictor and scorer groups submitted only acceptable models among their top 5 submissions. The total number of groups submitting such models was however quite high (15 for T126 and 23 for T127; see Figure 9). On the other hand, higher quality models were obtained for T128 and T129, the AbnE complexes with the shorter sugars, arabinotetraose (A4) and arabinotriose (A3). Indeed, a number of groups (7 for T128 and 6 for T129) submitted at least one medium quality model for these targets, with many more groups submitting acceptable models among their top 5 predictions. The number of groups with this performance increased to 9 or 10, depending on the target, when the top 10 submissions were tallied (Figure 9). Five groups (Bonvin, Andreani/Guerois, Zacharias, Kihara and the LZERD server) performed best for T128 and 6 groups (Gray, Seok, Chang, Huang, Andreani/Guerois , and the HDOCK server) performed best for T129.

These results indicate that predictors performed better in modeling the AbnE complexes with the shorter chain arabinose sugars than the longer versions. Interestingly furthermore, they were marginally more successful in modeling the complex with arabinotriose (A3), which binds to sites 2-4 of the AbnE binding pocket, and not sites 1-3, as might have been expected from the binding modes adopted by the longer chain arabinose sugars (A4-A6). The likely reason for this trend is that the available templates consisted of complexes of AbnE homologs bound only to 6-membered sugar ligands. These complexes were helpful in more or less correctly positioning the flexible longer arabinose oligosaccharides in the AbnE binding pocket, but they provided limited guidance for optimizing the bound sugar conformation and protein-sugar contacts. To perform such optimizations predictors had to rely on various modeling and refinement procedures. These procedures may not have been very effective in sampling the conformational landscape of the larger bound sugars moieties, but produced better results for the smaller sugars, owing to the fewer degrees of freedom that need to be sampled. This seems to have allowed predictor groups, including several servers, to produce medium quality models also for AbnE/A3, where the arabinotriose occupies sites 2-4.

The 5 th sugar target, T130, the complex of AbnB with arabinopentaose, was the easiest of the lot, because templates representing the holo and apo forms of the enzymes were available, although the holo forms only bound shorter arabinose oligosaccharides. This was the only target of this category where high quality models were submitted. Nine predictor groups, including 2 servers (HADDOCK and LZERD), submitted such models among their top 5 predictions, and 5 of these groups (Venclovas, Chang, Grudinin, Seok, and Carbone) had a high quality model as their top ranking submission. Over 20 groups submitted at least one medium quality model for T130, either among the top 5 or top 10 submissions.

Lastly, as expected, the scorer performance mirrored that of predictors. It was relatively poor and roughly on par with those of predictors for T126 and 127; it was better and again essentially on par for T128 and 129, and excellent for T130.

Performance across predictor groups scorers and servers.

Groups (predictors, servers and scorers) were ranked according to their prediction performance for the 16 targets evaluated here. This ranking represents the official performance ranking for Rounds 38-45. We also ranked the performance of predictor groups and servers for each of the 3 categories of targets separately, as these categories represent distinct modeling problems. But these rankings are only presented for analysis purposes, as the number of targets per category was too small for such ranking to be sufficiently informative.

All the rankings presented followed the revised ranking protocol, which uses a more balanced weighting scheme for models of different accuracy levels, as detailed in the section on the assessment and ranking procedures.

For a full account of the results obtained by each group the reader is referred to the CAPRI web site (http://pdbe.org/capri).

Performance of predictor groups

The consolidated ranking of predictor groups (including servers) across all 16 targets assessed in this report (Table IV.a), shows the 5 best performing groups to be those of Andreani/Guerois, Seok, Venclovas, Kozakov/Vajda, and Zacharias. The first 5 groups submitted correct models for 11-13 targets, which included high quality models (4 by Andreani/Guerois and Venclovas; 3 by Zacharias, 2 by Seok and 1 by Kozakov/Vajda), as well as medium quality models (8, 7, 5, 4 and 3, by Kozakov/Vajda, Seok, Andreani/Guerois, Venclovas and Zacharias, respectively). The first server in the ranked list is CLUSPRO, with 10 correctly predicted targets, including 6 medium quality ones.

It is furthermore noteworthy that the three top ranking groups (Andrani/Guerois, Seok, and Venclovas) also rank highest when considering their single best ranking models for each target (top 1); and that the same 5 groups remain the top performers, when considering their top 10 ranking models for each target (Table IV.a), as was the practice in earlier CAPRI evaluations. This supports the current more stringent approach of ranking the performance on the basis of the top 5 submitted models per target.

Performance of scorer groups

As already observed for the prediction results for individual targets, the across target performance of scorer groups is also somewhat weaker than that of predictors. The consolidated ranking of scorer across all targets (Table IV.c) lists 7 top performing scorer groups, as those submitting correct models for at least 11 of the 16 evaluated targets. In order of their rank, these groups are: Kihara, Zou, Bonvin and Seok, followed by the servers MDOCKPP and LZERD, and the group of Bates. The models submitted by these groups included a sizable fraction of targets for which medium quality models were submitted (7 targets by Zou, 6 by Bonvin, Seok, Huang and the LZERD server, and 5 by Kihara). On the other hand, the 5 best performing groups obtained high quality models for only 1 or 2 targets, whereas the group of Venclovas, which ranks lower in the list, did obtain high quality models for 3 targets, but overall submitted correct predictions for only 10 targets.

On the whole scorer rankings were less consistent, when considering also the top 1 or top 10 submitted models for each target (Table IV.c).

Performance of prediction servers

Like in the last CAPRI assessment [START_REF] Lensink | Modeling protein-protein and protein-peptide complexes: CAPRI 6th edition[END_REF] , the overall performance of prediction servers in Rounds 38-45 (Table IV.b) is lower than that of human predictor groups, most likely due to their lower performance on the protein-peptide and protein-sugar complexes. These complexes -more particularly the protein sugar complexes -represent relatively recent target categories in CAPRI, for which automatic servers still need to be optimized.

Nonetheless, the CLUSPRO server maintains its high-ranking performance of previous CAPRI assessments, and is closely followed by those of HDOCK, a server that also performed very well in the recent CASP13-CAPRI challenge [START_REF] Lensink | Blind prediction of homo-and hetero-protein complexes: The CASP13-CAPRI experiment[END_REF] , MDOCKPP, LZERD, and the veteran HADDOCK server. CLUSPRO loses its high ranking position when considering only the single best predicted model (top 1), but ranks second (after HADDOCK) when the top 10 submitted models for each target are considered.

It is satisfying to see that as many as 20 predictor groups (and servers) submitted correct models for 8 to 12 targets (out of the 16 total), including models of medium quality or better, for between 4 to 9 targets (Table IV.a). The performance of predictor and scorer groups did display some differences between the 3 target categories. While the top ranking predictor groups across all 16 targets were also among the best performers in each of the 3 target categories (see Supplementary Tables S19-S21), depending on the target category, a few additional groups percolated to the top of the rank. For example, the group of Pierce moved to the 3rd position for the protein complexes, and the groups of Zacharias and Schueler-Furman, rose to the 1 st and 2 nd position in the ranking for the protein-peptide complexes. These differences are clearly not statistically significant, but they seem to correlate with the proficiency of the group or server in tackling the modeling problem of the corresponding target category. here as the baseline performance, analogous to that produced by the 'naïve' predictions considered previously [START_REF] Lensink | The challenge of modeling protein assemblies: the CASP12-CAPRI experiment[END_REF] , which employ 'off the shelf' standard modeling tools, although, given the consistently good performance of this server, this may put the bar for baseline performance rather high.

Examining the DockQ scores for the 7 protein-only hetero-complexes (T122-T125 and T131-T133), most of which are also deemed to be difficult targets, confirms that the prediction performance, measured as the fraction of models of acceptable quality or higher submitted across the ~40 human predictor and server groups, was uneven, and generally on the weak side. This global trend reflects the inherent difficulty of these targets. For predictors employing ab-initio docking approaches of various flavors, the availability of mostly distantly related templates for the individual subunits was often an important limitation, which allowed groups relying on template based modeling of the entire complex to gain some advantage. An exception was the well-predicted LLT1/NKR-P1 interface of T125, with several high accuracy models submitted for this complex, despite its relatively small size interface (775 Å 2 ). For this set of hetero-complex targets the baseline performance of the CLUSPRO server was often comparable to those of the best performing manual predictors. Other times however, the base line performance was also significantly lower, like for T131, T132, and for the targets or interfaces for which only incorrect models were submitted. The better performance for T133 (the redesigned DNAse/Im2 complex) and the homo-decamer target T136, the easier targets for which adequate templates were available for the entire complex, illustrates the advantage that such targets represent for the modeling task. This advantage is likewise witnessed by the relatively good baseline performance of the CLUSPRO server. Nonetheless the failure to produce high quality models for these targets suggest that the modeling procedures remain in general limited in their ability to further optimize the initial near-correct models that are being generated.

Not much can be concluded from the performance trends for the 3 protein-peptide targets.

The poor results obtained for T121 cannot be analyzed here due to confidentiality requirement at this time, whereas the good performance for T134-T135, the DLC8-peptide complexes, is not particularly revealing, given the accurate template available for the DLC8 dimer, and the well conserved peptide binding site of this protein. Interestingly nevertheless, for T134, the poor performance of CLUSPRO and a good fraction of predictor groups, is mainly due the failure to identify the correct 12-residue that binds DLC8 from the sequence of a 50residue peptide, a task which most groups performed using template based methods. These methods involved leveraging information on known structures of complexes of DLC8 with other peptides, aligning the sequences of these peptides and looking for conserved motif(s) that match the target sequence.

Lastly, the performance trend across the 5 protein-oligosaccharide targets (T126-T130) confirms the conclusions reached by examining the per-target results, namely that the main bottleneck for both predictors and servers was the limited ability to optimize the binding modes of the larger sugar ligands, the arabinopentaose (A5) and arabinohexaose (A6), due to their high degree of conformational flexibility. homodimer (interface 1 of T125), most likely because submitted models were derived from better templates for this homodimer.

A less equivocal relationship is observed between the accuracy of the modeled side chains of interface residues in predicted complexes and the quality level of these complexes, as illustrated in Figure 11a. This Figure displays the distributions of the root mean square deviation of side chain atoms (S-rms) of interface residues, color-coded by the CAPRI model quality categories for all the interfaces in individual models submitted by predictors (human and servers) for the evaluated targets. These distributions reveal that for the vast majority of the target or interfaces, the S-rms values decrease as the model quality level improves, highlighting the close relationship between the CAPRI model quality measures, which notably evaluate inter-residue contacts at the binding interface, and the accuracy with which the side chains of residues at this interface are modeled. On the other hand, S-rms values remain in general rather high (up to 2.4 Å) even for high quality models, and may span a wide range among models with similar quality level. This indicates that even in models of high quality, according to CAPRI criteria, side chain conformations of interface residues may not be modeled at high enough accuracy for quantitative downstream applications such as rational protein or drug design.

Lastly, having noticed that some predicted complexes evaluated in previous Rounds, notably in the recent CASP13-CAPRI challenge, tended to feature a significant fraction of non-native residues at the interface [START_REF] Lensink | Blind prediction of homo-and hetero-protein complexes: The CASP13-CAPRI experiment[END_REF] and hence to over-predict the interface regions, we examine if this also occurs in predicted complexes for the 16 targets evaluated in this study. To this end we plotted the quantity f(non-nat) in models submitted by predictor and scorer groups for individual targets, as a function of the DockQ score of the corresponding models, which are color coded according to the CAPRI model quality category (see Figure 12). f(non-nat) represents the fraction of contacts formed between residues the interface of predicted complexes, which are not found in the interface of the target. This quantity is routinely computed in CAPRI assessments, but has so far not been used for model ranking.

As one would hope, the scatter plots of Figure 12 reveal a clear linear inverse correlation between f(non-nat) and the DockQ score of models submitted by predictor groups (including servers) (Figure 12a; Pearson Correlation -0.915), and by scorers (Figure 12b; Pearson Correlation -0.912). These plots also show quite a good correspondence of the plotted quantities with the 4 CAPRI model quality categories, each of which can be characterized by a pair of f(non-nat) and DockQ values. But values for individual models display a significant spread, with those of f(non-nat) spreading systematically toward higher values across models of all 4 CAPRI quality categories. Most strikingly, a majority of the medium quality models and a large fraction of the high quality models in Figure 12 feature f(non-nat) values of 40% or higher. Thus, even though interfaces in this fraction of high quality models recall between 60-95% of the native contacts of the target complexes, nearly half of all the predicted contacts in some of these interfaces are incorrect. As a result, the interfaces are of limited predictive value for any downstream application, as the probability of picking a native contact in such interfaces is only slightly above random.

Upon closer inspection we could verify however, that the high quality models displaying this behavior, numbering 60 in total, were all submitted for interface 2 of T125 (that of the LLT1 homodimer) by different scorer and predictor groups (including servers). With several templates available for this homodimer, it is reasonable to assume that most of these high quality models were generated by template-based modeling protocols, but that these protocols probably involved limited subsequent model refinement. Interestingly, the HADDOCK prediction server, and the scorer group of Bonvin, the developer of this server, submitted 2 high quality models each, for this complex. These models featured, in contrast, a very small fraction (< 10%) of non-native contacts, demonstrating that it was possible to reach a high predictive value for this complex.

NEW TRENDS AND FUTURE DIRECTIONS

This 7 th CAPRI report presented the evaluation of 36114 models of predicted protein complexes and their interfaces, submitted by about 60 groups, for 16 targets in CAPRI Round 38-45. These targets belonged to 3 different categories: protein-protein, protein-peptide and protein-oligosaccharide complexes, each representing distinct modeling challenges.

Of the 8 protein-protein complexes, most of which were hetero-complexes, at least 5 turned out to be non-trivial modeling problems. For T122, the IL23/IL23R complex, a bottleneck was the inter-domain flexibility of 2 of the subunits of this hetero-trimer and their homologs, requiring additional information and human expertise to guide the modeling. For T123 and T124, the C-ter and N-ter PorM domain/nb complexes, the complete absence of templates for the PorM moieties was the reason that no correct models were submitted. For T131 and T132, the CEACAM1/HopQ-Type I and II complexes, differences between the interface region in the target and templates, and more particularly the presence of several loops contributing to the interface of T131, that were absent in the templates, represented the major challenge for these targets.

Not too surprisingly, the prediction performance was overall poor for these targets, with only a relatively small number of groups submitting models of acceptable quality or better for most of these targets. As can be seen from the reports of individual groups in this issue, the groups that performed best for these difficult targets used increasingly sophisticated 'integrative' template-based modeling techniques. These techniques leveraged information not only on templates for the individual subunits of the complex, but also on templates for the complex as a whole, whenever available, and on the interfaces of these templates. Information from different templates and interfaces was often also combined. Furthermore, in cases where the templates suggested several competing solutions to the modeling problem, these solutions were prioritized using additional approaches such as re-docking of the modeled subunits, So far, similar but in general more straightforward template-based modeling of the entire complex has been successfully applied to predict the structure of homo-oligomer targets, such as those commonly offered in the CASP-CAPRI challenges [START_REF] Lensink | The challenge of modeling protein assemblies: the CASP12-CAPRI experiment[END_REF][START_REF] Lensink | Blind prediction of homo-and hetero-protein complexes: The CASP13-CAPRI experiment[END_REF] . It is therefore quite encouraging to see that this type of template-based modeling in its augmented and more 'integrative' forms is making important headway in the prediction of hetero-complexes, even for difficult targets. It hence comes as no surprise that these methods performed particularly well for 2 of the interfaces of T125, and for T133, where excellent templates were available for the binary complexes of the corresponding assemblies.

Nevertheless, the integrative template-based modeling of the full complex still falls short of generating accurate models in cases where more consequential conformational adjustments or remodeling part of the interface are necessary (like for T131, CEACAM1/HopQ-Type I, and T133 the redesigned DNase/Im2 complex). Other limitations reside in the fact that crystal structures of hetero-complexes of related proteins are more prone to displaying different binding modes than homomeric protein assemblies, owing to their transient nature and to the influence of other protein components (e.g. nanobodies as well as additional native interactions partners), with whom they may be co-crystallized. A related issue played out in the prediction of the LLT1/NKR-P1 hetero complex (T125), where inconsistencies between the announced stoichiometry of the target and the interfaces formed in available templates, adversely affected the prediction performance of the complex as a whole.

Template-based modeling also played an important role in the successful predictions for T134 and T135, the DLC8-peptide complex, and for the 5 protein-oligosaccharide complexes (T126-T130). Nevertheless, for the AbnE/arabinose oligosaccharide complexes (T126-T129), the available templates featured different sugar ligands, and could therefore serve mainly to guide the modeling procedures. The latter involved various strategies, including specialized flexible docking and model refinement methods, which alternate conformational perturbations of the sugar ligand with side chain adjustments of the protein binding pocket (see e.g. Burman et al.

this issue).

Highlighting the progress in template-based modeling of the full protein-protein of proteinligand complex should not be taken to mean that docking algorithms, which use as input homology built models of individual subunits of the complex, have not been holding their own in terms of prediction performance. Indeed, these algorithms have significantly progressed in their ability to speed up costly computations by using for example GPU (Graphic Processing In cases where such templates cannot be found, even if only for one of the interacting components the prediction task cannot be tackled, as was the case for T123 and T124, the Cter and N-ter PorM/nb complexes. Hopefully this may change in the not too distant future, if Deep Learning techniques confirm their recent successful performance in predicting the 3D structure of individual protein subunits [START_REF] Senior | Protein structure prediction using multiple deep neural networks in CASP13[END_REF] , and these ab-initio predicted subunits are taken as input for docking procedures to generate useful models for the full assembly.

  sometimes guided by biochemical information (Padhorny et al., Burman et al., this issue), or computing various model quality scores (Dapkunas et al., this issue) including approximate binding affinities, (Park et al., this issue) to evaluate and rank the corresponding interfaces.

  Unit) processors. A number of algorithms are now able to more efficiently incorporate conformation flexibility during the docking calculations (Glaschagen et al.; Torchala et al.; Burman et al., this issue), and to employ more accurate scoring functions to prioritize docking solutions, allowing their authors to rank prominently among the top performing groups. Docking procedures, and the criteria they use to score docking poses are also particularly helpful in augmenting the performance of template-based modeling (Burman et al.; Padhorny et al.; Park et al., this issue), and they clearly remain the only viable approach when templates are available only for the individual subunits of the complex.

Acknowledgements

We gratefully acknowledge the EU grant WestLife (H2020-EINFRA-2015-1-675858) for support to Sameer Velankar and Nurul Nadzirin. We express gratitude to all the structural biologists who provided the targets of Rounds 38-45.

Our thanks also go to the CAPRI management team and all predictor groups for stimulating discussion, valuable input and cooperation.

General trends and factors influencing prediction performance

The 16 targets of Round 38-45 comprised 3 very different categories of complexes representing distinct modeling challenges. Furthermore, with the exception of LdcA homodecamer (T136), all the targets were hetero-complexes, where the partners are different proteins, a protein and a peptide or a protein and an oligosaccharide.

The prediction performance for these targets critically depended on several factors. For the 7 protein hetero-complexes, and considering the increased reliance of predictor groups on template-based modeling rather than on classical docking procedures, an important role was played by their ability to leverage information on the binding modes and interfaces in structures of related hetero-complexes, and use this information to model the structure of the complex directly or to guide docking calculations. For all three target categories -but more particularly for the protein-peptide and protein-oligosaccharide complexes -a decisive factor was the successful optimization of the interactions at the binding interfaces by sampling both internal and rigid-body degrees of freedom of the interacting partners.

A convenient overview of how these challenges were met by predictor groups and servers across all three categories of targets is presented in To gain insight into the factors contributing to the quality of predicted models we examined the relationships between the quality of these models and 2 important parameters: the accuracy of the 3D structures of individual subunits in the predicted complexes and the accuracy of side chain conformations of interface residues in these complexes. Figure 11b displays the distributions of the M-rms values (the backbone rms values of the individual subunits of the submitted models versus those of the target), color-coded by the CAPRI model quality categories for all the interfaces in individual models submitted by predictors (human and severs) for the evaluated targets. For each target/interface, distributions are displayed for the ligand (L) and receptor (R) components of each complex (see legend of Figure 11 for definition). These distributions reveal interesting trends. The M-rms values of models submitted for individual targets span a wide range, even within models of the same CAPRI quality category. As expected, the lower M-rms values (< 1 Å) are generally observed for the high quality models (interfaces 1 and 2 of T125, the protein-peptide complexes T134 and T135, and the protein sugar complex T130), confirming that accurately modeling the 3D structures of the individual subunits contributes to the accuracy of the predicted complex. For some targets however, submitted models ranked at different CAPRI quality levels comprise components that feature very similar M-rms values. For example, models submitted for T122 and ranked as medium-quality, acceptable, or incorrect, all feature very similar M-rms values for the component proteins. This indicates in turn that accurately modeling the 3D structure of the individual subunits, while helpful, is not sufficient for accurately, or even correctly, predicting the resulting complex. A similar conclusion can be drawn for the more poorly predicted interfaces of T125 (interfaces 3, the alternate LLT1/NKR-P1 interface).

Interestingly, the low M-rms values for interface 2 of T125 are not reproduced in the LLT1