Pavel A Dub

Rinaldo Poli
email: poli@lcc-toulouse.fr

A computational study of solution equilibria of platinum-based ethylene hydroamination catalytic species including solvation and counterion effects: proper treatment of the free energy of solvation

Keywords: platinum, catalytic hydroamination, aniline complexes, solvent models, DFT calculations, counterion effect

A DFT/B3LYP study of the effect of the explicit inclusion of the Me4P + cation (as a model of nBu4P + ) on the calculation of solution equilibria involving anionic Pt II complexes is reported.

The calculated complexes are models of species that potentially participate in the low-energy portion of the catalytic cycle of the ethylene hydroamination by aniline catalyzed by the PtBr2/(nBu4P)Br system, namely (nBu4P

The relative energies are based on gas-phase geometry optimizations followed by C-PCM calculations of the solvation effects in dichloromethane and aniline at 25°C and 150°C. Three different approaches have been considered to describe the relative energies in solution: ∆E CPCM (∆Eel gas + ∆∆G solv ), ∆Gv CPCM (∆Eel gas + ∆EZPVE gas + ∆Ev gas -T∆Sv gas + ∆∆G solv ) and ∆G CPCM (∆H gas -T∆S gas + ∆∆G solv ), where ∆Ev gas and ∆Sv gas includes only the vibrational contribution and ∆∆G solv for each compound is the solvation free energy resulting from the C-PCM calculation. The cation-anion association was found favourable in both solvents at the ∆E CPCM and ∆Gv CPCM levels, but nearly neutral at the ∆G CPCM level. Consideration of the associated salts has a drastic effect on the energy scheme but significantly changes the relative energies only when doubly charged complexes are involved. The energy changes for equilibria that involve only neutral of singly charged species are not greatly affected by the cation inclusion. The ∆G CPCM approach provides results in greater agreement with the available equilibrium data.

Introduction

Brunet and co-workers have unveiled in 2004 a simple and efficient catalystone of the most performing reported to datefor the intermolecular hydroamination of ethylene and higher olefins by anilines, which consists of ligandless PtBr2 activated by nBu4PBr [1][2][3][4]. A mechanism involving olefin activation followed by amine nucleophilic addition (as opposed to amine activation followed by olefin coordination and insertion) seems operative for this platinum catalyst [4] on the basis of a previous computational study on the [PtCl(C2H4)(PH3)2] + model [5] and of the known stoichiometric reactivity of Pt-(olefin) complexes with amines [6][7][8][9][10][11][12].

However, the previous computational study addresses only models of phosphine-containing complexes, whereas the Brunet catalyst was found to be poisoned by phosphine ligands [1] while it is activated by halide anions (bromide being the best). Therefore, we set out to investigate the mechanistic details of this catalytic system both experimentally and computationally.

We have recently reported the synthesis and characterization of a variety of complexes formed by PtBr2 in the presence of the activator (Br -) and substrates (ethylene, aniline) and a preliminary DFT investigation of their relative stability [13]. Thus, complexes (nBu4P)2 [PtBr4] (1), (nBu4P)2[Pt2Br6] (1'), (nBu4P)[PtBr3(C2H4)] (2), (nBu4P)[PtBr3(PhNH2)] (3), trans-[PtBr2(C2H4)(PhNH2)] (4), cis-[PtBr2(C2H4)(PhNH2)] (5), and cis-[PtBr2(PhNH2)2] (6) have been isolated and fully characterized. Complexes trans-[PtBr2(C2H4)2] (7) and cis-[PtBr2(C2H4)2] (8), although not synthesized, could also potentially be implicated as intermediates or off-loop catalytic species, since our preliminary DFT calculations show that they are energetically accessible by ligand exchange from [PtBr3(C2H4)] -and C2H4. It should also be mentioned that the related chlorido complexes, cis and trans [PtCl2(C2H4)2], have been reported by other authors [14][15][16].

The experimental studies of the solution equilibria (in a variety of solvents at room temperature) backed up by the DFT study indicated that complex [PtBr3(C2H4)] -is the most stable species and that all other complexes mentioned above are thermally accessible by simple ligand exchange. Scheme 1 shows the relationship between all these different complexes. A roman numeral is used to describe the complexes in solution, corresponding to the Arabic number of the isolated compounds. While this may seem redundant for the neutral compounds, it is important to distinguish the free complexes in solution from the isolated compounds in the case of charged species (e.g.: 1 is the nBu4P + salt of the dianion I). All the ethylene containing complexes -II, IV, V, VII and VIIIare therefore potential candidates for aniline nucleophilic addition.

Scheme 1

The above mentioned theoretical calculations were performed without consideration of the counterion for the anionic species, using the B3LYP functional and applying the C-PCM model [17,18] on the gas-phase optimized geometries. The solution behaviour was described on the basis of the ∆G CPCM parameter (∆H gas -T∆S gas + ∆∆G solv ) [19], which gave results in qualitative agreement with the experimental equilibrium studies [13]. ∆∆G solv is the difference between the solvation free energy corrective terms for the products and the reactants. There are questions, however, related to the partial quenching of translational and rotational modes, as well as the (PV) term, upon going from the gas phase to a condensed state. The problem of the accurate calculation of enthalpies and entropies in solution is a major computational challenge and no universal solution is apparently available [20][21][22]. A very common approach to the computational study of solution energetics involves the complete neglect of the gas phase thermochemical parameters (∆E CPCM = ∆Eel gas + ∆∆G solv ), as this term is directly reported in the Gaussian output, but it should be noted that this approach mixes an electronic energy term with a free energy term and the result has no clear physical meaning. Another approach is based on the neglect of only the translational and rotational components of the gas phase entropy term (∆H gas -T∆Sv gas + ∆∆G solv ), where ∆Sv gas represent the vibrational component of the entropy change for the system optimized in the gas phase [23]. Note, however, that this term has also no precise relevance, because if we assume complete quenching of the translational and rotational components of the molecular motions upon going from the gas phase to the solution, then the translational and rotational components of the overall partition function should be removed. That is, the translational and rotational contributions to the thermal energy should also be neglected, as well as the PV term, not only the translational and rotational contributions to the entropic term. Note that equalizing enthalpy and energy in condensed phases is a common approximation.

In this paper, we shall define a vibrational-only reaction free energy in solution, ∆Gv CPCM = ∆Eel gas + ∆EZPVE gas + ∆Ev gas -T∆Sv gas + ∆∆G solv , where the gas phase electronic energy term of the reaction, ∆Eel gas , is only corrected by the ZPVE term, ∆EZPVE gas , by the thermal vibrational contribution to the energy, ∆Ev gas , by the vibrational entropic contribution, -T∆Sv gas , and by the solvation free energy corrective term, ∆∆G solv . A general problem is also the use of gas-phase frequencies for the calculation of the thermochemical parameters, because solution-phase optimizations are often haunted by several convergence problems. Another complication in our system, however, is the energetic effect of cation-anion association (ion pairing). Salts are generally present in solutions of low dielectric constant solvents as associated species, which may be energetically stabilized not only by Coulombic forces but also be specific non covalent interactions (i.e. hydrogen bonds).

We have therefore wondered how the agreement of the above mentioned different energy approaches (∆E CPCM , ∆Gv CPCM and ∆G CPCM ) would change after inclusion of the counterion in the calculations. The present contribution reports new theoretical studies (DFT/B3LYP) of the ionic complexes of Scheme 1 (I, I', II and III) after inclusion of the cation, and new considerations on the agreement of the above mentioned approaches to the description of the solution equilibria involving species I-VIII.

Computational methods

All geometry optimizations were performed with the Gaussian03 suite of programs [START_REF] Frisch | Gaussian 03, Revision C.02[END_REF] using the B3LYP functional which includes the three-parameter gradient-corrected exchange functional of Becke [START_REF] Becke | [END_REF] and the correlation functional of Lee, Yang, and Parr which includes both local and non-local terms [26,27]. In spite of several problems such as the underestimation of barrier heights, inaccuracy to describe weak interactions and overestimation of spin polarization, this functional was selected because it remains one of the most popular functionals used in transition metal computational chemistry [28]. In addition, all molecules described in this work are diamagnetic and the paramagnetic excited states are expected to be at much higher energy, thus the RHF description is appropriate. The basis set chosen was the standard 6-31+G*, which includes both polarization and diffuse functions that are necessary to allow angular and radial flexibility to the highly anionic systems, for all atoms of type H, C, N, P and Br. The Pt atom was described by the LANL2TZ(f) basis, which is an uncontracted version of LANL2DZ

and includes an f polarization function and an ECP [29]. The starting geometries for the calculations were those previously optimized for the free ion, after introduction of the cations at reasonable distances. Several relative positions of the cation and anion were used as input geometry in each case, and only the lower energy one in solution is reported (the others were generally very close in energy, typically at < 1 kcal mol -1 from the most stable structure).

Stability tests carried out on several molecules with the Gaussian03 default options showed stable wavefunctions in all cases. Frequency calculations were carried out for all optimized geometries in order to verify their nature as local minima and for the calculation of thermodynamic parameters at 298.15 K and at 423.15 K under the gas-phase and harmonic approximations. For the calculation of GV CPCM of each compound (equal to Eel gas + EZPVE gas + Ev gas -TSv gas + Gsolv CPCM ), the (Eel gas + EZPVE gas + Ev gas ) term was obtained as [H gas (T) -3.5RT]

[30], whereas the S v gas term was read directly from the Gaussian output.

Solvent effects were included by means of C-PCM single point calculations on the gasphase optimized geometries [17,18]. Among various continuum solvent models, the C-PCM was selected because of its generally better performance [31][32][33], even though there remains an inherent error when working with charged species [31]. The solvent cavity is created by a series of overlapping spheres by the default UA0 model and all standard settings as implemented in Gaussian03 were used for the C-PCM calculations. Selected calculations have also been carried out with the IEF-PCM model [34][35][36][37][38] (see Results and Discussion section). The reaction freeenergy changes in solution were corrected for the change of standard state from the gas phase (1 atm) to solution (1 M) [39]. does not participate in the catalytic cycle. However, it was experimentally found to result from the slow transformation (25°C) of I in the absence of free bromide. Therefore, calculations on (TMP)2I' were also carried out for comparison. For all the neutral complexes (IV-VIII) the geometries were already optimized in our previous study at the same level of theory [13].

Results and discussion

The gas phase optimized geometries of (TMP)2I, (TMP)2I', (TMP)II, (TMP)III, and

(TMP)Br are shown in Figure 1. In general the addition of the cation does not significantly affect the geometry of the anionic complex, except for a minor Pt-Br bond shortening for I, I', The cation-anion interactions are established through hydrogen bonds between the methyl C-H bonds as proton donors and the bromide anion or the bromido ligands for all the anionic complexes as proton acceptors. These are relatively weak interactions (the shortest H•••Br distances are ca. 2.61 Å). For compounds (TMP)2I' and (TMP)III, short interactions with the metal center (C-H•••Pt) were also observed. A comparison of the normal modes in the presence and absence of the cation reveals a correlation between the frequency shift and the bond length changes, with the greater shift (18 cm -1 to higher frequency) being observed for the Eu out-ofphase Br-Pt-Br stretching vibration in I. Certain Pt-Br stretching modes also reveal vibrational coupling with the cation vibrations. The cation effect on the calculated frequencies has also been analyzed in a recent contribution for the Me3SiO -K + salt [40]. The extrapolation of these results to the real nBu4P + salts clearly involves approximations related to the steric and inductive effects of the nBu group and to the possibility that other C-H bonds of the nBu chains also participate in the cation-anion interactions. It is likely that the nBu4P + cation also interacts with the anion through the  C-H bonds, since these should have the greater acidic character being closer to the positively charged P atom.

and

(b) Ion pairing energies

Since the equilibrium studies reported in our previous contribution [13] were carried out in dichloromethane at room temperature, the C-PCM calculations were also carried out in this solvent, as single point calculations on the gas-phase optimized geometries. An approximation is related to use of this procedure, since the best geometry could in principle change upon going from the gas phase to a condensed phase. However, a recent test carried our in our group for ion pairs of type [Cp*Mo(PMe 3 ) 2 (CO)H 2 ] + [BF 4 ] -has shown insignificant changes when the geometry was reoptimized with the C-PCM in either THF or dichloromethane. The internal geometries of the ions were practically unaffected, whereas the cation-anion interactions became slightly looser, but the effect on the energy was in each case below 0.2 kcal mol -1 relative to the corresponding fixed-point C-PCM calculations on the gas-phase optimized geometries [41].

Considering also the convergence problems that are frequently associated with the geometry optimizations in the presence of a C-PCM, we opted to use only the gas-phase optimized geometries. The thermal and entropy corrections to the gas phase electronic energies were done at 298.15 K to allow comparison with the experiment. C-PCM calculations were also run in aniline at 298.15 K and at 423.15 K. These cannot be compared with experimental results, but are useful for prediction purposes because the catalytic experiments are run in aniline at 150°C. Therefore, they are only provided in the Supporting Information. Calculations of solvation free energies on specific neutral, cationic and anionic species were also carried out with the IEF-PCM model (see Table 1). These showed very small changes, < 0.5 kcal mol -1 in most cases, with the notable exception of the PMe4 + Br -ion pair (difference of ca. 1 kcal mol -1 ). The combination of these solvation energy changes for reactants and products in the ion pairing (equation 1) and ligand exchange (equation 7) processes described later yields only minimal energy effects (< 1 kcal mol -1 under all three approaches, E CPCM , Gv CPCM or G CPCM ). It is first useful to analyze the outcome of the three different approaches to describe the energetic of the ion pair formation. The processes of interest are outlined in equations 1-5 and the results are reported in Table 2. The E CPCM data show that the association is more favourable as expected for the 2:1 salts than for the 1:1 salts. This trend remains valid at the ∆Gv CPCM level. A more detailed analysis reveals an interesting trend. On going from E CPCM to Gv CPCM , the difference between them being the (EZPVE gas +Ev gas -TSv gas ) term, the change is very small for the Br -system (-0.6 kcal mol -1 ), intermediate for the other 1:1 salts (-5.8 kcal mol -1 for II and -5.1 kcal mol -1 for III) and greater for the 2:1 salts (-10.4 kcal mol -1 for I and -13.1 kcal mol -1 for I'). The major cause for this trend is the difference of the Sv gas term, which is also reported for convenience in Table 2. All the Sv gas terms are positive, because a number of new low-frequency vibrational modes in the product (related to the relative movement of the cation and anion with respect to each other) are generated from translational and rotational modes in the reagents. For the bromide salt, the separated ions have 9 translational and rotational modes (no rotational modes for the spherical Br -ion), thus only 3 new vibrational modes are generated in the ion pair. For the salts of II and III, both separate ions have 6 non-vibrational modes, thus 6 new vibrational modes result in the product. Finally, for the 2:1 salts of I and I', the product contains 12 additional vibrational modes relative to the separate ions. Indeed, the value of Sv gas is roughly proportional to the number of new vibrational modes. The actual values are of course determined by the frequencies of the new vibrational modes, as well as from the frequency changes in the other modes.

Br -+ PMe4 + → (PMe4)Br (1) [PtBr4] 2-+ 2 PMe4 + → (PMe4)2[PtBr4] (2)
I (TMP)2I [Pt2Br6] 2-+ 2 PMe4 + → (PMe4)2[Pt2Br6] (3)
I' (TMP)2I' [PtBr3(C2H4)] -+ PMe4 + → (PMe4)[PtBr3(C2H4)] (4)
II (TMP)II [PtBr3(PhNH2)] -+ PMe4 + → (PMe4)[PtBr3(PhNH2)] (5)

III

(TMP)III [46], and 3.8(3)•10 3 for [Cp*2Fe]PF6. The association constants for the nBu4P + salts of II and III could also be reasonably estimated as being greater than 10 3 . We could not find data for 2:1 salts in dichloromethane. For equilibrium constants of 10 3 and 10 4 at 298 K, the corresponding G°298 is -5.5 and -4.1 kcal mol -1 , respectively.

Comparison between these G values and the results in Table 2 shows that, as could be expected, the anticipated experimental results lay in-between the G CPCM and Gv CPCM values.

In other words, a full gas-phase entropy correction overshoots the solution G value. On the other hand, because of the above discussed transformation of translational and rotational modes into vibrational modes, the vibrational entropy term (-TSv) yields a negative correction for this particular ion association process, thus the E CPCM values are placed in-between the Gv CPCM and G CPCM values for all salts. These E CPCM values appear to be in closer agreement with the expected experimental values than the values of either the Gv CPCM of the G CPCM approach, at least for the 1:1 salts (TMP)II and (TMP)III. Therefore, use of E CPCM values for the evaluation of ion pairing equilibria, at least for 1:1 salts, should not give results far off reality in general.

(c) Application to the solution equilibria.

The relative G CPCM values for species I-VIII were already reported and discussed in our previous contribution [13], but no analysis of the corresponding E CPCM and Gv CPCM was offered.

For this reason, the complete set of values for these species according to the three models is given in Figure 2 for the dichloromethane solution. The calculation of the energy value in each case takes into account all species consumed and produced starting from the reference system I, as outlined in equations 6-11. The corresponding diagrams in aniline at 25° and 150°C are presented in SI. The data are presented according to the synthetic strategy. Starting from I and I' in the center, addition of ethylene or aniline leads to II (right) and III (left). Then, complex IV, VII and VIII, shown further on the right, are obtained or can be imagined to result from the addition of aniline or ethylene to II, whereas complexes V and VI, further to the left of I, are obtained by addition of ethylene or aniline to complex III.

2 [PtBr4] 2-→ [Pt2Br6] 2-+ 2 Br - (6)
I I' [PtBr4] 2-+ C2H4 → [PtBr3(C2H4)] -+ Br - (7)
I II

[PtBr4] 2-+ PhNH2 → [PtBr3(PhNH2)] -+ Br - (8)
I III

[PtBr4] 2-+ C2H4 + PhNH2 → [PtBr2(C2H4)(PhNH2)] + 2 Br - (9)
I IV(trans), V(cis)

[PtBr4] 2-+ 2 PhNH2 → cis-[PtBr2(PhNH2)2] + 2 Br - (10)
I VI (dotted lines) approaches for the anionic model in dichloromethane at 25°C. All values are reported in kcal mol -1 relative to I. Species VII' is a different conformer of VII, where one C2H4 ligand has the C-C axis coplanar with the coordination plane [13].

[PtBr4] 2-+ 2 C2H4 → [PtBr2(C2H4)2] + 2 Br - (11)
For all complexes, except for I', ∆G CPCM > ∆Gv CPCM and generally ∆Gv CPCM > ∆E CPCM (except for I', III and VI). The ∆Gv CPCM and ∆E CPCM values are relatively close to each other, whereas ∆G CPCM is significantly greater. This reflects the fact that, for most of the equations 6-11, ∆Sv gas and ∆(Strans gas +Srot gas ) are negative and the former is smaller than the latter. The ∆(Etr gas +Erot gas +PV) term gives a zero contribution to all reactions with equal number of species on the reactants and products sides, and the ∆Ev gas term is small. Equation 6, where the molecularity increases, is the only one where both ∆(Strans gas +Srot gas ) and ∆Sv gas are positive. The same trends are observed in aniline (see SI). The lowest energy species is complex IV according to the ∆E CPCM and ∆Gv CPCM approaches, while complex II is most stable within the ∆G CPCM approach.

The energies differences between different neutral compounds are not affected by the presence of the counterion. Hence, they can already be analyzed on the basis of the results in Figure 2. For instance, for the mixed ligand system [PtBr2(C2H4)(PhNH2)] the trans isomer has a lower energy than the cis (IV < V) by 2.0 kcal mol -1 for all energy models. This agrees with the experimental evidence reported in our previous contribution [13]. On the other hand, the cis isomer has a lower energy than the trans for the PtBr2(C2H4)2 system (VIII < VII; 2-3 kcal mol -1 depending on the energy model). This is consistent with the experimental evidence reported for the related dichloride system [15,16]. The neutral ligand exchange processes leading from the mixed-ligand complexes [PtBr2(C2H4)(PhNH2)] (IV and V) to the dianiline (VI) or diethylene (VII and VIII) complexes are unfavourable for all energy models. This is again consistent with the experimental evidence, since addition of ethylene to III stops at V and addition of aniline to II stops at IV. As it can be appreciated, solvation entropy plays a very minor role in these equilibria between neutral species, because the same number of molecules appears on each side.

Hence, all approaches reproduce equally well the experiment.

Combination of the ion pairing energies of Table 2 with the relative energies shown in Figure 2 for the solution equilibria of equations 6-11 yields the results summarised in Figure 3 for dichloromethane at 25°C, where the system used as reference is (TMP)2I. The same schemes in aniline solution at 25°C and 150°C are presented in SI. The processes leading to the species of interest are the same ones already outlined in equations 6-11, but with all charged species in the associated form with the PMe4 + cation.

1.0 The association of the ionic complexes with the PMe4 + cation has a drastic effect on the energy scheme, cf. Figure 2 and Figure 3. The ion association process (see Table 2) has the effect of stabilizing all charged species, but the more highly charged species benefit from a greater stabilization. The three monoanionic species (complexes II and III and the Br -ion) are stabilized by approximately the same amount, especially at the ∆E CPCM level, whereas the stabilization of the dianionic complexes is worth more than twice as much on the ∆E CPCM and ∆Gv CPCM scales.

G CPCM G CPCM G v CPCM G v CPCM E CPCM E CPCM 0.0 -4.8
Therefore, the energy values for all equilibria relating a monoanionic complex with a neutral complex (with release of a Br -ion) are not affected very much, whereas the equilibria relating the doubly charged species I with the singly charged species II and III or with the neutral species are strongly affected by ion pairing at the ∆E CPCM and ∆Gv CPCM levels. On the other hand, the picture changes relatively little at the ∆G CPCM level because the ∆G CPCM values of all ion association equilibria (Table 2) are close to zero. Hence, the relative stabilization of all compounds (except I') relative to I are greatly reduced on the ∆E CPCM and ∆Gv CPCM scales but not as much on the ∆G CPCM scale (cf. Figure 2 and Figure 3) and consequently ∆Gv CPCM >> ∆G CPCM > ∆E CPCM for all compounds, except I', relative to compound (TMP)2I. The process leading from (TMP)2I to (TMP)2I' transforms two doubly charged species to a new dianionic species and two monoanionic ones. The result is a great destabilization on the ∆E CPCM and ∆Gv CPCM scales.

The ion pair model of Figure 3 entails small qualitative changes relative to the free ion model of Figure 2 concerning the nature of the most stable species in solution: the ∆G CPCM approach now suggests a greater stability for complex IV, whereas it favoured II under the free ion model, while the ∆Gv CPCM approach now slightly favours (TMP)II, whereas it favoured IV under the free ion model. However, the difference between (TMP)II and IV is only 0.6 kcal/mol in favour of the latter (∆G CPCM ) or 0.4 kcal/mol in favour of the former (∆Gv CPCM ). Recall that the experimental studies indicate a greater thermodynamic stability for compound 2 (equilibrium 12 is largely shifted to the left). This result would seem better reproduced by the ∆Gv CPCM approach for the neutral model and by the ∆G CPCM approach for the ionic model.

(nBu4P)[PtBr3(C2H4)] + PhNH2 trans-[PtBr2(C2H4)(PhNH2)]+ (nBu4P)Br (12) 2 4
It is also useful to recall the other experimental results [13] relating ionic and neutral species, or ionic species of different charge, and compare them with the computational results in Figure 2 and Figure 3. Compound 2 is stable relative to 1 in the presence of excess (nBu4P)Br, but a certain amount of 1 was generated unless a protected atmosphere of ethylene was present.

This suggests that equilibrium 13 is largely shifted to the left hand side, but the free energy difference cannot be more than a few kcal mol -1 . Similarly, equilibrium 14 is also largely shifted toward the left hand side because addition of excess (nBu4P)Br to 3 resulted in only minor transformation to 1. It is difficult to make estimations because the experimental evidence was only qualitative with no accurate determination of the equilibrium concentrations. However, the large stabilization predicted for II and III relative to I by the ionic model (Figure 2) should lead to no significant formation of 1 from either 2 or 3 under essentially any conditions. The energy differences shown in Figure 3 seem more reasonable, confirming the importance of the cationanion associations in the energy calculations. Equilibrium 15 between compounds 2 and 5 was found displaced toward the left hand side, although the conversion of 5 to 2 in the presence of a slightly substoichiometric amount of (nBu4P)Br did not lead to complete bromide consumption [13] (this study was carried out in DFM due to the insolubility of 5 in dichloromethane). Equilibrium 16 between compounds 3 and 6 was also found displaced toward the left hand side, in this case with apparent quantitative conversion of 6 to 3. These results are consistent for the ionic model only under the ∆G CPCM approach, whereas the neutral model predicts the correct result for both equilibria under both approaches (∆G CPCM and ∆Gv CPCM ).

(

)[PtBr3(C2H4)] + PhNH2 cis-[PtBr2(C2H4)(PhNH2)] + (nBu4P)Br (15) 2 5 (nBu4P)[PtBr3(PhNH2)] + PhNH2 cis-[PtBr2(PhNH2)2] + (nBu4P)Br (16) nBu4P
Finally, equilibrium 17 appears displaced toward the left but not by a large amount, because a small amount of 1' was spontaneously generated upon prolonged standing from dichloromethane solutions of 1 in the absence of free bromide. As shown in Figure 2, the free ion model would predict an essentially quantitative conversion of 1 to 1', whereas the ion pair model of Figure 3 can be reconciled with the experiment, under the logical assumption of a partial quenching of the translational and rotational components of the reaction entropy.

2 (nBu4P)2[PtBr4] (nBu4P)2[Pt2Br6] + 2 (nBu4P)Br (17) 1 1'
In summary, the utility of the associated salt model is evident for the description of the equilibria involving highly charged species. When only monocharged species are involved, roughly speaking, both the free ion and the ion pair models describe the solution equilibria with acceptable accuracy, but only when the thermochemical parameters (PV and -TS) are introduced to some degree. The ∆E CPCM approach, although apparently the best one to describe the ion association equilibria, does not yield results in agreement with experiment for the ligand exchange equilibria (except for the equilibria relating neutral species). Concerning the best approach for handling the solution entropy problem, conventional wisdom suggests that the real solution must lay in-between the ∆G CPCM and ∆Gv CPCM solutions and the qualitative comparison of the computational results according to the ion pair model with the available equilibria shows that this indeed the case. At a very qualitative level, it seems that the experimental result lies closer to the ∆G CPCM prediction than to the ∆Gv CPCM prediction for equilibria involving large changes in charge localization (ion associations for 1:1 salts in Table 2 andequilibrium 17). This amounts to saying that there is no significant quenching of the translational and rotational modes of the various compounds on going from the gas phase to the dichloromethane solution.

Otherwise stated, these modes are transformed to very active (low frequency) librations in the solvent cage, greatly contributing to the free energy of the system.

It is not possible to exclude, however, that the shortcoming of various approximations used in the computations lead to systematic errors in favour of ∆G CPCM approach. Amongst these approximations, we can mention the inaccuracy of the harmonic approximation, use of Me4P + as a model of nBu4P + , use of gas-phase optimized rather than solution-phase optimized geometries, and the nature of the functional. However, one additional approximation that might turn out to affect significantly the quality of the calculations is the total neglect of specific interactions (notably hydrogen bonding) between the reagents participating in the considered equilibria with each other and with solvent molecules. For instance, the free aniline and the aniline ligand coordinated to III in equilibrium (8) can serve as proton donors in hydrogen bonding with the bromido ligands of I and III and with the free bromide ion. Similar effects would operate on equilibria 9 and 10.

As a final remark, the investigation of the platinum-catalyzed hydroamination by aniline potentially involves several platinum complexes as intermediates and off-loop species, some of which may be neutral, others anionic. Insofar as these species have a single negative charge, the use of the ionic model seems to provide relevant results, while the participation of doubly charged anions may require a more elaborate study using the associated species. It should be mentioned that other theoretical studies have reported the need to include the counterion. Most typically, the counterion in these previous contributions interacts with the catalytic system by coordinating the metal (e.g. F -or Cl -) [47,48] or assists intramolecular rearrangement steps (e.g.

proton transfer by TfO - [49]) and thus changes the nature of the catalytic resting state and/or transition state of the rate determining step. Great attention has been devoted to the control of the structure and relative stability of dormant species and transition states in polymerization catalysis [50][51][52][53][54][55][56][57][58][59][60][61], while other studies address the orientation or steric influence of the counterion other types of potential catalytic sites [62][63][64]. In our case, the nBu4P + cation should in principle not play any role other than ion pairing. The explicit effect of the counterion on the relative energetics through cation-anion association equilibria in relation with catalytic processes does not appear to be systematically investigated.

Conclusions

The availability of solution equilibrium information relating neutral and charged Pt II complexes (precatalysts, intermediates or off-loop species for the ethylene hydroamination by aniline) has given us the opportunity to test the accuracy of various energy approaches within the C-PCM model for their description. Consideration of the cation-anion association in the low permittivity dichloromethane and aniline solvents has drastic effect on the energy scheme in comparison to the free ion model, although practically no effect is observed on the geometries and frequencies of the anionic complexes. Consideration of ion pairing is essential for equilibria involving highly charged anionic complexes (< -1), whereas those implicating only neutral and singly charged species are described reasonably well by both the free ion and ion pair models.

The most important point is the inadequacy of the common ∆E CPCM (∆Eel gas + ∆∆Gsolv) approach, whereas models that introduce the gas phase thermodynamic parameters, ∆Gv CPCM and ∆G CPCM , provide results in better agreement with the experimental results. However, the ∆E CPCM gives better estimates for the solution free energy of the ion pairing process. The correct handling of

(a) Gas phase geometries Compounds 1 , 2

 12 and 3 were modelled by simplifying the nBu 4 P + cation to the smaller PMe4 + (TMP), yielding systems (TMP)2I, (TMP)II and (TMP)III. The dianion [Pt2Br6] 2-(I')

  the two Pt-Br bonds trans to each other in II and III, whereas the unique Pt-Br bond (trans to C2H4 in II and to PhNH2 in III) slightly lengthens (comparisons are shown in SI). In addition, while free I' is planar, a slight bend across the Brbr•••Brbr edge occurs for the geometry optimized in the presence of the two PMe4 + cations. This is certainly caused by the slightly asymmetric positioning of the two cations on the opposite sides of the dianion (see Figure1) and by the soft nature of the potential energy surface along this bending mode, as shown by the low frequencies of 13 and 33 cm -1 where this molecular motion participates (vibrationally coupled with cation modes

Figure 1 .

 1 Figure 1. Optimized geometries of the ion pairs used in this study. The closer cation-anion contacts are highlighted by dotted lines with the distances reported in Ångstroms.

Figure 2 .

 2 Figure 2. Energy diagram for the ∆E CPCM (plain lines), ∆Gv CPCM (dashed lines), and ∆G CPCM(dotted lines) approaches for the anionic model in dichloromethane at 25°C. All values are reported in kcal mol -1 relative to I. Species VII' is a different conformer of VII, where one C2H4 ligand has the C-C axis coplanar with the coordination plane[13].

-4. 5 (

 5 TMP) 2 I (TMP)II (TMP)III (TMP) 2

Figure 3 .

 3 Figure 3. Energy diagram for the ∆E CPCM (plain lines), ∆Gv CPCM (dashed lines), and ∆G CPCM (dotted lines) approaches for the neutral model in dichloromethane at 25°C. All values are reported in kcal mol -1 relative to (TMP)2I.

Table 1 .

 1 Solvation free energies for selected species in CH2Cl2 at 298.15 K under different continuum model approaches.

	Species	Gsolv CPCM , kcal mol -1 Gsolv IEFPCM , kcal mol -1
	PMe4 +	-37.16	-36.88
	C2H4	1.79	1.88
	PMe4 + Br -	-16.25	-15.21
	Br -	-58.93	-58.93
	[PtBr3(C2H4)] -	-39.51	-39.01
	[PtBr 4 ] 2-	-149.52	-148.97

Table 2 .

 2 Relative energies changes for the ion pair formations at 298.15 K in dichloromethane.On going from Gv CPCM to G CPCM , on the other hand, the association becomes nearly ergoneutral for all salts. The major difference between these two parameters corresponds to the Note the cation dependence, the values of the ammonium salts being greater than those of the bulkier Ph4As + and PPN + salts. We can predict intermediate values for the nBu4P + salt, i.e.

	Anionic system	E CPCM	Gv CPCM	G CPCM	Sv gas
		kcal/mol kcal/mol kcal/mol	e.u.
	Br -(equation 1)	-8.1	-8.7	-2.5	8.8
	I (equation 2)	-24.5	-34.9	-5.9	53.2
	I' (equation 3)	-18.3	-31.4	-0.7	65.3
	II (equation 4)	-7.2	-13.0	1.5	30.4
	III (equation 5)	-7.6	-12.7	2.4	28.2

(-TStrans gas -TSrot gas ) term, which is much greater for the 2:1 salts (10.2, Br -; 18.4, II; 19.1, III; 37.0, I; 38.7, I'; all values are in kcal mol -1 ), plus a smaller term due to (Etr gas +Erot gas +PV). This trend is related to the loss of a greater number of translational and rotational modes in the order Br -(3) < II,III (6) < I, I'(12).

The question now revolves around which approach yields results in better agreement with the experiment. We do not have quantitative values for the experimental association constants of these salts in dichloromethane, but values for similar salts are available in the literature. For simple bromide salts in dichloromethane, relevant values are 2.5(2)•10 4 for nBu4NBr

[42]

,

Acknowledgement. We thank the CNRS and the RFBR for support through a France-Russia (RFBR -CNRS) bilateral grant № 08-03-92506, and the MENESR (Ministère de l'Éducation nationale de l'enseignement supérieur et de la recherche de France) for the Ph.D. fellowship to P.A.D.

Graphics for the Table of Contents

the solute translational and rotational partition function for accurate calculation of solution equilibrium positions is one of the outstanding challenges for computational chemistry. The additional complication of ion pairing equilibria adds another dimension to the complexity of the calculation, but the present contribution shows that relatively significant results can be obtained.

We believe that extensive benchmarking using simple ion pairing equilibria that are experimentally well determined at the quantitative level can be of great help for the analysis of the partially quenched translational and rotational motions of solutes.

In terms of our exploration of the Pt II -catalyzed hydroamination mechanism and more specifically the investigation of the high-energy portion of the catalytic cycle, the results presented in the present contribution will be of guidance for the selection of the most suitable computational approach. This investigation is currently ongoing and the results will be reported in due course.


Juan Antonio Vizcaíno

Mathias Walzer

Rafael C Jiménez

Wout Bittremieux

David Bouyssié

Christine Carapito

Fernando Corrales

Myriam Ferro

Albert J R Heck

Peter Horvatovich

Martin Hubalek

Lydie Lane

Kris Laukens

Fredrik Levander

Frederique Lisacek

Petr Novak

Magnus Palmblad

Damiano Piovesan

Alfred Pühler

Veit Schwämmle

Dirk Valkenborg

Merlijn Van Rijswijk

Jiri Vondrasek

Martin Eisenacher

Lennart Martens

Oliver Kohlbacher

A community proposal to integrate proteomics activities in ELIXIR [version 1; peer review: 2 approved]

Keywords: proteomics, mass spectrometry, computational proteomics, databases, bioinformatics infrastructure, data standards, training, multi-omics approaches

Computational approaches have been major drivers behind the progress of proteomics in recent years. The aim of this white paper is to provide a framework for integrating computational proteomics into ELIXIR in the near future, and thus to broaden the portfolio of omics technologies supported by this European distributed infrastructure. This white paper is the direct result of a strategy meeting on 'The Future of Proteomics in ELIXIR' that took place in March 2017 in Tübingen (Germany), and involved representatives of eleven ELIXIR nodes.

These discussions led to a list of priority areas in computational proteomics that would complement existing activities and close gaps in the portfolio of tools and services offered by ELIXIR so far. We provide some suggestions on how these activities could be integrated into ELIXIR's existing platforms, and how it could lead to a new ELIXIR use case in proteomics. We also highlight connections to the related field of metabolomics, where similar activities are ongoing. This white paper could thus serve as a starting point for the integration of computational proteomics into ELIXIR. Over the next few months we will be working closely with all stakeholders involved, and in particular with other representatives of the proteomics community, to further refine this paper.

Competing interests:

The meeting was funded by funds provided by the ELIXIR Central Hub (supported by EU H2020 Research Infrastructures).

Grant information:

This activity is included in the ELIXIR Implementation Project entitled 'Mining the Proteome: Enabling Automated Processing and Analysis of Large-Scale Proteomics Data'. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Introduction

Proteomics is generally defined as the large-scale experimental study of the proteome. High-throughput proteomics approaches have matured significantly, becoming an increasingly used tool in biological research. The rapid development of the field over the last decade has been primarily driven by technological progress in mass spectrometry instrumentation, chromatographic separation, genomics (increased availability of sequenced genomes) and bioinformatics [START_REF] Mallick | Proteomics: a pragmatic perspective[END_REF][START_REF] Aebersold | Mass-spectrometric exploration of proteome structure and function[END_REF] . The primary workhorse of proteomics today is mass spectrometry coupled to liquid chromatography (LC-MS), with less commonly used high-throughput proteomics approaches based on antibodies (e.g., protein arrays and other immunofluorescence-based techniques). Key applications of proteomics are the study of (differential) protein expression in time and space, characterization of protein primary structures and their post-translational modifications (PTMs), such as phosphorylation and glycosylation, elucidating protein structures, and proteinprotein interactions. It is the primary technology driving progress in unravelling signalling networks (e.g. protein phosphorylation driven signalling) and protein interaction networks, and is indispensable for understanding biological function of protein isoforms and disentangling their specific functions. In complex systems biology and systems medicine studies, proteomics often complements information gained from other omics levels, such as genomics and transcriptomics (the so-called proteogenomics and proteotranscriptomics studies [START_REF] Menschaert | Proteogenomics from a bioinformatics angle: A growing field[END_REF] ), metagenomics (metaproteomics), glycomics, and metabolomics.

As already highlighted, remarkable advances in computational methods have been a key driver in the fast development of the field of proteomics. ELIXIR (https://www.elixir-europe.org/) is a European Research Infrastructure (ESFRI), which coordinates, integrates and sustains bioinformatics resources across its member states. Some of the most prominent research groups in proteomics are active in Europe, such as Prof. Matthias Mann (Martinsried, Munich, Germany), Prof. Ruedi Aebersold (Zurich, Switzerland), Prof. Albert Heck (Utrecht, the Netherlands) and Prof. Mathias Uhlén (Stockholm, Sweden). In addition, Europe also hosts worldwide renowned groups that are focused on the development and application of widely-used bioinformatics tools and resources, including MaxQuant 4 , the OpenMS framework 5 , CompOmics 6 tools, such as PeptideShaker [START_REF] Vaudel | PeptideShaker enables reanalysis of MS-derived proteomics data sets[END_REF] , the PRIDE database, as the worldleading proteomics repository [START_REF] Vizcaino | 2016 update of the PRIDE database and its related tools[END_REF] (also coordinating the global ProteomeXchange Consortium of proteomics resources [START_REF] Deutsch | The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition[END_REF] ), and a collection of open data standards and related software 10 , emphasizing the European leading role in the activities of the HUPO (Human Proteome Organisation) Proteomics Standards Initiative (PSI).

Outside mass spectrometry proteomics, the Human Protein Atlas [START_REF] Uhlén | Proteomics. Tissue-based map of the human proteome[END_REF] , located in Sweden, is the world-leading resource for antibodybased characterization of the human proteome. In this context, it should also be highlighted that two of the three sites behind the development of UniProt 12 , the most-widely used protein knowledgebase, are European: the Swiss Institute of Bioinformatics (SIB) and the European Bioinformatics Institute (EMBL-EBI). Furthermore, neXtProt [START_REF] Lane | neXtProt: a knowledge platform for human proteins[END_REF] , which is the reference knowledgebase for human proteins in the context of the HUPO Human Proteome Project, is also developed and maintained at SIB. Additionally, it is of note to highlight that a number of national proteomics-dedicated infrastructures have already prioritised structuring and developing computational proteomics among their activities. This is the case of the French proteomics infrastructure ProFI (which has, for instance, devoted a major investment to develop the Proline tool) and the Spanish infrastructure ProteoRed (which has, for example, contributed heavily to PSI activities). Also, proteomics is represented in other national scientific infrastructures. One example is the Netherlands DTL (Dutch Techcentre for Life Sciences), having an active role in advocating for FAIR data management.

In the context of providing infrastructure for storing proteomics data, it is worth highlighting here that, although it was not the case just a few years ago, thanks to many of these efforts, and with the support of scientific publishers and funders, public availability of proteomics data has increased exponentially in recent years, becoming a common scientific practise, similarly to how it routinely happens in disciplines such as genomics and transcriptomics [START_REF] Martens | A Golden Age for Working with Public Proteomics Data[END_REF] . Figure 1 summarises the growth of the PRIDE database in recent years.

Although the European bioinformatics community has been very active in the proteomics field (see above), proteomics activities have not been highly represented in ELIXIR so far. There was a proteomics component in two small ELIXIR pilot actions (collaborations between EMBL-EBI, now ELIXIR central node, and Bioinformatics Services to Swedish Life Science [BILS], now ELIXIR-Sweden). In addition, a selection of proteomics tools and training events have been included in the ELIXIR tool registry [START_REF] Ison | Tools and data services registry: a community effort to document bioinformatics resources[END_REF] (http://bio.tools) and in TeSS, the ELIXIR training portal (https:// tess.elixir-europe.org/), respectively. These platforms were recently presented to the proteomics community [START_REF] Willems | Proceedings of the EuBIC Winter School[END_REF] . However, we propose that, due to the growing importance of the field and the prominence of proteomics bioinformatics activities in Europe, it is the right time to formally integrate proteomics activities in ELIXIR.

In this context, in February 2017, EMBL-EBI and ELIXIR-DE initiated the first ELIXIR 'Implementation study' involving proteomics approaches, as a starting point for the field. Within this implementation study, suggested by ELIXIR management, the meeting "The future of proteomics in ELIXIR" took place in Tübingen (Germany), as a general strategy meeting for future proteomics activities in ELIXIR. In this white paper, we first summarize the main conclusions of the meeting, and then explain possible future directions for the incorporation of proteomics activities in ELIXIR, taking into account the current overall ELIXIR structure, split in platforms and use cases.

Methods

Meeting "The future of proteomics in ELIXIR"

The meeting took place on March 1 st -2 nd 2017 in Tübingen (Germany). Attendance was widely advertised through ELIXIR dissemination channels (e.g., mailing lists, newsletter) and was open to any interested member of the community. There were 24 attendees representing eleven ELIXIR nodes: Germany (host), Belgium, Czech Republic, Denmark, France, Italy, Netherlands, Sweden, Switzerland, EMBL-EBI, and one representative from the ELIXIR Hub. The detailed minutes of the meeting are available as Supplementary File 1. The meeting started with a presentation given by Rafael Jiménez (ELIXIR Chief Technical Officer) who provided a general overview of the current ELIXIR activities. This initial talk was followed by a series of presentations where the representatives of each node summarized their ongoing activities related to proteomics. All the presentations are freely available at http://tinyurl.com/elixir-proteomics.

The remainder of the meeting was devoted to an open discussion on how to bring together activities, experience, stakeholders, and emerging needs. First of all, ten potential ELIXIR stakeholders in this domain were identified, namely: funding agencies, regulatory bodies, educators, infrastructures, publishers, core facilities, bioinformaticians, life scientists, industries and hospitals/patients. Second, a series of needs and challenges were outlined for each of the stakeholders, and these were then mapped to each of the existing ELIXIR platforms: Data, Tools, Interoperability, Compute and Training (see below). The output of this activity is summarized in Supplementary Table 1. In the second day of the meeting, more concrete topics, derived from the identified needs and challenges were outlined by the attendees, and then organised in wider areas, so called "clusters". Finally, they were prioritised, with the idea that these could form the basis for future proteomics activities in ELIXIR.

Current ELIXIR internal structure

Here we describe the current status of ELIXIR platforms and use cases, by May 2017. ELIXIR's activities are structured around platforms and use cases. They bring together resources and expertise from the ELIXIR Nodes and form the basic unit of operation within ELIXIR. The ELIXIR platforms are responsible of the implementation of the ELIXIR programme and are organised in five key areas: Data, Tools, Compute, Interoperability and Training. The platforms are complemented by four use cases that represent four scientific communities: Human data, Rare diseases, Marine metagenomics and Plant sciences (Figure 2). The use cases drive the work of the ELIXIR platforms by defining their bioinformatics needs and requirements. The close collaboration between the ELIXIR use cases and platforms ensures that the services developed by the ELIXIR platforms are fit for purpose. Each platform and use case is led by a group of senior scientists from across the ELIXIR nodes. In addition to the funding available in each national ELIXIR node, the main source of financial support for ELIXIR activities comes from the ELIXIR-EXCELERATE EU H2020 project. Additional activities are funded through other complementary grants as well as 'Implementation studies' supported by the ELIXIR Hub.

The ELIXIR platforms. The Data platform focuses on sustaining Europe's life science data infrastructure. This platform is working on guidelines and indicators for data resources to improve their impact and sustainability [START_REF] Durinx | Identifying ELIXIR Core Data Resources [version 2; referees: 2 approved[END_REF] . It also works on improving links between curated data resources and literature data.

The Tools platform is dedicated to services and connectors to drive access and exploitation of bioinformatics research software. The main key activities within this platform are centred to facilitate the discovery, benchmarking and interoperability of software. It does also focus on software development best practices, as well as on a strategy for workflows and software containers.

The Interoperability platform supports the discovery, integration and analysis of biological data. Activities driven by this platform are organised in projects around identifiers, metadata standards and linked data. It also works on the description of interoperability services as well as specialised workshops named BYOD (Bring Your Own Data) [START_REF] Eijssen | The Dutch Techcentre for Life Sciences: Enabling data-intensive life science research in the Netherlands [version 2; referees: 2 approved, 1[END_REF] to improve the "FAIRness" (Findable, Accessible, Interoperable and Re-usable) of data resources [START_REF] Wilkinson | The FAIR Guiding Principles for scientific data management and stewardship[END_REF] .

The Compute platform is dedicated to the compute, storage, transfer, authentication and authorization of biological data relying on services provided by ELIXIR nodes and e-infrastructures. Finally, the Training platform aims to increase the professional skills for managing and exploiting data. Part of activities are meant to train researchers, trainers and service providers, but it also includes other activities related to e-learning, to improve the discovery and availability of training materials and to measure the impact of training.

Current ELIXIR use cases.

There are four use cases. First of all, the Human data use case develops long-term strategies for managing and accessing sensitive human data. The Rare diseases use case supports the development of new therapies for rare diseases. The Marine metagenomics use case works towards a sustainable metagenomics infrastructure to nurture research and innovation in the marine domain. Finally, the Plant sciences use case develops an infrastructure to support genotype-phenotype analysis for crop and tree species.

Results and discussion

Areas prioritised in the meeting

In an attempt to assess the relative priorities of the various areas of proteomics that might steer integration into ELIXIR, attendees voted on the relative importance of the topics. The following list shows the top-ranked areas for future ELIXIR related proteomics activities (called "Clusters" from now on), sorted in descending order by the number of votes received: Cluster 1. Multi-omics approaches. It includes topics such as data integration of proteomics and other types of omics data, correlation between gene and protein expression, and development of data standards for "multi-omics" data types. A closely related group of activities was "Cancer proteomics", comprising topics such as support for clinical proteomics data (including large patient cohorts) and cancer "multi-omics" (proteogenomics) studies.

Cluster 2. Proteoforms and PTMs.

The term proteoform [START_REF] Smith | Consortium for Top Down Proteomics: Proteoform: a single term describing protein complexity[END_REF] represents the different molecular forms in which the protein product of a single gene can be found, including changes due to genetic variations, alternatively spliced RNA transcripts and PTMs, among other events. This cluster of activities included topics such as the handling, validation of proteoforms and creation of standards for their description, improvement of the existing connection between proteoforms, genes and metabolites (topic related to Cluster 1 above), and activities devoted to explain unidentified spectral signals.

Cluster 3. Quality Control (QC) activities. In any analytical discipline, QC is essential. Due to the fact that proteomics is a newer and still rapidly developing field, QC has historically not been as welldeveloped in proteomics as in, for instance, the more established small molecule mass spectrometry field [START_REF] Martens | A Golden Age for Working with Public Proteomics Data[END_REF] . This cluster is therefore focused on activities to develop automatic and reliable pipelines for QC of proteomics data at different levels.

Cluster 4. Data analysis workflows and cloud computing.

The concrete activities outlined here could be summarized as the development of robust, reproducible, scalable, user-friendly, integrated, QC-controlled, data analysis pipelines, ideally enabling the use of compatible cloud infrastructures, which in addition, could also be used for data storage. Infrastructure supporting efficient development of such pipelines and workflows is also important, including tool repositories and documentation, workflow management systems and interfaces for accessing computational resources.

Cluster 5. Protein quantification and statistics.

This topic encompasses the improvement of protein inference in shotgun proteomics approaches, the use of peptides that match to more than one protein precursor, and enhanced data integration/harmonisation for quantitative proteomics. It was perceived by many attendees that, although many of these issues are often considered solved, there are still many improvements possible in this area. Cluster 6. Metadata, standardisation, annotation, and data management. This topic also includes the improvement in the annotation of proteomics datasets, in particular in data repositories like PRIDE (to facilitate public data reuse by third parties), the development and/or extension of existing Laboratory Information Management Systems (LIMS), standard data formats, and guidelines summarising best practises for data management, following FAIR principles.

The rest of the areas discussed got only one or two votes from the attendees, and included activities related to interactomics, structural proteomics, metabolomics, metaproteomics, the development of benchmarking datasets, and training efforts. Finally, it is worth highlighting an additional proposal for the creation of a repository for tool-related ideas.

Alignment between ELIXIR activities and the needs of the proteomics community

As mentioned in the 'Methods' section, ELIXIR activities are currently structured in five platforms (Data, Tools, Interoperability, Compute and Training) and four longitudinal use cases (Human data, Rare diseases, Marine metagenomics and Plant sciences) (Figure 2). Our preferred option is that proteomics becomes the main focus of one additional ELIXIR use case in the near future. If there is no scope for proteomics to have its own use case, other options could be possible, for instance the integration of proteomics activities into the existing ELIXIR use cases. We think that the current use cases, heavily focused on genomics data, would also benefit from having a "multi-omics" perspective. Out of the existing cases, Human data would be an obvious choice. The other three could also benefit from proteomics activities: Rare diseases (clinical proteomics), Marine metagenomics (metaproteomics), and Plant sciences (plant proteomics).

In any case, and without considering specific use cases, it is clear that there are several topics that, in our opinion, would fit very well into the scope of the current five ELIXIR platforms:

1-Data platform. Metadata, standardisation, annotation and data management activities (Cluster 6), and "multi-omics" approaches (Cluster 1), involving data integration efforts from different omics data types, would be highly relevant in this context. Moreover, QC efforts (Cluster 3) are essential for all such types of data re-use. Indeed, data re-use is a very important aspect in proteomics data, as only 30-40% of the acquired data is typically exploited [START_REF] Martens | A Golden Age for Working with Public Proteomics Data[END_REF] . This creates extremely exciting opportunities for proteomics data re-use with specialized tools that can lead to the discovery of new biological information [START_REF] Vaudel | Exploring the potential of public proteomics data[END_REF] .

2-Tools platform. In this platform, the overall aim would be to increase the visibility, quality and sustainability of proteomics software developed following best practices. First of all, more proteomics tools should be effectively included into the ELIXIR tool registry, and highlighted there appropriately. However, it is worth highlighting that there are around 350 tools proteomics tools represented in this resource already. In addition, the development of improved and user-friendly quantification algorithms and tools, along with direct coupling to dedicated and performant statistical analysis (Cluster 5), also connects directly to the ELIXIR tools platform. Other possible activities would be related to Cluster 4, and would involve the improvement of the description and sharing of proteomics workflows, facilitating the encapsulation of workflows, data and tools into proteomics software containers that could be shared across ELIXIR, taking advantage of existing resources. Finally, the proposed idea in the meeting to create of a repository for tool-related ideas, would also be applicable in the wider ELIXIR context.

3-Interoperability platform. Obviously, the activities of this platform are very relevant in the case of "multi-omics" approaches (Cluster 1), for instance the development of data standards for these type of approaches, e.g. to enable better data integration and visualisation. A close connection can also be made to metabolomics with regards to QC (Cluster 3), as the main technology of choice (mass spectrometry) is shared between these two fields.

4-Compute platform. Workflow analysis pipelines and activities related to the development of cloud infrastructures (Cluster 4) and QC activities (Cluster 3) should be highlighted here. This would involve different pieces of infrastructure supporting the efficient development of such workflows, e.g. the EDAM ontology and the ELIXIR tool registry [START_REF] Ison | Tools and data services registry: a community effort to document bioinformatics resources[END_REF] , open data formats, and workflow management systems, among others.

It is important to note that, although originally set up in the Data platform, the proteomics ELIXIR implementation study mentioned in the 'Introduction' section aims to make a first step forward in this direction, for the popular shotgun (MS/MS) approaches. As a proof of concept, these pipelines will be deployed first in the EMBL-EBI "Embassy Cloud", a cloud infrastructure based in the Open Stack operating system, with the idea that in the future they can be made available in other cloud systems (e.g. Amazon EC2, Google Cloud, Microsoft Azure), so that the developed pipelines can be freely reused by any interested researcher in the community. In the context of the implementation study, the pipelines are connected to the PRIDE database, bringing the analysis tools closer to the data as datasets become larger in size and complexity. This trend is ongoing for other omics technologies in the context of ELIXIR: for instance, the pipelines developed in the context of the Marine metagenomics use case [START_REF] Robertsen | ELIXIR pilot action: Marine metagenomics -towards a domain specific set of sustainable services [version 1; referees: 1 approved[END_REF] .

5-Training platform. ELIXIR is already working actively in coordinating training activities across Europe (e.g. the already mentioned TeSS portal). Although training was not specifically highlighted as one of the main areas for future development during the meeting, the reason is that everyone assumes that this is implicitly a key need in all bioinformatics fields. While excellent training courses and workshops have already been created in the field, a higher degree of coordination across Europe should be achieved for bioinformatics training activities, and in particular for computational proteomics topics, possibly in coordination with other fields such as metabolomics (see next section).

Connection to metabolomics and other ESFRIs

As mentioned already, metabolomics and proteomics share a common experimental platform: mass spectrometry. A parallel effort is currently ongoing to integrate metabolomics activities in ELIXIR. There are many topics of common interest where both fields could benefit from a closer interaction: e.g. the development of common software (for QC, data visualisation, signal processing, to name but a few) and open data standards. Some initiatives are already working towards this goal, e.g. the computational mass spectrometry group (http://compms.org/), and some recent activities recently carried out by the PSI, but a lot of work remains to be done. One concrete proposal would be to coordinate efforts concerning training activities. ELIXIR would be an ideal platform to enable this coordination effort.

Coming back to the proteomics field, PRIME-XS (http://www. primexs.eu) was a four-year Infrastructure project funded by the EU FP7 Programme (2011-2015), coordinated by Prof. Albert Heck. The main aim of PRIME-XS was to provide funded access to an infrastructure of state-of-the-art proteomics technology to the European biological and biomedical research community. It is anticipated that future tailored EU H2020 infrastructure grant calls might provide an opportunity to create a second iteration of this successful project. The potential synergies with ELIXIR, considering the outlined activities in this white paper, would be obvious at different levels, for instance the provision of a cloud computing infrastructure to store and analyse the acquired data in a scalable and reproducible way, and the development of pipelines to allow data integration between proteomics and other omics data types.

Conclusions

We hope that this white paper acts as a guide to achieve the overall goal of integrating proteomics into ELIXIR. In our opinion, this would not only make perfect sense from a scientific point of view (proteomics information provides an essential ingredient in a "full picture" of life), but also because it would enable computational proteomics to work in close contact with bioinformatics activities in other high-throughput fields, which will undoubtedly trigger many possible interactions, exchanges, and exciting novel developments.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

  juan@ebi.ac.uk : Conceptualization, Funding Acquisition, Investigation, Project Administration, Supervision, Writing -Original Draft Author roles: Vizcaíno JA Preparation, Writing -Review & Editing; : Formal Analysis, Investigation, Writing -Original Draft Preparation, Writing -Review & Editing; Walzer M : Conceptualization, Investigation, Writing -Review & Editing; : Investigation, Writing -Review & Editing; : , Investigation, Writing -Original Draft Preparation, Writing -Review & Editing; : Eisenacher M Martens L Investigation, Writing -Original Draft Preparation, Writing -Review & Editing; : Conceptualization, Funding Acquisition, Kohlbacher O Investigation, Writing -Original Draft Preparation, Writing -Review & Editing No competing interests were disclosed.

Figure 1 .

 1 Figure 1. Number of datasets (A), and the total size of the PRIDE database (B), from 2005 to 2016. Data was retrieved directly from the PRIDE Archive Oracle TM database instance, which contains the file sizes and the dates when the datasets where originally submitted.

Figure 2 .

 2 Figure 2. Overview of current ELIXIR platforms and use cases (by May 2017).

  /doi.org/10.5256/f1000research.12693.r23454 © 2017 Elortza F. This is an open access peer review report distributed under the terms of the Creative Commons , which permits unrestricted use, distribution, and reproduction in any medium, provided the original Attribution License work is properly cited. Felix Elortza CIC bioGUNE, CIBERehd, ProteoRed-ISCIII, Derio, Spain Proteomics has reached a point in which bioinformatics is of paramount importance for the process to extract the maximum information out of the enormous amount of data obtained in practically any study. The scientific community in general and proteomics in particular is eager to get new and effective solutions that may implement that way. In this white paper manuscript, Vizcaino explain the ELIXIR et al. initiative and the importance of integrating proteomics in this European Research Infrastructure. Authors cover in a detailed way what was covered on the meeting that took place in Tübingen (Germany) on February 2017 under the name "The future of proteomics in ELIXIR". The paper is well written and structured. Conclusions are clearly stated.Is the topic of the opinion article discussed accurately in the context of the current literature? YesAre all factual statements correct and adequately supported by citations? YesAre arguments sufficiently supported by evidence from the published literature? YesAre the conclusions drawn balanced and justified on the basis of the presented arguments? YesNo competing interests were disclosed.Competing Interests:I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.The benefits of publishing with F1000Research: Your article is published within days, with no editorial bias

F1000Research 2017, 6:875 Last updated: 09 APR 2020

Acknowledgements

We want to acknowledge P. Velek (ELIXIR Hub) for his help in drafting Figure 2.

Competing interests

No competing interests were disclosed.

Grant information

The meeting was funded by funds provided by the ELIXIR Central Hub (supported by EU H2020 Research Infrastructures). This activity is included in the ELIXIR Implementation Project entitled 'Mining the Proteome: Enabling Automated Processing and Analysis of Large-Scale Proteomics Data'.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Supplementary material

Supplementary File 1: Detailed minutes of the meeting "The future of proteomics in ELIXIR". Click here to access the data. Supplementary File 2: Table S1 Minor change: On page 7 of PDF version of paper, paragraph "2 -Tools platform ...": "it is worth highlighting that there are around 350 tools proteomics tools represented in this resource already", the first "tools" should be removed.

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Email: agnes.labande@lcc-toulouse.fr RECEIVED DATE (to be automatically inserted after your manuscript is accepted if required according to the journal that you are submitting your paper to) ABSTRACT. Air-stable neutral and cationic palladium complexes bearing chiral phosphine-Nheterocyclic carbene ligands with planar chirality only have been prepared in moderate to good yields and characterized by NMR and X-ray diffraction studies. They are shown to catalyze the asymmetric coupling of aryl bromides with arylboronic acids in good yields and moderate enantioselectivities (up to 42% ee) with low catalyst loadings (0.1-0.5 mol%).

N-Heterocyclic carbenes (NHCs) have received a great deal of attention recently and are now considered as ligands of choice for various catalytic reactions, 1 among which the Suzuki-Miyaura reaction using aryl bromides and less reactive aryl chlorides. 2 They have several advantages over phosphines, such as air and thermal stability of the resulting complexes. We have focused our attention on the synthesis of functionalized NHC ligands, with the aim to produce robust and yet highly active catalysts. 3 The combination of a phosphine and a NHC allows the access to stable chelate complexes with interesting electronic properties that have been successfully used in catalysis. 4 Among C-C crosscoupling reactions, the Suzuki-Miyaura reaction has become one of the most appealing for its tolerance to functional groups and the low toxicity of its by-products. 5 Although axially chiral biaryls are present in numerous natural products and constitute an important class of ligands for asymmetric catalysis, there are few reports to date of asymmetric Suzuki-Miyaura reactions. 6 One of the challenges remains to find catalysts that are able to induce high levels of enantioselectivities at low loadings. Surprisingly, NHC ligands have never been used so far in the asymmetric version of the Suzuki-Miyaura reaction. We report here such first example, using a chiral palladium-NHC complex with planar chirality only.

We presented recently the synthesis of achiral and racemic ferrocenyl phosphine-NHC ligands and showed their efficiency in the rhodium-catalyzed hydrosilylation of ketones. 3c,d Here we describe for the first time the synthesis of such ligands in their enantiopure form, as well as their palladium complexes. Building on our experience in the preparation of enantiopure ferrocenyl ligands, 7 we obtained enantiopure (R)-and (S)-ferrocenyl phosphine-imidazolium salts 1a,b in good yields (Scheme 1). Both racemic and enantiopure imidazolium salts were prepared by reaction of the racemic or enantiopure ferrocenyl alcohol -respectively -with HBF4, immediately followed by the N-substituted imidazole. The phosphine was then cleanly deprotected with an excess of Raney nickel in MeCN. Oddly enough, all our attempts to synthesize the neutral complex bearing a bulky mesityl group on the NHC unit (2b) failed. PdCl2(PhCN)2 was tested under similar conditions, but also failed to give the expected product. We finally treated the allyl complex 3b-rac in CH2Cl2 with a solution of aqueous HCl, and obtained again an intractable mixture.

All complexes, even those possessing an allyl moiety, are air-stable and can be purified by column chromatography on silicagel.

NMR data of the allylic compounds indicated the presence of two species in a 55:45 (3a) or 75:25 (3b) ratio, which we can attribute to exo and endo configurational isomers. This refers to the position of the allyl group relative to the ferrocene, as described by Togni et al. for similar complexes. 4g

Unfortunately, the NMR data did not allow us to conclude on the configuration of the major isomer. The structures of complexes 2a-rac, 2a-(R), 3a-rac and 3b-rac were confirmed by crystallographic methods (Figures 1 and2). Both 3a-rac and 3b-rac structures display the endo isomer, with the central C-H of the allyl group pointing towards the ferrocenyl moiety. The allyl group of complex 3a is disordered, which is not uncommon for this type of complexes. 4g,8 The bond lengths and angles are all within the expected range, either for neutral or for cationic complexes, with a square-planar or slightly distorted square-planar geometry. Pd-CNHC and Pd-P distances are longer in the cationic complexes 3. In the neutral complex 2a, surprisingly, the Pd-Cl bond trans to the NHC is slightly but significantly shorter than the Pd-Cl bond trans to the phosphine. We would have expected the opposite, since the NHC should be more donating and exert a higher trans influence. However, this has already been observed for a similar complex. 9 Finally, a noticeable difference is observed in the bond angles between allyl complexes 3a and 3b. Indeed, the CNHC-Pd-P angle is 88.52( 11 Preliminary catalytic tests were carried out with racemic complexes, in order to evaluate their activity in the Suzuki-Miyaura coupling reaction between aryl bromides and phenylboronic acid (Table 1). a Reagents and conditions: aryl bromide (1.0 equiv), phenylboronic acid (1.2 equiv), Pd cat. (0.1 mol%), K2CO3 (2.4 equiv), toluene, 70°C, 1h. b GC yield.

After optimization of the reaction conditions with 4-bromoanisole, toluene and K2CO3 were chosen respectively as best solvent and base. Complex 2a-rac was found to give the best results, with 83 % yield (entry 1) after 1 h at 70°C and a low catalyst loading (0.1 mol%). Cationic allyl complexes 3a-rac and 3b-rac were less active, giving respectively 51% and 42% product (entries 2 and 3) under the same conditions. The introduction of an ortho substituent did not lower the yield with 2a-rac: 87% were obtained with 2-bromotoluene (entry 4). Finally, sterically crowded 2-bromomesitylene was successfully coupled to phenylboronic acid under these conditions (entry 5), which prompted us to turn to naphthyl derivatives (Table 2). Complex 2a-(R) successfully catalyzed the coupling of naphthyl bromides to naphthylboronic acid under these conditions. Reaction times were not optimized and were extended to 24h to maximize conversions. Enantioselectivities obtained initially were modest and we did not observe any substantial improvement when the temperature was lowered (Table 2, entries 1-3 and4-7). Reducing the temperature to 40°C allowed us to reach a 42% ee for R 1 =Me (entry 4). The same level of enantioselectivity was observed when the reaction was stopped after 8h instead of 24h (entry 5), which means we probably have the same catalytic species throughout the reaction. As expected, similar levels of enantioselectivities were observed with the opposite enantiomer 2a-(S) (entries 8-10). However, no product was obtained when the methyl substituent was introduced ortho to the boronic acid instead of to the bromide (entry 11). Allyl palladium complexes 3a-(S) and 3b-(S) gave surprising results. Indeed, complex 3a-(S), bearing a methyl substituent on the nitrogen atom of the imidazol-2-ylidene, gave the expected binaphthyls in very low yields (entries 12-14), no product being observed in the case of methyl-substituted naphthyl bromide (entry 12). The catalyst seemed to decompose in the course of the reaction, giving a black precipitate in the reaction medium. Complex 3b-(S), bearing a bulkier mesityl substituent on nitrogen, reacted with methyl-and methoxy-substituted naphthyl bromides (entries 15-16), but again decomposed in the course of the reaction in the presence of ethoxy-substituted naphthyl bromide (entry 17). Moreover, ees observed with this catalyst are very low. Our first hypothesis was that steric crowding near the palladium was probably too important in the case of 3b and became detrimental to the reaction. However, X-ray structures indicated important variations in the CNHC-Pd-P angles between 3b-rac (102.06(8)°) and other complexes (84.95( 9)° for 2a-rac to 88.52(11)° for 3a-rac) that could account for the differences in reactivity.

In conclusion, new palladium(II) complexes bearing planar chiral ferrocenyl phosphane-NHC ligands showed very good activities in the Suzuki-Miyaura reaction of aryl bromides with arylboronic acids.

Although enantioselectivities are moderate, this is the first example of asymmetric Suzuki-Miyaura reaction with complexes bearing N-heterocyclic carbene ligands. Future studies include the use of other planar chiral NHC ligands in order to improve enantioselectivities.

SYNOPSIS TOC.

Neutral and cationic palladium complexes bearing chiral phosphine-N-heterocyclic carbene ligands with planar chirality only have been synthesized and characterized by NMR and X-ray diffraction studies. They are shown to catalyze the asymmetric coupling of aryl bromides with arylboronic acids in good yields and moderate enantioselectivities with low catalyst loadings.

1

  Palladium Complexes of Planar Chiral Ferrocenyl Phosphine-NHC Ligands: New Catalysts for the Asymmetric Suzuki-Miyaura Reaction Nathalie Debono, a Agnès Labande,* a Eric Manoury, a Jean-Claude Daran a and Rinaldo Poli a,b . a CNRS ; LCC (Laboratoire de Chimie de Coordination) ; 205, route de Narbonne, F-31077 Toulouse, France, and Université de Toulouse ; UPS, INPT ; LCC ; F-31077 Toulouse, France. b Institut Universitaire de France, 103, bd Saint-Michel, 75005 Paris, France.

Scheme 1 .

 1 Scheme 1. Synthesis of racemic and enantiopure ferrocenyl phosphine-imidazolium salts 1a,b.

Figure 1 .Figure 2 .

 12 Figure 1. ORTEP representations of complexes 2a-rac (left) and 2a-(R) (right). Ellipsoids are shown at

Table 1 .

 1 Suzuki-Miyaura reaction between aryl bromides and phenylboronic acid. a

	Entry	Catalyst	Rn	% Yield b

Table 2 .

 2 Asymmetric Suzuki-Miyaura reaction between naphthyl bromides and naphthylboronic acids. a Reagents and conditions: naphthyl bromide (1.0 equiv), boronic acid (1.2 equiv), Pd cat., K2CO3 (2.4 equiv), toluene, 24h. b Isolated yield.c Determined by HPLC with a Chiracel-OJ column. d reaction stopped after 8h.

	Entry	Pd cat. (mol%) R 1	R 2	T (°C)	% Yield b	% ee c
	1	2a-(R) (0.1) Me	H	70	89	38
	2	"	OMe	H	70	86	33
	3	"	OEt	H	70	89	30
	4	2a-(R) (0.5) Me	H	40	88	42
	5	"	Me	H	40	57 d	39 d
	6	"	OMe	H	40	93	33

a

Acknowledgment. We thank the CNRS and the Agence Nationale de la Recherche (ANR-07-JCJC-0041, postdoctoral grant to N.D.) for financial support of this work.

Supporting Information Available. Scheme summarizing the preparation of racemic and enantiopure 1,2-disubstituted ferrocenyl alcohols, text and figures giving full experimental details and spectroscopic data for the preparation of all new compounds, NMR spectra, CIF files giving crystallographic information. This material is available free of charge via the Internet at http://pubs.acs.org.

References.

(1) (a) Nolan, S. P. N-Heterocyclic Carbenes in Synthesis; Wiley-VCH: Weinheim, Germany, 2006.