Albert Solernou

Present and future challenges and limitations in protein-protein docking.

The study of protein–protein interactions that are involved in essential life processes can largely benefit from the recent upraising of computational docking approaches. Predicting the structure of a protein–protein complex from their separate components is still a highly challenging task, but the field is rapidly improving. Recent advances in sampling algorithms and rigid‐body scoring functions allow to produce, at least for some cases, high quality docking models that are perfectly suitable for biological and functional annotations, as it has been shown in the CAPRI blind tests. However, important challenges still remain in docking prediction. For example, in cases with significant mobility, such as multidomain proteins, fully unrestricted rigid‐body docking approaches are clearly insufficient so they need to be combined with restraints derived from domain–domain linker residues, evolutionary information, or binding site predictions. Other challenging cases are weak or transient interactions, such as those between proteins involved in electron transfer, where the existence of alternative bound orientations and encounter complexes complicates the binding energy landscape. Docking methods also struggle when using in silico structural models for the interacting subunits. Bringing these challenges to a practical point of view, we have studied here the limitations of our docking and energy‐based scoring approach, and have analyzed different parameters to overcome the limitations and improve the docking performance. For that, we have used the standard benchmark and some practical cases from CAPRI. Based on these results, we have devised a protocol to estimate the success of a given docking run. Proteins 2010.

Blind prediction of interfacial water positions in CAPRI.

We report the first assessment of blind predictions of water positions at protein–protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community‐wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions—20 groups submitted a total of 195 models—were assessed by measuring the recall fraction of water‐mediated protein contacts. Of the 176 high‐ or medium‐quality docking models—a very good docking performance per se—only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high‐quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein–water interactions and their role in stabilizing protein complexes. Proteins 2014; 82:620–632.

Prediction and scoring of docking poses with pyDock

The two previous CAPRI experiments showed the success of our rigid‐body and refinement approach. For this third edition of CAPRI, we have used a new faster protocol called pyDock, which uses electrostatics and desolvation energy to score docking poses generated with FFT‐based algorithms. In target T24 (unbound/model), our best prediction had the highest value of fraction of native contacts (40%) among all participants, although it was not considered as acceptable by the CAPRI criteria. In target T25 (unbound/bound), we submitted a model with medium quality. In target T26 (unbound/unbound), we did not submit any acceptable model (but we would have submitted acceptable predictions if we had included available mutational information about the binding site). For targets T27 (unbound/unbound) and T28 (homo‐dimer using model), nobody (including us) submitted any acceptable model. Intriguingly, the crystal structure of target T27 shows an alternative interface that correlates with available biological data (we would have submitted acceptable predictions if we had included this). We also participated in all targets of the SCORERS experiment, with at least acceptable accuracy in all valid cases. We submitted two medium and four acceptable scoring models of T25. Using additional distance restraints (from mutational data), we had two medium and two acceptable scoring models of T26. For target T27, we submitted two acceptable scoring models of the alternative interface in the crystal structure. In summary, CAPRI showed the excellent capabilities of pyDock in identifying near‐native docking poses. Proteins 2007.

Optimization of pyDock for the new CAPRI challenges: Docking of homology‐based models, domain–domain assembly and protein‐RNA binding

We describe here our results in the last CAPRI edition. We have participated in all targets, both as predictors and as scorers, using our pyDock docking methodology. The new challenges (homology‐based modeling of the interacting subunits, domain–domain assembling, and protein‐RNA interactions) have pushed our computer tools to the limits and have encouraged us to devise new docking approaches. Overall, the results have been quite successful, in line with previous editions, especially considering the high difficulty of some of the targets. Our docking approaches succeeded in five targets as predictors or as scorers (T29, T34, T35, T41, and T42). Moreover, with the inclusion of available information on the residues expected to be involved in the interaction, our protocol would have also succeeded in two additional cases (T32 and T40). In the remaining targets (except T37), results were equally poor for most of the groups. We submitted the best model (in ligand RMSD) among scorers for the unbound‐bound target T29, the second best model among scorers for the protein‐RNA target T34, and the only correct model among predictors for the domain assembly target T35. In summary, our excellent results for the new proposed challenges in this CAPRI edition showed the limitations and applicability of our approaches and encouraged us to continue developing methodologies for automated biomolecular docking. Proteins 2010.

Expanding the frontiers of protein–protein modeling: From docking and scoring to binding affinity predictions and other challenges

In addition to protein–protein docking, this CAPRI edition included new challenges, like protein–water and protein–sugar interactions, or the prediction of binding affinities and ΔΔG changes upon mutation. Regarding the standard protein–protein docking cases, our approach, mostly based on the pyDock scheme, submitted correct models as predictors and as scorers for 67% and 57% of the evaluated targets, respectively. In this edition, available information on known interface residues hardly made any difference for our predictions. In one of the targets, the inclusion of available experimental small‐angle X‐ray scattering (SAXS) data using our pyDockSAXS approach slightly improved the predictions. In addition to the standard protein–protein docking assessment, new challenges were proposed. One of the new problems was predicting the position of the interface water molecules, for which we submitted models with 20% and 43% of the water‐mediated native contacts predicted as predictors and scorers, respectively. Another new problem was the prediction of protein–carbohydrate binding, where our submitted model was very close to being acceptable. A set of targets were related to the prediction of binding affinities, in which our pyDock scheme was able to discriminate between natural and designed complexes with area under the curve = 83%. It was also proposed to estimate the effect of point mutations on binding affinity. Our approach, based on machine learning methods, showed high rates of correctly classified mutations for all cases. The overall results were highly rewarding, and show that the field is ready to move forward and face new interesting challenges in interactomics. Proteins 2013; 81:2192–2200.

pyDockCG: new coarse-grained potential for protein-protein docking.

Protein-protein interactions are fundamental for the majority of biological processes, so their structural, functional, and energetic characterization is of enormous biotechnological and therapeutic interest. In recent years, a variety of computational docking approaches to the structural prediction of protein-protein complexes have been reported, with encouraging results. However, a major bottleneck is found in cases with conformational movements upon binding, for which docking algorithms have to be extended beyond the rigid-body framework by introducing flexibility. Given the high computational cost of flexible docking, coarse-grained models offer an efficient alternative to full-atom descriptions. This work describes pyDockCG, a new coarse-grained potential for protein-protein docking scoring and refinement, based on the known UNRES model for polypeptide chains. The main novelty is the inclusion of two new terms accounting for the Coulomb electrostatics and the solvation energy. The latter has been devised by adapting the EEF1 model to the coarse-grained approach, with optimal parameters for protein-protein docking. The coarse-grained potential yielded highly similar values to the full-atom scoring function pyDock when applied to the rigid body docking sets, but at much lower computational cost. This efficiency makes it suitable for the treatment of flexibility during docking.

Efficient Relaxation of Protein-Protein Interfaces by Discrete Molecular Dynamics Simulations.

Protein-protein interactions are responsible for the transfer of information inside the cell and represent one of the most interesting research fields in structural biology. Unfortunately, after decades of intense research, experimental approaches still have difficulties in providing 3D structures for the hundreds of thousands of interactions formed between the different proteins in a living organism. The use of theoretical approaches like docking aims to complement experimental efforts to represent the structure of the protein interactome. However, we cannot ignore that current methods have limitations due to problems of sampling of the protein-protein conformational space and the lack of accuracy of available force fields. Cases that are especially difficult for prediction are those in which complex formation implies a non-negligible change in the conformation of the interacting proteins, i.e., those cases where protein flexibility plays a key role in protein-protein docking. In this work, we present a new approach to treat flexibility in docking by global structural relaxation based on ultrafast discrete molecular dynamics. On a standard benchmark of protein complexes, the method provides a general improvement over the results obtained by rigid docking. The method is especially efficient in cases with large conformational changes upon binding, in which structure relaxation with discrete molecular dynamics leads to a predictive success rate double that obtained with state-of-the-art rigid-body docking.

Protein docking by Rotation-Based Uniform Sampling (RotBUS) with fast computing of intermolecular contact distance and residue desolvation.

BackgroundProtein-protein interactions are fundamental for the majority of cellular processes and their study is of enormous biotechnological and therapeutic interest. In recent years, a variety of computational approaches to the protein-protein docking problem have been reported, with encouraging results. Most of the currently available protein-protein docking algorithms are composed of two clearly defined parts: the sampling of the rotational and translational space of the interacting molecules, and the scoring and clustering of the resulting orientations. Although this kind of strategy has shown some of the most successful results in the CAPRI blind test http://www.ebi.ac.uk/msd-srv/capri, more efforts need to be applied. Thus, the sampling protocol should generate a pool of conformations that include a sufficient number of near-native ones, while the scoring function should discriminate between near-native and non-near-native proposed conformations. On the other hand, protocols to efficiently include full flexibility on the protein structures are increasingly needed.ResultsIn these work we present new computational tools for protein-protein docking. We describe here the RotBUS (Rotation-Based Uniform Sampling) method to generate uniformly distributed sets of rigid-body docking poses, with a new fast calculation of the optimal contacting distance between molecules. We have tested the method on a standard benchmark of unbound structures and we can find near-native solutions in 100% of the cases. After applying a new fast filtering scheme based on residue-based desolvation, in combination with FTDock plus pyDock scoring, near-native solutions are found with rank ≤ 50 in 39% of the cases. Knowledge-based experimental restraints can be easily included to reduce computational times during sampling and improve success rates, and the method can be extended in the future to include flexibility of the side-chains.ConclusionsThis new sampling algorithm has the advantage of its high speed achieved by fast computing of the intermolecular distance based on a coarse representation of the interacting surfaces. In addition, a fast desolvation scoring permits the screening of millions of conformations at low computational cost, without compromising accuracy. The protocol presented here can be used as a framework to include restraints, flexibility and ensemble docking approaches.

Refinement of rigid-body protein–protein docking using backbone and side-chain minimization with a coarse-grained model

Understanding protein-protein recognition is one of the main goals in structural biology. Most of the key biological processes involve the formation of specific protein complexes, for which a detailed structural knowledge is essential to understand the mechanism of protein association and their functional implications. Computational docking methods are currently able to predict the structure of a protein-protein complex with a high degree of accuracy in some cases. However, in the majority of cases, with conformational movements upon binding, we have to go beyond the current rigid-body approach and introduce flexibility. Given the dif - ficulties of using full-atom descriptions during flexible docking, we need to focus our efforts in coarse-grain models. Here, we have implemented and tested a version of the united residue (UNRES) forcefield for protein-protein docking refinement. The results indicate improvement in the geometry of the docking solutions, and better docking energy landscapes, although in general, the scoring did not improve with respect to rigid-body pyDock function. However, as opposed to other scoring algorithms, the UNRES scoring does not seem to be biased towards cases that are over-represented in the structural databases (typically enzyme-inhibitor and anti- body-antigen cases). This consistency among all types of complexes suggests its use as a solid basis for developing better unbiased scoring methods.

Computational Tools for Exploration of the Energy Landscape in Protein-Protein Association

We show here our work in structural prediction of protein‐protein interactions. Our computational docking methodology has two major components: the sampling of mutual orientations of the interacting molecules, and the scoring and clustering of these orientations for the identification of near‐native docking poses. Our procedure can generate a uniformly distributed set of rigid‐body docking poses, which can be easily extended to explore the flexibility of the side‐chains. The method is able to find near‐native orientations in line with other state‐of‐the‐art docking programs, and it has been successfully applied together with our pyDock scoring scheme in the most recent rounds of the CAPRI worl‐wide experiment (http://capri.ebi.ac.uk). We have also devised a new measure to compare rigid‐body docking poses based on angular distance (instead of RMSD), which describes the relative orientations of the two molecules in the different docking poses, as evaluated in a large benchmark of known protein‐protein cases...