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Antibodies are key proteins produced by the immune system to target pathogen proteins termed antigens via specific binding to surface regions called epitopes. Given an antigen and the sequence of an antibody the knowledge of the epitope is critical for the discovery and development of antibody based therapeutics. In this work, we present a computational protocol that uses template‐based modeling and docking to predict epitope residues. This protocol is implemented in three major steps. First, a template‐based modeling approach is used to build the antibody structures. We tested several options, including generation of models using AlphaFold2. Second, each antibody model is docked to the antigen using the fast Fourier transform (FFT) based docking program PIPER. Attention is given to optimally selecting the docking energy parameters depending on the input data. In particular, the van der Waals energy terms are reduced for modeled antibodies relative to x‐ray structures. Finally, ranking of antigen surface residues is produced. The ranking relies on the docking results, that is, how often the residue appears in the docking poses' interface, and also on the energy favorability of the docking pose in question. The method, called PIPER‐Map, has been tested on a widely used antibody–antigen docking benchmark. The results show that PIPER‐Map improves upon the existing epitope prediction methods. An interesting observation is that epitope prediction accuracy starting from antibody sequence alone does not significantly differ from that of starting from unbound (i.e., separately crystallized) antibody structure.

 

An important question is how well the models submitted to CASP retain the properties of target structures. We investigate several properties related to binding. First we explore the binding of small molecules as probes, and count the number of interactions between each residue and such probes, resulting in a binding fingerprint. The similarity between two fingerprints, one for the X‐ray structure and the other for a model, is determined by calculating their correlation coefficient. The fingerprint similarity weakly correlates with global measures of accuracy, and GDT_TS higher than 80 is a necessary but not sufficient condition for the conservation of surface binding properties. The advantage of this approach is that it can be carried out without information on potential ligands and their binding sites. The latter information was available for a few targets, and we explored whether the CASP14 models can be used to predict binding sites and to dock small ligands. Finally, we tested the ability of models to reproduce protein–protein interactions by docking both the X‐ray structures and the models to their interaction partners in complexes. The analysis showed that in CASP14 the quality of individual domain models is approaching that offered by X‐ray crystallography, and hence such models can be successfully used for the identification of binding and regulatory sites, as well as for assembling obligatory protein–protein complexes. Success of ligand docking, however, often depends on fine details of the binding interface, and thus may require accounting for conformational changes by simulation methods.

 

Peptide‐protein docking is challenging due to the considerable conformational freedom of the peptide. CAPRI rounds 38‐45 included two peptide‐protein interactions, both characterized by a peptide forming an additional beta strand of a beta sheet in the receptor. Using theRosetta FlexPepDockpeptide docking protocol we generated top‐performing, high‐accuracy models for targets 134 and 135, involving an interaction between a peptide derived from L‐MAG with DLC8. In addition, we were able to generate the only medium‐accuracy models for a particularly challenging target, T121. In contrast to the classical peptide‐mediated interaction, in which receptor side chains contact both peptide backbone and side chains, beta‐sheet complementation involves a major contribution to binding by hydrogen bonds between main chain atoms. To establish how binding affinity and specificity are established in this special class of peptide‐protein interactions, we extractedPeptiDBeta, a benchmark of solved structures of different protein domains that are bound by peptides via beta‐sheet complementation, and tested our protocol for global peptide‐dockingPIPER‐FlexPepDockon this dataset. We find that the beta‐strand part of the peptide is sufficient to generate approximate and even high resolution models of many interactions, but inclusion of adjacent motif residues often provides additional information necessary to achieve high resolution model quality.

 

Targets in the protein docking experiment CAPRI (Critical Assessment of Predicted Interactions) generally present new challenges and contribute to new developments in methodology. In rounds 38 to 45 of CAPRI, most targets could be effectively predicted using template‐based methods. However, the server ClusPro required structures rather than sequences as input, and hence we had to generate and dock homology models. The available templates also provided distance restraints that were directly used as input to the server. We show here that such an approach has some advantages. Free docking with template‐based restraints using ClusPro reproduced some interfaces suggested by weak or ambiguous templates while not reproducing others, resulting in correct server predicted models. More recently we developed the fully automated ClusPro TBM server that performs template‐based modeling and thus can use sequences rather than structures of component proteins as input. The performance of the server, freely available for noncommercial use athttps://tbm.cluspro.org, is demonstrated by predicting the protein‐protein targets of rounds 38 to 45 of CAPRI.

 

As a participant in the joint CASP13‐CAPRI46 assessment, the ClusPro server debuted its new template‐based modeling functionality. The addition of this feature, called ClusPro TBM, was motivated by the previous CASP‐CAPRI assessments and by the proven ability of template‐based methods to produce higher‐quality models, provided templates are available. In prior assessments, ClusPro submissions consisted of models that were produced via free docking of pre‐generated homology models. This method was successful in terms of the number of acceptable predictions across targets; however, analysis of results showed that purely template‐based methods produced a substantially higher number of medium‐quality models for targets for which there were good templates available. The addition of template‐based modeling has expanded ClusPro's ability to produce higher accuracy predictions, primarily for homomeric but also for some heteromeric targets. Here we review the newest additions to the ClusPro web server and discuss examples of CASP‐CAPRI targets that continue to drive further development. We also describe ongoing work not yet implemented in the server. This includes the development of methods to improve template‐based models and the use of co‐evolutionary information for data‐assisted free docking.

 

Starting with a crystal structure of a macromolecule, computational structural modeling can help to understand the associated biological processes, structure and function, as well as to reduce the number of further experiments required to characterize a given molecular entity. In the past decade, two classes of powerful automated tools for investigating the binding properties of proteins have been developed: the protein–protein docking program ClusPro and the FTMap and FTSite programs for protein hotspot identification. These methods have been widely used by the research community by means of publicly available online servers, and models built using these automated tools have been reported in a large number of publications. Importantly, additional experimental information can be leveraged to further improve the predictive power of these approaches. Here, an overview of the methods and their biological applications is provided together with a brief interpretation of the results. 

Predicting protein side-chains is important for both protein structure prediction and protein design. Modeling approaches to predict side-chains such as SCWRL4 have become one of the most widely used tools of its type due to fast and highly accurate predictions. Motivated by the recent success of AlphaFold2 in CASP14, our group adapted a 3D equivariant neural network architecture to predict protein side-chain conformations, specifically within a protein-protein interface, a problem that has not been fully addressed by AlphaFold2.