Search for: All records

Major histocompatibility complex Class I (MHC-I) molecules bind to peptides derived from intracellular antigens and present them on the surface of cells, allowing the immune system (T cells) to detect them. Elucidating the process of this presentation is essential for regulation and potential manipulation of the cellular immune system. Predicting whether a given peptide binds to an MHC molecule is an important step in the above process and has motivated the introduction of many computational approaches to address this problem. NetMHCPan, a pan-specific model for predicting binding of peptides to any MHC molecule, is one of the most widely used methods which focuses on solving this binary classification problem using shallow neural networks. The recent successful results of Deep Learning (DL) methods, especially Natural Language Processing (NLP-based) pretrained models in various applications, including protein structure determination, motivated us to explore their use in this problem. Specifically, we consider the application of deep learning models pretrained on large datasets of protein sequences to predict MHC Class I-peptide binding. Using the standard performance metrics in this area, and the same training and test sets, we show that our models outperform NetMHCpan4.1, currently considered as the-state-of-the-art. 

Abstract In the ligand prediction category of CASP15, the challenge was to predict the positions and conformations of small molecules binding to proteins that were provided as amino acid sequences or as models generated by the AlphaFold2 program. For most targets, we used our template‐based ligand docking program ClusPro ligTBM, also implemented as a public server available athttps://ligtbm.cluspro.org/. Since many targets had multiple chains and a number of ligands, several templates, and some manual interventions were required. In a few cases, no templates were found, and we had to use direct docking using the Glide program. Nevertheless, ligTBM was shown to be a very useful tool, and by any ranking criteria, our group was ranked among the top five best‐performing teams. In fact, all the best groups used template‐based docking methods. Thus, it appears that the AlphaFold2‐generated models, despite the high accuracy of the predicted backbone, have local differences from the x‐ray structure that make the use of direct docking methods more challenging. The results of CASP15 confirm that this limitation can be frequently overcome by homology‐based docking. 

Abstract 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. 

Abstract 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.