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.
This content will become publicly available on February 23, 2025
- Award ID(s):
- 2200052
- PAR ID:
- 10527731
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in Bioinformatics
- Volume:
- 4
- ISSN:
- 2673-7647
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
more » « less
Abstract The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major histocompatibility complex molecules (MHC) is fundamental to the immune response. Accurate prediction of TCR–epitope interactions is crucial for advancing the understanding of various diseases and their prevention and treatment. Existing methods primarily rely on sequence-based approaches, overlooking the inherent topology structure of TCR–epitope interaction networks. In this study, we present $GTE$, a novel heterogeneous Graph neural network model based on inductive learning to capture the topological structure between TCRs and Epitopes. Furthermore, we address the challenge of constructing negative samples within the graph by proposing a dynamic edge update strategy, enhancing model learning with the nonbinding TCR–epitope pairs. Additionally, to overcome data imbalance, we adapt the Deep AUC Maximization strategy to the graph domain. Extensive experiments are conducted on four public datasets to demonstrate the superiority of exploring underlying topological structures in predicting TCR–epitope interactions, illustrating the benefits of delving into complex molecular networks. The implementation code and data are available at https://github.com/uta-smile/GTE.
-
more » « less
Abstract Peptide therapeutics have gained great interest due to their multiple advantages over small molecule and antibody‐based drugs. Peptide drugs are easier to synthesize, have the potential for oral bioavailability, and are large enough to target protein‐protein interactions that are undruggable by small molecules. However, two major limitations have made it difficult to develop novel peptide therapeutics not derived from natural products, including the metabolic instability of peptides and the difficulty of reaching antibody‐like potencies and specificities. Compared to linear and disulfide‐monocyclized peptides, multicyclic peptides can provide increased conformational rigidity, enhanced metabolic stability, and higher potency in inhibiting protein‐protein interactions. The identification of novel multicyclic peptide binders can be difficult, however, recent advancements in the construction of multicyclic phage libraries have greatly advanced the process of identifying novel multicyclic peptide binders for therapeutically relevant protein targets. This review will describe the current approaches used to create multicyclic peptide libraries, highlighting the novel chemistries developed and the proof‐of‐concept work done on validating these libraries against different protein targets.
-
Ozkan, Banu (Ed.)Abstract Evaluation of immunogenic epitopes for universal vaccine development in the face of ongoing SARS-CoV-2 evolution remains a challenge. Herein, we investigate the genetic and structural conservation of an immunogenically relevant epitope (C662–C671) of spike (S) protein across SARS-CoV-2 variants to determine its potential utility as a broad-spectrum vaccine candidate against coronavirus diseases. Comparative sequence analysis, structural assessment, and molecular dynamics simulations of C662–C671 epitope were performed. Mathematical tools were employed to determine its mutational cost. We found that the amino acid sequence of C662–C671 epitope is entirely conserved across the observed major variants of SARS-CoV-2 in addition to SARS-CoV. Its conformation and accessibility are predicted to be conserved, even in the highly mutated Omicron variant. Costly mutational rate in the context of energy expenditure in genome replication and translation can explain this strict conservation. These observations may herald an approach to developing vaccine candidates for universal protection against emergent variants of coronavirus.more » « less
-
more » « less
Abstract A challenge in studying viral immune escape is determining how mutations combine to escape polyclonal antibodies, which can potentially target multiple distinct viral epitopes. Here we introduce a biophysical model of this process that partitions the total polyclonal antibody activity by epitope and then quantifies how each viral mutation affects the antibody activity against each epitope. We develop software that can use deep mutational scanning data to infer these properties for polyclonal antibody mixtures. We validate this software using a computationally simulated deep mutational scanning experiment and demonstrate that it enables the prediction of escape by arbitrary combinations of mutations. The software described in this paper is available at https://jbloomlab.github.io/polyclonal.
