Accurate prediction of peptide binding affinity to the major histocompatibility complex (MHC) proteins has the potential to design better therapeutic vaccines. Previous work has shown that pan‐specific prediction algorithms can achieve better prediction performance than other approaches. However, most of the top algorithms are neural networks based black box models. Here, we propose DeepAttentionPan, an improved pan‐specific model, based on convolutional neural networks and attention mechanisms for more flexible, stable and interpretable MHC‐I binding prediction. With the attention mechanism, our ensemble model consisting of 20 trained networks achieves high and more stabilized prediction performance. Extensive tests on IEDB's weekly benchmark dataset show that our method achieves state‐of‐the‐art prediction performance on 21 test allele datasets. Analysis of the peptide positional attention weights learned by our model demonstrates its capability to capture critical binding positions of the peptides, which leads to mechanistic understanding of MHC‐peptide binding with high alignment with experimentally verified results. Furthermore, we show that with transfer learning, our pan model can be fine‐tuned for alleles with few samples to achieve additional performance improvement. DeepAttentionPan is freely available as an open‐source software at
Peptide binding to major histocompatibility complexes (MHCs) is a central component of the immune system, and understanding the mechanism behind stable peptide–MHC binding will aid the development of immunotherapies. While MHC binding is mostly influenced by the identity of the so-called anchor positions of the peptide, secondary interactions from nonanchor positions are known to play a role in complex stability. However, current MHC-binding prediction methods lack an analysis of the major conformational states and might underestimate the impact of secondary interactions. In this work, we present an atomically detailed analysis of peptide–MHC binding that can reveal the contributions of any interaction toward stability. We propose a simulation framework that uses both umbrella sampling and adaptive sampling to generate a Markov state model (MSM) for a coronavirus-derived peptide (QFKDNVILL), bound to one of the most prevalent MHC receptors in humans (HLA-A24:02). While our model reaffirms the importance of the anchor positions of the peptide in establishing stable interactions, our model also reveals the underestimated importance of position 4 (p4), a nonanchor position. We confirmed our results by simulating the impact of specific peptide mutations and validated these predictions through competitive binding assays. By comparing the MSM of the wild-type system with those of the D4A and D4P mutations, our modeling reveals stark differences in unbinding pathways. The analysis presented here can be applied to any peptide–MHC complex of interest with a structural model as input, representing an important step toward comprehensive modeling of the MHC class I pathway.
more » « less- NSF-PAR ID:
- 10201549
- Publisher / Repository:
- Proceedings of the National Academy of Sciences
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 117
- Issue:
- 48
- ISSN:
- 0027-8424
- Page Range / eLocation ID:
- p. 30610-30618
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract https://github.com/jjin49/DeepAttentionPan . -
more » « less
Abstract Human leukocyte antigen class I (HLA-I) molecules bind and present peptides at the cell surface to facilitate the induction of appropriate CD8+ T cell-mediated immune responses to pathogen- and self-derived proteins. The HLA-I peptide-binding cleft contains dominant anchor sites in the B and F pockets that interact primarily with amino acids at peptide position 2 and the C-terminus, respectively. Nonpocket peptide–HLA interactions also contribute to peptide binding and stability, but these secondary interactions are thought to be unique to individual HLA allotypes or to specific peptide antigens. Here, we show that two positively charged residues located near the top of peptide-binding cleft facilitate interactions with negatively charged residues at position 4 of presented peptides, which occur at elevated frequencies across most HLA-I allotypes. Loss of these interactions was shown to impair HLA-I/peptide binding and complex stability, as demonstrated by both in vitro and in silico experiments. Furthermore, mutation of these Arginine-65 (R65) and/or Lysine-66 (K66) residues in HLA-A*02:01 and A*24:02 significantly reduced HLA-I cell surface expression while also reducing the diversity of the presented peptide repertoire by up to 5-fold. The impact of the R65 mutation demonstrates that nonpocket HLA-I/peptide interactions can constitute anchor motifs that exert an unexpectedly broad influence on HLA-I-mediated antigen presentation. These findings provide fundamental insights into peptide antigen binding that could broadly inform epitope discovery in the context of viral vaccine development and cancer immunotherapy.
-
more » « less
Abstract Modern large‐scale agricultural practices that incorporate high density farming with subtherapeutic antibiotic dosing are considered a major contributor to the rise of antibiotic‐resistant bacterial infections of humans with species of
Salmonella being a leading agriculture‐based bacterial infection. Microcin J25, a potent and highly stable antimicrobial peptide active against Enterobacteriaceae, is a candidate antimicrobial against multipleSalmonella species. Emerging evidence supports the hypothesis that the composition of the microbiota of the gastrointestinal tract prevents a variety of diseases by preventing infectious agents from proliferating. Reducing clearance of off‐target bacteria may decrease susceptibility to secondary infection. Of the Enterobacteriaceae susceptible to microcin J25,Escherichia coli are the most abundant within the human gut. To explore the modulation of specificity, a collection of 207 mutants encompassing 12 positions in both the ring and loop of microcin J25 was built and tested for activity againstSalmonella andE. coli strains. As has been found previously, mutational tolerance of ring residues was lower than loop residues, with 22% and 51% of mutations, respectively, retaining activity toward at least one target within the target organism test panel. The multitarget screening elucidated increased mutational tolerance at position G2, G3, and G14 than previously identified in panels composed of single targets. Multiple mutations conferred differential response between the different targets. Examination of specificity differences between mutants found that 30% showed significant improvements to specificity toward any of the targets. Generation and testing of a combinatorial library designed from the point‐mutant study revealed that microcin J25I13Treduces off‐target activity toward commensal human‐derivedE. coli isolates by 81% relative toSalmonella enterica serovar Enteritidis. These in vitro specificity improvements are likely to improve in vivo treatment efficacy by reducing clearance of commensal bacteria in the gastrointestinal tract of hosts. -
more » « less
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.
-
more » « less
The s2m, a highly conserved 41-nt hairpin structure in the SARS-CoV-2 genome, serves as an attractive therapeutic target that may have important roles in the virus life cycle or interactions with the host. However, the conserved s2m in Delta SARS-CoV-2, a previously dominant variant characterized by high infectivity and disease severity, has received relatively less attention than that of the original SARS-CoV-2 virus. The focus of this work is to identify and define the s2m changes between Delta and SARS-CoV-2 and the subsequent impact of those changes upon the s2m dimerization and interactions with the host microRNA miR-1307-3p. Bioinformatics analysis of the GISAID database targeting the s2m element reveals a >99% correlation of a single nucleotide mutation at the 15th position (G15U) in Delta SARS-CoV-2. Based on1H NMR spectroscopy assignments comparing the imino proton resonance region of s2m and the s2m G15U at 19°C, we show that the U15–A29 base pair closes, resulting in a stabilization of the upper stem without overall secondary structure deviation. Increased stability of the upper stem did not affect the chaperone activity of the viral N protein, as it was still able to convert the kissing dimers formed by s2m G15U into a stable duplex conformation, consistent with the s2m reference. However, we show that the s2m G15U mutation drastically impacts the binding of host miR-1307-3p. These findings demonstrate that the observed G15U mutation alters the secondary structure of s2m with subsequent impact on viral binding of host miR-1307-3p, with potential consequences on immune responses.
