T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response. Therefore, identifying peptides that can bind to MHC class-I molecules plays a vital role in the design of peptide vaccines. Many computational methods, for example, the state-of-the-art allele-specific method MHCflurry, have been developed to predict the binding affinities between peptides and MHC molecules. In this manuscript, we develop two allele-specific Convolutional Neural Network (CNN)-based methods named ConvM and SpConvM to tackle the binding prediction problem. Specifically, we formulate the problem as to optimize the rankings of peptide-MHC bindings via ranking-based learning objectives. Such optimization is more robust and tolerant to the measurement inaccuracy of binding affinities, and therefore enables more accurate prioritization of binding peptides. In addition, we develop a new position encoding method in ConvM and SpConvM to better identify the most important amino acids for the binding events. Our experimental results demonstrate that our models significantly outperform the state-of-the-art methods including MHCflurry with an average percentage improvement of 6.70% on AUC and 17.10% on ROC5 across 128 alleles.
The major histocompatibility complex (MHC) class-I pathway supports the detection of cancer and viruses by the immune system. It presents parts of proteins (peptides) from inside a cell on its membrane surface enabling visiting immune cells that detect non-self peptides to terminate the cell. The ability to predict whether a peptide will get presented on MHC Class I molecules helps in designing vaccines so they can activate the immune system to destroy the invading disease protein. We designed a prediction model using a BERT-based architecture (ImmunoBERT) that takes as input a peptide and its…
Major histocompatibility complex class two (MHC-II) molecules are trans-membrane proteins and key components of the cellular immune system. Upon recognition of foreign peptides expressed on the MHC-II binding groove, helper T cells mount an immune response against invading pathogens. Therefore, mechanistic identification and knowledge of physico-chemical features that govern interactions between peptides and MHC-II molecules is useful for the design of effective epitope-based vaccines, as well as for understanding of immune responses. In this paper, we present a comprehensive trans-allelic pre…
T-cell receptor specificity landscape revealed through de novo peptide design
Paul G. Allen School of Computer Science and Engineering, University of Washington（保罗·G·阿伦计算机科学与工程学院，华盛顿大学）；Department of Physics, University of Washington（物理系，华盛顿大学）；St. Jude Children’s Research Hospital（圣 Jude儿童研究医院）；Fred Hutchinson Cancer Center（弗雷德 Hutchinson癌症中心）；Department of Applied Mathematics, University of Washington（应用数学系，华盛顿大学）
An unsolved challenge in the development of antigen specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen-MHC binding is paramount towards achieving this goal. Here, we present CASTELO, a combined machine learning-molecular dynamics (ML-MD) approach to design novel antigens of increased MHC binding affinity for a Type 1 diabetes (T1D)-implicated system. We build upon a small molecule lead optimization algorithm by training a convolutional variational autoencoder (CVAE) on MD trajectories of 48 different systems across 4 antigens and 4 HLA serotypes. W…
Revolutionizing Personalized Cancer Vaccines with NEO: Novel Epitope Optimization Using an Aggregated Feed Forward and Recurrent Neural Network with LSTM Architecture
T cell receptors (TCRs) bind foreign or self-peptides attached to major histocompatibility complex (MHC) molecules, and the strength of this interaction determines T cell activation. Optimizing the ability of T cells to recognize a diversity of foreign peptides yet be tolerant of self-peptides is crucial for the adaptive immune system to properly function. This is achieved by selection of T cells in the thymus, where immature T cells expressing unique, stochastically generated TCRs interact with a large number of self-peptide-MHC; if a TCR does not bind strongly enough to any self-peptide-MHC,…
Many biological processes are governed by protein-ligand interactions. One such example is the recognition of self and nonself cells by the immune system. This immune response process is regulated by the major histocompatibility complex (MHC) protein which is encoded by the human leukocyte antigen (HLA) complex. Understanding the binding potential between MHC and peptides can lead to the design of more potent, peptide-based vaccines and immunotherapies for infectious autoimmune diseases. We apply machine learning techniques from the natural language processing (NLP) domain to address the task …
Generation of structure-guided pMHC-I libraries using Diffusion Models
Center for Computational Biology, University of California, Berkeley, Berkeley, California, USA（计算生物学中心，加州大学伯克利分校）；Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, California, USA（电气工程与计算机科学系，加州大学伯克利分校）
It has been verified that only a small fraction of the neoantigens presented by MHC class I molecules on the cell surface can elicit T cells. The limitation can be attributed to the binding specificity of T cell receptor (TCR) to peptide-MHC complex (pMHC). Computational prediction of T cell binding to neoantigens is an challenging and unresolved task. In this paper, we propose an attentive-mask contrastive learning model, ATMTCR, for inferring TCR-antigen binding specificity. For each input TCR sequence, we used Transformer encoder to transform it to latent representation, and then masked a p…
Ludwig Maximilian Universität München（路德维希-马克西米利安-慕尼黑大学）；Helmholtz Zentrum München（慕尼黑亥姆霍兹中心）；Technical University of Munich（慕尼黑工业大学）
Accurate in silico modeling of the antigen processing pathway is crucial to enable personalized epitope vaccine design for cancer. An important step of such pathway is the degradation of the vaccine into smaller peptides by the proteasome, some of which are going to be presented to T cells by the MHC complex. While predicting MHC-peptide presentation has received a lot of attention recently, proteasomal cleavage prediction remains a relatively unexplored area in light of recent advancesin high-throughput mass spectrometry-based MHC ligandomics. Moreover, as such experimental techniques do not …
Motivation: In silico methods for the prediction of antigenic peptides binding to MHC class I molecules play an increasingly important role in the identification of T-cell epitopes. Statistical and machine learning methods, in particular, are widely used to score candidate epitopes based on their similarity with known epitopes and non epitopes. The genes coding for the MHC molecules, however, are highly polymorphic, and statistical methods have difficulties to build models for alleles with few known epitopes. In this case, recent works have demonstrated the utility of leveraging information ac…
De novo peptide sequencing algorithms have been widely used in proteomics to analyse tandem mass spectra (MS/MS) and assign them to peptides, but quality-control methods to evaluate the confidence of de novo peptide sequencing are lagging behind. A fundamental part of a quality-control method is the scoring function used to evaluate the quality of peptide-spectrum matches (PSMs). Here, we propose a genetic programming (GP) based method, called GP-PSM, to learn a PSM scoring function for improving the rate of confident peptide identification from MS/MS data. The GP method learns from thousands …
The isoelectric point (IEP or pI) is the pH where the net charge on the molecular ensemble of peptides and proteins is zero. This physical-chemical property is dependent on protonable/deprotonable sidechains and their pKa values. Here an pI prediction model is trained from a database of peptide sequences and pIs using a recurrent neural network (RNN) with long short-term memory (LSTM) cells. The trained model obtains an RMSE and R$^2$ of 0.28 and 0.95 for the external test set. The model is not based on pKa values, but prediction of constructed test sequences show similar rankings as already k…
Donor T cell mediated graft vs. host effects may result from the aggregate alloreactivity to minor histocompatibility antigens (mHA) presented by the HLA in each donor-recipient pair (DRP) undergoing stem cell transplantation (SCT). Whole exome sequencing has demonstrated extensive nucleotide sequence variation in HLA-matched DRP. Non-synonymous single nucleotide polymorphisms (nsSNPs) in the GVH direction (polymorphisms present in recipient and absent in donor) were identified in 4 HLA-matched related and 5 unrelated DRP. The nucleotide sequence flanking each SNP was obtained utilizing the AN…
TCR-EML: Explainable Model Layers for TCR-pMHC Prediction
Department of Computer Science, Tulane University（计算机科学系，路易斯安那大学）；Department of Biochemistry and Molecular Biology, Tulane University School of Medicine（生物化学与分子生物学系，路易斯安那大学医学学院）
