Multiple-Allele MHC Class II Epitope Engineering by a Molecular Dynamics-Based Evolution Protocol

Epitopes that bind simultaneously to all human alleles of Major Histocompatibility Complex class II (MHC II) are considered one of the key factors for the development of improved vaccines and cancer immunotherapies. To engineer MHC II multiple-allele binders, we developed a protocol called PanMHC-PARCE, based on the unsupervised optimization of the epitope sequence by single-point mutations, parallel explicit-solvent molecular dynamics simulations and scoring of the MHC II-epitope complexes. The key idea is accepting mutations that not only improve the affinity but also reduce the affinity gap between the alleles. We applied this methodology to enhance a Plasmodium vivax epitope for multiple-allele binding. In vitro rate-binding assays showed that four engineered peptides were able to bind with improved affinity toward multiple human MHC II alleles. Moreover, we demonstrated that mice immunized with the peptides exhibited interferon-gamma cellular immune response. Overall, the method enables the engineering of peptides with improved binding properties that can be used for the generation of new immunotherapies.

[1]  Chris Bailey-Kellogg,et al.  MHCEpitopeEnergy, a Flexible Rosetta-Based Biotherapeutic Deimmunization Platform , 2021, J. Chem. Inf. Model..

[2]  J. Thornton,et al.  Impact of Structural Observables From Simulations to Predict the Effect of Single-Point Mutations in MHC Class II Peptide Binders , 2021, Frontiers in Molecular Biosciences.

[3]  Miguel A. Soler,et al.  PARCE: Protocol for Amino acid Refinement through Computational Evolution , 2020, Computer Physics Communications.

[4]  R. Monasson,et al.  RBM-MHC: A Semi-Supervised Machine-Learning Method for Sample-Specific Prediction of Antigen Presentation by HLA-I Alleles , 2020, Cell systems.

[5]  E. Jaffee,et al.  Role of in silico structural modeling in predicting immunogenic neoepitopes for cancer vaccine development , 2020, JCI insight.

[6]  Daniel W. Kulp,et al.  Incorporation of a Novel CD4+ Helper Epitope Identified from Aquifex aeolicus Enhances Humoral Responses Induced by DNA and Protein Vaccinations , 2020, iScience.

[7]  O. Pleguezuelos,et al.  Safety and immunogenicity of a mosquito saliva peptide-based vaccine: a randomised, placebo-controlled, double-blind, phase 1 trial , 2020, The Lancet.

[8]  Morten Nielsen,et al.  T Cell Epitope Predictions. , 2020, Annual review of immunology.

[9]  J. Lai,et al.  Peptide-Based Vaccines: Current Progress and Future Challenges , 2019, Chemical reviews.

[10]  M. Nielsen,et al.  NNAlign_MA; MHC Peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T-cell Epitope Predictions. , 2019, Molecular & cellular proteomics : MCP.

[11]  Alessandro Laio,et al.  A consensus protocol for the in silico optimisation of antibody fragments. , 2019, Chemical communications.

[12]  Russ B. Altman,et al.  Predicting HLA class II antigen presentation through integrated deep learning , 2019, Nature Biotechnology.

[13]  Alessandro Laio,et al.  Predicting the affinity of peptides to MHC class II by scoring molecular dynamics simulations. , 2019, Journal of chemical information and modeling.

[14]  Massimo Andreatta,et al.  NNAlign_MA; MHC Peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T-cell Epitope Predictions , 2019, Molecular & Cellular Proteomics.

[15]  K. Eberle,et al.  Design of a mimotope-peptide based double epitope vaccine against disseminated candidiasis. , 2019, Vaccine.

[16]  Yuwei Zhang,et al.  In silico design of MHC class I high binding affinity peptides through motifs activation map , 2018, BMC Bioinformatics.

[17]  Alyssa R. Richman,et al.  Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial , 2018, Nature.

[18]  Weilong Zhao,et al.  Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes , 2018, PLoS Comput. Biol..

[19]  Miguel A. Soler,et al.  Assessing the capability of in silico mutation protocols for predicting the finite temperature conformation of amino acids. , 2018, Physical chemistry chemical physics : PCCP.

[20]  L. Zitvogel,et al.  Trial watch: Peptide-based vaccines in anticancer therapy , 2018, Oncoimmunology.

[21]  J. Greenbaum,et al.  Improved methods for predicting peptide binding affinity to MHC class II molecules , 2018, Immunology.

[22]  Ali A. Faruqi,et al.  Microbiota epitope similarity either dampens or enhances the immunogenicity of disease-associated antigenic epitopes , 2018, PloS one.

[23]  Brett J. Hos,et al.  Approaches to Improve Chemically Defined Synthetic Peptide Vaccines , 2018, Front. Immunol..

[24]  Abdoelnaser M. Degoot,et al.  Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions , 2017, Front. Immunol..

[25]  Claudia S Neuhaus,et al.  Rational Design of Membrane-Pore-Forming Peptides. , 2017, Small.

[26]  Charles H. Yoon,et al.  An immunogenic personal neoantigen vaccine for patients with melanoma , 2017, Nature.

[27]  Rainer Merkl,et al.  Rosetta:MSF: a modular framework for multi-state computational protein design , 2017, PLoS Comput. Biol..

[28]  Frank Noé,et al.  Major Histocompatibility Complex (MHC) Class I and MHC Class II Proteins: Conformational Plasticity in Antigen Presentation , 2017, Front. Immunol..

[29]  Roland L. Dunbrack,et al.  The Rosetta all-atom energy function for macromolecular modeling and design , 2017, bioRxiv.

[30]  A Ganesan,et al.  Oral Administration of Peptide‐Based Drugs: Beyond Lipinski's Rule , 2016, ChemMedChem.

[31]  Alistair Miles,et al.  Genomic analysis of local variation and recent evolution in Plasmodium vivax , 2016, Nature Genetics.

[32]  W. Pickl,et al.  On Peptides and Altered Peptide Ligands: From Origin, Mode of Action and Design to Clinical Application (Immunotherapy) , 2016, International Archives of Allergy and Immunology.

[33]  M. Skwarczynski,et al.  Peptide-based synthetic vaccines , 2015, Chemical science.

[34]  B. Leimkuhler,et al.  A Stochastic Algorithm for the Isobaric-Isothermal Ensemble with Ewald Summations for All Long Range Forces. , 2015, Journal of chemical theory and computation.

[35]  J. Kalil,et al.  Multiple Approaches for Increasing the Immunogenicity of an Epitope-Based Anti-HIV Vaccine. , 2015, AIDS research and human retroviruses.

[36]  Alessandro Laio,et al.  Designing High-Affinity Peptides for Organic Molecules by Explicit Solvent Molecular Dynamics. , 2015, The journal of physical chemistry. B.

[37]  Morten Nielsen,et al.  Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification , 2015, Immunogenetics.

[38]  Charlotte M. Deane,et al.  Rapid, Precise, and Reproducible Prediction of Peptide-MHC Binding Affinities from Molecular Dynamics That Correlate Well with Experiment. , 2015, Journal of chemical theory and computation.

[39]  Mark M. Davis,et al.  A Population Response Analysis Approach To Assign Class II HLA-Epitope Restrictions , 2015, The Journal of Immunology.

[40]  P. Soema,et al.  Development of Cross-Protective Influenza A Vaccines Based on Cellular Responses , 2015, Front. Immunol..

[41]  Deborah Hix,et al.  The immune epitope database (IEDB) 3.0 , 2014, Nucleic Acids Res..

[42]  Fabian Sievers,et al.  Clustal Omega , 2014, Current protocols in bioinformatics.

[43]  Lenna X. Peterson,et al.  Assessment of protein side‐chain conformation prediction methods in different residue environments , 2014, Proteins.

[44]  C. Fraser,et al.  Generation of a universal CD4 memory T cell recall peptide effective in humans, mice and non-human primates. , 2014, Vaccine.

[45]  David Baker,et al.  Removing T-cell epitopes with computational protein design , 2014, Proceedings of the National Academy of Sciences.

[46]  David Baker,et al.  Proof of principle for epitope-focused vaccine design , 2014, Nature.

[47]  Lucy Dorrell,et al.  Vaccine-elicited human T cells recognizing conserved protein regions inhibit HIV-1. , 2014, Molecular therapy : the journal of the American Society of Gene Therapy.

[48]  Alessandro Laio,et al.  BACHSCORE. A tool for evaluating efficiently and reliably the quality of large sets of protein structures , 2013, Comput. Phys. Commun..

[49]  M. Cranfield,et al.  Malarial parasite diversity in chimpanzees: the value of comparative approaches to ascertain the evolution of Plasmodium falciparum antigens , 2013, Malaria Journal.

[50]  Alessandra Corazza,et al.  Bluues: a program for the analysis of the electrostatic properties of proteins based on generalized Born radii , 2012, BMC Bioinformatics.

[51]  J. Kalil,et al.  A Recombinant Adenovirus Encoding Multiple HIV-1 Epitopes Induces Stronger CD4+ T cell Responses than a DNA Vaccine in Mice. , 2011, Journal of vaccines & vaccination.

[52]  J. Skolnick,et al.  GOAP: a generalized orientation-dependent, all-atom statistical potential for protein structure prediction. , 2011, Biophysical journal.

[53]  Z. Weng,et al.  Integrating atom‐based and residue‐based scoring functions for protein–protein docking , 2011, Protein science : a publication of the Protein Society.

[54]  D. Baker,et al.  RosettaRemodel: A Generalized Framework for Flexible Backbone Protein Design , 2011, PloS one.

[55]  J. Gorman,et al.  Preparation and Analysis of Proteins and Peptides Using MALDI TOF/TOF Mass Spectrometry , 2011, Current protocols in protein science.

[56]  Bono Lučić,et al.  Knowledge-based computational methods for identifying or designing novel, non-homologous antimicrobial peptides , 2011, European Biophysics Journal.

[57]  J. Sidney,et al.  Towards an immunosense vaccine to prevent toxoplasmosis: protective Toxoplasma gondii epitopes restricted by HLA-A*0201. , 2011, Vaccine.

[58]  Morten Nielsen,et al.  Peptide binding predictions for HLA DR, DP and DQ molecules , 2010, BMC Bioinformatics.

[59]  Hans D. Mittelmann,et al.  MultiRTA: A simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes , 2010, BMC Bioinformatics.

[60]  R. Dror,et al.  Improved side-chain torsion potentials for the Amber ff99SB protein force field , 2010, Proteins.

[61]  Jack S. Richards,et al.  The Relationship between Anti-merozoite Antibodies and Incidence of Plasmodium falciparum Malaria: A Systematic Review and Meta-analysis , 2010, PLoS medicine.

[62]  Arthur J. Olson,et al.  AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading , 2009, J. Comput. Chem..

[63]  Eileen Kraemer,et al.  PlasmoDB: a functional genomic database for malaria parasites , 2008, Nucleic Acids Res..

[64]  M. Davenport,et al.  Cutting Edge: TLR Ligands Increase TCR Triggering by Slowing Peptide-MHC Class I Decay Rates1 , 2008, The Journal of Immunology.

[65]  Yaoqi Zhou,et al.  Specific interactions for ab initio folding of protein terminal regions with secondary structures , 2008, Proteins.

[66]  John Sidney,et al.  A Systematic Assessment of MHC Class II Peptide Binding Predictions and Evaluation of a Consensus Approach , 2008, PLoS Comput. Biol..

[67]  Carsten Kutzner,et al.  GROMACS 4:  Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. , 2008, Journal of chemical theory and computation.

[68]  Ruth Nussinov,et al.  FireDock: Fast interaction refinement in molecular docking , 2007, Proteins.

[69]  K. Henrick,et al.  Inference of macromolecular assemblies from crystalline state. , 2007, Journal of molecular biology.

[70]  Morten Nielsen,et al.  Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method , 2007, BMC Bioinformatics.

[71]  M. Parrinello,et al.  Canonical sampling through velocity rescaling. , 2007, The Journal of chemical physics.

[72]  J. Sidney,et al.  Immunogenicity of a recombinant protein containing the Plasmodium vivax vaccine candidate MSP1(19) and two human CD4+ T-cell epitopes administered to non-human primates (Callithrix jacchus jacchus). , 2006, Microbes and infection.

[73]  Francisco A. Chaves,et al.  The relationship between immunodominance, DM editing, and the kinetic stability of MHC class II:peptide complexes , 2005, Immunological reviews.

[74]  G. Crooks,et al.  WebLogo: a sequence logo generator. , 2004, Genome research.

[75]  J. Sidney,et al.  In Silico Prediction of Peptides Binding to Multiple HLA-DR Molecules Accurately Identifies Immunodominant Epitopes from gp43 of Paracoccidioides brasiliensis Frequently Recognized in Primary Peripheral Blood Mononuclear Cell Responses from Sensitized Individuals , 2003, Molecular medicine.

[76]  A. Nesburn,et al.  Identification of Novel Immunodominant CD4+ Th1-Type T-Cell Peptide Epitopes from Herpes Simplex Virus Glycoprotein D That Confer Protective Immunity , 2003, Journal of Virology.

[77]  Federico Fogolari,et al.  Amino acid empirical contact energy definitions for fold recognition in the space of contact maps , 2003, BMC Bioinformatics.

[78]  C. Dominguez,et al.  HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. , 2003, Journal of the American Chemical Society.

[79]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[80]  U. Şahin,et al.  Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices , 1999, Nature Biotechnology.

[81]  Dusanka Janezic,et al.  An Efficient Symplectic Integration Algorithm for Molecular Dynamics Simulations , 1995, J. Chem. Inf. Comput. Sci..

[82]  C Oseroff,et al.  Development of high potency universal DR-restricted helper epitopes by modification of high affinity DR-blocking peptides. , 1994, Immunity.

[83]  Don C. Wiley,et al.  Three-dimensional structure of a human class II histocompatibility molecule complexed with superantigen , 1994, Nature.

[84]  Don C. Wiley,et al.  Crystal structure of the human class II MHC protein HLA-DR1 complexed with an influenza virus peptide , 1994, Nature.

[85]  M. A. Saper,et al.  Structure of the human class I histocompatibility antigen, HLA-A2 , 1987, Nature.

[86]  D. Eisenberg,et al.  The hydrophobic moment detects periodicity in protein hydrophobicity. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[87]  W. L. Jorgensen,et al.  Comparison of simple potential functions for simulating liquid water , 1983 .

[88]  M. Parrinello,et al.  Crystal structure and pair potentials: A molecular-dynamics study , 1980 .