ArticlePloS one2014
OptMAVEn--a new framework for the de novo design of antibody variable region models targeting specific antigen epitopes.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Antibody screening for tumor and immune hotspot targets: The frontier of new methods and technologies.Journal of pharmaceutical analysis · 2026Review
- Artificial intelligence advancements in monoclonal antibody development technology.Frontiers in immunology · 2026Review
- Antibody Affinity Maturation by Computational Design.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Nanodesigner: resolving the complex-CDR interdependency with iterative refinement.Journal of cheminformatics · 2025Article
- Extracellular Peptide-Ligand Dimerization Actuator Receptor Design for Reversible and Spatially Dosed 3D Cell-Material Communication.ACS synthetic biology · 2025Article
- Using Short Molecular Dynamics Simulations to Determine the Important Features of Interactions in Antibody-Protein Complexes.Proteins · 2025Article
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances.Molecular biotechnology · 2025Review
- Applying computational protein design to therapeutic antibody discovery - current state and perspectives.Frontiers in immunology · 2025Review
- Improved HIV-1 neutralization breadth and potency of V2-apex antibodies by in silico design.Cell reports · 2023Article
- Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries.Nature communications · 2023Article
- Modeling and affinity maturation of an anti-CD20 nanobody: a comprehensive in-silico investigation.Scientific reports · 2023Article
- Rational strategies for enhancing mAb binding to SARS-CoV-2 variants through CDR diversification and antibody-escape prediction.Frontiers in immunology · 2023Article
- How can we discover developable antibody-based biotherapeutics?Frontiers in molecular biosciences · 2023Review
- Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery.Briefings in bioinformatics · 2022Review
- Understanding and Modulating Antibody Fine Specificity: Lessons from Combinatorial Biology.Antibodies (Basel, Switzerland) · 2022Review
- Affinity maturation of TCR-like antibodies using phage display guided by structural modeling.Protein engineering, design & selection : PEDS · 2022Article
- Higher Affinity Antibodies Bind With Lower Hydration and Flexibility in Large Scale Simulations.Frontiers in immunology · 2022Article
- Shape Complementarity Optimization of Antibody-Antigen Interfaces: The Application to SARS-CoV-2 Spike Protein.Frontiers in molecular biosciences · 2022Article
- Redesigning an antibody H3 loop by virtual screening of a small library of human germline-derived sequences.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antibody-based therapeutics provides novel and efficacious treatments for a number of diseases. Traditional experimental approaches for designing therapeutic antibodies rely on raising antibodies against a target antigen in an immunized animal or directed evolution of antibodies with low affinity for the desired antigen. However, these methods remain time consuming, cannot target a specific epitope and do not lead to broad design principles informing other studies. Computational design methods can overcome some of these limitations by using biophysics models to rationally select antibody parts that maximize affinity for a target antigen epitope. This has been addressed to some extend by OptCDR for the design of complementary determining regions. Here, we extend this earlier contribution by addressing the de novo design of a model of the entire antibody variable region against a given antigen epitope while safeguarding for immunogenicity (Optimal Method for Antibody Variable region Engineering, OptMAVEn). OptMAVEn simulates in silico the in vivo steps of antibody generation and evolution, and is capable of capturing the critical structural features responsible for affinity maturation of antibodies. In addition, a humanization procedure was developed and incorporated into OptMAVEn to minimize the potential immunogenicity of the designed antibody models. As case studies, OptMAVEn was applied to design models of neutralizing antibodies targeting influenza hemagglutinin and HIV gp120. For both HA and gp120, novel computational antibody models with numerous interactions with their target epitopes were generated. The observed rates of mutations and types of amino acid changes during in silico affinity maturation are consistent with what has been observed during in vivo affinity maturation. The results demonstrate that OptMAVEn can efficiently generate diverse computational antibody models with both optimized binding affinity to antigens and reduced immunogenicity.
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