ArticleNucleic acids research2020
mmCSM-AB: guiding rational antibody engineering through multiple point mutations.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed.
- MutPPI+: a multimodal framework for predicting mutation effects on protein-protein interactions via mutation-path-based data augmentation.Briefings in bioinformatics · 2026Article
- Computational Design of Broad-Spectrum Ebola Antibodies through Framework and Complementarity-Determining Region Synergistic Optimization.Research (Washington, D.C.) · 2026Article
- Screening and Validation of Functional Residues of the Antimicrobial PeptideBiomolecules · 2025Article
- Allosteric inhibition of trypanosomatid pyruvate kinases by a camelid single-domain antibody.eLife · 2025Article
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- Predicting Antibody Affinity Changes upon Mutation Based on Unbound Protein Structures.International journal of molecular sciences · 2025Article
- From immune evasion to broadFrontiers in immunology · 2025Article
- PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery.Scientific data · 2024Article
- De novo generation of SARS-CoV-2 antibody CDRH3 with a pre-trained generative large language model.Nature communications · 2024Article
- Engineering G protein-coupled receptors for stabilization.Protein science : a publication of the Protein Society · 2024Article
- BiSpec Pairwise AI: guiding the selection of bispecific antibody target combinations with pairwise learning and GPT augmentation.Journal of cancer research and clinical oncology · 2024Article
- A dual computational and experimental strategy to enhance TSLP antibody affinity for improved asthma treatment.PLoS computational biology · 2024Article
- Characterization on the oncogenic effect of the missense mutations of p53 via machine learning.Briefings in bioinformatics · 2023Article
- Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens.Journal of chemical theory and computation · 2023Review
- Accelerating therapeutic protein design with computational approaches toward the clinical stage.Computational and structural biotechnology journal · 2023Review
- Using Graph-Based Signatures to Guide Rational Antibody Engineering.Methods in molecular biology (Clifton, N.J.) · 2023Article
- An optimized thermodynamics integration protocol for identifying beneficial mutations in antibody design.Frontiers in immunology · 2023Article
- kinCSM: Using graph-based signatures to predict small molecule CDK2 inhibitors.Protein science : a publication of the Protein Society · 2022Article
- Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery.Briefings in bioinformatics · 2022Review
- cropCSM: designing safe and potent herbicides with graph-based signatures.Briefings in bioinformatics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
While antibodies are becoming an increasingly important therapeutic class, especially in personalized medicine, their development and optimization has been largely through experimental exploration. While there have been many efforts to develop computational tools to guide rational antibody engineering, most approaches are of limited accuracy when applied to antibody design, and have largely been limited to analysing a single point mutation at a time. To overcome this gap, we have curated a dataset of 242 experimentally determined changes in binding affinity upon multiple point mutations in antibody-target complexes (89 increasing and 153 decreasing binding affinity). Here, we have shown that by using our graph-based signatures and atomic interaction information, we can accurately analyse the consequence of multi-point mutations on antigen binding affinity. Our approach outperformed other available tools across cross-validation and two independent blind tests, achieving Pearson's correlations of up to 0.95. We have implemented our new approach, mmCSM-AB, as a web-server that can help guide the process of affinity maturation in antibody design. mmCSM-AB is freely available at http://biosig.unimelb.edu.au/mmcsm_ab/.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.