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mCSM-AB2: guiding rational antibody design using graph-based signatures.
Bioinformatics. 2020 03 01; 36(5):1453-1459.B

Abstract

MOTIVATION

A lack of accurate computational tools to guide rational mutagenesis has made affinity maturation a recurrent challenge in antibody (Ab) development. We previously showed that graph-based signatures can be used to predict the effects of mutations on Ab binding affinity.

RESULTS

Here we present an updated and refined version of this approach, mCSM-AB2, capable of accurately modelling the effects of mutations on Ab-antigen binding affinity, through the inclusion of evolutionary and energetic terms. Using a new and expanded database of over 1800 mutations with experimental binding measurements and structural information, mCSM-AB2 achieved a Pearson's correlation of 0.73 and 0.77 across training and blind tests, respectively, outperforming available methods currently used for rational Ab engineering.

AVAILABILITY AND IMPLEMENTATION

mCSM-AB2 is available as a user-friendly and freely accessible web server providing rapid analysis of both individual mutations or the entire binding interface to guide rational antibody affinity maturation at http://biosig.unimelb.edu.au/mcsm_ab2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Authors+Show Affiliations

Department of Biochemistry and Molecular Biology. ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, VIC 3010, Australia. Structural Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.Department of Biochemistry and Molecular Biology. ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, VIC 3010, Australia. Structural Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.Department of Biochemistry and Molecular Biology. ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, VIC 3010, Australia. Structural Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia. Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK.Department of Biochemistry and Molecular Biology. ACRF Facility for Innovative Cancer Drug Discovery, Bio21 Institute, University of Melbourne, Melbourne, VIC 3010, Australia. Structural Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia. School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3010, Australia.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

31665262

Citation

Myung, Yoochan, et al. "MCSM-AB2: Guiding Rational Antibody Design Using Graph-based Signatures." Bioinformatics (Oxford, England), vol. 36, no. 5, 2020, pp. 1453-1459.
Myung Y, Rodrigues CHM, Ascher DB, et al. MCSM-AB2: guiding rational antibody design using graph-based signatures. Bioinformatics. 2020;36(5):1453-1459.
Myung, Y., Rodrigues, C. H. M., Ascher, D. B., & Pires, D. E. V. (2020). MCSM-AB2: guiding rational antibody design using graph-based signatures. Bioinformatics (Oxford, England), 36(5), 1453-1459. https://doi.org/10.1093/bioinformatics/btz779
Myung Y, et al. MCSM-AB2: Guiding Rational Antibody Design Using Graph-based Signatures. Bioinformatics. 2020 03 1;36(5):1453-1459. PubMed PMID: 31665262.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - mCSM-AB2: guiding rational antibody design using graph-based signatures. AU - Myung,Yoochan, AU - Rodrigues,Carlos H M, AU - Ascher,David B, AU - Pires,Douglas E V, PY - 2019/05/09/received PY - 2019/10/07/revised PY - 2019/10/23/accepted PY - 2019/10/31/pubmed PY - 2020/9/18/medline PY - 2019/10/31/entrez SP - 1453 EP - 1459 JF - Bioinformatics (Oxford, England) JO - Bioinformatics VL - 36 IS - 5 N2 - MOTIVATION: A lack of accurate computational tools to guide rational mutagenesis has made affinity maturation a recurrent challenge in antibody (Ab) development. We previously showed that graph-based signatures can be used to predict the effects of mutations on Ab binding affinity. RESULTS: Here we present an updated and refined version of this approach, mCSM-AB2, capable of accurately modelling the effects of mutations on Ab-antigen binding affinity, through the inclusion of evolutionary and energetic terms. Using a new and expanded database of over 1800 mutations with experimental binding measurements and structural information, mCSM-AB2 achieved a Pearson's correlation of 0.73 and 0.77 across training and blind tests, respectively, outperforming available methods currently used for rational Ab engineering. AVAILABILITY AND IMPLEMENTATION: mCSM-AB2 is available as a user-friendly and freely accessible web server providing rapid analysis of both individual mutations or the entire binding interface to guide rational antibody affinity maturation at http://biosig.unimelb.edu.au/mcsm_ab2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. SN - 1367-4811 UR - https://www.unboundmedicine.com/medline/citation/31665262/mCSM_AB2:_guiding_rational_antibody_design_using_graph_based_signatures_ L2 - https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btz779 DB - PRIME DP - Unbound Medicine ER -
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