OptMAVEn-2.0: De novo Design of Variable Antibody Regions Against Targeted Antigen Epitopes
Monoclonal antibodies are becoming increasingly important therapeutic agents for the treatment of cancers, infectious diseases, and autoimmune disorders. However, laboratory-based methods of developing therapeutic monoclonal antibodies (e.g., immunized mice, hybridomas, and phage display) are time-c...
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute (MDPI)
2018
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| Online Access: | http://hdl.handle.net/1721.1/118300 https://orcid.org/0000-0001-8182-7402 |
| _version_ | 1811078076548775936 |
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| author | Chowdhury, Ratul Maranas, Costas D. Allan, Matthew Frederick |
| author2 | Massachusetts Institute of Technology. Computational and Systems Biology Program |
| author_facet | Massachusetts Institute of Technology. Computational and Systems Biology Program Chowdhury, Ratul Maranas, Costas D. Allan, Matthew Frederick |
| author_sort | Chowdhury, Ratul |
| collection | MIT |
| description | Monoclonal antibodies are becoming increasingly important therapeutic agents for the treatment of cancers, infectious diseases, and autoimmune disorders. However, laboratory-based methods of developing therapeutic monoclonal antibodies (e.g., immunized mice, hybridomas, and phage display) are time-consuming and are often unable to target a specific antigen epitope or reach (sub)nanomolar levels of affinity. To this end, we developed Optimal Method for Antibody Variable region Engineering (OptMAVEn) for de novo design of humanized monoclonal antibody variable regions targeting a specific antigen epitope. In this work, we introduce OptMAVEn-2.0, which improves upon OptMAVEn by (1) reducing computational resource requirements without compromising design quality; (2) clustering the designs to better identify high-affinity antibodies; and (3) eliminating intra-antibody steric clashes using an updated set of clashing parts from the Modular Antibody Parts (MAPs) database. Benchmarking on a set of 10 antigens revealed that OptMAVEn-2.0 uses an average of 74% less CPU time and 84% less disk storage relative to OptMAVEn. Testing on 54 additional antigens revealed that computational resource requirements of OptMAVEn-2.0 scale only sub-linearly with respect to antigen size. OptMAVEn-2.0 was used to design and rank variable antibody fragments targeting five epitopes of Zika envelope protein and three of hen egg white lysozyme. Among the top five ranked designs for each epitope, recovery of native residue identities is typically 45–65%. MD simulations of two designs targeting Zika suggest that at least one would bind with high affinity. OptMAVEn-2.0 can be downloaded from our GitHub repository and webpage as (links in Summary and Discussion section). Keywords: de novo antibody design; zika envelope protein; computational protein design; specific antigen epitope |
| first_indexed | 2024-09-23T10:53:01Z |
| format | Article |
| id | mit-1721.1/118300 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T10:53:01Z |
| publishDate | 2018 |
| publisher | Multidisciplinary Digital Publishing Institute (MDPI) |
| record_format | dspace |
| spelling | mit-1721.1/1183002022-09-27T15:41:44Z OptMAVEn-2.0: De novo Design of Variable Antibody Regions Against Targeted Antigen Epitopes Chowdhury, Ratul Maranas, Costas D. Allan, Matthew Frederick Massachusetts Institute of Technology. Computational and Systems Biology Program Allan, Matthew Frederick Monoclonal antibodies are becoming increasingly important therapeutic agents for the treatment of cancers, infectious diseases, and autoimmune disorders. However, laboratory-based methods of developing therapeutic monoclonal antibodies (e.g., immunized mice, hybridomas, and phage display) are time-consuming and are often unable to target a specific antigen epitope or reach (sub)nanomolar levels of affinity. To this end, we developed Optimal Method for Antibody Variable region Engineering (OptMAVEn) for de novo design of humanized monoclonal antibody variable regions targeting a specific antigen epitope. In this work, we introduce OptMAVEn-2.0, which improves upon OptMAVEn by (1) reducing computational resource requirements without compromising design quality; (2) clustering the designs to better identify high-affinity antibodies; and (3) eliminating intra-antibody steric clashes using an updated set of clashing parts from the Modular Antibody Parts (MAPs) database. Benchmarking on a set of 10 antigens revealed that OptMAVEn-2.0 uses an average of 74% less CPU time and 84% less disk storage relative to OptMAVEn. Testing on 54 additional antigens revealed that computational resource requirements of OptMAVEn-2.0 scale only sub-linearly with respect to antigen size. OptMAVEn-2.0 was used to design and rank variable antibody fragments targeting five epitopes of Zika envelope protein and three of hen egg white lysozyme. Among the top five ranked designs for each epitope, recovery of native residue identities is typically 45–65%. MD simulations of two designs targeting Zika suggest that at least one would bind with high affinity. OptMAVEn-2.0 can be downloaded from our GitHub repository and webpage as (links in Summary and Discussion section). Keywords: de novo antibody design; zika envelope protein; computational protein design; specific antigen epitope 2018-10-01T14:41:56Z 2018-10-01T14:41:56Z 2018-06 2018-06 2018-09-21T07:11:46Z Article http://purl.org/eprint/type/JournalArticle 2073-4468 http://hdl.handle.net/1721.1/118300 Chowdhury, Ratul et al. "OptMAVEn-2.0: De novo Design of Variable Antibody Regions against Targeted Antigen Epitopes." Antibodies 7, 3 (June 2018): 23 © 2018 The Authors https://orcid.org/0000-0001-8182-7402 http://dx.doi.org/10.3390/antib7030023 Antibodies Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute (MDPI) Multidisciplinary Digital Publishing Institute |
| spellingShingle | Chowdhury, Ratul Maranas, Costas D. Allan, Matthew Frederick OptMAVEn-2.0: De novo Design of Variable Antibody Regions Against Targeted Antigen Epitopes |
| title | OptMAVEn-2.0: De novo Design of Variable Antibody Regions Against Targeted Antigen Epitopes |
| title_full | OptMAVEn-2.0: De novo Design of Variable Antibody Regions Against Targeted Antigen Epitopes |
| title_fullStr | OptMAVEn-2.0: De novo Design of Variable Antibody Regions Against Targeted Antigen Epitopes |
| title_full_unstemmed | OptMAVEn-2.0: De novo Design of Variable Antibody Regions Against Targeted Antigen Epitopes |
| title_short | OptMAVEn-2.0: De novo Design of Variable Antibody Regions Against Targeted Antigen Epitopes |
| title_sort | optmaven 2 0 de novo design of variable antibody regions against targeted antigen epitopes |
| url | http://hdl.handle.net/1721.1/118300 https://orcid.org/0000-0001-8182-7402 |
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