Developing similarity matrices for antibody-protein binding interactions.
The inventions of AlphaFold and RoseTTAFold are revolutionizing computational protein science due to their abilities to reliably predict protein structures. Their unprecedented successes are due to the parallel consideration of several types of information, one of which is protein sequence similarit...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0293606&type=printable |
| _version_ | 1827352679197704192 |
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| author | Sumaiya Islam Robert J Pantazes |
| author_facet | Sumaiya Islam Robert J Pantazes |
| author_sort | Sumaiya Islam |
| collection | DOAJ |
| description | The inventions of AlphaFold and RoseTTAFold are revolutionizing computational protein science due to their abilities to reliably predict protein structures. Their unprecedented successes are due to the parallel consideration of several types of information, one of which is protein sequence similarity information. Sequence homology has been studied for many decades and depends on similarity matrices to define how similar or different protein sequences are to one another. A natural extension of predicting protein structures is predicting the interactions between proteins, but similarity matrices for protein-protein interactions do not exist. This study conducted a mutational analysis of 384 non-redundant antibody-protein antigen complexes to calculate antibody-protein interaction similarity matrices. Every important residue in each antibody and each antigen was mutated to each of the other 19 commonly occurring amino acids and the percentage changes in interaction energies were calculated using three force fields: CHARMM, Amber, and Rosetta. The data were used to construct six interaction similarity matrices, one for antibodies and another for antigens using each force field. The matrices exhibited both commonalities, such as mutations of aromatic and charged residues being the most detrimental, and differences, such as Rosetta predicting mutations of serines to be better tolerated than either Amber or CHARMM. A comparison to nine previously published similarity matrices for protein sequences revealed that the new interaction matrices are more similar to one another than they are to any of the previous matrices. The created similarity matrices can be used in force field specific applications to help guide decisions regarding mutations in protein-protein binding interfaces. |
| first_indexed | 2024-03-08T03:09:29Z |
| format | Article |
| id | doaj.art-0ae7d84574b44edd9c1fe01020182f13 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-08T03:09:29Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-0ae7d84574b44edd9c1fe01020182f132024-02-13T05:34:27ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-011810e029360610.1371/journal.pone.0293606Developing similarity matrices for antibody-protein binding interactions.Sumaiya IslamRobert J PantazesThe inventions of AlphaFold and RoseTTAFold are revolutionizing computational protein science due to their abilities to reliably predict protein structures. Their unprecedented successes are due to the parallel consideration of several types of information, one of which is protein sequence similarity information. Sequence homology has been studied for many decades and depends on similarity matrices to define how similar or different protein sequences are to one another. A natural extension of predicting protein structures is predicting the interactions between proteins, but similarity matrices for protein-protein interactions do not exist. This study conducted a mutational analysis of 384 non-redundant antibody-protein antigen complexes to calculate antibody-protein interaction similarity matrices. Every important residue in each antibody and each antigen was mutated to each of the other 19 commonly occurring amino acids and the percentage changes in interaction energies were calculated using three force fields: CHARMM, Amber, and Rosetta. The data were used to construct six interaction similarity matrices, one for antibodies and another for antigens using each force field. The matrices exhibited both commonalities, such as mutations of aromatic and charged residues being the most detrimental, and differences, such as Rosetta predicting mutations of serines to be better tolerated than either Amber or CHARMM. A comparison to nine previously published similarity matrices for protein sequences revealed that the new interaction matrices are more similar to one another than they are to any of the previous matrices. The created similarity matrices can be used in force field specific applications to help guide decisions regarding mutations in protein-protein binding interfaces.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0293606&type=printable |
| spellingShingle | Sumaiya Islam Robert J Pantazes Developing similarity matrices for antibody-protein binding interactions. PLoS ONE |
| title | Developing similarity matrices for antibody-protein binding interactions. |
| title_full | Developing similarity matrices for antibody-protein binding interactions. |
| title_fullStr | Developing similarity matrices for antibody-protein binding interactions. |
| title_full_unstemmed | Developing similarity matrices for antibody-protein binding interactions. |
| title_short | Developing similarity matrices for antibody-protein binding interactions. |
| title_sort | developing similarity matrices for antibody protein binding interactions |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0293606&type=printable |
| work_keys_str_mv | AT sumaiyaislam developingsimilaritymatricesforantibodyproteinbindinginteractions AT robertjpantazes developingsimilaritymatricesforantibodyproteinbindinginteractions |