Sequence-based prediction of protein binding mode landscapes.
Interactions between disordered proteins involve a wide range of changes in the structure and dynamics of the partners involved. These changes can be classified in terms of binding modes, which include disorder-to-order (DO) transitions, when proteins fold upon binding, as well as disorder-to-disord...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-05-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1007864 |
| _version_ | 1818901942143811584 |
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| author | Attila Horvath Marton Miskei Viktor Ambrus Michele Vendruscolo Monika Fuxreiter |
| author_facet | Attila Horvath Marton Miskei Viktor Ambrus Michele Vendruscolo Monika Fuxreiter |
| author_sort | Attila Horvath |
| collection | DOAJ |
| description | Interactions between disordered proteins involve a wide range of changes in the structure and dynamics of the partners involved. These changes can be classified in terms of binding modes, which include disorder-to-order (DO) transitions, when proteins fold upon binding, as well as disorder-to-disorder (DD) transitions, when the conformational heterogeneity is maintained in the bound states. Furthermore, systematic studies of these interactions are revealing that proteins may exhibit different binding modes with different partners. Proteins that exhibit this context-dependent binding can be referred to as fuzzy proteins. Here we investigate amino acid code for fuzzy binding in terms of the entropy of the probability distribution of transitions towards decreasing order. We implement these entropy calculations into the FuzPred (http://protdyn-fuzpred.org) algorithm to predict the range of context-dependent binding modes of proteins from their amino acid sequences. As we illustrate through a variety of examples, this method identifies those binding sites that are sensitive to the cellular context or post-translational modifications, and may serve as regulatory points of cellular pathways. |
| first_indexed | 2024-12-19T20:27:46Z |
| format | Article |
| id | doaj.art-6344c5a22233420cac8362bb072b923d |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-19T20:27:46Z |
| publishDate | 2020-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-6344c5a22233420cac8362bb072b923d2022-12-21T20:06:47ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-05-01165e100786410.1371/journal.pcbi.1007864Sequence-based prediction of protein binding mode landscapes.Attila HorvathMarton MiskeiViktor AmbrusMichele VendruscoloMonika FuxreiterInteractions between disordered proteins involve a wide range of changes in the structure and dynamics of the partners involved. These changes can be classified in terms of binding modes, which include disorder-to-order (DO) transitions, when proteins fold upon binding, as well as disorder-to-disorder (DD) transitions, when the conformational heterogeneity is maintained in the bound states. Furthermore, systematic studies of these interactions are revealing that proteins may exhibit different binding modes with different partners. Proteins that exhibit this context-dependent binding can be referred to as fuzzy proteins. Here we investigate amino acid code for fuzzy binding in terms of the entropy of the probability distribution of transitions towards decreasing order. We implement these entropy calculations into the FuzPred (http://protdyn-fuzpred.org) algorithm to predict the range of context-dependent binding modes of proteins from their amino acid sequences. As we illustrate through a variety of examples, this method identifies those binding sites that are sensitive to the cellular context or post-translational modifications, and may serve as regulatory points of cellular pathways.https://doi.org/10.1371/journal.pcbi.1007864 |
| spellingShingle | Attila Horvath Marton Miskei Viktor Ambrus Michele Vendruscolo Monika Fuxreiter Sequence-based prediction of protein binding mode landscapes. PLoS Computational Biology |
| title | Sequence-based prediction of protein binding mode landscapes. |
| title_full | Sequence-based prediction of protein binding mode landscapes. |
| title_fullStr | Sequence-based prediction of protein binding mode landscapes. |
| title_full_unstemmed | Sequence-based prediction of protein binding mode landscapes. |
| title_short | Sequence-based prediction of protein binding mode landscapes. |
| title_sort | sequence based prediction of protein binding mode landscapes |
| url | https://doi.org/10.1371/journal.pcbi.1007864 |
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