Using genome-wide measurements for computational prediction of SH2–peptide interactions
Peptide-recognition modules (PRMs) are used throughout biology to mediate protein–protein interactions, and many PRMs are members of large protein domain families. Recent genome-wide measurements describe networks of peptide–PRM interactions. In these networks, very similar PRMs recognize distinct s...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Oxford University Press (OUP)
2012
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| Online Access: | http://hdl.handle.net/1721.1/70946 https://orcid.org/0000-0002-0785-5410 |
| _version_ | 1811093355958894592 |
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| author | Wunderlich, Zeba Mirny, Leonid A. |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Wunderlich, Zeba Mirny, Leonid A. |
| author_sort | Wunderlich, Zeba |
| collection | MIT |
| description | Peptide-recognition modules (PRMs) are used throughout biology to mediate protein–protein interactions, and many PRMs are members of large protein domain families. Recent genome-wide measurements describe networks of peptide–PRM interactions. In these networks, very similar PRMs recognize distinct sets of peptides, raising the question of how peptide-recognition specificity is achieved using similar protein domains. The analysis of individual protein complex structures often gives answers that are not easily applicable to other members of the same PRM family. Bioinformatics-based approaches, one the other hand, may be difficult to interpret physically. Here we integrate structural information with a large, quantitative data set of SH2 domain–peptide interactions to study the physical origin of domain–peptide specificity. We develop an energy model, inspired by protein folding, based on interactions between the amino-acid positions in the domain and peptide. We use this model to successfully predict which SH2 domains and peptides interact and uncover the positions in each that are important for specificity. The energy model is general enough that it can be applied to other members of the SH2 family or to new peptides, and the cross-validation results suggest that these energy calculations will be useful for predicting binding interactions. It can also be adapted to study other PRM families, predict optimal peptides for a given SH2 domain, or study other biological interactions, e.g. protein–DNA interactions. |
| first_indexed | 2024-09-23T15:43:56Z |
| format | Article |
| id | mit-1721.1/70946 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:43:56Z |
| publishDate | 2012 |
| publisher | Oxford University Press (OUP) |
| record_format | dspace |
| spelling | mit-1721.1/709462022-09-29T15:47:28Z Using genome-wide measurements for computational prediction of SH2–peptide interactions Wunderlich, Zeba Mirny, Leonid A. Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Department of Physics Mirny, Leonid A. Mirny, Leonid A. Peptide-recognition modules (PRMs) are used throughout biology to mediate protein–protein interactions, and many PRMs are members of large protein domain families. Recent genome-wide measurements describe networks of peptide–PRM interactions. In these networks, very similar PRMs recognize distinct sets of peptides, raising the question of how peptide-recognition specificity is achieved using similar protein domains. The analysis of individual protein complex structures often gives answers that are not easily applicable to other members of the same PRM family. Bioinformatics-based approaches, one the other hand, may be difficult to interpret physically. Here we integrate structural information with a large, quantitative data set of SH2 domain–peptide interactions to study the physical origin of domain–peptide specificity. We develop an energy model, inspired by protein folding, based on interactions between the amino-acid positions in the domain and peptide. We use this model to successfully predict which SH2 domains and peptides interact and uncover the positions in each that are important for specificity. The energy model is general enough that it can be applied to other members of the SH2 family or to new peptides, and the cross-validation results suggest that these energy calculations will be useful for predicting binding interactions. It can also be adapted to study other PRM families, predict optimal peptides for a given SH2 domain, or study other biological interactions, e.g. protein–DNA interactions. National Institutes of Health. National Centers for Biomedical Computing (Informatics for Integrating Biology and the Bedside) National Institutes of Health (U.S.) (grant U54LM008748) 2012-05-25T16:51:55Z 2012-05-25T16:51:55Z 2009-04 2009-04 Article http://purl.org/eprint/type/JournalArticle 0305-1048 1362-4962 http://hdl.handle.net/1721.1/70946 Wunderlich, Z., and L. A. Mirny. “Using Genome-wide Measurements for Computational Prediction of SH2-peptide Interactions.” Nucleic Acids Research 37.14 (2009): 4629–4641. Web. 25 May 2012. https://orcid.org/0000-0002-0785-5410 en_US http://dx.doi.org/10.1093/nar/gkp394 Nucleic Acids Research Creative Commons Attribution Non-Commercial http://creativecommons.org/licenses/by-nc/2.5 application/pdf Oxford University Press (OUP) Oxford |
| spellingShingle | Wunderlich, Zeba Mirny, Leonid A. Using genome-wide measurements for computational prediction of SH2–peptide interactions |
| title | Using genome-wide measurements for computational prediction of SH2–peptide interactions |
| title_full | Using genome-wide measurements for computational prediction of SH2–peptide interactions |
| title_fullStr | Using genome-wide measurements for computational prediction of SH2–peptide interactions |
| title_full_unstemmed | Using genome-wide measurements for computational prediction of SH2–peptide interactions |
| title_short | Using genome-wide measurements for computational prediction of SH2–peptide interactions |
| title_sort | using genome wide measurements for computational prediction of sh2 peptide interactions |
| url | http://hdl.handle.net/1721.1/70946 https://orcid.org/0000-0002-0785-5410 |
| work_keys_str_mv | AT wunderlichzeba usinggenomewidemeasurementsforcomputationalpredictionofsh2peptideinteractions AT mirnyleonida usinggenomewidemeasurementsforcomputationalpredictionofsh2peptideinteractions |