Machine learning approaches to evaluate correlation patterns in allosteric signaling: A case study of the PDZ2 domain
Autor: | Mohsen Botlani, Ahnaf Siddiqui, Sameer Varma |
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Rok vydání: | 2018 |
Předmět: |
0301 basic medicine
010304 chemical physics Computer science business.industry Allosteric regulation General Physics and Astronomy Thermal fluctuations Model protein Context (language use) Machine learning computer.software_genre 01 natural sciences Domain (software engineering) Correlation 03 medical and health sciences Molecular dynamics 030104 developmental biology 0103 physical sciences Artificial intelligence Physical and Theoretical Chemistry business Conformational ensembles computer |
Zdroj: | The Journal of chemical physics. 148(24) |
ISSN: | 1089-7690 |
Popis: | Many proteins are regulated by dynamic allostery wherein regulator-induced changes in structure are comparable with thermal fluctuations. Consequently, understanding their mechanisms requires assessment of relationships between and within conformational ensembles of different states. Here we show how machine learning based approaches can be used to simplify this high-dimensional data mining task and also obtain mechanistic insight. In particular, we use these approaches to investigate two fundamental questions in dynamic allostery. First, how do regulators modify inter-site correlations in conformational fluctuations (Cij)? Second, how are regulator-induced shifts in conformational ensembles at two different sites in a protein related to each other? We address these questions in the context of the human protein tyrosine phosphatase 1E’s PDZ2 domain, which is a model protein for studying dynamic allostery. We use molecular dynamics to generate conformational ensembles of the PDZ2 domain in both the regulat... |
Databáze: | OpenAIRE |
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