Machine Learning-Based Modeling of Olfactory Receptors in Their Inactive State: Human OR51E2 as a Case Study.
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ABSTRACT:
SUBMITTER: Alfonso-Prieto M
PROVIDER: S-EPMC10207261 | biostudies-literature | 2023 May
REPOSITORIES: biostudies-literature
Publications
Machine Learning-Based Modeling of Olfactory Receptors in Their Inactive State: Human OR51E2 as a Case Study.
Alfonso-Prieto Mercedes M Capelli Riccardo R
Journal of chemical information and modeling 20230505 10
Atomistic-level investigation of olfactory receptors (ORs) is a challenging task due to the experimental/computational difficulties in the structural determination/prediction for members of this family of G-protein coupled receptors. Here, we have developed a protocol that performs a series of molecular dynamics simulations from a set of structures predicted <i>de novo</i> by recent machine learning algorithms and apply it to a well-studied receptor, the human OR51E2. Our study demonstrates the ...[more]
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