首页|Modeling Alternative Conformational States of Pseudo-Symmetric Solute Carrier Tr ansporters using Methods from Machine Learning
Modeling Alternative Conformational States of Pseudo-Symmetric Solute Carrier Tr ansporters using Methods from Machine Learning
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – According to news reporting based on a preprint abstract, our journalists obtained thefollowing quote sourced from bi orxiv.org:“The Solute Carrier (SLC) superfamily of integral membrane proteins function to transport a widearray of solutes across the plasma and organelle membranes. SLC proteins also function as importantdrug transporters and as viral receptors. D espite being classified as a single superfamily, SLC proteins donot share a sin gle common fold classification; however, most belong to multi-pass transmembrane helicalprotein fold families. SLC proteins populate different conformational s tates during the solute transportprocess, including outward-open, intermediate (occluded), and inward-open conformational states. Forsome SLC fold families th is structural \’flipping\’ c orresponds to swapping between conformations oftheir N-terminal and C-terminal symmetry-related sub-structures. Conventional AlphaFold2 or EvolutionaryScale M odeling methods typically generate models for only one of these multiple conform ational statesof SLC proteins.
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