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1.
Nat Commun ; 15(1): 2986, 2024 Apr 06.
Article in English | MEDLINE | ID: mdl-38582862

ABSTRACT

Recent cryoEM studies elucidated details of the structural basis for the substrate selectivity and translocation of heteromeric amino acid transporters. However, Asc1/CD98hc is the only neutral heteromeric amino acid transporter that can function through facilitated diffusion, and the only one that efficiently transports glycine and D-serine, and thus has a regulatory role in the central nervous system. Here we use cryoEM, ligand-binding simulations, mutagenesis, transport assays, and molecular dynamics to define human Asc1/CD98hc determinants for substrate specificity and gain insights into the mechanisms that govern substrate translocation by exchange and facilitated diffusion. The cryoEM structure of Asc1/CD98hc is determined at 3.4-3.8 Å resolution, revealing an inward-facing semi-occluded conformation. We find that Ser 246 and Tyr 333 are essential for Asc1/CD98hc substrate selectivity and for the exchange and facilitated diffusion modes of transport. Taken together, these results reveal the structural bases for ligand binding and transport features specific to human Asc1.


Subject(s)
Amino Acid Transport Systems , Fusion Regulatory Protein 1, Heavy Chain , Humans , Amino Acid Transport Systems/genetics , Amino Acid Transport Systems/metabolism , Fusion Regulatory Protein 1, Heavy Chain/chemistry , Ligands , Molecular Dynamics Simulation
2.
Int J Mol Sci ; 23(24)2022 Dec 17.
Article in English | MEDLINE | ID: mdl-36555731

ABSTRACT

Computer simulation techniques are gaining a central role in molecular pharmacology. Due to several factors, including the significant improvements of traditional molecular modelling, the irruption of machine learning methods, the massive data generation, or the unlimited computational resources through cloud computing, the future of pharmacology seems to go hand in hand with in silico predictions. In this review, we summarize our recent efforts in such a direction, centered on the unconventional Monte Carlo PELE software and on its coupling with machine learning techniques. We also provide new data on combining two recent new techniques, aquaPELE capable of exhaustive water sampling and fragPELE, for fragment growing.


Subject(s)
Drug Discovery , Software , Computer Simulation , Drug Discovery/methods , Models, Molecular , Monte Carlo Method , Drug Design
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