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      Conformational ensemble-dependent lipid recognition and segregation by prenylated intrinsically disordered regions in small GTPases

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          Abstract

          We studied diverse prenylated intrinsically disordered regions (PIDRs) of Ras and Rho family small GTPases using long timescale atomistic molecular dynamics simulations in an asymmetric model membrane of phosphatidylcholine (PC) and phosphatidylserine (PS) lipids. Here we show that conformational plasticity is a key determinant of lipid sorting by polybasic PIDRs and provide evidence for lipid sorting based on both headgroup and acyl chain structures. We further show that conformational ensemble-based lipid recognition is generalizable to all polybasic PIDRs, and that the sequence outside the polybasic domain (PBD) modulates the conformational plasticity, bilayer adsorption, and interactions of PIDRs with membrane lipids. Specifically, we find that palmitoylation, the ratio of basic to acidic residues, and the hydrophobic content of the sequence outside the PBD significantly impact the diversity of conformational substates and hence the extent of conformation-dependent lipid interactions. We thus propose that the PBD is required but not sufficient for the full realization of lipid sorting by prenylated PBD-containing membrane anchors, and that the membrane anchor is not only responsible for high affinity membrane binding but also directs the protein to the right target membrane where it participates in lipid sorting.

          Abstract

          Conformational plasticity is a key determinant of lipid sorting by polybasic prenylated intrinsically disordered regions and lipid sorting might be based on both headgroup and acyl chain structures.

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          Most cited references54

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          VMD: Visual molecular dynamics

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            CHARMM36m: an improved force field for folded and intrinsically disordered proteins

            An all-atom protein force field, CHARMM36m, offers improved accuracy for simulating intrinsically disordered peptides and proteins.
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              Scalable molecular dynamics on CPU and GPU architectures with NAMD.

              NAMDis a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.
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                Author and article information

                Contributors
                Alemayehu.G.Abebe@uth.tmc.edu
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                2 November 2023
                2 November 2023
                2023
                : 6
                : 1111
                Affiliations
                [1 ]McGovern Medical School, University of Texas Health Science Center at Houston, Department of Integrative Biology and Pharmacology, ( https://ror.org/03gds6c39) 6431 Fannin St., Houston, TX 77030 USA
                [2 ]GRID grid.240145.6, ISNI 0000 0001 2291 4776, Biochemistry and Cell Biology Program & Therapeutics and Pharmacology Program, , UTHealth MD Anderson Cancer Center Graduate School of Biomedical Sciences, ; Houston, 6431 Fannin St., TX 77030 USA
                Author information
                http://orcid.org/0000-0002-9328-4692
                Article
                5487
                10.1038/s42003-023-05487-6
                10622456
                37919400
                1f3f1b17-dc3a-4ec0-88bb-1c473b7ff504
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 May 2023
                : 19 October 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: R01GM144836.
                Award ID: R01GM116961
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | National Institutes of Health (NIH)
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                © Springer Nature Limited 2023

                cellular signalling networks,computational biophysics

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