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      Mobile barrier mechanisms for Na +-coupled symport in an MFS sugar transporter

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          Abstract

          While many 3D structures of cation-coupled transporters have been determined, the mechanistic details governing the obligatory coupling and functional regulations still remain elusive. The bacterial melibiose transporter (MelB) is a prototype of the Na +-coupled major facilitator superfamily transporters. With a conformational nanobody (Nb), we determined a low-sugar affinity inward-facing Na +-bound cryoEM structure. Collectively with the available outward-facing sugar-bound structures, both the outer and inner barriers were localized. The N- and C-terminal residues of the inner barrier contribute to the sugar selectivity pocket. When the inner barrier is broken as shown in the inward-open conformation, the sugar selectivity pocket is also broken. The binding assays by isothermal titration calorimetry revealed that this inward-facing conformation trapped by the conformation-selective Nb exhibited a greatly decreased sugar-binding affinity, suggesting the mechanisms for the substrate intracellular release and accumulation. While the inner/outer barrier shift directly regulates the sugar-binding affinity, it has little or no effect on the cation binding, which is also supported by molecular dynamics simulations. Furthermore, the use of this Nb in combination with the hydron/deuterium exchange mass spectrometry allowed us to identify dynamic regions; some regions are involved in the functionally important inner barrier-specific salt-bridge network, which indicates their critical roles in the barrier switching mechanisms for transport. These complementary results provided structural and dynamic insights into the mobile barrier mechanism for cation-coupled symport.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            CHARMM-GUI: a web-based graphical user interface for CHARMM.

            CHARMM is an academic research program used widely for macromolecular mechanics and dynamics with versatile analysis and manipulation tools of atomic coordinates and dynamics trajectories. CHARMM-GUI, http://www.charmm-gui.org, has been developed to provide a web-based graphical user interface to generate various input files and molecular systems to facilitate and standardize the usage of common and advanced simulation techniques in CHARMM. The web environment provides an ideal platform to build and validate a molecular model system in an interactive fashion such that, if a problem is found through visual inspection, one can go back to the previous setup and regenerate the whole system again. In this article, we describe the currently available functional modules of CHARMM-GUI Input Generator that form a basis for the advanced simulation techniques. Future directions of the CHARMM-GUI development project are also discussed briefly together with other features in the CHARMM-GUI website, such as Archive and Movie Gallery. 2008 Wiley Periodicals, Inc.
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              UCSF ChimeraX: Meeting modern challenges in visualization and analysis

              UCSF ChimeraX is next‐generation software for the visualization and analysis of molecular structures, density maps, 3D microscopy, and associated data. It addresses challenges in the size, scope, and disparate types of data attendant with cutting‐edge experimental methods, while providing advanced options for high‐quality rendering (interactive ambient occlusion, reliable molecular surface calculations, etc.) and professional approaches to software design and distribution. This article highlights some specific advances in the areas of visualization and usability, performance, and extensibility. ChimeraX is free for noncommercial use and is available from http://www.rbvi.ucsf.edu/chimerax / for Windows, Mac, and Linux.
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                25 November 2023
                : 2023.09.18.558283
                Affiliations
                [1 ]Department of Cell Physiology and Molecular Biophysics, Center for Membrane Protein Research, Texas Tech University Health Sciences Center, School of Medicine, Lubbock, TX 79424, USA
                [2 ]Thermo Fisher Scientific, San Jose, CA 95134, USA
                [3 ]VIB-VUB Center for Structural Biology, 1050 Brussel, Belgium
                [4 ]Division of CryoEM and Bioimaging, Stanford Synchrotron Radiation Light Source, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
                [5 ]Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX 79409, USA
                [6 ]Department of Physiology, University of California, Los Angeles, Los Angeles, CA, USA
                Author notes
                [#]

                Deceased

                [§ ]To whom correspondence should be addressed: Lan Guan, Lan.Guan@ 123456ttuhsc.edu
                Author information
                http://orcid.org/0000-0002-2274-361X
                Article
                10.1101/2023.09.18.558283
                10542114
                37790566
                b405d792-b979-4117-9625-7b6e87d51263

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

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