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      Integrated AlphaFold2 and DEER investigation of the conformational dynamics of a pH-dependent APC antiporter

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          Significance

          The transporter GadC contributes to acid resistance in bacterial pathogens by exchanging two substrates, glutamate and γ-aminobutyric acid (GABA), using a mechanism termed alternating access. In this study, the conformational dynamics underlying alternating access were studied using a combination of spectroscopy and computational modeling. A conformationally diverse ensemble of models, generated using AlphaFold2, guided the design and interpretation of double electron-electron resonance spectroscopy experiments. We found that whereas GadC was inactive and conformationally homogeneous at neutral pH, low pH induced isomerization between two conformations. From our integrated computational/experimental investigation emerges a transport model that may be relevant to eukaryotic homologs that are involved in other cellular processes.

          Abstract

          The Amino Acid–Polyamine-Organocation (APC) transporter GadC contributes to the survival of pathogenic bacteria under extreme acid stress by exchanging extracellular glutamate for intracellular γ-aminobutyric acid (GABA). Its structure, determined in an inward-facing conformation at alkaline pH, consists of the canonical LeuT-fold with a conserved five-helix inverted repeat, thereby resembling functionally divergent transporters such as the serotonin transporter SERT and the glucose-sodium symporter SGLT1. However, despite this structural similarity, it is unclear if the conformational dynamics of antiporters such as GadC follow the blueprint of these or other LeuT-fold transporters. Here, we used double electron-electron resonance (DEER) spectroscopy to monitor the conformational dynamics of GadC in lipid bilayers in response to acidification and substrate binding. To guide experimental design and facilitate the interpretation of the DEER data, we generated an ensemble of structural models in multiple conformations using a recently introduced modification of AlphaFold2 . Our experimental results reveal acid-induced conformational changes that dislodge the Cterminus from the permeation pathway coupled with rearrangement of helices that enables isomerization between inward- and outward-facing states. The substrate glutamate, but not GABA, modulates the dynamics of an extracellular thin gate without shifting the equilibrium between inward- and outward-facing conformations. In addition to introducing an integrated methodology for probing transporter conformational dynamics, the congruence of the DEER data with patterns of structural rearrangements deduced from ensembles of AlphaFold2 models illuminates the conformational cycle of GadC underpinning transport and exposes yet another example of the divergence between the dynamics of different families in the LeuT-fold.

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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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            SciPy 1.0: fundamental algorithms for scientific computing in Python

            SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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              ColabFold: making protein folding accessible to all

              ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold’s 40−60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com . ColabFold is a free and accessible platform for protein folding that provides accelerated prediction of protein structures and complexes using AlphaFold2 or RoseTTAFold.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                15 August 2022
                23 August 2022
                15 February 2023
                : 119
                : 34
                : e2206129119
                Affiliations
                [1] aDepartment of Molecular Physiology and Biophysics, Vanderbilt University , Nashville, TN 37212;
                [2] bDepartment of Chemistry, Vanderbilt University , Nashville, TN 37212;
                [3] cInstitute for Drug Discovery, Leipzig University , Leipzig, Germany 04109
                Author notes
                2To whom correspondence may be addressed. Email: hassane.mchaourab@ 123456vanderbilt.edu .

                Edited by Michael Grabe, University of California, San Francisco, California; received April 7, 2022; accepted July 8, 2022 by Editorial Board Member William F. DeGrado

                Author contributions: D.d.A, S.R., J.M., and H.S.M. designed research; D.d.A., L.D., R.M.N., and S.R. performed research; D.d.A., L.D., R.M.N., and S.R. analyzed data; D.d.A. and H.S.M. wrote the paper; and J.M. and H.S.M. provided funding and resources for the study.

                1Present address: Computational Sciences, GlaxoSmithKline Research and Development, 6300 Zug, Switzerland.

                Author information
                https://orcid.org/0000-0003-1757-9971
                https://orcid.org/0000-0001-5016-743X
                Article
                202206129
                10.1073/pnas.2206129119
                9407458
                35969794
                60e01af0-e9e1-457f-b496-39f16941d1f3
                Copyright © 2022 the Author(s). Published by PNAS.

                This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                : 08 July 2022
                Page count
                Pages: 11
                Funding
                Funded by: HHS | National Institutes of Health (NIH) 100000002
                Award ID: GM 128087
                Award Recipient : Hassane S Mchaourab
                Funded by: Deutsche Forschungsgemeinschaft (DFG) 501100001659
                Award ID: CRC 1423
                Award ID: project 421152132
                Award ID: subproject Z04
                Award Recipient : Jens Meiler
                Categories
                408
                Biological Sciences
                Biophysics and Computational Biology

                amino acid transport,acid resistance,membrane protein biophysics,structure prediction

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