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      Mass spectrometry of intact membrane proteins: shifting towards a more native-like context

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          Abstract

          Integral membrane proteins are involved in a plethora of biological processes including cellular signalling, molecular transport, and catalysis. Many of these functions are mediated by non-covalent interactions with other proteins, substrates, metabolites, and surrounding lipids. Uncovering such interactions and deciphering their effect on protein activity is essential for understanding the regulatory mechanisms underlying integral membrane protein function. However, the detection of such dynamic complexes has proven to be challenging using traditional approaches in structural biology. Native mass spectrometry has emerged as a powerful technique for the structural characterisation of membrane proteins and their complexes, enabling the detection and identification of protein-binding partners. In this review, we discuss recent native mass spectrometry-based studies that have characterised non-covalent interactions of membrane proteins in the presence of detergents or membrane mimetics. We additionally highlight recent progress towards the study of membrane proteins within native membranes and provide our perspective on how these could be combined with recent developments in instrumentation to investigate increasingly complex biomolecular systems.

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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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            Accurate prediction of protein structures and interactions using a 3-track neural network

            DeepMind presented remarkably accurate predictions at the recent CASP14 protein structure prediction assessment conference. We explored network architectures incorporating related ideas and obtained the best performance with a 3-track network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The 3-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short circuiting traditional approaches which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.
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              A comprehensive map of molecular drug targets

              The success of mechanism-based drug discovery depends on the definition of the drug target. This definition becomes even more important as we try to link drug response to genetic variation, understand stratified clinical efficacy and safety, rationalize the differences between drugs in the same therapeutic
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                Author and article information

                Contributors
                Journal
                Essays Biochem
                Essays Biochem
                ebc
                Essays in Biochemistry
                Portland Press Ltd.
                0071-1365
                1744-1358
                March 2023
                29 March 2023
                : 67
                : 2 , Structural Mass Spectrometry
                : 201-213
                Affiliations
                [1 ]Department of Chemistry, University of Oxford, South Parks Road, Oxford OX1 3QZ, U.K.
                [2 ]The Kavli Institute for Nanoscience Discovery, Oxford, South Parks Road, Oxford OX1 3QU, U.K.
                [3 ]Department of Biology, University of Oxford, South Parks Road, Oxford OX1 3RB, U.K.
                Author notes
                Correspondence: Jani R. Bolla ( jani.bolla@ 123456biology.ox.ac.uk )
                Author information
                https://orcid.org/0000-0003-4346-182X
                Article
                EBC20220169
                10.1042/EBC20220169
                10070488
                36807530
                64e8a24a-8567-4b7a-bb3e-8a2f3ee83227
                © 2023 The Author(s).

                This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY). Open access for this article was enabled by the participation of University of Oxford in an all-inclusive Read & Publish agreement with Portland Press and the Biochemical Society under a transformative agreement with JISC.

                History
                : 17 November 2022
                : 20 January 2023
                : 23 January 2023
                Page count
                Pages: 13
                Categories
                Biophysics
                Molecular Interactions
                Structural Biology
                Review Articles

                lipids,membrane proteins,native mass spectrometry,structural biology

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