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      BeStSel: webserver for secondary structure and fold prediction for protein CD spectroscopy

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          Abstract

          Circular dichroism (CD) spectroscopy is widely used to characterize the secondary structure composition of proteins. To derive accurate and detailed structural information from the CD spectra, we have developed the Beta Structure Selection (BeStSel) method (PNAS, 112, E3095), which can handle the spectral diversity of β-structured proteins. The BeStSel webserver provides this method with useful accessories to the community with the main goal to analyze single or multiple protein CD spectra. Uniquely, BeStSel provides information on eight secondary structure components including parallel β-structure and antiparallel β-sheets with three different groups of twist. It overperforms any available method in accuracy and information content, moreover, it is capable of predicting the protein fold down to the topology/homology level of the CATH classification. A new module of the webserver helps to distinguish intrinsically disordered proteins by their CD spectrum. Secondary structure calculation for uploaded PDB files will help the experimental verification of protein MD and in silico modelling using CD spectroscopy. The server also calculates extinction coefficients from the primary sequence for CD users to determine the accurate protein concentrations which is a prerequisite for reliable secondary structure determination. The BeStSel server can be freely accessed at https://bestsel.elte.hu.

          Graphical Abstract

          Graphical Abstract

          Functions of the BeStSel web server for the analysis of protein CD spectra.

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          Most cited references51

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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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            Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features.

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              Highly accurate protein structure prediction for the human proteome

              Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure 1 . Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold 2 , at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective. AlphaFold is used to predict the structures of almost all of the proteins in the human proteome—the availability of high-confidence predicted structures could enable new avenues of investigation from a structural perspective.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                05 July 2022
                11 May 2022
                11 May 2022
                : 50
                : W1
                : W90-W98
                Affiliations
                ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University , Budapest H-1117, Hungary
                ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University , Budapest H-1117, Hungary
                Synchrotron SOLEIL , Gif-sur-Yvette 91192, France
                Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University , Budapest H-1117, Hungary
                ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University , Budapest H-1117, Hungary
                Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University , Budapest H-1117, Hungary
                Institute of Enzymology, Research Centre for Natural Sciences , Budapest H-1117, Hungary
                Research Center of Bioconvergence Analysis, Korea Basic Science Institute (KBSI) , Ochang 28119, Republic of Korea
                Bio-Analytical Science, University of Science and Technology (UST) , Daejeon 34113, Republic of Korea
                Graduate School of Analytical Science and Technology (GRAST), Chungnam National University (CNU) , Daejeon 34134, Republic of Korea
                ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University , Budapest H-1117, Hungary
                Synchrotron SOLEIL , Gif-sur-Yvette 91192, France
                Centre de Biophysique Moléculaire, CNRS UPR4301 , Orléans, France
                Global Center for Medical Engineering and Informatics, Osaka University , Osaka 565-0871, Japan
                Institute of Enzymology, Research Centre for Natural Sciences , Budapest H-1117, Hungary
                ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University , Budapest H-1117, Hungary
                Author notes
                To whom correspondence should be addressed. Tel: +36 1 372 2500/1795; Fax: +36 1 381 2172; Email: kardos@ 123456elte.hu
                Author information
                https://orcid.org/0000-0002-2135-2932
                Article
                gkac345
                10.1093/nar/gkac345
                9252784
                35544232
                47ddd443-7e27-417a-9076-2df97a63a189
                © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 09 May 2022
                : 18 April 2022
                : 28 March 2022
                Page count
                Pages: 9
                Funding
                Funded by: National Research, Development and Innovation Fund, DOI 10.13039/501100012550;
                Award ID: K120391
                Award ID: K138937
                Award ID: K125340
                Award ID: PD135510
                Award ID: 2017-1.2.1-NKP-2017-00002
                Funded by: International Collaboration;
                Award ID: 2019-2.1.11-TÉT-2019-00079
                Award ID: 2018-2.1.17-TÉT-KR-2018-00008
                Award ID: 2019-2.1.6-NEMZ_KI-2019-00012
                Award ID: 2019-2.1.11-TÉT-2020-00101
                Funded by: SOLEIL Synchrotron, France;
                Award ID: 20181890
                Award ID: 20191810
                Award ID: 20200751
                Funded by: Institute for Protein Research, Osaka University, DOI 10.13039/501100018787;
                Funded by: Japan Society for the Promotion of Science, DOI 10.13039/501100001691;
                Categories
                AcademicSubjects/SCI00010
                Web Server Issue

                Genetics
                Genetics

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