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      VARIDT 2.0: structural variability of drug transporter

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          Abstract

          The structural variability data of drug transporter (DT) are key for research on precision medicine and rational drug use. However, these valuable data are not sufficiently covered by the available databases. In this study, a major update of VARIDT (a database previously constructed to provide DTs’ variability data) was thus described. First, the experimentally resolved structures of all DTs reported in the original VARIDT were discovered from PubMed and Protein Data Bank. Second, the structural variability data of each DT were collected by literature review, which included: (a) mutation-induced spatial variations in folded state, (b) difference among DT structures of human and model organisms, (c) outward/inward-facing DT conformations and (d) xenobiotics-driven alterations in the 3D complexes. Third, for those DTs without experimentally resolved structural variabilities, homology modeling was further applied as well-established protocol to enrich such valuable data. As a result, 145 mutation-induced spatial variations of 42 DTs, 1622 inter-species structures originating from 292 DTs, 118 outward/inward-facing conformations belonging to 59 DTs, and 822 xenobiotics-regulated structures in complex with 57 DTs were updated to VARIDT ( https://idrblab.org/varidt/ and http://varidt.idrblab.net/). All in all, the newly collected structural variabilities will be indispensable for explaining drug sensitivity/selectivity, bridging preclinical research with clinical trial, revealing the mechanism underlying drug-drug interaction, and so on.

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          Graphical Abstract

          Structural variability of drug transporter (VARIDT 2.0).

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          Most cited references112

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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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            SWISS-MODEL: homology modelling of protein structures and complexes

            Abstract Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.
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              UniProt: the universal protein knowledgebase in 2021

              (2020)
              Abstract The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.
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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
                07 January 2022
                08 November 2021
                08 November 2021
                : 50
                : D1
                : D1417-D1431
                Affiliations
                College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, China
                Institute of Theoretical Chemistry, College of Chemistry, Jilin University , Changchun 130023, China
                College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, China
                Department of Pharmacology, Hebei Medical University , Shijiazhuang 050017, China
                College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, China
                Department of Surgery, HKU-SZH & Faculty of Medicine, The University of Hong Kong , Hong Kong, China
                Department of Pharmacology, Hebei Medical University , Shijiazhuang 050017, China
                Department of Pharmacology, Hebei Medical University , Shijiazhuang 050017, China
                Department of Pharmacology, Hebei Medical University , Shijiazhuang 050017, China
                Department of Pharmacology, Hebei Medical University , Shijiazhuang 050017, China
                College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, China
                College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, China
                Westlake Laboratory of Life Sciences and Biomedicine , Hangzhou, Zhejiang, China
                Institute of Theoretical Chemistry, College of Chemistry, Jilin University , Changchun 130023, China
                College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, China
                College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, China
                Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare , Hangzhou 330110, China
                Author notes
                To whom correspondence should be addressed. Tel: +86 189 8946 6518; Fax: +86 571 8820 8444; Email: zhufeng@ 123456zju.edu.cn
                Correspondence may also be addressed to Su Zeng. Email: zengsu@ 123456zju.edu.cn
                Correspondence may also be addressed to Qingchuan Zheng. Email: zhengqc@ 123456jlu.edu.cn

                The authors wish it to be known that, in their opinion, the first four authors should be regarded as Joint First Authors.

                Author information
                https://orcid.org/0000-0001-8069-0053
                Article
                gkab1013
                10.1093/nar/gkab1013
                8728241
                34747471
                6f6c2c7b-4098-47fa-a80c-3a8b04a64c56
                © The Author(s) 2021. 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
                : 04 November 2021
                : 08 October 2021
                : 08 September 2021
                Page count
                Pages: 15
                Funding
                Funded by: Natural Science Foundation of Zhejiang Province, DOI 10.13039/501100004731;
                Award ID: LR21H300001
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 81973394
                Award ID: 81872798
                Award ID: U1909208
                Funded by: National High-Level Talents Special Support Plan of China;
                Funded by: Fundamental Research Fund for the Central Universities, DOI 10.13039/501100012226;
                Award ID: 2018QNA7023
                Funded by: ‘Double Top-Class’ University Project;
                Award ID: 181201*194232101
                Funded by: Key R&D Program of Zhejiang Province;
                Award ID: 2020C03010
                Funded by: Westlake Laboratory;
                Funded by: Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare;
                Funded by: Alibaba Cloud;
                Funded by: Information Technology Center of Zhejiang University;
                Categories
                AcademicSubjects/SCI00010
                Database Issue

                Genetics
                Genetics

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