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      DrugDomain: The evolutionary context of drugs and small molecules bound to domains

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          Abstract

          Interactions between proteins and small organic compounds play a crucial role in regulating protein functions. These interactions can modulate various aspects of protein behavior, including enzymatic activity, signaling cascades, and structural stability. By binding to specific sites on proteins, small organic compounds can induce conformational changes, alter protein–protein interactions, or directly affect catalytic activity. Therefore, many drugs available on the market today are small molecules (72% of all approved drugs in the last 5 years). Proteins are composed of one or more domains: evolutionary units that convey function or fitness either singly or in concert with others. Understanding which domain(s) of the target protein binds to a drug can lead to additional opportunities for discovering novel targets. The evolutionary classification of protein domains (ECOD) classifies domains into an evolutionary hierarchy that focuses on distant homology. Previously, no structure‐based protein domain classification existed that included information about both the interaction between small molecules or drugs and the structural domains of a target protein. This data is especially important for multidomain proteins and large complexes. Here, we present the DrugDomain database that reports the interaction between ECOD of human target proteins and DrugBank molecules and drugs. The pilot version of DrugDomain describes the interaction of 5160 DrugBank molecules associated with 2573 human proteins. It describes domains for all experimentally determined structures of these proteins and incorporates AlphaFold models when such structures are unavailable. The DrugDomain database is available online: http://prodata.swmed.edu/DrugDomain/.

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          Most cited references33

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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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            UniProt: a worldwide hub of protein knowledge

            (2018)
            Abstract The UniProt Knowledgebase is a collection of sequences and annotations for over 120 million proteins across all branches of life. Detailed annotations extracted from the literature by expert curators have been collected for over half a million of these proteins. These annotations are supplemented by annotations provided by rule based automated systems, and those imported from other resources. In this article we describe significant updates that we have made over the last 2 years to the resource. We have greatly expanded the number of Reference Proteomes that we provide and in particular we have focussed on improving the number of viral Reference Proteomes. The UniProt website has been augmented with new data visualizations for the subcellular localization of proteins as well as their structure and interactions. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.
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              DrugBank 5.0: a major update to the DrugBank database for 2018

              Abstract DrugBank (www.drugbank.ca) is a web-enabled database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets. First described in 2006, DrugBank has continued to evolve over the past 12 years in response to marked improvements to web standards and changing needs for drug research and development. This year’s update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years. In many cases, existing data content has grown by 100% or more over the last update. For instance, the total number of investigational drugs in the database has grown by almost 300%, the number of drug-drug interactions has grown by nearly 600% and the number of SNP-associated drug effects has grown more than 3000%. Significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions. A great deal of brand new data have also been added to DrugBank 5.0. This includes information on the influence of hundreds of drugs on metabolite levels (pharmacometabolomics), gene expression levels (pharmacotranscriptomics) and protein expression levels (pharmacoprotoemics). New data have also been added on the status of hundreds of new drug clinical trials and existing drug repurposing trials. Many other important improvements in the content, interface and performance of the DrugBank website have been made and these should greatly enhance its ease of use, utility and potential applications in many areas of pharmacological research, pharmaceutical science and drug education.
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                Author and article information

                Contributors
                kirill.medvedev@utsouthwestern.edu
                Journal
                Protein Sci
                Protein Sci
                10.1002/(ISSN)1469-896X
                PRO
                Protein Science : A Publication of the Protein Society
                John Wiley & Sons, Inc. (Hoboken, USA )
                0961-8368
                1469-896X
                09 July 2024
                August 2024
                09 July 2024
                : 33
                : 8 ( doiID: 10.1002/pro.v33.8 )
                : e5116
                Affiliations
                [ 1 ] Department of Biophysics University of Texas Southwestern Medical Center Dallas Texas USA
                [ 2 ] Department of Biochemistry University of Texas Southwestern Medical Center Dallas Texas USA
                Author notes
                [*] [* ] Correspondence

                Kirill E. Medvedev, 5323 Harry Hines Blvd., Dallas, Texas 75390‐9050, USA.

                Email: kirill.medvedev@ 123456utsouthwestern.edu

                Author information
                https://orcid.org/0000-0002-7982-4242
                https://orcid.org/0000-0001-6502-1425
                Article
                PRO5116
                10.1002/pro.5116
                11231930
                38979784
                f333553c-6ab2-42f7-b918-eeaf63caa076
                © 2024 The Author(s). Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 27 June 2024
                : 22 March 2024
                : 29 June 2024
                Page count
                Figures: 4, Tables: 0, Pages: 7, Words: 3800
                Funding
                Funded by: National Institute of General Medical Sciences , doi 10.13039/100000057;
                Award ID: GM127390
                Award ID: GM147367
                Funded by: Welch Foundation , doi 10.13039/100000928;
                Award ID: I‐1505
                Funded by: National Science Foundation , doi 10.13039/100000001;
                Award ID: DBI 2224128
                Categories
                Tools for Protein Science
                Tools for Protein Science
                Custom metadata
                2.0
                August 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.4.5 mode:remove_FC converted:09.07.2024

                Biochemistry
                domain,drugs,protein structure,small molecules,target
                Biochemistry
                domain, drugs, protein structure, small molecules, target

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