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      Drug ReposER: a web server for predicting similar amino acid arrangements to known drug binding interfaces for potential drug repositioning

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          Abstract

          A common drug repositioning strategy is the re-application of an existing drug to address alternative targets. A crucial aspect to enable such repurposing is that the drug's binding site on the original target is similar to that on the alternative target. Based on the assumption that proteins with similar binding sites may bind to similar drugs, the 3D substructure similarity data can be used to identify similar sites in other proteins that are not known targets. The Drug ReposER (DRug REPOSitioning Exploration Resource) web server is designed to identify potential targets for drug repurposing based on sub-structural similarity to the binding interfaces of known drug binding sites. The application has pre-computed amino acid arrangements from protein structures in the Protein Data Bank that are similar to the 3D arrangements of known drug binding sites thus allowing users to explore them as alternative targets. Users can annotate new structures for sites that are similarly arranged to the residues found in known drug binding interfaces. The search results are presented as mappings of matched sidechain superpositions. The results of the searches can be visualized using an integrated NGL viewer. The Drug ReposER server has no access restrictions and is available at http://mfrlab.org/drugreposer/.

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          Most cited references36

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          MUSCLE: multiple sequence alignment with high accuracy and high throughput.

          We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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            The Pfam protein families database in 2019

            Abstract The last few years have witnessed significant changes in Pfam (https://pfam.xfam.org). The number of families has grown substantially to a total of 17,929 in release 32.0. New additions have been coupled with efforts to improve existing families, including refinement of domain boundaries, their classification into Pfam clans, as well as their functional annotation. We recently began to collaborate with the RepeatsDB resource to improve the definition of tandem repeat families within Pfam. We carried out a significant comparison to the structural classification database, namely the Evolutionary Classification of Protein Domains (ECOD) that led to the creation of 825 new families based on their set of uncharacterized families (EUFs). Furthermore, we also connected Pfam entries to the Sequence Ontology (SO) through mapping of the Pfam type definitions to SO terms. Since Pfam has many community contributors, we recently enabled the linking between authorship of all Pfam entries with the corresponding authors’ ORCID identifiers. This effectively permits authors to claim credit for their Pfam curation and link them to their ORCID record.
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              PLIP: fully automated protein–ligand interaction profiler

              The characterization of interactions in protein–ligand complexes is essential for research in structural bioinformatics, drug discovery and biology. However, comprehensive tools are not freely available to the research community. Here, we present the protein–ligand interaction profiler (PLIP), a novel web service for fully automated detection and visualization of relevant non-covalent protein–ligand contacts in 3D structures, freely available at projects.biotec.tu-dresden.de/plip-web . The input is either a Protein Data Bank structure, a protein or ligand name, or a custom protein–ligand complex (e.g. from docking). In contrast to other tools, the rule-based PLIP algorithm does not require any structure preparation. It returns a list of detected interactions on single atom level, covering seven interaction types (hydrogen bonds, hydrophobic contacts, pi-stacking, pi-cation interactions, salt bridges, water bridges and halogen bonds). PLIP stands out by offering publication-ready images, PyMOL session files to generate custom images and parsable result files to facilitate successive data processing. The full python source code is available for download on the website. PLIP's command-line mode allows for high-throughput interaction profiling.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                02 July 2019
                20 May 2019
                20 May 2019
                : 47
                : W1
                : W350-W356
                Affiliations
                [1 ]Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia
                [2 ]School of Computing, Engineering and Intelligent Systems, Ulster University, Northlands Road, Magee Campus, Londonderry BT48 7JL, UK
                [3 ]Malaysia Genome Institute, National Institutes of Biotechnology Malaysia, Jalan Bangi, 43000 Kajang, Selangor, Malaysia
                [4 ]Institute of Systems Biology, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia
                Author notes
                To whom correspondence should be addressed. Tel: +6 03 89267446; Fax: +6 03 89267972; Email: firdaus@ 123456mfrlab.org
                Author information
                http://orcid.org/0000-0003-4275-4663
                Article
                gkz391
                10.1093/nar/gkz391
                6602481
                31106379
                74d25784-c853-40ae-8f1c-c1155178a1fd
                © The Author(s) 2019. 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 Non-Commercial License ( http://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
                : 02 May 2019
                : 24 April 2019
                : 27 February 2019
                Page count
                Pages: 7
                Funding
                Funded by: Universiti Kebangsaan Malaysia 10.13039/501100004515
                Award ID: DIP-2017-013
                Funded by: Ministry of Science, Technology and Innovation, Malaysia 10.13039/501100003200
                Award ID: 02-01-02-SF1278
                Categories
                Web Server Issue

                Genetics
                Genetics

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