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      Benchmarking fold detection by DaliLite v.5

      1 , 2
      Bioinformatics
      Oxford University Press (OUP)

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          Abstract

          Motivation

          Protein structure comparison plays a fundamental role in understanding the evolutionary relationships between proteins. Here, we release a new version of the DaliLite standalone software. The novelties are hierarchical search of the structure database organized into sequence based clusters, and remote access to our knowledge base of structural neighbors. The detection of fold, superfamily and family level similarities by DaliLite and state-of-the-art competitors was benchmarked against a manually curated structural classification.

          Results

          Database search strategies were evaluated using Fmax with query-specific thresholds. DaliLite and DeepAlign outperformed TM-score based methods at all levels of the benchmark, and DaliLite outperformed DeepAlign at fold level. Hierarchical and knowledge-based searches got close to the performance of systematic pairwise comparison. The knowledge-based search was four times as efficient as the hierarchical search. The knowledge-based search dynamically adjusts the depth of the search, enabling a trade-off between speed and recall.

          Availability and implementation

          http://ekhidna2.biocenter.helsinki.fi/dali/README.v5.html.

          Supplementary information

          Supplementary data are available at Bioinformatics online.

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          Most cited references3

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          DaliLite workbench for protein structure comparison.

          DaliLite is a program for pairwise structure comparison and for structure database searching. It is a standalone version of the search engine of the popular Dali server. A web interface is provided to view the results, multiple alignments and 3D superimpositions of structures.
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            Is Open Access

            Protein structure alignment beyond spatial proximity

            Protein structure alignment is a fundamental problem in computational structure biology. Many programs have been developed for automatic protein structure alignment, but most of them align two protein structures purely based upon geometric similarity without considering evolutionary and functional relationship. As such, these programs may generate structure alignments which are not very biologically meaningful from the evolutionary perspective. This paper presents a novel method DeepAlign for automatic pairwise protein structure alignment. DeepAlign aligns two protein structures using not only spatial proximity of equivalent residues (after rigid-body superposition), but also evolutionary relationship and hydrogen-bonding similarity. Experimental results show that DeepAlign can generate structure alignments much more consistent with manually-curated alignments than other automatic tools especially when proteins under consideration are remote homologs. These results imply that in addition to geometric similarity, evolutionary information and hydrogen-bonding similarity are essential to aligning two protein structures.
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              Is Open Access

              Discovery of Ubiquitin Deamidases in the Pathogenic Arsenal of Legionella pneumophila

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                Author and article information

                Journal
                Bioinformatics
                Oxford University Press (OUP)
                1367-4803
                1460-2059
                December 15 2019
                December 15 2019
                July 02 2019
                December 15 2019
                December 15 2019
                July 02 2019
                : 35
                : 24
                : 5326-5327
                Affiliations
                [1 ]Institute of Biotechnology, Helsinki Institute of Life Sciences
                [2 ]Organismal and Evolutionary Biology Research Program, Faculty of Biosciences, University of Helsinki, Helsinki, Finland
                Article
                10.1093/bioinformatics/btz536
                31263867
                7b380b0b-d4af-4e72-9d44-b49338ce1afe
                © 2019

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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