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      ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules

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          Abstract

          The degree of evolutionary conservation of an amino acid in a protein or a nucleic acid in DNA/RNA reflects a balance between its natural tendency to mutate and the overall need to retain the structural integrity and function of the macromolecule. The ConSurf web server ( http://consurf.tau.ac.il), established over 15 years ago, analyses the evolutionary pattern of the amino/nucleic acids of the macromolecule to reveal regions that are important for structure and/or function. Starting from a query sequence or structure, the server automatically collects homologues, infers their multiple sequence alignment and reconstructs a phylogenetic tree that reflects their evolutionary relations. These data are then used, within a probabilistic framework, to estimate the evolutionary rates of each sequence position. Here we introduce several new features into ConSurf, including automatic selection of the best evolutionary model used to infer the rates, the ability to homology-model query proteins, prediction of the secondary structure of query RNA molecules from sequence, the ability to view the biological assembly of a query (in addition to the single chain), mapping of the conservation grades onto 2D RNA models and an advanced view of the phylogenetic tree that enables interactively rerunning ConSurf with the taxa of a sub-tree.

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          Dating of the human-ape splitting by a molecular clock of mitochondrial DNA.

          A new statistical method for estimating divergence dates of species from DNA sequence data by a molecular clock approach is developed. This method takes into account effectively the information contained in a set of DNA sequence data. The molecular clock of mitochondrial DNA (mtDNA) was calibrated by setting the date of divergence between primates and ungulates at the Cretaceous-Tertiary boundary (65 million years ago), when the extinction of dinosaurs occurred. A generalized least-squares method was applied in fitting a model to mtDNA sequence data, and the clock gave dates of 92.3 +/- 11.7, 13.3 +/- 1.5, 10.9 +/- 1.2, 3.7 +/- 0.6, and 2.7 +/- 0.6 million years ago (where the second of each pair of numbers is the standard deviation) for the separation of mouse, gibbon, orangutan, gorilla, and chimpanzee, respectively, from the line leading to humans. Although there is some uncertainty in the clock, this dating may pose a problem for the widely believed hypothesis that the pipedal creature Australopithecus afarensis, which lived some 3.7 million years ago at Laetoli in Tanzania and at Hadar in Ethiopia, was ancestral to man and evolved after the human-ape splitting. Another likelier possibility is that mtDNA was transferred through hybridization between a proto-human and a proto-chimpanzee after the former had developed bipedalism.
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            An evolutionary trace method defines binding surfaces common to protein families.

            X-ray or NMR structures of proteins are often derived without their ligands, and even when the structure of a full complex is available, the area of contact that is functionally and energetically significant may be a specialized subset of the geometric interface deduced from the spatial proximity between ligands. Thus, even after a structure is solved, it remains a major theoretical and experimental goal to localize protein functional interfaces and understand the role of their constituent residues. The evolutionary trace method is a systematic, transparent and novel predictive technique that identifies active sites and functional interfaces in proteins with known structure. It is based on the extraction of functionally important residues from sequence conservation patterns in homologous proteins, and on their mapping onto the protein surface to generate clusters identifying functional interfaces. The SH2 and SH3 modular signaling domains and the DNA binding domain of the nuclear hormone receptors provide tests for the accuracy and validity of our method. In each case, the evolutionary trace delineates the functional epitope and identifies residues critical to binding specificity. Based on mutational evolutionary analysis and on the structural homology of protein families, this simple and versatile approach should help focus site-directed mutagenesis studies of structure-function relationships in macromolecules, as well as studies of specificity in molecular recognition. More generally, it provides an evolutionary perspective for judging the functional or structural role of each residue in protein structure.
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              Predicting functionally important residues from sequence conservation.

              All residues in a protein are not equally important. Some are essential for the proper structure and function of the protein, whereas others can be readily replaced. Conservation analysis is one of the most widely used methods for predicting these functionally important residues in protein sequences. We introduce an information-theoretic approach for estimating sequence conservation based on Jensen-Shannon divergence. We also develop a general heuristic that considers the estimated conservation of sequentially neighboring sites. In large-scale testing, we demonstrate that our combined approach outperforms previous conservation-based measures in identifying functionally important residues; in particular, it is significantly better than the commonly used Shannon entropy measure. We find that considering conservation at sequential neighbors improves the performance of all methods tested. Our analysis also reveals that many existing methods that attempt to incorporate the relationships between amino acids do not lead to better identification of functionally important sites. Finally, we find that while conservation is highly predictive in identifying catalytic sites and residues near bound ligands, it is much less effective in identifying residues in protein-protein interfaces. Data sets and code for all conservation measures evaluated are available at http://compbio.cs.princeton.edu/conservation/
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                08 July 2016
                10 May 2016
                10 May 2016
                : 44
                : Web Server issue
                : W344-W350
                Affiliations
                [1 ]Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
                [2 ]Department of Molecular Biology and Ecology of Plants, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
                [3 ]Department of Microbiology, University of Massachusetts, Amherst, MA 01003, USA
                [4 ]Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +972 3 640 6709; Fax: +972 3 640 6834; Email: bental@ 123456tauex.tau.ac.il
                Correspondence may also be addressed to Itay Mayrose. Email: itaymay@ 123456post.tau.ac.il
                Correspondence may also be addressed to Tal Pupko. Email: talp@ 123456post.tau.ac.il
                Article
                10.1093/nar/gkw408
                4987940
                27166375
                aa4e09f0-fe86-469c-8b9b-8f3adbac2980
                © The Author(s) 2016. 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 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
                : 03 May 2016
                : 21 April 2016
                : 20 February 2016
                Page count
                Pages: 7
                Categories
                Web Server issue
                Custom metadata
                08 July 2016

                Genetics
                Genetics

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