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      Enrich: software for analysis of protein function by enrichment and depletion of variants

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          Abstract

          Summary: Measuring the consequences of mutation in proteins is critical to understanding their function. These measurements are essential in such applications as protein engineering, drug development, protein design and genome sequence analysis. Recently, high-throughput sequencing has been coupled to assays of protein activity, enabling the analysis of large numbers of mutations in parallel. We present Enrich, a tool for analyzing such deep mutational scanning data. Enrich identifies all unique variants (mutants) of a protein in high-throughput sequencing datasets and can correct for sequencing errors using overlapping paired-end reads. Enrich uses the frequency of each variant before and after selection to calculate an enrichment ratio, which is used to estimate fitness. Enrich provides an interactive interface to guide users. It generates user-accessible output for downstream analyses as well as several visualizations of the effects of mutation on function, thereby allowing the user to rapidly quantify and comprehend sequence–function relationships.

          Availability and Implementation: Enrich is implemented in Python and is available under a FreeBSD license at http://depts.washington.edu/sfields/software/enrich/. Enrich includes detailed documentation as well as a small example dataset.

          Contact: dfowler@ 123456uw.edu ; fields@ 123456uw.edu

          Supplementary Information: Supplementary data is available at Bioinformatics online.

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          Most cited references9

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          High Resolution Mapping of Protein Sequence–Function Relationships

          We present a large-scale approach to investigate the functional consequences of sequence variation in a protein. The approach entails the display of hundreds of thousands of protein variants, moderate selection for activity, and high throughput DNA sequencing to quantify the performance of each variant. Using this strategy, we tracked the performance of >600,000 variants of a human WW domain after three and six rounds of selection by phage display for binding to its peptide ligand. Binding properties of these variants defined a high-resolution map of mutational preference across the WW domain; each position possessed unique features that could not be captured by a few representative mutations. Our approach could be applied to many in vitro or in vivo protein assays, providing a general means for understanding how protein function relates to sequence.
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            Experimental illumination of a fitness landscape.

            The genes of all organisms have been shaped by selective pressures. The relationship between gene sequence and fitness has tremendous implications for understanding both evolutionary processes and functional constraints on the encoded proteins. Here, we have exploited deep sequencing technology to experimentally determine the fitness of all possible individual point mutants under controlled conditions for a nine-amino acid region of Hsp90. Over the past five decades, limited glimpses into the relationship between gene sequence and function have sparked a long debate regarding the distribution, relative proportion, and evolutionary significance of deleterious, neutral, and advantageous mutations. Our systematic experimental measurement of fitness effects of Hsp90 mutants in yeast, evaluated in the light of existing population genetic theory, are remarkably consistent with a nearly neutral model of molecular evolution.
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              Rapid mapping of protein functional epitopes by combinatorial alanine scanning.

              A combinatorial alanine-scanning strategy was used to determine simultaneously the functional contributions of 19 side chains buried at the interface between human growth hormone and the extracellular domain of its receptor. A phage-displayed protein library was constructed in which the 19 side chains were preferentially allowed to vary only as the wild type or alanine. The library pool was subjected to binding selections to isolate functional clones, and DNA sequencing was used to determine the alanine/wild-type ratio at each varied position. This ratio was used to calculate the effect of each alanine substitution as a change in free energy relative to that of wild type. Only seven side chains contribute significantly to the binding interaction, and these conserved residues form a compact cluster in the human growth hormone tertiary structure. The results were in excellent agreement with free energy data previously determined by conventional alanine-scanning mutagenesis and suggest that this technology should be useful for analyzing functional epitopes in proteins.
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                Author and article information

                Journal
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                15 December 2011
                17 October 2011
                17 October 2011
                : 27
                : 24
                : 3430-3431
                Affiliations
                1Department of Genome Sciences, 2Howard Hughes Medical Institute and 3Department of Medicine, University of Washington, Seattle, WA 98195, USA
                Author notes
                * To whom correspondence should be addressed.

                Associate Editor: Burkhard Rost

                Article
                btr577
                10.1093/bioinformatics/btr577
                3232369
                22006916
                2e62864a-89c6-495b-87da-78ff7e5817d7
                © The Author(s) 2011. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 8 June 2011
                : 5 October 2011
                : 11 October 2011
                Page count
                Pages: 2
                Categories
                Applications Note
                Sequence Analysis

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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