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      Malleable nature of mRNA-protein compositional complementarity and its functional significance

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      Nucleic Acids Research

      Oxford University Press

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          Abstract

          It has recently been demonstrated that nucleobase-density profiles of typical mRNA coding sequences exhibit a complementary relationship with nucleobase-interaction propensity profiles of their cognate protein sequences. This finding supports the idea that the genetic code developed in response to direct binding interactions between amino acids and appropriate nucleobases, but also suggests that present-day mRNAs and their cognate proteins may be physicochemically complementary to each other and bind. Here, we computationally recode complete Methanocaldococcus jannaschii, Escherichia coli and Homo sapiens mRNA transcriptomes and analyze how much complementary matching of synonymous mRNAs can vary, while keeping protein sequences fixed. We show that for most proteins there exist cognate mRNAs that improve, but also significantly worsen the level of native matching (e.g. by 1.8 viz. 7.6 standard deviations on average for H. sapiens, respectively), with the least malleable proteins in this sense being strongly enriched in nuclear localization and DNA-binding functions. Even so, we show that the majority of recodings for most proteins result in pronounced complementarity. Our results suggest that the genetic code was designed for favorable, yet tunable compositional complementarity between mRNAs and their cognate proteins, supporting the hypothesis that the interactions between the two were an important defining element behind the code's origin.

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          Most cited references 35

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          Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

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            Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

            DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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              Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

              Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
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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
                31 March 2015
                08 March 2015
                08 March 2015
                : 43
                : 6
                : 3012-3021
                Affiliations
                Department of Structural and Computational Biology, Max F. Perutz Laboratories, University of Vienna, Campus Vienna Biocenter 5, 1030 Vienna, Austria
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +43 1 4277 9522; Fax: +43 1 4277 9522; Email: bojan.zagrovic@ 123456univie.ac.at
                Article
                10.1093/nar/gkv166
                4381073
                25753660
                © The Author(s) 2015. 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/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                Counts
                Pages: 10
                Product
                Categories
                Computational Biology
                Custom metadata
                31 March 2015

                Genetics

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