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      Molecular determinants underlying functional innovations of TBP and their impact on transcription initiation

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          Abstract

          TATA-box binding protein (TBP) is required for every single transcription event in archaea and eukaryotes. It binds DNA and harbors two repeats with an internal structural symmetry that show sequence asymmetry. At various times in evolution, TBP has acquired multiple interaction partners and different organisms have evolved TBP paralogs with additional protein regions. Together, these observations raise questions of what molecular determinants (i.e. key residues) led to the ability of TBP to acquire new interactions, resulting in an increasingly complex transcriptional system in eukaryotes. We present a comprehensive study of the evolutionary history of TBP and its interaction partners across all domains of life, including viruses. Our analysis reveals the molecular determinants and suggests a unified and multi-stage evolutionary model for the functional innovations of TBP. These findings highlight how concerted chemical changes on a conserved structural scaffold allow for the emergence of complexity in a fundamental biological process.

          Abstract

          The TATA-box binding protein (TBP) is required for transcription initiation in archaea and eukaryotes. Here the authors delineate how TBP’s function has evolved new functional features through context-dependent interactions with various protein partners.

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          Most cited references71

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          Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

          The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
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            Hidden Markov model speed heuristic and iterative HMM search procedure

            Background Profile hidden Markov models (profile-HMMs) are sensitive tools for remote protein homology detection, but the main scoring algorithms, Viterbi or Forward, require considerable time to search large sequence databases. Results We have designed a series of database filtering steps, HMMERHEAD, that are applied prior to the scoring algorithms, as implemented in the HMMER package, in an effort to reduce search time. Using this heuristic, we obtain a 20-fold decrease in Forward and a 6-fold decrease in Viterbi search time with a minimal loss in sensitivity relative to the unfiltered approaches. We then implemented an iterative profile-HMM search method, JackHMMER, which employs the HMMERHEAD heuristic. Due to our search heuristic, we eliminated the subdatabase creation that is common in current iterative profile-HMM approaches. On our benchmark, JackHMMER detects 14% more remote protein homologs than SAM's iterative method T2K. Conclusions Our search heuristic, HMMERHEAD, significantly reduces the time needed to score a profile-HMM against large sequence databases. This search heuristic allowed us to implement an iterative profile-HMM search method, JackHMMER, which detects significantly more remote protein homologs than SAM's T2K and NCBI's PSI-BLAST.
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              Database resources of the National Center for Biotechnology Information

              In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR, OrfFinder, Spidey, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs), Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Entrez Probe, GENSAT, Online Mendelian Inheritance in Man (OMIM), Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART) and the PubChem suite of small molecule databases. Augmenting many of the web applications is custom implementation of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.
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                Author and article information

                Contributors
                ravarani@mrc-lmb.cam.ac.uk
                bsanthan@mrc-lmb.cam.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                13 May 2020
                13 May 2020
                2020
                : 11
                : 2384
                Affiliations
                [1 ]MRC Laboratory of Molecular Biology, Francis Crick Ave, Cambridge, UK CB2 0QH
                [2 ]ISNI 0000000121885934, GRID grid.5335.0, Fitzwilliam College of the University of Cambridge, ; Storey’s Way, Cambridge, CB3 0DG UK
                [3 ]GRID grid.494635.9, Department of Biology, , Indian Institute of Science Education and Research (IISER) Tirupati, ; Tirupati, Andhra Pradesh India
                Author information
                http://orcid.org/0000-0001-8307-1645
                http://orcid.org/0000-0002-4237-0048
                http://orcid.org/0000-0003-0556-6196
                http://orcid.org/0000-0001-8523-4145
                Article
                16182
                10.1038/s41467-020-16182-z
                7221094
                32404905
                62450d38-bbcf-4cc5-bfc8-29483f233a69
                © Crown 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 February 2020
                : 13 April 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000265, RCUK | Medical Research Council (MRC);
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                protein sequence analyses,molecular evolution
                Uncategorized
                protein sequence analyses, molecular evolution

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