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      Functional annotation of hypothetical proteins from the Exiguobacterium antarcticum strain B7 reveals proteins involved in adaptation to extreme environments, including high arsenic resistance

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          Abstract

          Exiguobacterium antarcticum strain B7 is a psychrophilic Gram-positive bacterium that possesses enzymes that can be used for several biotechnological applications. However, many proteins from its genome are considered hypothetical proteins (HPs). These functionally unknown proteins may indicate important functions regarding the biological role of this bacterium, and the use of bioinformatics tools can assist in the biological understanding of this organism through functional annotation analysis. Thus, our study aimed to assign functions to proteins previously described as HPs, present in the genome of E. antarcticum B7. We used an extensive in silico workflow combining several bioinformatics tools for function annotation, sub-cellular localization and physicochemical characterization, three-dimensional structure determination, and protein-protein interactions. This genome contains 2772 genes, of which 765 CDS were annotated as HPs. The amino acid sequences of all HPs were submitted to our workflow and we successfully attributed function to 132 HPs. We identified 11 proteins that play important roles in the mechanisms of adaptation to adverse environments, such as flagellar biosynthesis, biofilm formation, carotenoids biosynthesis, and others. In addition, three predicted HPs are possibly related to arsenic tolerance. Through an in vitro assay, we verified that E. antarcticum B7 can grow at high concentrations of this metal. The approach used was important to precisely assign function to proteins from diverse classes and to infer relationships with proteins with functions already described in the literature. This approach aims to produce a better understanding of the mechanism by which this bacterium adapts to extreme environments and to the finding of targets with biotechnological interest.

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          Prediction of protein subcellular localization.

          Because the protein's function is usually related to its subcellular localization, the ability to predict subcellular localization directly from protein sequences will be useful for inferring protein functions. Recent years have seen a surging interest in the development of novel computational tools to predict subcellular localization. At present, these approaches, based on a wide range of algorithms, have achieved varying degrees of success for specific organisms and for certain localization categories. A number of authors have noticed that sequence similarity is useful in predicting subcellular localization. For example, Nair and Rost (Protein Sci 2002;11:2836-2847) have carried out extensive analysis of the relation between sequence similarity and identity in subcellular localization, and have found a close relationship between them above a certain similarity threshold. However, many existing benchmark data sets used for the prediction accuracy assessment contain highly homologous sequences-some data sets comprising sequences up to 80-90% sequence identity. Using these benchmark test data will surely lead to overestimation of the performance of the methods considered. Here, we develop an approach based on a two-level support vector machine (SVM) system: the first level comprises a number of SVM classifiers, each based on a specific type of feature vectors derived from sequences; the second level SVM classifier functions as the jury machine to generate the probability distribution of decisions for possible localizations. We compare our approach with a global sequence alignment approach and other existing approaches for two benchmark data sets-one comprising prokaryotic sequences and the other eukaryotic sequences. Furthermore, we carried out all-against-all sequence alignment for several data sets to investigate the relationship between sequence homology and subcellular localization. Our results, which are consistent with previous studies, indicate that the homology search approach performs well down to 30% sequence identity, although its performance deteriorates considerably for sequences sharing lower sequence identity. A data set of high homology levels will undoubtedly lead to biased assessment of the performances of the predictive approaches-especially those relying on homology search or sequence annotations. Our two-level classification system based on SVM does not rely on homology search; therefore, its performance remains relatively unaffected by sequence homology. When compared with other approaches, our approach performed significantly better. Furthermore, we also develop a practical hybrid method, which combines the two-level SVM classifier and the homology search method, as a general tool for the sequence annotation of subcellular localization.
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            Biofilms: the matrix revisited.

            Microbes often construct and live within surface-associated multicellular communities known as biofilms. The precise structure, chemistry and physiology of the biofilm all vary with the nature of its resident microbes and local environment. However, an important commonality among biofilms is that their structural integrity critically depends upon an extracellular matrix produced by their constituent cells. Extracellular matrices might be as diverse as biofilms, and they contribute significantly to the organization of the community. This review discusses recent advances in our understanding of the extracellular matrix and its role in biofilm biology.
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              Protein structure homology modeling using SWISS-MODEL workspace.

              Homology modeling aims to build three-dimensional protein structure models using experimentally determined structures of related family members as templates. SWISS-MODEL workspace is an integrated Web-based modeling expert system. For a given target protein, a library of experimental protein structures is searched to identify suitable templates. On the basis of a sequence alignment between the target protein and the template structure, a three-dimensional model for the target protein is generated. Model quality assessment tools are used to estimate the reliability of the resulting models. Homology modeling is currently the most accurate computational method to generate reliable structural models and is routinely used in many biological applications. Typically, the computational effort for a modeling project is less than 2 h. However, this does not include the time required for visualization and interpretation of the model, which may vary depending on personal experience working with protein structures.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: VisualizationRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: MethodologyRole: ResourcesRole: ValidationRole: Visualization
                Role: Data curationRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: ResourcesRole: Software
                Role: Data curationRole: MethodologyRole: VisualizationRole: Writing – review & editing
                Role: Funding acquisitionRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                25 June 2018
                2018
                : 13
                : 6
                : e0198965
                Affiliations
                [1 ] Laboratory of Genomic and Bioinformatics, Center of Genomics and System Biology, Institute of Biological Science, Federal University of Para, Belém, Pará, Brazil
                [2 ] Biotechnology Center, Federal University of Paraiba, João Pessoa, Paraíba, Brazil
                [3 ] Biology Department & CESAM, University of Aveiro, Aveiro, Portugal
                Boston University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-5243-7891
                http://orcid.org/0000-0003-1522-9871
                Article
                PONE-D-18-01222
                10.1371/journal.pone.0198965
                6016940
                29940001
                174fffe1-9799-4d61-a6ba-c91fe8d12201
                © 2018 da Costa et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 12 January 2018
                : 28 May 2018
                Page count
                Figures: 4, Tables: 2, Pages: 28
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100002322, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior;
                Award ID: 88881.068052/2014-01
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001871, Fundação para a Ciência e a Tecnologia;
                Award ID: UID/AMB/50017/2013 - POCI-01-0145-FEDER-007638
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001871, Fundação para a Ciência e a Tecnologia;
                Award ID: FCT Investigator Programme – IF/00492/2013
                Award Recipient :
                Authors thank the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES (88881.068052/2014-01) and PROPESP/UFPA for the financial support on this study. We also acknowledge FCT (Fundação para a Ciência e Tecnologia, Portugal) financing to CESAM (UID/AMB/50017/2013 - POCI-01-0145-FEDER-007638) and Isabel Henriques (FCT Investigator Programme – IF/00492/2013). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Domains
                Physical Sciences
                Chemistry
                Chemical Elements
                Arsenic
                Biology and life sciences
                Biochemistry
                Proteins
                DNA-binding proteins
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Interactions
                Biology and Life Sciences
                Genetics
                Gene Expression
                Gene Regulation
                Computer and Information Sciences
                Network Analysis
                Protein Interaction Networks
                Biology and Life Sciences
                Biochemistry
                Proteomics
                Protein Interaction Networks
                Research and Analysis Methods
                Database and Informatics Methods
                Biological Databases
                Sequence Databases
                Research and Analysis Methods
                Database and Informatics Methods
                Bioinformatics
                Sequence Analysis
                Sequence Databases
                Biology and Life Sciences
                Microbiology
                Bacteriology
                Bacterial Biofilms
                Biology and Life Sciences
                Microbiology
                Biofilms
                Bacterial Biofilms
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

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