Computational approaches to selecting and optimising targets for structural biology

Ian M. Overton, Geoffrey J. Barton

    Research output: Contribution to journalArticle

    6 Citations (Scopus)

    Abstract

    Selection of protein targets for study is central to structural biology and may be influenced by numerous factors. A key aim is to maximise returns for effort invested by identifying proteins with the balance of biophysical properties that are conducive to success at all stages (e.g. solubility, crystallisation) in the route towards a high resolution structural model. Selected targets can be optimised through construct design (e.g. to minimise protein disorder), switching to a homologous protein, and selection of experimental methodology (e.g. choice of expression system) to prime for efficient progress through the structural proteomics pipeline.

    Here we discuss computational techniques in target selection and optimisation, with more detailed focus on tools developed within the Scottish Structural Proteomics Facility (SSPF); namely XANNpred, ParCrys, OB-Score (target selection) and Tar (target optimisation). TarO runs a large number of algorithms, searching for homologues and annotating the pool of possible alternative targets. This pool of putative homologues is presented in a ranked, tabulated format and results are also visualised as an automatically generated and annotated multiple sequence alignment. The target selection algorithms each predict the propensity of a selected protein target to progress through the experimental stages leading to diffracting crystals. This single predictor approach has advantages for target selection, when compared with an approach using two or more predictors that each predict for success at a single experimental stage. The tools described here helped SSPF achieve a high (21%) success rate in progressing cloned targets to diffraction-quality crystals. (C) 2011 Elsevier Inc. All rights reserved.

    Original languageEnglish
    Pages (from-to)311
    Number of pages9
    JournalJournal of Neuroscience Methods
    Volume55
    Issue number1
    DOIs
    Publication statusPublished - Sep 2011

    Keywords

    • Target selection
    • Crystallisation
    • Structural genomics
    • Structural biology
    • Bioinformatics
    • Construct design
    • MULTIPLE SEQUENCE ALIGNMENT
    • STRUCTURE PREDICTION SERVER
    • HIGH-THROUGHPUT
    • WEB SERVER
    • PROTEIN CRYSTALLIZATION
    • PHOSPHORYLATION SITES
    • THERMOTOGA-MARITIMA
    • INTRINSIC DISORDER
    • ESCHERICHIA-COLI
    • DRUG DISCOVERY

    Cite this

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    title = "Computational approaches to selecting and optimising targets for structural biology",
    abstract = "Selection of protein targets for study is central to structural biology and may be influenced by numerous factors. A key aim is to maximise returns for effort invested by identifying proteins with the balance of biophysical properties that are conducive to success at all stages (e.g. solubility, crystallisation) in the route towards a high resolution structural model. Selected targets can be optimised through construct design (e.g. to minimise protein disorder), switching to a homologous protein, and selection of experimental methodology (e.g. choice of expression system) to prime for efficient progress through the structural proteomics pipeline.Here we discuss computational techniques in target selection and optimisation, with more detailed focus on tools developed within the Scottish Structural Proteomics Facility (SSPF); namely XANNpred, ParCrys, OB-Score (target selection) and Tar (target optimisation). TarO runs a large number of algorithms, searching for homologues and annotating the pool of possible alternative targets. This pool of putative homologues is presented in a ranked, tabulated format and results are also visualised as an automatically generated and annotated multiple sequence alignment. The target selection algorithms each predict the propensity of a selected protein target to progress through the experimental stages leading to diffracting crystals. This single predictor approach has advantages for target selection, when compared with an approach using two or more predictors that each predict for success at a single experimental stage. The tools described here helped SSPF achieve a high (21{\%}) success rate in progressing cloned targets to diffraction-quality crystals. (C) 2011 Elsevier Inc. All rights reserved.",
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    language = "English",
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    Computational approaches to selecting and optimising targets for structural biology. / Overton, Ian M.; Barton, Geoffrey J.

    In: Journal of Neuroscience Methods, Vol. 55, No. 1, 09.2011, p. 311.

    Research output: Contribution to journalArticle

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    AU - Overton, Ian M.

    AU - Barton, Geoffrey J.

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    AB - Selection of protein targets for study is central to structural biology and may be influenced by numerous factors. A key aim is to maximise returns for effort invested by identifying proteins with the balance of biophysical properties that are conducive to success at all stages (e.g. solubility, crystallisation) in the route towards a high resolution structural model. Selected targets can be optimised through construct design (e.g. to minimise protein disorder), switching to a homologous protein, and selection of experimental methodology (e.g. choice of expression system) to prime for efficient progress through the structural proteomics pipeline.Here we discuss computational techniques in target selection and optimisation, with more detailed focus on tools developed within the Scottish Structural Proteomics Facility (SSPF); namely XANNpred, ParCrys, OB-Score (target selection) and Tar (target optimisation). TarO runs a large number of algorithms, searching for homologues and annotating the pool of possible alternative targets. This pool of putative homologues is presented in a ranked, tabulated format and results are also visualised as an automatically generated and annotated multiple sequence alignment. The target selection algorithms each predict the propensity of a selected protein target to progress through the experimental stages leading to diffracting crystals. This single predictor approach has advantages for target selection, when compared with an approach using two or more predictors that each predict for success at a single experimental stage. The tools described here helped SSPF achieve a high (21%) success rate in progressing cloned targets to diffraction-quality crystals. (C) 2011 Elsevier Inc. All rights reserved.

    KW - Target selection

    KW - Crystallisation

    KW - Structural genomics

    KW - Structural biology

    KW - Bioinformatics

    KW - Construct design

    KW - MULTIPLE SEQUENCE ALIGNMENT

    KW - STRUCTURE PREDICTION SERVER

    KW - HIGH-THROUGHPUT

    KW - WEB SERVER

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    KW - THERMOTOGA-MARITIMA

    KW - INTRINSIC DISORDER

    KW - ESCHERICHIA-COLI

    KW - DRUG DISCOVERY

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    SP - 311

    JO - Journal of Neuroscience Methods

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