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  1. Article ; Online: Order in Disorder as Observed by the "Hydrophobic Cluster Analysis" of Protein Sequences.

    Bitard-Feildel, Tristan / Lamiable, Alexis / Mornon, Jean-Paul / Callebaut, Isabelle

    Proteomics

    2018  Volume 18, Issue 21-22, Page(s) e1800054

    Abstract: Hydrophobic cluster analysis (HCA) is an original approach for protein sequence analysis ... disordered regions (IDRs) undergoing disorder to order transitions. In this review, how HCA can be used ... which provides access to the foldable repertoire of the protein universe, including yet unannotated protein ...

    Abstract Hydrophobic cluster analysis (HCA) is an original approach for protein sequence analysis, which provides access to the foldable repertoire of the protein universe, including yet unannotated protein segments ("dark proteome"). Foldable segments correspond to ordered regions, as well as to intrinsically disordered regions (IDRs) undergoing disorder to order transitions. In this review, how HCA can be used to give insight into this last category of foldable segments is illustrated, with examples matching known 3D structures. After reviewing the HCA principles, examples of short foldable segments are given, which often contain short linear motifs, typically matching hydrophobic clusters. These segments become ordered upon contact with partners, with secondary structure preferences generally corresponding to those observed in the 3D structures within the complexes. Such small foldable segments are sometimes larger than the segments of known 3D structures, including flanking hydrophobic clusters that may be critical for interaction specificity or regulation, as well as intervening sequences allowing fuzziness. Cases of larger conditionally disordered domains are also presented, with lower density in hydrophobic clusters than well-folded globular domains or with exposed hydrophobic patches, which are stabilized by interaction with partners.
    MeSH term(s) Cluster Analysis ; Hydrophobic and Hydrophilic Interactions ; Protein Structure, Secondary ; Sequence Analysis, Protein/methods
    Keywords covid19
    Language English
    Publishing date 2018-10-30
    Publishing country Germany
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 2032093-0
    ISSN 1615-9861 ; 1615-9853
    ISSN (online) 1615-9861
    ISSN 1615-9853
    DOI 10.1002/pmic.201800054
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Protein disorder prediction by condensed PSSM considering propensity for order or disorder.

    Su, Chung-Tsai / Chen, Chien-Yu / Ou, Yu-Yen

    BMC bioinformatics

    2006  Volume 7, Page(s) 319

    Abstract: ... than their parent properties in predicting protein disorder. In order to get an effective and compact feature set ... Distinguishing disordered regions from ordered regions in protein sequences facilitates the exploration ... disorder. As more and more machine learning techniques have been introduced to protein disorder detection ...

    Abstract Background: More and more disordered regions have been discovered in protein sequences, and many of them are found to be functionally significant. Previous studies reveal that disordered regions of a protein can be predicted by its primary structure, the amino acid sequence. One observation that has been widely accepted is that ordered regions usually have compositional bias toward hydrophobic amino acids, and disordered regions are toward charged amino acids. Recent studies further show that employing evolutionary information such as position specific scoring matrices (PSSMs) improves the prediction accuracy of protein disorder. As more and more machine learning techniques have been introduced to protein disorder detection, extracting more useful features with biological insights attracts more attention.
    Results: This paper first studies the effect of a condensed position specific scoring matrix with respect to physicochemical properties (PSSMP) on the prediction accuracy, where the PSSMP is derived by merging several amino acid columns of a PSSM belonging to a certain property into a single column. Next, we decompose each conventional physicochemical property of amino acids into two disjoint groups which have a propensity for order and disorder respectively, and show by experiments that some of the new properties perform better than their parent properties in predicting protein disorder. In order to get an effective and compact feature set on this problem, we propose a hybrid feature selection method that inherits the efficiency of uni-variant analysis and the effectiveness of the stepwise feature selection that explores combinations of multiple features. The experimental results show that the selected feature set improves the performance of a classifier built with Radial Basis Function Networks (RBFN) in comparison with the feature set constructed with PSSMs or PSSMPs that adopt simply the conventional physicochemical properties.
    Conclusion: Distinguishing disordered regions from ordered regions in protein sequences facilitates the exploration of protein structures and functions. Results based on independent testing data reveal that the proposed predicting model DisPSSMP performs the best among several of the existing packages doing similar tasks, without either under-predicting or over-predicting the disordered regions. Furthermore, the selected properties are demonstrated to be useful in finding discriminating patterns for order/disorder classification.
    MeSH term(s) Amino Acid Sequence ; Cluster Analysis ; Computer Simulation ; Databases, Protein ; Models, Molecular ; Molecular Sequence Data ; Proteins/chemistry ; Sequence Analysis, Protein ; Software
    Chemical Substances Proteins
    Language English
    Publishing date 2006-06-23
    Publishing country England
    Document type Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2041484-5
    ISSN 1471-2105 ; 1471-2105
    ISSN (online) 1471-2105
    ISSN 1471-2105
    DOI 10.1186/1471-2105-7-319
    Database MEDical Literature Analysis and Retrieval System OnLINE

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