TMFoldRec: a statistical potential-based transmembrane protein fold recognition tool
نویسندگان
چکیده
منابع مشابه
Transmembrane Protein Alignment and Fold Recognition Based on Predicted Topology
BACKGROUND Although Transmembrane Proteins (TMPs) are highly important in various biological processes and pharmaceutical developments, general prediction of TMP structures is still far from satisfactory. Because TMPs have significantly different physicochemical properties from soluble proteins, current protein structure prediction tools for soluble proteins may not work well for TMPs. With the...
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Protein fold recognition is an important problem in molecular biology. Machine learning symbolic approaches have been applied to automatically discover local structural signatures and relate these to the concept of fold in SCOP. However, most of these methods cannot handle uncertainty being therefore not able to solve multiple prediction problems. In this paper we present an application of the ...
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We present FORTE, a profile-profile comparison tool for protein fold recognition. Users can submit a protein sequence to explore the possibilities of structural similarity existing in known structures. Results are reported via email in the form of pairwise alignments.
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The need to perform large-scale studies of protein fold recognition, structure prediction and protein-protein interactions has led to novel developments of residue-level minimal models of proteins. A minimum requirement for useful protein force-fields is that they be successful in the recognition of native conformations. The balance between the level of detail in describing the specific interac...
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In fold recognition by threading one takes the amino acid sequence of a protein and evaluates how well it fits into one of the known three-dimensional (3D) protein structures. The quality of sequence-structure fit is typically evaluated using inter-residue potentials of mean force or other statistical parameters. Here, we present an alternative approach to evaluating sequence-structure fitness....
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ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2015
ISSN: 1471-2105
DOI: 10.1186/s12859-015-0638-5