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dc.contributor.authorGandhi, Neha
dc.contributor.authorMancera, Ricardo
dc.date.accessioned2017-01-30T12:57:41Z
dc.date.available2017-01-30T12:57:41Z
dc.date.created2009-03-05T00:55:49Z
dc.date.issued2008
dc.identifier.citationGandhi, Neha and Mancera, Ricardo. 2008. Computational Methods for the Prediction of the Structure and Interactions of Coiled-Coil Peptides. Current Bioinformatics 3: pp. 149-161.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/27216
dc.description.abstract

The past several years have seen significant advances in the development of computational methods for the prediction of the structure and interactions of coiled-coil peptides. These methods are generally based on pairwise correlations of amino acids, helical propensity, thermal melts and the energetics of sidechain interactions, as well as statistical patterns based on Hidden Markov Model (HMM) and Support Vector Machine (SVM) techniques. These methods are complemented by a number of public databases that contain sequences, motifs, domains and other details of coiled-coil structures identified by various algorithms. Some of these computational methods have been developed to make predictions of coiled-coil structure on the basis of sequence information; however, structural predictions of the oligomerisation state of these peptides still remains largely an open question due to the dynamic behaviour of these molecules. This review focuses on existing in silico methods for the prediction of coiled-coil peptides of functional importance using sequenceand/or three-dimensional structural data.

dc.publisherBentham Science Publishers Limited
dc.titleComputational Methods for the Prediction of the Structure and Interactions of Coiled-Coil Peptides
dc.typeJournal Article
dcterms.source.volume3
dcterms.source.startPage149
dcterms.source.endPage161
dcterms.source.issn15748936
dcterms.source.titleCurrent Bioinformatics
curtin.accessStatusFulltext not available
curtin.facultyFaculty of Health Sciences
curtin.facultySchool of Biomedical Sciences


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