Exploiting Protein Structures to Predict Protein Functions View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2011-03-29

AUTHORS

Alison Cuff , Oliver Redfern , Benoit Dessailly , Christine Orengo

ABSTRACT

The exponential growth of experimentally determined protein structures in the Protein Data Bank (PDB) has provided structural data for an ever increasing proportion of genomic sequences. In combination with enhanced functional annotation from sequence, it has become possible to predict protein function from structure. In this chapter we discuss a range of methods which aim to recognise enzyme active sites and predict protein-ligand interactions. We then focus on algorithms developed as part of the CATH database of structural domains, where an evolutionary approach is used to recognise proteins with similar functions. While protein domains that exhibit the same structural fold tend to display related functional activities, there are a several large domain structure superfamilies that show a high degree of functional diversity. In these cases, we have built novel tools (FLORA and GeMMA) which are able to effectively identify sub-families of functionally linked domains, where standard methods of homologue detection (e.g. sequence profile and global structure alignment) fail. More... »

PAGES

107-123

Book

TITLE

Protein Function Prediction for Omics Era

ISBN

978-94-007-0880-8
978-94-007-0881-5

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-94-007-0881-5_6

DOI

http://dx.doi.org/10.1007/978-94-007-0881-5_6

DIMENSIONS

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50 schema:description The exponential growth of experimentally determined protein structures in the Protein Data Bank (PDB) has provided structural data for an ever increasing proportion of genomic sequences. In combination with enhanced functional annotation from sequence, it has become possible to predict protein function from structure. In this chapter we discuss a range of methods which aim to recognise enzyme active sites and predict protein-ligand interactions. We then focus on algorithms developed as part of the CATH database of structural domains, where an evolutionary approach is used to recognise proteins with similar functions. While protein domains that exhibit the same structural fold tend to display related functional activities, there are a several large domain structure superfamilies that show a high degree of functional diversity. In these cases, we have built novel tools (FLORA and GeMMA) which are able to effectively identify sub-families of functionally linked domains, where standard methods of homologue detection (e.g. sequence profile and global structure alignment) fail.
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