Linked Life Data

11021970

Source:http://linkedlifedata.com/resource/pubmed/id/11021970

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2000-10-25
pubmed:abstractText
The increasing number and diversity of protein sequence families requires new methods to define and predict details regarding function. Here, we present a method for analysis and prediction of functional sub-types from multiple protein sequence alignments. Given an alignment and set of proteins grouped into sub-types according to some definition of function, such as enzymatic specificity, the method identifies positions that are indicative of functional differences by comparison of sub-type specific sequence profiles, and analysis of positional entropy in the alignment. Alignment positions with significantly high positional relative entropy correlate with those known to be involved in defining sub-types for nucleotidyl cyclases, protein kinases, lactate/malate dehydrogenases and trypsin-like serine proteases. We highlight new positions for these proteins that suggest additional experiments to elucidate the basis of specificity. The method is also able to predict sub-type for unclassified sequences. We assess several variations on a prediction method, and compare them to simple sequence comparisons. For assessment, we remove close homologues to the sequence for which a prediction is to be made (by a sequence identity above a threshold). This simulates situations where a protein is known to belong to a protein family, but is not a close relative of another protein of known sub-type. Considering the four families above, and a sequence identity threshold of 30 %, our best method gives an accuracy of 96 % compared to 80 % obtained for sequence similarity and 74 % for BLAST. We describe the derivation of a set of sub-type groupings derived from an automated parsing of alignments from PFAM and the SWISSPROT database, and use this to perform a large-scale assessment. The best method gives an average accuracy of 94 % compared to 68 % for sequence similarity and 79 % for BLAST. We discuss implications for experimental design, genome annotation and the prediction of protein function and protein intra-residue distances.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0022-2836
pubmed:author
pubmed:copyrightInfo
Copyright 2000 Academic Press.
pubmed:issnType
Print
pubmed:day
13
pubmed:volume
303
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
61-76
pubmed:dateRevised
2009-11-19
pubmed:meshHeading
pubmed-meshheading:11021970-Adenylate Cyclase, pubmed-meshheading:11021970-Algorithms, pubmed-meshheading:11021970-Amino Acid Sequence, pubmed-meshheading:11021970-Animals, pubmed-meshheading:11021970-Computational Biology, pubmed-meshheading:11021970-Databases as Topic, pubmed-meshheading:11021970-Entropy, pubmed-meshheading:11021970-Guanylate Cyclase, pubmed-meshheading:11021970-Humans, pubmed-meshheading:11021970-L-Lactate Dehydrogenase, pubmed-meshheading:11021970-Malate Dehydrogenase, pubmed-meshheading:11021970-Models, Molecular, pubmed-meshheading:11021970-Molecular Sequence Data, pubmed-meshheading:11021970-Protein Conformation, pubmed-meshheading:11021970-Protein Kinases, pubmed-meshheading:11021970-Proteins, pubmed-meshheading:11021970-Sensitivity and Specificity, pubmed-meshheading:11021970-Sequence Alignment, pubmed-meshheading:11021970-Serine Endopeptidases, pubmed-meshheading:11021970-Software, pubmed-meshheading:11021970-Structure-Activity Relationship, pubmed-meshheading:11021970-Substrate Specificity
pubmed:year
2000
pubmed:articleTitle
Analysis and prediction of functional sub-types from protein sequence alignments.
pubmed:affiliation
Bioinformatics Research Group, SmithKline Beecham Pharmaceuticals Research & Development, 709 Swedeland Road, King of Prussia, PA 19406, USA.
pubmed:publicationType
Journal Article