@article {38110,
	title = {Analysis and prediction of functional sub-types from protein sequence alignments},
	journal = {Journal of Molecular BiologyJournal of Molecular Biology},
	volume = {303},
	year = {2000},
	type = {10.1006/jmbi.2000.4036},
	abstract = {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.},
	keywords = {prediction, protein function, protein structure, sequence alignment},
	isbn = {0022-2836},
	author = {Sridhar Hannenhalli and Russell, Robert B.}
}
