@article {19716,
	title = {TIGRFAMs and Genome Properties in 2013.},
	journal = {Nucleic Acids Res},
	volume = {41},
	year = {2013},
	month = {2013 Jan},
	pages = {D387-95},
	abstract = {<p>TIGRFAMs, available online at http://www.jcvi.org/tigrfams is a database of protein family definitions. Each entry features a seed alignment of trusted representative sequences, a hidden Markov model (HMM) built from that alignment, cutoff scores that let automated annotation pipelines decide which proteins are members, and annotations for transfer onto member proteins. Most TIGRFAMs models are designated equivalog, meaning they assign a specific name to proteins conserved in function from a common ancestral sequence. Models describing more functionally heterogeneous families are designated subfamily or domain, and assign less specific but more widely applicable annotations. The Genome Properties database, available at http://www.jcvi.org/genome-properties, specifies how computed evidence, including TIGRFAMs HMM results, should be used to judge whether an enzymatic pathway, a protein complex or another type of molecular subsystem is encoded in a genome. TIGRFAMs and Genome Properties content are developed in concert because subsystems reconstruction for large numbers of genomes guides selection of seed alignment sequences and cutoff values during protein family construction. Both databases specialize heavily in bacterial and archaeal subsystems. At present, 4284 models appear in TIGRFAMs, while 628 systems are described by Genome Properties. Content derives both from subsystem discovery work and from biocuration of the scientific literature.</p>},
	keywords = {Databases, Protein, Genome, Archaeal, Genome, Bacterial, Genomics, Internet, Markov chains, Molecular Sequence Annotation, Proteins, sequence alignment},
	issn = {1362-4962},
	doi = {10.1093/nar/gks1234},
	author = {Haft, Daniel H and Jeremy D Selengut and Richter, Roland A and Harkins, Derek and Basu, Malay K and Beck, Erin}
}
@article {14594,
	title = {Analysis and prediction of functional sub-types from protein sequence alignments},
	journal = {Journal of Molecular Biology},
	volume = {303},
	year = {2000},
	month = {2000/10/13/},
	pages = {61 - 76},
	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},
	doi = {10.1006/jmbi.2000.4036},
	url = {http://www.sciencedirect.com/science/article/pii/S0022283600940361},
	author = {Hannenhalli, Sridhar and Russell,Robert B.}
}
