Computational annotation of protein function

Proceedings of the National Academy of Sciences, 2002

Bioinformatics, 2000

Sequence determination and analysis began on proteins in the 1950s, with RNA starting about a decade later and DNA a similar period later still. Hence many of the concepts for function prediction were first developed by looking at amino acid sequences. Over time these methods have become much more sophisticated, allowing better discrimination of only weak similarities. The most recent developments concern an examination of contextual information, such as operon structure, metabolic reconstruction or co-expression profiles.

Journal of Molecular Biology, 1998

Predicting' function from sequence using computational tools is a highly complicated procedure that is generally done for each gene individually. This review focuses on the added value that is provided by completely sequenced genomes in function prediction. Various levels of sequence annotation and function prediction are discussed, raneing from genomic sequence to that of complex cellular processes. Protern function is currently best described in the context of molecular interactions. In the near future it will be possible to predict protein function in the context of higher order processes such as the regulation of gene expression, metabolic pathways and signalling cascades. The analysis of such higher levels of function description uses, besides the information from completely sequenced genomes, also the additional information from proteomics and expression data. The final goal w i l l be to elucidate the mapping between genotype and phenotype.

Structure, 2005

Debnath Pal and David Eisenberg* teractions between these functions form the basis for sustainable homeostasis. These multiple levels of func-UCLA-DOE Institute for Genomics and Proteomics tion are reflected in our procedure, described below, of Howard Hughes Medical Institute linking protein features to annotations at various levels. Box 951570 The repertoire of methods for in silico annotation of Los Angeles, California 90095 function has grown enormously over the past two decades. A protein with a high degree of sequence similarity to a family of well-characterized proteins can be Summary detected by BLAST (Altschul et al., 1990). With lower sequence similarity, more subtle methods such as "pro-Structural genomics has brought us three-dimensional files" (where patterns obvious from multiple sequence structures of proteins with unknown functions. To shed alignment are evident) (Altschul et al., 1997; Bork and light on such structures, we have developed ProKnow Gibson, 1996; Gribskov et al., 1987) or hidden Markov (http://www.doe-mbi.ucla.edu/Services/ProKnow/), which models (HMM) (Eddy et al., 1995) are required. These annotates proteins with Gene Ontology functional methods are based on the assumption that similar seterms. The method extracts features from the protein quences have descended from a common ancestor such as 3D fold, sequence, motif, and functional linkand share similar function. The assumption is, howages and relates them to function via the ProKnow ever, limited in validity, as demonstrated by numerous knowledgebase of features, which links features to studies (Devos and Valencia, 2000; Gerlt and Babbitt, annotated functions via annotation profiles. Bayes' 2000; Karp, 1998; Rost, 2002; Rost et al., 2003; Rost theorem is used to compute weights of the functions and Valencia, 1996; Tian and Skolnick, 2003; Whisstock assigned, using likelihoods based on the extracted and Lesk, 2003). To enhance accuracy of functional asfeatures. The description level of the assigned funcsignment, functional annotations can be inferred from tion is quantified by the ontology depth (from 1 = information on fold (Bowie et al., 1991; Holm and general to 9 = specific). Jackknife tests show 89% Sander, 1998; Jones et al., 1992), motif (Attwood et al., correct assignments at ontology depth 1 and 40% at 2003; Henikoff et al., 2000; Hulo et al., 2004), domain depth 9, with 93% coverage of 1507 distinct folded (Bateman et al., 2004), and orthology (Tatusov et al., proteins. Overall, about 70% of the assignments were 1997). Another class of annotation algorithms infers inferred correctly. This level of performance suggests protein function based on identification of functionally that ProKnow is a useful resource in functional assignificant residues. This class includes biodictionary sessments of novel proteins. "seqlets" mapping sequence patterns to their properties (Rigoutsos et al., 2002), evolutionary tracing (Land

Protein Science, 2006