Protein function prediction

Protein function prediction methods are techniques that bioinformatics researchers use to assign biological or biochemical roles to proteins. These proteins are usually ones that are poorly studied or predicted based on genomic sequence data. These predictions are often driven by data-intensive computational procedures. Information may come from nucleic acid sequence homology, gene expression profiles, protein domain structures, text mining of publications, phylogenetic profiles, phenotypic profiles, and protein-protein interaction. Protein function is a broad term: the roles of proteins range from catalysis of biochemical reactions to transport to signal transduction, and a single protein may play a role in multiple processes or cellular pathways. Generally, function can be thought of as, "anything that happens to or through a protein". The Gene Ontology Consortium provides a useful classification of functions, based on a dictionary of well-defined terms divided into three main categories of molecular function, biological process and cellular component. Researchers can query this database with a protein name or accession number to retrieve associated Gene Ontology (GO) terms or annotations based on computational or experimental evidence. While techniques such as microarray analysis, RNA interference, and the yeast two-hybrid system can be used to experimentally demonstrate the function of a protein, advances in sequencing technologies have made the rate at which proteins can be experimentally characterized much slower than the rate at which new sequences become available. Thus, the annotation of new sequences is mostly by prediction through computational methods, as these types of annotation can often be done quickly and for many genes or proteins at once. The first such methods inferred function based on homologous proteins with known functions (homology-based function prediction). The development of context-based and structure based methods have expanded what information can be predicted, and a combination of methods can now be used to get a picture of complete cellular pathways based on sequence data. The importance and prevalence of computational prediction of gene function is underlined by an analysis of 'evidence codes' used by the GO database: as of 2010, 98% of annotations were listed under the code IEA (inferred from electronic annotation) while only 0.6% were based on experimental evidence.

Homology-based methods

Proteins of similar sequence are usually homologous and thus have a similar function. Hence proteins in a newly sequenced genome are routinely annotated using the sequences of similar proteins in related genomes. However, closely related proteins do not always share the same function. For example, the yeast Gal1 and Gal3 proteins are paralogs (73% identity and 92% similarity) that have evolved very different functions with Gal1 being a galactokinase and Gal3 being a transcriptional inducer. There is no hard sequence-similarity threshold for "safe" function prediction; many proteins of barely detectable sequence similarity have the same function while others (such as Gal1 and Gal3) are highly similar but have evolved different functions. As a rule of thumb, sequences that are more than 30-40% identical are usually considered as having the same or a very similar function. For enzymes, predictions of specific functions are especially difficult, as they only need a few key residues in their active site, hence very different sequences can have very similar activities. By contrast, even with sequence identity of 70% or greater, 10% of any pair of enzymes have different substrates; and differences in the actual enzymatic reactions are not uncommon near 50% sequence identity.

Sequence motif-based methods The development of protein domain databases such as Pfam (Protein Families Database) allow us to find known domains within a query sequence, providing evidence for likely functions. The dcGO website contains annotations to both the individual domains and supra-domains (i.e., combinations of two or more successive domains), thus via dcGO Predictor allowing for the function predictions in a more realistic manner. Within protein domains, shorter signatures known as 'motifs' are associated with particular functions, and motif databases such as PROSITE ('database of protein domains, families and functional sites') can be searched using a query sequence. Motifs can, for example, be used to predict subcellular localization of a protein (where in the cell the protein is sent after synthesis). Short signal peptides direct certain proteins to a particular location such as the mitochondria, and various tools exist for the prediction of these signals in a protein sequence. For example, SignalP, which has been updated several times as methods are improved. Thus, aspects of a protein's function can be predicted without comparison to other full-length homologous protein sequences.

Structure-based methods

Because 3D protein structure is generally more well conserved than protein sequence, structural similarity is a good indicator of similar function in two or more proteins. Many programs have been developed to screen a known protein structure against the Protein Data Bank and report similar structures (for example, FATCAT (Flexible structure AlignmenT by Chaining AFPs (Aligned Fragment Pairs) with Twists), CE (combinatorial extension)) and DeepAlign (protein structure alignment beyond spatial proximity). Similarly, the main protein databases, such as UniProt, have built-in tools to search any given protein sequences against structure databases, and link to related proteins of known structure.

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