A heuristic algorithm for associating Gene Ontology (GO) defined molecular functions to protein domains as listed in the ProDom and CDD databases is described. The algorithm generates rules for function-domain associations based on the intersection of functions assigned to gene products by the GO consortium that contain ProDom and/or CDD domains at varying levels of sequence similarity. The hierarchical nature of GO molecular functions is incorporated into rule generation. Manual review of a subset of the rules generated indicates an accuracy rate of 87% for ProDom rules and 84% for CDD rules. The utility of these associations is that novel sequences can be assigned a putative function if sufficient similarity exists to a ProDom or CDD domain for which one or more GO functions has been associated. Although functional assignments are increasingly being made for gene products from model organisms, it is likely that the needs of investigators will continue to outpace the efforts of curators, particularly for nonmodel organisms. A comparison with other methods in terms of coverage and agreement was performed, indicating the utility of the approach. The domain-function associations and function assignments are available from our website http://www.cbil.upenn.edu/GO. An important early step in the postgenomic era is the characterization of the biochemical functions of gene products. Accurate computational predictions are a useful resource for both the community at large and the curators that eventually assign function to gene products. The Gene Ontology (GO) (Gene Ontology Consortium 2001) is an ontology, that is, a database of agreed-to terms for molecular functions, biological processes and cellular components. GO also includes relationships between terms such as specialization or part-whole relations. GO was developed to facilitate effective use of this information. We present an automatic method for leveraging curated GO function annotation of proteins to associate GO terms with protein domains that can then be applied to proteins that contain any of the domains. We make the assumption that the functions that a protein is capable of performing are determined by the protein domains that it contains. We use the simplest possible model of this kind; each domain contributes a function independently of any other domain in the protein. The basis of the algorithm, illustrated in Figure 1 , is to identify, using an intersection procedure, the GO functions common to a set of proteins that each contain a domain. We determine whether or not a protein has a domain using sequence similarity. As part of the process of associating GO functions with a domain, we determine a p-value threshold for each domain that indicates the level of similarity needed to confer function. As demonstrated by Hegyi and Gerstein (2001) , this is an important consideration. We contrast this approach with the comparison of entire proteins where similarities may be found between regions that are not responsible for the transferred annotation (Myers et al. 2000) . An evaluation of all sequences assigned to a function is needed to ascertain the essential sequence features of the function (Henikoff and Henikoff 1994; Bateman et al. 1999; Corpet et al. 1999; Hofmann et al. 1999) . The GO function ontology consists of function classifications arranged in a directed acyclic graph, that is, a parentchild hierarchy where components may belong to more than one component but no component can be its own descendant. The top level has general terms such as enzyme and nucleic acid binding; more specific terms are located deeper in the hierarchy. The length of a path to a term is termed its depth. The terms in a path to a term are called the ancestors of the term. A term is a leaf if it is at the end of a path. Our method takes advantage of the structure of the GO ontology to make only as specific a prediction as is consistent with the data. We chose the ProDom (Corpet et al. 1999) and CDD (Bryant and NCBI Structure Group 2001) databases as our initial sources of domain models. ProDom contains protein signatures determined from sequences organized into classes of functional domains by sequence clustering. The Conserved Domain Database (CDD) is comprised of a mirror of two major domain databases, the Simple Modular Architecture Research Tool (SMART) (Schultz et al. 2000) and Pfam (Bateman et al. 2000); it also includes entries from the Library Of Ancient Domains (LOAD), maintained by I. Aravind, E. Koonin, and colleagues at the National Center for Biotechnology Information. We applied our algorithm (Fig. 2)
An alternative approach uses sequence similarity and protein domain data in order to predict functional annotations REF .
Predicting Gene Ontology Functions from ProDom and CDD Protein Domains
Genome research
Genome research
Biology Medicine