Multitasking or moonlighting is the capability of some proteins to execute two or more biochemical functions. Usually, moonlighting proteins are experimentally revealed by serendipity. For this reason, it would be helpful that Bioinformatics could predict this multifunctionality, especially because of the large amounts of sequences from genome projects. In the present work, we analyze and describe several approaches that use sequences, structures, interactomics, and current bioinformatics algorithms and programs to try to overcome this problem. Among these approaches are (a) remote homology searches using Psi-Blast, (b) detection of functional motifs and domains, (c) analysis of data from protein-protein interaction databases (PPIs), (d) match the query protein sequence to 3D databases (i.e., algorithms as PISITE), and (e) mutation correlation analysis between amino acids by algorithms as MISTIC. Programs designed to identify functional motif/domains detect mainly the canonical function but usually fail in the detection of the moonlighting one, Pfam and ProDom being the best methods. Remote homology search by Psi-Blast combined with data from interactomics databases (PPIs) has the best performance. Structural information and mutation correlation analysis can help us to map the functional sites. Mutation correlation analysis can only be used in very specific situations - it requires the existence of multialigned family protein sequences - but can suggest how the evolutionary process of second function acquisition took place. The multitasking protein database MultitaskProtDB (http://wallace.uab.es/multitask/), previously published by our group, has been used as a benchmark for the all of the analyses.
Keywords: bioinformatics; moonlighting protein; multifunctional; multitasking; protein evolution; protein function.