Phylogenomic Inference of Protein Molecular Function

Abstract

With the explosion in sequence data, accurate prediction of protein function has become a vital task in prioritizing experimental investigation. While computationally efficient methods for homology‐based function prediction have been developed to make this approach feasible in high‐throughput mode, it is not without its dangers. Biological processes such as gene duplication, domain shuffling, and speciation produce families of related genes whose gene products can have vastly different molecular functions. Standard sequence‐comparison approaches may not discriminate effectively among these candidate homologs, leading to errors in database annotations. In this unit, we describe phylogenomic approaches to reduce the error rate in function prediction. Phylogenomic inference of protein molecular function consists of a series of subtasks. Once a cluster of homologs is identified, a multiple sequence alignment and phylogenetic tree are constructed. Finally, the phylogenetic tree is overlaid with experimental data culled for the members of the family, and changes in biochemical function can be traced along the evolutionary tree.

Classification of the short‐chain dehydrogenase/reductase superfamily using hidden Markov models

“…Another approach uses evolutionary trees [17] to achieve subfamily classification. In comparison with the method presented herein, this approach arrives at much more fine‐grained families; for example, our 3β‐hydroxysteroid dehydrogenase family is split into eight subfamilies, and our family with retinol dehydrogenases is split into as many as 19 subfamilies.…”

Pathway Projector: Web-Based Zoomable Pathway Browser Using KEGG Atlas and Google Maps API

“…The pathway map has been made clickable by sending the coordinate information and asynchronously retrieving related information upon the user mouse click event, which is displayed through the InfoWindow function of Google Maps API as an information window. The information window contains the retrieved annotation of each component, including the common name, identifier, structural formula or chemical equation, and links to external databases such as KEGG, PubChem [31], ChEBI [32], and MSDchem [33] for metabolites and, ExPASy [34], MetaCyc [35], Brenda [36], IntEnz [37], PUMA2 [38], and IUBMB [39] for enzymes (see Table S1 for a complete listing for organism-specific database references). Although the default reference pathway only contains external links to enzymes, when an organism specific pathway map is opened from the “Organism Selection” tab, the information window on the nodes also shows gene-centric links to suitable databases for the organism ( e.g.…”