Novel function discovery through sequence and structural data mining. (June 2016)

• Record Type: Journal Article
• Authors: Lobb, Briallen; Doxey, Andrew C
• Is Part Of: Current opinion in structural biology. Volume 38(2016)
• Page Start: 53
• Page End: 61
• Publication Date: 2016-06
• Subjects: Molecular biology -- Periodicals; 570
• DOI: 10.1016/j.sbi.2016.05.017
• Languages: English
• ISSNs: 0959-440X

Abstract

Highlights: Homology detection remains essential for many function prediction methods. Homologs found in unexpected places may reveal profound functional novelty. Untapped functional novelty lies within protein 'dark matter'. Existing families are also a significant source of new and diverse activities. State-of-the-art methods combine sequence, structural, evolutionary and genomic approaches. Abstract : Large-scale sequence and structural data is a goldmine of novel proteins, but how can this data be effectively mined for new functions? Here, we review protein function prediction methods and recent studies that apply these methods to discover new functionality. Core approaches include sequence-based homology detection, phylogenetic analysis, structural bioinformatics, and inference of functional associations using genomic context and related methods. With such a wide range of approaches, sequences may reveal new functionality regardless of their similarity to a characterized reference. Homologs of known function may be identified in unexpected species or associations. Detection of functional shifts in sequences may reveal new activities and specificities. New protein functions may also be predicted in uncharacterized sequences and structures. Finally, methods and data may be integrated and applied at increasingly large scales due to improved protein domain knowledge and structural coverage, which amplifies the ability to predict and discover novel protein functions.