Novel function discovery through sequence and structural data mining

被引:26
|
作者
Lobb, Briallen [1 ]
Doxey, Andrew C. [1 ]
机构
[1] Univ Waterloo, Dept Biol, 200 Univ Ave West, Waterloo, ON N2L 3G1, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
PROTEIN-PROTEIN INTERACTIONS; LARGE-SCALE; LINEAR MOTIFS; COMPLETE NITRIFICATION; STRUCTURE PREDICTION; ENZYME; EVOLUTION; SPECIFICITY; BACTERIA; SURFACE;
D O I
10.1016/j.sbi.2016.05.017
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:53 / 61
页数:9
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