Computational Approaches for Enzyme Functional Class Prediction: A Review
Mahesh Sharma and Prabha Garg
Affiliation: Computer Centre, National Institute of Pharmaceutical Education and Research (NIPER), Sector-67, S.A.S. Nagar, Punjab-160062, India.
Numerous genome sequence projects of various organisms have resulted in generation of large amount of data
on genes and proteins sequence information. Functional annotation of these proteins is important to bridge gap between
sequence information and functional characterization. As experimental approaches for characterizing the functional class
of an enzyme are expensive and time consuming, computational prediction methods are an effective alternative. Various
approaches like homology-based function transfer and machine learning methods have been utilized for in silico enzyme
functional classifications in terms of Enzyme Commission number (EC number) for a protein. Different types of features
have been used in various machine learning techniques and each has its own advantages and limitations. The critical
evaluation of performance measure in terms of predictive ability of these methods is necessary. Here, a systematic review
on the various approaches used by different research groups, their utility and inference is presented.
Keywords: Bioinformatics, chemoinformatics, computational function prediction, enzyme commission number, functional
annotation, machine learning methods.
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