Format

Send to

Choose Destination
Curr Opin Chem Biol. 2017 Apr;37:89-96. doi: 10.1016/j.cbpa.2017.01.021. Epub 2017 Feb 21.

Computational tools for enzyme improvement: why everyone can - and should - use them.

Author information

1
Département de biochimie and Center for Green Chemistry and Catalysis (CGCC), Université de Montréal, Montréal, QC H3T 1J4, Canada; PROTEO, The Québec Network for Research on Protein Function, Engineering and Applications, Québec, QC G1V 0A6, Canada.
2
Département de biochimie and Center for Green Chemistry and Catalysis (CGCC), Université de Montréal, Montréal, QC H3T 1J4, Canada; PROTEO, The Québec Network for Research on Protein Function, Engineering and Applications, Québec, QC G1V 0A6, Canada; Département de chimie, Université de Montréal, Montréal, QC H3T 1J4, Canada. Electronic address: joelle.pelletier@umontreal.ca.

Abstract

This review presents computational methods that experimentalists can readily use to create smart libraries for enzyme engineering and to obtain insights into protein-substrate complexes. Computational tools have the reputation of being hard to use and inaccurate compared to experimental methods in enzyme engineering, yet they are essential to probe datasets of ever-increasing size and complexity. In recent years, bioinformatics groups have made a huge leap forward in providing user-friendly interfaces and accurate algorithms for experimentalists. These methods guide efficient experimental planning and allow the enzyme engineer to rationalize time and resources. Computational tools nevertheless face challenges in the realm of transient modern technology.

PMID:
28231515
DOI:
10.1016/j.cbpa.2017.01.021
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center