Copyright ©The Author(s) 2015. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Med Genet. Aug 27, 2015; 5(3): 46-51
Published online Aug 27, 2015. doi: 10.5496/wjmg.v5.i3.46
Value of predictive bioinformatics in inherited metabolic diseases
David J Timson
David J Timson, School of Biological Sciences and Institute for Global Food Security, Queen’s University Belfast, BT9 7BL Belfast, United Kingdom
Author contributions: Timson DJ conceived and wrote the paper
Conflict-of-interest statement: The author has no conflicts of interest to declare.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Dr. David J Timson, School of Biological Sciences and Institute for Global Food Security, Queen’s University Belfast, 97 Lisburn Road, BT9 7BL Belfast, United Kingdom. d.timson@qub.ac.uk
Telephone: +44-028-90975875 Fax: +44-028-90975877
Received: February 27, 2015
Peer-review started: March 2, 2015
First decision: April 27, 2015
Revised: April 28, 2015
Accepted: May 16, 2015
Article in press: May 18, 2015
Published online: August 27, 2015

Typically, inherited metabolic diseases arise from point mutations in genes encoding metabolic enzymes. Although some of these mutations directly affect amino acid residues in the active sites of these enzymes, the majority do not. It is now well accepted that the majority of these disease-associated mutations exert their effects through alteration of protein stability, which causes a reduction in enzymatic activity. This finding suggests a way to predict the severity of newly discovered mutations. In silico prediction of the effects of amino acid sequence alterations on protein stability often correlates with disease severity. However, no stability prediction tool is perfect and, in general, better results are obtained if the predictions from a variety of tools are combined and then interpreted. In addition to predicted alterations to stability, the degree of conservation of a particular residue can also be a factor which needs to be taken into account: alterations to highly conserved residues are more likely to be associated with severe forms of the disease. The approach has been successfully applied in a variety of inherited metabolic diseases, but further improvements are necessary to enable robust translation into clinically useful tools.

Keywords: Genetic disease, Metabolism, In silico method, Protein stability, Disease-associated mutation

Core tip: Bioinformatics and other in silico methods are increasingly being used to predict the severity of disease-associated mutations in inherited metabolic diseases. In general, severity correlates with altered protein stability and the best predictions occur when a variety of tools are applied.