A structural bioinformatics approach to the analysis of nsSNPs and prediction of
disease association.
Catherine L. Worth, G. Richard J. Bickerton, Adrian Schreyer, Julia R. Forman,
Tammy M.K. Cheng, Semin Lee, Sungsam Gong, David F. Burke and Tom L.
Blundell
Understanding the impact that non-synonymous single nucleotide polymorphisms
(nsSNPs) have on the structures of gene products, proteins, is important in identifying
the origins of complex diseases. Predicting the effects that these mutations have on
protein function depends critically on exploiting all information available on the
three-dimensional structures of proteins. We have developed software and databases
for the analysis of nsSNPs that allows a user to move from SNP to sequence to
structure to function. In both structure prediction and in the analysis of the effects of
nsSNPs, we exploit information about protein evolution, in particular, that derived
from investigation of the relation of sequence to structure gained from the study of
amino acid substitutions in divergent evolution. The techniques developed in our
laboratory have allowed fast and automated sequence-structure homology recognition
to identify templates and to perform comparative modelling, as well as simple, robust
and generally applicable algorithms to assess the likely impact of amino acid
substitutions on structure and interactions. We describe our strategy for relating SNPs
to disease1 and the results of benchmarking our approach on a set of human proteins
of known structure and recognized mutation2.
1. Burke DF, Worth CL, Priego EM, Cheng TMK, Smink LJ, Todd JA and Blundell
TL (2007) Genome bioinformatic analysis of nonsynonymous SNPs. BMC
Bioinformatics 8:301.
2. Worth CL*, Bickerton GRJ*, Schreyer A, Forman JR, Cheng TMK, Lee S, Gong
S, Burke DF and Blundell TL (2007) A structural bioinformatics approach to the
analysis of non-synonymous single nucleotide polymorphisms and their relation to
disease. Journal of Bioinformatics and Computational Biology special issue: Making
Sense of Mutations requires Knowledge Management vol.5 no 6. *these authors
contributed equally to this work