Details

Autor(en) / Beteiligte

• Cheng, Tammy M K
• Lu, Yu-En
• Vendruscolo, Michele
• Lio', Pietro
• Blundell, Tom L
• Nussinov, Ruth

Titel

Prediction by graph theoretic measures of structural effects in proteins arising from non-synonymous single nucleotide polymorphisms

Ist Teil von

• PLoS computational biology, 2008-07, Vol.4 (7), p.e1000135-e1000135

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2008

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• Recent analyses of human genome sequences have given rise to impressive advances in identifying non-synonymous single nucleotide polymorphisms (nsSNPs). By contrast, the annotation of nsSNPs and their links to diseases are progressing at a much slower pace. Many of the current approaches to analysing disease-associated nsSNPs use primarily sequence and evolutionary information, while structural information is relatively less exploited. In order to explore the potential of such information, we developed a structure-based approach, Bongo (Bonds ON Graph), to predict structural effects of nsSNPs. Bongo considers protein structures as residue-residue interaction networks and applies graph theoretical measures to identify the residues that are critical for maintaining structural stability by assessing the consequences on the interaction network of single point mutations. Our results show that Bongo is able to identify mutations that cause both local and global structural effects, with a remarkably low false positive rate. Application of the Bongo method to the prediction of 506 disease-associated nsSNPs resulted in a performance (positive predictive value, PPV, 78.5%) similar to that of PolyPhen (PPV, 77.2%) and PANTHER (PPV, 72.2%). As the Bongo method is solely structure-based, our results indicate that the structural changes resulting from nsSNPs are closely associated to their pathological consequences.