Predicting disease-associated substitution of a single amino acid by analyzing residue interactions

BMC Bioinformatics

Research Article

Li Yang¹  Hui Yin¹  Lezheng Yu¹  Menglong Li¹  Zhining Wen¹  Yizhou Li¹  Jiamin Xiao¹ 

[1] Key Laboratory of Green Chemistry and Technology, Ministry of Education, College of Chemistry, Sichuan University, 610064, Chengdu, PRChina;

关键词: Random Forest;    Cluster Coefficient;    Topological Feature;    Conservation Score;    Residue Interaction;   

DOI  :  10.1186/1471-2105-12-14

 received in 2010-06-10, accepted in 2011-01-12,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundThe rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues.ResultsWe found that SAPs can be well characterized by network topological features. Mutations are probably disease-associated when they occur at a site with a high centrality value and/or high degree value in a protein structure network. We also discovered that study of the neighboring residues around a mutation site can help to determine whether the mutation is disease-related or not. We compiled a dataset from the Swiss-Prot variant pages and constructed a model to predict disease-associated SAPs based on the random forest algorithm. The values of total accuracy and MCC were 83.0% and 0.64, respectively, as determined by 5-fold cross-validation. With an independent dataset, our model achieved a total accuracy of 80.8% and MCC of 0.59, respectively.ConclusionsThe satisfactory performance suggests that network topological features can be used as quantification measures to determine the importance of a site on a protein, and this approach can complement existing methods for prediction of disease-associated SAPs. Moreover, the use of this method in SAP studies would help to determine the underlying linkage between SAPs and diseases through extensive investigation of mutual interactions between residues.

【 授权许可】

CC BY   
© Li et al; licensee BioMed Central Ltd. 2011