Predicting residue contacts using pragmatic correlated mutations method: reducing the false positives

• Published in: BMC bioinformatics 2006-11, Vol.7 (1), p.503-503, Article 503
• Main Authors: Kundrotas, Petras J, Alexov, Emil G
• Format: Article
• Language: English
• Subjects: Algorithms; Biophysics - methods; Computational Biology - methods; Databases, Protein; False Positive Reactions; Genetic Variation; Imaging, Three-Dimensional; Internet; Models, Statistical; Mutation; Proteins - chemistry; Reproducibility of Results; Software
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-7-503

Predicting residues' contacts using primary amino acid sequence alone is an important task that can guide 3D structure modeling and can verify the quality of the predicted 3D structures. The correlated mutations (CM) method serves as the most promising approach and it has been used to predict amino acids pairs that are distant in the primary sequence but form contacts in the native 3D structure of homologous proteins. Here we report a new implementation of the CM method with an added set of selection rules (filters). The parameters of the algorithm were optimized against fifteen high resolution crystal structures with optimization criterion that maximized the confidentiality of the predictions. The optimization resulted in a true positive ratio (TPR) of 0.08 for the CM without filters and a TPR of 0.14 for the CM with filters. The protocol was further benchmarked against 65 high resolution structures that were not included in the optimization test. The benchmarking resulted in a TPR of 0.07 for the CM without filters and to a TPR of 0.09 for the CM with filters. Thus, the inclusion of selection rules resulted to an overall improvement of 30%. In addition, the pair-wise comparison of TPR for each protein without and with filters resulted in an average improvement of 1.7. The methodology was implemented into a web server http://www.ces.clemson.edu/compbio/recon that is freely available to the public. The purpose of this implementation is to provide the 3D structure predictors with a tool that can help with ranking alternative models by satisfying the largest number of predicted contacts, as well as it can provide a confidence score for contacts in cases where structure is known.