ProCKSI: a decision support system for Protein (Structure) Comparison, Knowledge, Similarity and Information

Daniel Barthel¹ , Jonathan D Hirst² , Jacek Błażewicz³^,4 , Edmund K Burke¹ and Natalio Krasnogor¹

¹ASAP, School of Computer Science and IT, University of Nottingham, Nottingham, NG8 1BB, UK

²School of Chemistry, University of Nottingham, Nottingham, NG7 2RD, UK

³Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland

⁴The Institute of Computing Science, 60-965 Poznan, Poland

author email corresponding author email

BMC Bioinformatics 2007, 8:416doi:10.1186/1471-2105-8-416


Published:	26 October 2007

Abstract

Background

We introduce the decision support system for Protein (Structure) Comparison, Knowledge, Similarity and Information (ProCKSI). ProCKSI integrates various protein similarity measures through an easy to use interface that allows the comparison of multiple proteins simultaneously. It employs the Universal Similarity Metric (USM), the Maximum Contact Map Overlap (MaxCMO) of protein structures and other external methods such as the DaliLite and the TM-align methods, the Combinatorial Extension (CE) of the optimal path, and the FAST Align and Search Tool (FAST). Additionally, ProCKSI allows the user to upload a user-defined similarity matrix supplementing the methods mentioned, and computes a similarity consensus in order to provide a rich, integrated, multicriteria view of large datasets of protein structures.

Results

We present ProCKSI's architecture and workflow describing its intuitive user interface, and show its potential on three distinct test-cases. In the first case, ProCKSI is used to evaluate the results of a previous CASP competition, assessing the similarity of proposed models for given targets where the structures could have a large deviation from one another. To perform this type of comparison reliably, we introduce a new consensus method. The second study deals with the verification of a classification scheme for protein kinases, originally derived by sequence comparison by Hanks and Hunter, but here we use a consensus similarity measure based on structures. In the third experiment using the Rost and Sander dataset (RS126), we investigate how a combination of different sets of similarity measures influences the quality and performance of ProCKSI's new consensus measure. ProCKSI performs well with all three datasets, showing its potential for complex, simultaneous multi-method assessment of structural similarity in large protein datasets. Furthermore, combining different similarity measures is usually more robust than relying on one single, unique measure.

Conclusion

Based on a diverse set of similarity measures, ProCKSI computes a consensus similarity profile for the entire protein set. All results can be clustered, visualised, analysed and easily compared with each other through a simple and intuitive interface.

ProCKSI is publicly available at http://www.procksi.net webcite for academic and non-commercial use.