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

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 for academic and non-commercial use.

Figure 1

ProCKSI's Architecture and Workflow. Overview over the workflow and architecture of ProCKSI with its multiple similarity comparison methods: Universal Similarity Metric (USM), Maximum Contact Map Overlap (MaxCMO), and other local and external methods. Currently, these are the DaliLite and TM-align methods, the Combinatorial Extension (CE) of the optimal path, and the FAST Align and Search Tool (FAST).

Figure 2

Building the Protein Universe. 3D proteins structures (left, [68]) can be represented as 2D contact maps that are used to compare pairs of proteins (centre) according to their USM and MaxCMO similarities. The resulting similarity matrices can be clustered in order to produce a hierarchical tree (right, [51]). Spoof protein from Bacillus Subtilis (1NAT) from the Skolnick dataset [23].

Figure 3

Evaluation of Models against CASP Target T0196. CASP target T0196 was compared against all submitted server models using ProCKSI's similarity methods USM, MaxCMO, and its new Consensus method. The most similar structures to the target detected by each method are displayed next to each other. – The protein structure pictures were generated by ProCKSI using MolScript [68].

Figure 4

Clustering the Kinase Dataset using Single Similarity Measures. The Kinase dataset was clustered with the Ward's Minimum Variance (WMV) method in conjunction with the MaxCMO/Overlap (left), the USM (middle), and the DaliLite/Z (right) similarity measures. The meaning of the different coloured boxes is explained in the text in detail. – The hierarchical tree images were generated by ProCKSI using HyperTree [51].

Figure 5

Clustering the Kinase Dataset using Consensus Similarity Measures. The Kinase dataset was clustered with the Ward's Minimum Variance (WMV) method in conjunction with a consensus similarity measure of the USM and DaliLite/Z (left), and the USM, DaliLite/Z, and MaxCMO/Overlap (right) similarity measures. The meaning of the different coloured boxes is explained in the text in detail. – The hierarchical tree images were generated by ProCKSI using HyperTree [51].

Figure 6

ROC Analysis for the Rost and Sander Dataset. ROC analysis for all available similarity comparison methods in ProCKSI for the Rost and Sander dataset using SCOP's Class level as gold standard (top). ROC analysis showing the better performance of ProCKSI's Consensus/Best3 method compared to each contributing single one using SCOP's Superfamily level as gold standard (bottom).

Figure 7

Benchmark Tests of ProCKSI. ProCKSI's response times for the completion of a request using all available similarity measures as a function of the number of all chains (top) and the number of residues (middle and bottom) in the corresponding dataset. The bottom panel is a magnification of the middle one, displaying only the fastest three methods and the contact map preparation for more clarity.