Abstract
BackgroundWe 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.ResultsWe 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.ConclusionBased 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 for academic and non-commercial use.
Highlights
Introduction to Receiver Operator Characteristics (ROC) AnalysisROC analyses have been widely employed, e.g. in signal detection theory [89], machine learning [90], and diagnostic testing in medicine [91]
ProCKSI is publicly available at http://www.procksi.net for academic and non-commercial use
ProCKSI returns a large variety of data and intermediate results that are handled through the Structures, Task and Analysis Management subsystems
Summary
ProCKSI's core technologies, namely the USM and MaxCMO methods, and external servers and methods, namely the DaliLite, CE, FAST, and TM-align methods, have been introduced and evaluated independently in the past [8,11,20,22,23,24,27,28,49,50]. We take the step towards a fully-automated decision support system by analysing the quality and performance of the six different similarity comparison methods currently included in ProCKSI by means of Receiver Operator Characteristics (ROC) [86] These are USM, MaxCMO, DaliLite, CE, TM-align, and FAST, providing a total number of 15 similarity measures (compare section Task Management for details). We compare the proteins' secondary structures, but analyse the performance of ProCKSI's similarity comparison methods according to the proteins' classification as given by SCOP, release 1.69 [85] We adopted this manually curated database as our gold standard containing expert knowledge for each of its hierarchical classification levels: Class, Fold, Superfamily, Family, Protein, and Species.
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