Abstract
BackgroundThe unforgiving pace of growth of available biological data has increased the demand for efficient and scalable paradigms, models and methodologies for automatic annotation. In this paper, we present a novel structure-based protein function prediction and structural classification method: Cutoff Scanning Matrix (CSM). CSM generates feature vectors that represent distance patterns between protein residues. These feature vectors are then used as evidence for classification. Singular value decomposition is used as a preprocessing step to reduce dimensionality and noise. The aspect of protein function considered in the present work is enzyme activity. A series of experiments was performed on datasets based on Enzyme Commission (EC) numbers and mechanistically different enzyme superfamilies as well as other datasets derived from SCOP release 1.75.ResultsCSM was able to achieve a precision of up to 99% after SVD preprocessing for a database derived from manually curated protein superfamilies and up to 95% for a dataset of the 950 most-populated EC numbers. Moreover, we conducted experiments to verify our ability to assign SCOP class, superfamily, family and fold to protein domains. An experiment using the whole set of domains found in last SCOP version yielded high levels of precision and recall (up to 95%). Finally, we compared our structural classification results with those in the literature to place this work into context. Our method was capable of significantly improving the recall of a previous study while preserving a compatible precision level.ConclusionsWe showed that the patterns derived from CSMs could effectively be used to predict protein function and thus help with automatic function annotation. We also demonstrated that our method is effective in structural classification tasks. These facts reinforce the idea that the pattern of inter-residue distances is an important component of family structural signatures. Furthermore, singular value decomposition provided a consistent increase in precision and recall, which makes it an important preprocessing step when dealing with noisy data.
Highlights
The unforgiving pace of growth of available biological data has increased the demand for efficient and scalable paradigms, models and methodologies for automatic annotation
53% of the Protein Data Bank (PDB) [4] entries are classified by the current release of SCOP (1.75) as of April 2011, and after removing redundancy, the coverage drops to about 41%
We showed our methodology to be, in general, independent of the classifiers utilized, giving even results for different classification heuristics. Having in mind these considerations, we showed that the patterns derived from Cutoff Scanning Matrix (CSM) might effectively be used in automatic protein function prediction and structural classification
Summary
The unforgiving pace of growth of available biological data has increased the demand for efficient and scalable paradigms, models and methodologies for automatic annotation. We present a novel structure-based protein function prediction and structural classification method: Cutoff Scanning Matrix (CSM). CSM generates feature vectors that represent distance patterns between protein residues These feature vectors are used as evidence for classification. The Pfam database of protein families [2] represents about 12,000 families, and about 20% of these are domains of unknown function (DUFs), revealing that state-of-the-art sequence similarity-based and even profile-based annotation methods have had limited success in assigning functions to novel proteins. Protein structural classification databases, such as SCOP [3], present difficulties in keeping up with the increasing number of protein structures solved and deposited in public repositories. As international structural genomics initiatives have produced a huge number of structures of unknown function, attempting to automatically assign functions to these proteins is becoming even more necessary, and significant efforts have been devoted to this task [5,6,7,8]
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