Mining SARS-CoV protease cleavage data using non-orthogonal decision trees: a novel method for decisive template selection.
BACKGROUND
Although the outbreak of the severe acute respiratory syndrome (SARS) is currently over, it is expected that it will return to attack human beings. A critical challenge to scientists from various disciplines worldwide is to study the specificity of cleavage activity of SARS-related coronavirus (SARS-CoV) and use the knowledge obtained from the study for effective inhibitor design to fight the disease. The most commonly used inductive programming methods for knowledge discovery from data assume that the elements of input patterns are orthogonal to each other. Suppose a sub-sequence is denoted as P2-P1-P1'-P2', the conventional inductive programming method may result in a rule like 'if P1 = Q, then the sub-sequence is cleaved, otherwise non-cleaved'. If the site P1 is not orthogonal to the others (for instance, P2, P1' and P2'), the prediction power of these kind of rules may be limited. Therefore this study is aimed at developing a novel method for constructing non-orthogonal decision trees for mining protease data.


RESULTS
Eighteen sequences of coronavirus polyprotein were downloaded from NCBI (http://www.ncbi.nlm.nih.gov). Among these sequences, 252 cleavage sites were experimentally determined. These sequences were scanned using a sliding window with size k to generate about 50,000 k-mer sub-sequences (for short, k-mers). The value of k varies from 4 to 12 with a gap of two. The bio-basis function proposed by Thomson et al. is used to transform the k-mers to a high-dimensional numerical space on which an inductive programming method is applied for the purpose of deriving a decision tree for decision-making. The process of this transform is referred to as a bio-mapping. The constructed decision trees select about 10 out of 50,000 k-mers. This small set of selected k-mers is regarded as a set of decisive templates. By doing so, non-orthogonal decision trees are constructed using the selected templates and the prediction accuracy is significantly improved.
Yang
Zheng Rong
ZR
Department of Computer Science, Exeter University, United Kingdom. z.r.yang@exeter.ac.uk
D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D003198:Computer Simulation; D000086782:Coronavirus 3C Proteases; D003546:Cysteine Endopeptidases; D030562:Databases, Protein; D003661:Decision Support Techniques; D010450:Endopeptidases / Q000032:analysis / Q000737:chemistry*; D008956:Models, Chemical; D008958:Models, Molecular; D011485:Protein Binding; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D014764:Viral Proteins / Q000032:analysis / Q000737:chemistry*
D014764:Viral Proteins; D010450:Endopeptidases; D003546:Cysteine Endopeptidases; D000086782:Coronavirus 3C Proteases