Characterizing proteolytic cleavage site activity using bio-basis function neural networks.
BACKGROUND
In protein chemistry, proteomics and biopharmaceutical development, there is a desire to know not only where a protein is cleaved by a protease, but also the susceptibility of its cleavage sites. The current tools for proteolytic cleavage prediction have often relied purely on regular expressions, or involve models that do not represent biological data well.


RESULTS
A novel methodology for characterizing proteolytic cleavage site activities has been developed, which incorporates two fundamental features: activity class prediction and the use of an amino acid similarity matrix for (non-parametric) neural learning. The first solved the problem of predicting proteolytic efficiency. The second significantly improved the robustness in prediction and reduced the time complexity for learning. This study shows that activity class prediction is successful when applying this methodology to the prediction and characterization of Trypsin cleavage sites and the prediction of HIV protease cleavage sites.


BACKGROUND
Requests for software and data should be made respectively to Dr Zheng Rong Yang and Miss Rebecca Thomson.
D000595:Amino Acid Sequence; D001665:Binding Sites; D016000:Cluster Analysis; D030562:Databases, Protein; D004789:Enzyme Activation; D016333:HIV Protease / Q000737:chemistry; D006868:Hydrolysis; D008969:Molecular Sequence Data; D009415:Nerve Net; D010363:Pattern Recognition, Automated; D010446:Peptide Fragments / Q000737:chemistry*; D010447:Peptide Hydrolases / Q000737:chemistry* / Q000145:classification*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D014357:Trypsin / Q000737:chemistry
D010446:Peptide Fragments; D010447:Peptide Hydrolases; D014357:Trypsin; D016333:HIV Protease