Why neural networks should not be used for HIV-1 protease cleavage site prediction.
Several papers have been published where nonlinear machine learning algorithms, e.g. artificial neural networks, support vector machines and decision trees, have been used to model the specificity of the HIV-1 protease and extract specificity rules. We show that the dataset used in these studies is linearly separable and that it is a misuse of nonlinear classifiers to apply them to this problem. The best solution on this dataset is achieved using a linear classifier like the simple perceptron or the linear support vector machine, and it is straightforward to extract rules from these linear models. We identify key residues in peptides that are efficiently cleaved by the HIV-1 protease and list the most prominent rules, relating them to experimental results for the HIV-1 protease.


BACKGROUND
Understanding HIV-1 protease specificity is important when designing HIV inhibitors and several different machine learning algorithms have been applied to the problem. However, little progress has been made in understanding the specificity because nonlinear and overly complex models have been used.


RESULTS
We show that the problem is much easier than what has previously been reported and that linear classifiers like the simple perceptron or linear support vector machines are at least as good predictors as nonlinear algorithms. We also show how sets of specificity rules can be generated from the resulting linear classifiers.


BACKGROUND
The datasets used are available at http://www.hh.se/staff/bioinf/
Intelligent Systems Laboratory, School of Information Science, Computer and Electrical Engineering, Halmstad University, Box 823, 301 18 Sweden. denni@ide.hh.se
Thorsteinn
T
Rögnvaldsson
Liwen
L
You
D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D003198:Computer Simulation; D030562:Databases, Protein; D004789:Enzyme Activation; D016333:HIV Protease / Q000737:chemistry*; D016014:Linear Models; D008956:Models, Chemical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*
D016333:HIV Protease