The importance of physicochemical characteristics and nonlinear classifiers in determining HIV-1 protease specificity

Abstract

This paper reviews recent research relating to the application of bioinformatics approaches to determining HIV-1 protease specificity, outlines outstanding issues, and presents a new approach to addressing these issues. Leading machine learning theory for the problem currently suggests that the direct encoding of the physicochemical properties of the amino acid substrates is not required for optimal performance. A number of amino acid encoding approaches which incorporate potentially relevant physicochemical properties of the substrate are identified, and are evaluated using a nonlinear task decomposition based neuroevolution algorithm. The results are evaluated, and compared against a recent benchmark set on a nonlinear classifier using only amino acid sequence and identity information. Ensembles of these nonlinear classifiers using the physicochemical properties of the substrate are demonstrated to consistently outperform the recently published state-of-the-art linear support vector machine based approach in out-of-sample evaluations.

SARS-CoV-2 3CLpro whole human proteome cleavage prediction and enrichment/depletion analysis

“… [ 63 ] Rognvaldsson et al 64 , 65 argue that nonlinear models including neural networks should not be used for cleavage prediction, however the HIV dataset from Cai et al [ 66 ] that they used and their expanded dataset only included 299 and 746 samples, respectively. Additionally, physiochemical or structural encodings have outperformed one-hot encoding (also called orthogonal encoding) for their small HIV datasets [ 67 ] and have moreover eliminated differences between linear and nonlinear classifiers in an equivalent HCV dataset with 891 samples. [ 68 ] To my knowledge no one has expanded the 3CLpro cleavage dataset to the point where nonlinearity becomes significant, investigated the entire human proteome for 3CLpro cleavages sites with any method, or performed enrichment analysis and classification of these affected proteins.…”