Physics and chemistry-driven artificial neural network for predicting bioactivity of peptides and proteins and their design

Predicting the bioactivity of peptides and proteins is an important challenge in drug development and protein engineering. In this study we introduce a novel approach, the so-called “physics and chemistry-driven artificial neural network (Phys–Chem ANN)”, to deal with such a problem. Unlike the existing ANN approaches, which were designed under the inspiration of biological neural system, the Phys–Chem ANN approach is based on the physical and chemical principles, as well as the structural features of proteins. In the Phys–Chem ANN model the “hidden layers” are no longer virtual “neurons”, but real structural units of proteins and peptides. It is a hybridization approach, which combines the linear free energy concept of quantitative structure-activity relationship (QSAR) with the advanced mathematical technique of ANN. The Phys–Chem ANN approach has adopted an iterative and feedback procedure, incorporating both machine-learning and artificial intelligence capabilities. In addition to making more accurate predictions for the bioactivities of proteins and peptides than is possible with the traditional QSAR approach, the Phys–Chem ANN approach can also provide more insights about the relationship between bioactivities and the structures involved than the ANN approach does. As an example of the application of the Phys–Chem ANN approach, a predictive model for the conformational stability of human lysozyme is presented.