Development of machine learning-based frameworks to predict permeability of peptides through cell membrane and blood-brain barrier

Abstract

Peptides comprise a versatile class of biomolecules with diverse physicochemical and structural properties, in addition to numerous pharmacological and biotechnological applications. Some groups of peptides can cross biological membranes, such as the cell membrane and the human blood-brain barrier. Researchers have explored this property over the years as an alternative to developing more powerful drugs, given that some peptides can also be drug carriers. Although some machine learning-based tools have been developed to predict cell-penetrating peptides (CPPs) and blood-brain barrier penetrating peptides (B3PPs), some points have not yet been explored within this theme. These points encompass the use of dimensionality reduction (DR) techniques in the preprocessing stage, molecular descriptors related to drug bioavailability, and data structures that encode peptides with chemical modifications. Therefore, the primary purpose of this thesis is to develop and test two frameworks based on DR, the first one to predict CPPs and the second to predict B3PPs, also evaluating the molecular descriptors and data structure of interest. The results of this thesis show that for the prediction of penetration in the cell membrane, the proposed framework reached 92% accuracy in the best performance in an independent test, outperforming other tools created for the same purpose, besides evidencing the contribution between the junction of molecular descriptors based on amino acid sequence and those related to bioavailability and cited in Lipinski’s rule of five. Furthermore, the prediction of B3PPs by the proposed framework reveals that the best model using structural, electric, and bioavailability-associated molecular descriptors achieved average accuracy values exceeding 93% in the 10-fold cross-validation and between 75% and 90% accuracy in the independent test for all simulations, outperforming other machine learning (ML) tools developed to predict B3PPs. These results show that the proposed frameworks can be used as an additional tool in predicting the penetration of peptides in these two biomembranes and are available as free-touse web servers.