Integrating Protein Structure Prediction and Bayesian Optimization for Peptide Design

Negin Manshour, Fei He, Duolin Wang, Dong Xu

Keywords: Peptide Design, Bayesian Optimization, Deep Learning, Protein Structure Prediction

TL;DR: Designing an appropriate peptide by using Bayesian optimization and protein structure prediction methods

Abstract: Peptide design, with the goal of identifying peptides possessing unique biological properties, stands as a crucial challenge in peptide-based drug discovery. While traditional and computational methods have made significant strides, they often encounter hurdles due to the complexities and costs of laboratory experiments. Recent advancements in deep learning and Bayesian Optimization have paved the way for innovative research in this domain. In this context, our study presents a novel approach that effectively combines protein structure prediction with Bayesian Optimization for peptide design. By applying carefully designed objective functions, we guide and enhance the optimization trajectory for new peptide sequences. Benchmarked against multiple native structures, our methodology is tailored to generate new peptides to their optimal potential biological properties.

Submission Number: 55