Computational Methods for Peptide Macrocycle Drug Design

Abstract

Peptide macrocycles represent a promising class of therapeutics, albeit one that is under-represented amongst existing drugs. One disadvantage of macrocycles, however, is that they can be more conformationally heterogeneous than small molecules or large, well-folded proteins. This flexibility can impede high-affinity binding. In recent years, the development of new computational tools has made possible the structure-based design of macrocycles that are able to fold into rigid structures compatible with binding to target proteins of therapeutic interest. This chapter is intended to introduce biologists, chemists, and drug developers to current computational methods for peptide macrocycle drug design. It introduces computational concepts such as parallelism and algorithmic complexity and outlines general algorithmic approaches such as Monte Carlo and simulated annealing methods. It also describes the thermodynamics of a flexible macrocycle binding to a target protein and explores molecular dynamics and Monte Carlo methods for sampling backbone conformations, deterministic and heuristic methods for designing amino acid sequences, and large-scale sampling-based methods for computationally validating and ranking designs to prioritize the likeliest candidates for chemical synthesis and experimental validation. Particular focus is given to methods implemented in the Rosetta software suite, with detailed examination of a Rosetta design protocol that was previously used to create peptide macrocycle inhibitors of an antibiotic resistance factor, the New Delhi metallo-β-lactamase 1 (NDM-1). Finally, this chapter describes new and emerging technologies that promise to enhance computational peptide macrocycle drug design, such as deep learning and quantum computing.