Protocol for iterative optimization of modified peptides bound to protein targets

Abstract

Peptides are commonly used as therapeutic agents. However, they suffer from easy degradation and instability. Replacing natural by non-natural amino acids can avoid these problems, and potentially improve the affinity towards the target protein. Here, we present a computational pipeline to optimize peptides based on adding non-natural amino acids while improving their binding affinity. The workflow is an iterative computational evolution algorithm, inspired by the PARCE protocol, that performs single-point mutations on the peptide sequence using modules from the Rosetta framework. The modifications can be guided based on the structural properties or previous knowledge of the biological system. At each mutation step, the affinity to the protein is estimated by sampling the complex conformations and applying a consensus metric using various open protein-ligand scoring functions. The mutations are accepted based on the score differences, allowing for an iterative optimization of the initial peptide. The sampling/scoring scheme was benchmarked with a set of protein-peptide complexes where experimental affinity values have been reported. In addition, a basic application using a known protein-peptide complex is also provided. The structure- and dynamic-based approach allows users to optimize bound peptides, with the option to personalize the code for further applications. The protocol, called mPARCE, is available at: https://github.com/rochoa85/mPARCE/.

PepFun 2.0: improved protocols for the analysis of natural and modified peptides

“…Nowadays, including subtle modifications on the peptide sequences is crucial to increase the peptide's structural and metabolic stability, among other applications [12,13]. For example, being able to run automatic pipelines able to predict conformers, analyze interactions and mutate residues by non-natural monomers is a practice commonly used in both industrial and academic environments [14].…”

A simple method for developing lysine targeted covalent protein reagents

“…Due to the size of peptides and their conformational flexibility, computational modeling can aid in the design and placement of the electrophile. Computational modeling has been used extensively to model and design peptide and peptidomimetic binders for proteins [48][49][50][51][52][53][54] . We developed CovPepDock 55 , a Rosetta-based framework for modeling covalent protein-peptide interactions and for the design and virtual screening of potential covalent peptide binders.…”