Rapid and accurate structure‐based therapeutic peptide design using GPU accelerated thermodynamic integration

Abstract

Peptide‐based therapeutics are an alternative to small molecule drugs as they offer superior specificity, lower toxicity, and easy synthesis. Here we present an approach that leverages the dramatic performance increase afforded by the recent arrival of GPU accelerated thermodynamic integration (TI). GPU TI facilitates very fast, highly accurate binding affinity optimization of peptides against therapeutic targets. We benchmarked TI predictions using published peptide binding optimization studies. Prediction of mutations involving charged side‐chains was found to be less accurate than for non‐charged, and use of a more complex 3‐step TI protocol was found to boost accuracy in these cases. Using the 3‐step protocol for non‐charged side‐chains either had no effect or was detrimental. We use the benchmarked pipeline to optimize a peptide binding to our recently discovered cancer target: EME1. TI calculations predict beneficial mutations using both canonical and non‐canonical amino acids. We validate these predictions using fluorescence polarization and confirm that binding affinity is increased. We further demonstrate that this increase translates to a significant reduction in pancreatic cancer cell viability.

Predicting changes in protein stability caused by mutation using sequence‐and structure‐based methods in a CAGI5 blind challenge

“…A pre‐equilibrated structure tends to generate more stable ensembles and therefore more accurate estimations of the free energy (Garton et al, ). To that end, all water and ions atoms were removed from the structure with PDB code 1EKG.…”

“…Each simulation was repeated five times to calculate the ensemble-averaged values. More information on the recommended setup protocol found in the references (Garton et al, 2018;Lee et al, 2017;Seeliger & de Groot, 2010). Input files and scripts can be found here: https://gitlab.com/kimlab/rapid.…”

How Computational Chemistry and Drug Delivery Techniques Can Support the Development of New Anticancer Drugs

“…Once a structural model of the complex is available both MD simulations and/or free energy calculations demonstrated that they can significantly contribute to design peptide modifications that results in an improved affinity and selectivity [61,62]. In particular, starting from the assumption that a significant increment of the affinity for the target would result also in an augmented selectivity, multiple methodologies with different degree of complexity such as thermodynamic integration or free energy perturbation [63,64] metadynamics [65,66], MM-GB(PB)SA [5,67,68] aimed to estimate the binding free energy (∆G) computationally, have been applied to evaluate which peptide modifications could result in a more favorable (more negative) binding energy. Additionally, in this case, literature reports some interesting examples about the use of computer simulations in the design of anticancer peptides, therefore we selected some of them to give to the reader a flavor of the impact that computer simulation had on this research field in the last ten years.…”

Rigorous Computational and Experimental Investigations on MDM2/MDMX-Targeted Linear and Macrocyclic Peptides

“…Depending on computing resources, CMDboltzmann can be used to further and more accurately prioritize 100s to 1000s of the sequences of greatest interest. Indeed, the results of these calculations could be used to prioritize further synthesis or could be used for more computationally expensive methods such as molecular dynamics [21,22], free energy perturbation [23,24] or thermodynamic integration [25,26].…”