The emerging role of computational design in peptide macrocycle drug discovery

ABSTRACT

Drug discovery is a laborious process with rising cost per new drug. Peptide macrocycles are promising therapeutics, though conformational flexibility can reduce target affinity and specificity. Recent computational advancements address this problem by enabling rational design of rigidly folded peptide macrocycles.

Areas Covered

This review summarizes currently approved peptide macrocycle therapeutics and discusses advantages of mesoscale drugs over small molecules or protein therapeutics. It describes the history, rationale, and state of the art of computational tools, such as Rosetta, that allow the design of rigidly structured peptide macrocycles. The emerging pipeline for designing peptide macrocycle drugs is described, including current challenges in designing permeable molecules that can emulate the chameleonic behavior of natural macrocycles. Prospects for reducing computational cost and improving accuracy with emerging computational technologies are also discussed.

Expert opinion

To embrace computational design of peptide macrocycle drugs, we must shift current attitudes regarding the role of computation in drug discovery, and move beyond Lipinski’s rules. This technology has the potential to shift failures to earlier in silico stages of the drug discovery process, improving success rates in costly clinical trials. Given the available tools, now is the time for drug developers to incorporate peptide macrocycle design into drug discovery pipelines.

KEYWORDS:

• Peptides
• macrocycles
• design
• computation
• simulation
• rosetta
• folding
• proteins

Article Highlights

• Drug discovery typically involves costly library screens, yielding diminishing returns on time and resources invested.

• Peptide macrocycles represent an underexplored class of drugs with properties that could address current deficiencies of small-molecule drugs.

• Recently developed computational peptide macrocycle design tools, and validation tools able to predict a peptide’s rigidity in a binding-competent conformation, are described.

• Ongoing challenges include design for membrane permeability and computational cost of validation, but current research aims to address these.

• Now is an excellent time for drug developers to incorporate computational peptide macrocycle design into existing drug discovery pipelines, to shift failures to earlier, less-costly in silico steps and increase success in later laboratory and clinical steps.

Acknowledgments

Illustrations were rendered with Blender 2.81 (https://www.blender.org/). An award of computer time provided to the author by the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program was used to produce the output shown in Figure 3. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.

Declaration of interest

V Mulligan is a co-founder of Menten AI, in which he owns equity. He has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer Disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

The author is funded by the Simons Foundation.