Advanced Computational Methods May Lead to New Peptide Drugs

New computational strategies reported that might help realize the promise of peptide-based drugs. Peptides are similar to protein molecules, but differ in their smaller size, structure and functions. In addition, peptides such as cyclosporine are stable like small molecules and selective like antibodies.

The scientists explain that naturally occurring peptides that might serve as scaffolds are few and often fail to perform as expected when repurposed. As a result, the traditional approach has been to screen large, randomly generated compound libraries to find peptides of interest.

The methods covered in the study (“Comprehensive Computational Design of Ordered Peptide Macrocycles”), which is published in Science, solve these problems, claim the researchers from the University of Washington, School of Medicine department of biochemistry and the UW Institute of Protein Design. The senior author is David Baker, Ph.D., Professor of biochemistry and head of the institute.

“In our paper,” the researchers noted, “we describe computational strategies for designing peptides that adopt diverse shapes with very high accuracy and for providing comprehensive coverage of the structures that can be formed by short peptides.”

“Mixed-chirality peptide macrocycles such as cyclosporine are among the most potent therapeutics identified to date, but there is currently no way to systematically search the structural space spanned by such compounds. Natural proteins do not provide a useful guide: Peptide macrocycles lack regular secondary structures and hydrophobic cores, and can contain local structures not accessible with L-amino acids. Here, we enumerate the stable structures that can be adopted by macrocyclic peptides composed of L- and D-amino acids by near-exhaustive backbone sampling followed by sequence design and energy landscape calculations,” write the investigators.

“We identify more than 200 designs predicted to fold into single stable structures, many times more than the number of currently available unbound peptide macrocycle structures. Nuclear magnetic resonance structures of 9 of 12 designed 7- to 10-residue macrocycles, and three 11- to 14-residue bicyclic designs, are close to the computational models. Our results provide a nearly complete coverage of the rich space of structures possible for short peptide macrocycles and vastly increase the available starting scaffolds for both rational drug design and library selection methods.”

The paper shows that the team was able to design and compile a library of many new stable peptide scaffolds that can provide the basic platforms for drug-candidate architecture. Their methods also can be used to design additional custom peptides with arbitrary shapes on demand.

“We sampled the diverse landscape of shapes that peptides can form, as a guide for designing the next generation of drugs,” notes Dr. Baker.

Key to control of the geometry and chemistry of molecules was the design of peptides with L- and D-amino acids. The D-amino acids improved pharmacological properties by increasing resistance to natural enzymes that break down peptides and allowed for a more diverse range of shapes.

Designing peptides takes intensive computer power, resulting in expensive calculations, adds Dr. Baker, whose team credited citizen scientists and volunteers who donated their spare cellular smartphone minutes and computer time. The Hyak Supercomputer at the University of Washington also ran some of the programs.

Future plans for computational design approaches include peptides that can permeate cell membranes and go inside living cells. The team also wants to add new functionalities to peptide structures by stabilizing the binding motifs at protein–protein interfaces for basic science studies. For clinical applications, they anticipate using their methods and scaffolds for developing peptide-based drugs.