High-precision Free Energy Perturbation (FEP+) accurately predicts macrocycle and cyclic peptide binding affinities.
Background
Macrocycles and cyclic peptides are a compelling therapeutic modality for engaging historically challenging biological targets, particularly those involved in protein-protein interactions. However, their inherent structural complexity, often falling into the beyond-rule-of-five (bRo5) category, and laborious synthetic demands pose significant hurdles in drug discovery. Traditional high-throughput screening methods are often inefficient for navigating their multidimensional conformational landscapes. Integrating rigorous, physics-based computational methods like Free Energy Perturbation (FEP) is crucial to accurately prioritize design candidates and derisk the high resource investment required for macrocyclic synthesis, addressing a critical gap in efficient drug development.
Study Design
Researchers conducted a retrospective validation of the FEP+ framework across five diverse macrocyclic and cyclic peptide inhibitor series: KRAS, PCSK9, MCL-1, JAK2, and Cyclin A/B. The study encompassed over 230 unique peptidic and nonpeptidic analogues. The primary objective was to demonstrate robust predictive accuracy and reliable intraseries rank-ordering of binding affinities. The FEP+ framework was applied to these complex bRo5 macrocycles, utilizing specialized enhanced sampling protocols and careful consideration of receptor conformational states to effectively navigate their intricate conformational landscapes.
Results
The consolidated data set demonstrated robust predictive accuracy for macrocyclic and cyclic peptide binding affinities. The global pairwise RMSEΔΔG was found to be 1.06 kcal/mol, indicating high precision in affinity prediction. The framework achieved reliable intraseries rank-ordering and robust absolute accuracy across an experimental dynamic range exceeding 10 kcal/mol, which translates to more than 7 orders of magnitude in binding affinity. This analysis also revealed critical insights into the complex interplay between ligand preorganization, hydration dynamics, and binding energetics for these challenging molecules.
The
FEP+framework achieved a global pairwiseRMSEΔΔGof 1.06 kcal/mol across 230 diverse macrocyclic and cyclic peptide analogues, demonstrating high predictive power.
Key Findings
- FEP+ achieved a global pairwise
RMSEΔΔGof 1.06 kcal/mol for binding affinity prediction. - The framework demonstrated robust absolute accuracy across an experimental dynamic range exceeding 10 kcal/mol.
- It reliably rank-ordered intraseries binding affinities for over 230 macrocyclic and cyclic peptide analogues.
- Insights were gained into ligand preorganization, hydration dynamics, and binding energetics.
Why It Matters
This validation establishes FEP+ as an effective computational method for derisking and accelerating the discovery of clinically viable bRo5 therapeutics. For peptide users and biohackers, this means that the development of novel macrocyclic peptides for challenging targets could become significantly faster and more predictable, reducing the need for extensive, costly synthesis and empirical testing. The ability to accurately predict binding affinities computationally can guide lead optimization, potentially leading to more potent and selective compounds. This advancement could transform early-stage drug discovery for complex peptides, making the path from concept to a usable protocol more efficient by prioritizing the most promising candidates before costly experimental synthesis.
free energy perturbation
computational drug discovery
macrocycles
cyclic peptides
affinity prediction
bRo5