Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension

Jiahan Li, Tong Chen, Shitong Luo, Chaoran Cheng, Jiaqi Guan, Ruihan Guo, Sheng Wang, Ge Liu, Jian Peng, Jianzhu Ma

Keywords: AI for Science; Protein Design; Generative Models;

TL;DR: Designing peptides based on hot-spot estimation and autoregressive fragment extension.

Abstract:

Peptides, short chains of amino acids, interact with target proteins, making them a unique class of protein-based therapeutics for treating human diseases. Recently, deep generative models have shown great promise in peptide generation. However, several challenges remain in designing effective peptide binders. First, not all residues contribute equally to peptide-target interactions. Second, the generated peptides must adopt correct geometries due to the constraints of peptide bonds. Third, realistic benchmarks for peptide drug development are still lacking.
To address these challenges, we introduce **PepHAR**, a hot-spot-driven autoregressive generative model for designing peptides targeting specific proteins. Building on the observation that certain hot spot residues have higher interaction potentials, we first use an energy-based density model to fit and sample these key residues. Next, to ensure proper peptide geometry, we autoregressively extend peptide fragments by estimating dihedral angles between residue frames. Finally, we apply an optimization process to iteratively refine fragment assembly, ensuring valid peptide structures.
By combining hot spot sampling with fragment-based extension, our approach enables de novo peptide design tailored to a target protein and allows the incorporation of key hot spot residues into peptide scaffolds. Extensive experiments, including peptide design and peptide scaffold generation, demonstrate the strong potential of **PepHAR** in computational peptide design.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 8588