PepCARES: A Comprehensive Advanced Refinement and Evaluation System for Peptide Design and Affinity Screening

Wen Xu, Zhipeng Wu, Chengyun Zhang, Cheng Zhu, Hongliang Duan

Research output: Contribution to journalArticlepeer-review

Abstract

Peptides are crucial in vaccine research, and their remarkable specificity and efficacy make them a promising potential drug class. However, designing and screening these peptides computationally is challenging. Here, we present the comprehensive advanced refinement and evaluation system (PepCARES), a program utilizing our novel model called PeptideMPNN and score evaluation for peptide design and affinity screening. PeptideMPNN, built on ProteinMPNN with transfer learning, significantly enhances sequence recovery (by 26.26%) and reduces perplexity (by 0.536) in a sequence generation task. We designed peptides targeting two HLA alleles and, using MHCfovea and PDBePISA, identified candidates with high potential. From 20 designed peptides, 14 and 7 peptides were selected, respectively. Our research provides a method for designing and screening peptides, making an important step toward the development of peptide-based vaccines.

Original languageEnglish
Pages (from-to)46429-46438
Number of pages10
JournalACS Omega
Volume9
Issue number46
DOIs
Publication statusPublished - 19 Nov 2024

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