ArticleACS omega2024
PepCARES: A Comprehensive Advanced Refinement and Evaluation System for Peptide Design and Affinity Screening.
Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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