ArticleCommunications chemistry2025
Topological deep learning for enhancing peptide-protein complex prediction.
Article in Communications chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- TINA: a geometry-aware post-hoc scoring framework for protein-protein interaction.Acta pharmacologica Sinica · 2026Article
- Peptide-protein docking: from physics-based models to generative intelligence.Chemical communications (Cambridge, England) · 2026Review
- Recent advances in multimodal foundation model-enabled peptide screening and optimization for smart biomaterials and functional tissue engineering.Frontiers in bioengineering and biotechnology · 2026Review
- A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences.Journal of chemical information and modeling · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Peptide-protein interactions are essential to biological processes and drug discovery, but selecting high-quality models from predicted complexes remains challenging due to high false positive rates (FPR). Here we introduce TopoDockQ, a topological deep learning model leveraging persistent combinatorial Laplacian (PCL) features to predict DockQ scores (p-DockQ) for accurately evaluating peptide-protein interface quality, aimed at enhancing precision and mitigating FPR in model selection. Compared to AlphaFold2's built-in confidence score, TopoDockQ reduces false positives by at least 42% and increases precision by 6.7% across five evaluation datasets filtered to ≤70% peptide-protein sequence identity, while maintaining relatively high recall and F1 scores. To support flexible peptide design, we introduce ResidueX, a workflow incorporating non-canonical amino acids (ncAA) into peptide scaffolds. Together, TopoDockQ and ResidueX advance peptide-protein modeling by refining confidence scoring and supporting ncAA incorporation, enabling precise, customizable design and accelerating next-generation peptide therapeutics development.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.