Paper List
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An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System
This paper addresses the critical gap between theoretical AI research and real-world clinical implementation by providing a practical framework for as...
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The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
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Hierarchical Molecular Language Models (HMLMs)
This paper addresses the core challenge of accurately modeling context-dependent signaling, pathway cross-talk, and temporal dynamics across multiple ...
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Stability analysis of action potential generation using Markov models of voltage‑gated sodium channel isoforms
This work addresses the challenge of systematically characterizing how the high-dimensional parameter space of Markov models for different sodium chan...
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Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution
This paper addresses the core challenge of inferring the relative contributions of multiple, simultaneous generative mechanisms in network formation w...
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EnzyCLIP: A Cross-Attention Dual Encoder Framework with Contrastive Learning for Predicting Enzyme Kinetic Constants
This paper addresses the core challenge of jointly predicting enzyme kinetic parameters (Kcat and Km) by modeling dynamic enzyme-substrate interaction...
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Tissue stress measurements with Bayesian Inversion Stress Microscopy
This paper addresses the core challenge of measuring absolute, tissue-scale mechanical stress without making assumptions about tissue rheology, which ...
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DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction
This study addresses the critical gap between high predictive accuracy and biological interpretability in DeepFRI, revealing that the model often prio...
Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A
Shanghai Jiao Tong University | QuietD Biotech
30秒速读
IN SHORT: This paper addresses the critical bottleneck in therapeutic peptide design: how to efficiently optimize lead peptides with geometric constraints while bridging the gap between computational generation and experimental validation.
核心创新
- Methodology Introduces POTFlow, the first lead peptide-conditioned flow matching model that incorporates secondary structure priors and optimal transport for shorter, disentangled generation paths
- Methodology Proposes a dry-to-wet framework that integrates computational design with experimental validation spanning in vitro assays and in vivo PDX models
- Biology Demonstrates successful optimization of ATP5A-binding peptides for glioblastoma, achieving improved tumor selectivity and in vivo efficacy
主要结论
- POTFlow outperforms five state-of-the-art methods across multiple metrics, achieving 53.44% similarity, 95.07% compactness, 30.56% affinity, and 1.66Å RMSD on benchmark datasets
- Generated peptide candidates showed 18-68% higher inhibition of viability rate (IVR) in GBM cells compared to non-cancerous cells (<10%), demonstrating improved tumor selectivity
- High-dose candidate 4 (20mg/kg) significantly prolonged survival in PDX models (p-value = 0.02) with 40% of mice surviving beyond week 18 compared to 0% in control group
摘要: Glioblastoma (GBM) remains the most aggressive tumor, urgently requiring novel therapeutic strategies. Here, we present a dry-to-wet framework combining generative modeling and experimental validation to optimize peptides targeting ATP5A, a potential peptide-binding protein for GBM. Our framework introduces the first lead-conditioned generative model, which focuses exploration on geometrically relevant regions around lead peptides and mitigates the combinatorial complexity of de novo methods. Specifically, we propose POTFlow, a Prior and Optimal Transport-based Flow-matching model for peptide optimization. POTFlow employs secondary structure information (e.g., helix, sheet, loop) as geometric constraints, which are further refined by optimal transport to produce shorter flow paths. With this design, our method achieves state-of-the-art performance compared with five popular approaches. When applied to GBM, our method generates peptides that selectively inhibit cell viability and significantly prolong survival in a patient-derived xenograft (PDX) model. As the first lead peptide-conditioned flow matching model, POTFlow holds strong potential as a generalizable framework for therapeutic peptide design.