Paper List
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SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
This paper addresses the core challenge of robust single-cell annotation across heterogeneous datasets with batch effects and the critical need to ide...
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Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
This paper addresses the core challenge of efficiently and accurately sampling the conformational landscape of biomolecules from diffusion-based struc...
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Personalized optimization of pediatric HD-tDCS for dose consistency and target engagement
This paper addresses the critical limitation of one-size-fits-all HD-tDCS protocols in pediatric populations by developing a personalized optimization...
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Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
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Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
This paper addresses the core pain point of low sequence-structure alignment in existing synthetic datasets (e.g., AFDB), which severely limits the pe...
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MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
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.