Paper List
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Pharmacophore-based design by learning on voxel grids
This paper addresses the computational bottleneck and limited novelty in conventional pharmacophore-based virtual screening by introducing a voxel cap...
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CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design
This paper addresses the critical limitation of current protein structure prediction models (like AlphaFold3) where high-confidence scores (pLDDT) can...
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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 ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A
Shanghai Jiao Tong University | QuietD Biotech
The 30-Second View
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.
Innovation (TL;DR)
- 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
Key conclusions
- 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
Abstract: 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.