Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A
- URL: http://arxiv.org/abs/2512.02030v1
- Date: Wed, 19 Nov 2025 11:17:17 GMT
- Title: Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A
- Authors: Hao Qian, Pu You, Lin Zeng, Jingyuan Zhou, Dengdeng Huang, Kaicheng Li, Shikui Tu, Lei Xu,
- Abstract summary: We present a framework combining generative modeling and experimental validation to optimize peptides targeting ATP5A.<n>As the first lead peptide-conditioned flow matching model, POTFlow holds strong potential as a generalizable framework for therapeutic peptide design.
- Score: 13.133911733817563
- License: http://creativecommons.org/licenses/by/4.0/
- 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 \underline{P}rior and \underline{O}ptimal \underline{T}ransport-based \underline{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.
Related papers
- MuCO: Generative Peptide Cyclization Empowered by Multi-stage Conformation Optimization [30.75292632688159]
We propose a generative peptide cyclization method that models the distribution of cyclic peptide conformations conditioned on the corresponding linear peptide.<n>MuCO decouples the peptide cyclization task into three stages: topology-aware backbone design, generative side-chain packing, and physics-aware all-atom optimization.<n> Experiments on the large-scale CPSea dataset demonstrate that MuCO significantly outperforms state-of-the-art methods in consistently in physical stability, structural diversity, secondary structure recovery, and computational efficiency.
arXiv Detail & Related papers (2026-01-30T10:02:15Z) - Surface-based Molecular Design with Multi-modal Flow Matching [64.00572241268597]
SurfFlow is a novel surface-based generative algorithm that enables comprehensive co-design of sequence, structure, and surface for peptides.<n> evaluated on the comprehensive PepMerge benchmark, SurfFlow consistently outperforms full-atom baselines across all metrics.
arXiv Detail & Related papers (2026-01-08T02:19:29Z) - Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints [65.77915791312634]
We propose CP-Composer, a novel generative framework that enables zero-shot cyclic peptide generation.<n>Our approach decomposes complex cyclization patterns into unit constraints, which are incorporated into a diffusion model.<n>Our model, despite trained with linear peptides, is capable of generating diverse target-binding cyclic peptides, reaching success rates from 38% to 84%.
arXiv Detail & Related papers (2025-07-06T03:30:45Z) - Protein Inverse Folding From Structure Feedback [78.27854221882572]
We introduce a novel approach to fine-tune an inverse folding model using feedback from a protein folding model.<n>Our results on the CATH 4.2 test set demonstrate that DPO fine-tuning leads to a significant improvement in average TM-Score.
arXiv Detail & Related papers (2025-06-03T16:02:12Z) - CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and Optimization [19.795752582745397]
Target-specific peptides, such as conotoxins, exhibit exceptional binding affinity and selectivity toward ion channels and receptors.<n>Here, we present CreoPep, a deep learning-based conditional generative framework that integrates masked language modeling with a progressive masking scheme to design high-affinity peptide mutants.<n>We validate this approach by designing conotoxin inhibitors targeting the $alpha$7 nicotinic acetylcholine receptor, achieving submicromolar potency in electrophysiological tests.
arXiv Detail & Related papers (2025-05-05T15:56:39Z) - THFlow: A Temporally Hierarchical Flow Matching Framework for 3D Peptide Design [14.436723124352817]
multimodal temporal inconsistency problem is a key factor contributing to low binding generated peptides.<n>We propose a novel flow-based generative model that explicitly models the temporal hierarchy between peptide position and conformation.<n> THFlow outperforms existing methods in generating peptides with superior stability, affinity, and diversity.
arXiv Detail & Related papers (2025-02-21T06:49:49Z) - PPFlow: Target-aware Peptide Design with Torsional Flow Matching [52.567714059931646]
We propose a target-aware peptide design method called textscPPFlow to model the internal geometries of torsion angles for the peptide structure design.<n>Besides, we establish a protein-peptide binding dataset named PPBench2024 to fill the void of massive data for the task of structure-based peptide drug design.
arXiv Detail & Related papers (2024-03-05T13:26:42Z) - Efficient Prediction of Peptide Self-assembly through Sequential and
Graphical Encoding [57.89530563948755]
This work provides a benchmark analysis of peptide encoding with advanced deep learning models.
It serves as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc.
arXiv Detail & Related papers (2023-07-17T00:43:33Z) - EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based
Models [53.17320541056843]
We propose a fully-differentiable approach for protein structure optimization, guided by a data-driven generative network.
Our EBM-Fold approach can efficiently produce high-quality decoys, compared against traditional Rosetta-based structure optimization routines.
arXiv Detail & Related papers (2021-05-11T03:40:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.