Sign in
BioinformaticsbioRxiv
Preprint

trFlow: ultrafast all-atom protein conformational ensemble generation via conditional flow matching

Chenxiao Xiang, Kailong Zhao, Zhenling PengiD, Wenkai WangiD, Jianyi YangiD

Abstract

Generating diverse, all-atom protein conformations remains challenging, especially for recovering alternative functional states and modeling conformational ensembles. We present trFlow, an ultrafast approach for generating all-atom conformations using geometry-guided flow matching. trFlow leverages three core innovations: (1) a hybrid single- and multi-step sampling scheme that balances accuracy and speed; (2) a decoupled representation-generation architecture that maximizes computational efficiency; and (3) fine-tuning on NMR ensembles to better capture conformational heterogeneity. Extensive benchmarking across dual-conformation proteins, NMR ensembles, and a CASP16 dynamic target shows that trFlow outperforms competing methods in recovering challenging states and reproducing experimental heterogeneity, while being an order of magnitude faster than existing all-atom approaches. These results establish trFlow as a practical and scalable framework for large-scale generation of all-atom conformational ensembles.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.