arXiv cs.AI· Lisheng Zhang, Qingsong Li, Zinan Xie, Yanting Yang, Qikui Yang, Peng Lu, Zhican Fu, Wentao Cui, Runtong Hou, Zhengwei Xie·· 11 小时前AI 评分44
FoldScape：从序列预测蛋白质任意折叠阶段的结构
Predicting protein structures at any folding stage from sequence
AI 导读
FoldScape 是一个序列条件深度学习框架，可按指定折叠分数 Q 生成蛋白质构象系综，训练数据为 10,000 条蛋白质序列的约 100 万个构象。在匹配样本对比中，其构象空间覆盖率在回转半径、天然接触分数 Q 和二级结构分数上分别达到 0.989、0.722 和 0.877，而 AlphaFold 3 系综仅为 0.05、0.05 和 0.10。
正文 · 原文
Abstract:Protein structure prediction has largely focused on native conformations, leaving the continuum of unfolded states, folding intermediates, and native structures difficult to access. We present FoldScape, a sequence-conditioned deep learning framework for generating protein conformational ensembles at specified folding fractions $Q$. Trained on approximately one million conformations from 10,000 protein sequences, FoldScape combines coarse-grained folding and all-atom mechanical-unfolding trajectories. It reconstructs three-dimensional $C_\alpha$ chains through hierarchical sliding-window regressions of inter-residue distances, chain-level assembly, and a rectified-flow decoder. By conditioning on folding progress, FoldScape generates conformations from unfolded states through intermediates to native-state structures without protein-specific molecular dynamics at inference. In matched-sample comparisons, FoldScape achieves median conformational-space coverage of 0.989, 0.722, and 0.877 along radius of gyration, fraction of native contacts $Q$, and secondary-structure fraction, respectively, versus 0.05, 0.05, and 0.10 for seed-varied AlphaFold 3 ensembles. Coverage advantages are consistent across the evaluated proteins and robust to binning choices. Reconstruction accuracy remains approximately stable across the evaluated stages of a folding trajectory, and stride-one crop-and-superpose inference extends predictions to chains several times longer than the single-pass limit. Analyses of mechanical-unfolding trajectories show residue-resolved pathways, reproducible extension-conditioned observables across pulling velocities, and geometrically coherent segments in partially unfolded chains. FoldScape extends sequence-based structure prediction beyond a single folded endpoint to controllable conformational-ensemble generation across protein folding.
| Subjects: | Biomolecules (q-bio.BM); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.04287 [q-bio.BM] |
| (or arXiv:2611.04287v2 [q-bio.BM] for this version) | |
| https://doi.org/10.48550/arXiv.2611.04287 arXiv-issued DOI via DataCite |
Submission history
From: Lisheng Zhang [view email]
[v1]
Tue, 3 Nov 2026 04:15:20 UTC (7,583 KB)
[v2]
Wed, 4 Nov 2026 03:46:56 UTC (7,583 KB)
来源：arXiv cs.AI · arxiv.org