用几何图结构建模无序蛋白构象，提升预测精度。
GeoGraph: Geometric and Graph-based Ensemble Descriptors for Intrinsically Disordered Proteins
- 基于粗粒度模拟构建残基级图特征，捕捉序列与空间拓扑关系。
- 直接预测残基接触图的平均统计特性，优于现有方法。
- 适合研究无序蛋白动力学与功能机制的生物物理学者。
尽管深度学习已革新刚性蛋白质结构预测，但对内在无序蛋白（IDPs）构象集合的建模仍是前沿挑战。当前人工智能范式存在权衡：蛋白质语言模型（PLMs）捕捉进化统计信息，但缺乏显式物理基础；而训练全集合生成模型则计算成本高昂。本文批判性评估这些局限，并提出新路径。我们提出GeoGraph，一种基于模拟启发的代理模型，可直接从序列预测残基-残基接触图拓扑的集合平均统计特性。通过将粗粒度分子动力学模拟转化为残基与序列层级的图描述符，构建出鲁棒且信息丰富的学习目标。实验表明，该方法得到的表征在预测关键生物物理性质方面优于现有方法。
原文摘要 · Abstract (English)
While deep learning has revolutionized the prediction of rigid protein structures, modelling the conformational ensembles of Intrinsically Disordered Proteins (IDPs) remains a key frontier. Current AI paradigms present a trade-off: Protein Language Models (PLMs) capture evolutionary statistics but lack explicit physical grounding, while generative models trained to model full ensembles are computationally expensive. In this work we critically assess these limits and propose a path forward. We introduce GeoGraph, a simulation-informed surrogate trained to predict ensemble-averaged statistics of residue-residue contact-map topology directly from sequence. By featurizing coarse-grained molecular dynamics simulations into residue- and sequence-level graph descriptors, we create a robust and information-rich learning target. Our evaluation demonstrates that this approach yields representations that are more predictive of key biophysical properties than existing methods.
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