美国华盛顿大学医学院Alex S. Holehouse团队近期取得重要工作进展，他们研究提出了从序列直接预测内在无序的蛋白质构象性质的方法。相关研究成果2024年1月31日在线发表于《自然—方法学》杂志上。

据介绍，内在无序区（IDR）普遍存在于生命的所有领域，并发挥着一系列功能作用。虽然折叠域通常用稳定的三维结构来很好地描述，但IDR存在于一组称为系综的相互转换状态中。这种结构异质性意味着IDR在很大程度上不存在于蛋白质数据库中，这导致缺乏从序列中预测整体构象特性的计算方法。

研究人员将合理的序列设计、大规模分子模拟和深度学习相结合，开发ALBATROS，这是一种深度学习模型，用于直接从蛋白质组范围的序列中预测IDR的集合维度，包括回转半径、端到端距离、聚合物标度指数和整体非球面度。ALBATROSS是轻量级的，易于使用，可作为本地安装的软件包和通过Google Colab笔记本电脑的点击式界面访问。研究人员首先通过检查IDR中序列-集合关系的可推广性来证明这一预测因子的适用性。然后，研究人员利用ALBATROSS的高通量特性，来表征蛋白质组内和蛋白质组间IDR的序列特异性生物物理行为。

附：英文原文

Title: Direct prediction of intrinsically disordered protein conformational properties from sequences

Author: Lotthammer, Jeffrey M., Ginell, Garrett M., Griffith, Daniel, Emenecker, Ryan J., Holehouse, Alex S.

Issue&Volume: 2024-01-31

Abstract: Intrinsically disordered regions (IDRs) are ubiquitous across all domains of life and play a range of functional roles. While folded domains are generally well described by a stable three-dimensional structure, IDRs exist in a collection of interconverting states known as an ensemble. This structural heterogeneity means that IDRs are largely absent from the Protein Data Bank, contributing to a lack of computational approaches to predict ensemble conformational properties from sequence. Here we combine rational sequence design, large-scale molecular simulations and deep learning to develop ALBATROSS, a deep-learning model for predicting ensemble dimensions of IDRs, including the radius of gyration, end-to-end distance, polymer-scaling exponent and ensemble asphericity, directly from sequences at a proteome-wide scale. ALBATROSS is lightweight, easy to use and accessible as both a locally installable software package and a point-and-click-style interface via Google Colab notebooks. We first demonstrate the applicability of our predictors by examining the generalizability of sequence–ensemble relationships in IDRs. Then, we leverage the high-throughput nature of ALBATROSS to characterize the sequence-specific biophysical behavior of IDRs within and between proteomes.

DOI: 10.1038/s41592-023-02159-5

Source: https://www.nature.com/articles/s41592-023-02159-5