NMR chemical shifts (CSs) are delicate reporters of local protein structure, and recent advances in random coil CS (RCCS) prediction and interpretation now offer the compelling prospect of inferring small populations of structure from small deviations from RCCSs. Here, we present CheSPI, a simple and efficient method that provides unbiased and sensitive aggregate measures of local structure and disorder. It is demonstrated that CheSPI can predict even very small amounts of residual structure and robustly delineate subtle differences into four structural classes for intrinsically disordered proteins. For structured regions and proteins, CheSPI provides predictions for up to eight structural classes, which coincide with the well-known DSSP classification. The program is freely available, and can either be invoked from URL www.protein-nmr.org as a web implementation, or run locally from command line as a python program. CheSPI generates comprehensive numeric and graphical output for intuitive annotation and visualization of protein structures. A number of examples are provided.

NMR 化学位移 (CS) 是局部蛋白质结构的微妙报告者，并且最近在无规卷曲 CS (RCCS) 预测和解释方面的进展提供了从 RCCS 的小偏差推断小结构群的令人信服的前景。在这里，我们展示了 CheSPI，这是一种简单有效的方法，可提供对局部结构和无序的无偏和敏感的聚合测量。事实证明，CheSPI 甚至可以预测非常少量的残留结构，并将本质上无序的蛋白质的细微差异准确地划分为四个结构类别。对于结构化区域和蛋白质，CheSPI 提供多达八个结构类别的预测，这与众所周知的 DSSP 分类一致。该程序是免费提供的，并且可以从 URL www.protein-nmr 调用。org 作为 Web 实现，或从命令行作为 Python 程序在本地运行。CheSPI 生成全面的数字和图形输出，用于蛋白质结构的直观注释和可视化。提供了许多示例。