Protein folds and the local environments they create can be compared using a variety of differently designed measures, such as the root mean squared deviation, the global distance test, the template modeling score or the local distance difference test. Although these measures have proven to be useful for a variety of tasks, each fails to fully incorporate the valuable chemical information inherent to atoms and residues, and considers these only partially and indirectly. Here, we develop the highly flexible local composition Hellinger distance (LoCoHD) metric, which is based on the chemical composition of local residue environments. Using LoCoHD, we analyze the chemical heterogeneity of amino acid environments and identify valines having the most conserved-, and arginines having the most variable chemical environments. We use LoCoHD to investigate structural ensembles, to evaluate critical assessment of structure prediction (CASP) competitors, to compare the results with the local distance difference test (lDDT) scoring system, and to evaluate a molecular dynamics simulation. We show that LoCoHD measurements provide unique information about protein structures that is distinct from, for example, those derived using the alignment-based RMSD metric, or the similarly distance matrix-based but alignment-free lDDT metric.

蛋白质折叠和它们创建的局部环境可以使用各种不同设计的测量进行比较，例如均方根偏差、全局距离测试、模板建模得分或局部距离差异测试。尽管这些措施已被证明对各种任务有用，但每种措施都未能完全纳入原子和残留物固有的有价值的化学信息，并且仅部分和间接地考虑这些信息。在这里，我们开发了高度灵活的局部成分海林格距离（LoCoHD）度量，该度量基于局部残留物环境的化学成分。使用 LoCoHD，我们分析了氨基酸环境的化学异质性，并识别了具有最保守化学环境的缬氨酸和具有最可变化学环境的精氨酸。我们使用 LoCoHD 来研究结构集成，评估结构预测 (CASP) 竞争对手的关键评估，将结果与局部距离差异测试 (lDDT) 评分系统进行比较，并评估分子动力学模拟。我们表明，LoCoHD 测量提供了有关蛋白质结构的独特信息，这些信息不同于使用基于对齐的 RMSD 度量或类似的基于距离矩阵但无对齐的 IDDT 度量导出的信息。