蛋白质 pKa 预测的高通量方法的性能比较,Journal of Chemical Information and Modeling

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蛋白质 pKa 预测的高通量方法的性能比较
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2023-08-07 , DOI: 10.1021/acs.jcim.3c00165
Wanlei Wei ₁ , Hervé Hogues ₁ , Traian Sulea ₁

Affiliation

基于蛋白质的疗法的医学相关领域引发了对具有生物相关性的不同 pH 环境中的蛋白质工程的需求。计算机工程工作流程通常采用高通量筛选活动，需要通过快速而准确的计算算法评估大量蛋白质残基和点突变。虽然存在几种高通量 p K _a预测方法，但由于目前缺乏全面的基准测试，它们的准确性尚不清楚。在这里，对来自 pKa 数据库 (PKAD) 的 408 个测量蛋白质残基 pKa 位移的_非冗余子集_系统地测试了七种快速、高效且易于使用的方法，包括 PROPKA3、DeepKa、PKAI、PKAI+、DelPhiPKa、MCCE2 和 H++。。虽然没有任何方法可以自信地超越原假设，如统计引导所示，但 DeepKa、PKAI+、PROPKA3 和 H++ 具有实用性。更具体地说，DeepKa 在多个和单个氨基酸残基类型的测试中始终表现良好，这体现在较低的错误、较高的相关性和改进的分类上。将最佳经验预测变量算术平均为简单共识，提高了整体可转移性和准确性，均方根误差高达 0.76 p K _a单位，相关系数 ( R 2 ) 与实验 p K _a变化的相关系数 ( R ² ) 为 0.45 。该分析应该为进一步的方法学发展提供基础并指导未来的应用，这些应用需要嵌入计算成本低廉的 pKa预测_方法，例如优化 pH 依赖性抗原结合的抗体。

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Comparative Performance of High-Throughput Methods for Protein pKa Predictions

The medically relevant field of protein-based therapeutics has triggered a demand for protein engineering in different pH environments of biological relevance. In silico engineering workflows typically employ high-throughput screening campaigns that require evaluating large sets of protein residues and point mutations by fast yet accurate computational algorithms. While several high-throughput pK_a prediction methods exist, their accuracies are unclear due to the lack of a current comprehensive benchmarking. Here, seven fast, efficient, and accessible approaches including PROPKA3, DeepKa, PKAI, PKAI+, DelPhiPKa, MCCE2, and H++ were systematically tested on a nonredundant subset of 408 measured protein residue pK_a shifts from the pK_a database (PKAD). While no method outperformed the null hypotheses with confidence, as illustrated by statistical bootstrapping, DeepKa, PKAI+, PROPKA3, and H++ had utility. More specifically, DeepKa consistently performed well in tests across multiple and individual amino acid residue types, as reflected by lower errors, higher correlations, and improved classifications. Arithmetic averaging of the best empirical predictors into simple consensuses improved overall transferability and accuracy up to a root-mean-square error of 0.76 pK_a units and a correlation coefficient (R²) of 0.45 to experimental pK_a shifts. This analysis should provide a basis for further methodological developments and guide future applications, which require embedding of computationally inexpensive pK_a prediction methods, such as the optimization of antibodies for pH-dependent antigen binding.

更新日期：2023-08-07

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