Department of BioModeling BioInformatics and BioProcesses, Université Libre de Bruxelles and Interuniversity Institute of Bioinformatics in Brussels, Triumph Bld, 1050 Brussels, Belgium.
Bioinformatics. 2017 Nov 1;33(21):3415-3422. doi: 10.1093/bioinformatics/btx417.
The molecular bases of protein stability remain far from elucidated even though substantial progress has been made through both computational and experimental investigations. One of the most challenging goals is the development of accurate prediction tools of the temperature dependence of the standard folding free energy ΔG(T). Such predictors have an enormous series of potential applications, which range from drug design in the biopharmaceutical sector to the optimization of enzyme activity for biofuel production. There is thus an important demand for novel, reliable and fast predictors.
We present the SCooP algorithm, which is a significant step towards accurate temperature-dependent stability prediction. This automated tool uses the protein structure and the host organism as sole entries and predicts the full T-dependent stability curve of monomeric proteins assumed to follow a two-state folding transition. Equivalently, it predicts all the thermodynamic quantities associated to the folding transition, namely the melting temperature Tm, the standard folding enthalpy ΔHm measured at Tm, and the standard folding heat capacity ΔCp. The cross-validated performances are good, with correlation coefficients between predicted and experimental values equal to [0.80, 0.83, 0.72] for ΔHm, ΔCp and Tm, respectively, which increase up to [0.88, 0.90, 0.78] upon the removal of 10% outliers. Moreover, the stability curve prediction of a target protein is very fast: it takes less than a minute. SCooP can thus potentially be applied on a structurome scale. This opens new perspectives of large-scale analyses of protein stability, which is of considerable interest for protein engineering.
The SCooP webserver is freely available at http://babylone.ulb.ac.be/SCooP.
fapucci@ulb.ac.be, jkwasigr@ulb.ac.be or mrooman@ulb.ac.be.
Supplementary data are available at Bioinformatics online.
尽管通过计算和实验研究已经取得了相当大的进展，但蛋白质稳定性的分子基础仍远未阐明。最具挑战性的目标之一是开发准确预测标准折叠自由能 ΔG(T)温度依赖性的预测工具。这些预测器具有广泛的潜在应用，从生物制药领域的药物设计到生物燃料生产中酶活性的优化。因此，对新的、可靠的和快速的预测器有很大的需求。
我们提出了 SCooP 算法，这是朝着准确的温度依赖性稳定性预测迈出的重要一步。该自动化工具仅使用蛋白质结构和宿主生物体作为唯一输入，并预测假定遵循两态折叠转变的单体蛋白质的完整 T 依赖性稳定性曲线。等效地，它预测与折叠转变相关的所有热力学量，即熔点 Tm、在 Tm 测量的标准折叠焓 ΔHm 以及标准折叠热容 ΔCp。交叉验证的性能良好，预测值与实验值之间的相关系数分别为[0.80、0.83、0.72]，对于 ΔHm、ΔCp 和 Tm，当去除 10%异常值时，增加到[0.88、0.90、0.78]。此外，目标蛋白质的稳定性曲线预测非常快：不到一分钟。因此，SCooP 可能适用于结构组学规模。这为蛋白质稳定性的大规模分析开辟了新的前景，这对于蛋白质工程具有重要意义。
SCooP 网络服务器可免费在 http://babylone.ulb.ac.be/SCooP 获得。
fapucci@ulb.ac.be、jkwasigr@ulb.ac.be 或 mrooman@ulb.ac.be。
补充数据可在 Bioinformatics 在线获得。
