虽然许多计算方法准确地预测不稳定突变,识别稳定突变仍然是一个挑战,因为它们相对罕见。我们测试了来自Rosetta等计算预测因子的ΔΔG0预测,ThermoMPNN,RaSP,DeepDDG,使用细菌毒素CcdB的82个突变体作为测试案例。在这个数据集上,最好的计算预测因子是ThermoMPNN,它以68%的精确度识别稳定突变。然而,对于CcdB,这些预测突变的Tm平均增加仅为1°C,对于更具挑战性的目标,预测较差,流感神经氨酸酶.使用来自多个先前描述的酵母表面展示文库和体外热稳定性测量的数据,我们训练了logistic回归模型,以识别稳定性突变,准确率为90%,CcdB的Tm平均增加为3°C.当此类文库含有相对于相应野生型具有显著增强的结合的突变体群体时,使用计算预测因子没有好处。然后可以在没有任何训练的情况下预测稳定突变,只需检查突变结合得分的分布。这避免了费力的体外表达步骤,净化,和稳定性表征。如果不是这样,将来自计算预测因子的数据与高通量实验结合数据相结合,增强了对稳定突变的预测.然而,这需要对已知稳定突变体在体外测量的稳定性数据进行训练。因此,对于可以进行结合选择或筛选的任何感兴趣的系统,快速且准确地预测稳定化突变是可行的。
While many computational methods accurately predict destabilizing mutations, identifying stabilizing mutations has remained a challenge, because of their relative rarity. We tested ΔΔG0 predictions from computational predictors such as Rosetta, ThermoMPNN, RaSP, and DeepDDG, using 82 mutants of the bacterial toxin CcdB as a test case. On this dataset, the best computational predictor is ThermoMPNN, which identifies stabilizing mutations with a precision of 68%. However, the average increase in Tm for these predicted mutations was only 1°C for CcdB, and predictions were poorer for a more challenging target, influenza neuraminidase. Using data from multiple previously described yeast surface display libraries and in vitro thermal stability measurements, we trained logistic regression models to identify stabilizing mutations with a precision of 90% and an average increase in Tm of 3°C for CcdB. When such libraries contain a population of mutants with significantly enhanced binding relative to the corresponding wild type, there is no benefit in using computational predictors. It is then possible to predict stabilizing mutations without any training, simply by examining the distribution of mutational binding scores. This avoids laborious steps of in vitro expression, purification, and stability characterization. When this is not the case, combining data from computational predictors with high-throughput experimental binding data enhances the prediction of stabilizing mutations. However, this requires training on stability data measured in vitro with known stabilized mutants. It is thus feasible to predict stabilizing mutations rapidly and accurately for any system of interest that can be subjected to a binding selection or screen.