Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India.
PLoS One. 2018 Oct 4;13(10):e0203036. doi: 10.1371/journal.pone.0203036. eCollection 2018.
Attaining recombinant thermostable proteins is still a challenge for protein engineering. The complexity is the length of time and enormous efforts required to achieve the desired results. Present work proposes a novel and economic strategy of attaining protein thermostability by predicting site-specific mutations at the shortest possible time. The success of the approach can be attributed to Analytical Hierarchical Process and the outcome was a rationalized thermostable mutation(s) prediction tool- RankProt. Briefly the method involved ranking of 17 biophysical protein features as class predictors, derived from 127 pairs of thermostable and mesostable proteins. Among the 17 predictors, ionic interactions and main-chain to main-chain hydrogen bonds were the highest ranked features with eigen value of 0.091. The success of the tool was judged by multi-fold in silico validation tests and it achieved the prediction accuracy of 91% with AUC 0.927. Further, in vitro validation was carried out by predicting thermostabilizing mutations for mesostable Bacillus subtilis lipase and performing the predicted mutations by multi-site directed mutagenesis. The rationalized method was successful to render the lipase thermostable with optimum temperature stability and Tm increase by 20°C and 7°C respectively. Conclusively it can be said that it was the minimum number of mutations in comparison to the number of mutations incorporated to render Bacillus subtilis lipase thermostable, by directed evolution techniques. The present work shows that protein stabilizing mutations can be rationally designed by balancing the biophysical pleiotropy of proteins, in accordance to the selection pressure.
获得重组热稳定蛋白仍然是蛋白质工程的一个挑战。其复杂性在于需要花费很长时间和大量精力才能达到预期的结果。目前的工作提出了一种通过在最短时间内预测特定位置突变来获得蛋白质热稳定性的新颖且经济的策略。该方法的成功可以归因于层次分析法，其结果是一个合理化的热稳定突变预测工具——RankProt。简而言之，该方法涉及对 17 种生物物理蛋白特征进行排序作为分类预测因子，这些特征源自 127 对热稳定和中稳定蛋白。在 17 个预测因子中，离子相互作用和主链到主链氢键是排名最高的特征，特征值为 0.091。该工具的成功通过多次计算机模拟验证测试来判断，其预测准确率达到 91%，AUC 为 0.927。此外，通过预测中稳定性枯草芽孢杆菌脂肪酶的热稳定突变，并通过多位点定向诱变进行预测突变，进行了体外验证。通过合理化方法成功地使脂肪酶热稳定，最佳温度稳定性和 Tm 分别增加了 20°C 和 7°C。可以说，与定向进化技术使枯草芽孢杆菌脂肪酶热稳定所引入的突变数量相比，突变数量最少。本工作表明，可以通过平衡蛋白质的生物物理多效性，根据选择压力，合理设计蛋白质稳定突变。
