Protein stability: computation, sequence statistics, and new experimental methods.
Protein stability: computation, sequence statistics, and new experimental methods.
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DOI：
10.1016/j.sbi.2015.09.002
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发表时间：
2015-08
影响因子：
6.8
通讯作者：
Magliery TJ
中科院分区：
文献类型：
--
作者：
Magliery TJ
计算蛋白质稳定性和预测稳定突变仍然是非常困难的任务，主要是由于势函数的不足，熵和展开状态建模的困难，以及采样的挑战，特别是骨架构象。然而，近年来，计算机设计已经产生了一些非常稳定的蛋白质，显然是由于结构和序列特征接近理想。需要注意的是，稳定性的计算预测可用于指导突变，并且来自共有序列分析的突变，特别是通过最近的共变过滤器改进的突变，很可能在不牺牲功能的情况下稳定。计算和统计方法与文库方法的结合，包括深度测序和高通量稳定性测量等新技术，为稳定性工程指明了一个非常令人兴奋的近期未来，即使存在困难的计算问题。
Calculating protein stability and predicting stabilizing mutations remain exceedingly difficult tasks, largely due to the inadequacy of potential functions, the difficulty of modeling entropy and the unfolded state, and challenges of sampling, particularly of backbone conformations. Yet, computational design has produced some remarkably stable proteins in recent years, apparently owing to near ideality in structure and sequence features. With caveats, computational prediction of stability can be used to guide mutation, and mutations derived from consensus sequence analysis, especially improved by recent co-variation filters, are very likely to stabilize without sacrificing function. The combination of computational and statistical approaches with library approaches, including new technologies such as deep sequencing and high throughput stability measurements, point to a very exciting near term future for stability engineering, even with difficult computational issues remaining.