Liu Haiyan, Chen Quan
School of Life Sciences, University of Science and Technology of China, China; Hefei National Laboratory for Physical Sciences at the Microscales, China; Collaborative Innovation Center of Chemistry for Life Sciences, Hefei, Anhui 230027, China; Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China.
School of Life Sciences, University of Science and Technology of China, China.
Curr Opin Struct Biol. 2016 Aug;39:89-95. doi: 10.1016/j.sbi.2016.06.013. Epub 2016 Jun 24.
To achieve high success rate in protein design requires a reliable sequence design method to find amino acid sequences that stably fold into a desired backbone structure. This problem is addressed by computational protein design through the approach of energy minimization. Here we review recent method progresses related to improving the accuracy of this approach. First, the quality of the energy model is a key factor. Second, with structure sensitive energy functions, whether and how backbone flexibility is considered can have large effects on design accuracy, although usually only small adjustments of the backbone structure itself are involved. Third, the effective accuracy of design results can be boosted by post-processing a small number of designed sequences with complementary models that may not be efficient enough for full sequence optimization. Finally, computational method development will benefit greatly from increasingly efficient experimental approaches that can be applied to obtain extensive feedbacks.
要在蛋白质设计中实现高成功率，需要一种可靠的序列设计方法来找到能稳定折叠成所需主链结构的氨基酸序列。通过能量最小化方法的计算蛋白质设计解决了这个问题。在此，我们综述了与提高该方法准确性相关的近期方法进展。首先，能量模型的质量是一个关键因素。其次，对于结构敏感的能量函数，是否考虑以及如何考虑主链柔性会对设计准确性产生很大影响，尽管通常只涉及主链结构本身的微小调整。第三，通过用互补模型对少量设计序列进行后处理，可以提高设计结果的有效准确性，而这些互补模型对于完整序列优化可能效率不够高。最后，计算方法的发展将极大地受益于越来越高效的实验方法，这些方法可用于获得广泛的反馈。
