Center for Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109-2218, USA.
J Mol Biol. 2011 Apr 15;407(5):764-76. doi: 10.1016/j.jmb.2011.02.017. Epub 2011 Feb 15.
Protein design aims at designing new protein molecules of desired structure and functionality. One of the major obstacles to large-scale protein design are the extensive time and manpower requirements for experimental validation of designed sequences. Recent advances in protein structure prediction have provided potentials for an automated assessment of the designed sequences via folding simulations. We present a new protocol for protein design and validation. The sequence space is initially searched by Monte Carlo sampling guided by a public atomic potential, with candidate sequences selected by the clustering of sequence decoys. The designed sequences are then assessed by I-TASSER folding simulations, which generate full-length atomic structural models by the iterative assembly of threading fragments. The protocol is tested on 52 nonhomologous single-domain proteins, with an average sequence identity of 24% between the designed sequences and the native sequences. Despite this low sequence identity, three-dimensional models predicted for the first designed sequence have an RMSD of <2 Å to the target structure in 62% of cases. This percentage increases to 77% if we consider the three-dimensional models from the top 10 designed sequences. Such a striking consistency between the target structure and the structural prediction from nonhomologous sequences, despite the fact that the design and folding algorithms adopt completely different force fields, indicates that the design algorithm captures the features essential to the global fold of the target. On average, the designed sequences have a free energy that is 0.39 kcal/(mol residue) lower than in the native sequences, potentially affording a greater stability to synthesized target folds.
蛋白质设计旨在设计具有所需结构和功能的新蛋白质分子。大规模蛋白质设计的主要障碍之一是设计序列的实验验证需要大量的时间和人力。最近蛋白质结构预测的进展为通过折叠模拟对设计序列进行自动评估提供了潜力。我们提出了一种新的蛋白质设计和验证方案。首先通过公共原子势的蒙特卡罗采样搜索序列空间,然后通过序列诱饵的聚类选择候选序列。然后通过 I-TASSER 折叠模拟评估设计序列,该模拟通过串联片段的迭代组装生成全长原子结构模型。该方案在 52 个非同源单域蛋白上进行了测试,设计序列与天然序列之间的平均序列同一性为 24%。尽管序列同一性较低,但对于第一个设计序列,预测的三维模型在 62%的情况下与目标结构的 RMSD<2 Å。如果我们考虑前 10 个设计序列中的三维模型,则该百分比增加到 77%。尽管设计和折叠算法采用完全不同的力场,但在非同源序列中,目标结构和结构预测之间存在如此显著的一致性,这表明设计算法捕捉到了目标全局折叠的关键特征。平均而言,设计序列的自由能比天然序列低 0.39 kcal/(mol 残基),这可能为合成目标折叠提供更大的稳定性。