Ohio State Biochemistry Program, The Ohio State University, Columbus, OH 43210, USA.
J Mol Biol. 2012 Jul 20;420(4-5):384-99. doi: 10.1016/j.jmb.2012.04.025. Epub 2012 May 1.
Understanding the determinants of protein stability remains one of protein science's greatest challenges. There are still no computational solutions that calculate the stability effects of even point mutations with sufficient reliability for practical use. Amino acid substitutions rarely increase the stability of native proteins; hence, large libraries and high-throughput screens or selections are needed to stabilize proteins using directed evolution. Consensus mutations have proven effective for increasing stability, but these mutations are successful only about half the time. We set out to understand why some consensus mutations fail to stabilize, and what criteria might be useful to predict stabilization more accurately. Overall, consensus mutations at more conserved positions were more likely to be stabilizing in our model, triosephosphate isomerase (TIM) from Saccharomyces cerevisiae. However, positions coupled to other sites were more likely not to stabilize upon mutation. Destabilizing mutations could be removed both by removing sites with high statistical correlations to other positions and by removing nearly invariant positions at which "hidden correlations" can occur. Application of these rules resulted in identification of stabilizing mutations in 9 out of 10 positions, and amalgamation of all predicted stabilizing positions resulted in the most stable yeast TIM variant we produced (+8 °C). In contrast, a multimutant with 14 mutations each found to stabilize TIM independently was destabilized by 2 °C. Our results are a practical extension to the consensus concept of protein stabilization, and they further suggest the importance of positional independence in the mechanism of consensus stabilization.
理解蛋白质稳定性的决定因素仍然是蛋白质科学面临的最大挑战之一。目前还没有计算方法能够足够可靠地计算出甚至点突变对蛋白质稳定性的影响，使其能够实际应用。氨基酸取代很少能增加天然蛋白质的稳定性；因此，需要使用定向进化来稳定蛋白质，需要构建大型文库并进行高通量筛选或选择。共识突变已被证明可有效提高稳定性，但这些突变只有大约一半的成功率。我们着手研究为什么有些共识突变不能稳定蛋白质，并确定哪些标准可能有助于更准确地预测稳定性。总体而言，在我们的模型中，来自酿酒酵母的磷酸丙糖异构酶（TIM）中，更保守位置的共识突变更有可能稳定蛋白质。然而，与其他位置偶联的位置在突变后更不可能稳定。通过去除与其他位置具有高统计相关性的位置以及去除可能发生“隐藏相关性”的几乎不变位置，可以去除破坏稳定性的突变。应用这些规则可在 10 个位置中的 9 个位置中识别出稳定突变，并且合并所有预测的稳定位置可产生我们生产的最稳定的酵母 TIM 变体（+8°C）。相比之下，一个独立发现有 14 个突变稳定 TIM 的多突变体却降低了 2°C 的稳定性。我们的结果是对蛋白质稳定的共识概念的实际扩展，并且进一步表明了在共识稳定机制中位置独立性的重要性。
