Institute of Science and Technology Austria, Klosterneuburg, Austria.
Synthetic Biology Group, MRC London Institute of Medical Sciences, London, United Kingdom.
Elife. 2022 May 5;11:e75842. doi: 10.7554/eLife.75842.
Studies of protein fitness landscapes reveal biophysical constraints guiding protein evolution and empower prediction of functional proteins. However, generalisation of these findings is limited due to scarceness of systematic data on fitness landscapes of proteins with a defined evolutionary relationship. We characterized the fitness peaks of four orthologous fluorescent proteins with a broad range of sequence divergence. While two of the four studied fitness peaks were sharp, the other two were considerably flatter, being almost entirely free of epistatic interactions. Mutationally robust proteins, characterized by a flat fitness peak, were not optimal templates for machine-learning-driven protein design - instead, predictions were more accurate for fragile proteins with epistatic landscapes. Our work paves insights for practical application of fitness landscape heterogeneity in protein engineering.
研究蛋白质适应度景观揭示了指导蛋白质进化的生物物理限制，并能够预测功能蛋白。然而，由于缺乏具有明确进化关系的蛋白质适应度景观的系统数据，这些发现的推广受到限制。我们对四个具有广泛序列差异的同源荧光蛋白的适应度峰进行了特征描述。虽然研究的四个适应度峰中的两个是尖锐的，但另外两个则相当平坦，几乎完全没有上位性相互作用。具有平坦适应度峰的突变稳健蛋白并不是机器学习驱动的蛋白质设计的最佳模板 - 相反，对于具有上位性景观的脆弱蛋白，预测更为准确。我们的工作为在蛋白质工程中实际应用适应度景观异质性提供了新的见解。
