omputational energy-based redesign of robust proteins
omputational energy-based redesign of robust proteins
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发表时间：
2010
影响因子：
15.9
通讯作者：
iovanni Stracquadanio;Giuseppe Nicosia
中科院分区：
文献类型：
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作者：
iovanni Stracquadanio;Giuseppe Nicosia
一个系统的鲁棒性是一个性质，渗透到自然界的各个方面。一个系统适应内部和外部因素、老化、磨损、环境变化等扰动的能力是进化的驱动力之一。在分子水平上，了解蛋白质的鲁棒性对多肽链和药物的硅设计有很大的影响;计算检查蛋白质在天然状态下保持其结构和功能的能力的机会可以导致设计新的化合物，这些化合物可以在活细胞中更有效地工作。受电子设计自动化中鲁棒性分析框架的启发，我们引入了蛋白质鲁棒性的概念和两个无量纲量：能量产率和能量相对熵。我们使用能量产率来量化蛋白质的鲁棒性，并检测蛋白质中的敏感区域和敏感残基，而我们采用能量相对熵来衡量两个势能分布之间的差异。随后，我们实现了一种新的鲁棒性为中心的蛋白质设计算法，称为鲁棒蛋白质设计（RPD）;该算法的目的是发现具有高产量值的特定功能的新构象。我们对许多肽、蛋白质和药物的稳健性进行了广泛的表征。此外，我们发现，鲁棒性和相对熵是相互冲突的目标，构成了一个权衡有用的新蛋白质和药物的设计原则。最后，我们对Crambin蛋白（1CRN）使用了RPD算法;所获得的结果证实，该算法能够找到比野生型高23%的Crambin样蛋白。
The robustness of a system is a property that pervades all aspects of Nature. The ability of a system to adapt itself to perturbations due to internal and external agents, to aging, to wear, to environmental changes is one of the driving forces of evolution. At the molecular level, understanding the robustness of a protein has a great impact on the in silicon design of polypeptide chains and drugs; the chance of computationally checking the ability of a protein to preserve its structure and function in the native state can lead to the design of new compounds that can work in a living cell more effectively. Inspired by the well known robustness analysis framework used in Electronic Design Automation, we introduced a notion of robustness for proteins and two dimensionless quantities: the energetic yield and the energetic relative entropy. We used the energetic yield in order to quantify the robustness of a protein, and to detect sensitive regions and sensitive residues in the protein, whereas we adopted the energetic relative entropy to measure the discrepancy between two potential energy distributions. Subsequently, we implemented a new robustness-centred protein design algorithm called Robust-Protein-Design (RPD); the aim of the algorithm is to discover new conformations with a specific function with high yield values. We performed an extensive characterization of the robustness property of many peptides, proteins, and drugs. Moreover, we found that robustness and relative entropy are conflicting objectives which constitute a trade-off useful as design principle for new proteins and drugs. Finally, we used the RPD algorithm on the Crambin protein (1CRN); the obtained results confirm that the algorithm was able to find out a Crambin-like protein that is 23% more robust than the wild type.