Development of an accessible integrated computational protein design software suite

这项研究计划的目标是在我们现有的方法，即正常模式分析方法Encom和FlexAID(Gaudreault等人，2014)的基础上，开发一套与现有软件竞争但不同于现有CPD软件的集成计算蛋白质设计(CPD)软件套件，易于使用；使非专家能够进行计算蛋白质设计。我们先前已经证明，基于熵的Encom方法非常适合于稳定突变的预测(Frappier等人，2014年)，因为它们旨在达到可证明的全球最小能量配置(GMEC)。我们的第二个目标是利用我们的对接软件FlexAID已经使用的遗传算法(GA)搜索方法来执行概率搜索，以允许对序列和构象空间进行更广泛的搜索。第三个目标是在GA适应度函数中集成溶解度预测和在保持稳定性和灵活性的同时消除二硫键的过程。我们的第四个目标是将上述方法集成到我们用户友好的NRG套件图形用户界面中(Gaudreault，2015)。我们将使用人乳铁蛋白作为实验模型系统。乳铁蛋白具有广泛的抗菌特性，在人类健康和食品技术中有着广泛的应用(布罗克，2011)。我们预测的额外实验验证将由Amai对thaumatin和monellin进行优化。拟议使用Encom作为拟议的新型CPD套件中的中心工具的一个独特优势是，它允许我们测量突变对蛋白质结构所有部分动力学的影响。这反过来又允许我们引入突变，这些突变可能会改变稳定性，而不会影响蛋白质特定区域的动态，这些区域对特定功能可能是重要的。上述项目在使用我们现有的工具方面具有很高的创新性，并将产生一个高性能的计算蛋白质设计软件套件，所有人都可以使用。最后，开发更稳定形式的乳铁蛋白和甜蛋白的潜在好处可能预示着在人类健康和食品技术中的重要应用，此外还有助于在实验上验证我们的计算预测。这些试验性应用程序还将指导我们的软件功能的进一步开发，以适应学术和工业用户的需求。

The goal of this research program is to build upon our existing methods, namely the Normal Mode Analysis method ENCoM and FlexAID (Gaudreault et al., 2014), to develop an integrated computational protein design (CPD) software suite that performs competitively with existing software but unlike existing CPD software, is easy to use; Making computational protein design accessible to non-experts. We previously showed that the entropy-based ENCoM method is well suited for the prediction of stabilizing mutations (Frappier et al., 2014 tations and minimal backbone movements as they aim at reaching provable global minimum energy configurations (GMEC). Our second goal is to utilize our genetic-algorithm (GA) search method already used for our docking software FlexAID to perform probabilistic searches to allow a wider search of sequence and conformational space. The third objective is to integrate within the GA fitness function the prediction of solubility and a procedure to eliminate dissulfide bonds while maintaining stability and flexibility. Our fourth objective is to integrate the above methods within our user-friendly NRGsuite GUI(Gaudreault, 2015). We will utilize human lactoferrin as experimental model system. Lactoferrin has ample applications in human health and food technology due to among others, its broad antibacterial properties (Brock, 2011). Additional experimental validation for our predictions will be performed by Amai on the optimisation of thaumatin and monellin. One unique advantage of the proposed use of ENCoM as a central tool within the proposed novel CPD suite is that it allows us to measure the effect of mutations on dynamics across all sections of a protein structure. This in turn lets us to introduce mutations that may change stability without affecting the dynamics in specific areas of the protein that may be important for a particular function. The above project is highly innovative in its use of our existing tools and will produce a high-performing computational protein design software suite accessible to all. Lastly, the potential benefits from developing more stable forms of lactoferrin as well as sweet proteins may herald important applications in human health and food technology in addition to serving to validate experimentally our computational predictions. These experimental applications will also guide further development of the capabilities of our software to suit the needs of both academic and industrial users.