Development of an accessible integrated computational protein design software suite

该研究计划的目标是建立在我们现有的方法，即简正波分析方法ENCoM和FlexAID（Gaudreault等人，2014年），开发一个集成的计算蛋白质设计（CPD）软件套件，与现有的软件竞争，但不同于现有的CPD软件，易于使用;使计算蛋白质设计非专家访问。我们先前表明，基于熵的ENCoM方法非常适合于预测稳定突变（Frappier等人，2014 & 2015）。这使得我们在与Amai蛋白的现有合作中利用它来帮助他们提高甜蛋白的热稳定性。该计划的第一个目标是在集成软件中引入不同的焓预测方法。与CPD相关的一个重要问题是当考虑多个突变位点上的多个侧链构象以及主链灵活性时，搜索空间的巨大尺寸。现有的方法仅限于非常少量的突变和最小的骨架移动，因为它们的目的是达到可证明的全局最小能量构型（GMEC）。我们的第二个目标是利用我们的遗传算法（GA）搜索方法已经用于我们的对接软件FlexAID进行概率搜索，以允许更广泛的搜索序列和构象空间。第三个目标是在GA适应度函数中集成溶解度的预测和在保持稳定性和灵活性的同时消除二硫键的程序。我们的第四个目标是将上述方法集成到用户友好的NRG套件GUI中（Gaudreault，2015）。我们将利用人乳铁蛋白作为实验模型系统。乳铁蛋白在人类健康和食品技术中具有广泛的应用，尤其是由于其广泛的抗菌特性（Brock，2011）。Amai将对我们的预测进行额外的实验验证，优化奇异果甜蛋白和莫内林。建议使用ENCoM作为所提出的新型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的所有部分的动态的影响。这反过来又让我们引入突变，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，这些区域可能对特定功能很重要。上述项目在使用我们现有的工具方面具有高度创新性，并将产生一个所有人都可以使用的高性能计算蛋白质设计软件套件。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，除了用于实验验证我们的计算预测。这些实验性应用也将指导我们软件功能的进一步开发，以满足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 & 2015). This led to its utilisation in our existing collaboration with Amai proteins to help them increase the thermal stability of sweet proteins. The first goal of this program is to introduce different enthalpy prediction methods within an integrated software. One important issue related to CPD is the massive size of search space when considering multiple side-chain conformations over several mutation sites as well as backbone flexibility. Existing methods are limited to a very small number of mutations 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.