Development of an accessible integrated computational protein design software suite

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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.