The importance of engineering protein stability is well-known and has the potential to impact many fields ranging from pharmaceuticals to food sciences. Engineering proteins can be both a time-consuming and expensive experimental process. The use of computation is a potential solution to mitigating some of the time and expenses required to engineer a protein. This process has been previously hindered by inaccurate force fields or energy equations and slow computational processors; however, improved software and hardware have made this goal much more attainable. Here we find that Schrödinger's new FEP+, although still imperfect, proves more successful in predicting protein stability than other simpler methods of investigation. This increased accuracy comes at a cost of computational time and resources when compared to simpler methods. This work adds to the initial testing of FEP+ by offering options for more accurately predicting protein stability in an efficient manner.
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Ford et al. (2017) studied this question.
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