Abstract
It has become customary in engineering to require a modelling component in research endeavours. In addition, as the code for these models becomes more byzantine in complexity, it is difficult for reviewers and readers to discern their value and understand the underlying code. This opinion piece summarizes the negative experience of the author with the IPRO and OptMAVEn computational protein engineering models as well as problems with the optStoic metabolic pathway model. In our hands, these models often fail to predict reliable ways to engineer proteins and metabolic pathways.
Highlights
In the following, I describe our experience with three computational programmes that were designed to improve proteins and metabolic pathways
As part of a National Science Foundation grant (CBET 1133040), we investigated the de novo protein design of fully human antibody variable domains for binding a specified antigen using OptMAVEn [1]
We focused on trying to improve an existing antibody, 2D10, which is a single-chain antibody that recognizes the dodecapeptide DVFYPYPYASGS, a peptide mimic of mannose-containing carbohydrates
Summary
I describe our experience with three computational programmes that were designed to improve proteins and metabolic pathways. As a result of the OptMAVEn predictions [2], we cloned and purified five de novo designed scFvs and verified their correct folding. We had hoped to use computational methods to help identify additional substitutions that would be beneficial for increasing the activity of the dioxygenase for nitroaromatic pollutants. With this goal, IPRO [4] was performed on the large subunit NagAc of NDO with the aim of optimizing the docking of the substrate 2,3-dinitrotoluene to favour the formation of the intermediate 4-methyl-3-nitrocatechol. In our experience, IPRO was unable to predict amino acid substitutions that increase protein activity
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