Flexible sites are potential targets for engineering the stability of enzymes. Nevertheless, the success rate of the rigidifying flexible sites (RFS) strategy is still low due to a limited understanding of how to determine the best mutation candidates. In this study, two parallel strategies were applied to identify mutation candidates within the flexible loops of Escherichia coli transketolase (TK). The first was a "back to consensus mutations" approach, and the second was computational design based on ΔΔG calculations in Rosetta. Forty-nine single variants were generated and characterised experimentally. From these, three single-variants I189H, A282P, D143K were found to be more thermostable than wildtype TK. The combination of A282P with H192P, a variant constructed previously, resulted in the best all-round variant with a 3-fold improved half-life at 60 °C, 5-fold increased specific activity at 65 °C, 1.3-fold improved k cat and a T m increased by 5 °C above that of wild type. Based on a statistical analysis of the stability changes for all variants, the qualitative prediction accuracy of the Rosetta program reached 65.3%. Both of the two strategies investigated were useful in guiding mutation candidates to flexible loops, and had the potential to be used for other enzymes.Transketolase (TK), a thiamine diphosphate dependent (ThDP) enzyme, catalyses the reversible transfer of a C2-ketol unit from D-xylulose-5-phosphate to either D-ribose-5-phosphate or D-erythrose-4-phosphate, linking glycolysis to the pentose phosphate pathway in all living cells 1,2 . The stereospecifically controlled carbon-carbon bond forming ability of TK makes it promising as a biocatalyst in industry, for the synthesis of complex carbohydrates and other high-value compounds 3,4 . The use of β -hydroxypyruvate (HPA) as the ketol donor renders the donor-half reaction irreversible, thus increasing the atom efficiency of the reaction favourably for industrial syntheses. Escherichia coli (E. coli) TK converts HPA with a rate of 60 U/mg, significantly higher than the rates of 2 U/mg and 9 U/mg reported for its orthologs from spinach and yeast 5 .Wild-type (WT) E. coli TK has been successfully engineered to have improved and inverted enantioselectivity 6 , as well as an expanded aldol-acceptor substrate range including polar aliphatics 7 , non-polar aliphatics 8 , and heteroaromatics 9,10 . Most recently, E. coli TK has been engineered to synthesize L-gluco-heptulose from L-arabinose, thus transforming a major component of the carbohydrates in sugar beet pulp, into a rare naturally-occurring ketoheptose with potential therapeutic applications in hypoglycaemia and cancer 11 . Additionally, the substrate range accepted by TK has been recently extended to aromatic benzaldehyde derivatives, which opened up potential routes to chiral aromatic amino-alcohols such as chloramphenicol antibiotics, nor-ephedrine, and their analogues with alternative aromatic substituents 12,13 .As a mesophilic enzyme E. coli TK suffers the limitation of low stability t...

Computationally guided semirational design has significant potential for improving the aggregation kinetics of protein biopharmaceuticals. While improvement in the global conformational stability can stabilize proteins to aggregation under some conditions, previous studies suggest that such an approach is limited, because thermal transition temperatures ( T) and the fraction of protein unfolded ( f) tend to only correlate with aggregation kinetics where the protein is incubated at temperatures approaching the T. This is because under these conditions, aggregation from globally unfolded protein becomes dominant. However, under native conditions, the aggregation kinetics are presumed to be dependent on local structural fluctuations or partial unfolding of the native state, which reveal regions of high propensity to form protein-protein interactions that lead to aggregation. In this work, we have targeted the design of stabilizing mutations to regions of the A33 Fab surface structure, which were predicted to be more flexible. This Fab already has high global stability, and global unfolding is not the main cause of aggregation under most conditions. Therefore, the aim was to reduce the conformational flexibility and entropy of the native protein at various locations and thus identify which of those regions has the greatest influence on the aggregation kinetics. Highly dynamic regions of structure were identified through both molecular dynamics simulation and B-factor analysis of related X-ray crystal structures. The most flexible residues were mutated into more stable variants, as predicted by Rosetta, which evaluates the ΔΔ G for each potential point mutation. Additional destabilizing variants were prepared as controls to evaluate the prediction accuracy and also to assess the general influence of conformational stability on aggregation kinetics. The thermal conformational stability, and aggregation rates of 18 variants at 65 °C, were each examined at pH 4, 200 mM ionic strength, under which conditions the initial wild-type protein was <5% unfolded. Variants with decreased T values led to more rapid aggregation due to an increase in the fraction of protein unfolded under the conditions studied. As expected, no significant improvements were observed in the global conformational stability as measured by T. However, 6 of the 12 stable variants led to an increase in the cooperativity of unfolding, consistent with lower conformational flexibility and entropy in the native ensemble. Three of these had 5-11% lower aggregation rates, and their structural clustering indicated that the local dynamics of the C-terminus of the heavy chain had a role in influencing the aggregation rate.

Protein formulation development relies on the selection of excipients that inhibit protein–protein interactions preventing aggregation. Empirical strategies involve screening many excipient and buffer combinations using force degradation studies. Such methods do not readily provide information on intermolecular interactions responsible for the protective effects of excipients. This study describes a molecular docking approach to screen and rank interactions allowing for the identification of protein–excipient hotspots to aid in the selection of excipients to be experimentally screened. Previously published work with Drosophila Su(dx) was used to develop and validate the computational methodology, which was then used to determine the formulation hotspots for Fab A33. Commonly used excipients were examined and compared to the regions in Fab A33 prone to protein–protein interactions that could lead to aggregation. This approach could provide information on a molecular level about the protective interactions of excipients in protein formulations to aid the more rational development of future formulations.

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