Computational tools help improve protein stability but with a solubility tradeoff

Abstract

Accurately predicting changes in protein stability upon amino acid substitution is a much sought after goal. Destabilizing mutations are often implicated in disease, whereas stabilizing mutations are of great value for industrial and therapeutic biotechnology. Increasing protein stability is an especially challenging task, with random substitution yielding stabilizing mutations in only ∼2% of cases. To overcome this bottleneck, computational tools that aim to predict the effect of mutations have been developed; however, achieving accuracy and consistency remains challenging. Here, we combined 11 freely available tools into a meta-predictor (meieringlab.uwaterloo.ca/stabilitypredict/). Validation against ∼600 experimental mutations indicated that our meta-predictor has improved performance over any of the individual tools. The meta-predictor was then used to recommend 10 mutations in a previously designed protein of moderate thermodynamic stability, ThreeFoil. Experimental characterization showed that four mutations increased protein stability and could be amplified through ThreeFoil's structural symmetry to yield several multiple mutants with >2-kcal/mol stabilization. By avoiding residues within functional ties, we could maintain ThreeFoil's glycan-binding capacity. Despite successfully achieving substantial stabilization, however, almost all mutations decreased protein solubility, the most common cause of protein design failure. Examination of the 600-mutation data set revealed that stabilizing mutations on the protein surface tend to increase hydrophobicity and that the individual tools favor this approach to gain stability. Thus, whereas currently available tools can increase protein stability and combining them into a meta-predictor yields enhanced reliability, improvements to the potentials/force fields underlying these tools are needed to avoid gaining protein stability at the cost of solubility.

Improvement in the Thermostability of a β‐Amino Acid Converting ω‐Transaminase by Using FoldX

“…performed their predictions by using a sophisticated approach to gain a consensus sequence by comparing calculations with closely related enzymes to increase the stability . Using this strategy, the thermostability was increased for the fungal cellobiohydrolase I by 2.1 °C by single mutation, and the authors showed that 24 % of their selected FoldX predictions were stabilizing . We also found that approximately 20 % of the predicted mutant proteins that we expressed resulted in stabilization.…”

“…We also found that approximately 20 % of the predicted mutant proteins that we expressed resulted in stabilization. In contrast, by using only random‐based approaches, the success rate tends to be approximately 2 % …”

“…The low correlation of ΔΔ G Foldx to ΔΔ G real has also been observed by others and shows that FoldX can be used to predict stability trends but not to predict accurate values, as the accuracy tends to be low, especially for ΔΔ G values, which is indicative of highly stabilizing mutations . However, the general standard error of the FoldX algorithm is known to be only approximately 1.7 kcal mol −1 , which is small in comparison with the theoretical ΔΔ G values of G165M (−54.3) and E391K (−42.9) . To improve the suggestions obtained by the FoldX algorithm further, an additional conservation analysis should be performed to avoid nonfunctional proteins by excluding highly conserved amino‐acid positions.…”

High proportion of transient neonatal zinc deficiency causing alleles in the general population

“…The thermal stability meta‐predictor tool was used to predict the effect of missense ZnT2 mutations on the thermal stability of the protein. This tool combines the predictive power of 11 tools to generate two predictive scores, an average from all the tools, as well as a weighted average which takes into consideration the amino acid environment . The weighted average is considered more accurate .…”

Recent advances in automated protein design and its future challenges

“…Alterations to the protein core from complete protein redesign may result in low thermodynamic stability and periodic fluctuations that expose the interior of the protein to solvent, creating a surface favorable for aggregation. Explicit consideration of solubility in design, either with a scoring term that disfavors hydrophobic patches on the surface [189], or solubility prediction based on sequence threading [190,191], may be effective in increasing protein yield and in vivo efficacy.…”