Computational tools help improve protein stability but with a solubility tradeoff

Aron Broom, Zachary Jacobi, Kyle Trainor, Elizabeth M. Meiering
2017 Journal of Biological Chemistry  
Edited by Wolfgang Peti 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 ϳ2% of cases. To overcome this bottleneck, computational tools that aim to predict the
more » ... 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 ϳ600 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, Three-Foil. 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 glycanbinding 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. 2 The abbreviations used are: PDB, Protein Data Bank; MCC, Matthews correlation coefficient; GuSCN, guanidine thiocyanate. Figure 4. Taking advantage of symmetry to improve stability. ThreeFoil structure (PDB entry 3PG0) is shown from a side view, perpendicular to the 3-fold axis of symmetry (top) and rotated 90°to look along the axis of symmetry toward the capping ␤-hairpin triplets (bottom). The first, second, and third subdomains are shown in magenta, cyan, and yellow, respectively. Sites of mutations used for MMut1 (K6V/K53V/K100V, A15V/A62V/A109V, and D38P/ D85P/D132P) and MMut2 (same as MMut1 plus D2N/D49N/D96N) are shown as thick sticks. Tradeoff between protein stability and solubility
doi:10.1074/jbc.m117.784165 pmid:28710274 fatcat:y5wy7fx63ffbblo5twb7uerdse