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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.

SUBMITTER: Broom A

PROVIDER: S-EPMC5582830 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Computational tools help improve protein stability but with a solubility tradeoff.

Broom Aron A Jacobi Zachary Z Trainor Kyle K Meiering Elizabeth M EM

The Journal of biological chemistry 20170714 35

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 ...[more]

PMID: 28710274

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Project description:BackgroundHeterologous expression of active, native-folded protein in Escherichia coli is critical in both academic research and biotechnology settings. When expressing non-native recombinant proteins in E. coli, obtaining soluble and active protein can be challenging. Numerous techniques can be used to enhance a proteins solubility, and largely focus on either altering the expression strain, plasmid vector features, growth conditions, or the protein coding sequence itself. However, there is no one-size-fits-all approach for addressing issues with protein solubility, and it can be both time and labor intensive to find a solution. An alternative approach is to use the co-expression of chaperones to assist with increasing protein solubility. By designing a genetic system where protein solubility is linked to viability, the appropriate protein folding factor can be selected for any given protein of interest. To this end, we developed a Split Antibiotic Selection (SAS) whereby an insoluble protein is inserted in-frame within the coding sequence of the hygromycin B resistance protein, aminoglycoside 7″-phosphotransferase-Ia (APH(7″)), to generate a tripartite fusion. By creating this tripartite fusion with APH(7″), the solubility of the inserted protein can be assessed by measuring the level of hygromycin B resistance of the cells.ResultsWe demonstrate the functionality of this system using a known protein and co-chaperone pair, the human mitochondrial Hsp70 ATPase domain (ATPase70) and its co-chaperone human escort protein (Hep). Insertion of the insoluble ATPase70 within APH(7'') renders the tripartite fusion insoluble and results in sensitivity to hygromycin B. Antibiotic resistance can be rescued by expression of the co-chaperone Hep which assists in the folding of the APH(7'')-ATPase70-APH(7'') tripartite fusion and find that cellular hygromycin B resistance correlates with the total soluble fusion protein. Finally, using a diverse chaperone library, we find that SAS can be used in a pooled genetic selection to identify chaperones capable of improving client protein solubility.ConclusionsThe tripartite APH(7'') fusion links the in vivo solubility of the inserted protein of interest to hygromycin B resistance. This construct can be used in conjunction with a chaperone library to select for chaperones that increase the solubility of the inserted protein. This selection system can be applied to a variety of client proteins and eliminates the need to individually test chaperone-protein pairs to identify those that increase solubility.

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Computational tools help improve protein stability but with a solubility tradeoff.

Publications

Computational tools help improve protein stability but with a solubility tradeoff.

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