title: Thermal stabilization of dihydrofolate reductase using monte carlo unfolding simulations and its functional consequences.
abstract: Design of proteins with desired thermal properties is important for scientific and biotechnological applications. Here we developed a theoretical approach to predict the effect of mutations on protein stability from non-equilibrium unfolding simulations. We establish a relative measure based on apparent simulated melting temperatures that is independent of simulation length and, under certain assumptions, proportional to equilibrium stability, and we justify this theoretical development with extensive simulations and experimental data. Using our new method based on all-atom Monte-Carlo unfolding simulations, we carried out a saturating mutagenesis of Dihydrofolate Reductase (DHFR), a key target of antibiotics and chemotherapeutic drugs. The method predicted more than 500 stabilizing mutations, several of which were selected for detailed computational and experimental analysis. We find a highly significant correlation of r=0.65-0.68 between predicted and experimentally determined melting temperatures and unfolding denaturant concentrations for WT DHFR and 42 mutants. The correlation between energy of the native state and experimental denaturation temperature was much weaker, indicating the important role of entropy in protein stability. The most stabilizing point mutation was D27F, which is located in the active site of the protein, rendering it inactive. However for the rest of mutations outside of the active site we observed a weak yet statistically significant positive correlation between thermal stability and catalytic activity indicating the lack of a stability-activity tradeoff for DHFR. By combining stabilizing mutations predicted by our method, we created a highly stable catalytically active E. coli DHFR mutant with measured denaturation temperature 7.2°C higher than WT. Prediction results for DHFR and several other proteins indicate that computational approaches based on unfolding simulations are useful as a general technique to discover stabilizing mutations.
authors.affiliation: Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America; Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Tian
authors.affiliation: Graduate Program in Biophysics, Harvard University, Cambridge, Massachusetts, United States of America.
authors.forename: Jaie C
authors.initials: JC
authors.lastname: Woodard
authors.affiliation: Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America.
authors.forename: Andrew
authors.initials: A
authors.lastname: Whitney
authors.affiliation: Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America.
authors.forename: Eugene I
authors.initials: EI
authors.lastname: Shakhnovich
mesh_terms: D003198:Computer Simulation; D004795:Enzyme Stability; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D009010:Monte Carlo Method; D016297:Mutagenesis, Site-Directed; D011487:Protein Conformation; D011489:Protein Denaturation; D017510:Protein Folding; D013329:Structure-Activity Relationship; D013762:Tetrahydrofolate Dehydrogenase / Q000737:chemistry* / Q000235:genetics / Q000648:ultrastructure*; D044366:Transition Temperature
chemical_list: D013762:Tetrahydrofolate Dehydrogenase