Inferring Stabilizing Mutations from Protein Phylogenies: Application to Influenza Hemagglutinin

One selection pressure shaping sequence evolution is the requirement that a protein fold with sufficient stability to perform its biological functions. We present a conceptual framework that explains how this requirement causes the probability that a particular amino acid mutation is fixed during evolution to depend on its effect on protein stability. We mathematically formalize this framework to develop a Bayesian approach for inferring the stability effects of individual mutations from homologous protein sequences of known phylogeny. This approach is able to predict published experimentally measured mutational stability effects (ΔΔG values) with an accuracy that exceeds both a state-of-the-art physicochemical modeling program and the sequence-based consensus approach. As a further test, we use our phylogenetic inference approach to predict stabilizing mutations to influenza hemagglutinin. We introduce these mutations into a temperature-sensitive influenza virus with a defect in its hemagglutinin gene and experimentally demonstrate that some of the mutations allow the virus to grow at higher temperatures. Our work therefore describes a powerful new approach for predicting stabilizing mutations that can be successfully applied even to large, complex proteins such as hemagglutinin. This approach also makes a mathematical link between phylogenetics and experimentally measurable protein properties, potentially paving the way for more accurate analyses of molecular evolution.     

How protein stability and new functions trade off

Numerous studies have noted that the evolution of new enzymatic specificities is accompanied by loss of the protein's thermodynamic stability (DeltaDeltaG), thus suggesting a tradeoff between the acquisition of new enzymatic functions and stability. However, since most mutations are destabilizing (DeltaDeltaG>0), one should ask how destabilizing mutations that confer new or altered enzymatic functions relative to all other mutations are. We applied DeltaDeltaG computations by FoldX to analyze the effects of 548 mutations that arose from the directed evolution of 22 different enzymes. The stability effects, location, and type of function-altering mutations were compared to DeltaDeltaG changes arising from all possible point mutations in the same enzymes. We found that mutations that modulate enzymatic functions are mostly destabilizing (average DeltaDeltaG = +0.9 kcal/mol), and are almost as destabilizing as the "average" mutation in these enzymes (+1.3 kcal/mol). Although their stability effects are not as dramatic as in key catalytic residues, mutations that modify the substrate binding pockets, and thus mediate new enzymatic specificities, place a larger stability burden than surface mutations that underline neutral, non-adaptive evolutionary changes. How are the destabilizing effects of functional mutations balanced to enable adaptation? Our analysis also indicated that many mutations that appear in directed evolution variants with no obvious role in the new function exert stabilizing effects that may compensate for the destabilizing effects of the crucial function-altering mutations. Thus, the evolution of new enzymatic activities, both in nature and in the laboratory, is dependent on the compensatory, stabilizing effect of apparently "silent" mutations in regions of the protein that are irrelevant to its function.     

Thermal Stabilization of Dihydrofolate Reductase Using Monte Carlo Unfolding Simulations and Its Functional Consequences

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.     

Identification of stabilizing point mutations through mutagenesis of destabilized protein libraries

Although there have been recent transformative advances in the area of protein structure prediction, prediction of point mutations that improve protein stability remains challenging. It is possible to construct and screen large mutant libraries for improved activity or ligand binding. However, reliable screens for mutants that improve protein stability do not yet exist, especially for proteins that are well folded and relatively stable. Here, we demonstrate that incorporation of a single, specific, destabilizing mutation termed parent inactivating mutation into each member of a single-site saturation mutagenesis library, followed by screening for suppressors, allows for robust and accurate identification of stabilizing mutations. We carried out fluorescence-activated cell sorting of such a yeast surface display, saturation suppressor library of the bacterial toxin CcdB, followed by deep sequencing of sorted populations. We found that multiple stabilizing mutations could be identified after a single round of sorting. In addition, multiple libraries with different parent inactivating mutations could be pooled and simultaneously screened to further enhance the accuracy of identification of stabilizing mutations. Finally, we show that individual stabilizing mutations could be combined to result in a multi-mutant that demonstrated an increase in thermal melting temperature of about 20 °C, and that displayed enhanced tolerance to high temperature exposure. We conclude that as this method is robust and employs small library sizes, it can be readily extended to other display and screening formats to rapidly isolate stabilized protein mutants.     

Facile Method for High-throughput Identification of Stabilizing Mutations

Characterizing the effects of mutations on stability is critical for understanding the function and evolution of proteins and improving their biophysical properties. High throughput folding and abundance assays have been successfully used to characterize missense mutations associated with reduced stability. However, screening for increased thermodynamic stability is more challenging since such mutations are rarer and their impact on assay readout is more subtle. Here, a multiplex assay for high throughput screening of protein folding was developed by combining deep mutational scanning, fluorescence-activated cell sorting, and deep sequencing. By analyzing a library of 2000 variants of Adenylate kinase we demonstrate that the readout of the method correlates with stability and that mutants with up to 13 °C increase in thermal melting temperature could be identified with low false positive rate. The discovery of many stabilizing mutations also enabled the analysis of general substitution patterns associated with increased stability in Adenylate kinase. This high throughput method to identify stabilizing mutations can be combined with functional screens to identify mutations that improve both stability and activity.     

Molecular dynamics studies on HIV-1 protease drug resistance and folding pathways

Drug resistance to HIV-1 protease involves the accumulation of multiple mutations in the protein. We investigate the role of these mutations by using molecular dynamics simulations that exploit the influence of the native-state topology in the folding process. Our calculations show that sites contributing to phenotypic resistance of FDA-approved drugs are among the most sensitive positions for the stability of partially folded states and should play a relevant role in the folding process. Furthermore, associations between amino acid sites mutating under drug treatment are shown to be statistically correlated. The striking correlation between clinical data and our calculations suggest a novel approach to the design of drugs tailored to bind regions crucial not only for protein function, but for folding as well.     

The Stability Landscape of de novo TIM Barrels Explored by a Modular Design Approach

The ability to design stable proteins with custom-made functions is a major goal in biochemistry with practical relevance for our environment and society. Understanding and manipulating protein stability provide crucial information on the molecular determinants that modulate structure and stability, and expand the applications of de novo proteins. Since the (β/⍺)₈-barrel or TIM-barrel fold is one of the most common functional scaffolds, in this work we designed a collection of stable de novo TIM barrels (DeNovoTIMs), using a computational fixed-backbone and modular approach based on improved hydrophobic packing of sTIM11, the first validated de novo TIM barrel, and subjected them to a thorough folding analysis. DeNovoTIMs navigate a region of the stability landscape previously uncharted by natural TIM barrels, with variations spanning 60 degrees in melting temperature and 22 kcal per mol in conformational stability throughout the designs. Significant non-additive or epistatic effects were observed when stabilizing mutations from different regions of the barrel were combined. The molecular basis of epistasis in DeNovoTIMs appears to be related to the extension of the hydrophobic cores. This study is an important step towards the fine-tuned modulation of protein stability by design.     

Importance of mutant position in Ramachandran plot for predicting protein stability of surface mutations

Understanding the mechanisms by which mutations affect protein stability is one of the most important problems in molecular biology. In this work, we analyzed the relationship between changes in protein stability caused by surface mutations and changes in 49 physicochemical, energetic, and conformational properties of amino acid residues. We found that the hydration entropy was the major contributor to the stability of surface mutations in helical segments; other properties responsible for size and volume of molecule also correlated significantly with stability. Classification of coil mutations based on their locations in the (phi-psi) map improved the correlation significantly, demonstrating the existence of a relationship between stability and strain energy, which indicates that the role of strain energy is very important for the stability of surface mutations. We observed that the inclusion of sequence and structural information raised the correlation, indicating the influence of surrounding residues on the stability of surface mutations. Further, we examined the previously reported "inverse relationship" between stability and hydrophobicity, and observed that the inverse hydrophobic effect was generally applicable only to coil mutations. The present study leads to a simple method for predicting protein stability changes caused by amino acid substitutions, which will be useful for protein engineering in designing novel proteins with increased stability and altered function.     

Average assignment method for predicting the stability of protein mutants

Prediction of protein stability upon amino acid substitutions is an important problem in molecular biology and it will be helpful for designing stable mutants. In this work, we have analyzed the stability of protein mutants using three different data sets of 1791, 1396, and 2204 mutants, respectively, for thermal stability (DeltaTm), free energy change due to thermal (DeltaDeltaG), and denaturant denaturations (DeltaDeltaGH2O), obtained from the ProTherm database. We have classified the mutants into 380 possible substitutions and assigned the stability of each mutant using the information obtained with similar type of mutations. We observed that this assignment could distinguish the stabilizing and destabilizing mutants to an accuracy of 70-80% at different measures of stability. Further, we have classified the mutants based on secondary structure and solvent accessibility (ASA) and observed that the classification significantly improved the accuracy of prediction. The classification of mutants based on helix, strand, and coil distinguished the stabilizing/destabilizing mutants at an average accuracy of 82% and the correlation is 0.56; information about the location of residues at the interior, partially buried, and surface regions of a protein correctly identified the stabilizing/destabilizing residues at an average accuracy of 81% and the correlation is 0.59. The nine subclassifications based on three secondary structures and solvent accessibilities improved the accuracy of assigning stabilizing/destabilizing mutants to an accuracy of 84-89% for the three data sets. Further, the present method is able to predict the free energy change (DeltaDeltaG) upon mutations within a deviation of 0.64 kcal/mol. We suggest that this method could be used for predicting the stability of protein mutants.     

Optimized variants of the cold shock protein from in vitro selection: structural basis of their high thermostability

The bacterial cold shock proteins (Csp) are widely used as models for the experimental and computational analysis of protein stability. In a previous study, in vitro evolution was employed to identify strongly stabilizing mutations in Bs-CspB from Bacillus subtilis. The best variant found by this approach contained the mutations M1R, E3K and K65I, which raised the midpoint of thermal unfolding of Bs-CspB from 53.8 degrees C to 83.7 degrees C, and increased the Gibbs free energy of stabilization by 20.9 kJ mol(-1). Another selected variant with the two mutations A46K and S48R was stabilized by 11.1 kJ mol(-1). To elucidate the molecular basis of these stabilizations, we determined the crystal structures of these two Bs-CspB variants. The mutated residues are generally well ordered and provide additional stabilizing interactions, such as charge interactions, additional hydrogen bonds and improved side-chain packing. Several mutations improve the electrostatic interactions, either by the removal of unfavorable charges (E3K) or by compensating their destabilizing interactions (A46K, S48R). The stabilizing mutations are clustered at a contiguous surface area of Bs-CspB, which apparently is critically important for the stability of the beta-barrel structure but not well optimized in the wild-type protein.     

Review: Protein function at thermal extremes: balancing stability and flexibility.

No organism can survive across the entire temperature range found in the biosphere, and a given species can rarely support active metabolism across more than a few tens of degrees C. Nevertheless, life can be maintained at surprisingly extreme temperatures, from below -50 to over 110 degrees C. That proteins, which are assembled with the same 20 amino acids in all species, can function well at both extremes of this range illustrates the plasticity available in the construction of these macromolecules. In studying proteins from extremophiles, researchers have found no new amino acids, covalent modifications or structural motifs that explain the ability of these molecules to function in such harsh environments. Rather, subtle redistributions of the same intramolecular interactions required for protein stabilization at moderate temperatures are sufficient to maintain structural integrity at hot or cold extremes. The key to protein function, whether in polar seas or hot springs, is the maintenance of an appropriate balance between molecular stability on the one hand and structural flexibility on the other. Stability is needed to ensure the appropriate geometry for ligand binding, as well as to avoid denaturation, while flexibility is necessary to allow catalysis at a metabolically appropriate rate. Comparisons of homologous proteins from organisms spanning a wide range of thermal habitats show that adaptive mutations, as well as stabilizing solutes, maintain a balance between these two attributes, regardless of the temperature at which the protein functions.     

Experimental evaluation of single‐domain antibodies predicted by molecular dynamics simulations to have elevated thermal stability

Recently Bekker et al. [Bekker G‐J et al. Protein Sci. 2019;28:429–438.] described a computational strategy of applying molecular‐dynamics simulations to estimate the relative stabilities of single‐domain antibodies, and utilized their method to design changes with the aim of increasing the stability of a single‐domain antibody with a known crystal structure. The structure from which they generated potentially stabilizing mutations is an anti‐cholera toxin single domain antibody selected from a naïve library which has relatively low thermal stability, reflected by a melting point of 48°C. Their work was purely theoretical, so to examine their predictions, we prepared the parental and predicted stabilizing mutant single domain antibodies and examined their thermal stability, ability to refold and affinity. We found that the mutation that improved stability the most (~7°C) was one which changed an amino acid in CDR1 from an asparagine to an aspartic acid. This change unfortunately was also accompanied by a reduction in affinity. Thus, while their modeling did appear to successfully predict stabilizing mutations, introducing mutations in the binding regions is problematic. Of further interest, the mutations selected via their high temperature simulations, did improve refolding, suggesting that they were successful in stabilizing the structure at high temperatures and thereby decrease aggregation. Our result should permit them to reassess and refine their model and may one day lead to a usefulin silico approach to protein stabilization.     

Mechanism of phage P22 tailspike protein folding mutations.

Temperature-sensitive folding (tsf) and global-tsf-suppressor (su) point mutations affect the folding yields of the trimeric, thermostable phage P22 tailspike endorhamnosidase at elevated temperature, both in vivo and in vitro, but they have little effect on function and stability of the native folded protein. To delineate the mechanism by which these mutations modify the partitioning between productive folding and off-pathway aggregation, the kinetics of refolding after dilution from acid-urea solutions and the thermal stability of folding intermediates were analyzed. The study included five tsf mutations of varying severity, the two known su mutations, and four tsf/su double mutants. At low temperature (10 degrees C), subunit-folding rates, measured as an increase in fluorescence, were similar for wild-type and mutants. At 25 degrees C, however, tsf mutations reduced the rate of subunit folding. The su mutations increased this rate, when present in the tsf-mutant background, but had no effect in the wild-type background. Conversely, tsf mutations accelerated, and su mutations retarded the irreversible off-pathway reaction, as revealed by temperature down-shifts after varied times during refolding at high temperature (40 degrees C). The kinetic results are consistent with tsf mutations destabilizing and su mutations stabilizing an essential subunit folding intermediate. In accordance with this interpretation, tsf mutations decreased, and su mutations increased the temperature resistance of folding intermediates, as disclosed by temperature up-shifts during refolding at 25 degrees C. The stabilizing and destabilizing effects were most pronounced early during refolding. However, they were not limited to subunit-folding intermediates and were also observable during thermal unfolding of the native protein.     

Cataract-causing mutation R233H affects the stabilities of βB1- and βA3/βB1-crystallins with different pH-dependence

Disease-causing mutations can be stabilizing or destabilizing. Missense mutations of structural residues are generally destabilizing, while stabilizing mutations are usually linked to alterations in protein functions. Stabilizing mutations are rarely identified in mutations linked to congenital cataract, a disease caused by the opacification of the lens. In this research, we found that R233H mutation had little impact on βB1-crystallin structure, solubility and thermal stability under neutral solution pH conditions. The mutation increased βB1 stability against guanidine hydrochloride-induced denaturation, suggesting that Arg233 might be a functional residue. Further analysis indicated that the R233H mutation did not affect the formation of βA3/βB1 heteromer, but significantly reduced heteromer stability against heat- and guanidine hydrochloride-induced denaturation. The R233H mutation negatively affected the thermal stabilities and aggregatory propensities of βB1 and βA3/βB1 with different pH-dependence, implying that the protonation of His side chains during acidification played a regulatory role in crystallin stability and aggregation. Molecular dynamic simulations indicated that Arg233 is one of the residues forming an inter-subunit ion-pairing network with intrinsically dynamic nature. Based on these observations, we proposed that the highly dynamic ion-pairing network contributed to the tradeoff among βB1 solubility, stability, aggregatory propensity and function of protecting βA3.     

Improving the stability of an antibody variable fragment by a combination of knowledge-based approaches: validation and mechanisms

Numerous approaches have been described to obtain variable fragments of antibodies (Fv or scFv) that are sufficiently stable for their applications. Here, we combined several knowledge-based methods to increase the stability of pre-existing scFvs by design. Firstly, the consensus sequence approach was used in a non-stringent way to predict a large basic set of potentially stabilizing mutations. These mutations were then prioritized by other methods of design, mainly the formation of additional hydrogen bonds, an increase in the hydrophilicity of solvent exposed residues, and previously described mutations in other antibodies. We validated this combined method with antibody mAbD1.3, directed against lysozyme. Fourteen potentially stabilizing mutations were designed and introduced into scFvD1.3 by site-directed mutagenesis, either individually or in combinations. We characterized the effects of the mutations on the thermodynamic stability of scFvD1.3 by experiments of unfolding with urea, monitored by spectrofluorometry, and tested the additivity of their effects by double-mutant cycles. We also quantified the individual contributions of the resistance to denaturation ([urea](1/2)) and cooperativity of unfolding (m) to the variations of stability and the energy of coupling between mutations by a novel approach. Most mutations (75%) were stabilizing and none was destabilizing. The progressive recombination of the mutations into the same molecule of scFvD1.3 showed that their effects were mostly additive or synergistic, provided a large overall increase in protein stability (9.1 kcal/mol), and resulted in a highly stable scFvD1.3 derivative. The mechanisms of the mutations and of their combinations involved variations in the resistance to denaturation, cooperativity of unfolding, and likely residual structures of the denatured state, which was constrained by two disulfide bonds. This combined method should be applicable to any recombinant antibody fragment, through a single step of mutagenesis.     

Computational Assessment of Protein–protein Binding Affinity by Reversely Engineering the Energetics in Protein Complexes

The cellular functions of proteins are maintained by forming diverse complexes. The stability of these complexes is quantified by the measurement of binding affinity, and mutations that alter the binding affinity can cause various diseases such as cancer and diabetes. As a result, accurate estimation of the binding stability and the effects of mutations on changes of binding affinity is a crucial step to understanding the biological functions of proteins and their dysfunctional consequences. It has been hypothesized that the stability of a protein complex is dependent not only on the residues at its binding interface by pairwise interactions but also on all other remaining residues that do not appear at the binding interface. Here, we computationally reconstruct the binding affinity by decomposing it into the contributions of interfacial residues and other non-interfacial residues in a protein complex. We further assume that the contributions of both interfacial and non-interfacial residues to the binding affinity depend on their local structural environments such as solvent-accessible surfaces and secondary structural types. The weights of all corresponding parameters are optimized by Monte-Carlo simulations. After cross-validation against a large-scale dataset, we show that the model not only shows a strong correlation between the absolute values of the experimental and calculated binding affinities, but can also be an effective approach to predict the relative changes of binding affinity from mutations. Moreover, we have found that the optimized weights of many parameters can capture the first-principle chemical and physical features of molecular recognition, therefore reversely engineering the energetics of protein complexes. These results suggest that our method can serve as a useful addition to current computational approaches for predicting binding affinity and understanding the molecular mechanism of protein–protein interactions.     

Directed evolution of novel protein functions

Directed evolution has been successfully used to engineer proteins for basic and applied biological research. However, engineering of novel protein functions by directed evolution remains an overwhelming challenge. This challenge may come from the fact that multiple simultaneously or synergistic mutations are required for the creation of a novel protein function. Here we review the key developments in engineering of novel protein functions by using either directed evolution or a combined directed evolution and rational or computational design approach. Specific attention will be paid to a molecular evolution model for generation of novel proteins. The engineered novel proteins should not only broaden the range of applications of proteins but also provide new insights into protein structure-function relationship and protein evolution.     

The linear interaction energy method for the prediction of protein stability changes upon mutation

The coupling of protein energetics and sequence changes is a critical aspect of computational protein design, as well as for the understanding of protein evolution, human disease, and drug resistance. To study the molecular basis for this coupling, computational tools must be sufficiently accurate and computationally inexpensive enough to handle large amounts of sequence data. We have developed a computational approach based on the linear interaction energy (LIE) approximation to predict the changes in the free-energy of the native state induced by a single mutation. This approach was applied to a set of 822 mutations in 10 proteins which resulted in an average unsigned error of 0.82 kcal/mol and a correlation coefficient of 0.72 between the calculated and experimental ΔΔG values. The method is able to accurately identify destabilizing hot spot mutations; however, it has difficulty in distinguishing between stabilizing and destabilizing mutations because of the distribution of stability changes for the set of mutations used to parameterize the model. In addition, the model also performs quite well in initial tests on a small set of double mutations. On the basis of these promising results, we can begin to examine the relationship between protein stability and fitness, correlated mutations, and drug resistance.     

Stabilizing proteins from sequence statistics: the interplay of conservation and correlation in triosephosphate isomerase stability

Understanding the determinants of protein stability remains one of protein science's greatest challenges. There are still no computational solutions that calculate the stability effects of even point mutations with sufficient reliability for practical use. Amino acid substitutions rarely increase the stability of native proteins; hence, large libraries and high-throughput screens or selections are needed to stabilize proteins using directed evolution. Consensus mutations have proven effective for increasing stability, but these mutations are successful only about half the time. We set out to understand why some consensus mutations fail to stabilize, and what criteria might be useful to predict stabilization more accurately. Overall, consensus mutations at more conserved positions were more likely to be stabilizing in our model, triosephosphate isomerase (TIM) from Saccharomyces cerevisiae. However, positions coupled to other sites were more likely not to stabilize upon mutation. Destabilizing mutations could be removed both by removing sites with high statistical correlations to other positions and by removing nearly invariant positions at which "hidden correlations" can occur. Application of these rules resulted in identification of stabilizing mutations in 9 out of 10 positions, and amalgamation of all predicted stabilizing positions resulted in the most stable yeast TIM variant we produced (+8 °C). In contrast, a multimutant with 14 mutations each found to stabilize TIM independently was destabilized by 2 °C. Our results are a practical extension to the consensus concept of protein stabilization, and they further suggest the importance of positional independence in the mechanism of consensus stabilization.