A computational approach for the rational design of stable proteins and enzymes: optimization of surface charge-charge interactions

Computer-aided rational design strategy based on protein surface charge to improve the thermal stability of a novel esterase from Geobacillus jurassicus

OBJECTIVES

Although Geobacillus are significant thermophilic bacteria source, there are no reports of thermostable esterase gene in Geobacillus jurassicus or rational design strategies to increase the thermal stability of esterases.

RESULTS

Gene gju768 showed a highest similarity of 15.20% to esterases from Geobacillus sp. with detail enzymatic properties. Using a combination of Gibbs Unfolding Free Energy (∆∆G) calculator and the distance from the mutation site to the catalytic site (DsCα-Cα) to screen suitable mutation sites with elimination of negative surface charge, the mutants (D24N, E221Q, and E253Q) displayed stable mutants with higher thermal stability than the wild-type (WT). Mutant E253Q exhibited the best thermal stability, with a half-life (T1/2) at 65 °C of 32.4 min, which was 1.8-fold of the WT (17.9 min).

CONCLUSION

Cloning of gene gju768 and rational design based on surface charge engineering contributed to the identification of thermostable esterase from Geobacillus sp. and the exploration of evolutionary strategies for thermal stability.

Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning

We developed a method to improve protein thermostability, “loop-walking method”. Three consecutive positions in 12 loops of Burkholderia cepacia lipase were subjected to random mutagenesis to make 12 libraries. Screening allowed us to identify L7 as a hot-spot loop having an impact on thermostability, and the P233G/L234E/V235M mutant was found from 214 variants in the L7 library. Although a more excellent mutant might be discovered by screening all the 8000 P233X/L234X/V235X mutants, it was difficult to assay all of them. We therefore employed machine learning. Using thermostability data of the 214 mutants, a computational discrimination model was constructed to predict thermostability potentials. Among 7786 combinations ranked in silico, 20 promising candidates were selected and assayed. The P233D/L234P/V235S mutant retained 66% activity after heat treatment at 60 °C for 30 min, which was higher than those of the wild-type enzyme (5%) and the P233G/L234E/V235M mutant (35%).

Investigation of Supercharging as A Strategy to Enhance the Solubility and Plasminogen Cleavage Activity of Reteplase

Background

Reteplase, the recombinant form of tissue plasminogen activator, is a thrombolytic drug with outstanding characteristics, while demonstrating limited solubility and reduced plasminogen activation. Previously, we in silico designed a variant of Reteplase with positively supercharged surface, which showed promising stability, solubility and activity. This study was devoted to evaluation of the utility of supercharging technique for enhancing these characteristics in Reteplase.

Objective

To test the hypothesis that reinforced surface charge of a rationally-designed Reteplase variant will not compromise its stability, will increase its solubility, and will enhance its plasminogen cleavage activity.

Materials and Methods

Supercharged Reteplase coding sequence was cloned in pDest527 vector and expressed in E. coli BL21 (DE3). The expressed protein was extracted by cell disruption. Inclusion bodies were solubilized using guanidine hydrochloride, followed by dialysis for protein refolding. After confirmation with SDS-PAGE and western blotting, extracted proteins were assayed for solubility and tested for bioactivity.

Results

SDS-PAGE and western blot analysis confirmed the successful expression of Reteplase. Western blot experiments showed most of Reteplase expressed in the insoluble form. Plasminogen cleavage assay showed significantly higher activity of the supercharged variant than the wild type protein (P < 0.001). The stability of the supercharged variant was also comparable to the wild type.

Conclusion

Our findings, i.e. the contribution of the surface supercharging technique to retained stability, enhanced plasminogen cleavage activity, while inefficiently changed solubility of Reteplase, contain implications for future designs of soluble variants of this fibrinolytic protein drug.

TKSA-MC: A web server for rational mutation through the optimization of protein charge interactions

The TKSAMC is a web server which calculates protein charge-charge interactions via the Tanford-Kirkwood Surface Accessibility model with the Monte Carlo method for sampling different protein protonation states. The optimization of charge-charge interactions via directed mutations has successfully enhanced the thermal stability of different proteins and could be a key to protein engineering improvement. The server presents the electrostatic free energy contribution of each polar-charged residue to the protein native state stability. Specific residues are suggested to be mutated for improving thermal stability. The choice of a residue is based on its fraction of side chain exposed to solvent and its positive free energy contribution, which tends to destabilize the protein native state. Any residue energy contribution can be shown as a function of pH condition. The web server is freely available at UNESP (São Paulo State University - DF/IBILCE): http://tksamc.df.ibilce.unesp.br and also on GitHub https://github.com/contessoto/tksamc.

Strategy for Stabilization of CutA1 Proteins Due to Ion-Ion Interactions at Temperatures of over 100 °C

In order to elucidate the contribution of charged residues to protein stabilization at temperatures of over 100 °C, we constructed many mutants of the CutA1 protein ( EcCutA1) from Escherichia coli. The goal was to see if one can achieve the same stability as for a CutA1 from hyperthermophile Pyrococcus horikoshii that has the denaturation temperature near 150 °C. The hydrophobic mutant of EcCutA1 ( Ec0VV) with denaturation temperature ( T_d) of 113.2 °C was used as a template for mutations. The highest T_d of Ec0VV mutants substituted by a single charged residue was 118.4 °C. Multiple ion mutants were also constructed by combination of single mutants and found to have an increased thermostability. The highest stability of multiple mutants was a mutant substituted by nine charged residues that had a T_d of 142.2 °C. To evaluate the energy of ion-ion interactions of mutant proteins, we used the structural ensemble obtained by a molecular dynamics simulation at 300 K. The T_d of ionic mutants linearly increases with the increments of the computed energy of ion-ion interactions for ionic mutant proteins even up to the temperatures near 140 °C, suggesting that ion-ion interactions cumulatively contribute to the stabilization of a protein at high temperatures.

Proton pump inhibitors have pH-dependent effects on the thermostability of the carboxyl-terminal domain of voltage-gated proton channel Hv1

The voltage-gated proton channel Hv1 is highly selective for H⁺ and is activated by membrane depolarization and pH gradient. An increased external and decreased internal pH opens the Hv1 channel. The intracellular C-terminal domain of Hv1 is responsible for channel dimerization, cooperative, and thermosensitive gating. Here, we found that proton pump inhibitors (PPIs) interact with the C-terminal domain of human Hv1. The interaction between PPIs and the C-terminal domain, which is pH-dependent, lowered the thermal and structural stability of the protein at pH 4, but enhanced the thermal and structural stability at pH 8. Furthermore, we investigated in vitro the interaction of PPIs with the C-terminal domain of Hv1 by fluorescence and micro-Raman spectra. Fluorescence quenching measurements revealed that the interaction between the C-terminal domain and PPIs is a mainly hydrophobic interaction. The micro-Raman spectra showed that PPIs did not form stable disulfide bonds with the unique thiol group within this domain (Cys²⁴⁹ residue). The preferential interaction of PPIs with the inactive form of Hv1 stabilizes the high pH inactive state of the C-terminal domain, indicating a mechanism by which PPIs might act explicitly on the stabilization of a closed state of the proton channel.

Predicting stabilizing mutations in proteins using Poisson-Boltzmann based models: study of unfolded state ensemble models and development of a successful binary classifier based on residue interaction energies

In many cases the stability of a protein has to be increased to permit its biotechnological use. Rational methods of protein stabilization based on optimizing electrostatic interactions have provided some fine successful predictions. However, the precise calculation of stabilization energies remains challenging, one reason being that the electrostatic effects on the unfolded state are often neglected. We have explored here the feasibility of incorporating Poisson-Boltzmann model electrostatic calculations performed on representations of the unfolded state as large ensembles of geometrically optimized conformations calculated using the ProtSA server. Using a data set of 80 electrostatic mutations experimentally tested in two-state proteins, the predictive performance of several such models has been compared to that of a simple one that considers an unfolded structure of non-interacting residues. The unfolded ensemble models, while showing correlation between the predicted stabilization values and the experimental ones, are worse than the simple model, suggesting that the ensembles do not capture well the energetics of the unfolded state. A more attainable goal is classifying potential mutations as either stabilizing or non-stabilizing, rather than accurately calculating their stabilization energies. To implement a fast classification method that can assist in selecting stabilizing mutations, we have used a much simpler electrostatic model based only on the native structure and have determined its precision using different stabilizing energy thresholds. The binary classifier developed finds 7 true stabilizing mutants out of every 10 proposed candidates and can be used as a robust tool to propose stabilizing mutations.

Thermodynamics of Ion Pair Formations Between Charged Poly(Amino Acid)s

Electrostatic interactions between the positively and negatively charged amino acids in proteins play an important role in macromolecular stability, binding, and recognition. Numerous amino acids in proteins are ionizable and may exist in negatively (e.g., Glu, Asp, Cys, Tyr) or positively (e.g., Arg, Lys, His, Orn) charged form dependent on pH and their pKas. In this work, isothermal titration calorimetry was used to determine the average standard values of thermodynamic parameters (the Gibbs free energy, enthalpy, entropy, and the heat capacity) of interaction between the positively charged amino acid homopolymers (polyarginine, polylysine, and polyornithine) and the negatively charged homopolymers (polyaspartic and polyglutamic acids). These values are of potential use in the computational models of interacting proteins and other biological macromolecules. The study showed that oppositely charged poly(amino acid)s bound each other with the stoichiometry of one positive to one negative charge. Arginine bound to the negatively charged amino acids with exothermic enthalpy and higher affinity than lysine. This result also suggests that positive charges in proteins should not be considered entirely equivalent if carried by lysine or arginine. The difference in binding energy of arginine and lysine association with the negatively charged amino acids was attributed to the enthalpy of the second ionic hydrogen bond formation between the guanidine and carboxylic groups. Despite the favorable enthalpic contribution, all such ion pair formation reactions were largely entropy-driven. Consistent with previously observed ionic interactions, the positive heat capacity was always observed during the amino acid ion pair formation.

Mining the entire Protein DataBank for frequent spatially cohesive amino acid patterns

BACKGROUND

The three-dimensional structure of a protein is an essential aspect of its functionality. Despite the large diversity in protein structures and functionality, it is known that there are common patterns and preferences in the contacts between amino acid residues, or between residues and other biomolecules, such as DNA. The discovery and characterization of these patterns is an important research topic within structural biology as it can give fundamental insight into protein structures and can aid in the prediction of unknown structures.

RESULTS

Here we apply an efficient spatial pattern miner to search for sets of amino acids that occur frequently in close spatial proximity in the protein structures of the Protein DataBank. This allowed us to mine for a new class of amino acid patterns, that we term FreSCOs (Frequent Spatially Cohesive Component sets), which feature synergetic combinations. To demonstrate the relevance of these FreSCOs, they were compared in relation to the thermostability of the protein structure and the interaction preferences of DNA-protein complexes. In both cases, the results matched well with prior investigations using more complex methods on smaller data sets.

CONCLUSIONS

The currently characterized protein structures feature a diverse set of frequent amino acid patterns that can be related to the stability of the protein molecular structure and that are independent from protein function or specific conserved domains.

Modulation of folding energy landscape by charge-charge interactions: linking experiments with computational modeling

The kinetics of folding-unfolding of a structurally diverse set of four proteins optimized for thermodynamic stability by rational redesign of surface charge-charge interactions is characterized experimentally. The folding rates are faster for designed variants compared with their wild-type proteins, whereas the unfolding rates are largely unaffected. A simple structure-based computational model, which incorporates the Debye-Hückel formalism for the electrostatics, was used and found to qualitatively recapitulate the experimental results. Analysis of the energy landscapes of the designed versus wild-type proteins indicates the differences in refolding rates may be correlated with the degree of frustration of their respective energy landscapes. Our simulations indicate that naturally occurring wild-type proteins have frustrated folding landscapes due to the surface electrostatics. Optimization of the surface electrostatics seems to remove some of that frustration, leading to enhanced formation of native-like contacts in the transition-state ensembles (TSE) and providing a less frustrated energy landscape between the unfolded and TS ensembles. Macroscopically, this results in faster folding rates. Furthermore, analyses of pairwise distances and radii of gyration suggest that the less frustrated energy landscapes for optimized variants are a result of more compact unfolded and TS ensembles. These findings from our modeling demonstrates that this simple model may be used to: (i) gain a detailed understanding of charge-charge interactions and their effects on modulating the energy landscape of protein folding and (ii) qualitatively predict the kinetic behavior of protein surface electrostatic interactions.

Characterization and comparative analysis of psychrophilic and mesophilic alpha-amylases from Euplotes species: a contribution to the understanding of enzyme thermal adaptation

The eukaryotic α-amylase isolated from the psychrophilic ciliated protozoon Euplotes focardii (EfAmy) was expressed in Escherichia coli and biochemically characterized. Its enzymatic activity was compared to that of the homologous protein from the mesophilic congeneric species Euplotes crassus (EcAmy). The comparison of the amino acid composition and the surface residue composition of the two enzymes indicated a preference for tiny residues and the avoidance of charged, aromatic and hydrophobic residues in EfAmy. Our comparative homology modeling study reveals a lack of surface salt bridges, a decreased number of the surface charged residues, decreased hydrogen bonds and bound ions, and a reduction of aromatic-sulfur interactions, cationic-π interactions and disulfide interactions in EfAmy. In contrast, sequence alignment and homology modeling showed five unconserved prolines located on the surface loops of EcAmy. By analyzing amylolytic activity towards soluble starch as the substrate, we determined the temperature and pH dependence, thermostability and kinetic parameters of these two enzymes. We demonstrated that EfAmy shows the characteristics of a psychrophilic α-amylase, such as the highest hydrolytic activity at low temperatures and high thermolability. In contrast, the EcAmy showed mesophilic characteristics with the highest activity at moderate temperatures and a more than 2-fold increased half-life at 50°C compared to EfAmy. The kcat and KM values of EfAmy were higher than those of the mesophilic EcAmy at all tested temperatures. Furthermore, both EfAmy and EcAmy showed maximum activities at pH 9 and maintained high activities in the presence of surfactants. These results suggest the potential applications of EfAmy and EcAmy as ingredients in detergents for industrial applications.

Engineering a more thermostable blue light photo receptor Bacillus subtilis YtvA LOV domain by a computer aided rational design method

The ability to design thermostable proteins offers enormous potential for the development of novel protein bioreagents. In this work, a combined computational and experimental method was developed to increase the T_m of the flavin mononucleotide based fluorescent protein Bacillus Subtilis YtvA LOV domain by 31 Celsius, thus extending its applicability in thermophilic systems. Briefly, the method includes five steps, the single mutant computer screening to identify thermostable mutant candidates, the experimental evaluation to confirm the positive selections, the computational redesign around the thermostable mutation regions, the experimental reevaluation and finally the multiple mutations combination. The adopted method is simple and effective, can be applied to other important proteins where other methods have difficulties, and therefore provides a new tool to improve protein thermostability.

The bacterial blue-light photoreceptor YtvA from Bacillus subtilis, which fluoresces in the presence or absence of oxygen, provides new opportunities to study oxygen-limited cellular systems. However its thermostability is poor, hindering its applicability in thermophilic systems. In this work, we develop an iterative combined computational and experimental method to significantly improve this protein's thermostability. The method is simple and effective, which in principle can be applied to other protein systems, thus adds a new tool to the protein engineering arsenal.

PROTS-RF: a robust model for predicting mutation-induced protein stability changes

The ability to improve protein thermostability via protein engineering is of great scientific interest and also has significant practical value. In this report we present PROTS-RF, a robust model based on the Random Forest algorithm capable of predicting thermostability changes induced by not only single-, but also double- or multiple-point mutations. The model is built using 41 features including evolutionary information, secondary structure, solvent accessibility and a set of fragment-based features. It achieves accuracies of 0.799,0.782, 0.787, and areas under receiver operating characteristic (ROC) curves of 0.873, 0.868 and 0.862 for single-, double- and multiple- point mutation datasets, respectively. Contrary to previous suggestions, our results clearly demonstrate that a robust predictive model trained for predicting single point mutation induced thermostability changes can be capable of predicting double and multiple point mutations. It also shows high levels of robustness in the tests using hypothetical reverse mutations. We demonstrate that testing datasets created based on physical principles can be highly useful for testing the robustness of predictive models.

In silico classification of proteins from acidic and neutral cytoplasms

Protein acidostability is a common problem in biopharmaceutical and other industries. However, it remains a great challenge to engineer proteins for enhanced acidostability because our knowledge of protein acidostabilization is still very limited. In this paper, we present a comparative study of proteins from bacteria with acidic (AP) and neutral cytoplasms (NP) using an integrated statistical and machine learning approach. We construct a set of 393 non-redundant AP-NP ortholog pairs and calculate a total of 889 sequence based features for these proteins. The pairwise alignments of these ortholog pairs are used to build a residue substitution propensity matrix between APs and NPs. We use Gini importance provided by the Random Forest algorithm to rank the relative importance of these features. A scoring function using the 10 most significant features is developed and optimized using a hill climbing algorithm. The accuracy of the score function is 86.01% in predicting AP-NP ortholog pairs and is 76.65% in predicting non-ortholog AP-NP pairs, suggesting that there are significant differences between APs and NPs which can be used to predict relative acidostability of proteins. The overall trends uncovered in the study can be used as general guidelines for designing acidostable proteins. To best of our knowledge, this work represents the first systematic comparative study of the acidostable proteins and their non-acidostable orthologs.

Electrostatic contribution of surface charge residues to the stability of a thermophilic protein: benchmarking experimental and predicted pKa values

Optimization of the surface charges is a promising strategy for increasing thermostability of proteins. Electrostatic contribution of ionizable groups to the protein stability can be estimated from the differences between the pKa values in the folded and unfolded states of a protein. Using this pKa-shift approach, we experimentally measured the electrostatic contribution of all aspartate and glutamate residues to the stability of a thermophilic ribosomal protein L30e from Thermococcus celer. The pKa values in the unfolded state were found to be similar to model compound pKas. The pKa values in both the folded and unfolded states obtained at 298 and 333 K were similar, suggesting that electrostatic contribution of ionizable groups to the protein stability were insensitive to temperature changes. The experimental pKa values for the L30e protein in the folded state were used as a benchmark to test the robustness of pKa prediction by various computational methods such as H++, MCCE, MEAD, pKD, PropKa, and UHBD. Although the predicted pKa values were affected by crystal contacts that may alter the side-chain conformation of surface charged residues, most computational methods performed well, with correlation coefficients between experimental and calculated pKa values ranging from 0.49 to 0.91 (p<0.01). The changes in protein stability derived from the experimental pKa-shift approach correlate well (r = 0.81) with those obtained from stability measurements of charge-to-alanine substituted variants of the L30e protein. Our results demonstrate that the knowledge of the pKa values in the folded state provides sufficient rationale for the redesign of protein surface charges leading to improved protein stability.

Computational design of a thermostable mutant of cocaine esterase via molecular dynamics simulations

Cocaine esterase (CocE) has been known as the most efficient native enzyme for metabolizing naturally occurring cocaine. A major obstacle to the clinical application of CocE is the thermoinstability of native CocE with a half-life of only ∼11 min at physiological temperature (37 °C). It is highly desirable to develop a thermostable mutant of CocE for therapeutic treatment of cocaine overdose and addiction. To establish a structure-thermostability relationship, we carried out molecular dynamics (MD) simulations at 400 K on wild-type CocE and previously known thermostable mutants, demonstrating that the thermostability of the active form of the enzyme correlates with the fluctuation (characterized as the root-mean square deviation and root-mean square fluctuation of atomic positions) of the catalytic residues (Y44, S117, Y118, H287, and D259) in the simulated enzyme. In light of the structure-thermostability correlation, further computational modelling including MD simulations at 400 K predicted that the active site structure of the L169K mutant should be more thermostable. The prediction has been confirmed by wet experimental tests showing that the active form of the L169K mutant had a half-life of 570 min at 37 °C, which is significantly longer than those of the wild-type and previously known thermostable mutants. The encouraging outcome suggests that the high-temperature MD simulations and the structure-thermostability relationship may be considered as a valuable tool for the computational design of thermostable mutants of an enzyme.

Thermostabilization of Bacillus circulans xylanase: computational optimization of unstable residues based on thermal fluctuation analysis

Low thermostability often hampers the applications of xylanases in industrial processes operated at high temperature, such as degradation of biomass or pulp bleaching. Thermostability of enzymes can be improved by the optimization of unstable residues via protein engineering. In this study, computational modeling instead of random mutagenesis was used to optimize unstable residues of Bacillus circulans xylanase (Bcx). The thermal fluctuations of unstable residues known as important to the thermal unfolding of Bcx were investigated by the molecular dynamics (MD) simulations at 300 K and 330 K to identify promising residues. The N52 site in unstable regions showed the highest thermal fluctuations. Subsequently, computational design was conducted to predict the optimal sequences of unstable residues. Five optimal single mutants were predicted by the computational design, and the N52Y mutation showed the thermostabilization effect. The N52 residue is conserved in Bacillus species xylanases and the structure analysis revealed that the N52Y mutation introduced more hydrophobic clusters for thermostability, as well as a more favorable aromatic stacking environment for substrate binding. We confirm that flexible residues at high temperature in unstable regions can be promising targets to improve thermostability of enzymes.

Increasing protein stability: importance of DeltaC(p) and the denatured state

Increasing the conformational stability of proteins is an important goal for both basic research and industrial applications. In vitro selection has been used successfully to increase protein stability, but more often site-directed mutagenesis is used to optimize the various forces that contribute to protein stability. In previous studies, we showed that improving electrostatic interactions on the protein surface and improving the beta-turn sequences were good general strategies for increasing protein stability, and used them to increase the stability of RNase Sa. By incorporating seven of these mutations in RNase Sa, we increased the stability by 5.3 kcal/mol. Adding one more mutation, D79F, gave a total increase in stability of 7.7 kcal/mol, and a melting temperature 28 degrees C higher than the wild-type enzyme. Surprisingly, the D79F mutation lowers the change in heat capacity for folding, DeltaC(p), by 0.6 kcal/mol/K. This suggests that this mutation stabilizes structure in the denatured state ensemble. We made other mutants that give some insight into the structure present in the denatured state. Finally, the thermodynamics of folding of these stabilized variants of RNase Sa are compared with those observed for proteins from thermophiles.

A novel scoring function for discriminating hyperthermophilic and mesophilic proteins with application to predicting relative thermostability of protein mutants

Background

The ability to design thermostable proteins is theoretically important and practically useful. Robust and accurate algorithms, however, remain elusive. One critical problem is the lack of reliable methods to estimate the relative thermostability of possible mutants.

Results

We report a novel scoring function for discriminating hyperthermophilic and mesophilic proteins with application to predicting the relative thermostability of protein mutants. The scoring function was developed based on an elaborate analysis of a set of features calculated or predicted from 540 pairs of hyperthermophilic and mesophilic protein ortholog sequences. It was constructed by a linear combination of ten important features identified by a feature ranking procedure based on the random forest classification algorithm. The weights of these features in the scoring function were fitted by a hill-climbing algorithm. This scoring function has shown an excellent ability to discriminate hyperthermophilic from mesophilic sequences. The prediction accuracies reached 98.9% and 97.3% in discriminating orthologous pairs in training and the holdout testing datasets, respectively. Moreover, the scoring function can distinguish non-homologous sequences with an accuracy of 88.4%. Additional blind tests using two datasets of experimentally investigated mutations demonstrated that the scoring function can be used to predict the relative thermostability of proteins and their mutants at very high accuracies (92.9% and 94.4%). We also developed an amino acid substitution preference matrix between mesophilic and hyperthermophilic proteins, which may be useful in designing more thermostable proteins.

Conclusions

We have presented a novel scoring function which can distinguish not only HP/MP ortholog pairs, but also non-homologous pairs at high accuracies. Most importantly, it can be used to accurately predict the relative stability of proteins and their mutants, as demonstrated in two blind tests. In addition, the residue substitution preference matrix assembled in this study may reflect the thermal adaptation induced substitution biases. A web server implementing the scoring function and the dataset used in this study are freely available at http://www.abl.ku.edu/thermorank/.