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.     

HIV-1 protease function and structure studies with the simplicial neighborhood analysis of protein packing method

The Simplicial Neighborhood Analysis of Protein Packing (SNAPP) method was used to predict the effect of mutagenesis on the enzymatic activity of the HIV-1 protease (HIVP). SNAPP relies on a four-body statistical scoring function derived from the analysis of spatially nearest neighbor residue compositional preferences in a diverse and representative subset of protein structures from the Protein Data Bank. The method was applied to the analysis of HIVP mutants with residue substitutions in the hydrophobic core as well as at the interface between the two protease monomers. Both wild-type and tethered structures were employed in the calculations. We obtained a strong correlation, with R(2) as high as 0.96, between DeltaSNAPP score (i.e., the difference in SNAPP scores between wild-type and mutant proteins) and the protease catalytic activity for tethered structures. However, a weaker but significant correlation was obtained for nontethered structures. Our analysis identified residues both in the hydrophobic core and at the dimeric interface that are very important for the protease function. This study demonstrates a potential utility of the SNAPP method for rational design of mutagenesis studies and protein engineering.     

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.     

A systematic and comprehensive combinatorial approach to simultaneously improve the activity, reaction specificity, and thermal stability of p-hydroxybenzoate hydroxylase

We have simultaneously improved the activity, reaction specificity, and thermal stability of p-hydroxybenzoate hydroxylase by means of systematic and comprehensive combinatorial mutagenesis starting from available single mutations. Introduction of random mutations at the positions of four cysteine and eight methionine residues provided 216 single mutants as stably expressed forms in Escherichia coli host cells. Four characteristics, hydroxylase activity toward p-hydroxybenzoate (main activity), protocatechuate-dependent NADPH oxidase activity (sub-activity), ratio of sub-activity to main activity (reaction specificity), and thermal stability, of the purified mutants were determined. To improve the above characteristics for diagnostic use of the enzyme, 11 single mutations (C152V, C211I, C332A, M52V, M52Q, M110L, M110I, M213G, M213L, M276Q, and M349A) were selected for further combinatorial mutagenesis. All possible combinations of the mutations provided 18 variants with double mutations and further combinatorial mutagenesis provided 6 variants with triple mutations and 9 variants with quadruple mutations with the simultaneously improved four properties.     

Evolution of three human GPCRs for higher expression and stability

We recently developed a display method for the directed evolution of integral membrane proteins in the inner membrane of Escherichia coli for higher expression and stability. For the neurotensin receptor 1, a G-protein-coupled receptor (GPCR), we had evolved a mutant with a 10-fold increase in functional expression that largely retains wild-type binding and signaling properties and shows higher stability in detergent-solubilized form. We have now evolved three additional human GPCRs. Unmodified wild-type receptor cDNA was subjected to successive cycles of mutagenesis and fluorescence-activated cell sorting, and functional expression could be increased for all three GPCR targets. We also present a new stability screening method in a 96-well assay format to quickly identify evolved receptors showing increased thermal stability in detergent-solubilized form and rapidly evaluate them quantitatively. Combining the two methods turned out to be very powerful; even for the most challenging GPCR target—the tachykinin receptor NK₁, which is hardly expressed in E. coli and cannot be functionally solubilized—receptor mutants that are functionally expressed at 1 mg/l levels in E. coli and are stable in detergent solution could be quickly evolved. The improvements result from cumulative small changes in the receptor sequence. This combinatorial approach does not require preconceived notions for designing mutations. Our results suggest that this method is generally applicable to GPCRs. Existing roadblocks in structural and biophysical studies can now be removed by providing sufficient quantities of correctly folded and stable receptor protein.     

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.     

Cover Image,Volume 116, Number 2, February 2019

A key point of protein stability engineering is to identify specific target residues whose mutations can stabilize the protein structure without negatively affecting the function or activity of the protein. Here, we propose a method called RiSLnet (Rapid i dentification of Smart mutant Library using residue network) to identify such residues by combining network analysis for protein residue interactions, identification of conserved residues, and evaluation of relative solvent accessibility. To validate its performance, the method was applied to four proteins, that is, T4 lysozyme, ribonuclease H, barnase, and cold shock protein B. Our method predicted beneficial mutations in thermal stability with ~62% average accuracy when the thermal stability of the mutants was compared with the ones in the Protherm database. It was further applied to lysine decarboxylase (CadA) to experimentally confirm its accuracy and effectiveness. RiSLnet identified mutations increasing the thermal stability of CadA with the accuracy of ~60% and significantly reduced the number of candidate residues (~99%) for mutation. Finally, combinatorial mutations designed by RiSLnet and in silico saturation mutagenesis yielded a thermally stable triple mutant with the half-life (T _1/2) of 114.9 min at 58°C, which is approximately twofold higher than that of the wild-type.     

Four-body potentials reveal protein-specific correlations to stability changes caused by hydrophobic core mutations

Mutational experiments show how changes in the hydrophobic cores of proteins affect their stabilities. Here, we estimate these effects computationally, using four-body likelihood potentials obtained by simplicial neighborhood analysis of protein packing (SNAPP). In this procedure, the volume of a known protein structure is tiled with tetrahedra having the center of mass of one amino acid side-chain at each vertex. Log-likelihoods are computed for the 8855 possible tetrahedra with equivalent compositions from structural databases and amino acid frequencies. The sum of these four-body potentials for tetrahedra present in a given protein yields the SNAPP score. Mutations change this sum by changing the compositions of tetrahedra containing the mutated residue and their related potentials. Linear correlation coefficients between experimental mutational stability changes, Delta(DeltaG(unfold)), and those based on SNAPP scoring range from 0.70 to 0.94 for hydrophobic core mutations in five different proteins. Accurate predictions for the effects of hydrophobic core mutations can therefore be obtained by virtual mutagenesis, based on changes to the total SNAPP likelihood potential. Significantly, slopes of the relation between Delta(DeltaG(unfold)) and DeltaSNAPP for different proteins are statistically distinct, and we show that these protein-specific effects can be estimated using the average SNAPP score per residue, which is readily derived from the analysis itself. This result enhances the predictive value of statistical potentials and supports previous suggestions that "comparable" mutations in different proteins may lead to different Delta(DeltaG(unfold)) values because of differences in their flexibility and/or conformational entropy.     

Effective scoring function for protein sequence design

We have developed an effective scoring function for protein design. The atomic solvation parameters, together with the weights of energy terms, were optimized so that residues corresponding to the native sequence were predicted with low energy in the training set of 28 protein structures. The solvation energy of non-hydrogen-bonded hydrophilic atoms was considered separately and expressed in a nonlinear way. As a result, our scoring function predicted native residues as the most favorable in 59% of the total positions in 28 proteins. We then tested the scoring function by comparing the predicted stability changes for 103 T4 lysozyme mutants with the experimental values. The correlation coefficients were 0.77 for surface mutations and 0.71 for all mutations. Finally, the scoring function combined with Monte Carlo simulation was used to predict favorable sequences on a fixed backbone. The designed sequences were similar to the natural sequences of the family to which the template structure belonged. The profile of the designed sequences was helpful for identification of remote homologues of the native sequence.     

A method for multi-site-directed mutagenesis based on homologous recombination

We have developed an efficient method for the simultaneous introduction of up to three mutations in a plasmid DNA via homologous recombination. The strategy is compatible with a variety of mutations, including degenerate codons in plasmids of different sizes. In contrast to other methodologies, this approach employs the same set of reagents for both single- and multi-site mutagenesis assays, minimizes the required protocol steps, and exhibits remarkably high mutagenesis efficiencies.     

Stabilizing the subtilisin BPN' pro-domain by phage display selection: How restrictive is the amino acid code for maximum protein stability?

We have devised a procedure using monovalent phage display to select for stable mutants in the pro-domain of the serine protease, subtilisin BPN'. In complex with subtilisin, the pro-domain assumes a compact structure with a four-stranded antiparallel beta-sheet and two three-turn alpha-helices. When isolated, however, the pro-domain is 97% unfolded. These experiments use combinatorial mutagenesis to select for stabilizing amino acid combinations at a particular structural locus and determine how many combinations are close to the maximum protein stability. The selection for stability is based on the fact that the independent stability of the pro-domain is very low and that binding to subtilisin is thermodynamically linked to folding. Two libraries of mutant pro-domains were constructed and analyzed to determine how many combinations of amino acids at a particular structural locus result in the maximum stability. A library comprises all combinations of four amino acids at a structural locus. Previous studies using combinatorial genetics have shown that many different combinations of amino acids can be accommodated in a selected locus without destroying function. The present results indicate that the number of sequence combinations at a structural locus, which are close to the maximum stability, is small. The most striking example is a selection at an interior locus of the pro-domain. After two rounds of phagemid selection, one amino acid combination is found in 40% of sequenced mutants. The most frequently selected mutant has a deltaG(unfolding) = 4 kcal/mol at 25 degrees C, an increase of 6 kcal/mol relative to the naturally occurring sequence. Some implications of these results on the amount of sequence information needed to specify a unique tertiary fold are discussed. Apart from possible implications on the folding code, the phage display selection described here should be useful in optimizing the stability of other proteins, which can be displayed on the phage surface.     

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.     

Enhanced Mutant Screening in One-step PCR-based Multiple Site-directed Plasmid Mutagenesis by Introduction of Silent Restriction Sites for Structural and Functional Study of Proteins

Site-directed mutagenesis (SDM) has been widely used for studying the structure and function of proteins. A one-step polymerase chain reaction (PCR)-based multiple site-directed plasmid mutagenesis method with extended non-overlapping sequence at the 3′ end of the primer increases the PCR amplification efficiency and the capacity of multi-site mutagenesis. Here, we introduced silent restriction sites in the primers used in this PCR-based SDM method by utilizing SDM-Assist software to generate mutants of Helicobacter pylori neutrophil-activating protein (HP-NAP), whose gene has low GC content. The HP-NAP mutants were efficiently generated by this modified mutagenesis method and quickly identified by a simple restriction digest due to the presence of the silent restriction site. This modified PCR-based SDM method with the introduction of a silent restriction site on the primer is efficient for generation and identification of mutations in the gene of interest.     

Identifying functionally important mutations from phenotypically diverse sequence data

Here we present a simple statistical method to determine the phenotypic contribution of a single mutation from libraries of mutants with diverse phenotypes in which each mutant contains a multitude of mutations. The central premise of this method is that, given M phenotypic classes, mutations that do not affect the phenotype should partition among the M classes according to a multinomial distribution. Deviations from this distribution are indicative of a link between specific mutations and phenotypes. We suggest that this method will aid the engineering of functional nucleic acids, proteins, and other biomolecules by uncovering target sites for rational mutagenesis. As a proof of the principle, we show how the method can be used to deduce the individual effects of mutations in a set of 69 P(L)-lambda promoter variants. Each of these promoters was generated by error-prone PCR and incorporated numerous mutations. The activity of the promoters was assayed using flow cytometry to measure the fluorescence of a green fluorescent protein reporter gene. Our analysis of the sequences of these mutants revealed seven positions having a statistically significant correlation with promoter activity. Using site-directed mutagenesis, we constructed point mutations for several sites, both statistically significant and insignificant, and combinations of these sites. Our results show that the statistical method correctly elucidated the phenotypic manifestations of these mutations. We suggest that this method may be useful for expediting directed evolution experiments by allowing both desired and undesired mutations to be identified and incorporated between rounds of mutagenesis.     

Optimization of combinatorial mutagenesis

Protein engineering by combinatorial site-directed mutagenesis evaluates a portion of the sequence space near a target protein, seeking variants with improved properties (e.g., stability, activity, immunogenicity). In order to improve the hit-rate of beneficial variants in such mutagenesis libraries, we develop methods to select optimal positions and corresponding sets of the mutations that will be used, in all combinations, in constructing a library for experimental evaluation. Our approach, OCoM (Optimization of Combinatorial Mutagenesis), encompasses both degenerate oligonucleotides and specified point mutations, and can be directed accordingly by requirements of experimental cost and library size. It evaluates the quality of the resulting library by one- and two-body sequence potentials, averaged over the variants. To ensure that it is not simply recapitulating extant sequences, it balances the quality of a library with an explicit evaluation of the novelty of its members. We show that, despite dealing with a combinatorial set of variants, in our approach the resulting library optimization problem is actually isomorphic to single-variant optimization. By the same token, this means that the two-body sequence potential results in an NP-hard optimization problem. We present an efficient dynamic programming algorithm for the one-body case and a practically-efficient integer programming approach for the general two-body case. We demonstrate the effectiveness of our approach in designing libraries for three different case study proteins targeted by previous combinatorial libraries--a green fluorescent protein, a cytochrome P450, and a beta lactamase. We found that OCoM worked quite efficiently in practice, requiring only 1 hour even for the massive design problem of selecting 18 mutations to generate 10? variants of a 443-residue P450. We demonstrate the general ability of OCoM in enabling the protein engineer to explore and evaluate trade-offs between quality and novelty as well as library construction technique, and identify optimal libraries for experimental evaluation.     

Design of highly stable functional GroEL minichaperones.

GroEL minichaperones have potential in the biotechnology industry for the refolding of recombinant proteins. With the aim of enhancing and widening their use, we have created two highly stable functional variants of minichaperone GroEL(193-345). A sequence alignment of 130 members of the chaperonin 60 (Cpn60) family was used to design 37 single mutations. Two small-to-large mutations, A223T, A223V and one similar-size mutation, M233L, all located in the hydrophobic core were found to stabilize the protein by more than 1 kcal mol(-1) each. Six stabilizing mutations were combined, yielding two multiple mutants that were 6.99 and 6.15 kcal mol(-1) more stable than wild-type protein. Even though some of the substituted residue pairs are close to each other in the protein structure, the energetic effects of mutation are approximately additive. In particular, the stabilizing substitution A223T is unexpected and would have been missed by purely structural analysis. In the light of previously reported successes employing similar methods with several other proteins, our results show that a homology based approach is a simple and efficient method of increasing the stability of a protein.     

First-step mutations for adaptation at elevated temperature increase capsid stability in a virus

The relationship between mutation, protein stability and protein function plays a central role in molecular evolution. Mutations tend to be destabilizing, including those that would confer novel functions such as host-switching or antibiotic resistance. Elevated temperature may play an important role in preadapting a protein for such novel functions by selecting for stabilizing mutations. In this study, we test the stability change conferred by single mutations that arise in a G4-like bacteriophage adapting to elevated temperature. The vast majority of these mutations map to interfaces between viral coat proteins, suggesting they affect protein-protein interactions. We assess their effects by estimating thermodynamic stability using molecular dynamic simulations and measuring kinetic stability using experimental decay assays. The results indicate that most, though not all, of the observed mutations are stabilizing.     

iPTREE-STAB: interpretable decision tree based method for predicting protein stability changes upon mutations

We have developed a web server, iPTREE-STAB for discriminating the stability of proteins (stabilizing or destabilizing) and predicting their stability changes (delta deltaG) upon single amino acid substitutions from amino acid sequence. The discrimination and prediction are mainly based on decision tree coupled with adaptive boosting algorithm, and classification and regression tree, respectively, using three neighboring residues of the mutant site along N- and C-terminals. Our method showed an accuracy of 82% for discriminating the stabilizing and destabilizing mutants, and a correlation of 0.70 for predicting protein stability changes upon mutations. AVAILABILITY: http://bioinformatics.myweb.hinet.net/iptree.htm. SUPPLEMENTARY INFORMATION: Dataset and other details are given.