There is great interest in increasing proteins’ stability to enhance their utility as biocatalysts, therapeutics, diagnostics and nanomaterials. Directed evolution is a powerful, but experimentally strenuous approach. Computational methods offer attractive alternatives. However, due to the limited reliability of predictions and potentially antagonistic effects of substitutions, only single-point mutations are usually predicted in silico, experimentally verified and then recombined in multiple-point mutants. Thus, substantial screening is still required. Here we present FireProt, a robust computational strategy for predicting highly stable multiple-point mutants that combines energy- and evolution-based approaches with smart filtering to identify additive stabilizing mutations. FireProt’s reliability and applicability was demonstrated by validating its predictions against 656 mutations from the ProTherm database. We demonstrate that thermostability of the model enzymes haloalkane dehalogenase DhaA and γ-hexachlorocyclohexane dehydrochlorinase LinA can be substantially increased (ΔTm = 24°C and 21°C) by constructing and characterizing only a handful of multiple-point mutants. FireProt can be applied to any protein for which a tertiary structure and homologous sequences are available, and will facilitate the rapid development of robust proteins for biomedical and biotechnological applications.
Proteins are increasingly used in biotechnological applications as therapeutics [1], diagnostics [2], nanomaterials [1] and biocatalysts [3]. Despite numerous advantages, the utility of proteins is frequently restricted by their limited stability under practical conditions, such as high temperatures, extreme pH, or the presence of organic solvents or proteases. Their thermostability is usually positively correlated with stability and performance in the presence of denaturing agents [4], expression yield [5], serum survival time [6] and shelf-life [7]. Thus, it is a key determinant of proteins’ applicability in biotechnological processes. High temperatures may also be required to prevent bacterial contamination during enzymatic food processing [8]. Moreover, thermostable proteins can tolerate much larger numbers of mutations than mesophilic variants and show enhanced evolvability in protein engineering projects [9]. Protein engineering is frequently applied to obtain more stable proteins. If successful, such efforts typically enhance the melting temperature (Tm) of engineered proteins by 2 to 15°C [7,10]. Extremely stabilized proteins with even greater increases in melting temperature (ΔTm) have been engineered by incorporating multiple mutations, and several outstanding increases of up to 35°C have been achieved using directed evolution methods [8]. However, these methods generally require extensive experiments, including screening up to 108 colonies of organisms expressing mutant variants to identify stable constructs, and appropriate high-throughput screening assays must be available [11]. A currently popular strategy is saturated mutagenesis of hotspots identified by (semi-) rational approaches [7,8, 12], such as the most flexible residues [10]; tunnel-forming residues [13]; or residues at multimeric interfaces [14]. The selected hotspots are then subjected to site-saturation mutagenesis (while leaving the rest of the protein unchanged) to create smaller smart libraries, markedly reducing the required screening to thousands of colonies. A long-sought alternative to screening-based approaches is reliable in silico design of stability-enhancing mutations. Numerous stable proteins have been computationally engineered via diverse approaches (singly or in combination), e. g. , identification of back-to-consensus or ancestral mutations, calculation of changes in folding free energies upon mutation, introduction of disulfide bridges and elimination of highly flexible regions [7,8, 12]. However, mutants generated using computational methods have rarely surpassed the 15°C ΔTm threshold of outstanding stabilization as a result of neutral, destabilizing or function-corrupting mutations that were predicted as stabilizing due to moderate accuracy of these methods [15,16]. To overcome this obstacle and provide substantial stabilization, predicted mutations are usually introduced by site-directed mutagenesis and tested individually. The most viable mutations are then recombined in multiple-point mutants assuming they have additive effects, but this is often invalid due to antagonistic epistatic effects of individual mutations [17]. For all those reasons, no computational method capable of directly designing highly stable multiple-point mutants has been previously published. Here we introduce a strategy, FireProt, for computationally designing multiple-point mutants, enabling significant improvements of protein stability with minimal experimental effort. We demonstrate its power by stabilizing the model proteins haloalkane dehalogenase (HLD) DhaA and γ-hexachlorocyclohexane dehydrochlorinase LinA. The method’s general applicability was further verified by validation against information from the ProTherm database [18], demonstrating that it can be used to identify stabilizing mutations in diverse proteins with known tertiary structures and homologous sequences allowing phylogenetic analysis, and thus should have broad utility in protein stabilization projects.
The FireProt strategy for protein stabilization is based on combining the best multiple-point mutants obtained from predictions of ΔΔG following mutation from a set of crystal structures and evolutionary information derived from multiple sequence alignment (Fig 1). Additional pre- and post-processing filters are applied in both approaches to improve prediction reliability and reduce the required computational effort. DhaA enzyme was selected as the first model protein due to the wealth of knowledge available on mutants engineered towards higher thermostability, prolonged half-life and stability in organic co-solvents that enables quantitative comparison of their performance with the mutants designed by FireProt [13,28]. γ-hexachlorocyclohexane dehydrochlorinase LinA enzyme was selected as the second model protein to illustrate broader applicability of FireProt strategy to other proteins of very different characteristics: (i) LinA is natively homotrimer (DhaA is monomer), (ii) LinA monomers form α+β barrel fold (DhaA possesses α/β-hydrolase fold), (iii) LinA is mainly composed of β-sheets (α-helices and β-sheets and equally represented in DhaA) and (iv) LinA is with 156 amino acids two-times shorter (DhaA has 294 residues). Visual inspection of mutant structures coupled with detailed analysis of their individual energy terms calculated by Rosetta provided indications of the possible structural basis of protein stabilization by mutations of DhaA115 and LinA01 (S11 Table). These mutations were introduced to various locations in the protein structure with different types of secondary structures.
The last decade has seen significant advances towards more rational approaches to reduce the experimental effort required to engineer highly stable proteins (Fig 4 and S12 Table). As a contribution to these efforts we have developed a hybrid strategy integrating energy-based and evolution-based approaches, with smart filtering of mutations that are destabilizing or may impair enzymes’ functions, enabling the identification of additively stabilizing substitutions in multiple-point mutants. It is essential to correctly configure all of the tools used in both the energy- and evolution-based approaches of the FireProt workflow in order to achieve robust and reliable predictions. Therefore, individual steps of the workflow were verified using a dataset featuring diverse proteins from the ProTherm database. The predictions carried out for 656 mutations confirmed the FireProt' s precision: the energy- and evolution-based approaches identified stabilizing mutations with success rates of 100% and 80%, respectively. Strikingly, only one stabilizing mutation that exceeded our thresholds was identified by both approaches, suggesting that they are highly complementary. The potential downside of the stringent conditions imposed to avoid false positives was that 92% of the available stabilizing mutations were discarded. However, the remaining correctly identified stabilizing mutations should be more than sufficient to construct highly stable catalysts (S3 Table). When the energy-based approach was applied to DhaA and LinA enzymes, the removal of conserved and correlated positions from analysis helped to avoid modification of structurally and functionally important residues, thereby greatly reducing the number of possible mutations requiring evaluation by computationally intensive free energy calculation. Since FoldX computation is about an order of magnitude faster than Rosetta, it was applied as a pre-filter, further reducing numbers of mutations to be analyzed by Rosetta. Regarding the prediction of multiple-point mutants, simple recombination of the most promising mutants could weaken stabilization, since strong antagonistic effects were detected even at the level of double-point mutants. The thermostability enhancement for the eight- and four-point mutants predicted by this approach, DhaA112 (ΔTm 16°C) and LinA01 (ΔTm 21°C), both exceeded the threshold for outstanding stabilization, although none of the introduced mutations optimized either hydrogen bonds or charge-charge interactions. This may be due to sampling limited rotamer libraries during the calculations and the requirement for both FoldX and Rosetta to unambiguously evaluate selected mutations as stabilizing. FoldX and Rosetta employ simplified scoring functions and despite using three protein structures for analysis, only limited protein flexibility is allowed, implying that it should be possible to supplement mutations proposed by free energy calculations with beneficial substitutions identified using different principles. To this end, additional mutations were selected by the evolution-based approach. The mutations predicted by the back-to-consensus method were filtered by FoldX to discard mutations proposed due to function-related evolutionary constraints rather than structural stabilization. This filtering step proved to be very important as over half of the mutations were discarded as potentially destabilizing. Interestingly, all five multiple-point mutants DhaA100-DhaA103 and LinA02 were predicted as destabilizing by Rosetta and had to be tested experimentally. While this prediction was accurate for three of them (DhaA100, DhaA102 and LinA02), the other two mutants (DhaA101 and DhaA103) were clearly more stable than the wild-type. This result suggests that some underlying principles important for stability detected by the back-to-consensus method are not captured by the applied Rosetta protocol. We speculate that these may include larger backbone rearrangements, interactions with ions present in the solvent, or other entropic contributions that are not well accounted for in the current protocols. Experimental characterization of these mutants by microcalorimetry, temperature-jump stopped-flow and protein crystallography is currently on-going in our laboratory. Despite its lower reliability, the evolution-based approach should still be considered as useful supplement to the energy-based approach, potentially enabling further improvement in the stability of designed proteins. The final 11-point mutant DhaA115 arising from this hybrid prediction strategy is one of the most stable HLD protein known to date (ΔTm > 24°C) [13,30]. We have compared our strategy against several methods providing exceptional protein stabilization (S12 Table). The experimentally intensive protocols of directed evolution and hot-spot predictions can provide engineered enzymes with comparable enhancement. However, since their success rate is generally below 1%, stable proteins can only be obtained after extensive screening. Notably, two of these studies also focused on improving stability of the enzyme DhaA. In one, an eight-point mutant DhaA was obtained with a ΔTm of 18°C after screening all 121,000 possible variants [28]. We have obtained a clearly superior enzyme after experimental evaluation of as few as six mutants, highlighting the importance of removing mutations with antagonistic and uncertain stabilizing effects. In the other study performed with DhaA, four hotspots in an access tunnel were experimentally randomized, requiring experimental screening of 5,000 mutations [13], and the ΔTm for the best four-point mutant was 19°C. Highly stable proteins have been obtained by in silico prediction of stabilizing effects of single-point mutations in four recently published studies [15,31,32,33]. In one, 67 variants of epoxide hydrolase with mutations identified as potentially stabilizing by the FRESCO method were experimentally tested, 24 were reportedly more stable than the parent protein, and the variant with the best permutation of mutations had remarkably enhanced thermostability (ΔTm 36°C) [15]. Much of this enhancement arose from disulfide bridges at the dimer interface, making this approach particularly suitable for multimeric proteins. In another of the studies, four out of six engineered methionine aminopeptidases designed by the RosettaVIP method were found to be stabilizing and a combined five-point mutant reportedly had a ΔTm of 18°C [31]. The authors noted that their final construct is still less stable than the most thermostable native aminopeptidases and that the method is particularly effective for mutagenesis of buried residues around internal cavities. In the other study, a 12-point mutant of Tobacco 5-epi-aristolochene synthase was generated using the SCADS method with an impressive ΔTm (45°C), but at the expense of 98% of catalytic activity at the optimal temperature [32]. In comparison to the methods applied in these and other relevant studies (S12 Table), FireProt affords a reduction of experimental screening effort due to robust identification of stabilizing mutations and ensuring their additivity. In addition, it has promising applicability to diverse proteins, potentially all proteins with known tertiary structure and homologous sequences, due to the diverse locations of introduced mutations and universal applicability of underlying principles. In summary, the presented hybrid strategy FireProt affords rapid design of stable proteins. Consideration of the additivity of identified potentially beneficial mutations enables prediction of multiple-point mutants with significantly enhanced stability. Despite a dramatic reduction in experimental effort, the workflow provided two proteins with outstanding stability. One of them a HLD with greater thermostability than all known HLD enzymes, either obtained from thermophilic organisms or engineered using extensive combinatorial screening. Furthermore, owing to the smart filtering, this strategy is affordable by users with limited access to powerful computer facilities. In addition, implementation of the FireProt strategy in the web-based protein engineering tool Hotspot Wizard [34] is currently on-going in our laboratory to ensure user convenience.
Proteins are increasingly used in numerous biotechnological applications. A key property determining proteins’ applicability is their stability under operating conditions. Natural proteins can be stabilized by modification of their structure. Methods of molecular biology allow introduction of modifications–mutations–to the protein structure at will, but it is not straightforward where to mutate and which amino acid to introduce for better stability. Computational methods can be used for prediction of stabilizing mutations using computers. Current computational methods predict libraries of single-point mutations, which need to be constructed individually, tested and recombined, resulting in non-trivial experimental effort. Here we present a robust computational strategy for predicting multiple-point mutants, providing extremely stabilized proteins with a minimal experimental effort.
FireProt: Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants