Evolutionary Analysis As a Powerful Complement to Energy Calculations for Protein Stabilization

Computer-aided engineering of stabilized fibroblast growth factor 21

FGF21 is an endocrine signaling protein belonging to the family of fibroblast growth factors (FGFs). It has emerged as a molecule of interest for treating various metabolic diseases due to its role in regulating glucogenesis and ketogenesis in the liver. However, FGF21 is prone to heat, proteolytic, and acid-mediated degradation, and its low molecular weight makes it susceptible to kidney clearance, significantly reducing its therapeutic potential. Protein engineering studies addressing these challenges have generally shown that increasing the thermostability of FGF21 led to improved pharmacokinetics. Here, we describe the computer-aided design and experimental characterization of FGF21 variants with enhanced melting temperature up to 15 °C, uncompromised efficacy at activation of MAPK/ERK signaling in Hep G2 cell culture, and ability to stimulate proliferation of Hep G2 and NIH 3T3 fibroblasts cells comparable with FGF21-WT. We propose that stabilizing the FGF21 molecule by rational design should be combined with other reported stabilization strategies to maximize the pharmaceutical potential of FGF21.

Computational design towards a boiling-resistant single-chain sweet protein monellin

Sweet proteins offer a promising solution as sugar substitutes by providing a sugar-like sweetness without the negative health impacts linked to sugar or artificial sweeteners. However, the low thermal stability of sweet proteins has hindered their applications. In this study, we took a computational approach utilizing ΔΔG calculations in PyRosetta to enhance the thermostability of single-chain monellin (MNEI). By generating and characterizing 21 variants with single mutation, we identified 11 variants with higher melting temperature (Tm) than that of MNEI. To further enhance the thermal stability, we conducted structural analysis and designed an additional set of 14 variants with multiple mutations. Among these variants, four exhibited a significant improvement in thermal stability, with an increase of at least 20 °C (Tm > 96 °C) compared to MNEI, while maintaining their sweetness. Remarkably, these variants remained soluble even after being heated in boiling water for one hour. Moreover, they displayed exceptional stability across alkaline, acidic and neutral environments. These findings highlight the tremendous potential of these variants for applications in the food and beverage industry. Additionally, this study provides valuable strategies for protein engineering to enhance the thermal stability of sweet proteins.

Mining and Characterization of Thermophilic Glucose Isomerase Based on Virtual Probe Technology

Fructose, which is produced by the isomerization of glucose isomerase, is a crucial precursor for the biosynthesis of rare sugars. In this study, thermophilic glucose isomerases (GI) from Caldicellulosiruptor acetigenus (CAGI), Thermoanaerobacter thermocopriae (TTGI), and Thermotoga petrophila (TPGI) were screened from GenBank database by a virtual probe and were successfully expressed in Escherichia coli BL21(DE3). The results of characterization demonstrated that the optimal pH for CAGI and TTGI were 8.0 and were maintained at 80% in a slightly acidic environment. The relative residual activities of CAGI and TTGI were found to be 40.6% and 52.6%, respectively, following an incubation period of 24 h at 90 ℃. Furthermore, CAGI and TTGI exhibited superior catalytic performance that their reaction equilibrium both reached only after an hour at 85 ℃ with 200 g/L glucose, and the highest conversion rates were 54.2% and 54.1%, respectively. This study identifies competitive enzyme candidates for fructose production in the industry with appreciable cost reduction.

Efficient Exploration of Sequence Space by Sequence-Guided Protein Engineering and Design

The rapid growth of sequence databases over the past two decades means that protein engineers faced with optimizing a protein for any given task will often have immediate access to a vast number of related protein sequences. These sequences encode information about the evolutionary history of the protein and the underlying sequence requirements to produce folded, stable, and functional protein variants. Methods that can take advantage of this information are an increasingly important part of the protein engineering tool kit. In this Perspective, we discuss the utility of sequence data in protein engineering and design, focusing on recent advances in three main areas: the use of ancestral sequence reconstruction as an engineering tool to generate thermostable and multifunctional proteins, the use of sequence data to guide engineering of multipoint mutants by structure-based computational protein design, and the use of unlabeled sequence data for unsupervised and semisupervised machine learning, allowing the generation of diverse and functional protein sequences in unexplored regions of sequence space. Altogether, these methods enable the rapid exploration of sequence space within regions enriched with functional proteins and therefore have great potential for accelerating the engineering of stable, functional, and diverse proteins for industrial and biomedical applications.

CalFitter 2.0: Leveraging the power of singular value decomposition to analyse protein thermostability

The importance of the quantitative description of protein unfolding and aggregation for the rational design of stability or understanding the molecular basis of protein misfolding diseases is well established. Protein thermostability is typically assessed by calorimetric or spectroscopic techniques that monitor different complementary signals during unfolding. The CalFitter webserver has already proved integral to deriving invaluable energy parameters by global data analysis. Here, we introduce CalFitter 2.0, which newly incorporates singular value decomposition (SVD) of multi-wavelength spectral datasets into the global fitting pipeline. Processed time- or temperature-evolved SVD components can now be fitted together with other experimental data types. Moreover, deconvoluted basis spectra provide spectral fingerprints of relevant macrostates populated during unfolding, which greatly enriches the information gains of the CalFitter output. The SVD analysis is fully automated in a highly interactive module, providing access to the results to users without any prior knowledge of the underlying mathematics. Additionally, a novel data uploading wizard has been implemented to facilitate rapid and easy uploading of multiple datasets. Together, the newly introduced changes significantly improve the user experience, making this software a unique, robust, and interactive platform for the analysis of protein thermal denaturation data. The webserver is freely accessible at https://loschmidt.chemi.muni.cz/calfitter.

Enhancing the Thermostability of Phytase to Boiling Point by Evolution-Guided Design

The good thermostability of enzymes is an important basis for their wide application in industry. In this study, the phytase APPA from Yersinia intermedia was designed by evolution-guided design. Through the collection of homologous sequences in the NCBI database, we obtained a sequence set composed of 5,569 sequences, counted the number and locations of motif N-X-T/S, and selected the sites with high frequency in evolution as candidate sites for experiments. Based on the principle that N-glycosylation modification sites are located on the protein surface, 13 mutants were designed to optimize the number and location of N-glycosylation sites. Through experimental verification, 7 single mutants with improved thermostability were obtained. The best mutant, M14, with equal catalytic efficiency as the wild-type was obtained through combined mutation. The half-life (t_1/2) value of mutant M14 was improved from 3.32 min at 65°C to 25 min of at 100°C, allowing it to withstand boiling water treatment, retaining approximately 75% initial activity after a 10-min incubation at 100°C. Differential scanning calorimetry analysis revealed that while the mutants' thermodynamic stability was nearly unchanged, their kinetic stability was greatly improved, and the combined mutant exhibited strong refolding ability. The results of a in vitro digestibility test indicated that the application effect of mutant M14 was about 4.5 times that of wild-type APPA, laying a foundation for its industrial application. IMPORTANCE Due to the harsh reaction conditions of industrial production, the relative instability of enzymes limits their application in industrial production, such as for food, pharmaceuticals, and feed. For example, the pelleting process of feed includes a brief high temperature (80 to 85°C), which requires the enzyme to have excellent thermostability. Therefore, a simple and effective method to improve the thermostability of enzymes has important practical value. In this study, we make full use of the existing homologous sequences (5,569) in the database to statistically analyze the existence frequency of N-X-T/S motifs in this large sequence space to design the phytase APPA with improved thermostability and a high hit rate (~50%). We obtained the best combination mutant, M14, that can tolerate boiling water treatment and greatly improved its kinetic stability without damaging its specific activity. Simultaneously, we proved that its performance improvement is due to its enhanced refolding ability, which comes from N-glycan modification rather than amino acid replacement. Our results provide a feasible and effective method for the modification of enzyme thermostability.

Exploring the Effect of Enhanced Sampling on Protein Stability Prediction

Changes in protein stability due to side-chain mutations are evaluated by alchemical free-energy calculations based on classical molecular dynamics (MD) simulations in explicit solvent using the GROMOS force field. Three proteins of different complexity with a total number of 93 single-point mutations are analyzed, and the relative free-energy differences are discussed with respect to configurational sampling and (dis)agreement with experimental data. For the smallest protein studied, a 34-residue WW domain, the starting structure dependence of the alchemical free-energy changes, is discussed in detail. Deviations from previous simulations for the two other proteins are shown to result from insufficient sampling in the earlier studies. Hamiltonian replica exchange in combination with multiple starting structures and sufficient sampling time of more than 100 ns per intermediate alchemical state is required in some cases to reach convergence.

Reviewing Challenges of Predicting Protein Melting Temperature Change Upon Mutation Through the Full Analysis of a Highly Detailed Dataset with High-Resolution Structures

Predicting the effects of mutations on protein stability is a key problem in fundamental and applied biology, still unsolved even for the relatively simple case of small, soluble, globular, monomeric, two-state-folder proteins. Many articles discuss the limitations of prediction methods and of the datasets used to train them, which result in low reliability for actual applications despite globally capturing trends. Here, we review these and other issues by analyzing one of the most detailed, carefully curated datasets of melting temperature change (ΔTm) upon mutation for proteins with high-resolution structures. After examining the composition of this dataset to discuss imbalances and biases, we inspect several of its entries assisted by an online app for data navigation and structure display and aided by a neural network that predicts ΔTm with accuracy close to that of programs available to this end. We pose that the ΔTm predictions of our network, and also likely those of other programs, account only for a baseline-like general effect of each type of amino acid substitution which then requires substantial corrections to reproduce the actual stability changes. The corrections are very different for each specific case and arise from fine structural details which are not well represented in the dataset and which, despite appearing reasonable upon visual inspection of the structures, are hard to encode and parametrize. Based on these observations, additional analyses, and a review of recent literature, we propose recommendations for developers of stability prediction methods and for efforts aimed at improving the datasets used for training. We leave our interactive interface for analysis available online at http://lucianoabriata.altervista.org/papersdata/proteinstability2021/s1626navigation.html so that users can further explore the dataset and baseline predictions, possibly serving as a tool useful in the context of structural biology and protein biotechnology research and as material for education in protein biophysics.

Decoding the intricate network of molecular interactions of a hyperstable engineered biocatalyst

Computational design of protein catalysts with enhanced stabilities for use in research and enzyme technologies is a challenging task. Using force-field calculations and phylogenetic analysis, we previously designed the haloalkane dehalogenase DhaA115 which contains 11 mutations that confer upon it outstanding thermostability (T m = 73.5 °C; ΔT m > 23 °C). An understanding of the structural basis of this hyperstabilization is required in order to develop computer algorithms and predictive tools. Here, we report X-ray structures of DhaA115 at 1.55 Å and 1.6 Å resolutions and their molecular dynamics trajectories, which unravel the intricate network of interactions that reinforce the αβα-sandwich architecture. Unexpectedly, mutations toward bulky aromatic amino acids at the protein surface triggered long-distance (∼27 Å) backbone changes due to cooperative effects. These cooperative interactions produced an unprecedented double-lock system that: (i) induced backbone changes, (ii) closed the molecular gates to the active site, (iii) reduced the volumes of the main and slot access tunnels, and (iv) occluded the active site. Despite these spatial restrictions, experimental tracing of the access tunnels using krypton derivative crystals demonstrates that transport of ligands is still effective. Our findings highlight key thermostabilization effects and provide a structural basis for designing new thermostable protein catalysts.

Structures of hyperstable ancestral haloalkane dehalogenases show restricted conformational dynamics

Ancestral sequence reconstruction is a powerful method for inferring ancestors of modern enzymes and for studying structure-function relationships of enzymes. We have previously applied this approach to haloalkane dehalogenases (HLDs) from the subfamily HLD-II and obtained thermodynamically highly stabilized enzymes (ΔT m up to 24 °C), showing improved catalytic properties. Here we combined crystallographic structural analysis and computational molecular dynamics simulations to gain insight into the mechanisms by which ancestral HLDs became more robust enzymes with novel catalytic properties. Reconstructed ancestors exhibited similar structure topology as their descendants with the exception of a few loop deviations. Strikingly, molecular dynamics simulations revealed restricted conformational dynamics of ancestral enzymes, which prefer a single state, in contrast to modern enzymes adopting two different conformational states. The restricted dynamics can potentially be linked to their exceptional stabilization. The study provides molecular insights into protein stabilization due to ancestral sequence reconstruction, which is becoming a widely used approach for obtaining robust protein catalysts.

Yosshi: a web-server for disulfide engineering by bioinformatic analysis of diverse protein families

Disulfide bonds play a significant role in protein stability, function or regulation but are poorly conserved among evolutionarily related proteins. The Yosshi can help to understand the role of S–S bonds by comparing sequences and structures of homologs with diverse properties and different disulfide connectivity patterns within a common structural fold of a superfamily, and assist to select the most promising hot-spots to improve stability of proteins/enzymes or modulate their functions by introducing naturally occurring crosslinks. The bioinformatic analysis is supported by the integrated Mustguseal web-server to construct large structure-guided sequence alignments of functionally diverse protein families that can include thousands of proteins based on all available information in public databases. The Yosshi+Mustguseal is a new integrated web-tool for a systematic homology-driven analysis and engineering of S–S bonds that facilitates a broader interpretation of disulfides not just as a factor of structural stability, but rather as a mechanism to implement functional diversity within a superfamily. The results can be downloaded as a content-rich PyMol session file or further studied online using the HTML5-based interactive analysis tools. Both web-servers are free and open to all users at https://biokinet.belozersky.msu.ru/yosshi and there is no login requirement.