Protein stability: computation, sequence statistics, and new experimental methods

Precise redesign for improving enzyme robustness based on coevolutionary analysis and multidimensional virtual screening

Natural enzymes are able to function effectively under optimal physiological conditions, but the intrinsic performance often fails to meet the demands of industrial production. Existing strategies are based mainly on the evaluation and subsequent combination of single-point mutations; however, this approach often suffers from a limited number of designable residues and from low accuracy. Here, we propose a strategy (Co-MdVS) based on coevolutionary analysis and multidimensional virtual screening for precise design to improve enzyme robustness, employing nattokinase as a model. Using this strategy, we efficiently screened 8 dual mutants with enhanced thermostability from a virtual mutation library containing 7980 mutants. After further iterative combination, the optimal mutant M6 exhibited a 31-fold increase in half-life at 55 °C, significantly enhanced acid resistance, and improved catalytic efficiency with different substrates. Molecular dynamics simulations indicated that the reduced flexibility of thermal and acid-sensitive regions resulted in a significantly increased robustness of M6. Furthermore, the potential of multidimensional virtual screening in enhancing design precision has been validated on l-rhamnose isomerase and PETase. Therefore, the Co-MdVS strategy introduced in this research may offer a viable approach for developing enzymes with enhanced robustness.

Unlocking the potential of enzyme engineering via rational computational design strategies

Enzymes play a pivotal role in various industries by enabling efficient, eco-friendly, and sustainable chemical processes. However, the low turnover rates and poor substrate selectivity of enzymes limit their large-scale applications. Rational computational enzyme design, facilitated by computational algorithms, offers a more targeted and less labor-intensive approach. There has been notable advancement in employing rational computational protein engineering strategies to overcome these issues, it has not been comprehensively reviewed so far. This article reviews recent developments in rational computational enzyme design, categorizing them into three types: structure-based, sequence-based, and data-driven machine learning computational design. Case studies are presented to demonstrate successful enhancements in catalytic activity, stability, and substrate selectivity. Lastly, the article provides a thorough analysis of these approaches, highlights existing challenges and potential solutions, and offers insights into future development directions.

The importance of input sequence set to consensus-derived proteins and their relationship to reconstructed ancestral proteins

A protein sequence encodes its energy landscape-all the accessible conformations, energetics, and dynamics. The evolutionary relationship between sequence and landscape can be probed phylogenetically by compiling a multiple sequence alignment of homologous sequences and generating common ancestors via Ancestral Sequence Reconstruction or a consensus protein containing the most common amino acid at each position. Both ancestral and consensus proteins are often more stable than their extant homologs-questioning the differences between them and suggesting that both approaches serve as general methods to engineer thermostability. We used the Ribonuclease H family to compare these approaches and evaluate how the evolutionary relationship of the input sequences affects the properties of the resulting consensus protein. While the consensus protein derived from our full Ribonuclease H sequence alignment is structured and active, it neither shows properties of a well-folded protein nor has enhanced stability. In contrast, the consensus protein derived from a phylogenetically-restricted set of sequences is significantly more stable and cooperatively folded, suggesting that cooperativity may be encoded by different mechanisms in separate clades and lost when too many diverse clades are combined to generate a consensus protein. To explore this, we compared pairwise covariance scores using a Potts formalism as well as higher-order sequence correlations using singular value decomposition (SVD). We find the SVD coordinates of a stable consensus sequence are close to coordinates of the analogous ancestor sequence and its descendants, whereas the unstable consensus sequences are outliers in SVD space.

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.

Towards the understanding of molecular motors and its relationship with local unfolding

Molecular motors are machines essential for life since they convert chemical energy into mechanical work. However, the precise mechanism by which nucleotide binding, catalysis, or release of products is coupled to the work performed by the molecular motor is still not entirely clear. This is due, in part, to a lack of understanding of the role of force in the mechanical-structural processes involved in enzyme catalysis. From a mechanical perspective, one promising hypothesis is the Haldane-Pauling hypothesis which considers the idea that part of the enzymatic catalysis is strain-induced. It suggests that enzymes cannot be efficient catalysts if they are fully complementary to the substrates. Instead, they must exert strain on the substrate upon binding, using enzyme-substrate energy interaction (binding energy) to accelerate the reaction rate. A novel idea suggests that during catalysis, significant strain energy is built up, which is then released by a local unfolding/refolding event known as 'cracking'. Recent evidence has also shown that in catalytic reactions involving conformational changes, part of the heat released results in a center-of-mass acceleration of the enzyme, raising the possibility that the heat released by the reaction itself could affect the enzyme's integrity. Thus, it has been suggested that this released heat could promote or be linked to the cracking seen in proteins such as adenylate kinase (AK). We propose that the energy released as a consequence of ligand binding/catalysis is associated with the local unfolding/refolding events (cracking), and that this energy is capable of driving the mechanical work.

Biosynthetic production of anticoagulant heparin polysaccharides through metabolic and sulfotransferases engineering strategies

Heparin is an important anticoagulant drug, and microbial heparin biosynthesis is a potential alternative to animal-derived heparin production. However, effectively using heparin synthesis enzymes faces challenges, especially with microbial recombinant expression of active heparan sulfate N-deacetylase/N-sulfotransferase. Here, we introduce the monosaccharide N-trifluoroacetylglucosamine into Escherichia coli K5 to facilitate sulfation modification. The Protein Repair One-Stop Service-Focused Rational Iterative Site-specific Mutagenesis (PROSS-FRISM) platform is used to enhance sulfotransferase efficiency, resulting in the engineered NST-M8 enzyme with significantly improved stability (11.32-fold) and activity (2.53-fold) compared to the wild-type N-sulfotransferase. This approach can be applied to engineering various sulfotransferases. The multienzyme cascade reaction enables the production of active heparin from bioengineered heparosan, demonstrating anti-FXa (246.09 IU/mg) and anti-FIIa (48.62 IU/mg) activities. This study offers insights into overcoming challenges in heparin synthesis and modification, paving the way for the future development of animal-free heparins using a cellular system-based semisynthetic strategy.

Computer-directed rational design enhanced the thermostability of carbonyl reductase LsCR for the synthesis of ticagrelor precursor

Carbonyl reductases are useful for producing optically active alcohols from their corresponding prochiral ketones. Herein, we applied a computer-assisted strategy to increase the thermostability of a previously constructed carbonyl reductase, LsCRM4 (N101D/A117G/F147L/E145A), which showed an outstanding activity in the synthesis of the ticagrelor precursor (1S)-2-chloro-1-(3,4-difluorophenyl)ethanol. The stability changes introduced by mutations at the flexible sites were predicted using the computational tools FoldX, I-Mutant 3.0, and DeepDDG, which demonstrated that 12 virtually screened mutants could be thermally stable; 11 of these mutants exhibited increased thermostability. Then a superior mutant LsCRM4-V99L/D150F was screened out from the library that was constructed by iteratively combining the beneficial sites, which showed a 78% increase in activity and a 17.4°C increase in melting temperature compared to LsCRM4. Our computer-assisted design and combinatorial strategy dramatically increased the efficiency of thermostable enzyme production.

Design and Thermodynamics Principles to Program the Cooperativity of Molecular Assemblies

Most functional nanosystems in living organisms are constructed using multimeric assemblies that provide multiple advantages over their monomeric counterparts such as cooperative or anti-cooperative responses, integration of multiple signals and self-regulation. Inspired by these natural nanosystems, chemists have been synthesizing self-assembled supramolecular systems over the last 50 years with increasing complexity with applications ranging from biosensing, drug delivery, synthetic biology, and system chemistry. Although many advances have been made concerning the design principles of novel molecular architectures and chemistries, little is still known, however, about how to program their dynamic of assembly so that they can assemble at the required concentration and with the right sensitivity. Here, we used synthetic DNA assemblies and double-mutant cycle analysis to explore the thermodynamic basis to program the cooperativity of molecular assemblies. The results presented here exemplify how programmable molecular assemblies can be efficiently built by fusing interacting domains and optimizing their compaction. They may also provide the rational basis for understanding the thermodynamic and mechanistic principles driving the evolution of multimeric biological complexes.

Improving Protein Expression, Stability, and Function with ProteinMPNN

Natural proteins are highly optimized for function but are often difficult to produce at a scale suitable for biotechnological applications due to poor expression in heterologous systems, limited solubility, and sensitivity to temperature. Thus, a general method that improves the physical properties of native proteins while maintaining function could have wide utility for protein-based technologies. Here, we show that the deep neural network ProteinMPNN, together with evolutionary and structural information, provides a route to increasing protein expression, stability, and function. For both myoglobin and tobacco etch virus (TEV) protease, we generated designs with improved expression, elevated melting temperatures, and improved function. For TEV protease, we identified multiple designs with improved catalytic activity as compared to the parent sequence and previously reported TEV variants. Our approach should be broadly useful for improving the expression, stability, and function of biotechnologically important proteins.

Characterization of a Novel Thermostable 7α-Hydroxysteroid Dehydrogenase

BACKGROUND

7α-Hydroxysteroid dehydrogenase (7α-HSDH) plays a pivotal role in vivo in the biotransformation of secondary bile acids and has great potential in industrial biosynthesis due to its broad substrate specificity. In this study, we expressed and characterized a novel thermostable 7α-HSDH (named Sa 7α-HSDH).

METHODS

The DNA sequence was derived from the black bear gut microbiome metagenomic sequencing data, and the coding sequence of Sa 7α-HSDH was chemically synthesized. The heterologous expression of the enzyme was carried out using the pGEX-6p-1 vector. Subsequently, the activity of the purified enzyme was studied by measuring the absorbance change at 340 nm. Finally, the three-dimensional structure was predicted with AlphaFold2.

RESULTS

Coenzyme screening results confirmed it to be NAD(H) dependent. Substrate specificity test revealed that Sa 7α-HSDH could catalyze taurochenodeoxycholic acid (TCDCA) with catalytic efficiency (kcat/Km) 3.81 S-1 mM-1. The optimum temperature of Sa 7α-HSDH was measured to be 75°C, confirming that it belongs to thermophilic enzymes. Additionally, its thermostability was assessed using an accelerated stability test over 32 hours. The catalytic activity of Sa 7α-HSDH remained largely unchanged for the first 24 hours and retained over 90% of its functionality after 32 hours at 50°C. Sa 7α-HSDH exhibited maximal activity at pH 10. The effect of metal ions-K+, Na+, Mg2+ and Cu2+-on the enzymatic activity of Sa 7α-HSDH was investigated. Only Mg2+ was observed to enhance the enzyme's activity by 27% at a concentration of 300 mM. Neither K+ nor Na+ had a significant influence on activity. Only Cu2+ was found to reduce enzyme activity.

CONCLUSION

We characterized the thermostable 7α-HSDH, which provides a promising biocatalyst for bioconversion of steroids at high reaction temperatures.

Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review

In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy achievements. This advancement in protein intelligent design holds great potential to accelerate the development of new drugs, enhance the efficiency of biocatalysts, and even create entirely new biomaterials. Protein characterization is the key to the performance of intelligent protein design. However, there is no consensus on the most suitable characterization method for intelligent protein design tasks. This review describes the methods, characteristics, and representative applications of traditional descriptors, sequence-based and structure-based protein characterization. It discusses their advantages, disadvantages, and scope of application. It is hoped that this could help researchers to better understand the limitations and application scenarios of these methods, and provide valuable references for choosing appropriate protein characterization techniques for related research in the field, so as to better carry out protein research.

Computational saturation mutagenesis to explore the effect of pathogenic mutations on extra-cellular domains of TREM2 associated with Alzheimer's and Nasu-Hakola disease

CONTEXT

The specialised family of triggering receptors expressed on myeloid cells (TREMs) plays a pivotal role in causing neurodegenerative disorders and activating microglial anti-inflammatory responses. Nasu-Hakola disease (NHD), a rare autosomal recessive disorder, has been associated with mutations in TREM2, which is also responsible for raising the risk of Alzheimer's disease (AD). Herein, we have made an endeavour to differentiate the confirmed pathogenic variants in TREM2 extra-cellular domain (ECD) linked with NHD and AD using mutation-induced fold stability change (∆∆G), with the computation of 12distinct structure-based methods through saturation mutagenesis. Correlation analysis between relative solvent accessibility (RSA) and ∆∆G expresses the discrete distributive behaviour of mutants associated with TREM2 in AD (R2 = 0.061) and NHD (R2 = 0.601). Our findings put an emphasis on W50 and V126 as major players in maintaining V-like domain in TREM2. Interestingly, we discern that both of them interact with a common residue Y108, which is dissolved upon mutation. This Y108 could have structural or functional role for TREM2 which can be an ideal candidate for further study. Furthermore, the residual interaction network highlights the importance of R47 and R62 in maintaining the CDR loops that are crucial for ligand binding. Future studies using biophysical characterisation of ligand interactions in TREM2-ECD would be helpful for the development of novel therapeutics for AD and NHD.

METHODS

ConSurf algorithm and ENDscript were used to determine the position and conservation of each residue in the wild-type ECD of TREM2. The mutation-induced fold stability change (∆∆G) of confirmed pathogenic mutants associated with NHD and AD was estimated using 12 state-of-the-art structure-based protein stability tools. Furthermore, we also computed the effect of random mutation on these sites using computational saturation mutagenesis. Linear regression analysis was performed using mutants ∆∆G and RSA through GraphPad software. In addition, a comprehensive non-bonded residual interaction network (RIN) of wild type and its mutants of TREM2-ECD was enumerated using RING3.0.

Genome mining yields putative disease-associated ROMK variants with distinct defects

Bartter syndrome is a group of rare genetic disorders that compromise kidney function by impairing electrolyte reabsorption. Left untreated, the resulting hyponatremia, hypokalemia, and dehydration can be fatal, and there is currently no cure. Bartter syndrome type II specifically arises from mutations in KCNJ1, which encodes the renal outer medullary potassium channel, ROMK. Over 40 Bartter syndrome-associated mutations in KCNJ1 have been identified, yet their molecular defects are mostly uncharacterized. Nevertheless, a subset of disease-linked mutations compromise ROMK folding in the endoplasmic reticulum (ER), which in turn results in premature degradation via the ER associated degradation (ERAD) pathway. To identify uncharacterized human variants that might similarly lead to premature degradation and thus disease, we mined three genomic databases. First, phenotypic data in the UK Biobank were analyzed using a recently developed computational platform to identify individuals carrying KCNJ1 variants with clinical features consistent with Bartter syndrome type II. In parallel, we examined genomic data in both the NIH TOPMed and ClinVar databases with the aid of Rhapsody, a verified computational algorithm that predicts mutation pathogenicity and disease severity. Subsequent phenotypic studies using a yeast screen to assess ROMK function-and analyses of ROMK biogenesis in yeast and human cells-identified four previously uncharacterized mutations. Among these, one mutation uncovered from the two parallel approaches (G228E) destabilized ROMK and targeted it for ERAD, resulting in reduced cell surface expression. Another mutation (T300R) was ERAD-resistant, but defects in channel activity were apparent based on two-electrode voltage clamp measurements in X. laevis oocytes. Together, our results outline a new computational and experimental pipeline that can be applied to identify disease-associated alleles linked to a range of other potassium channels, and further our understanding of the ROMK structure-function relationship that may aid future therapeutic strategies to advance precision medicine.

Forensic Proteomics for the Discovery of New post mortem Interval Biomarkers: A Preliminary Study

Estimating the time since death (post mortem interval, PMI) represents one of the most important tasks in daily forensic casework. For decades, forensic scientists have investigated changes in post mortem body composition, focusing on different physical, chemical, or biological aspects, to discover a reliable method for estimating PMI; nevertheless, all of these attempts remain unsuccessful considering the currently available methodical spectrum characterized by great inaccuracies and limitations. However, recent promising approaches focus on the post mortem decomposition of biomolecules. In particular, significant advances have been made in research on the post mortem degradation of proteins. In the present study, we investigated early post mortem changes (during the first 24 h) in the proteome profile of the pig skeletal muscle looking for new PMI specific biomarkers. By mass spectrometry (MS)-based proteomics, we were able to identify a total of nine potential PMI biomarkers, whose quantity changed constantly and progressively over time, directly or inversely proportional to the advancement of post mortem hours. Our preliminary study underlines the importance of the proteomic approach in the search for a reliable method for PMI determination and highlights the need to characterize a large number of reliable marker proteins useful in forensic practice for PMI estimation.

LibGENiE - A bioinformatic pipeline for the design of information-enriched enzyme libraries

Enzymes are potent catalysts with high specificity and selectivity. To leverage nature's synthetic potential for industrial applications, various protein engineering techniques have emerged which allow to tailor the catalytic, biophysical, and molecular recognition properties of enzymes. However, the many possible ways a protein can be altered forces researchers to carefully balance between the exhaustiveness of an enzyme screening campaign and the required resources. Consequently, the optimal engineering strategy is often defined on a case-by-case basis. Strikingly, while predicting mutations that lead to an improved target function is challenging, here we show that the prediction and exclusion of deleterious mutations is a much more straightforward task as analyzed for an engineered carbonic acid anhydrase, a transaminase, a squalene-hopene cyclase and a Kemp eliminase. Combining such a pre-selection of allowed residues with advanced gene synthesis methods opens a path toward an efficient and generalizable library construction approach for protein engineering. To give researchers easy access to this methodology, we provide the website LibGENiE containing the bioinformatic tools for the library design workflow.

The importance of input sequence set to consensus-derived proteins and their relationship to reconstructed ancestral proteins

A protein sequence encodes its energy landscape - all the accessible conformations, energetics, and dynamics. The evolutionary relationship between sequence and landscape can be probed phylogenetically by compiling a multiple sequence alignment of homologous sequences and generating common ancestors via Ancestral Sequence Reconstruction or a consensus protein containing the most common amino acid at each position. Both ancestral and consensus proteins are often more stable than their extant homologs - questioning the differences and suggesting that both approaches serve as general methods to engineer thermostability. We used the Ribonuclease H family to compare these approaches and evaluate how the evolutionary relationship of the input sequences affects the properties of the resulting consensus protein. While the overall consensus protein is structured and active, it neither shows properties of a well-folded protein nor has enhanced stability. In contrast, the consensus protein derived from a phylogenetically-restricted region is significantly more stable and cooperatively folded, suggesting that cooperativity may be encoded by different mechanisms in separate clades and lost when too many diverse clades are combined to generate a consensus protein. To explore this, we compared pairwise covariance scores using a Potts formalism as well as higher-order couplings using singular value decomposition (SVD). We find the SVD coordinates of a stable consensus sequence are close to coordinates of the analogous ancestor sequence and its descendants, whereas the unstable consensus sequences are outliers in SVD space.

Fibroblast Growth Factor 11 Inhibits Hepatitis B Virus Gene Expression Through FXRα Suppression

Fibroblast growth factor 11 (FGF11) is a member of the intracellular FGF family, which shows different signal transmission compared with other FGF superfamily members. The molecular function of FGF11 is not clearly understood. In this study, we identified the inhibitory effect of FGF11 on hepatitis B virus (HBV) gene expression through transcriptional suppression. FGF11 decreased the mRNA and protein expression of HBV genes in liver cells. While the nuclear receptor FXRα1 increased HBV promoter transactivation, FGF11 decreased the FXRα-mediated gene induction of the HBV promoter by the FXRα agonist. Reduced endogenous levels of FXRα by siRNA and the dominant negative mutant protein (aa 1-187 without ligand binding domain) of FXRα expression indicated that HBV gene suppression by FGF11 is dependent on FXRα inhibition. In addition, FGF11 interacts with FXRα protein and reduces FXRα protein stability. These results indicate that FGF11 inhibits HBV replicative expression through the liver cell-specific transcription factor, FXRα, and suppresses HBV promoter activity. Our findings may contribute to the establishment of better regimens for the treatment of chronic HBV infections by including FGF11 to alter the bile acid mediated FXR pathway.

Improving thermostability of a PL 5 family alginate lyase with combination of rational design strategies

Alginate lyases with strict substrate specificity possess potential in directed production of alginate oligosaccharides with specific composition. However, their poor thermostability hampered their applications in industry. In this study, an efficient comprehensive strategy including sequence-based analysis, structure-based analysis, and computer-aid ΔΔGfold value calculation was proposed. It was successfully performed on alginate lyase (PMD) with strict poly-β-D-mannuronic acid substrate specificity. Four single-point variants A74V, G75V, A240V, and D250G with increased T_m of 3.94 °C, 5.21 °C, 2.56 °C, and 4.80 °C, respectively, were selected out. After ordered combined mutations, a four-point mutant (M4) was finally generated which displayed remarkable increase on thermostability. The T_m of M4 increased from 42.25 °C to 51.59 °C and its half-life at 50 °C was about 58.9-fold of PMD. Meanwhile, there was no obvious loss of enzyme activity (more than 90% retained). Molecular dynamics simulation analysis insisted that the improvement of thermostability might be attribute to the rigidified region A which might be caused by the newly formed hydrogen bonds and salt bridges introduced by mutations, the lower distance of original hydrogen bonds, and the more compact overall structures.

Accelerating therapeutic protein design with computational approaches toward the clinical stage

Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.

Engineering the kinetic stability of a β-trefoil protein by tuning its topological complexity

Kinetic stability, defined as the rate of protein unfolding, is central to determining the functional lifetime of proteins, both in nature and in wide-ranging medical and biotechnological applications. Further, high kinetic stability is generally correlated with high resistance against chemical and thermal denaturation, as well as proteolytic degradation. Despite its significance, specific mechanisms governing kinetic stability remain largely unknown, and few studies address the rational design of kinetic stability. Here, we describe a method for designing protein kinetic stability that uses protein long-range order, absolute contact order, and simulated free energy barriers of unfolding to quantitatively analyze and predict unfolding kinetics. We analyze two β-trefoil proteins: hisactophilin, a quasi-three-fold symmetric natural protein with moderate stability, and ThreeFoil, a designed three-fold symmetric protein with extremely high kinetic stability. The quantitative analysis identifies marked differences in long-range interactions across the protein hydrophobic cores that partially account for the differences in kinetic stability. Swapping the core interactions of ThreeFoil into hisactophilin increases kinetic stability with close agreement between predicted and experimentally measured unfolding rates. These results demonstrate the predictive power of readily applied measures of protein topology for altering kinetic stability and recommend core engineering as a tractable target for rationally designing kinetic stability that may be widely applicable.

Target-template relationships in protein structure prediction and their effect on the accuracy of thermostability calculations

Improving protein thermostability has been a labor- and time-consuming process in industrial applications of protein engineering. Advances in computational approaches have facilitated the development of more efficient strategies to allow the prioritization of stabilizing mutants. Among these is FEP+, a free energy perturbation implementation that uses a thoroughly tested physics-based method to achieve unparalleled accuracy in predicting changes in protein thermostability. To gauge the applicability of FEP+ to situations where crystal structures are unavailable, here we have applied the FEP+ approach to homology models of 12 different proteins covering 316 mutations. By comparing predictions obtained with homology models to those obtained using crystal structures, we have identified that local rather than global sequence conservation between target and template sequence is a determining factor in the accuracy of predictions. By excluding mutation sites with low local sequence identity (<40%) to a template structure, we have obtained predictions with comparable performance to crystal structures (R2 of 0.67 and 0.63 and an RMSE of 1.20 and 1.16 kcal/mol for crystal structure and homology model predictions, respectively) for identifying stabilizing mutations when incorporating residue scanning into a cascade screening strategy. Additionally, we identify and discuss inherent limitations in sequence alignments and homology modeling protocols that translate into the poor FEP+ performance of a few select examples. Overall, our retrospective study provides detailed guidelines for the application of the FEP+ approach using homology models for protein thermostability predictions, which will greatly extend this approach to studies that were previously limited by structure availability.

Insights Into The Effects of Amino Acid Substitutions on The Stability of Reteplase Structure: A Molecular Dynamics Simulation Study

Background

Reteplase (recombinant plasminogen activator, r-PA) is a recombinant protein designed to imitate the endogenous tissue plasminogen activator and catalyze the plasmin production. It is known that the application of reteplase is limited by the complex production processes and protein's stability challenges. Computational redesign of proteins has gained momentum in recent years, particularly as a powerful tool for improving protein stability and consequently its production efficiency. Hence, in the current study, we implemented computational approaches to improve r-PA conformational stability, which fairly correlates with protein's resistance to proteolysis.

Objectives

The current study was developed in order to evaluate the effect of amino acid substitutions on the stability of reteplase structure using molecular dynamic simulations and computational predictions.

Materials and Methods

Several web servers designed for mutation analysis were utilized to select appropriate mutations. Additionally, the experimentally reported mutation, R103S, converting wild type r-PA into non-cleavable form, was also employed. Firstly, mutant collection, consisting of 15 structures, was constructed based on the combinations of four designated mutations. Then, 3D structures were generated using MODELLER. Finally, 17 independent 20-ns molecular dynamics (MD) simulations were conducted and different analysis were performed like root-mean-square deviation (RMSD), root-mean-square fluctuations (RMSF), secondary structure analysis, number of hydrogen bonds, principal components analysis (PCA), eigenvector projection, and density analysis.

Results

Predicted mutations successfully compensated the more flexible conformation caused by R103S substitution, so, improved conformational stability was analyzed from MD simulations. In particular, R103S/A286I/G322I indicated the best results and remarkably enhanced the protein stability.

Conclusion

The conformational stability conferred by these mutations will probably lead to more protection of r-PA in protease-rich environments in various recombinant systems and potentially enhance its production and expression level.

An efficient method to predict protein thermostability in alanine mutation

The relationship between protein sequence and its thermodynamic stability is a critical aspect of computational protein design. In this work, we present a new theoretical method to calculate the free energy change (ΔΔG) resulting from a single-point amino acid mutation to alanine in a protein sequence. The method is derived based on physical interactions and is very efficient in estimating the free energy changes caused by a series of alanine mutations from just a single molecular dynamics (MD) trajectory. Numerical calculations are carried out on a total of 547 alanine mutations in 19 diverse proteins whose experimental results are available. The comparison between the experimental ΔΔG_exp and the calculated values shows a generally good correlation with a correlation coefficient of 0.67. Both the advantages and limitations of this method are discussed. This method provides an efficient and valuable tool for protein design and engineering.

Active site center redesign increases protein stability preserving catalysis in thioredoxin

The stabilization of natural proteins is a long-standing desired goal in protein engineering. Optimizing the hydrophobicity of the protein core often results in extensive stability enhancements. However, the presence of totally or partially buried catalytic charged residues, essential for protein function, has limited the applicability of this strategy. Here, focusing on the thioredoxin, we aimed to augment protein stability by removing buried charged residues in the active site without loss of catalytic activity. To this end, we performed a charged-to-hydrophobic substitution of a buried and functional group, resulting in a significant stability increase yet abolishing catalytic activity. Then, to simulate the catalytic role of the buried ionizable group, we designed a combinatorial library of variants targeting a set of seven surface residues adjacent to the active site. Notably, more than 50% of the library variants restored, to some extent, the catalytic activity. The combination of experimental study of 2% of the library with the prediction of the whole mutational space by partial least squares regression revealed that a single point mutation at the protein surface is sufficient to fully restore the catalytic activity without thermostability cost. As a result, we engineered one of the highest thermal stabilities reported for a protein with a natural occurring fold (137°C). Further, our hyperstable variant preserves the catalytic activity both in vitro and in vivo.

Enhancing the secretion of a feruloyl esterase in Bacillus subtilis by signal peptide screening and rational design

Feruloyl esterase is a subclass of α/β hydrolase, which could release ferulic acid from biomass residues for use as an efficient additive in food or pharmaceutical industries. In the present study, a feruloyl esterase with broad substrate specificity was characterised and secreted by Bacillus subtilis WB600. After codon usage optimisation and signal peptide library screening, the secretion amount of feruloyl esterase was enhanced by up to 10.2-fold in comparison with the base strain. The site-specific amino acid substitutions that facilitate protein folding further improved the secretion by about 1.5-fold. The purified rationally designed enzyme exhibited maximal activity against methyl ferulate at pH 6.5 and 65 °C. In the solid-state fermentation, the genetically engineered B. subtilis released about 37% of the total alkali-extractable ferulic acid in maize bran. This study provides a promising candidate for ferulic acid production and demonstrates that the secretion of a heterologous enzyme from B. subtilis can be cumulatively improved by changes in protein sequence features.

Tuning Enzyme Thermostability via Computationally Guided Covalent Stapling and Structural Basis of Enhanced Stabilization

Enhancing the thermostability of enzymes without impacting their catalytic function represents an important yet challenging goal in protein engineering and biocatalysis. We recently introduced a novel method for enzyme thermostabilization that relies on the computationally guided installation of genetically encoded thioether "staples" into a protein via cysteine alkylation with the noncanonical amino acid O-2-bromoethyl tyrosine (O2beY). Here, we demonstrate the functionality of an expanded set of electrophilic amino acids featuring chloroacetamido, acrylamido, and vinylsulfonamido side-chain groups for protein stapling using this strategy. Using a myoglobin-based cyclopropanase as a model enzyme, our studies show that covalent stapling with p-chloroacetamido-phenylalanine (pCaaF) provides higher stapling efficiency and enhanced stability (thermodynamic and kinetic) compared to the other stapled variants and the parent protein. Interestingly, molecular simulations of conformational flexibility of the cross-links show that the pCaaF staple allows fewer energetically feasible conformers than the other staples, and this property may be a broader indicator of stability enhancement. Using this strategy, pCaaF-stapled variants with significantly enhanced stability against thermal denaturation (ΔTm' = +27 °C) and temperature-induced heme loss (ΔT50 = +30 °C) were obtained while maintaining high levels of catalytic activity and stereoselectivity. Crystallographic analyses of singly and doubly stapled variants provide key insights into the structural basis for stabilization, which includes both direct interactions of the staples with protein residues and indirect interactions through adjacent residues involved in heme binding. This work expands the toolbox of protein stapling strategies available for protein stabilization.

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.

Large-scale design and refinement of stable proteins using sequence-only models

Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we use a high-throughput, low-fidelity assay to experimentally evaluate the stability of approximately 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We build a neural network model that predicts protein stability given only sequences of amino acids, and compare its performance to the assayed values. We also report another network model that is able to generate the amino acid sequences of novel stable proteins given requested secondary sequences. Finally, we show that the predictive model-despite weaknesses including a noisy data set-can be used to substantially increase the stability of both expert-designed and model-generated proteins.

A cell-free self-replenishing CO2-fixing system

Biological CO2 fixation is so far the most effective means for CO2 reduction at scale and accounts for most of the CO2 fixed on Earth. Through this process, carbon is fixed in cellular components and biomass during organismal growth. To uncouple CO2 fixation from growth and cellular regulation, cell-free CO2 fixation systems represent an alternative approach since the rate can be independently manipulated. Here we designed an oxygen-insensitive, self-replenishing CO2 fixation system with opto-sensing. The system comprises a synthetic reductive glyoxylate and pyruvate synthesis (rGPS) cycle and the malyl-CoA-glycerate (MCG) pathway to produce acetyl-coenzyme A (CoA), pyruvate and malate from CO2, which are also intermediates in the cycle. We solved various problems associated with the in vitro system, and implemented opto-sensing modules to control the regeneration of cofactors. We accomplished sustained operation for 6 hours with a CO2-fixing rate comparable to or greater than typical CO2 fixation rates of photosynthetic or lithoautotrophic organisms.

Is Protein Folding a Thermodynamically Unfavorable, Active, Energy-Dependent Process?

The prevailing current view of protein folding is the thermodynamic hypothesis, under which the native folded conformation of a protein corresponds to the global minimum of Gibbs free energy G. We question this concept and show that the empirical evidence behind the thermodynamic hypothesis of folding is far from strong. Furthermore, physical theory-based approaches to the prediction of protein folds and their folding pathways so far have invariably failed except for some very small proteins, despite decades of intensive theory development and the enormous increase of computer power. The recent spectacular successes in protein structure prediction owe to evolutionary modeling of amino acid sequence substitutions enhanced by deep learning methods, but even these breakthroughs provide no information on the protein folding mechanisms and pathways. We discuss an alternative view of protein folding, under which the native state of most proteins does not occupy the global free energy minimum, but rather, a local minimum on a fluctuating free energy landscape. We further argue that ΔG of folding is likely to be positive for the majority of proteins, which therefore fold into their native conformations only through interactions with the energy-dependent molecular machinery of living cells, in particular, the translation system and chaperones. Accordingly, protein folding should be modeled as it occurs in vivo, that is, as a non-equilibrium, active, energy-dependent process.

A Protein-Engineered, Enhanced Yeast Display Platform for Rapid Evolution of Challenging Targets

Here, we enhanced the popular yeast display method by multiple rounds of DNA and protein engineering. We introduced surface exposure-tailored reporters, eUnaG2 and DnbALFA, creating a new platform of C and N terminal fusion vectors. The optimization of eUnaG2 resulted in five times brighter fluorescence and 10 °C increased thermostability than UnaG. The optimized DnbALFA has 10-fold the level of expression of the starting protein. Following this, different plasmids were developed to create a complex platform allowing a broad range of protein expression organizations and labeling strategies. Our platform showed up to five times better separation between nonexpressing and expressing cells compared with traditional pCTcon2 and c-myc labeling, allowing for fewer rounds of selection and achieving higher binding affinities. Testing 16 different proteins, the enhanced system showed consistently stronger expression signals over c-myc labeling. In addition to gains in simplicity, speed, and cost-effectiveness, new applications were introduced to monitor protein surface exposure and protein retention in the secretion pathway that enabled successful protein engineering of hard-to-express proteins. As an example, we show how we optimized the WD40 domain of the ATG16L1 protein for yeast surface and soluble bacterial expression, starting from a nonexpressing protein. As a second example, we show how using the here-presented enhanced yeast display method we rapidly selected high-affinity binders toward two protein targets, demonstrating the simplicity of generating new protein-protein interactions. While the methodological changes are incremental, it results in a qualitative enhancement in the applicability of yeast display for many applications.

Improving the Thermostability and Catalytic Activity of an Inulosucrase by Rational Engineering for the Biosynthesis of Microbial Inulin

Thermostability and enzymatic activity are two vital indexes determining the application of an enzyme on an industrial scale. A truncated inulosucrase, Laga-ISΔ138-702, from Lactobacillus gasseri showed high catalysis activity. To further enhance its thermostability and activity, multiple sequence alignment (MSA) and rational design based on the modeled structure were performed. Variants A446E, S482A, I614M, and A627S were identified with an improved denaturation temperature (T_m) of more than 1 °C. A combinational mutation method was further carried out to explore the synergistic promotion effects of single-point mutants. Additionally, 33 residues at the N-terminus were truncated to construct mutant M4^N-33. The half-life of M4^N-33 at 55 °C increased by 120 times compared to that of Laga-ISΔ138-702, and the relative activity of M4^N-33 increased up to 152% at the optimal pH and temperature (pH 5.5 and 60 °C). Molecular dynamics (MD) simulations illustrated the decreased b-factor of the surface loop of M4^N-33.

Computational and Experimental Assessment of Backbone Templates for Computational Redesign of the Thioredoxin Fold

Computational protein design has taken big strides in recent years; however, the tools available are still not at a state where a sequence can be designed to fold into a given protein structure at will and with high probability. We have applied here a recent release of Rosetta Design to redesign a set of structurally very similar proteins belonging to the thioredoxin fold. We used a genetic screening tool to estimate solubility/folding of the designed proteins in E. coli and to select the best hits from this for further biochemical characterization. We have previously used this set of template proteins for redesign and found that success was highly dependent on template structure, a trait which was also found in this study. Nevertheless, state-of-the-art design software is now able to predict the best template, most likely due to the introduction of an energy term that reports on stress in covalent bond lengths and angles. The template that led to the greatest fraction of successful designs was the same (a thioredoxin from spinach) as that identified in our previous study. Our previously described redesign of thioredoxin, which also used the spinach protein as a template, however also performed well as a template. In the present study, both of these templates yielded proteins with compact folded structures and enforced the conclusion that any design project must carefully consider different design templates. Fortunately, selecting designs based on energies appears to correctly identify such templates.

Identification of a Zika NS2B epitope as a biomarker for severe clinical phenotypes

The identification of specific biomarkers for Zika infection and its clinical complications is fundamental to mitigate the infection spread, which has been associated with a broad range of neurological sequelae. We present the characterization of antibody responses in serum samples from individuals infected with Zika, presenting non-severe (classical) and severe (neurological disease) phenotypes, with high-density peptide arrays comprising the Zika NS1 and NS2B proteins. The data pinpoints one strongly IgG-targeted NS2B epitope in non-severe infections, which is absent in Zika patients, where infection progressed to the severe phenotype. This differential IgG profile between the studied groups was confirmed by multivariate data analysis. Molecular dynamics simulations and circular dichroism have shown that the peptide in solution presents itself in a sub-optimal conformation for antibody recognition, which led us to computationally engineer an artificial protein able to stabilize the NS2B epitope structure. The engineered protein was used to interrogate paired samples from mothers and their babies presenting Zika-associated microcephaly and confirmed the absence of NS2B IgG response in those samples. These findings suggest that the assessment of antibody responses to the herein identified NS2B epitope is a strong candidate biomarker for the diagnosis and prognosis of Zika-associated neurological disease.

Protein Macrocyclization for Tertiary Structure Stabilization

Proteins possess unique molecular recognition capabilities and enzymatic activities, features that are usually tied to a particular tertiary structure. To make use of proteins for biotechnological and biomedical purposes, it is often required to enforce their tertiary structure in order to ensure sufficient stability under the conditions inherent to the application of interest. The introduction of intramolecular crosslinks has proven efficient in stabilizing native protein folds. Herein, we give an overview of methods that allow the macrocyclization of expressed proteins, discussing involved reaction mechanisms and structural implications.

Surface salt bridges contribute to the extreme thermal stability of an FN3-like domain from a thermophilic bacterium

This study uses differential scanning calorimetry, X-ray crystallography, and molecular dynamics simulations to investigate the structural basis for the high thermal stability (melting temperature 97.5°C) of a FN3-like protein domain from thermophilic bacteria Thermoanaerobacter tengcongensis (FN3tt). FN3tt adopts a typical FN3 fold with a three-stranded beta sheet packing against a four-stranded beta sheet. We identified three solvent exposed arginine residues (R23, R25, and R72), which stabilize the protein through salt bridge interactions with glutamic acid residues on adjacent strands. Alanine mutation of the three arginine residues reduced melting temperature by up to 22°C. Crystal structures of the wild type (WT) and a thermally destabilized (∆Tm -19.7°C) triple mutant (R23L/R25T/R72I) were found to be nearly identical, suggesting that the destabilization is due to interactions of the arginine residues. Molecular dynamics simulations showed that the salt bridge interactions in the WT were stable and provided a dynamical explanation for the cooperativity observed between R23 and R25 based on calorimetry measurements. In addition, folding free energy changes computed using free energy perturbation molecular dynamics simulations showed high correlation with melting temperature changes. This work is another example of surface salt bridges contributing to the enhanced thermal stability of thermophilic proteins. The molecular dynamics simulation methods employed in this study may be broadly useful for in silico surface charge engineering of proteins.

A Brief History of Colour, the Environmental Impact of Synthetic Dyes and Removal by Using Laccases

The history of colour is fascinating from a social and artistic viewpoint because it shows the way; use; and importance acquired. The use of colours date back to the Stone Age (the first news of cave paintings); colour has contributed to the social and symbolic development of civilizations. Colour has been associated with hierarchy; power and leadership in some of them. The advent of synthetic dyes has revolutionized the colour industry; and due to their low cost; their use has spread to different industrial sectors. Although the percentage of coloured wastewater discharged by the textile; food; pharmaceutical; cosmetic; and paper industries; among other productive areas; are unknown; the toxic effect and ecological implications of this discharged into water bodies are harmful. This review briefly shows the social and artistic history surrounding the discovery and use of natural and synthetic dyes. We summarise the environmental impact caused by the discharge of untreated or poorly treated coloured wastewater to water bodies; which has led to physical; chemical and biological treatments to reduce the colour units so as important physicochemical parameters. We also focus on laccase utility (EC 1.10.3.2), for discolouration enzymatic treatment of coloured wastewater, before its discharge into water bodies. Laccases (p-diphenol: oxidoreductase dioxide) are multicopper oxidoreductase enzymes widely distributed in plants, insects, bacteria, and fungi. Fungal laccases have employed for wastewater colour removal due to their high redox potential. This review includes an analysis of the stability of laccases, the factors that influence production at high scales to achieve discolouration of high volumes of contaminated wastewater, the biotechnological impact of laccases, and the degradation routes that some dyes may follow when using the laccase for colour removal.

Network-based strategies for protein characterization

Protein structure characterization is fundamental to understand protein properties, such as folding process and protein resistance to thermal stress, up to unveiling organism pathologies (e.g., prion disease). In this chapter, we provide an overview on how the spectral properties of the networks reconstructed from the Protein Contact Map (PCM) can be used to generate informative observables. As a specific case study, we apply two different network approaches to an example protein dataset, for the aim of discriminating protein folding state, and for the reconstruction of protein 3D structure.

Discovery of a microbial rhodopsin that is the most stable in extreme environments

Microbial rhodopsin is a retinal protein that functions as an ion pump, channel, and sensory transducer, as well as a light sensor, as in biosensors and biochips. Tara76 rhodopsin is a typical proton-pumping rhodopsin that exhibits strong stability against extreme pH, detergent, temperature, salt stress, and dehydration stress and even under dual and triple conditions. Tara76 rhodopsin has a thermal stability approximately 20 times higher than that of thermal rhodopsin at 80°C and is even stable at 85°C. Tara76 rhodopsin is also stable at pH 0.02 to 13 and exhibits strong resistance in detergent, including Triton X-100 and SDS. We tested the current flow that electrical current flow across dried proteins on the paper at high temperatures using an electrode device, which was measured stably from 25°C up to 120°C. These properties suggest that this Tara76 rhodopsin is suitable for many applications in the fields of bioengineering and biotechnology.

Protein analysis and stability: Overcoming trial-and-error by grouping according to physicochemical properties

Today proteins are possibly the most important class of substances. Yet new tasks for proteins are still often solved by trial-and-error approaches. However, in some areas these euphemistically called "screening approaches" are not suitable. E.g. stability tests just take too long and therefore require a more strategic, target-orientated concept. This concept is available by grouping proteins according to their physicochemical properties and then pulling out the right drawer for new tasks. These properties include size, then charge and hydrophobicity as well as their patchinesses, and the degree of order. In addition, solubility, the content of (free) enthalpy, aromatic-amino-acid- and α/β-frequency as well as helix capping, and corresponding patchiness, the number of specific motifs and domains as well as the typical concentration range can be helpful to discriminate between different groups of proteins. Analyzing correlations will reduce the necessary amount of parameters and additional ones, which may be still undiscovered at the present time, can be identified looking at protein subgroups with similar physicochemical properties which still behave heterogeneously. Step-by-step the methodology will be improved. Possibly protein stability will be the driver of this process, but all other areas such as production, purification and analytics including sample pre-treatment and the choice of appropriate separation conditions for e.g. chromatography and electrophoresis will profit from a rational strategy.

Rational thermostabilisation of four-helix bundle dimeric de novo proteins

The stability of proteins is an important factor for industrial and medical applications. Improving protein stability is one of the main subjects in protein engineering. In a previous study, we improved the stability of a four-helix bundle dimeric de novo protein (WA20) by five mutations. The stabilised mutant (H26L/G28S/N34L/V71L/E78L, SUWA) showed an extremely high denaturation midpoint temperature (Tm). Although SUWA is a remarkably hyperstable protein, in protein design and engineering, it is an attractive challenge to rationally explore more stable mutants. In this study, we predicted stabilising mutations of WA20 by in silico saturation mutagenesis and molecular dynamics simulation, and experimentally confirmed three stabilising mutations of WA20 (N22A, N22E, and H86K). The stability of a double mutant (N22A/H86K, rationally optimised WA20, ROWA) was greatly improved compared with WA20 (ΔTm = 10.6 °C). The model structures suggested that N22A enhances the stability of the α-helices and N22E and H86K contribute to salt-bridge formation for protein stabilisation. These mutations were also added to SUWA and improved its Tm. Remarkably, the most stable mutant of SUWA (N22E/H86K, rationally optimised SUWA, ROSA) showed the highest Tm (129.0 °C). These new thermostable mutants will be useful as a component of protein nanobuilding blocks to construct supramolecular protein complexes.

Surface charged amino acid-based strategy for rational engineering of kinetic stability and specific activity of enzymes: Linking experiments with computational modeling

A rational workflow for engineering kinetically stable enzymes with good specific activity by surface charged amino acids engineering was proposed based on systematically analyzing the results of mutating 44 negatively charged surface amino acids of a thermophilic β-mannanase (ManAK). Computational data, combined with experimental results indicated that percentage side-chain solvent accessibility (PSSA), changes in Gibbs free energy of unfolding (∆∆G_mut) and root-mean-square fluctuations (RMSF) could be suitable for screening kinetically stable mutants. A combinational standard (∆∆G_mut < -0.5 kJ/mol and RMSF >0.68 Å) resulted a decrease in the proportion of destabilizing mutants to 12.5%. The perturbations of substrate affinity and specific activity caused by mutation were weakened as the shortest distance from C_α of mutated site to C_α of catalytic sites (Ds_Cα-Cα) increased. Results indicated that hotspot zones contributing to the local stability and integrity of catalytic motif at elevated temperatures might be widely distributed across spatial structure of the protein, while the mutation perturbation on enzyme specific activity demonstrated a gradually weakening trend from the catalytic core to the protein surface. These findings further our understanding of the structural-functional relationships of protein and highlight a deduced workflow to engineering industrially useful enzymes.

An enzyme-based biosensor for monitoring and engineering protein stability in vivo

Protein stability affects the physiological functions of proteins and is also a desirable trait in many protein engineering tasks, yet improving protein stability is challenging because of limitations in methods for directly monitoring protein stability in cells. Here, we report an in vivo stability biosensor wherein a protein of interest (POI) is inserted into a microbial enzyme (CysGA) that catalyzes the formation of endogenous fluorescent compounds, thereby coupling POI stability to simple fluorescence readouts. We demonstrate the utility of the biosensor in directed evolution to obtain stabilized, less aggregation-prone variants of two POIs (including nonamyloidogenic variants of human islet amyloid polypeptide). Beyond engineering applications, we exploited our biosensor in deep mutational scanning for experimental delineation of the stability-related contributions of all residues throughout the catalytic domain of a histone H3K4 methyltransferase, thereby revealing its scientifically informative stability landscape. Thus, our highly accessible method for in vivo monitoring of the stability of diverse proteins will facilitate both basic research and applied protein engineering efforts.

Community-Wide Experimental Evaluation of the PROSS Stability-Design Method

Recent years have seen a dramatic improvement in protein-design methodology. Nevertheless, most methods demand expert intervention, limiting their widespread adoption. By contrast, the PROSS algorithm for improving protein stability and heterologous expression levels has been successfully applied to a range of challenging enzymes and binding proteins. Here, we benchmark the application of PROSS as a stand-alone tool for protein scientists with no or limited experience in modeling. Twelve laboratories from the Protein Production and Purification Partnership in Europe (P4EU) challenged the PROSS algorithm with 14 unrelated protein targets without support from the PROSS developers. For each target, up to six designs were evaluated for expression levels and in some cases, for thermal stability and activity. In nine targets, designs exhibited increased heterologous expression levels either in prokaryotic and/or eukaryotic expression systems under experimental conditions that were tailored for each target protein. Furthermore, we observed increased thermal stability in nine of ten tested targets. In two prime examples, the human Stem Cell Factor (hSCF) and human Cadherin-Like Domain (CLD12) from the RET receptor, the wild type proteins were not expressible as soluble proteins in E. coli, yet the PROSS designs exhibited high expression levels in E. coli and HEK293 cells, respectively, and improved thermal stability. We conclude that PROSS may improve stability and expressibility in diverse cases, and that improvement typically requires target-specific expression conditions. This study demonstrates the strengths of community-wide efforts to probe the generality of new methods and recommends areas for future research to advance practically useful algorithms for protein science.

Rational Design of Adenylate Kinase Thermostability through Coevolution and Sequence Divergence Analysis

Protein engineering is actively pursued in industrial and laboratory settings for high thermostability. Among the many protein engineering methods, rational design by bioinformatics provides theoretical guidance without time-consuming experimental screenings. However, most rational design methods either rely on protein tertiary structure information or have limited accuracies. We proposed a primary-sequence-based algorithm for increasing the heat resistance of a protein while maintaining its functions. Using adenylate kinase (ADK) family as a model system, this method identified a series of amino acid sites closely related to thermostability. Single- and double-point mutants constructed based on this method increase the thermal denaturation temperature of the mesophilic Escherichia coli (E. coli) ADK by 5.5 and 8.3 °C, respectively, while preserving most of the catalytic function at ambient temperatures. Additionally, the constructed mutants have improved enzymatic activity at higher temperature.

De novo sequence redesign of a functional Ras-binding domain globally inverted the surface charge distribution and led to extreme thermostability

To acquire extremely thermostable proteins of given functions is challenging for conventional protein engineering. Here we applied ABACUS, a statistical energy function we developed for de novo amino acid sequence design, to globally redesign a Ras-binding domain (RBD), and obtained an extremely thermostable RBD that unfolds reversibly at above 110°C, the redesigned RBD experimentally confirmed to have expected structure and Ras-binding interface. Directed evolution of the redesigned RBD improved its Ras-binding affinity to the native protein level without excessive loss of thermostability. The designed amino acid substitutions were mostly at the protein surface. For many substitutions, strong epistasis or significantly differentiated effects on thermostability in the native sequence context relative to the redesigned sequence context were observed, suggesting the globally redesigned sequence to be unreachable through combining beneficial mutations of the native sequence. Further analyses revealed that by replacing 38 of a total of 48 non-interfacial surface residues at once, ABACUS redesign was able to globally "invert" the protein's charge distribution pattern in an optimized way. Our study demonstrates that computational protein design provides powerful new tools to solve challenging protein engineering problems.

Improved thermostability of proteinase K and recognizing the synergistic effect of Rosetta and FoldX approaches

Proteinase K (PRK) is a proteolytic enzyme that has been widely used in industrial applications. However, poor stability has severely limited the uses of PRK. In this work, we used two structure-guided rational design methods, Rosetta and FoldX, to modify PRK thermostability. Fifty-two single amino acid conversion mutants were constructed based on software predictions of residues that could affect protein stability. Experimental characterization revealed that 46% (21 mutants) exhibited enhanced thermostability. The top four variants, D260V, T4Y, S216Q, and S219Q, showed improved half-lives at 69°C by 12.4-, 2.6-, 2.3-, and 2.2-fold that of the parent enzyme, respectively. We also found that selecting mutations predicted by both methods could increase the predictive accuracy over that of either method alone, with 73% of the shared predicted mutations resulting in higher thermostability. In addition to providing promising new variants of PRK in industrial applications, our findings also show that combining these programs may synergistically improve their predictive accuracy.

PROSS 2: a new server for the design of stable and highly expressed protein variants

Many natural and designed proteins are only marginally stable limiting their usefulness in research and applications. Recently, we described an automated structure and sequence-based design method, called PROSS, for optimizing protein stability and heterologous expression levels that has since been validated on dozens of proteins. Here, we introduce improvements to the method, workflow and presentation, including more accurate sequence analysis, error handling and automated analysis of the quality of the sequence alignment that is used in design calculations. PROSS2 is freely available for academic use at https://pross.weizmann.ac.il.

Postmortem Protein Degradation as a Tool to Estimate the PMI: A Systematic Review

Objectives: We provide a systematic review of the literature to evaluate the current research status of protein degradation-based postmortem interval (PMI) estimation. Special attention is paid to the applicability of the proposed approaches/methods in forensic routine practice. Method: A systematic review of the literature on protein degradation in tissues and organs of animals and humans was conducted. Therefore, we searched the scientific databases Pubmed and Ovid for publications until December 2019. Additional searches were performed in Google Scholar and the reference lists of eligible articles. Results: A total of 36 studies were included. This enabled us to consider the degradation pattern of over 130 proteins from 11 different tissues, studied with different methods including well-established and modern approaches. Although comparison between studies is complicated by the heterogeneity of study designs, tissue types, methods, proteins and outcome measurement, there is clear evidence for a high explanatory power of protein degradation analysis in forensic PMI analysis. Conclusions: Although only few approaches have yet exceeded a basic research level, the current research status provides strong evidence in favor of the applicability of a protein degradation-based PMI estimation method in routine forensic practice. Further targeted research effort towards specific aims (also addressing influencing factors and exclusion criteria), especially in human tissue will be required to obtain a robust, reliable laboratory protocol, and collect sufficient data to develop accurate multifactorial mathematical decomposition models.