Utility of B-Factors in Protein Science: Interpreting Rigidity, Flexibility, and Internal Motion and Engineering Thermostability

Computational design of α-amylase from Bacillus licheniformis to increase its activity and stability at high temperatures

The thermostable α-amylase derived from Bacillus licheniformis (BLA) has multiple advantages, including enhancing the mass transfer rate and by reducing microbial contamination in starch hydrolysis. Nonetheless, the application of BLA is constrained by the accessibility and stability of enzymes capable of achieving high conversion rates at elevated temperatures. Moreover, the thermotolerance of BLA requires further enhancement. Here, we developed a computational strategy for constructing small and smart mutant libraries to identify variants with enhanced thermostability. Initially, molecular dynamics (MD) simulations were employed to identify the regions with high flexibility. Subsequently, FoldX, a computational design predictor, was used to design mutants by rigidifying highly flexible residues, whereas the simultaneous decrease in folding free energy assisted in improving thermostability. Through the utilization of MD and FoldX, residues K251, T277, N278, K319, and E336, situated at a distance of 5 Å from the catalytic triad, were chosen for mutation. Seventeen mutants were identified and characterized by evaluating enzymatic characteristics and kinetic parameters. The catalytic efficiency of the E271L/N278K mutant reached 184.1 g L-1 s-1, which is 1.88-fold larger than the corresponding value determined for the WT. Furthermore, the most thermostable mutant, E336S, exhibited a 1.43-fold improvement in half-life at 95 ℃, compared with that of the WT. This study, by combining computational simulation with experimental verification, establishes that potential sites can be computationally predicted to increase the activity and stability of BLA and thus provide a possible strategy by which to guide protein design.

Data-driven probabilistic definition of the low energy conformational states of protein residues

Protein dynamics and related conformational changes are essential for their function but difficult to characterise and interpret. Amino acids in a protein behave according to their local energy landscape, which is determined by their local structural context and environmental conditions. The lowest energy state for a given residue can correspond to sharply defined conformations, e.g. in a stable helix, or can cover a wide range of conformations, e.g. in intrinsically disordered regions. A good definition of such low energy states is therefore important to describe the behaviour of a residue and how it changes with its environment. We propose a data-driven probabilistic definition of six low energy conformational states typically accessible for amino acid residues in proteins. This definition is based on solution NMR information of 1322 proteins through a combined analysis of structure ensembles with interpreted chemical shifts. We further introduce a conformational state variability parameter that captures, based on an ensemble of protein structures from molecular dynamics or other methods, how often a residue moves between these conformational states. The approach enables a different perspective on the local conformational behaviour of proteins that is complementary to their static interpretation from single structure models.

The evolution of cutinase Est1 based on the clustering strategy and its application for commercial PET bottles degradation

The rapid increase in global plastic consumption, especially the worldwide use of polyethylene terephthalate (PET), has caused serious pollution problems. Due to the low recycling rate of PET, a substantial amount of waste accumulates in the environment, which prompts a growing focus on enzymatic degradation for its efficiency and environmentally friendliness. This study systematically designed and modified a cutinase, Est1 from Thermobifida alba AHK119, known for its potential of plastic-degradation at high temperatures. Additionally, the introduction of clustering algorithms provided the ability to understand and modify biomolecules, to accelerate the process of finding the optimal mutations. K-means was further proceeded based on the positive mutations. After comprehensive screening for thermostability and activity mutation sites, the dominant mutation Est1_5M (Est1 with the mutations of N213M, T215P, S115P, Q93A, and L91W) exhibited satisfying degradation ability for commercial PET bottles. The results showed that Est1_5M achieved a degradation rate of 90.84% in 72 h, 65-fold higher than the wild type. This study offers reliable theoretical and practical support for the development of efficient PET-degrading enzymes, providing a reference for plastic pollution management.

Machine-learned molecular mechanics force fields from large-scale quantum chemical data

The development of reliable and extensible molecular mechanics (MM) force fields-fast, empirical models characterizing the potential energy surface of molecular systems-is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1 M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides and folded proteins, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest.

Structure-based characterization and compound identification of the wild-type THF class-II riboswitch

Riboswitches are conserved regulatory RNA elements participating in various metabolic pathways. Recently, a novel RNA motif known as the folE RNA motif was discovered upstream of folE genes. It specifically senses tetrahydrofolate (THF) and is therefore termed THF-II riboswitch. To unravel the ligand recognition mechanism of this newly discovered riboswitch and decipher the underlying principles governing its tertiary folding, we determined both the free-form and bound-form THF-II riboswitch in the wild-type sequences. Combining structural information and isothermal titration calorimetry (ITC) binding assays on structure-based mutants, we successfully elucidated the significant long-range interactions governing the function of THF-II riboswitch and identified additional compounds, including alternative natural metabolites and potential lead compounds for drug discovery, that interact with THF-II riboswitch. Our structural research on the ligand recognition mechanism of the THF-II riboswitch not only paves the way for identification of compounds targeting riboswitches, but also facilitates the exploration of THF analogs in diverse biological contexts or for therapeutic applications.

Design and Evolution of an Enzyme for the Asymmetric Michael Addition of Cyclic Ketones to Nitroolefins by Enamine Catalysis

Consistent introduction of novel enzymes is required for developing efficient biocatalysts for challenging biotransformations. Absorbing catalytic modes from organocatalysis may be fruitful for designing new-to-nature enzymes with novel functions. Herein we report a newly designed artificial enzyme harboring a catalytic pyrrolidine residue that catalyzes the asymmetric Michael addition of cyclic ketones to nitroolefins through enamine activation with high efficiency. Diverse chiral γ-nitro cyclic ketones with two stereocenters were efficiently prepared with excellent stereoselectivity (up to 97 % e.e., >20 : 1 d.r.) and good yield (up to 86 %). This work provides an efficient biocatalytic strategy for cyclic ketone functionalization, and highlights the usefulness of artificial enzymes for extending biocatalysis to further non-natural reactions.

Evolution of liquid phase during homogenous non-equilibrium melting of Ta at superheating temperature

Homogenous melting at superheating temperature is commonly described by classical nucleation theory (CNT), but the atomic mechanism of the formation and development of critical liquid nuclei is still unclear. Molecular dynamics simulations were conducted to analyze the melting process of Ta. It is found that the process of subcritical liquid clusters evolving into critical liquid nucleus occupies most of the melting time, and merging between neighboring liquid clusters is the main path for subcritical liquid clusters to grow in size. Total melting time is strongly affected by the distribution of formation sites of subcritical liquid clusters, which has been considered random in homogenous melting. This work depicts a clear picture of the formation and development of liquid phase during the homogeneous melting process at superheating temperature and suggests an internal factor of melting mechanism.

Quercetin and taxifolin inhibits TMPRSS2 activity and its interaction with EGFR in paclitaxel-resistant breast cancer cells: An in silico and in vitro study

Transmembrane protease/serine (TMPRSS2), a type II transmembrane serine protease, plays a crucial role in different stages of cancer. Recent studies have reported that the triggering epidermal growth factor receptor (EGFR) activation through protease action promotes metastasis. However, there are no reports on the interaction of TMPRSS2 with EGFR, especially in triple-negative triple negative (TNBC). The current study investigates the unexplored interaction between TMPRSS2 and EGFR, which are key partners mediating metastasis. This interaction is explored for potential targeting using quercetin (QUE) and taxifolin (TAX). TMPRSS2 expression patterns in breast cancer (BC) tissues and subtypes have been predicted, with the prognostic significance assessed using the GENT2.0 database. Validation of TMPRSS2 expression was performed in normal and TNBC tissues, including drug-resistant cell lines, utilizing GEO datasets. TMPRSS2 was further validated as a predictive biomarker for FDA-approved chemotherapeutics through transcriptomic data from BC patients. The study demonstrated the association of TMPRSS2 with EGFR through in silico analysis and validates the findings in TNBC cohorts using the TIMER2.0 web server and the TCGA dataset through C-Bioportal. Molecular docking and molecular dynamic simulation studies identified QUE and TAX as best leads targeting TMPRSS2. They inhibited cell-free TMPRSS2 activity like clinical inhibitor of TMPRSS2, Camostat mesylate. In cell-based assays focused on paclitaxel-resistant TNBC (TNBC/PR), QUE and TAX demonstrated potent inhibitory activity against extracellular and membrane-bound TMPRSS2, with low IC50 values. Furthermore, ELISA and cell-based AlphaLISA assays demonstrated that QUE and TAX inhibit the interaction of TMPRSS2 with EGFR. Additionally, QUE and TAX exhibited significant inhibition of proliferation and cell cycle accompanied by notable alterations in the morphology of TNBC/PR cells. This study provides valuable insights into potential of QUE and TAX targeting TMPRSS2 overexpressing TNBC.

Theory vs. experiment: The rise of the dynamic view of proteins

Over the past century, the scientific conception of the protein has evolved significantly. This paper focuses on the most recent stage of this evolution, namely, the origin of the dynamic view of proteins and the challenge it posed to the static view of classical molecular biology. Philosophers and scientists have offered two hypotheses to explain the origin of the dynamic view and its slow reception by structural biologists. Some have argued that the shift from the static to the dynamic view was a Kuhnian revolution, driven by the accumulation of dynamic anomalies, while others have argued that the shift was caused by new empirical findings made possible by technological advances. I analyze this scientific episode and ultimately reject both of these empiricist accounts. I argue that focusing primarily on technological advances and empirical discoveries overlooks the important role of theory in driving this scientific change. I show how the application of general thermodynamic principles to proteins gave rise to the dynamic view, and a commitment to these principles then led early adopters to seek out the empirical examples of protein dynamics, which would eventually convince their peers. My analysis of this historical case shows that empiricist accounts of modern scientific progress-at least those that aim to explain developments in the molecular life sciences-need to be tempered in order to capture the interplay between theory and experiment.

Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering

The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly those that are poorly characterized or unknown in biology. We introduce MODIFY, a machine learning (ML) algorithm that learns from natural protein sequences to infer evolutionarily plausible mutations and predict enzyme fitness. MODIFY co-optimizes predicted fitness and sequence diversity of starting libraries, prioritizing high-fitness variants while ensuring broad sequence coverage. In silico evaluation shows that MODIFY outperforms state-of-the-art unsupervised methods in zero-shot fitness prediction and enables ML-guided directed evolution with enhanced efficiency. Using MODIFY, we engineer generalist biocatalysts derived from a thermostable cytochrome c to achieve enantioselective C-B and C-Si bond formation via a new-to-nature carbene transfer mechanism, leading to biocatalysts six mutations away from previously developed enzymes while exhibiting superior or comparable activities. These results demonstrate MODIFY's potential in solving challenging enzyme engineering problems beyond the reach of classic directed evolution.

Water-mediated active conformational transitions of lipase on organic solvent interfaces

When it comes to enzyme stability and their application in organic solvents, enzyme biocatalysis has emerged as a popular substitute for conventional chemical processes. However, the demand for enzymes exhibiting improved stability remains a persistent challenge. Organic solvents can significantly impacts enzyme properties, thereby limiting their practical application. This study focuses on Lipase Thermomyces lanuginose, through molecular dynamics simulations and experiments, we quantified the effect of different solvent-lipase interfaces on the interfacial activation of lipase. Revealed molecular views of the complex solvation processes through the minimum distance distribution function. Solvent-protein interactions were used to interpret the factors influencing changes in lipase conformation and enzyme activity. We found that water content is crucial for enzyme stability, and the optimum water content for lipase activity was 35 % in the presence of benzene-water interface, which is closely related to the increase of its interfacial activation angle from 78° to 102°. Methanol induces interfacial activation in addition to significant competitive inhibition and denaturation at low water content. Our findings shed light on the importance of understanding solvent effects on enzyme function and provide practical insights for enzyme engineering and optimization in various solvent-lipase interfaces.

A review of in vitro stochastic and non-stochastic affinity maturation strategies for phage display derived monoclonal antibodies

Monoclonal antibodies identified using display technologies like phage display occasionally suffers from a lack of affinity making it unsuitable for application. This drawback is circumvented with the application of affinity maturation. Affinity maturation is an essential step in the natural evolution of antibodies in the immune system. The evolution of molecular based methods has seen the development of various mutagenesis approaches. This allows for the natural evolutionary process during somatic hypermutation to be replicated in the laboratories for affinity maturation to fine-tune the affinity and selectivity of antibodies. In this review, we will discuss affinity maturation strategies for mAbs generated through phage display systems. The review will highlight various in vitro stochastic and non-stochastic affinity maturation approaches that includes but are not limited to random mutagenesis, site-directed mutagenesis, and gene synthesis.

Structural Assessment of Chlamydia trachomatis Major Outer Membrane Protein (MOMP)-Derived Vaccine Antigens and Immunological Profiling in Mice with Different Genetic Backgrounds

Chlamydia trachomatis (Ct) is the most common cause of bacterial sexually transmitted infections (STIs) worldwide. Ct infections are often asymptomatic in women, leading to severe reproductive tract sequelae. Development of a vaccine against Chlamydia is crucial. The Chlamydia major outer membrane protein (MOMP) is a prime vaccine antigen candidate, and it can elicit both neutralizing antibodies and protective CD4+ T cell responses. We have previously designed chimeric antigens composed of immunogenic variable regions (VDs) and conserved regions (CDs) of MOMP from Chlamydia muridarum (Cm) expressed into a carrier protein (PorB), and we have shown that these were protective in a mouse model of Cm respiratory infection. Here, we generated corresponding constructs based on MOMP from Ct serovar F. Preliminary structure analysis of the three antigens, PorB/VD1-3, PorB/VD1-4 and PorB/VD1-2-4, showed that they retained structure features consistent with those of PorB. The antigens induced robust humoral and cellular responses in mice with different genetic backgrounds. The antibodies were cross-reactive against Ct, but only anti-PorB/VD1-4 and anti-PorB/VD1-2-4 IgG antibodies were neutralizing, likely due to the antigen specificity. The cellular responses included proliferation in vitro and production of IFN-γ by splenocytes following Ct re-stimulation. Our results support further investigation of the PorB/VD antigens as potential protective candidates for a Chlamydia subunit vaccine.

A dataset of alternately located segments in protein crystal structures

Protein Data Bank (PDB) files list the relative spatial location of atoms in a protein structure as the final output of the process of fitting and refining to experimentally determined electron density measurements. Where experimental evidence exists for multiple conformations, atoms are modelled in alternate locations. Programs reading PDB files commonly ignore these alternate conformations by default leaving users oblivious to the presence of alternate conformations in the structures they analyze. This has led to underappreciation of their prevalence, under characterisation of their features and limited the accessibility to this high-resolution data representing structural ensembles. We have trawled PDB files to extract structural features of residues with alternately located atoms. The output includes the distance between alternate conformations and identifies the location of these segments within the protein chain and in proximity of all other atoms within a defined radius. This dataset should be of use in efforts to predict multiple structures from a single sequence and support studies investigating protein flexibility and the association with protein function.

Multivariate Silicification-Assisted Single Enzyme Structure Augmentation for Improved Enzymatic Activity-Stability Trade-Off

The ability to finely tune/balance the structure and rigidity of enzymes to realize both high enzymatic activity and long-term stability is highly desired but highly challenging. Herein, we propose the concept of the "silicazyme", where solid inorganic silica undergoes controlled hybridization with the fragile enzyme under moderate conditions at the single-enzyme level, thus enabling simultaneous structure augmentation, long-term stability, and high enzymatic activity preservation. A multivariate silicification approach was utilized and occurred around individual enzymes to allow conformal coating. To realize a high activity-stability trade-off the structure flexibility/rigidity of the silicazyme was optimized by a component adjustment ternary (CAT) plot method. Moreover, the multivariate organosilica frameworks bring great advantages, including surface microenvironment adjustability, reversible modification capability, and functional extensibility through the rich chemistry of silica. Overall silicazymes represent a new class of enzymes with promise for catalysis, separations, and nanomedicine.

Engineering of a Baeyer-Villiger monooxygenase to Improve Substrate Scope, Stereoselectivity and Regioselectivity

Baeyer-Villiger monooxygenases belong to a family of flavin-binding proteins that catalyze the Baeyer-Villiger (BV) oxidation of ketones to produce lactones or esters, which are important intermediates in pharmaceuticals or sustainable materials. Phenylacetone monooxygenase (PAMO) from Thermobifida fusca with moderate thermostability catalyzes the oxidation of aryl ketone substrates, but is limited by high specificity and narrow substrate scope. In the present study, we applied loop optimization by loop swapping followed by focused saturation mutagenesis in order to evolve PAMO mutants capable of catalyzing the regioselective BV oxidation of cyclohexanone and cyclobutanone derivatives with formation of either normal or abnormal esters or lactones. We further modulated PAMO to increase enantioselectivity. Crystal structure studies indicate that rotation occurs in the NADP-binding domain and that the high B-factor region is predominantly distributed in the catalytic pocket residues. Computational analyses further revealed dynamic character in the catalytic pocket and reshaped hydrogen bond interaction networks, which is more favorable for substrate binding. Our study provides useful insights for studying enzyme-substrate adaptations.

Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning

Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity for fitness prediction. By combining meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. In silico benchmarks across 87 deep mutational scanning datasets demonstrate FSFP's superiority over both unsupervised and supervised baselines. Furthermore, we successfully apply FSFP to engineer the Phi29 DNA polymerase through wet-lab experiments, achieving a 25% increase in the positive rate. These results underscore the potential of our approach in aiding AI-guided protein engineering.

Enhanced Thermostability of Nattokinase by Computation-Based Rational Redesign of Flexible Regions

Nattokinase is a nutrient in healthy food natto that has the function of preventing and treating blood thrombus. However, its low thermostability and fibrinolytic activity limit its application in food and pharmaceuticals. In this study, we used bioinformatics analysis to identify two loops (loop10 and loop12) in the flexible region of nattokinase rAprY. Using this basis, we screened the G131S-S161T variant, which showed a 2.38-fold increase in half-life at 55 °C, and the M3 variant, which showed a 2.01-fold increase in activity, by using a thermostability prediction algorithm. Bioinformatics analysis revealed that the enhanced thermostability of the G131S-S161T variant was due to the increased rigidity and structural shrinkage of the overall structure. Additionally, the increased rigidity of the local region surrounding the active center and its mutated sites helps maintain its normal conformation in high-temperature environments. The increased catalytic activity of the M3 variant may be due to its more efficient substrate binding mechanism. We investigated strategies to improve the thermostability and fibrinolytic activity of nattokinase, and the resulting variants show promise for industrial production and application.

Immunoinformatics and Reverse Vaccinology Approach for the Identification of Potential Vaccine Candidates against Vandammella animalimors

Vandammella animalimorsus is a Gram-negative and non-motile bacterium typically transmitted to humans through direct contact with the saliva of infected animals, primarily through biting, scratches, or licks on fractured skin. The absence of a confirmed post-exposure treatment of V. animalimorsus bacterium highlights the imperative for developing an effective vaccine. We intended to determine potential vaccine candidates and paradigm a chimeric vaccine against V. animalimorsus by accessible public data analysis of the strain by utilizing reverse vaccinology. By subtractive genomics, five outer membranes were prioritized as potential vaccine candidates out of 2590 proteins. Based on the instability index and transmembrane helices, a multidrug transporter protein with locus ID A0A2A2AHJ4 was designated as a potential candidate for vaccine construct. Sixteen immunodominant epitopes were retrieved by utilizing the Immune Epitope Database. The epitope encodes the strong binding affinity, nonallergenic properties, non-toxicity, high antigenicity scores, and high solubility revealing the more appropriate vaccine construct. By utilizing appropriate linkers and adjuvants alongside a suitable adjuvant molecule, the epitopes were integrated into a chimeric vaccine to enhance immunogenicity, successfully eliciting both adaptive and innate immune responses. Moreover, the promising physicochemical features, the binding confirmation of the vaccine to the major innate immune receptor TLR-4, and molecular dynamics simulations of the designed vaccine have revealed the promising potential of the selected candidate. The integration of computational methods and omics data has demonstrated significant advantages in discovering novel vaccine targets and mitigating vaccine failure rates during clinical trials in recent years.

Engineering of Halide Methyltransferase BxHMT through Dynamic Cross-Correlation Network Analysis

Halide methyltransferases (HMTs) provide an effective way to regenerate S-adenosyl methionine (SAM) from S-adenosyl homocysteine and reactive electrophiles, such as methyl iodide (MeI) and methyl toluene sulfonate (MeOTs). As compared with MeI, the cost-effective unnatural substrate MeOTs can be accessed directly from cheap and abundant alcohols, but shows only limited reactivity in SAM production. In this study, we developed a dynamic cross-correlation network analysis (DCCNA) strategy for quickly identifying hot spots influencing the catalytic efficiency of the enzyme, and applied it to the evolution of HMT from Paraburkholderia xenovorans. Finally, the optimal mutant, M4 (V55T/C125S/L127T/L129P), exhibited remarkable improvement, with a specific activity of 4.08 U/mg towards MeOTs, representing an 82-fold increase as compared to the wild-type (WT) enzyme. Notably, M4 also demonstrated a positive impact on the catalytic ability with other methyl donors. The structural mechanism behind the enhanced enzyme activity was uncovered by molecular dynamics simulations. Our work not only contributes a promising biocatalyst for the regeneration of SAM, but also offers a strategy for efficient enzyme engineering.

Perspective on Agricultural Industrialization: Modification Strategies for Enhancing the Catalytic Capacity of Keratinase

Keratinases is a special hydrolytic enzyme produced by microorganisms, which has the ability to catalyze the degradation of keratin. Currently, keratinases show great potential for application in many agricultural and industrial fields, such as biofermented feed, leather tanning, hair removal, and fertilizer production. However, these potentials have not yet been fully unleashed on an industrial scale. This paper reviews the sources, properties, and catalytic mechanisms of keratinases. Strategies for the molecular modification of keratinases are summarized and discussed in terms of improving the substrate specificity, thermostability, and pH tolerance of keratinases. The modification strategies are also enriched by the introduction of immobilized enzymes and directed evolution. In addition, the selection of modification strategies when facing specific industrial applications is discussed and prospects are provided. We believe that this review serves as a reference for the future quest to extend the application of keratinases from the laboratory to industry.

Impact of single-residue mutations on protein thermal stability: The case of threonine 83 of BC2L-CN lectin

The thermal stability of trimeric lectin BC2L-CN was investigated and found to be considerably altered when mutating residue 83, originally a threonine, located at the fucose-binding loop. Mutants were analyzed using differential scanning calorimetry and isothermal microcalorimetry. Although most mutations decreased the affinity of the protein for oligosaccharide H type 1, six mutations increased the melting temperature (Tm) by >5 °C; one mutation, T83P, increased the Tm value by 18.2 °C(T83P, Tm = 96.3 °C). In molecular dynamic simulations, the investigated thermostable mutants, T83P, T83A, and T83S, had decreased fluctuations in the loop containing residue 83. In the T83S mutation, the side-chain hydroxyl group of serine formed a hydrogen bond with a nearby residue, suggesting that the restricted movement of the side-chain resulted in fewer fluctuations and enhanced thermal stability. Residue 83 is located at the interface and near the upstream end of the equivalent loop in a different protomer; therefore, fluctuations by this residue likely propagate throughout the loop. Our study of the dramatic change in thermal stability by a single amino acid mutation provides useful insights into the rational design of protein structures, especially the structures of oligomeric proteins.

Enhancing the stability of a novel D-allulose 3-epimerase from Ruminococcus sp. CAG55 by interface interaction engineering and terminally attached a self-assembling peptide

D-allulose, a highly desirable sugar substitute, is primarily produced using the D-allulose 3-epimerase (DAE). However, the availability of usable DAE enzymes is limited. In this study, we discovered and engineered a novel DAE Rum55, derived from a human gut bacterium Ruminococcus sp. CAG55. The activity of Rum55 was strictly dependent on the presence of Co2+, and it exhibited an equilibrium conversion rate of 30.6 % and a half-life of 4.5 h at 50 °C. To enhance its performance, we engineered the interface interaction of Rum55 to stabilize its tetramer structure, and the best variant E268R was then attached with a self-assembling peptide to form active enzyme aggregates as carrier-free immobilization. The half-life of the best variant E268R-EKL16 at 50 °C was dramatically increased 30-fold to 135.3 h, and it maintained 90 % of its activity after 13 consecutive reaction cycles. Additionally, we identified that metal ions played a key role in stabilizing the tetramer structure of Rum55, and the dependence on metal ions for E268R-EKL16 was significantly reduced. This study provides a useful route for improving the thermostability of DAEs, opening up new possibilities for the industrial production of D-allulose.

Engineering a Carotenoid Cleavage Oxygenase for Coenzyme-Free Synthesis of Vanillin from Ferulic Acid

One-pot biosynthesis of vanillin from ferulic acid without providing energy and cofactors adds significant value to lignin waste streams. However, naturally evolved carotenoid cleavage oxygenase (CCO) with extreme catalytic conditions greatly limited the above pathway for vanillin bioproduction. Herein, CCO from Thermothelomyces thermophilus (TtCCO) was rationally engineered for achieving high catalytic activity under neutral pH conditions and was further utilized for constructing a one-pot synthesis system of vanillin with Bacillus pumilus ferulic acid decarboxylase. TtCCO with the K192N-V310G-A311T-R404N-D407F-N556A mutation (TtCCOM3) was gradually obtained using substrate access channel engineering, catalytic pocket engineering, and pocket charge engineering. Molecular dynamics simulations revealed that reducing the site-blocking effect in the substrate access channel, enhancing affinity for substrates in the catalytic pocket, and eliminating the pocket's alkaline charge contributed to the high catalytic activity of TtCCOM3 under neutral pH conditions. Finally, the one-pot synthesis of vanillin in our study could achieve a maximum rate of up to 6.89 ± 0.3 mM h-1. Therefore, our study paves the way for a one-pot biosynthetic process of transforming renewable lignin-related aromatics into valuable chemicals.

Engineering the activity and thermostability of a carboxylic acid reductase in the conversion of vanillic acid to vanillin

Vanillin is a valuable natural product that can be used as a fragrance and additive. Recent research in the biosynthesis of vanillin has brought attention to a key enzyme, carboxylic acid reductase (CAR), which catalyzes the reduction of vanillic acid to vanillin. Nevertheless, the biosynthesis of vanillin is hampered by the low activity and stability of CAR. As such, a rational design campaign was conducted on a well-documented carboxylic acid reductase from Segniliparus rugosus (SrCAR), using vanillic acid as the model substrate. After combined active site saturation and iterative site-specific mutagenesis, the best quadruple mutant N292H/K524S/A627L/E1121W (M3) was successfully obtained. In comparison to the wildtype SrCAR, M3 demonstrated a 4.2-fold increase in catalytic efficiency (kcat/Km), and its half-life (t1/2) was enhanced by 3.8 times up to 385.08 minutes at 40 °C. In silico docking and molecular dynamics simulation provided insights into the improved activity and stability. In the subsequent preparative-scale reaction with 100 mM (16.8 g L-1) vanillic acid, the whole cell catalysis utilizing M3 produced 10.15 g·L-1 of vanillin and 1.11 g·L-1 of vanillyl alcohol, respectively. This work demonstrates a dual improvement in the activity and thermal stability of SrCAR, thereby potentially facilitating the application of carboxylic acid reductase in the biosynthesis of vanillin.

MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery

Large language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule-ligand interaction datasets are urgently needed for large language models. To address this, we present MISATO, a dataset that combines quantum mechanical properties of small molecules and associated molecular dynamics simulations of ~20,000 experimental protein-ligand complexes with extensive validation of experimental data. Starting from the existing experimental structures, semi-empirical quantum mechanics was used to systematically refine these structures. A large collection of molecular dynamics traces of protein-ligand complexes in explicit water is included, accumulating over 170 μs. We give examples of machine learning (ML) baseline models proving an improvement of accuracy by employing our data. An easy entry point for ML experts is provided to enable the next generation of drug discovery artificial intelligence models.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

Designing of a multi-epitopes based vaccine against Haemophilius parainfluenzae and its validation through integrated computational approaches

Haemophilus parainfluenzae is a Gram-negative opportunist pathogen within the mucus of the nose and mouth without significant symptoms and has an ability to cause various infections ranging from ear, eye, and sinus to pneumonia. A concerning development is the increasing resistance of H. parainfluenzae to beta-lactam antibiotics, with the potential to cause dental infections or abscesses. The principal objective of this investigation is to utilize bioinformatics and immuno-informatic methodologies in the development of a candidate multi-epitope Vaccine. The investigation focuses on identifying potential epitopes for both B cells (B lymphocytes) and T cells (helper T lymphocytes and cytotoxic T lymphocytes) based on high non-toxic and non-allergenic characteristics. The selection process involves identifying human leukocyte antigen alleles demonstrating strong associations with recognized antigenic and overlapping epitopes. Notably, the chosen alleles aim to provide coverage for 90% of the global population. Multi-epitope constructs were designed by using suitable linker sequences. To enhance the immunological potential, an adjuvant sequence was incorporated using the EAAAK linker. The final vaccine construct, comprising 344 amino acids, was achieved after the addition of adjuvants and linkers. This multi-epitope Vaccine demonstrates notable antigenicity and possesses favorable physiochemical characteristics. The three-dimensional conformation underwent modeling and refinement, validated through in-silico methods. Additionally, a protein-protein molecular docking analysis was conducted to predict effective binding poses between the multi-epitope Vaccine and the Toll-like receptor 4 protein. The Molecular Dynamics (MD) investigation of the docked TLR4-vaccine complex demonstrated consistent stability over the simulation period, primarily attributed to electrostatic energy. The docked complex displayed minimal deformation and enhanced rigidity in the motion of residues during the dynamic simulation. Furthermore, codon translational optimization and computational cloning was performed to ensure the reliability and proper expression of the multi-Epitope Vaccine. It is crucial to emphasize that despite these computational validations, experimental research in the laboratory is imperative to demonstrate the immunogenicity and protective efficacy of the developed vaccine. This would involve practical assessments to ascertain the real-world effectiveness of the multi-epitope Vaccine.

Case report: A severe clinical phenotype of pontocerebellar hypoplasia type 7 with compound heterozygous variants of TOE1

Pontocerebellar Hypoplasia (PCH) is a rare autosomal recessive hereditary neurological degenerative disease. To elaborate upon the clinical phenotypes of PCH and explore the correlation between TOE1 gene mutations and clinical phenotype, we analyze the clinical and genetic features of a Chinese infant afflicted with pontocerebellar dysplasia accompanied by gender reversal with bioinformatics methods. The main clinical features of this infant with TOE1 gene mutation included progressive lateral ventricle widening, hydrocephalus, severe postnatal growth retardation, and hypotonia, and simultaneously being accompanied by 46, XY female sex reversal. Whole exome sequencing revealed a compound heterozygous mutation in the TOE1 gene (c.299T > G, c.1414T > G), with the protein homology modeling-generated structure predicting a pathogenic variation, which is closely related to the clinical manifestations in the patient. The new mutation sites, c.299T > G and c.1414T > G, in the TOE1 gene are pathogenic variants of pontocerebellar hypoplasia type 7.

High-Temperature Tolerance Protein Engineering through Deep Evolution

Protein engineering aimed at increasing temperature tolerance through iterative mutagenesis and high-throughput screening is often labor-intensive. Here, we developed a deep evolution (DeepEvo) strategy to engineer protein high-temperature tolerance by generating and selecting functional sequences using deep learning models. Drawing inspiration from the concept of evolution, we constructed a high-temperature tolerance selector based on a protein language model, acting as selective pressure in the high-dimensional latent spaces of protein sequences to enrich those with high-temperature tolerance. Simultaneously, we developed a variant generator using a generative adversarial network to produce protein sequence variants containing the desired function. Afterward, the iterative process involving the generator and selector was executed to accumulate high-temperature tolerance traits. We experimentally tested this approach on the model protein glyceraldehyde 3-phosphate dehydrogenase, obtaining 8 variants with high-temperature tolerance from just 30 generated sequences, achieving a success rate of over 26%, demonstrating the high efficiency of DeepEvo in engineering protein high-temperature tolerance.

S-adenosyl-l-methionine interaction signatures in methyltransferases

The switching on or off of methylation, a change from a normal methylation to hyper or hypo methylation is implicated in many diseases that include cancers, infectious, neurodegenerative diseases and others. Methyltransferases are one of the most sought targets that have diversified for the methylation of a variety of substrates. However, without S-adenosyl-l-methionine (SAM), the universal methyl donor, the majority of the methyltransferases remain functionally inactive. In this article, we did a comprehensive analysis of all available SAM-receptor crystal structures at atom, moiety and structure levels to gain deeper insights into the structure and function of SAM. SAM demonstrated flexibility in binding to a variety of receptors irrespective of the size of the binding pockets. Further analysis of the binding pockets resulted in all SAM conformations clustering into four natural shapes. The conserved interaction analysis provides an unambiguous orientation of SAM binding to receptors which has been elusive till now. SAM peptide moiety (SPM) and SAM nucleobase moiety (SNM) show up to 89% interactions with receptors whereas only 11% interactions with SAM ribose moiety (SRM). It is found that SPM and SNM terminal atoms anchor to the highly conserved receptor subsites creating a workbench for catalysis. It is seen that every interacting atom and its position is crucial in the methyl transfer phenomenon. A very unique observation is that the methyl group of SAM does not have even one interaction with the receptor. The deep insights gained help in the design and development of novel drugs against the methyltransferases.Communicated by Ramaswamy H. Sarma.

Rational engineering S1' substrate binding pocket to enhance substrate specificity and catalytic activity of thermal-stable keratinase for efficient keratin degradation

This study reports the rational engineering of the S1' substrate-binding pocket of a thermally-stable keratinase from Pseudomonas aeruginosa 4-3 (4-3Ker) to improve substrate specificity to typical keratinase (K/C > 0.5) and catalytic activity without compromising thermal stability for efficient keratin degradation. Of 10 chosen mutation hotspots in the S1' substrate-binding pocket, the top three mutations M128R, A138V, and V142I showing the best catalytic activity and substrate specificity were identified. Their double and triple combinatorial mutants synergistically overcame limitations of single mutants, fabricating an excellent M128R/A138V/V142I triple mutant which displayed a 1.21-fold increase in keratin catalytic activity, 1.10-fold enhancement in keratin/casein activity ratio, and a 3.13 °C increase in half-inactivation temperature compared to 4-3Ker. Molecular dynamics simulations revealed enhanced flexibility of critical amino acid residues at the substrate access tunnel, improved global protein rigidity, and heightened hydrophobicity within the active site likely underpinned the increased catalytic activity and substrate specificity. Additionally, the triple mutant improved the feather degradation rate by 32.86 % over the wild-type, far exceeding commercial keratinase in substrate specificity and thermal stability. This study exemplified engineering a typical keratinase with enhanced substrate specificity, catalytic activity, and thermal stability from thermally-stable 4-3Ker, providing a more robust tool for feather degradation.

Changes in an enzyme ensemble during catalysis observed by high-resolution XFEL crystallography

Enzymes populate ensembles of structures necessary for catalysis that are difficult to experimentally characterize. We use time-resolved mix-and-inject serial crystallography at an x-ray free electron laser to observe catalysis in a designed mutant isocyanide hydratase (ICH) enzyme that enhances sampling of important minor conformations. The active site exists in a mixture of conformations, and formation of the thioimidate intermediate selects for catalytically competent substates. The influence of cysteine ionization on the ICH ensemble is validated by determining structures of the enzyme at multiple pH values. Large molecular dynamics simulations in crystallo and time-resolved electron density maps show that Asp17 ionizes during catalysis and causes conformational changes that propagate across the dimer, permitting water to enter the active site for intermediate hydrolysis. ICH exhibits a tight coupling between ionization of active site residues and catalysis-activated protein motions, exemplifying a mechanism of electrostatic control of enzyme dynamics.

Protein engineering of transaminase facilitating enzyme cascade reaction for the biosynthesis of azasugars

Azasugars, such as 1-deoxynojirimycin (1-DNJ), exhibit unique physiological functions and hold promising applications in medicine and health fields. However, the biosynthesis of 1-DNJ is hindered by the low activity and thermostability of the transaminase. In this study, the transaminase from Mycobacterium vanbaalenii (MvTA) with activity toward d-fructose was engineered through semi-rational design and high-throughput screening method. The final mutant M9-1 demonstrated a remarkable 31.2-fold increase in specific activity and an impressive 200-fold improvement in thermostability compared to the wild-type enzyme. Molecular dynamics (MD) simulations revealed that the mutation sites of H69R and K145R in M9-1 played crucial roles in the binding of the amino acceptor and donor, leading to the stable conformation of substrates within the active pocket. An enzyme cascade reaction was developed using M9-1 and the dehydrogenase from Paenibacillus polymyxa (GutB1) for the production of mannojirimycin (MJ), which provided a new idea for the in vitro biosynthesis of 1-DNJ.

Decoding Molecular Mechanism Underlying Human Olfactory Receptor OR8D1 Activation by Sotolone Enantiomers

Sotolone, a chiral compound, plays an important role in the food industry. Herein, (R)-/(S)-sotolone were separated to determine their odor characteristics and thresholds in air (R-form: smoky, burned, herb, and green aroma, 0.0514 μg/m3; S-form: sweet, milk, acid, and nutty aroma, 0.0048 μg/m3). OR8D1 responses to (R)-/(S)-sotolone were detected in a HEK293 cell-based luminescence assay. (S)-Sotolone was a more potent agonist than (R)-sotolone (EC50 values of 84.98 ± 1.05 and 167.20 ± 0.25 μmol/L, respectively). Molecular dynamics simulations and molecular mechanics Poisson-Boltzmann surface area analyses confirmed that the combination of (S)-sotolone and OR8D1 was more stable than that of (R)-sotolone. Odorant docking, multiple sequence alignments, site-directed mutagenesis, and functional studies with recombinant odorant receptors (ORs) in a cell-based luminescence assay identified 11 amino-acid residues that influence the enantioselectivity of OR8D1 toward sotolone significantly and that N2065.46 was indispensable to the activation of OR8D1 by (S)-sotolone.

Application of Machine Learning in the Quantitative Analysis of the Surface Characteristics of Highly Abundant Cytoplasmic Proteins: Toward AI-Based Biomimetics

Proteins in the crowded environment of human cells have often been studied regarding nonspecific interactions, misfolding, and aggregation, which may cause cellular malfunction and disease. Specifically, proteins with high abundance are more susceptible to these issues due to the law of mass action. Therefore, the surfaces of highly abundant cytoplasmic (HAC) proteins directly exposed to the environment can exhibit specific physicochemical, structural, and geometrical characteristics that reduce nonspecific interactions and adapt to the environment. However, the quantitative relationships between the overall surface descriptors still need clarification. Here, we used machine learning to identify HAC proteins using hydrophobicity, charge, roughness, secondary structures, and B-factor from the protein surfaces and quantified the contribution of each descriptor. First, several supervised learning algorithms were compared to solve binary classification problems for the surfaces of HAC and extracellular proteins. Then, logistic regression was used for the feature importance analysis of descriptors considering model performance (80.2% accuracy and 87.6% AUC) and interpretability. The HAC proteins showed positive correlations with negatively and positively charged areas but negative correlations with hydrophobicity, the B-factor, the proportion of beta structures, roughness, and the proportion of disordered regions. Finally, the details of each descriptor could be explained concerning adaptative surface strategies of HAC proteins to regulate nonspecific interactions, protein folding, flexibility, stability, and adsorption. This study presented a novel approach using various surface descriptors to identify HAC proteins and provided quantitative design rules for the surfaces well-suited to human cellular crowded environments.

The first crystal structure of a family 45 glycoside hydrolase from a brown-rot fungus, Gloeophyllum trabeum GtCel45A

Here we describe the first crystal structure of a beta-1,4-endoglucanase from a brown-rot fungus, Gloeophyllum trabeum GtCel45A, which belongs to subfamily C of glycoside hydrolase family 45 (GH45). GtCel45A is ~ 18 kDa in size and the crystal structure contains 179 amino acids. The structure is refined at 1.30 Å resolution and Rfree 0.18. The enzyme consists of a single catalytic module folded into a six-stranded double-psi beta-barrel domain surrounded by long loops. GtCel45A is very similar in sequence (82% identity) and structure to PcCel45A from the white-rot fungus Phanerochaete chrysosporium. Surprisingly though, initial hydrolysis of barley beta-glucan was almost twice as fast in GtCel45A as compared to PcCel45A.

Combining directed evolution with high cell permeability for high-level cadaverine production in engineered Escherichia coli

The biosynthesis of cadaverine from lysine is an environmentally promising technology, that could contribute to a more sustainable approach to manufacturing bio-nylon 5X. However, the titer of biosynthesized cadaverine has still not reached a sufficient level for industrial production. A powerful green cell factory was developed to enhance cadaverine production by regulating lipopolysaccharide (LPS) genes and improving membrane permeability. Firstly, 10 LPS mutant strains were constructed and the effect on the growth was investigated. Then, the lysine decarboxylase (CadA) was overexpressed in 10 LPS mutant strains of Escherichia coli MG1655 and the ability to produce cadaverine was compared. Using 20.0 g L-1 of L-lysine hydrochloride (L-lysine-HCl) as the substrate for the biotransformation reaction, Cad02 and Cad06 strains exhibited high production levels of cadaverine, with 8.95 g L-1 and 7.55 g L-1 respectively while the control strain Cad00 only 4.92 g L-1 . Directed evolution of CadA was also used to improve its stability under alkaline conditions. The cadaverine production of the Cad02-M mutant stain increased by 1.86 times at pH 8.0. Finally, the production process was scaled up using recombinant whole cells as catalysts, achieving a high titer of 211 g L-1 cadaverine (96.8%) by fed-batch bioconversion. This study demonstrates the potential role of LPS in enhancing the efficiency of mass transfer between substrate and enzymes in vivo by increasing cell permeability. The results indicate that the argumentation of cell permeability could not only significantly enhance the biotransformation efficiency of cadaverine, but also provide a universally applicable, straightforward, environment-friendly, and cost-effective method for the biosynthesis of other high-value chemicals.

Linking ATP and allosteric sites to achieve superadditive binding with bivalent EGFR kinase inhibitors

Bivalent molecules consisting of groups connected through bridging linkers often exhibit strong target binding and unique biological effects. However, developing bivalent inhibitors with the desired activity is challenging due to the dual motif architecture of these molecules and the variability that can be introduced through differing linker structures and geometries. We report a set of alternatively linked bivalent EGFR inhibitors that simultaneously occupy the ATP substrate and allosteric pockets. Crystal structures show that initial and redesigned linkers bridging a trisubstituted imidazole ATP-site inhibitor and dibenzodiazepinone allosteric-site inhibitor proved successful in spanning these sites. The re-engineered linker yielded a compound that exhibited significantly higher potency (~60 pM) against the drug-resistant EGFR L858R/T790M and L858R/T790M/C797S, which was superadditive as compared with the parent molecules. The enhanced potency is attributed to factors stemming from the linker connection to the allosteric-site group and informs strategies to engineer linkers in bivalent agent design.

Computer-Aided Rational Design Strategy to Improve the Thermal Stability of Alginate Lyase AlyMc

Alginate lyase degrades alginate by the β-elimination mechanism to produce unsaturated alginate oligosaccharides (UAOS), which have better bioactivities than saturated AOS. Enhancing the thermal stability of alginate lyases is crucial for their industrial applications. In this study, a feasible and efficient rational design strategy was proposed by combining the computer-aided ΔΔG value calculation with the B-factor analysis. Two thermal stability-enhanced mutants, Q246V and K249V, were obtained by site-directed mutagenesis. Particularly, the t1/2, 50 °C for mutants Q246V and K249V was increased from 2.36 to 3.85 and 3.65 h, respectively. Remarkably, the specific activities of Q246V and K249V were enhanced to 2.41- and 2.96-fold that of alginate lyase AlyMc, respectively. Structural analysis and molecular dynamics simulations suggested that mutations enhanced the hydrogen bond networks and the overall rigidity of the molecular structure. Notably, mutant Q246V exhibited excellent thermal stability among the PL-7 alginate lyase family, especially considering the heightened enzymatic activity. Moreover, the rational design strategy used in this study can effectively improve the thermal stability of enzymes and has important significance in advancing applications of alginate lyase.

Searching for Frataxin Function: Exploring the Analogy with Nqo15, the Frataxin-like Protein of Respiratory Complex I from Thermus thermophilus

Nqo15 is a subunit of respiratory complex I of the bacterium Thermus thermophilus, with strong structural similarity to human frataxin (FXN), a protein involved in the mitochondrial disease Friedreich's ataxia (FRDA). Recently, we showed that the expression of recombinant Nqo15 can ameliorate the respiratory phenotype of FRDA patients' cells, and this prompted us to further characterize both the Nqo15 solution's behavior and its potential functional overlap with FXN, using a combination of in silico and in vitro techniques. We studied the analogy of Nqo15 and FXN by performing extensive database searches based on sequence and structure. Nqo15's folding and flexibility were investigated by combining nuclear magnetic resonance (NMR), circular dichroism, and coarse-grained molecular dynamics simulations. Nqo15's iron-binding properties were studied using NMR, fluorescence, and specific assays and its desulfurase activation by biochemical assays. We found that the recombinant Nqo15 isolated from complex I is monomeric, stable, folded in solution, and highly dynamic. Nqo15 does not share the iron-binding properties of FXN or its desulfurase activation function.

DeepPPThermo: A Deep Learning Framework for Predicting Protein Thermostability Combining Protein-Level and Amino Acid-Level Features

Using wet experimental methods to discover new thermophilic proteins or improve protein thermostability is time-consuming and expensive. Machine learning methods have shown powerful performance in the study of protein thermostability in recent years. However, how to make full use of multiview sequence information to predict thermostability effectively is still a challenge. In this study, we proposed a deep learning-based classifier named DeepPPThermo that fuses features of classical sequence features and deep learning representation features for classifying thermophilic and mesophilic proteins. In this model, deep neural network (DNN) and bi-long short-term memory (Bi-LSTM) are used to mine hidden features. Furthermore, local attention and global attention mechanisms give different importance to multiview features. The fused features are fed to a fully connected network classifier to distinguish thermophilic and mesophilic proteins. Our model is comprehensively compared with advanced machine learning algorithms and deep learning algorithms, proving that our model performs better. We further compare the effects of removing different features on the classification results, demonstrating the importance of each feature and the robustness of the model. Our DeepPPThermo model can be further used to explore protein diversity, identify new thermophilic proteins, and guide directed mutations of mesophilic proteins.

Sequence identification and in silico characterization of novel thermophilic lipases from Geobacillus species

Microbial lipases are utilized in various biotechnological areas, including pharmaceuticals, food, biodiesel, and detergents. In this study, we cloned and sequenced Lip21 and Lip33 genes from Geobacillus sp. GS21 and Geobacillus sp. GS33, then we in silico and experimentally analyzed the encoded lipases. For this purpose, Lip21 and Lip33 were cloned, sequenced, and their amino acid sequences were investigated for determination of biophysicochemical characteristics, evolutionary relationships, and sequence similarities. 3D models were built and computationally affirmed by various bioinformatics tools, and enzyme-ligand interactions were investigated by docking analysis using six ligands. Biophysicochemical property of Lip21 and Lip33 was also determined experimentally and the results demonstrated that they had similar isoelectric point (pI) (6.21) and Tm (75.5°C) values as Tm was revealed by denatured protein analysis of the circular dichroism spectrum and pI was obtained by isoelectric focusing. Phylogeny analysis indicated that Lip21 and Lip33 were the closest to lipases from Geobacillus sp. SBS-4S and Geobacillus thermoleovorans, respectively. Alignment analysis demonstrated that S144-D348-H389 was catalytic triad residues in Lip21 and Lip33, and enzymes possessed a conserved Gly-X-Ser-X-Gly motif containing catalytic serine. 3D structure analysis indicated that Lip21 and Lip33 highly resembled each other and they were α/β hydrolase-fold enzymes with large lid domains. BANΔIT analysis results showed that Lip21 and Lip33 had higher thermal stability, compared to other thermostable Geobacillus lipases. Docking results revealed that Lip21- and Lip33-docked complexes possessed common residues (H112, K115, Q162, E163, and S141) that interacted with the substrates, except paranitrophenyl (pNP)-C10 and pNP-C12, indicating that these residues might have a significant action on medium and short-chain fatty acid esters. Thus, Lip21 and Lip33 can be potential candidates for different industrial applications.

Improving the thermal stability and catalytic activity of ulvan lyase by the combination of FoldX and KnowVolution campaign

Thermal stability is one of the most important properties of ulvan lyases for their application in algae biomass degradation. The Knowledge gaining directed eVolution (KnowVolution) protein engineering strategy could be employed to improve thermostability of ulvan lyase with less screening effort. Herein, the unfolding free energies (ΔΔG) of the loop region were calculated using FoldX and four sites (D103, G104, T113, Q229) were selected for saturation mutagenesis, resulting in the identification of a favorable single-site mutant Q229M. Subsequently, iteration mutation was carried out with the mutant N57P (previously obtained by our group) to further enhance the performance of ulvan lyase. The results showed that the most beneficial variant N57P/Q229M exhibited a 1.67-fold and 2-fold increase in residual activity compared to the wild type after incubation at 40 °C and 50 °C for 1 h, respectively. In addition, the variant produced 1.06 mg/mL of reducing sugar in 2 h, which was almost four times as much as the wild type. Molecular dynamics simulations revealed that N57P/Q229M mutant enhanced the structural rigidity by augmenting intramolecular hydrogen bonds. Meanwhile, the shorter proton transmission distance between the general base of the enzyme and the substrate contributed to the glycosidic bond breakage. Our research showed that in silico saturation mutagenesis using position scan module in FoldX allowed for faster screening of mutants with improved thermal stability, and combining it with KnowVolution enabled a balanced effect of thermal stability and enzyme activity in protein engineering.

Improving plastic degrading enzymes via directed evolution

Plastic degrading enzymes have immense potential for use in industrial applications. Protein engineering efforts over the last decade have resulted in considerable enhancement of many properties of these enzymes. Directed evolution, a protein engineering approach that mimics the natural process of evolution in a laboratory, has been particularly useful in overcoming some of the challenges of structure-based protein engineering. For example, directed evolution has been used to improve the catalytic activity and thermostability of polyethylene terephthalate (PET)-degrading enzymes, although its use for the improvement of other desirable properties, such as solvent tolerance, has been less studied. In this review, we aim to identify some of the knowledge gaps and current challenges, and highlight recent studies related to the directed evolution of plastic-degrading enzymes.

Small, solubilized platinum nanocrystals consist of an ordered core surrounded by mobile surface atoms

In situ structures of Platinum (Pt) nanoparticles (NPs) can be determined with graphene liquid cell transmission electron microscopy. Atomic-scale three-dimensional structural information about their physiochemical properties in solution is critical for understanding their chemical function. We here analyze eight atomic-resolution maps of small (<3 nm) colloidal Pt NPs. Their structures are composed of an ordered crystalline core surrounded by surface atoms with comparatively high mobility. 3D reconstructions calculated from cumulative doses of 8500 and 17,000 electrons/pixel, respectively, are characterized in terms of loss of atomic densities and atomic displacements. Less than 5% of the total number of atoms are lost due to dissolution or knock-on damage in five of the structures analyzed, whereas 10-16% are lost in the remaining three. Less than 5% of the atomic positions are displaced due to the increased electron irradiation in all structures. The surface dynamics will play a critical role in the diverse catalytic function of Pt NPs and must be considered in efforts to model Pt NP function computationally.

Enzyme engineering for functional lipids synthesis: recent advance and perspective

Functional lipids, primarily derived through the modification of natural lipids by various processes, are widely acknowledged for their potential to impart health benefits. In contrast to chemical methods for lipid modification, enzymatic catalysis offers distinct advantages, including high selectivity, mild operating conditions, and reduced byproduct formation. Nevertheless, enzymes face challenges in industrial applications, such as low activity, stability, and undesired selectivity. To address these challenges, protein engineering techniques have been implemented to enhance enzyme performance in functional lipid synthesis. This article aims to review recent advances in protein engineering, encompassing approaches from directed evolution to rational design, with the goal of improving the properties of lipid-modifying enzymes. Furthermore, the article explores the future prospects and challenges associated with enzyme-catalyzed functional lipid synthesis.

Structural insights and aggregation propensity of a super-stable monellin mutant: A new potential building block for protein-based nanostructured materials

Protein fibrillation is commonly associated with pathologic amyloidosis. However, under appropriate conditions several proteins form fibrillar structures in vitro that can be used for biotechnological applications. MNEI and its variants, firstly designed as single chain derivatives of the sweet protein monellin, are also useful models for protein fibrillary aggregation studies. In this work, we have drawn attention to a protein dubbed Mut9, already characterized as a "super stable" MNEI variant. Comparative analysis of the respective X-ray structures revealed how the substitutions present in Mut9 eliminate several unfavorable interactions and stabilize the global structure. Molecular dynamic predictions confirmed the presence of a hydrogen-bonds network in Mut9 which increases its stability, especially at neutral pH. Thioflavin-T (ThT) binding assays and Fourier transform infrared (FTIR) spectroscopy indicated that the aggregation process occurs both at acidic and neutral pH, with and without addition of NaCl, even if with a different kinetics. Accordingly, Transmission Electron Microscopy (TEM) showed a fibrillar organization of the aggregates in all the tested conditions, albeit with some differences in the quantity and in the morphology of the fibrils. Our data underline the great potential of Mut9, which combines great stability in solution with the versatile conversion into nanostructured biomaterials.

Structural and functional analysis of two SHMT8 variants associated with soybean cyst nematode resistance

Two amino acid variants in soybean serine hydroxymethyltransferase 8 (SHMT8) are associated with resistance to the soybean cyst nematode (SCN), a devastating agricultural pathogen with worldwide economic impacts on soybean production. SHMT8 is a cytoplasmic enzyme that catalyzes the pyridoxal 5-phosphate-dependent conversion of serine and tetrahydrofolate (THF) to glycine and 5,10-methylenetetrahydrofolate. A previous study of the P130R/N358Y double variant of SHMT8, identified in the SCN-resistant soybean cultivar (cv.) Forrest, showed profound impairment of folate binding affinity and reduced THF-dependent enzyme activity, relative to the highly active SHMT8 in cv. Essex, which is susceptible to SCN. Given the importance of SCN-resistance in soybean agriculture, we report here the biochemical and structural characterization of the P130R and N358Y single variants to elucidate their individual effects on soybean SHMT8. We find that both single variants have reduced THF-dependent catalytic activity relative to Essex SHMT8 (10- to 50-fold decrease in kcat /Km ) but are significantly more active than the P130R/N368Y double variant. The kinetic data also show that the single variants lack THF-substrate inhibition as found in Essex SHMT8, an observation with implications for regulation of the folate cycle. Five crystal structures of the P130R and N358Y variants in complex with various ligands (resolutions from 1.49 to 2.30 Å) reveal distinct structural impacts of the mutations and provide new insights into allosterism. Our results support the notion that the P130R/N358Y double variant in Forrest SHMT8 produces unique and unexpected effects on the enzyme, which cannot be easily predicted from the behavior of the individual variants.

Non-canonical helical transitions and conformational switching are associated with characteristic flexibility and disorder indices in TRP and Kv channels

Structural evidence and much experimental data have demonstrated the presence of non-canonical helical substructures (π and 3₁₀) in regions of great functional relevance both in TRP as in Kv channels. Through an exhaustive compositional analysis of the sequences underlying these substructures, we find that each of them is associated with characteristic local flexibility profiles, which in turn are implicated in significant conformational rearrangements and interactions with specific ligands. We found that α-to-π helical transitions are associated with patterns of local rigidity whereas α-to-3₁₀ transitions are mainly leagued with high local flexibility profiles. We also study the relationship between flexibility and protein disorder in the transmembrane domain of these proteins. By contrasting these two parameters, we located regions showing a sort of structural discrepancy between these similar but not identical protein attributes. Notably, these regions are presumably implicated in important conformational rearrangements during the gating in those channels. In that sense, finding these regions where flexibility and disorder are not proportional allows us to detect regions with potential functional dynamism. From this point of view, we highlighted some conformational rearrangements that occur during ligand binding events, the compaction, and refolding of the outer pore loops in several TRP channels, as well as the well-known S4 motion in Kv channels.