Insights on protein thermal stability: a graph representation of molecular interactions

De Novo Design of Highly Stable Binders Targeting Dihydrofolate Reductase in Klebsiella pneumoniae

The study aims to design novel therapeutic inhibitors targeting the DHFR protein of Klebsiella pneumoniae. However, challenges like bacterial resistance to peptides and the limitations of computational models in predicting in vivo behavior must be addressed to refine the design process and improve therapeutic efficacy. This study employed deep learning-based bioinformatics techniques to tackle these issues. The study involved retrieving DHFR protein sequences from Klebsiella strains, aligning them to identify conserved regions, and using deep learning models (OmegaFold, ProteinMPNN) to design de novo inhibitors. Cell-penetrating peptide (CPP) motifs were added to enhance delivery, followed by allergenicity and thermal stability assessments. Molecular docking and dynamics simulations evaluated the binding affinity and stability of the inhibitors with DHFR. A conserved 60-residue region was identified, and 60 de novo binders were generated, resulting in 7200 sequences. After allergenicity prediction and stability testing, 10 sequences with melting points near 70°C were shortlisted. Strong binding affinities were observed, especially for complexes 4OR7-1787 and 4OR7-1811, which remained stable in molecular dynamics simulations, indicating their potential as therapeutic agents. This study designed stable de novo peptides with cell-penetrating properties and strong binding affinity to DHFR. Future steps include in vitro validation to assess their effectiveness in inhibiting DHFR, followed by in vivo studies to evaluate their therapeutic potential and stability. These peptides offer a promising strategy against Klebsiella pneumoniae infections, providing potential alternatives to current antibiotics. Experimental validation will be key to assessing their clinical relevance.

Biochemical characterization and biocatalytic application of a hyperthermostable tagatose 4-epimerase from Infirmifilum uzonense

D-Tagatose is a representative rare sugar with the physiochemical properties of low energy and high sweetness, as well as excellent physiological functions such as blood sugar regulation, enhancement of intestinal flora, and prevention of dental caries. At present, D-tagatose production involves lactose hydrolysis and D-galactose isomerization processes, resulting in high production costs that hinder its industrial advancement. Tagatose 4-epimerase (T4Ease) has the capability to directly convert d-fructose into D-tagatose through C-4 epimerization, providing a new approach for D-tagatose production. In this study, a hyperthermostable T4Ease from Infirmifilum uzonense (Inuz-TE4ase) was identified from the Foldseek clustered AlphaFold database and its biochemical properties were characterized in detail. Under the optimal reaction conditions of 90 °C and pH 8.5 (Tris-HCl) with the addition of 1 mM Ni2+, the maximum catalytic activity towards d-fructose was determined to be 0.680 U/mg. Inuz-TE4ase exhibited exceptional thermostability, with half-life (t1/2) values of 19.3 h at 85 °C and 8.9 h at 90 °C, respectively. Inuz-TE4ase was strictly metal-dependent, and its stability could be enhanced by Ni2+ with an increase in the melting temperature (Tm) value from 101.1 °C to 105.7 °C. When 100 g/L d-fructose was used as the substrate, Inuz-TE4ase could catalyze the production of 21.67 g/L D-tagatose, indicating its significant potential for D-tagatose bioproduction.

Ancestral Sequence Reconstruction and Semirational Engineering of Glycosyltransferase for Efficient Synthesis of Rare Ginsenoside Rh1

Rare ginsenoside Rh1, exhibiting great potential in the food industry, is limited by its natural scarcity. This constraint has driven the development of biocatalytic synthesis approaches, yet robust enzymes capable of efficient production remain elusive. Here, we employed the ancestral sequence reconstruction (ASR) approach to create a thermostable UDP-dependent glycosyltransferase (UGT227) for Rh1 synthesis from 20(S)-protopanaxatriol (PPT). UGT227 exhibited enhanced thermostability (t1/2 = 44.2 h at 60 °C) but initially yielded only 15% Rh1. Semirational engineering generated the I83A/F285 M variant, increasing the yield to 92%. For economic viability, the I83A/F285 M variant was coexpressed with Arabidopsis thaliana sucrose synthase (AtSUS1), enabling the use of cost-effective sucrose for UDP-glucose regeneration. This integration achieved a 99.9% yield at a 1 mM PPT. Molecular dynamics simulations revealed that the enlarged binding pocket entrance of I83A/F285 M contributed to the enhanced Rh1 yield. Our findings offer strategies for efficient biosynthesis of Rh1 and pave the way for economically feasible production.

Discovery and In Silico Characterization of Anatolian Water Buffalo Rumen-Derived Bacterial Thermostable Xylanases: A Sequence-Based Metagenomic Approach

This study involved shotgun sequencing of rumen metagenomes from three Anatolian water buffalos, an exploration of the relationship between microbial flora and xylanases, and in silico analyses of thermostable xylanases, focusing on their sequence, structure, and dynamic properties. For this purpose, the rumen metagenome of three Anatolian water buffalos was sequenced and bioinformatically analyzed to determine microbial diversity and full-length xylanases. Analyses of BLAST, biophysicochemical characteristics, phylogenetic tree, and multiple sequence alignment were performed with Blastp, ProtParam, MEGA11 software, and Clustal Omega, respectively. Three-dimensional homology models of three xylanases (AWBRMetXyn5, AWBRMetXyn10, and AWBRMetXyn19) were constructed by SWISS-MODEL and validated by ProSA, ProCheck, and Verify3D. Also, their 3D models were structurally analyzed by PyMOL, BANΔIT, thermostability predictor, What If, and Protein Interaction Calculator (PIC) software. Protein-ligand interactions were examined by docking and MD simulation. Shotgun sequence and Blastp analyses showed that Clostridium (Clostridiales bacterial order), Ruminococcus (Oscillospiraceae bacterial family), Prevotella (Bacteroidales bacterial order), and Butyrivibrio (Lachnospiraceae bacterial family) were found as dominant potential xylanase-producer genera in three rumen samples. Furthermore, the biophysicochemical analysis indicated that three xylanases exhibited an aliphatic index above 80, an instability index below 40, and melting temperatures (T m) surpassing 65 °C. Phylogenetic analysis placed three xylanases within the GH10 family, clustering them with thermophilic xylanases, while homology modeling identified the optimal template as a xylanase from a thermophilic bacterium. The structural analysis indicated that three xylanases possessed the number of salt bridges, hydrophobic interactions, and T m score higher than 50, 165, and 70 °C, respectively; however, the reference thermophilic XynAS9 had 43, 145, and 54.41 °C, respectively. BANΔIT analysis revealed that three xylanases exhibited lower B'-factor values in the β3-α1 loop/short-helix at the N-terminal site compared to the reference thermophilic XynAS9. In contrast, six residues (G79, M123, D150, T199, A329, and G377) possessed higher B'-factor values in AWBRMetXyn5 and their aligned positions in AWBRMetXyn10 and AWBRMetXyn19, relative to XynAS9 including Gln, Glu, Ile, Lys, Ser, and Val at these positions, respectively. MD simulation results showed that the β9-η5 loop including catalytic nucleophile glutamic acid in the RMSF plot of three xylanases had a higher fluctuation than the aligned region in XynAS9. The distance analysis from the MD simulation showed that the nucleophile residue in AWBRMetXyn5 and AWBRMetXyn10 remained closer to the ligand throughout the simulation compared with XynAS9 and AWBRMetXyn19. The most notable difference between AWBRMetXyn5 and AWBRMetXyn10 was the increased amino acid fluctuations in two specific regions, the η3 short-helix and the η3-α3 loop, despite a minimal sequence difference of only 1.24%, which included three key amino acid variations (N345, N396, and T397 in AWBRMetXyn5; D345, K396, and A397 in AWBRMetXyn10). Thus, this study provided computational insights into xylanase function and thermostability, which could inform future protein engineering efforts. Additionally, three xylanases, especially AWBRMetXyn5, are promising candidates for various high-temperature industrial applications. In a forthcoming study, three xylanases will be experimentally characterized and considered for potential industrial applications. In addition, the amino acid substitutions (G79Q, M123E, D150I, T199K, A329S, and G377V) and the residues in the β3-α1 loop will be targeted for thermostability improvement of AWBRMetXyn5. The amino acids (N345, N396, and T397) and the residues on the β9-η5 loop, η3 short-helix, and η3-α3 loop will also be focused on development of the catalytic efficiency.

Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues

Predicting interactions between proteins is fundamental for understanding the mechanisms underlying cellular processes, since protein-protein complexes are crucial in physiological conditions but also in many diseases, for example by seeding aggregates formation. Despite the many advancements made so far, the performance of docking protocols is deeply dependent on their capability to identify binding regions. From this, the importance of developing low-cost and computationally efficient methods in this field. We present an integrated novel protocol mainly based on compact modeling of protein surface patches via sets of orthogonal polynomials to identify regions of high shape/electrostatic complementarity. By incorporating both hydrophilic and hydrophobic contributions, we define new binding matrices, which serve as effective inputs for training a neural network. In this work, we propose a new Neural Network (NN)-based architecture, Core Interacting Residues Network (CIRNet), which achieves a performance in terms of Area Under the Receiver Operating Characteristic Curve (ROC AUC) of approximately 0.87 in identifying pairs of core interacting residues on a balanced data set. In a blind search for core interacting residues, CIRNet distinguishes them from random decoys with an ROC AUC of 0.72. We test this protocol to enhance docking algorithms by filtering the proposed poses, addressing one of the still open problems in computational biology. Notably, when applied to the top ten models from three widely used docking servers, CIRNet improves docking outcomes, significantly reducing the average RMSD between the selected poses and the native state. Compared to another state-of-the-art tool for rescaling docking poses, CIRNet more efficiently identified the worst poses generated by the three docking servers under consideration and achieved superior rescaling performance in two cases.

Biomass-Based Microbial Protein Production: A Review of Processing and Properties

A rise in population and societal changes have increased pressure on resources required to meet the growing demand for food and changing dietary preferences. The increasing demand for animal protein is concerning and raises questions regarding sustainability due to its environmental impact. Subsequently, scientists seek alternative proteins, such as microbial proteins (MPs), as an environmentally friendly choice. The production of MPs promotes benefits, including reducing deforestation and CO2 emissions. Several microorganism types, such as bacteria, yeast, fungi, and algae, use a variety of substrates for MP production, from agricultural residues to lignocellulosic biomass. These complex substrates, including lignocellulosic biomass, are converted to fermentable sugar through either chemical, physical, or biological methods. Indeed, fermentation can occur through submerged cultures or other methods. However, this depends on the substrate and microorganisms being utilized. MPs have properties that make them versatile and useful ingredients in various applications. Using residues and lignocellulosic biomass as raw materials for producing MPs offers sustainability, cost-effectiveness, and waste reduction advantages. These properties are consistent with the principles established by green chemistry, which aims to conserve resources effectively and operate sustainably in all areas. This review highlights the importance of studying manufacturing aspects and the characteristics associated with MPs, which can be implemented to solve problems and encourage novel methods in the global food/feed industry.

Modulation of antioxidant enzyme by light and heavy rare earth metals: A case study with catalase

The present study highlights the hazardous effect of heavy and light rare earth elements (REEs) on bovine liver catalase (BLC) using a combination of spectroscopic and computational methods. The presence of Praseodymium chloride (PrCl3) and Gadolinium chloride (GdCl3) resulted in a substantial reduction in catalytic efficiency of BLC by approximately 1.8 and 2.6 fold, respectively. The compromised activity was further accompanied by conformational rearrangements at the secondary and tertiary levels as evidenced by circular dichroism (CD) and fluorescence spectroscopy. These analyses revealed a significant decrease in α-helical content and a simultaneous increase in random coils, disrupting intramolecular hydrogen bonding. Furthermore, the zeta potential (ζ) of BLC demonstrated a reversal from negative to positive ζ values upon the addition of PrCl3 and GdCl3, indicating BLC-lanthanide complex formation. Isothermal titration calorimetry (ITC) supports spontaneous interaction with negative free energy favouring endothermic reaction. This was further supported by docking studies which revealed the binding of PrCl3 and GdCl3 within the active site of BLC thus interfering with the catalytic ability to degrade hydrogen peroxide (H2O2). Nevertheless, a significant decline in the melting temperature (Tm) of BLC was observed in the presence of lanthanides suggesting the thermal instability of the enzyme. Thus, a similar approach could be applied to evaluate the hazardous effects of lanthanides on structural and functional changes in other proteins or similar biomolecules.

Osmolyte-induced protein stability changes explained by graph theory

Enhanced stabilization of protein structures via the presence of inert osmolytes is a key mechanism adopted both by physiological systems and in biotechnological applications. While the intrinsic stability of proteins is ultimately fixed by their amino acid composition and organization, the interactions between osmolytes and proteins together with their concentrations introduce an additional layer of complexity and in turn, a method of modulating protein stability. Here, we combined experimental measurements with molecular dynamics simulations and graph-theory-based analyses to predict the stabilizing/destabilizing effects of different kinds of osmolytes on proteins during heat-mediated denaturation. We found that (i) proteins in solution with stability-enhancing osmolytes tend to have more compact interaction networks than those assumed in the presence of destabilizing osmolytes; (ii) a strong negative correlation (R = -0.85) characterizes the relationship between the melting temperatureT m and the preferential interaction coefficient defined by the radial distribution functions of osmolytes and water around the protein and (iii) a positive correlation exists between osmolyte-osmolyte clustering and the extent of preferential exclusion from the local domain of the protein, suggesting that exclusion may be driven by enhanced steric hindrance of aggregated osmolytes.

Revealing SARS-CoV-2 Mpro mutation cold and hot spots: Dynamic residue network analysis meets machine learning

Deciphering the effect of evolutionary mutations of viruses and predicting future mutations is crucial for designing long-lasting and effective drugs. While understanding the impact of current mutations on protein drug targets is feasible, predicting future mutations due to natural evolution of viruses and environmental pressures remains challenging. Here, we leveraged existing mutation data during the evolution of the SARS-CoV-2 protein drug target main protease (Mpro) to test the predictive power of dynamic residue network (DRN) analysis in identifying mutation cold and hot spots. We conducted molecular dynamics simulations on the Mpro of SARS-CoV-2 (Wuhan strain) and calculated eight DRN metrics (averaged BC, CC, DC, EC, ECC, KC, L, PR), each of which identifies a unique network feature within the protein. The sets of residues with the highest and lowest values for each metric, comprising potential cold and hot spots, were compared to published biochemical analyses and per residue mutation frequencies observed across five SARS-CoV-2 lineages, encompassing a total of 191,878 sequences. Individual DRN metrics displayed only modest power to predict the mutation frequency of individual residues. However, integrating the eight DRN metrics with additional structural and sequence-derived metrics allowed us to develop machine learning models which significantly improved the prediction of residue mutation frequency. While further refinements should enhance accuracy, we demonstrated a robust method to understand pathogen evolution. This approach can also guide the development of long-lasting drugs by targeting functional residues located in and near active site, and allosteric sites, that are less prone to mutations.

Using residue interaction networks to understand protein function and evolution and to engineer new proteins

Residue interaction networks (RINs) provide graph-based representations of interaction networks within proteins, providing important insight into the factors driving protein structure, function, and stability relationships. There exists a wide range of tools with which to perform RIN analysis, taking into account different types of interactions, input (crystal structures, simulation trajectories, single proteins, or comparative analysis across proteins), as well as formats, including standalone software, web server, and a web application programming interface (API). In particular, the ability to perform comparative RIN analysis across protein families using "metaRINs" provides a valuable tool with which to dissect protein evolution. This, in turn, highlights hotspots to avoid (or target) for in vitro evolutionary studies, providing a powerful framework that can be exploited to engineer new proteins.

Computer-aided engineering of stabilized fibroblast growth factor 21

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

Engineering Bacterial Chitinases for Industrial Application: From Protein Engineering to Bacterial Strains Mutation! A Comprehensive Review of Physical, Molecular, and Computational Approaches

Bacterial chitinases are integral in breaking down chitin, the natural polymer in crustacean and insect exoskeletons. Their increasing utilization across various sectors such as agriculture, waste management, biotechnology, food processing, and pharmaceutical industries highlights their significance as biocatalysts. The current review investigates various scientific strategies to maximize the efficiency and production of bacterial chitinases for industrial use. Our goal is to optimize the heterologous production process using physical, molecular, and computational tools. Physical methods focus on isolating, purifying, and characterizing chitinases from various sources to ensure optimal conditions for maximum enzyme activity. Molecular techniques involve gene cloning, site-directed mutation, and CRISPR-Cas9 gene editing as an approach for creating chitinases with improved catalytic activity, substrate specificity, and stability. Computational approaches use molecular modeling, docking, and simulation techniques to accurately predict enzyme-substrate interactions and enhance chitinase variants' design. Integrating multidisciplinary strategies enables the development of highly efficient chitinases tailored for specific industrial applications. This review summarizes current knowledge and advances in chitinase engineering to serve as an indispensable guideline for researchers and industrialists seeking to optimize chitinase production for various uses.

Prediction of mutation-induced protein stability changes based on the geometric representations learned by a self-supervised method

BACKGROUND

Thermostability is a fundamental property of proteins to maintain their biological functions. Predicting protein stability changes upon mutation is important for our understanding protein structure-function relationship, and is also of great interest in protein engineering and pharmaceutical design.

RESULTS

Here we present mutDDG-SSM, a deep learning-based framework that uses the geometric representations encoded in protein structure to predict the mutation-induced protein stability changes. mutDDG-SSM consists of two parts: a graph attention network-based protein structural feature extractor that is trained with a self-supervised learning scheme using large-scale high-resolution protein structures, and an eXtreme Gradient Boosting model-based stability change predictor with an advantage of alleviating overfitting problem. The performance of mutDDG-SSM was tested on several widely-used independent datasets. Then, myoglobin and p53 were used as case studies to illustrate the effectiveness of the model in predicting protein stability changes upon mutations. Our results show that mutDDG-SSM achieved high performance in estimating the effects of mutations on protein stability. In addition, mutDDG-SSM exhibited good unbiasedness, where the prediction accuracy on the inverse mutations is as well as that on the direct mutations.

CONCLUSION

Meaningful features can be extracted from our pre-trained model to build downstream tasks and our model may serve as a valuable tool for protein engineering and drug design.

Fabrication and characterization of polyvinyl alcohol-chitosan composite nanofibers for carboxylesterase immobilization to enhance the stability of the enzyme

Electrospinning stands out as a flexible and viable method, presenting designed nanoscale materials with customized properties. This research demonstrates the immobilization of carboxylesterase protein Ha006a, reported for its adequacy in pesticide bioremediation by utilizing the electrospinning strategy. This strategy was utilized to create nanofibers by incorporating variable mixtures of biodegradable and cost-effective polyvinyl alcohol (PVA)-chitosan (CS) nanofiber solution (PVA100, PVA96, PVA94, PVA92 and PVA90). All the mixtures were electrospun at a reliable voltage of 21 kV, maintaining a gap of 12 cm from the nozzle. The Ha006a, sourced from Helicoverpa armigera, was consolidated into the optimized PVA90 polymer mixture. The electrospun nanofibers experienced comprehensive characterization utilizing distinctive microscopy and spectroscopy procedures counting FESEM, TGA, XRD and FTIR. The comparative investigation of the esterase property, ideal parameters and stability of the unbound and bound/immobilized Ha006a was scrutinized. The results uncovered an essential elevation in the ideal conditions of enzyme activity post-immobilization. The PVA-CS control nanofiber and Ha006a-PVA-CS showed a smooth structure, including an average breadth of around 170.5 ± 44.2 and 222.5 ± 66.5 nm, respectively. The enzyme-immobilized nanofibers displayed upgraded stability and comprehensive characterization of the nanofiber, which guaranteed genuineness and reproducibility, contributing to its potential as a potent device for bioremediation applications. This investigation opens the way for the manufacture of pesticide-resistant insect enzyme-based nanofibers, unlocking their potential for assorted applications, counting pesticide remediation and ensuring environmental sustainability.

Rheostatic contributions to protein stability can obscure a position's functional role

Rheostat positions, which can be substituted with various amino acids to tune protein function across a range of outcomes, are a developing area for advancing personalized medicine and bioengineering. Current methods cannot accurately predict which proteins contain rheostat positions or their substitution outcomes. To compare the prevalence of rheostat positions in homologs, we previously investigated their occurrence in two pyruvate kinase (PYK) isozymes. Human liver PYK contained numerous rheostat positions that tuned the apparent affinity for the substrate phosphoenolpyruvate (Kapp-PEP) across a wide range. In contrast, no functional rheostat positions were identified in Zymomonas mobilis PYK (ZmPYK). Further, the set of ZmPYK substitutions included an unusually large number that lacked measurable activity. We hypothesized that the inactive substitution variants had reduced protein stability, precluding detection of Kapp-PEP tuning. Using modified buffers, robust enzymatic activity was obtained for 19 previously-inactive ZmPYK substitution variants at three positions. Surprisingly, both previously-inactive and previously-active substitution variants all had Kapp-PEP values close to wild-type. Thus, none of the three positions were functional rheostat positions, and, unlike human liver PYK, ZmPYK's Kapp-PEP remained poorly tunable by single substitutions. To directly assess effects on stability, we performed thermal denaturation experiments for all ZmPYK substitution variants. Many diminished stability, two enhanced stability, and the three positions showed different thermal sensitivity to substitution, with one position acting as a "stability rheostat." The differences between the two PYK homologs raises interesting questions about the underlying mechanism(s) that permit functional tuning by single substitutions in some proteins but not in others.

Computational evidences of a misfolding event in an aggregation-prone light chain preceding the formation of the non-native pathogenic dimer

Antibody light chain amyloidosis is a disorder in which protein aggregates, mainly composed of immunoglobulin light chains, deposit in diverse tissues impairing the correct functioning of organs. Interestingly, due to the high susceptibility of antibodies to mutations, AL amyloidosis appears to be strongly patient-specific. Indeed, every patient will display their own mutations that will make the proteins involved prone to aggregation thus hindering the study of this disease on a wide scale. In this framework, determining the molecular mechanisms that drive the aggregation could pave the way to the development of patient-specific therapeutics. Here, we focus on a particular patient-derived light chain, which has been experimentally characterized. We investigated the early phases of the aggregation pathway through extensive full-atom molecular dynamics simulations, highlighting a structural rearrangement and the exposure of two hydrophobic regions in the aggregation-prone species. Next, we moved to consider the pathological dimerization process through docking and molecular dynamics simulations, proposing a dimeric structure as a candidate pathological first assembly. Overall, our results shed light on the first phases of the aggregation pathway for a light chain at an atomic level detail, offering new structural insights into the corresponding aggregation process.

Molecular Mechanisms Involved in the B Cell Growth and Clonogenic Activity of HIV-1 Matrix Protein p17 Variants

The human immunodeficiency virus (HIV-1) matrix protein p17 (p17) is released from infected cells as a protein capable of deregulating the biological activity of different cells. P17 variants (vp17s), more frequently detected in the plasma of HIV-1+ patients with rather than without lymphoma and characterized by amino acids insertions in their C-terminal region, were found to trigger B cell growth and clonogenicity. Vp17s endowed with B-cell-growth-promoting activity are drastically destabilized, whereas, in a properly folded state, reference p17 (refp17) does not exert any biological activity on B cell growth and clonogenicity. However, misfolding of refp17 is necessary to expose a masked functional epitope, interacting with the protease-activated receptor 1 (PAR-1), endowed with B cell clonogenicity. Indeed, it is worth noting that changes in the secondary structure can strongly impact the function of a protein. Here, we performed computational studies to show that the gain of function of vp17s is linked to dramatic conformational changes due to structural modification in the secondary-structure elements and in the rearrangement of the hydrogen bond (H-bond) network. In particular, all clonogenic vp17s showed the disengagement of two critical residues, namely Trp16 and Tyr29, from their hydrophobic core. Biological data showed that the mutation of Trp16 and Tyr29 to Ala in the refp17 backbone, alone or in combination, resulted in a protein endowed with B cell clonogenic activity. These data show the pivotal role of the hydrophobic component in maintaining refp17 stability and identify a novel potential therapeutic target to counteract vp17-driven lymphomagenesis in HIV-1+ patients.

Cellular and epigenetic perspective of protein stability and its implications in the biological system

Protein stability is a fundamental prerequisite in both experimental and therapeutic applications. Current advancements in high throughput experimental techniques and functional ontology approaches have elucidated that impairment in the structure and stability of proteins is intricately associated with the cause and cure of several diseases. Therefore, it is paramount to deeply understand the physical and molecular confounding factors governing the stability of proteins. In this review article, we comprehensively investigated the evolution of protein stability, examining its emergence over time, its relationship with organizational aspects and the experimental methods used to understand it. Furthermore, we have also emphasized the role of Epigenetics and its interplay with post-translational modifications (PTMs) in regulating the stability of proteins.

High-throughput system for the thermostability analysis of proteins

Thermal stability of proteins is a primary metric for evaluating their physical properties. Although researchers attempted to predict it using machine learning frameworks, their performance has been dependent on the quality and quantity of published data. This is due to the technical limitation that thermodynamic characterization of protein denaturation by fluorescence or calorimetry in a high-throughput manner has been challenging. Obtaining a melting curve that derives solely from the target protein requires laborious purification, making it far from practical to prepare a hundred or more samples in a single workflow. Here, we aimed to overcome this throughput limitation by leveraging the high protein secretion efficacy of Brevibacillus and consecutive treatment with plate-scale purification methodologies. By handling the entire process of expression, purification, and analysis on a per-plate basis, we enabled the direct observation of protein denaturation in 384 samples within 4 days. To demonstrate a practical application of the system, we conducted a comprehensive analysis of 186 single mutants of a single-chain variable fragment of nivolumab, harvesting the melting temperature (Tm) ranging from -9.3 up to +10.8°C compared to the wild-type sequence. Our findings will allow for data-driven stabilization in protein design and streamlining the rational approaches.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

PEGylation versus glycosylation: effect on the thermodynamics and thermostability of crisantaspase

Thermostability is an important and desired feature of therapeutic proteins and is critical for the success or failure of protein drugs development. It can be increased by PEGylation-binding of poly(ethylene glycol) moieties-or glycosylation-post-translational modification to add glycans. Here, the thermostability and thermodynamic parameters of native, PEGylated, and glycosylated versions of the antileukemic enzyme crisantaspase were investigated. First-order kinetics was found to describe the irreversible deactivation process. Activation energy of the enzyme-catalyzed reaction (E*) was estimated for native, PEGylated, and glycosylated enzyme (10.2, 14.8, and 18.8 kJ mol-1 respectively). Half-life decreased progressively with increasing temperature, and longer half-life was observed for PEG-crisantaspase (87.74 min) at 50 °C compared to the native form (9.79 min). The activation energy of denaturation of PEG-crisantaspase (307.1 kJ mol-1) was higher than for crisantaspase (218.1 kJ mol-1) and Glyco-crisantaspase (120.0 kJ mol-1), which means that more energy is required to overcome the energy barrier of the unfolding process. According to our results, PEG-crisantaspase is more thermostable than its native form, while Glyco-crisantaspase is more thermosensitive.

Quinaldine Red as a fluorescent probe for determining the melting temperature (Tm) of proteins: a simple, rapid and high-throughput assay

Proteins play an important role in biological systems and several proteins are used in diagnosis, therapy, food industry etc. Thus, knowledge about the physical properties of the proteins is of utmost importance, which will aid in understanding their function and subsequent applications. The melting temperature (Tm) of a protein is one of the essential parameters which gives information about the stability of a protein under different conditions. In the present study, we have demonstrated a method for determining the Tm of proteins using the supramolecular interaction between Quinaldine Red (QR) and proteins. Using this method, we have determined the Tm of 5 proteins and compared our results with established protocols. Our results showed good agreement with the other methods and published values. The method developed in this study is inexpensive, quick, and devoid of complex instruments and pre/post-treatment of the samples. In addition, this method can be adopted for high throughput in multi-plate mode. Thus, this study projects a new methodology for Tm determination of various proteins with user friendly operation.

Evidence for paraquat-pepsin interaction: In vitro and silico study

The use of the herbicide paraquat (PQ) has raised concerns about potential environmental consequences due to its toxicity and persistence in the environment. Considering the affinity of dangerous compounds to biological molecules, it is necessary to know their binding properties. This article focuses on the behavior of the pepsin enzyme following its contact with paraquat poison, and the interaction between paraquat and pepsin has been investigated in laboratory conditions and simulated physiological conditions using multispectral techniques. Fluorescence experiments showed that PQ uses a static method to quench pepsin's intrinsic fluorescence. By causing structural damage to pepsin, PQ may be detrimental as it alters its conformational function based on FT-IR spectroscopy. The coupling reaction is a spontaneous process caused by hydrogen bonding and van der Waals forces according to the analysis of the thermodynamic parameters of each system at three different temperatures. The molecular structure of pepsin changes when it binds to PQ. Also, the results showed that PQ is a pepsin inhibitor that changes the function of the enzyme.

Tunable CH/π Interactions within a Tryptophan Zipper Motif to Stabilize the Fold of Long β-Hairpin Peptides

The tryptophan zipper (Trpzip) is an iconic folding motif of β-hairpin peptides capitalizing on two pairs of cross-strand tryptophans, each stabilized by an aromatic-aromatic stacking in an edge-to-face (EtF) geometry. Yet, the origins and the contribution of this EtF packing to the unique Trpzip stability remain poorly understood. To address this question of structure-stability relationship, a library of Trpzip hairpins was developed by incorporating readily accessible nonproteinogenic tryptophans of varying electron densities. We found that each EtF geometry was, in fact, stabilized by an intricate combination of XH/π interactions. By tuning the π-electron density of Trpface rings, CH/π interactions are strengthened to gain additional stability. On the contrary, our DFT calculations support the notion that Trpedge modulations are challenging due to their simultaneous paradoxical engagement as H-bond donors in CH/π and acceptors in NH/π interactions.

Structure and Antidiabetic Activity of a Glycoprotein from Porphyra haitanensis

A novel antidiabetic glycoprotein (PG) was isolated and purified from Porphyra haitanensis, and its structure and inhibiting activity on α-amylase and α-glucosidase were analyzed. The purity of the PG was 95.29 ± 0.21%, and its molecular weight was 163.024 ± 5.55 kDa. The PG had a tetramer structure with α- and β-subunits, and it contained 54.12 ± 0.86% protein (with highly hydrophobic amino acids) and 41.19% ± 0.64% carbohydrate (composed of galactose). The PG was linked via an O-glycosidic bond, exhibiting an α-helical structure and high stability. In addition, the PG inhibited the activities of α-amylase and α-glucosidase, by changing the enzyme's structure toward the PG's structure in a noncompetitive inhibition mode. Molecular docking results showed that the PG inhibited α-amylase activity by hydrophobic interaction, whereas it inhibited α-glucosidase activity by hydrogen bonds and hydrophobic interaction. Overall, the PG was linked to polysaccharides via O-glycosidic bonds, showing an α-helical configuration and a hydrophobic effect, which altered the configuration of α-amylase and α-glucosidase and exerted hypoglycemic activity. This study provides insights into analyzing the structure and antidiabetic activity of glycoproteins.

Homologous Pairs of Low and High Temperature Originating Proteins Spanning the Known Prokaryotic Universe

Stability of proteins at high temperature has been a topic of interest for many years, as this attribute is favourable for applications ranging from therapeutics to industrial chemical manufacturing. Our current understanding and methods for designing high-temperature stability into target proteins are inadequate. To drive innovation in this space, we have curated a large dataset, learn2thermDB, of protein-temperature examples, totalling 24 million instances, and paired proteins across temperatures based on homology, yielding 69 million protein pairs - orders of magnitude larger than the current largest. This important step of pairing allows for study of high-temperature stability in a sequence-dependent manner in the big data era. The data pipeline is parameterized and open, allowing it to be tuned by downstream users. We further show that the data contains signal for deep learning. This data offers a new doorway towards thermal stability design models.

Model-driven insights into the effects of temperature on metabolism

Temperature affects cellular processes at different spatiotemporal scales, and identifying the genetic and molecular mechanisms underlying temperature responses paves the way to develop approaches for mitigating the effects of future climate scenarios. A systems view of the effects of temperature on cellular physiology can be obtained by focusing on metabolism since: (i) its functions depend on transcription and translation and (ii) its outcomes support organisms' development, growth, and reproduction. Here we provide a systematic review of modelling efforts directed at investigating temperature effects on properties of single biochemical reactions, system-level traits, metabolic subsystems, and whole-cell metabolism across different prokaryotes and eukaryotes. We compare and contrast computational approaches and theories that facilitate modelling of temperature effects on key properties of enzymes and their consideration in constraint-based as well as kinetic models of metabolism. In addition, we provide a summary of insights from computational approaches, facilitating integration of omics data from temperature-modulated experiments with models of metabolic networks, and review the resulting biotechnological applications. Lastly, we provide a perspective on how different types of metabolic modelling can profit from developments in machine learning and models of different cellular layers to improve model-driven insights into the effects of temperature relevant for biotechnological applications.

Molecular dynamics analysis of superoxide dismutase 1 mutations suggests decoupling between mechanisms underlying ALS onset and progression

Mutations in the superoxide dismutase 1 (SOD1) gene are the second most common known cause of ALS. SOD1 variants express high phenotypic variability and over 200 have been reported in people with ALS. It was previously proposed that variants can be broadly classified in two groups, 'wild-type like' (WTL) and 'metal binding region' (MBR) variants, based on their structural location and biophysical properties. MBR variants, but not WTL variants, were associated with a reduction of SOD1 enzymatic activity. In this study we used molecular dynamics and large clinical datasets to characterise the differences in the structural and dynamic behaviour of WTL and MBR variants with respect to the wild-type SOD1, and how such differences influence the ALS clinical phenotype. Our study identified marked structural differences, some of which are observed in both variant groups, while others are group specific. Moreover, collecting clinical data of approximately 500 SOD1 ALS patients carrying variants, we showed that the survival time of patients carrying an MBR variant is generally longer (∼6 years median difference, p < 0.001) with respect to patients with a WTL variant. In conclusion, our study highlighted key differences in the dynamic behaviour between WTL and MBR SOD1 variants, and between variants and wild-type SOD1 at an atomic and molecular level, that could be further investigated to explain the associated phenotypic variability. Our results support the hypothesis of a decoupling between mechanisms of onset and progression of SOD1 ALS, and an involvement of loss-of-function of SOD1 with the disease progression.

Alginate-based materials for enzyme encapsulation

Enzymes are widely used in industry due to their high efficiency and selectivity. However, their low stability during certain industrial processes can result in a significant loss of catalytic activity. Encapsulation is a promising technique that can stabilize enzymes by protecting them from environmental stresses such as extreme temperature and pH, mechanical force, organic solvents, and proteases. Alginate and alginate-based materials have emerged as effective carriers for enzyme encapsulation due to their biocompatibility, biodegradability, and ability to form gel beads through ionic gelation. This review presents various alginate-based encapsulation systems for enzyme stabilization and explores their applications in different industries. We discuss the preparation methods of alginate encapsulated enzymes and analyze the release mechanisms of enzymes from alginate materials. Additionally, we summarize the characterization techniques used for enzyme-alginate composites. This review provides insights into the use of alginate encapsulation as a means of stabilizing enzymes and highlights the potential benefits for various industrial applications.

Thermal Ring-Based Heat Switches in Hyperthermophilic Class II Bacterial Fructose Aldolase

Both thermophilic and hyperthermophilic enzymes in bacterial and archaeal species are activated above a specific temperature threshold but inactivated at another higher temperature. However, the underlying structural basis for these two heat switches is still unresolved. Here, graph theory was used to test if the temperature-dependent noncovalent interactions and metal bridges as identified in a series of crystal structures of the class II bacterial fructose 1,6-bisphosphate aldolase homodimer or homotetramer with or without natural substrates and products bound could form systematic fluidic grid-like mesh networks with topological grids as thermal rings to regulate their structural thermostability and functional thermoactivity. The results indicated that the second biggest grid in the Thermus aquaticus fructose 1,6-diphosphate aldolase dimer may control the specific temperature threshold to release the swapping flexible active sites at the dimeric interface for heat-evoked activation. Meanwhile, the third biggest grid may serve as a necessary structural motif against heat inactivation. Finally, the smallest grid may act as a stiff thermostable anchor. Its dissociation at the maximal melting temperature threshold may stop the catalytic activity. Taken as a whole, this computational study may render the structural motifs for the optimal growth temperature and the extreme heat stability of hyperthermophiles.

Differences in the organization of interface residues tunes the stability of the SARS-CoV-2 spike-ACE2 complex

The continuous emergence of novel variants represents one of the major problems in dealing with the SARS-CoV-2 virus. Indeed, also due to its prolonged circulation, more than ten variants of concern emerged, each time rapidly overgrowing the current viral version due to improved spreading features. As, up to now, all variants carry at least one mutation on the spike Receptor Binding Domain, the stability of the binding between the SARS-CoV-2 spike protein and the human ACE2 receptor seems one of the molecular determinants behind the viral spreading potential. In this framework, a better understanding of the interplay between spike mutations and complex stability can help to assess the impact of novel variants. Here, we characterize the peculiarities of the most representative variants of concern in terms of the molecular interactions taking place between the residues of the spike RBD and those of the ACE2 receptor. To do so, we performed molecular dynamics simulations of the RBD-ACE2 complexes of the seven variants of concern in comparison with a large set of complexes with different single mutations taking place on the RBD solvent-exposed residues and for which the experimental binding affinity was available. Analyzing the strength and spatial organization of the intermolecular interactions of the binding region residues, we found that (i) mutations producing an increase of the complex stability mainly rely on instaurating more favorable van der Waals optimization at the cost of Coulombic ones. In particular, (ii) an anti-correlation is observed between the shape and electrostatic complementarities of the binding regions. Finally, (iii) we showed that combining a set of dynamical descriptors is possible to estimate the outcome of point mutations on the complex binding region with a performance of 0.7. Overall, our results introduce a set of dynamical observables that can be rapidly evaluated to probe the effects of novel isolated variants or different molecular systems.

Electrostatic complementarity at the interface drives transient protein-protein interactions

Understanding the mechanisms driving bio-molecules binding and determining the resulting complexes' stability is fundamental for the prediction of binding regions, which is the starting point for drug-ability and design. Characteristics like the preferentially hydrophobic composition of the binding interfaces, the role of van der Waals interactions, and the consequent shape complementarity between the interacting molecular surfaces are well established. However, no consensus has yet been reached on the role of electrostatic. Here, we perform extensive analyses on a large dataset of protein complexes for which both experimental binding affinity and pH data were available. Probing the amino acid composition, the disposition of the charges, and the electrostatic potential they generated on the protein molecular surfaces, we found that (i) although different classes of dimers do not present marked differences in the amino acid composition and charges disposition in the binding region, (ii) homodimers with identical binding region show higher electrostatic compatibility with respect to both homodimers with non-identical binding region and heterodimers. Interestingly, (iii) shape and electrostatic complementarity, for patches defined on short-range interactions, behave oppositely when one stratifies the complexes by their binding affinity: complexes with higher binding affinity present high values of shape complementarity (the role of the Lennard-Jones potential predominates) while electrostatic tends to be randomly distributed. Conversely, complexes with low values of binding affinity exploit Coulombic complementarity to acquire specificity, suggesting that electrostatic complementarity may play a greater role in transient (or less stable) complexes. In light of these results, (iv) we provide a novel, fast, and efficient method, based on the 2D Zernike polynomial formalism, to measure electrostatic complementarity without the need of knowing the complex structure. Expanding the electrostatic potential on a basis of 2D orthogonal polynomials, we can discriminate between transient and permanent protein complexes with an AUC of the ROC of [Formula: see text] 0.8. Ultimately, our work helps shedding light on the non-trivial relationship between the hydrophobic and electrostatic contributions in the binding interfaces, thus favoring the development of new predictive methods for binding affinity characterization.

Towards a Better Understanding of Texturization during High-Moisture Extrusion (HME)-Part I: Modeling the Texturability of Plant-Based Proteins

This study focused on predicting high-moisture texturization of plant-based proteins (soy protein concentrate (SPC), soy protein isolate (SPI), pea protein isolate (PPI)) at different water contents (57.5%, 60%, 65%, 70%, and 72.5% (w/w db)) to optimize and guarantee the production of high-moisture meat analogs (HMMA). Therefore, high-moisture extrusion (HME) experiments were performed, and the texture of the obtained high-moisture extruded samples (HMES) was sensory evaluated and categorized into poorly-textured, textured, or well-textured. In parallel, data on heat capacity (cp) and phase transition behavior of the plant-based proteins were determined using differential scanning calorimetry (DSC). Based on the DSC data, a model for predicting cp of hydrated, but not extruded, plant-based proteins was developed. Furthermore, based on the aforementioned model for predicting cp and DSC data on phase transition behavior of the plant-based proteins in combination with conducted HME trials and the mentioned model for predicting cp, a texturization indicator was developed, which could be used to calculate the minimum threshold temperature required to texturize plant-based proteins during HME. The outcome of this study could help to minimize the resources of expensive extrusion trials in the industry to produce HMMA with defined textures.

Release systems based on self-assembling RADA16-I hydrogels with a signal sequence which improves wound healing processes

Self-assembling peptides can be used for the regeneration of severely damaged skin. They can act as scaffolds for skin cells and as a reservoir of active compounds, to accelerate scarless wound healing. To overcome repeated administration of peptides which accelerate healing, we report development of three new peptide biomaterials based on the RADA16-I hydrogel functionalized with a sequence (AAPV) cleaved by human neutrophil elastase and short biologically active peptide motifs, namely GHK, KGHK and RDKVYR. The peptide hybrids were investigated for their structural aspects using circular dichroism, thioflavin T assay, transmission electron microscopy, and atomic force microscopy, as well as their rheological properties and stability in different fluids such as water or plasma, and their susceptibility to digestion by enzymes present in the wound environment. In addition, the morphology of the RADA-peptide hydrogels was examined with a unique technique called scanning electron cryomicroscopy. These experiments enabled us to verify if the designed peptides increased the bioactivity of the gel without disturbing its gelling processes. We demonstrate that the physicochemical properties of the designed hybrids were similar to those of the original RADA16-I. The materials behaved as expected, leaving the active motif free when treated with elastase. XTT and LDH tests on fibroblasts and keratinocytes were performed to assess the cytotoxicity of the RADA16-I hybrids, while the viability of cells treated with RADA16-I hybrids was evaluated in a model of human dermal fibroblasts. The hybrid peptides revealed no cytotoxicity; the cells grew and proliferated better than after treatment with RADA16-I alone. Improved wound healing following topical delivery of RADA-GHK and RADA-KGHK was demonstrated using a model of dorsal skin injury in mice and histological analyses. The presented results indicate further research is warranted into the engineered peptides as scaffolds for wound healing and tissue engineering.

Encapsulation of enzymes in food industry using spray drying: recent advances and process scale-ups

Enzymes are widely used in the food industry due to their ability in improving the functional, sensory, and nutritional properties of food products. However, their poor stability under harsh industrial conditions and their compromised shelf-lives during long-term storage limit their applications. This review introduces typical enzymes and their functionality in the food industry and demonstrates spray drying as a promising approach for enzyme encapsulation. Recent studies on encapsulation of enzymes in the food industry using spray drying and the key achievements are summarized. The latest developments including the novel design of spray drying chambers, nozzle atomizers and advanced spray drying techniques are also analyzed and discussed in depth. In addition, the scale-up pathways connecting laboratory scale trials and industrial scale productions are illustrated, as most of the current studies have been limited to lab-scales. Enzyme encapsulation using spray drying is a versatile strategy to improve enzyme stability in an economical and industrial viable way. Various nozzle atomizers and drying chambers have recently been developed to increase process efficiency and product quality. A comprehensive understanding of the complex droplet-to-particle transformations during the drying process would be beneficial for both process optimization and scale-up design.

A comprehensive insight into the effects of caffeic acid (CA) on pepsin: Multi-spectroscopy and MD simulations methods

The interaction between caffeic acid (CA) and pepsin was investigated using multi-spectroscopy approaches and molecular dynamic simulations (MDS). The effects of CA on the structure, stability, and activity of pepsin were studied. Fluorescence emission spectra and UV-vis absorption peaks all represented the static quenching mechanism of pepsin by CA. Moreover, the fluorescence spectra displayed that the interaction of CA exposed the tryptophan chromophores of pepsin to a more hydrophilic micro-environment. Consistent with the simulation results, thermodynamic parameters revealed that CA was bound to pepsin with a high binding affinity. The Van der Waals force and Hydrogen bond interaction were the dominant driving forces during the binding process. The circular dichroism (CD) spectroscopy analysis showed that the CA binding to pepsin decreased the contents of α-Helix and Random Coil but increased the content of β-sheet in the pepsin structure. Accordingly, MD simulations confirmed all the experimental results. As a result, CA is considered an inhibitor with adverse effects on pepsin activity.

The Network Basis for the Structural Thermostability and the Functional Thermoactivity of Aldolase B

Thermostability is important for the thermoactivity of proteins including enzymes. However, it is still challenging to pinpoint the specific structural factors for different temperature thresholds to initiate their specific structural and functional perturbations. Here, graph theory was used to investigate how the temperature-dependent noncovalent interactions as identified in the structures of aldolase B and its prevalent A149P mutant could form a systematic fluidic grid-like mesh network with topological grids to regulate the structural thermostability and the functional thermoactivity upon cyclization against decyclization in an extended range of a subunit. The results showed that the biggest grid may determine the melting temperature thresholds for the changes in their secondary and tertiary structures and specific catalytic activities. Further, a highly conserved thermostable grid may serve as an anchor to secure the flexible active site to achieve the specific thermoactivity. Finally, higher grid-based systematic thermal instability may disfavor the thermoactivity. Thus, this computational study may provide critical clues for the structural thermostability and the functional thermoactivity of proteins including enzymes.

Investigation of the interaction behavior between quercetin and pepsin by spectroscopy and MD simulation methods

The ability of a therapeutic compound to bind to proteins is critical for characterizing its therapeutic impacts. We have selected quercetin (Qu), a most common flavonoid found in plants and vegetables among therapeutic molecules that are known to have anti-inflammatory, antioxidant, anti-genotoxic, and anti-cancer effects. The current study aimed to see how quercetin interacts with pepsin in an aqueous environment under physiological conditions. Absorbance and emission spectroscopy, circular dichroism (CD), and kinetic methods, as well as molecular dynamic (MD) simulation and docking, were applied to study the effects of Qu on the structure, dynamics, and kinetics of pepsin. Stern-Volmer (K_sv) constants were computed for the pepsin-quercetin complex at three temperatures, showing that Qu reduces enzyme emission spectra using a static quenching. With Qu binding, the V_max and the k_cat/K_m values decreased. UV-vis absorption spectra, fluorescence emission spectroscopy, and CD result indicated that Qu binding to pepsin leads to microenvironmental changes around the enzyme, which can alter the enzyme's secondary structure. Therefore, quercetin caused alterations in the function and structure of pepsin. Thermodynamic parameters, MD binding, and docking simulation analysis showed that non-covalent reactions, including the hydrophobic forces, played a key role in the interaction of Qu with pepsin. The findings conclude of spectroscopic experiments were supported by molecular dynamics simulations and molecular docking results.

A guide to studying protein aggregation

Disrupted protein folding or decreased protein stability can lead to the accumulation of (partially) un- or misfolded proteins, which ultimately cause the formation of protein aggregates. Much of the interest in protein aggregation is associated with its involvement in a wide range of human diseases and the challenges it poses for large-scale biopharmaceutical manufacturing and formulation of therapeutic proteins and peptides. On the other hand, protein aggregates can also be functional, as observed in nature, which triggered its use in the development of biomaterials or therapeutics as well as for the improvement of food characteristics. Thus, unmasking the various steps involved in protein aggregation is critical to obtain a better understanding of the underlying mechanism of amyloid formation. This knowledge will allow a more tailored development of diagnostic methods and treatments for amyloid-associated diseases, as well as applications in the fields of new (bio)materials, food technology and therapeutics. However, the complex and dynamic nature of the aggregation process makes the study of protein aggregation challenging. To provide guidance on how to analyse protein aggregation, in this review we summarize the most commonly investigated aspects of protein aggregation with some popular corresponding methods.

Structural features, temperature adaptation and industrial applications of microbial lipases from psychrophilic, mesophilic and thermophilic origins

Microbial lipases are very prominent biocatalysts because of their ability to catalyze a wide variety of reactions in aqueous and non-aqueous media. Here microbial lipases from different origins (psychrophiles, mesophiles, and thermophiles) have been reviewed. This review emphasizes an update of structural diversity in temperature adaptation and industrial applications, of psychrophilic, mesophilic, and thermophilic lipases. The microbial origins of lipases are logically dynamic, proficient, and also have an extensive range of industrial uses with the manufacturing of altered molecules. It is therefore of interest to understand the molecular mechanisms of adaptation to temperature in occurring lipases. However, lipases from extremophiles (psychrophiles, and thermophiles) are widely used to design biotransformation reactions with higher yields, fewer byproducts, or useful side products and have been predicted to catalyze those reactions also, which otherwise are not possible with the mesophilic lipases. Lipases as a multipurpose biological catalyst have given a favorable vision in meeting the needs of several industries such as biodiesel, foods, and drinks, leather, textile, detergents, pharmaceuticals, and medicals.

Screening of Buffers and Additives for Protein Stabilization by Thermal Shift Assay: A Practical Approach

Thermal shift assay (TSA), also commonly designed by differential scanning fluorimetry (DSF) or ThermoFluor, is a technique relatively easy to implement and perform, useful in a myriad of applications. In addition to versatility, it is also rather inexpensive, making it suitable for high-throughput approaches. TSA uses a fluorescent dye to monitor the thermal denaturation of the protein under study and determine its melting temperature (T_m). One of its main applications is to identify the best buffers and additives that enhance protein stability.Understanding the TSA operating mode and the main methodological steps is a central key to designing effective experiments and retrieving meaningful conclusions. This chapter intends to present a straightforward TSA protocol, with different troubleshooting tips, to screen effective protein stabilizers such as buffers and additives, as well as data treatment and analysis. TSA results provide conditions in which the protein of interest is stable and therefore suitable to carry out further biophysical and structural characterization.

Identification of Thermostable Xylose Reductase from Thermothelomyces thermophilus: A Biochemical Characterization Approach to Meet Biofuel Challenges

The constant rise in energy demands, costs, and concerns about global warming has created a demand for new renewable alternative fuels that can be produced sustainably. Lignocellulose biomass can act as an excellent energy source and various value-added compounds like xylitol. In this research study, we have explored the xylose reductase that was obtained from the genome of a thermophilic fungus Thermothelomyces thermophilus while searching for an enzyme to convert xylose to xylitol at higher temperatures. The recombinant thermostable TtXR histidine-tagged fusion protein was expressed in Escherichia coli and successfully purified for the first time. Further, it was characterized for its function and novel structure at varying temperatures and pH. The enzyme showed maximal activity at 7.0 pH and favored  d-xylose over other pentoses and hexoses. Biophysical approaches such as ultraviolet-visible (UV-visible), fluorescence spectrometry, and far-UV circular dichroism (CD) spectroscopy were used to investigate the structural integrity of pure TtXR. This research highlights the potential application of uncharacterized xylose reductase as an alternate source for the effective utilization of lignocellulose in fermentation industries at elevated temperatures. Moreover, this research would give environment-friendly and long-term value-added products, like xylitol, from lignocellulosic feedstock for both scientific and commercial purposes.

Proteins from Thermophilic Thermus thermophilus Often Do Not Fold Correctly in a Mesophilic Expression System Such as Escherichia coli

Majority of protein structure studies use Escherichia coli (E. coli) and other model organisms as expression systems for other species' genes. However, protein folding depends on cellular environment factors, such as chaperone proteins, cytoplasmic pH, temperature, and ionic concentrations. Because of differences in these factors, especially temperature and chaperones, native proteins in organisms such as extremophiles may fold improperly when they are expressed in mesophilic model organisms. Here we present a methodology of assessing the effects of using E. coli as the expression system on protein structures. We compare these effects between eight mesophilic bacteria and Thermus thermophilus (T. thermophilus), a thermophile, and found that differences are significantly larger for T. thermophilus. More specifically, helical secondary structures in T. thermophilus proteins are often replaced by coil structures in E. coli. Our results show unique directionality in misfolding when proteins in thermophiles are expressed in mesophiles. This indicates that extremophiles, such as thermophiles, require unique protein expression systems in protein folding studies.

Spatial organization of hydrophobic and charged residues affects protein thermal stability and binding affinity

What are the molecular determinants of protein–protein binding affinity and whether they are similar to those regulating fold stability are two major questions of molecular biology, whose answers bring important implications both from a theoretical and applicative point of view. Here, we analyze chemical and physical features on a large dataset of protein–protein complexes with reliable experimental binding affinity data and compare them with a set of monomeric proteins for which melting temperature data was available. In particular, we probed the spatial organization of protein (1) intramolecular and intermolecular interaction energies among residues, (2) amino acidic composition, and (3) their hydropathy features. Analyzing the interaction energies, we found that strong Coulombic interactions are preferentially associated with a high protein thermal stability, while strong intermolecular van der Waals energies correlate with stronger protein–protein binding affinity. Statistical analysis of amino acids abundances, exposed to the molecular surface and/or in interaction with the molecular partner, confirmed that hydrophobic residues present on the protein surfaces are preferentially located in the binding regions, while charged residues behave oppositely. Leveraging on the important role of van der Waals interface interactions in binding affinity, we focused on the molecular surfaces in the binding regions and evaluated their shape complementarity, decomposing the molecular patches in the 2D Zernike basis. For the first time, we quantified the correlation between local shape complementarity and binding affinity via the Zernike formalism. In addition, considering the solvent interactions via the residue hydropathy, we found that the hydrophobicity of the binding regions dictates their shape complementary as much as the correlation between van der Waals energy and binding affinity. In turn, these relationships pave the way to the fast and accurate prediction and design of optimal binding regions as the 2D Zernike formalism allows a rapid and superposition-free comparison between possible binding surfaces.

Investigating binding dynamics of trans resveratrol to HSA for an efficient displacement of aflatoxin B1 using spectroscopy and molecular simulation

Resveratrol is a polyphenol belonging to the class stilbenes. The active and stable form of resveratrol is trans-resveratrol. This polyphenol is bestowed with numerous biological properties. Aflatoxin B₁ is a hepato-carcinogen and mutagen that is produced by Aspergillus species. In this study, the interaction of trans-resveratrol with HSA followed by competitive dislodging of AFB₁ from HSA by trans-resveratrol has been investigated using spectroscopic studies. The UV-absorption studies revealed ground state complex formation between HSA and trans-resveratrol. Trans-resveratrol binds strongly to HSA with the binding constant of ~ 10⁷ M⁻¹ to a single binding site (n = 1.58), at 298.15 K. The Stern–Volmer quenching constant was calculated as 7.83 × 10⁴ M⁻¹ at 298.15 K, suggesting strong fluorescence quenching ability of trans-resveratrol. Site markers displacement assay projected subdomain IIA as the binding site of trans-resveratrol to HSA. The molecular docking approach envisages the amino acid residues involved in the formation of the binding pocket. As confirmed from the site marker displacement assays, both trans-resveratrol and AFB₁ binds to HSA in the same binding site, subdomain IIA. The study explores the ability of trans-resveratrol to displace AFB₁ from the HSA-AFB₁ complex, thereby affecting the toxicokinetic behavior of AFB₁ associated with AFB₁ exposure.

Protein Mutations and Stability, a Link with Disease: The Case Study of Frataxin

Protein mutations may lead to pathologies by causing protein misfunction or propensity to degradation. For this reason, several studies have been performed over the years to determine the capability of proteins to retain their native conformation under stress condition as well as factors to explain protein stabilization and the mechanisms behind unfolding. In this review, we explore the paradigmatic example of frataxin, an iron binding protein involved in Fe–S cluster biogenesis, and whose impairment causes a neurodegenerative disease called Friedreich’s Ataxia (FRDA). We summarize what is known about most common point mutations identified so far in heterozygous FRDA patients, their effects on frataxin structure and function and the consequences of its binding with partners.