Differences in amino acids composition and coupling patterns between mesophilic and thermophilic proteins

Computational analysis to comprehend the structure-function properties of fibrinolytic enzymes from Bacillus spp for their efficient integration into industrial applications

Background

The fibrinolytic enzymes from Bacillus sp. are proposed as therapeutics in preventing thrombosis. Computational-based analyses of these enzymes' amino acid composition, basic physiological properties, presence of functional domain and motifs, and secondary and tertiary structure analyses can lead to developing a specific enzyme with improved catalytic activity and other properties that may increase their therapeutic potential.

Methods

The nucleotide sequences of fibrinolytic enzymes produced by the genus Bacillus and its corresponding protein sequences were retrieved from the NCBI database and aligned using the PRALINE programme. The varied physiochemical parameters and structural and functional analysis of the enzyme sequences were carried out with the ExPASy-ProtParam tool, MEME server, SOPMA, PDBsum tool, CYS-REC tool, SWISS-MODEL, SAVES servers, TMHMM program, GlobPlot, and peptide cutter software. The assessed in-silico data were compared with the published experimental results for validation.

Results

The alignment of sixty fibrinolytic serine protease enzymes (molecular mass 12-86 kDa) sequences showed 49 enzymes possess a conserved domain with a catalytic triad of Asp196, His242, and Ser569. The predicted instability and aliphatic indexes were 1.94-37.77, and 68.9-93.41, respectively, indicating high thermostability. The random coil means value suggested the predominance of this secondary structure in these proteases. A set of 50 amino acid residues representing motif 3 signifies the Peptidase S8/S53 domain that was invariably observed in 56 sequences. Additionally, 28 sequences have transmembrane helices, with two having the most disordered areas, and they pose 25 enzyme cleavage sites. A comparative analysis of the experimental work with the results of in-silico study put forward the characteristics of the enzyme sequences JF739176.1 and MF677779.1 to be considered when creating a potential mutant enzyme as these sequences are stable at high pH with thermostability and to exhibit αβ-fibrinogenase activity in both experimental and in-silico studies.

TemBERTure: advancing protein thermostability prediction with deep learning and attention mechanisms

Motivation

Understanding protein thermostability is essential for numerous biotechnological applications, but traditional experimental methods are time-consuming, expensive, and error-prone. Recently, deep learning (DL) techniques from natural language processing (NLP) was extended to the field of biology, since the primary sequence of proteins can be viewed as a string of amino acids that follow a physicochemical grammar.

Results

In this study, we developed TemBERTure, a DL framework that predicts thermostability class and melting temperature from protein sequences. Our findings emphasize the importance of data diversity for training robust models, especially by including sequences from a wider range of organisms. Additionally, we suggest using attention scores from Deep Learning models to gain deeper insights into protein thermostability. Analyzing these scores in conjunction with the 3D protein structure can enhance understanding of the complex interactions among amino acid properties, their positioning, and the surrounding microenvironment. By addressing the limitations of current prediction methods and introducing new exploration avenues, this research paves the way for more accurate and informative protein thermostability predictions, ultimately accelerating advancements in protein engineering.

Availability and implementation

TemBERTure model and the data are available at: https://github.com/ibmm-unibe-ch/TemBERTure.

A Novel Non-Destructive Rapid Tool for Estimating Amino Acid Composition and Secondary Structures of Proteins in Solution

Amino-acid protein composition plays an important role in biology, medicine, and nutrition. Here, a groundbreaking protein analysis technique that quickly estimates amino acid composition and secondary structure across various protein sizes, while maintaining their natural states is introduced and validated. This method combines multivariate statistics and the thermostable Raman interaction profiling (TRIP) technique, eliminating the need for complex preparations. In order to validate the approach, the Raman spectra are constructed of seven proteins of varying sizes by utilizing their amino acid frequencies and the Raman spectra of individual amino acids. These constructed spectra exhibit a close resemblance to the actual measured Raman spectra. Specific vibrational modes tied to free amino and carboxyl termini of the amino acids disappear as signals linked to secondary structures emerged under TRIP conditions. Furthermore, the technique is used inversely to successfully estimate amino acid compositions and secondary structures of unknown proteins across a range of sizes, achieving impressive accuracy ranging between 1.47% and 5.77% of root mean square errors (RMSE). These results extend the uses for TRIP beyond interaction profiling, to probe amino acid composition and structure.

AI-Driven Spectral Decomposition: Predicting the Most Probable Protein Compositions from Surface Enhanced Raman Spectroscopy Spectra of Amino Acids

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for elucidating the molecular makeup of materials. It possesses the unique characteristics of single-molecule sensitivity and extremely high specificity. However, the true potential of SERS, particularly in capturing the biochemical content of particles, remains underexplored. In this study, we harnessed transformer neural networks to interpret SERS spectra, aiming to discern the amino acid profiles within proteins. By training the network on the SERS profiles of 20 amino acids of human proteins, we explore the feasibility of predicting the predominant proteins within the µL-scale detection volume of SERS. Our results highlight a consistent alignment between the model's predictions and the protein's known amino acid compositions, deepening our understanding of the inherent information contained within SERS spectra. For instance, the model achieved low root mean square error (RMSE) scores and minimal deviation in the prediction of amino acid compositions for proteins such as Bovine Serum Albumin (BSA), ACE2 protein, and CD63 antigen. This novel methodology offers a robust avenue not only for protein analytics but also sets a precedent for the broader realm of spectral analyses across diverse material categories. It represents a solid step forward to establishing SERS-based proteomics.

Taxonomic identification and temperature stress tolerance mechanisms of Aequorivita marisscotiae sp. nov

The deep sea harbours microorganisms with unique life characteristics and activities due to adaptation to particular environmental conditions, but the limited sample collection and pure culture techniques available constrain the study of deep-sea microorganisms. In this study, strain Ant34-E75 was isolated from Antarctic deep-sea sediment samples and showed the highest 16 S rRNA gene sequence similarity (97.18%) with the strain Aequorivita viscosa 8-1bT. Strain Ant34-E75 is psychrotrophic and can effectively increase the cold tolerance of Chlamydomonas reinhardtii (a model organism). Subsequent transcriptome analysis revealed multiple mechanisms involved in the Ant34-E75 response to temperature stress, and weighted gene co-expression network analysis (WGCNA) showed that the peptidoglycan synthesis pathway was the key component. Overall, this study provides insights into the characteristics of a deep-sea microorganism and elucidates mechanisms of temperature adaptation at the molecular level.

Structure of full-length cobalamin-dependent methionine synthase and cofactor loading captured in crystallo

Cobalamin-dependent methionine synthase (MS) is a key enzyme in methionine and folate one-carbon metabolism. MS is a large multi-domain protein capable of binding and activating three substrates: homocysteine, folate, and S-adenosylmethionine for methylation. Achieving three chemically distinct methylations necessitates significant domain rearrangements to facilitate substrate access to the cobalamin cofactor at the right time. The distinct conformations required for each reaction have eluded structural characterization as its inherently dynamic nature renders structural studies difficult. Here, we use a thermophilic MS homolog (tMS) as a functional MS model. Its exceptional stability enabled characterization of MS in the absence of cobalamin, marking the only studies of a cobalamin-binding protein in its apoenzyme state. More importantly, we report the high-resolution full-length MS structure, ending a multi-decade quest. We also capture cobalamin loading in crystallo, providing structural insights into holoenzyme formation. Our work paves the way for unraveling how MS orchestrates large-scale domain rearrangements crucial for achieving challenging chemistries.

Investigation of thermal stability characteristic in family A DNA polymerase - A theoretical study

DNA polymerases create complementary DNA strands in living cells and are crucial to genome transmission and maintenance. These enzymes possess similar human right-handed folds which contain thumb, fingers, and palm subdomains and contribute to polymerization activities. These enzymes are classified into seven evolutionary families, A, B, C, D, X, Y, and RT, based on amino acid sequence analysis and biochemical characteristics. Family A DNA polymerases exist in an extended range of organisms including mesophilic, thermophilic, and hyper-thermophilic bacteria, participate in DNA replication and repair, and have a broad application in molecular biology and biotechnology. In this study, we attempted to detect factors that play a role in the thermostability properties of this family member despite their remarkable similarities in structure and function. For this purpose, similarities and differences in amino acid sequences, structure, and dynamics of these enzymes have been inspected. Our results demonstrated that thermophilic and hyper-thermophilic enzymes have more charged, aromatic, and polar residues than mesophilic ones and consequently show further electrostatic and cation-pi interactions. In addition, in thermophilic enzymes, aliphatic residues tend to position in buried states more than mesophilic enzymes. These residues within their aliphatic parts increase hydrophobic core packing and therefore enhance the thermostability of these enzymes. Furthermore, a decrease in thermophilic cavities volumes assists in the protein compactness enhancement. Moreover, molecular dynamic simulation results revealed that increasing temperature impacts mesophilic enzymes further than thermophilic ones that reflect on polar and aliphatic residues surface area and hydrogen bonds changes.

Enhanced enzyme thermostability of a family I.3 lipase LipSR1 by T118A mutation at the calcium-binding site

OBJECTIVES

The lipase gene lipSR1 isolated from oil-contaminated soil exhibits high hydrolytic activity for short-chain fatty acid substrates. A single calcium ion is required to anchor the lid of LipSR1 in an open conformation by coordination with two aspartate residues and three other residues in the lid. The lid of LipSR1 is anchored by Ca2+, which is coordinated by side-chain carboxyl oxygens of Asp153 and Asp157, carbonyl oxygens of Thr118 and Ser144, and the side chain of Gln120.

RESULTS

D157A, D153R, Q120A, S144A, and T118A mutants were produced by site-directed mutagenesis in this study. Analyses of hydrolytic activity and thermostability showed that the properties of D157A, D153R, Q120A, and S144A were almost lost, suggesting that Asp157, Asp153, Gln120, and Ser144 are important residues for LipSR1. However, the catalytic performance of T118A was clearly maintained. Moreover, the thermostability of mutant T118A was higher than that of wild-type LipSR1.

CONCLUSIONS

These results indicated that mutation of threonine at position 118 improved the stability of the enzyme at high temperature.

Enhancing the thermostability of carboxypeptidase A by a multiple computer-aided rational design based on amino acids preferences at β-turns

Carboxypeptidase A (CPA) with efficient hydrolysis ability has shown vital potential in food and biological fields. In addition, it is also the earliest discovered enzyme with Ochratoxin A (OTA) degradation activity. Thermostability plays an imperative role to catalyze the reactions at high temperatures in industry, but the poor thermostability of CPA restricts its industrial application. In order to improve the thermostability of CPA, flexible loops were predicted through molecular dynamics (MD) simulation. Based on the amino acid preferences at β-turns, three ΔΔG-based computational programs (Rosetta, FoldX and PoPMuSiC) were employed to screen three variants from plentiful candidates and MD simulations were then used to verify two potential variants with enhanced thermostability (R124K and S134P). Results showed that compared to the wild-type CPA, the variants S134P and R124K exhibited rise of 4.2 min and 7.4 min in half-life (t1/2) at 45 °C, 3 °C and 4.1 °C in the half inactivation temperature (T5010), in addition to increase by 1.9 °C and 1.2 °C in the melting temperature (Tm), respectively. The mechanism responsible for the enhanced thermostability was elucidated through the comprehensive analysis of molecular structure. This study shows that the thermostability of CPA can be improved by the multiple computer-aided rational design based on amino acid preferences at β-turns, broadening its industrial applicability of OTA degradation and providing a valuable strategy for the protein engineering of mycotoxin degrading enzymes.

Characterization and expression of fungal defensin in Escherichia coli and its antifungal mechanism by RNA-seq analysis

Introduction

Invasive fungal infections (IFIs) are fatally threatening to critical patients. The fungal defensin as an antifungal protein can widely inhibit fungi.

Methods

In this study, eight antifungal genes from different filamentous fungi were optimized by synonymous codon bias and heterologously expressed in Escherichia coli.

Results and discussion

Only the antifungal protein (AFP) from Aspergillus giganteus was produced, whereas the AFP from its mutation of the chitin-binding domain could not be expressed, thereby suggesting the importance of the motif for protein folding. In addition, the recombinant AFP (rAFP, 100 μg/mL) pre-heated at 50°C for 1 h effectively inhibited Paecilomyces variotii CICC40716 of IFIs by 55%, and no cell cytotoxicity was observed in RAW264.7 cells. After being pre-heated at 50°C for 8 h, the fluorescence emission intensity of the rAFP decreased and shifted from 343 nm to 335 nm. Moreover, the helix and β-turn of the rAFP gradually decreased with the pre-heated treatment temperature of 50°C via circular dichroism spectroscopy. Propidium iodide staining revealed that the rAFP could cause damage to the cell membrane. Moreover, the corresponding differentially expressed genes (DEGs) for downregulation such as amino sugar and nucleotide sugar metabolism, as well as mitogen-activated protein kinase (MAPK) signaling pathway involved in the cell wall integrity were found via the RNA-seq of rAFP treatment. By contrast, the upregulated DEGs were enriched in response to the oxidative stress of Biological Process by the Gene Ontology (GO) database. The encoding proteins of laccase, multicopper oxidase, and nitroreductase that contributed to reactive oxygen species (ROS) scavenging could be recognized. These results suggested that the rAFP may affect the integrity of the cell wall and cell membrane, and promote the increase in ROS, thereby resulting in fungal death. Consequently, drug development could be based on the inhibitory effect of the rAFP on IFIs.

SVSBI: sequence-based virtual screening of biomolecular interactions

Virtual screening (VS) is a critical technique in understanding biomolecular interactions, particularly in drug design and discovery. However, the accuracy of current VS models heavily relies on three-dimensional (3D) structures obtained through molecular docking, which is often unreliable due to the low accuracy. To address this issue, we introduce a sequence-based virtual screening (SVS) as another generation of VS models that utilize advanced natural language processing (NLP) algorithms and optimized deep K-embedding strategies to encode biomolecular interactions without relying on 3D structure-based docking. We demonstrate that SVS outperforms state-of-the-art performance for four regression datasets involving protein-ligand binding, protein-protein, protein-nucleic acid binding, and ligand inhibition of protein-protein interactions and five classification datasets for protein-protein interactions in five biological species. SVS has the potential to transform current practices in drug discovery and protein engineering.

Effects of synonymous mutations on kinetic properties and structure of firefly luciferase: Molecular dynamics simulation, molecular docking, RNA folding, and experimental study

Although synonymous mutations have long been thought to lack striking results, a growing body of research shows these mutations have highly variable effects. In this study, the impact of synonymous mutations in the development of thermostable luciferase was investigated using a combination of experimental and theoretical approaches. Using bioinformatics analysis, the codon usage features in the Lampyridae family's luciferases were studied and four synonymous mutations of Arg in luciferase were created. An exciting result was that the analysis of kinetic parameters showed a slight increase in the thermal stability of the mutant luciferase. AutoDock Vina, %MinMax algorithm, and UNAFold Server were used to perform molecular docking, folding rate, and RNA folding, respectively. Here, it was assumed that in the region (Arg337) with a moderate propensity for coil, synonymous mutation altered the rate of translation, which in turn may lead to a slight change in the structure of the enzyme. According to the molecular dynamics simulation data, local minor global flexibility is observed in the context of the protein conformation. A plausible explanation is that this flexibility may strengthen hydrophobic interactions due to its sensitivity to a molecular collision. Accordingly, thermostability originated mainly from hydrophobic interaction.

Charge reversal mutations in mesophilic-thermophilic orthologous protein pairs and their role in enhancing coulombic interaction energy

Proteins from thermophilic organisms are a matter of immense interest for decades because of its application in fields like de-novo protein design, thermostable variants of biocatalysts etc. Previous studies have found several sequence and structural adaptations related to thermal stability, while charge reversal study remains ignored. Here we address whether charge reversal mutations naturally occur in mesophilic-thermophilic/hyperthermophilic orthologous proteins. Do they contribute to thermal stability? Our systematic study on 1550 mesophilic-thermophilic/hyperthermophilic orthologous protein pairs with remarkable structural and topological similarity, shows gain in coulombic interaction energy in thermophilic/hyperthermophilic proteins at short range associated with partially exposed and buried charge reversal mutations, which may enhance thermostability. Our findings call forth its application in future protein engineering studies. Communicated by Ramaswamy H. Sarma.

Discrimination of psychrophilic enzymes using machine learning algorithms with amino acid composition descriptor

Introduction

Psychrophilic enzymes are a class of macromolecules with high catalytic activity at low temperatures. Cold-active enzymes possessing eco-friendly and cost-effective properties, are of huge potential application in detergent, textiles, environmental remediation, pharmaceutical as well as food industry. Compared with the time-consuming and labor-intensive experiments, computational modeling especially the machine learning (ML) algorithm is a high-throughput screening tool to identify psychrophilic enzymes efficiently.

Methods

In this study, the influence of 4 ML methods (support vector machines, K-nearest neighbor, random forest, and naïve Bayes), and three descriptors, i.e., amino acid composition (AAC), dipeptide combinations (DPC), and AAC + DPC on the model performance were systematically analyzed.

Results and discussion

Among the 4 ML methods, the support vector machine model based on the AAC descriptor using 5-fold cross-validation achieved the best prediction accuracy with 80.6%. The AAC outperformed than the DPC and AAC + DPC descriptors regardless of the ML methods used. In addition, amino acid frequencies between psychrophilic and non-psychrophilic proteins revealed that higher frequencies of Ala, Gly, Ser, and Thr, and lower frequencies of Glu, Lys, Arg, Ile,Val, and Leu could be related to the protein psychrophilicity. Further, ternary models were also developed that could classify psychrophilic, mesophilic, and thermophilic proteins effectively. The predictive accuracy of the ternary classification model using AAC descriptor via the support vector machine algorithm was 75.8%. These findings would enhance our insight into the cold-adaption mechanisms of psychrophilic proteins and aid in the design of engineered cold-active enzymes. Moreover, the proposed model could be used as a screening tool to identify novel cold-adapted proteins.

DeepTP: A Deep Learning Model for Thermophilic Protein Prediction

Thermophilic proteins have important value in the fields of biopharmaceuticals and enzyme engineering. Most existing thermophilic protein prediction models are based on traditional machine learning algorithms and do not fully utilize protein sequence information. To solve this problem, a deep learning model based on self-attention and multiple-channel feature fusion was proposed to predict thermophilic proteins, called DeepTP. First, a large new dataset consisting of 20,842 proteins was constructed. Second, a convolutional neural network and bidirectional long short-term memory network were used to extract the hidden features in protein sequences. Different weights were then assigned to features through self-attention, and finally, biological features were integrated to build a prediction model. In a performance comparison with existing methods, DeepTP had better performance and scalability in an independent balanced test set and validation set, with AUC values of 0.944 and 0.801, respectively. In the unbalanced test set, DeepTP had an average precision (AP) of 0.536. The tool is freely available.

Stabilization of Enzymes by Using Thermophiles

Manufactured steroid compounds have many applications in the pharmaceutical industry. Due to the chemical complexity and chirality of steroids, there is an increasing demand for enzyme-based bioconversion processes to replace those based on chemical synthesis. In this context, thermostability of the involved enzymes is a highly desirable property as both the increased half-life of the enzyme and the enhanced solubility of substrates and products will improve the yield of the reactions. Metagenomic libraries from thermal environments are potential sources of thermostable enzymes of prokaryotic origin, but the number of expected hits could be quite low for enzymes handling substrates such as steroids, rarely found in prokaryotes. An alternative to metagenome screening is the selection of thermostable variants of well-known steroid-processing enzymes. Here we review and detail a protocol for such selection, where error-prone PCR (epPCR) is used to introduce random mutations into a gene to create a variants library for further selection of thermostable variants in the thermophile Thermus thermophilus. The method involves the use of folding interference vectors where the proper folding of the enzyme of interest at high temperature is linked to the folding of a reporter encoding a selectable property such as thermostable resistance to kanamycin, leading to a life-or-death selection of variants of reinforced folding independently of the activity of the enzyme.

Functional differentiation determines the molecular basis of the symbiotic lifestyle of Ca. Nanohaloarchaeota

BACKGROUND

Candidatus Nanohaloarchaeota, an archaeal phylum within the DPANN superphylum, is characterized by limited metabolic capabilities and limited phylogenetic diversity and until recently has been considered to exclusively inhabit hypersaline environments due to an obligate association with Halobacteria. Aside from hypersaline environments, Ca. Nanohaloarchaeota can also have been discovered from deep-subsurface marine sediments.

RESULTS

Three metagenome-assembled genomes (MAGs) representing a new order within the Ca. Nanohaloarchaeota were reconstructed from a stratified salt crust and proposed to represent a novel order, Nucleotidisoterales. Genomic features reveal them to be anaerobes capable of catabolizing nucleotides by coupling nucleotide salvage pathways with lower glycolysis to yield free energy. Comparative genomics demonstrated that these and other Ca. Nanohaloarchaeota inhabiting saline habitats use a "salt-in" strategy to maintain osmotic pressure based on the high proportion of acidic amino acids. In contrast, previously described Ca. Nanohaloarchaeota MAGs from geothermal environments were enriched with basic amino acids to counter heat stress. Evolutionary history reconstruction revealed that functional differentiation of energy conservation strategies drove diversification within Ca. Nanohaloarchaeota, further leading to shifts in the catabolic strategy from nucleotide degradation within deeper lineages to polysaccharide degradation within shallow lineages.

CONCLUSIONS

This study provides deeper insight into the ecological functions and evolution of the expanded phylum Ca. Nanohaloarchaeota and further advances our understanding on the functional and genetic associations between potential symbionts and hosts. Video Abstract.

SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins

Thermophilic proteins (TPPs) are important in the field of protein biochemistry and development of new enzymes. Thus, computational methods must be urgently developed to accurately and rapidly identify TPPs. To date, several computational methods have been developed for TPP identification; however, few limitations in terms of performance and utility remain. In this study, we present a novel computational method, SAPPHIRE, to achieve more accurate identification of TPPs using only sequence information without any need for structural information. We combined twelve different feature encodings representing different perspectives and six popular machine learning algorithms to train 72 baseline models and extract the key information of TPPs. Subsequently, the informative predicted probabilities from the baseline models were mined and selected using a genetic algorithm in conjunction with a self-assessment-report approach. Finally, the final meta-predictor, SAPPHIRE, was built and optimized by applying an optimal feature set. The performance of SAPPHIRE in the 10-fold cross-validation test showed that a superior predictive performance compared with several baseline models could be achieved. Moreover, SAPPHIRE yielded an accuracy of 0.942 and Matthew's coefficient correlation of 0.884, which were 7.68 and 5.12% higher than those of the current existing methods, respectively, as indicated by the independent test. The proposed computational approach is anticipated to facilitate large-scale identification of TPPs and accelerate their applications in the food industry. The codes and datasets are available at https://github.com/plenoi/SAPPHIRE.

iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy

Thermophilic proteins have important application value in biotechnology and industrial processes. The correct identification of thermophilic proteins provides important information for the application of these proteins in engineering. The identification method of thermophilic proteins based on biochemistry is laborious, time-consuming, and high cost. Therefore, there is an urgent need for a fast and accurate method to identify thermophilic proteins. Considering this urgency, we constructed a reliable benchmark dataset containing 1,368 thermophilic and 1,443 non-thermophilic proteins. A multi-layer perceptron (MLP) model based on a multi-feature fusion strategy was proposed to discriminate thermophilic proteins from non-thermophilic proteins. On independent data set, the proposed model could achieve an accuracy of 96.26%, which demonstrates that the model has a good application prospect. In order to use the model conveniently, a user-friendly software package called iThermo was established and can be freely accessed at http://lin-group.cn/server/iThermo/index.html. The high accuracy of the model and the practicability of the developed software package indicate that this study can accelerate the discovery and engineering application of thermally stable proteins.

TMPpred: A support vector machine-based thermophilic protein identifier

MOTIVATION

The thermostability of proteins will cause them to break the temperature binding and play more functions. Using machine learning, we explored the mechanism of and reasons for protein thermostability characteristics.

RESULTS

Different from other methods that only pursue the performance of models, we aim to find important features so as to provide a powerful reference for in vitro experiments. We transformed this problem into a binary classification problem, that is, the distinction between thermophilic proteins and nonthermophilic proteins. Using support vector machine-based model construction and analysis, we inferred that Gly, Ala, Ser and Thr may be the most important components at the residue level that determine the thermal stability of proteins. It is also noteworthy that our proposed model obtains an Sn of 0.892, an Sp of 0.857, an ACC of 0.87566 and an AUC of 0.874. To facilitate other researchers, we wrapped our model and deployed it as a web server, which is accessible at http://112.124.26.17:7000/TMPpred/index.html.

A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides

Owing to their ability to maintain a thermodynamically stable fold at extremely high temperatures, thermophilic proteins (TTPs) play a critical role in basic research and a variety of applications in the food industry. As a result, the development of computation models for rapidly and accurately identifying novel TTPs from a large number of uncharacterized protein sequences is desirable. In spite of existing computational models that have already been developed for characterizing thermophilic proteins, their performance and interpretability remain unsatisfactory. We present a novel sequence-based thermophilic protein predictor, termed SCMTPP, for improving model predictability and interpretability. First, an up-to-date and high-quality dataset consisting of 1853 TPPs and 3233 non-TPPs was compiled from published literature. Second, the SCMTPP predictor was created by combining the scoring card method (SCM) with estimated propensity scores of g-gap dipeptides. Benchmarking experiments revealed that SCMTPP had a cross-validation accuracy of 0.883, which was comparable to that of a support vector machine-based predictor (0.906-0.910) and 2-17% higher than that of commonly used machine learning models. Furthermore, SCMTPP outperformed the state-of-the-art approach (ThermoPred) on the independent test dataset, with accuracy and MCC of 0.865 and 0.731, respectively. Finally, the SCMTPP-derived propensity scores were used to elucidate the critical physicochemical properties for protein thermostability enhancement. In terms of interpretability and generalizability, comparative results showed that SCMTPP was effective for identifying and characterizing TPPs. We had implemented the proposed predictor as a user-friendly online web server at http://pmlabstack.pythonanywhere.com/SCMTPP in order to allow easy access to the model. SCMTPP is expected to be a powerful tool for facilitating community-wide efforts to identify TPPs on a large scale and guiding experimental characterization of TPPs.

Enhanced Thermostability of D-Psicose 3-Epimerase from Clostridium bolteae through Rational Design and Engineering of New Disulfide Bridges

D-psicose 3-epimerase (DPEase) catalyzes the isomerization of D-fructose to D-psicose (aka D-allulose, a low-calorie sweetener), but its industrial application has been restricted by the poor thermostability of the naturally available enzymes. Computational rational design of disulfide bridges was used to select potential sites in the protein structure of DPEase from Clostridium bolteae to engineer new disulfide bridges. Three mutants were engineered successfully with new disulfide bridges in different locations, increasing their optimum catalytic temperature from 55 to 65 °C, greatly improving their thermal stability and extending their half-lives (t_1/2) at 55 °C from 0.37 h to 4−4.5 h, thereby greatly enhancing their potential for industrial application. Molecular dynamics simulation and spatial configuration analysis revealed that introduction of a disulfide bridge modified the protein hydrogen–bond network, rigidified both the local and overall structures of the mutants and decreased the entropy of unfolded protein, thereby enhancing the thermostability of DPEase.

The Molecular Determinants of Thermoadaptation: Methanococcales as a Case Study

Previous reports have shown that environmental temperature impacts proteome evolution in Bacteria and Archaea. However, it is unknown whether thermoadaptation mainly occurs via the sequential accumulation of substitutions, massive horizontal gene transfers, or both. Measuring the real contribution of amino acid substitution to thermoadaptation is challenging, because of confounding environmental and genetic factors (e.g., pH, salinity, genomic G + C content) that also affect proteome evolution. Here, using Methanococcales, a major archaeal lineage, as a study model, we show that optimal growth temperature is the major factor affecting variations in amino acid frequencies of proteomes. By combining phylogenomic and ancestral sequence reconstruction approaches, we disclose a sequential substitutional scheme in which lysine plays a central role by fine tuning the pool of arginine, serine, threonine, glutamine, and asparagine, whose frequencies are strongly correlated with optimal growth temperature. Finally, we show that colonization to new thermal niches is not associated with high amounts of horizontal gene transfers. Altogether, although the acquisition of a few key proteins through horizontal gene transfer may have favored thermoadaptation in Methanococcales, our findings support sequential amino acid substitutions as the main factor driving thermoadaptation.

Novel insights of waterborne human rotavirus A in Riyadh (Saudi Arabia) involving G2 predominance and emergence of a thermotolerant sequence

The routine evaluation of water environments is necessary to manage enteric virus-mediated fecal contamination and the possible emergence of novel variants. Here, we detected human rotavirus A (HRVA) circulating in two wastewater treatment plants, two lakes, irrigation water and a wastewater landfill located in Riyadh. VP7-derived surface protein sequences were assessed by phylogenetic analyses and inspection of thermotolerance-mediated secondary structure and seasonal variation. HRVA was most prevalent at An-Nazim wastewater landfill (AN-WWLF; 63.89%). Phylogenetic analyzes revealed the predominance of HRVA G2 lineage for the first time in Saudi Arabia. Moreover, a single HRVA sequence (2B64I-ANLF3/2018) was recovered at 45 °C from AN-WWLF; secondary structure prediction indicated that this sequence was thermotolerant with a high hydrophobicity, an absence of Ramachandran outliers, and a higher content of proline patches on the protein surface. Varied relationships were significantly observed between sampling areas influenced by temperature ranges (p < 0.05). HRVA prevalence was influenced by seasonal variations, favoring moderate temperatures in late autumn and early winter in all locations. However, a significant temperature impact was detected in Wadi-Hanifah Lake (p = 0.01). Our study extends the knowledge of currently circulating HRVA genotypes, and indicates the probable emergence of thermotolerant strains and seasonally mediated HRVA prevalence.

Molecular cloning and characterization of a thermostable and halotolerant endo-β-1,4-glucanase from Microbulbifer sp. ALW1

The bacterium Microbulbifer sp. ALW1 was previously characterized with the capability to break down the cell wall of brown algae into fine pieces. The biological functions of strain ALW1 were yet to be elucidated. In this study, a gene, namely MaCel5A, was isolated from the ALW1 strain genome, encoding an endo-β-1,4-glucanase. MaCel5A was phylogenetically categorized under the glycoside hydrolase family GH5, with the highest identity to a putative cellulase of Microbulbifer thermotolerans. The recombinant MaCel5A protein purified from heterologous expression in E. coli exhibited maximum activity at 50 °C and pH 6.0, respectively, and functioned selectively toward carboxymethyl cellulose and barley β-glucan. Recombinant MaCel5A demonstrated considerable tolerance to the exposure to high temperature up to 80 °C for 30 min retaining 49% residual activity. In addition, MaCel5A showed moderate stability against pH 5.0-11.0 and strong stability in the presence of nonionic surfactant. MaCel5A exhibited strong halo-stability and halotolerance. The activity of the enzyme increased about tenfold at 0.5 M NaCl, and about fivefold even at 4.0 M NaCl compared to the enzyme activity without the addition of salt. The two conserved glutamic acid residues in MaCel5A featured the typical catalytic acid/base and nucleophile machinery of glycoside hydrolases. These characteristics highlight the industrial application potential of MaCel5A.

A meta-analysis of the activity, stability, and mutational characteristics of temperature-adapted enzymes

Understanding the characteristics that define temperature-adapted enzymes has been a major goal of extremophile enzymology in recent decades. In the present study, we explore these characteristics by comparing psychrophilic, mesophilic, and thermophilic enzymes. Through a meta-analysis of existing data, we show that psychrophilic enzymes exhibit a significantly larger gap (T_g) between their optimum and melting temperatures compared with mesophilic and thermophilic enzymes. These results suggest that T_g may be a useful indicator as to whether an enzyme is psychrophilic or not and that models of psychrophilic enzyme catalysis need to account for this gap. Additionally, by using predictive protein stability software, HoTMuSiC and PoPMuSiC, we show that the deleterious nature of amino acid substitutions to protein stability increases from psychrophiles to thermophiles. How this ultimately affects the mutational tolerance and evolutionary rate of temperature adapted organisms is currently unknown.

Parallel molecular mechanisms for enzyme temperature adaptation

The mechanisms that underly the adaptation of enzyme activities and stabilities to temperature are fundamental to our understanding of molecular evolution and how enzymes work. Here, we investigate the molecular and evolutionary mechanisms of enzyme temperature adaption, combining deep mechanistic studies with comprehensive sequence analyses of thousands of enzymes. We show that temperature adaptation in ketosteroid isomerase (KSI) arises primarily from one residue change with limited, local epistasis, and we establish the underlying physical mechanisms. This residue change occurs in diverse KSI backgrounds, suggesting parallel adaptation to temperature. We identify residues associated with organismal growth temperature across 1005 diverse bacterial enzyme families, suggesting widespread parallel adaptation to temperature. We assess the residue properties, molecular interactions, and interaction networks that appear to underly temperature adaptation.

Characterization of the Altai Maral Chymosin Gene, Production of a Chymosin Recombinant Analog in the Prokaryotic Expression System, and Analysis of Its Several Biochemical Properties

For the first time, the chymosin gene (CYM) of a maral was characterized. Its exon/intron organization was established using comparative analysis of the nucleotide sequence. The CYM mRNA sequence encoding a maral preprochymosin was reconstructed. Nucleotide sequence of the CYM maral mRNA allowed developing an expression vector to ensure production of a recombinant enzyme. Recombinant maral prochymosin was obtained in the expression system of Escherichia coli [strain BL21 (DE3)]. Total milk-coagulation activity (MCA) of the recombinant maral chymosin was 2330 AU/ml. The recombinant maral prochymosin relative activity was 52955 AU/mg. The recombinant maral chymosin showed 100-81% MCA in the temperature range 30-50°C, thermal stability (TS) threshold was 50°C, and the enzyme was completely inactivated at 70°C. Preparations of the recombinant chymosin of a single-humped camel and recombinant bovine chymosin were used as reference samples. Michaelis-Menten constant (K_m), turnover number (k_cat), and catalytic efficiency (k_cat/K_m) of the recombinant maral chymosin, were 1.18 ± 0.1 µM, 2.68 ± 0.08 s^-1 and 2.27± 0.10 µm M^-1·s^-1, respectively.

Structural and functional characterisation of a cold-active yet heat-tolerant dehydroquinase from Glaciozyma antarctica PI12

Dehydroquinase or 3-dehydroquinate dehydratase (DHQD) reversibly cleaves 3-dehydroquinate to form 3-dehydroshikimate. Here, we describe the functional and structural features of a cold active type II 3-dehydroquinate dehydratase from the psychrophilic yeast, Glaciozyma antarctica PI12 (GaDHQD). Functional studies showed that the enzyme was active at low temperatures (10-30 °C), but displayed maximal activity at 40 °C. Yet the enzyme was stable over a wide range of temperatures (10-70 °C) and between pH 6.0-10.0 with an optimum pH of 8.0. Interestingly, the enzyme was highly thermo-tolerant, denaturing only at approximately 84 °C. Three-dimensional structure analyses showed that the G. antarctica dehydroquinase (GaDHQD) possesses psychrophilic features in comparison with its mesophilic and thermophilic counterparts such as higher numbers of non-polar residues on the surface, lower numbers of arginine and higher numbers of glycine-residues with lower numbers of hydrophobic interactions. On the other hand, GaDHQD shares some traits (i.e. total number of hydrogen bonds, number of proline residues and overall folding) with its mesophilic and thermophilic counterparts. Combined, these features contribute synergistically towards the enzyme's ability to function at both low and high temperatures.

Evolution of Protein Structure and Stability in Global Warming

This review focuses on the molecular signatures of protein structures in relation to evolution and survival in global warming. It is based on the premise that the power of evolutionary selection may lead to thermotolerant organisms that will repopulate the planet and continue life in general, but perhaps with different kinds of flora and fauna. Our focus is on molecular mechanisms, whereby known examples of thermoresistance and their physicochemical characteristics were noted. A comparison of interactions of diverse residues in proteins from thermophilic and mesophilic organisms, as well as reverse genetic studies, revealed a set of imprecise molecular signatures that pointed to major roles of hydrophobicity, solvent accessibility, disulfide bonds, hydrogen bonds, ionic and π-electron interactions, and an overall condensed packing of the higher-order structure, especially in the hydrophobic regions. Regardless of mutations, specialized protein chaperones may play a cardinal role. In evolutionary terms, thermoresistance to global warming will likely occur in stepwise mutational changes, conforming to the molecular signatures, such that each "intermediate" fits a temporary niche through punctuated equilibrium, while maintaining protein functionality. Finally, the population response of different species to global warming may vary substantially, and, as such, some may evolve while others will undergo catastrophic mass extinction.

A Novel Thermostable Cytochrome P450 from Sequence-Based Metagenomics of Binh Chau Hot Spring as a Promising Catalyst for Testosterone Conversion

Biotechnological applications of cytochromes P450 show difficulties, such as low activity, thermal and/or solvent instability, narrow substrate specificity and redox partner dependence. In an attempt to overcome these limitations, an exploitation of novel thermophilic P450 enzymes from nature via uncultured approaches is desirable due to their great advantages that can resolve nearly all mentioned impediments. From the metagenomics library of the Binh Chau hot spring, an open reading frame (ORF) encoding a thermostable cytochrome P450—designated as P450-T3—which shared 66.6% amino acid sequence identity with CYP109C2 of Sorangium cellulosum So ce56 was selected for further identification and characterization. The ORF was synthesized artificially and heterologously expressed in Escherichia coli C43(DE3) using the pET17b system. The purified enzyme had a molecular weight of approximately 43 kDa. The melting temperature of the purified enzyme was 76.2 °C and its apparent half-life at 60 °C was 38.7 min. Redox partner screening revealed that P450-T3 was reduced well by the mammalian AdR-Adx4-108 and the yeast Arh1-Etp1 redox partners. Lauric acid, palmitic acid, embelin, retinoic acid (all-trans) and retinoic acid (13-cis) demonstrated binding to P450-T3. Interestingly, P450-T3 also bound and converted testosterone. Overall, P450-T3 might become a good candidate for biocatalytic applications on a larger scale.

Immunohistochemical analysis of IDH2 R172 hotspot mutations in breast papillary neoplasms: applications in the diagnosis of tall cell carcinoma with reverse polarity

Tall cell carcinoma with reverse polarity is a rare subtype of breast carcinoma with solid and papillary growth and nuclear features reminiscent of those of the tall cell variant of papillary thyroid carcinoma. These tumors harbor recurrent IDH2 R172 hotspot mutations or TET2 mutations, co-occurring with mutations in PI3K pathway genes. Diagnosis of tall cell carcinomas with reverse polarity is challenging in view of their rarity and the range of differential diagnosis. We sought to determine the sensitivity and specificity of IDH2 R172 immunohistochemistry for the detection of IDH2 R172 hotspot mutations in this entity. We evaluated 14 tall cell carcinomas with reverse polarity (ten excision and five core needle biopsy specimens), 13 intraductal papillomas, 16 solid papillary carcinomas, and 5 encapsulated papillary carcinomas by Sanger sequencing of the IDH2 R172 hotspot locus and of exons 9 and 20 of PIK3CA, and by immunohistochemistry using monoclonal antibodies (11C8B1) to the IDH2 R172S mutation. The 14 tall cell carcinomas with reverse polarity studied harbored IDH2 R172 hotspot mutations, which co-occurred with PIK3CA hotspot mutations in 50% of cases. None of the other papillary neoplasms analyzed displayed IDH2 R172 mutations, however PIK3CA hotspot mutations were detected in 54% of intraductal papillomas, 6% of solid papillary carcinomas, and 20% of encapsulated papillary carcinomas tested. Immunohistochemical analysis with anti-IDH2 R172S antibodies (11C8B1) detected IDH2 R172 mutated protein in 93% (14/15) of tall cell carcinomas with reverse polarity samples including excision (n = 9/10) and core needle biopsy specimens (n = 5), whereas the remaining papillary neoplasms (n = 34) were negative. Our findings demonstrate that immunohistochemical analysis of IDH2 R172 is highly sensitive and specific for the detection of IDH2 R172 hotspot mutations, and likely suitable as a diagnostic tool in the evaluation of excision and core needle biopsy material of tall cell carcinomas with reverse polarity.

A Method for Prediction of Thermophilic Protein Based on Reduced Amino Acids and Mixed Features

The thermostability of proteins is a key factor considered during enzyme engineering, and finding a method that can identify thermophilic and non-thermophilic proteins will be helpful for enzyme design. In this study, we established a novel method combining mixed features and machine learning to achieve this recognition task. In this method, an amino acid reduction scheme was adopted to recode the amino acid sequence. Then, the physicochemical characteristics, auto-cross covariance (ACC), and reduced dipeptides were calculated and integrated to form a mixed feature set, which was processed using correlation analysis, feature selection, and principal component analysis (PCA) to remove redundant information. Finally, four machine learning methods and a dataset containing 500 random observations out of 915 thermophilic proteins and 500 random samples out of 793 non-thermophilic proteins were used to train and predict the data. The experimental results showed that 98.2% of thermophilic and non-thermophilic proteins were correctly identified using 10-fold cross-validation. Moreover, our analysis of the final reserved features and removed features yielded information about the crucial, unimportant and insensitive elements, it also provided essential information for enzyme design.

Meltome atlas-thermal proteome stability across the tree of life

We have used a mass spectrometry-based proteomic approach to compile an atlas of the thermal stability of 48,000 proteins across 13 species ranging from archaea to humans and covering melting temperatures of 30-90 °C. Protein sequence, composition and size affect thermal stability in prokaryotes and eukaryotic proteins show a nonlinear relationship between the degree of disordered protein structure and thermal stability. The data indicate that evolutionary conservation of protein complexes is reflected by similar thermal stability of their proteins, and we show examples in which genomic alterations can affect thermal stability. Proteins of the respiratory chain were found to be very stable in many organisms, and human mitochondria showed close to normal respiration at 46 °C. We also noted cell-type-specific effects that can affect protein stability or the efficacy of drugs. This meltome atlas broadly defines the proteome amenable to thermal profiling in biology and drug discovery and can be explored online at http://meltomeatlas.proteomics.wzw.tum.de:5003/ and http://www.proteomicsdb.org.

Evaluation of Luciferase Thermal Stability by Arginine Saturation in the Flexible Loops

Background:

The firefly luciferase enzyme is widely used in protein engineering and diverse areas of biotechnology, but the main problem with this enzyme is low-temperature stability. Previous reports indicated that surface areas of thermostable proteins are rich in arginine, which increased their thermal stability. In this study, this aspect of thermophilic proteins evaluated by mutations of surface residues to Arg. Here, we report the construction, purification, and studying of these mutated luciferases.

Methods:

For mutagenesis, the QuikChange site-directed mutagenesis was used and the I108R, T156R, and N177R mutant luciferases were created. In the following, the expression and purification of wild-type and mutant luciferases were conducted and their kinetic and structural properties were analyzed. To analyze the role of these Arg in these loops, the 3D models of these mutants’ enzymes were constructed in the I-TASSER server and the exact situation of these mutants was studied by the SPDBV and PyMOL software.

Results:

Overall, the optimum temperature of these mutated enzymes was not changed. However, after 30 min incubation of these mutated enzymes at 30°C, the I108R, T156R, N177R, and wild-type kept the 80%, 50%, 20%, and 20% of their original activity, respectively. It should be noted that substitution of these residues by Arg preserved the specific activity of firefly luciferase.

Conclusion:

Based on these results, it can be concluded that T156R and N177R mutants by compacting local protein structure, increased the thermostability of luciferase. However, insertion of positively charged residues in these positions create the new hydrogen bonds that associated with a series of structural changes and confirmed by intrinsic and extrinsic fluorescence spectroscopy and homology modeling studies.

Finding the generalized molecular principles of protein thermal stability

Are there any generalized molecular principles of thermal adaptation? Here, integrating the concepts of structural bioinformatics, sequence analysis, and classical knot theory, we develop a robust computational framework that seeks for mechanisms of thermal adaptation by comparing orthologous mesophilic-thermophilic and mesophilic-hyperthermophilic proteins of remarkable structural and topological similarities, and still leads us to context-independent results. A comprehensive analysis of 4741 high-resolution, non-redundant X-ray crystallographic structures collected from 11 hyperthermophilic, 32 thermophilic and 53 mesophilic prokaryotes unravels at least five "nearly universal" signatures of thermal adaptation, irrespective of the enormous sequence, structure, and functional diversity of the proteins compared. A careful investigation further extracts a set of amino acid changes that can potentially enhance protein thermal stability, and remarkably, these mutations are overrepresented in protein crystallization experiments, in disorder-to-order transitions and in engineered thermostable variants of existing mesophilic proteins. These results could be helpful to find a precise, global picture of thermal adaptation.

Novel Sequence Feature of SecA Translocase Protein Unique to the Thermophilic Bacteria: Bioinformatics Analyses to Investigate Their Potential Roles

SecA is an evolutionarily conserved protein that plays an indispensable role in the secretion of proteins across the bacterial cell membrane. Comparative analyses of SecA homologs have identified two large conserved signature inserts (CSIs) that are unique characteristics of thermophilic bacteria. A 50 aa conserved insert in SecA is exclusively present in the SecA homologs from the orders Thermotogales and Aquificales, while a 76 aa insert in SecA is specific for the order Thermales and Hydrogenibacillus schlegelii. Phylogenetic analyses on SecA sequences show that the shared presence of these CSIs in unrelated groups of thermophiles is not due to lateral gene transfers, but instead these large CSIs have likely originated independently in these lineages due to their advantageous function. Both of these CSIs are located in SecA protein in a surface exposed region within the ATPase domain. To gain insights into the functional significance of the 50 aa CSI in SecA, molecular dynamics (MD) simulations were performed at two different temperatures using ADP-bound SecA from Thermotoga maritima. These analyses have identified a conserved network of water molecules near the 50 aa insert in which the Glu185 residue from the CSI is found to play a key role towards stabilizing these interactions. The results provide evidence for the possible role of the 50 aa CSI in stabilizing the binding interaction of ADP/ATP, which is required for SecA function. Additionally, the surface-exposed CSIs in SecA, due to their potential to make novel protein-protein interactions, could also contribute to the thermostability of SecA from thermophilic bacteria.

Three dimensional structure prediction of panomycocin, a novel Exo-β-1,3-glucanase isolated from Wickerhamomyces anomalus NCYC 434 and the computational site-directed mutagenesis studies to enhance its thermal stability for therapeutic applications

Panomycocin is a naturally produced potent antimycotic/antifungal protein secreted by the yeast Wickerhamomyces anomalus NCYC 434 with an exo-β-1,3-glucanase activity. In this study the three dimensional structure of panomycocin was predicted and the computational site-directed mutagenesis was performed to enhance its thermal stability in liquid formulations over the body temperature for topical therapeutic applications. Homology modeling was performed with MODELLER and I-TASSER. Among the generated models, the model with the lowest energy and DOPE score was selected for further loop modeling. The loop model was optimized and the reliability of the model was confirmed with ERRAT, Verify 3D and Ramachandran plot values. Enhancement of the thermal stability of the model was done using contemporary servers and programs such as SPDBViewer, CNA, I-Mutant2.0, Eris, AUTO-MUTE and MUpro. In the region outside the binding site of the model Leu52 Arg, Phe223Arg and Gly254Arg were found to be the best thermostabilizing mutations with 6.26 K, 6.26 K and 8.27 K increases, respectively. In the binding site Glu186Arg was found to be the best thermostabilizer mutation with a 9.58 K temperature increase. The results obtained in this study led us to design a mutant panomycocin that can be used as a novel antimycotic/antifungal drug in a liquid formulation for topical applications over the normal body temperature.

Thermozymes: Adaptive strategies and tools for their biotechnological applications

In today's scenario of global climate change, there is a colossal demand for sustainable industrial processes and enzymes from thermophiles. Plausibly, thermozymes are an important toolkit, as they are known to be polyextremophilic in nature. Small genome size and diverse molecular conformational modifications have been implicated in devising adaptive strategies. Besides, the utilization of chemical technology and gene editing attributions according to mechanical necessities are the additional key factor for efficacious bioprocess development. Microbial thermozymes have been extensively used in waste management, biofuel, food, paper, detergent, medicinal and pharmaceutical industries. To understand the strength of enzymes at higher temperatures different models utilize X-ray structures of thermostable proteins, machine learning calculations, neural networks, but unified adaptive measures are yet to be totally comprehended. The present review provides a recent updates on thermozymes and various interdisciplinary applications including the aspects of thermophiles bioengineering utilizing synthetic biology and gene editing tools.

In silico Design of Laccase Thermostable Mutants From Lacc 6 of Pleurotus Ostreatus

Fungal laccase enzymes have a great biotechnological potential for bioremediation processes due to their ability to degrade compounds such as ρ-diphenol, aminophenols, polyphenols, polyamines, and aryldiamines. These enzymes have activity at different pH and temperature values, however, high temperatures can cause partial or total loss of enzymatic activity, so it is appropriate to do research to modify their secondary and/or tertiary structure to make them more resistant to extreme temperature conditions. In silico, a structure of the Lacc 6 enzyme of Pleurotus ostreatus was constructed using a laccase of Trametes versicolor as a template. From this structure, 16 mutants with possible resistance at high temperature due to ionic interactions, salt bridges and disulfide bonds were also obtained in silico. It was determined that 12 mutants called 4-DB, 3-DB, D233C-T310C, F468P, 3-SB, L132T, N79D, N372D, P203C, P203V, T147E, and W85F, presented the lowest thermodynamic energy. Based on the previous criterion and determining the least flexibility in the protein structures, three mutants (4-DB, 3-DB, and P203C) were selected, which may present high stability at high temperatures without affecting their active site. The obtained results allow the understanding of the molecular base that increase the structural stability of the enzyme Lacc 6 of Pleurotus ostreatus, achieving the in silico generation of mutants, which could have activity at high temperatures.

Functional Properties of Amino Acid Side Chains as Biomarkers of Extraterrestrial Life

The present study proposes to search our solar system (Mars, Enceladus, Europa) for patterns of organic molecules that are universally associated with biological functions and structures. The functions are primarily catalytic because life could only have originated within volume/space-constrained compartments containing chemical reactions catalyzed by certain polymers. The proposed molecular structures are specific groups in the side chains of amino acids with the highest catalytic propensities related to life on Earth, that is, those that most frequently participate as key catalytic groups in the active sites of enzymes such as imidazole, thiol, guanidinium, amide, and carboxyl. Alternatively, these or other catalytic groups can be searched for on non-amino-acid organic molecules, which can be tested for certain hydrolytic catalytic activities. The first scenario assumes that life may have originated in a similar manner as the terrestrial set of α-amino acids, while the second scenario does not set such a requirement. From the catalytic propensity perspective proposed in the first scenario, life must have invented amino acids with high catalytic propensity (His, Cys, Arg) in order to overcome, and be complemented by, the low catalytic propensity of the initially available abiogenic amino acids. The abiogenic and the metabolically invented amino acids with the lowest catalytic propensity can also serve as markers of extraterrestrial life when searching for patterns on the basis of the following functional propensities related to protein secondary/quaternary structure: (1) amino acids that are able to form α-helical intramembrane peptide domains, which can serve as primitive transporters in protocell membrane bilayers and catalysts of simple biochemical reactions; (2) amino acids that tend to accumulate in extremophile proteins of Earth and possibly extraterrestrial life. The catalytic/structural functional propensity approach offers a new perspective in the search for extraterrestrial life and could help unify previous amino acid-based approaches.

Stabilizing HIV-1 envelope glycoprotein trimers to induce neutralizing antibodies

An effective HIV-1 vaccine probably will need to be able to induce broadly neutralizing HIV-1 antibodies (bNAbs) in order to be efficacious. The many bNAbs that have been isolated from HIV-1 infected patients illustrate that the human immune system is able to elicit this type of antibodies. The elucidation of the structure of the HIV-1 envelope glycoprotein (Env) trimer has further fueled the search for Env immunogens that induce bNAbs, but while native Env trimer mimetics are often capable of inducing strain-specific neutralizing antibodies (NAbs) against the parental virus, they have not yet induced potent bNAb responses. To improve the performance of Env trimer immunogens, researchers have studied the immune responses that Env trimers have induced in animals; they have evaluated how to best use Env trimers in various immunization regimens; and they have engineered increasingly stabilized Env trimer variants. Here, we review the different approaches that have been used to increase the stability of HIV-1 Env trimer immunogens with the aim of improving the induction of NAbs. In particular, we draw parallels between the various approaches to stabilize Env trimers and ones that have been used by nature in extremophile microorganisms in order to survive in extreme environmental conditions.