Correlating enzyme annotations with a large set of microbial growth temperatures reveals metabolic adaptations to growth at diverse temperatures

Enhancing cryo-enzymatic efficiency in cold-adapted lipase from Psychrobacter sp. C18 via site-directed mutagenesis

As industrial demands for cold-active enzymes have been increased, psychrophilic lipases present a promising solution with potential for innovation and growth in food, pharmaceutical, and detergent industries. Cold-adapted enzymes achieve high catalytic efficiency at low temperatures through their structural flexibility and conformational adaptability. Therefore, in this study, the lipase gene from Psychrobacter sp. C18 was cloned and subjected to site-directed mutagenesis based on computer aided predictions to enhance the enzyme's cold-adapted properties and flexibility. Mutations were strategically selected in loops of the active site to improve the enzyme's accessibility to the substrate under cold conditions. The P163G, L186G, and Q239W mutations were selected for further analysis. Enzyme activity, along with its stability and structural flexibility, was assessed using techniques including UV-Vis spectroscopy, fluorescence, and circular dichroism (CD) spectroscopy. The obtained data revealed that the optimal temperature for the wild-type lipase was 30 °C, which shifted to lower temperatures in the mutants: 15 °C for P163G and L186G, and 20 °C for Q239W. Additionally, the optimal pH of the mutant lipases shifted to more alkaline conditions compared to the wild-type enzyme. While the thermal and pH stability of the mutant enzymes slightly decreased, these findings can be attributed to their enhanced flexibility. Far-UV CD spectroscopy revealed a reduction in α-helical content of the mutant enzymes. Molecular dynamics simulations corroborated these findings, confirming increased structural flexibility in all three mutants compared to the wild-type enzyme. This research underlines the importance of applying engineered cold-adapted enzymes for industrial application.

Large-scale discovery, analysis, and design of protein energy landscapes

All folded proteins continuously fluctuate between their low-energy native structures and higher energy conformations that can be partially or fully unfolded. These rare states influence protein function, interactions, aggregation, and immunogenicity, yet they remain far less understood than protein native states. Although native protein structures are now often predictable with impressive accuracy, conformational fluctuations and their energies remain largely invisible and unpredictable, and experimental challenges have prevented large-scale measurements that could improve machine learning and physics-based modeling. Here, we introduce a multiplexed experimental approach to analyze the energies of conformational fluctuations for hundreds of protein domains in parallel using intact protein hydrogen-deuterium exchange mass spectrometry. We analyzed 5,778 domains 28-64 amino acids in length, revealing hidden variation in conformational fluctuations even between sequences sharing the same fold and global folding stability. Site-resolved hydrogen exchange NMR analysis of 13 domains showed that these fluctuations often involve entire secondary structural elements with lower stability than the overall fold. Computational modeling of our domains identified structural features that correlated with the experimentally observed fluctuations, enabling us to design mutations that stabilized low-stability structural segments. Our dataset enables new machine learning-based analysis of protein energy landscapes, and our experimental approach promises to reveal these landscapes at unprecedented scale.

A general temperature-guided language model to design proteins of enhanced stability and activity

Designing protein mutants with both high stability and activity is a critical yet challenging task in protein engineering. Here, we introduce PRIME, a deep learning model, which can suggest protein mutants with improved stability and activity without any prior experimental mutagenesis data for the specified protein. Leveraging temperature-aware language modeling, PRIME demonstrated superior predictive ability compared to current state-of-the-art models on the public mutagenesis dataset across 283 protein assays. Furthermore, we validated PRIME's predictions on five proteins, examining the impact of the top 30 to 45 single-site mutations on various protein properties, including thermal stability, antigen-antibody binding affinity, and the ability to polymerize nonnatural nucleic acid or resilience to extreme alkaline conditions. More than 30% of PRIME-recommended mutants exhibited superior performance compared to their premutation counterparts across all proteins and desired properties. We developed an efficient and effective method based on PRIME to rapidly obtain multisite mutants with enhanced activity and stability. Hence, PRIME demonstrates broad applicability in protein engineering.

qProtein: Exploring Physical Features of Protein Thermostability Based on Structural Proteomics

Thermostability, which is essential for the functional performance of enzymes, is largely determined by intramolecular physical interactions. Although many tools have been developed, existing computational methods have struggled to find the universal principles of protein thermostability. Recent advancements in structural proteomics have been driven by the introduction of deep neural networks such as AlphaFold2 and ESMFold. These innovations have enabled the characterization of protein structures with unprecedented speed and accuracy. Here, we introduce qProtein, a Python-implemented workflow designed for the quantitative analysis of physical interactions on the scale of structural proteomics. This platform accepts protein sequences as input and produces four structural features, including hydrophobic clusters, hydrogen bonds, electrostatic interactions, and disulfide bonds. To demonstrate the use of qProtein, we investigate the structural features related to protein thermostability in six glycoside hydrolase (GH) families, comprising a total of 3,811 protein structures. Our results indicate that in five enzyme families (GH11, GH12, GH5_2, GH10, and GH48), the thermophilic enzymes have a larger average area of hydrophobic clusters compared to the nonthermophilic enzymes within each family. Furthermore, our analysis of the local-structure regions reveals that the hydrophobic clusters are predominantly distributed in the distal regions of the GH11 enzymes. In addition, the average hydrophobic cluster area of the thermophilic enzymes is significantly higher than that of the nonthermophilic enzymes in the distal regions of the GH11 enzymes. Therefore, qProtein is a well-suited platform for analyzing the structural features of thermal stability at the level of structural proteomics. We provide the source code for qProtein at https://github.com/bj600800/qProtein, and the web server is available at http://qProtein.sdu.edu.cn:8888.

Evolutionary-Scale Enzymology Enables Biochemical Constant Prediction Across a Multi-Peaked Catalytic Landscape

Quantitatively mapping enzyme sequence-catalysis landscapes remains a critical challenge in understanding enzyme function, evolution, and design. Here, we expand an emerging microfluidic platform to measure catalytic constants-k cat and K M-for hundreds of diverse naturally occurring sequences and mutants of the model enzyme Adenylate Kinase (ADK). This enables us to dissect the sequence-catalysis landscape's topology, navigability, and mechanistic underpinnings, revealing distinct catalytic peaks organized by structural motifs. These results challenge long-standing hypotheses in enzyme adaptation, demonstrating that thermophilic enzymes are not slower than their mesophilic counterparts. Combining the rich representations of protein sequences provided by deep-learning models with our custom high-throughput kinetic data yields semi-supervised models that significantly outperform existing models at predicting catalytic parameters of naturally occurring ADK sequences. Our work demonstrates a promising strategy for dissecting sequence-catalysis landscapes across enzymatic evolution and building family-specific models capable of accurately predicting catalytic constants, opening new avenues for enzyme engineering and functional prediction.

Metamaterial Sensing of Cyanobacteria Using THz Thermal Curve Analysis

In this study, we perform thermal curve analyses based on terahertz (THz) metamaterials for the label-free sensing of cyanobacteria. In the presence of bacterial films, significant frequency shifts occur at the metamaterial resonance, but these shifts become saturated at a certain thickness owing to the limited sensing volume of the metamaterial. The saturation value was used to determine the dielectric constants of various cyanobacteria, which are crucial for dielectric sensing. For label-free identification, we performed thermal curve analysis of THz metamaterials coated with cyanobacteria. The resonant frequency of the cyanobacteria-coated metasensor changed with temperature. The differential thermal curves (DTC) obtained from temperature-dependent resonance exhibited peaks unique to individual cyanobacteria, which helped identify individual species. Interestingly, despite being classified as Gram negative, cyanobacteria exhibit DTC profiles similar to those of Gram-positive bacteria, likely due to their unique extracellular structures. DTC analysis can reveal unique characteristics of various cyanobacteria that are not easily accessible by conventional approaches.

Approaching Optimal pH Enzyme Prediction with Large Language Models

Enzymes are widely used in biotechnology due to their ability to catalyze chemical reactions: food making, laundry, pharmaceutics, textile, brewing─all these areas benefit from utilizing various enzymes. Proton concentration (pH) is one of the key factors that define the enzyme functioning and efficiency. Usually there is only a narrow range of pH values where the enzyme is active. This is a common problem in biotechnology to design an enzyme with optimal activity in a given pH range. A large part of this task can be completed in silico, by predicting the optimal pH of designed candidates. The success of such computational methods critically depends on the available data. In this study, we developed a language-model-based approach to predict the optimal pH range from the enzyme sequence. We used different splitting strategies based on sequence similarity, protein family annotation, and enzyme classification to validate the robustness of the proposed approach. The derived machine-learning models demonstrated high accuracy across proteins from different protein families and proteins with lower sequence similarities compared with the training set. The proposed method is fast enough for the high-throughput virtual exploration of protein space for the search for sequences with desired optimal pH levels.

ThermoFinder: A sequence-based thermophilic proteins prediction framework

Thermophilic proteins are important for academic research and industrial processes, and various computational methods have been developed to identify and screen them. However, their performance has been limited due to the lack of high-quality labeled data and efficient models for representing protein. Here, we proposed a novel sequence-based thermophilic proteins prediction framework, called ThermoFinder. The results demonstrated that ThermoFinder outperforms previous state-of-the-art tools on two benchmark datasets, and feature ablation experiments confirmed the effectiveness of our approach. Additionally, ThermoFinder exhibited exceptional performance and consistency across two newly constructed datasets, one of these was specifically constructed for the regression-based prediction of temperature optimum values directly derived from protein sequences. The feature importance analysis, using shapley additive explanations, further validated the advantages of ThermoFinder. We believe that ThermoFinder will be a valuable and comprehensive framework for predicting thermophilic proteins, and we have made our model open source and available on Github at https://github.com/Luo-SynBioLab/ThermoFinder.

TemStaPro: protein thermostability prediction using sequence representations from protein language models

MOTIVATION

Reliable prediction of protein thermostability from its sequence is valuable for both academic and industrial research. This prediction problem can be tackled using machine learning and by taking advantage of the recent blossoming of deep learning methods for sequence analysis. These methods can facilitate training on more data and, possibly, enable the development of more versatile thermostability predictors for multiple ranges of temperatures.

RESULTS

We applied the principle of transfer learning to predict protein thermostability using embeddings generated by protein language models (pLMs) from an input protein sequence. We used large pLMs that were pre-trained on hundreds of millions of known sequences. The embeddings from such models allowed us to efficiently train and validate a high-performing prediction method using over one million sequences that we collected from organisms with annotated growth temperatures. Our method, TemStaPro (Temperatures of Stability for Proteins), was used to predict thermostability of CRISPR-Cas Class II effector proteins (C2EPs). Predictions indicated sharp differences among groups of C2EPs in terms of thermostability and were largely in tune with previously published and our newly obtained experimental data.

AVAILABILITY AND IMPLEMENTATION

TemStaPro software and the related data are freely available from https://github.com/ievapudz/TemStaPro and https://doi.org/10.5281/zenodo.7743637.

Artificial Intelligence-Powered Construction of a Microbial Optimal Growth Temperature Database and Its Impact on Enzyme Optimal Temperature Prediction

Accurate prediction of enzyme optimal temperature (Topt) is crucial for identifying enzymes suitable for catalytic functions under extreme bioprocessing conditions. The optimal growth temperature (OGT) of microorganisms serves as a key indicator for estimating enzyme Topt, reflecting an evolutionary temperature balance between enzyme-catalyzed reactions and the organism's growth environments. Existing OGT databases, collected from culture collection centers, often fall short as culture temperature does not precisely represent the OGT. Models trained on such databases yield inadequate accuracy in enzyme Topt prediction, underscoring the need for a high-quality OGT database. Herein, we developed AI-based models to extract the OGT information from the scientific literature, constructing a comprehensive OGT database with 1155 unique organisms and 2142 OGT values. The top-performing model, BioLinkBERT, demonstrated exceptional information extraction ability with an EM score of 91.00 and an F1 score of 91.91 for OGT. Notably, applying this OGT database in enzyme Topt prediction achieved an R2 value of 0.698, outperforming the R2 value of 0.686 obtained using culture temperature. This emphasizes the superiority of the OGT database in predicting the enzyme Topt and underscores its pivotal role in identifying enzymes with optimal catalytic temperatures.

Metagenomic Thermometer

Various microorganisms exist in environments, and each of them has its optimal growth temperature (OGT). The relationship between genomic information and OGT of each species has long been studied, and one such study revealed that OGT of prokaryotes can be accurately predicted based on the fraction of seven amino acids (IVYWREL) among all encoded amino-acid sequences in its genome. Extending this discovery, we developed a 'Metagenomic Thermometer' as a means of predicting environmental temperature based on metagenomic sequences. Temperature prediction of diverse environments using publicly available metagenomic data revealed that the Metagenomic Thermometer can predict environmental temperatures with small temperature changes and little influx of microorganisms from other environments. The accuracy of the Metagenomic Thermometer was also confirmed by a demonstration experiment using an artificial hot water canal. The Metagenomic Thermometer was also applied to human gut metagenomic samples, yielding a reasonably accurate value for human body temperature. The result further suggests that deep body temperature determines the dominant lineage of the gut community. Metagenomic Thermometer provides a new insight into temperature-driven community assembly based on amino-acid composition rather than microbial taxa.

r/K selection of GC content in prokaryotes

The guanine/cytosine (GC) content of prokaryotic genomes is species-specific, taking values from 16% to 77%. This diversity of selection for GC content remains contentious. We analyse the correlations between GC content and a range of phenotypic and genotypic data in thousands of prokaryotes. GC content integrates well with these traits into r/K selection theory when phenotypic plasticity is considered. High GC-content prokaryotes are r-strategists with cheaper descendants thanks to a lower average amino acid metabolic cost, colonize unstable environments thanks to flagella and a bacillus form and are generalists in terms of resource opportunism and their defence mechanisms. Low GC content prokaryotes are K-strategists specialized for stable environments that maintain homeostasis via a high-cost outer cell membrane and endospore formation as a response to nutrient deprivation, and attain a higher nutrient-to-biomass yield. The lower proteome cost of high GC content prokaryotes is driven by the association between GC-rich codons and cheaper amino acids in the genetic code, while the correlation between GC content and genome size may be partly due to functional diversity driven by r/K selection. In all, molecular diversity in the GC content of prokaryotes may be a consequence of ecological r/K selection.

DeepTM: A deep learning algorithm for prediction of melting temperature of thermophilic proteins directly from sequences

Thermally stable proteins find extensive applications in industrial production, pharmaceutical development, and serve as a highly evolved starting point in protein engineering. The thermal stability of proteins is commonly characterized by their melting temperature (Tm). However, due to the limited availability of experimentally determined Tm data and the insufficient accuracy of existing computational methods in predicting Tm, there is an urgent need for a computational approach to accurately forecast the Tm values of thermophilic proteins. Here, we present a deep learning-based model, called DeepTM, which exclusively utilizes protein sequences as input and accurately predicts the Tm values of target thermophilic proteins on a dataset consisting of 7790 thermophilic protein entries. On a test set of 1550 samples, DeepTM demonstrates excellent performance with a coefficient of determination (R2) of 0.75, Pearson correlation coefficient (P) of 0.87, and root mean square error (RMSE) of 6.24 ℃. We further analyzed the sequence features that determine the thermal stability of thermophilic proteins and found that dipeptide frequency, optimal growth temperature (OGT) of the host organisms, and the evolutionary information of the protein significantly affect its melting temperature. We compared the performance of DeepTM with recently reported methods, ProTstab2 and DeepSTABp, in predicting the Tm values on two blind test datasets. One dataset comprised 22 PET plastic-degrading enzymes, while the other included 29 thermally stable proteins of broader classification. In the PET plastic-degrading enzyme dataset, DeepTM achieved RMSE of 8.25 ℃. Compared to ProTstab2 (20.05 ℃) and DeepSTABp (20.97 ℃), DeepTM demonstrated a reduction in RMSE of 58.85% and 60.66%, respectively. In the dataset of thermally stable proteins, DeepTM (RMSE=7.66 ℃) demonstrated a 51.73% reduction in RMSE compared to ProTstab2 (RMSE=15.87 ℃). DeepTM, with the sole requirement of protein sequence information, accurately predicts the melting temperature and achieves a fully end-to-end prediction process, thus providing enhanced convenience and expediency for further protein engineering.

Biochemical characterisation of a novel broad pH spectrum subtilisin from Fictibacillus arsenicus DSM 15822T

Subtilisins from microbial sources, especially from the Bacillaceae family, are of particular interest for biotechnological applications and serve the currently growing enzyme market as efficient and novel biocatalysts. Biotechnological applications include use in detergents, cosmetics, leather processing, wastewater treatment and pharmaceuticals. To identify a possible candidate for the enzyme market, here we cloned the gene of the subtilisin SPFA from Fictibacillus arsenicus DSM 15822T (obtained through a data mining-based search) and expressed it in Bacillus subtilis DB104. After production and purification, the protease showed a molecular mass of 27.57 kDa and a pI of 5.8. SPFA displayed hydrolytic activity at a temperature optimum of 80 °C and a very broad pH optimum between 8.5 and 11.5, with high activity up to pH 12.5. SPFA displayed no NaCl dependence but a high NaCl tolerance, with decreasing activity up to concentrations of 5 m NaCl. The stability enhanced with increasing NaCl concentration. Based on its substrate preference for 10 synthetic peptide 4-nitroanilide substrates with three or four amino acids and its phylogenetic classification, SPFA can be assigned to the subgroup of true subtilisins. Moreover, SPFA exhibited high tolerance to 5% (w/v) SDS and 5% H2 O2 (v/v). The biochemical properties of SPFA, especially its tolerance of remarkably high pH, SDS and H2 O2 , suggest it has potential for biotechnological applications.

Genomic estimates of mutation and substitution rates contradict the evolutionary speed hypothesis of the latitudinal diversity gradient

The latitudinal diversity gradient (LDG) refers to a decrease in biodiversity from the equator to the poles. The evolutionary speed hypothesis, backed by the metabolic theory of ecology, asserts that nucleotide mutation and substitution rates per site per year are higher and thereby speciation rates are higher at higher temperatures, generating the LDG. However, prior empirical investigations of the relationship between the temperature and mutation or substitution rate were based on a few genes and the results were mixed. We here revisit this relationship using genomic data. No significant correlation between the temperature and mutation rate is found in 13 prokaryotes or in 107 eukaryotes. An analysis of 234 diverse trios of bacterial taxa indicates that the synonymous substitution rate is not significantly associated with the growth temperature. The same data, however, reveal a significant negative association between the nonsynonymous substitution rate and temperature, which is explainable by a larger fraction of detrimental nonsynonymous mutations at higher temperatures due to a stronger demand for protein stability. We conclude that the evolutionary speed hypothesis of the LDG is unsupported by genomic data and advise that future mechanistic studies of the LDG should focus on other hypotheses.

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.

Whole genome sequence analyses of thermotolerant Bacillus sp. isolates from food

The Bacillus cereus group, also known as B. cereus sensu lato (B. cereus s.l.), is composed of various Bacillus species, some of which can cause diarrheal or emetic food poisoning. Several emerging highly heat-resistant Bacillus species have been identified, these include B. thermoamylovorans, B. sporothermodurans, and B. cytotoxicus NVH 391-98. Herein, we performed whole genome analysis of two thermotolerant Bacillus sp. isolates, Bacillus sp. B48 and Bacillus sp. B140, from an omelet with acacia leaves and fried rice, respectively. Phylogenomic analysis suggested that Bacillus sp. B48 and Bacillus sp. B140 are closely related to B. cereus and B. thuringiensis, respectively. Whole genome alignment of Bacillus sp. B48, Bacillus sp. B140, mesophilic strain B. cereus ATCC14579, and thermophilic strain B. cytotoxicus NVH 391-98 using the Mauve program revealed the presence of numerous homologous regions including genes responsible for heat shock in the dnaK gene cluster. However, the presence of a DUF4253 domain-containing protein was observed only in the genome of B. cereus ATCC14579 while the intracellular protease PfpI family was present only in the chromosome of B. cytotoxicus NVH 391-98. In addition, prophage Clp protease-like proteins were found in the genomes of both Bacillus sp. B48 and Bacillus sp. B140 but not in the genome of B. cereus ATCC14579. The genomic profiles of Bacillus sp. isolates were identified by using whole genome analysis especially those relating to heat-responsive gene clusters. The findings presented in this study lay the foundations for subsequent studies to reveal further insights into the molecular mechanisms of Bacillus species in terms of heat resistance mechanisms.

New robust subtilisins from halotolerant and halophilic Bacillaceae

The aim of the present study was the characterisation of three true subtilisins and one phylogenetically intermediate subtilisin from halotolerant and halophilic microorganisms. Considering the currently growing enzyme market for efficient and novel biocatalysts, data mining is a promising source for novel, as yet uncharacterised enzymes, especially from halophilic or halotolerant Bacillaceae, which offer great potential to meet industrial needs. Both halophilic bacteria Pontibacillus marinus DSM 16465^T and Alkalibacillus haloalkaliphilus DSM 5271^T and both halotolerant bacteria Metabacillus indicus DSM 16189 and Litchfieldia alkalitelluris DSM 16976^T served as a source for the four new subtilisins SPPM, SPAH, SPMI and SPLA. The protease genes were cloned and expressed in Bacillus subtilis DB104. Purification to apparent homogeneity was achieved by ethanol precipitation, desalting and ion-exchange chromatography. Enzyme activity could be observed between pH 5.0-12.0 with an optimum for SPPM, SPMI and SPLA around pH 9.0 and for SPAH at pH 10.0. The optimal temperature for SPMI and SPLA was 70 °C and for SPPM and SPAH 55 °C and 50 °C, respectively. All proteases showed high stability towards 5% (w/v) SDS and were active even at NaCl concentrations of 5 M. The four proteases demonstrate potential for future biotechnological applications. KEY POINTS: • Halophilic and halotolerant Bacillaceae are a valuable source of new subtilisins. • Four new subtilisins were biochemically characterised in detail. • The four proteases show potential for future biotechnological applications.

Data-driven strategies for the computational design of enzyme thermal stability: trends, perspectives, and prospects

Thermal stability is one of the most important properties of enzymes, which sustains life and determines the potential for the industrial application of biocatalysts. Although traditional methods such as directed evolution and classical rational design contribute greatly to this field, the enormous sequence space of proteins implies costly and arduous experiments. The development of enzyme engineering focuses on automated and efficient strategies because of the breakthrough of high-throughput DNA sequencing and machine learning models. In this review, we propose a data-driven architecture for enzyme thermostability engineering and summarize some widely adopted datasets, as well as machine learning-driven approaches for designing the thermal stability of enzymes. In addition, we present a series of existing challenges while applying machine learning in enzyme thermostability design, such as the data dilemma, model training, and use of the proposed models. Additionally, a few promising directions for enhancing the performance of the models are discussed. We anticipate that the efficient incorporation of machine learning can provide more insights and solutions for the design of enzyme thermostability in the coming years.

Optimized culture conditions facilitate the estrone biodegradation ability and laccase activity of Spirulina CPCC-695

Endocrine disrupting compounds (EDCs) are emerging contaminants that persist and contaminate the environment. They mimic hormones, block hormones, or modulate their synthesis, metabolism, transport, and action, affecting living organisms and their progeny. Steroid hormones from exogenous sources like water bodies are important EDCs. Their biodegradation is an urgent global need. The present study is a preliminary work to maximize the estrone degradation potential of Spirulina CPCC-695 and study the effect of optimized conditions on its laccase activity. It was observed that the exponential phase culture at pH 10.0, 30 ℃, and 200 rpm of agitation speed resulted in the maximum growth, estrone degradation efficiency (93.12%), and highest laccase activity (74%) of Spirulina CPCC-695.

Thermo-L-Asparaginases: From the Role in the Viability of Thermophiles and Hyperthermophiles at High Temperatures to a Molecular Understanding of Their Thermoactivity and Thermostability

L-asparaginase (L-ASNase) is a vital enzyme with a broad range of applications in medicine, food industry, and diagnostics. Among various organisms expressing L-ASNases, thermophiles and hyperthermophiles produce enzymes with superior performances-stable and heat resistant thermo-ASNases. This review is an attempt to take a broader view on the thermo-ASNases. Here we discuss the position of thermo-ASNases in the large family of L-ASNases, their role in the heat-tolerance cellular system of thermophiles and hyperthermophiles, and molecular aspects of their thermoactivity and thermostability. Different types of thermo-ASNases exhibit specific L-asparaginase activity and additional secondary activities. All products of these enzymatic reactions are associated with diverse metabolic pathways and are important for mitigating heat stress. Thermo-ASNases are quite distinct from typical mesophilic L-ASNases based on structural properties, kinetic and activity profiles. Here we attempt to summarize the current understanding of the molecular mechanisms of thermo-ASNases' thermoactivity and thermostability, from amino acid composition to structural-functional relationships. Research of these enzymes has fundamental and biotechnological significance. Thermo-ASNases and their improved variants, cloned and expressed in mesophilic hosts, can form a large pool of enzymes with valuable characteristics for biotechnological application.

Sourcing thermotolerant poly(ethylene terephthalate) hydrolase scaffolds from natural diversity

Enzymatic deconstruction of poly(ethylene terephthalate) (PET) is under intense investigation, given the ability of hydrolase enzymes to depolymerize PET to its constituent monomers near the polymer glass transition temperature. To date, reported PET hydrolases have been sourced from a relatively narrow sequence space. Here, we identify additional PET-active biocatalysts from natural diversity by using bioinformatics and machine learning to mine 74 putative thermotolerant PET hydrolases. We successfully express, purify, and assay 51 enzymes from seven distinct phylogenetic groups; observing PET hydrolysis activity on amorphous PET film from 37 enzymes in reactions spanning pH from 4.5-9.0 and temperatures from 30-70 °C. We conduct PET hydrolysis time-course reactions with the best-performing enzymes, where we observe differences in substrate selectivity as function of PET morphology. We employed X-ray crystallography and AlphaFold to examine the enzyme architectures of all 74 candidates, revealing protein folds and accessory domains not previously associated with PET deconstruction. Overall, this study expands the number and diversity of thermotolerant scaffolds for enzymatic PET deconstruction.

Eliminating host-guest incompatibility via enzyme mining enables the high-temperature production of N-acetylglucosamine

The host-guest incompatibility between a production host and non-native enzymes has posed an arduous challenge for synthetic biology, particularly between mesophile-derived enzymes and a thermophilic chassis. In the present study, we develop a thermophilic enzyme mining strategy comprising an automated co-evolution-based screening pipeline (http://cem.sjtu.edu.cn), computation-based enzyme characterization, and gene synthesis-based function validation. Using glucosamine-6-phosphate acetyltransferase (GNA1) as an example, we successfully mined four novel GNA1s with excellent thermostabilities and catalytic performances. Calculation and analysis based on AlphaFold2-generated structures were also conducted to uncover the mechanism underlying their excellent properties. Finally, our mined GNA1s were used to enable the high-temperature N-acetylglucosamine (GlcNAc) production with high titers of up to 119.3 g/L, with the aid of systems metabolic engineering and temperature programming. This study demonstrates the effectiveness of the enzyme mining strategy, highlighting the application prospects of mining new enzymes from massive databases and providing an effective solution for tackling host-guest incompatibility.

Learning deep representations of enzyme thermal adaptation

Temperature is a fundamental environmental factor that shapes the evolution of organisms. Learning thermal determinants of protein sequences in evolution thus has profound significance for basic biology, drug discovery, and protein engineering. Here, we use a data set of over 3 million BRENDA enzymes labeled with optimal growth temperatures (OGTs) of their source organisms to train a deep neural network model (DeepET). The protein-temperature representations learned by DeepET provide a temperature-related statistical summary of protein sequences and capture structural properties that affect thermal stability. For prediction of enzyme optimal catalytic temperatures and protein melting temperatures via a transfer learning approach, our DeepET model outperforms classical regression models trained on rationally designed features and other deep-learning-based representations. DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes.

The peripherally membrane-attached protein MbFACL6 of Mycobacterium tuberculosis activates a broad spectrum of substrates

The infectious disease tuberculosis is one of the fifteen most common causes of death worldwide (according to the WHO). About every fourth person is infected with the main causative agent Mycobacterium tuberculosis (Mb). A characteristic of the pathogen is its entrance into a dormant state in which a phenotypic antibiotic resistance is achieved. To target resistant strains, novel dormancy-specific targets are very promising. Such a possible target is the Mb "fatty acid-CoA ligase 6" (MbFACL6), which activates fatty acids and thereby modulates the accumulation of triacylglycerol-containing lipid droplets that are used by Mb as an energy source during dormancy. We investigated the membrane association of MbFACL6 in E. coli and its specific activity towards different substrates after establishing a novel MbFACL6 activity assay. Despite a high homology to the mammalian family of fatty acid transport proteins, which are typically transmembrane proteins, our results indicate that MbFACL6 is a peripheral membrane-attached protein. Furthermore, MbFACL6 tolerates a broad spectrum of substrates including saturated and unsaturated fatty acids (C12-C20), some cholic acid derivatives, and even synthetic fatty acids, such as 9(E)-nitrooleicacid. Therefore, the substrate selectivity of MbFACL6 appears to be much broader than previously assumed.

Terahertz thermal curve analysis for label-free identification of pathogens

In this study, we perform a thermal curve analysis with terahertz (THz) metamaterials to develop a label-free identification tool for pathogens such as bacteria and yeasts. The resonant frequency of the metasensor coated with a bacterial layer changes as a function of temperature; this provides a unique fingerprint specific to the individual microbial species without the use of fluorescent dyes and antibodies. Differential thermal curves obtained from the temperature-dependent resonance exhibit the peaks consistent with bacterial phases, such as growth, thermal inactivation, DNA denaturation, and cell wall destruction. In addition, we can distinguish gram-negative bacteria from gram-positive bacteria which show strong peaks in the temperature range of cell wall destruction. Finally, we perform THz melting curve analysis on the mixture of bacterial species in which the pathogenic bacteria are successfully distinguished from each other, which is essential for practical clinical and environmental applications such as in blood culture.

A label-free sensing method has been developed for identifying hazardous pathogens based on their intrinsic properties. This was possible by interrogating the temperature-dependent dielectric constant of the microbes in the far-infrared range.

Deep learning of a bacterial and archaeal universal language of life enables transfer learning and illuminates microbial dark matter

The majority of microbial genomes have yet to be cultured, and most proteins identified in microbial genomes or environmental sequences cannot be functionally annotated. As a result, current computational approaches to describe microbial systems rely on incomplete reference databases that cannot adequately capture the functional diversity of the microbial tree of life, limiting our ability to model high-level features of biological sequences. Here we present LookingGlass, a deep learning model encoding contextually-aware, functionally and evolutionarily relevant representations of short DNA reads, that distinguishes reads of disparate function, homology, and environmental origin. We demonstrate the ability of LookingGlass to be fine-tuned via transfer learning to perform a range of diverse tasks: to identify novel oxidoreductases, to predict enzyme optimal temperature, and to recognize the reading frames of DNA sequence fragments. LookingGlass enables functionally relevant representations of otherwise unknown and unannotated sequences, shedding light on the microbial dark matter that dominates life on Earth.

Computational methods to analyse microbial systems rely on reference databases which do not capture their full functional diversity. Here the authors develop a deep learning model and apply it using transfer learning, creating biologically useful models for multiple different tasks.

Biodegradation of bisphenol A using psychrotolerant bacterial strain Pseudomonas palleroniana GBPI_508

A psychrotolerant bacterial strain of Pseudomonas sp. (P. palleroniana GBPI_508), isolated from the Indian Himalayan region, is studied for analyzing its potential for degrading bisphenol A (BPA). Response surface methodology using Box-Behnken design was used to statistically optimize the environmental factors during BPA degradation and the maximum degradation (97%) was obtained at optimum conditions of mineral salt media pH 9, experimental temperature 25 °C, an inoculum volume of 10% (v/v), and agitation speed 130 rpm at the BPA concentration 270 mg L^-1. The Monod model was used for understanding bacterial degradation kinetics, and 37.5 mg^-1 half saturation coefficient (K_S) and 0.989 regression coefficient (R²) were obtained. Besides, the utmost specific growth rate µmax was witnessed as 0.080 h^-1 with the GBPI_508 during BPA degradation. Metabolic intermediates detected in this study by GC-MS were identified as valeric acid, propionic acid, diglycolic acid, and phenol. The psychrotolerant bacterial strain of Pseudomonas sp. (P. palleroniana GBPI_508), isolated from the Indian Himalayan region has shown good potential for remediation of BPA at variable conditions.

Precipitous Increase of Bacterial CRISPR-Cas Abundance at Around 45°C

Although performing adaptive immunity, CRISPR-Cas systems are present in only 40% of bacterial genomes. We observed an abrupt increase of bacterial CRISPR-Cas abundance at around 45°C. Phylogenetic comparative analyses confirmed that the abundance correlates with growth temperature only at the temperature range around 45°C. From the literature, we noticed that the diversities of cellular predators (like protozoa, nematodes, and myxobacteria) have a steep decline at this temperature range. The grazing risk faced by bacteria reduces substantially at around 45°C and almost disappears above 60°C. We propose that viral lysis would become the dominating factor of bacterial mortality, and antivirus immunity has a higher priority at higher temperatures. In temperature ranges where the abundance of cellular predators does not change with temperature, the growth temperatures of bacteria would not significantly affect their CRISPR-Cas contents. The hypothesis predicts that bacteria should also be rich in CRISPR-Cas systems if they live in other extreme conditions inaccessible to grazing predators.

Identification of novel extracellular putative chitinase and hydrolase from Geomyces sp. B10I with the biodegradation activity towards polyesters

Cold-adapted filamentous fungal strain Geomyces sp. B10I has been reported to decompose polyesters such as poly(e-caprolactone) (PCL), poly(butylene succinate) (PBS) and poly(butylene succinate-co-butylene adipate) (PBSA). Here, we identified the enzymes of Geomyces sp. B10I, which appear to be responsible for its biodegradation activity. We compared their amino acid sequences with sequences of well-studied fungal enzymes. Partial purification of an extracellular mixture of the two enzymes, named hydrGB10I and chitGB10I, using ammonium sulfate precipitation and ionic exchange chromatography gave 14.16-fold purity. The amino acid sequence of the proteins obtained from the MALDI-TOF analysis determined the molecular mass of 77.2 kDa and 46.5 kDa, respectively. Conserved domain homology analysis revealed that both proteins belong to the class of hydrolases; hydrGB10I belongs to the glycosyl hydrolase 81 superfamily, while chitGB10I contains the domain of the glycosyl hydrolase 18 superfamily. Phylogenetic analysis suggests a distinct nature of the hydrGB10I and chitGB10I of Geomyces sp. B10I when compared with other fungal polyester-degrading enzymes described to date.

A thermodynamic atlas of proteomes reveals energetic innovation across the tree of life

Protein stability is a fundamental molecular property enabling organisms to adapt to their biological niches. How this is facilitated and whether there are kingdom specific or more general universal strategies is not known. A principal obstacle to addressing this issue is that the vast majority of proteins lack annotation, specifically thermodynamic annotation, beyond the amino acid and chromosome information derived from genome sequencing. To address this gap and facilitate future investigation into large-scale patterns of protein stability and dynamics within and between organisms, we applied a unique ensemble-based thermodynamic characterization of protein folds to a substantial portion of extant sequenced genomes. Using this approach, we compiled a database resource focused on the position-specific variation in protein stability. Interrogation of the database reveals; 1) domains of life exhibit distinguishing thermodynamic features, with eukaryotes particularly different from both archaea and bacteria, 2) the optimal growth temperature of an organism is proportional to the average apolar enthalpy of its proteome, 3) intrinsic disorder content is also proportional to the apolar enthalpy (but unexpectedly not the predicted stability at 25 °C), and 4) secondary structure and global stability information of individual proteins is extractable. We hypothesize that wider access to residue-specific thermodynamic information of proteomes will result in deeper understanding of mechanisms driving functional adaptation and protein evolution. Our database is free for download at https://afc-science.github.io/thermo-env-atlas/.

Systematic investigation of the link between enzyme catalysis and cold adaptation

Cold temperature is prevalent across the biosphere and slows the rates of chemical reactions. Increased catalysis has been predicted to be a dominant adaptive trait of enzymes to reduced temperature, and this expectation has informed physical models for enzyme catalysis and influenced bioprospecting strategies. To systematically test rate enhancement as an adaptive trait to cold, we paired kinetic constants of 2223 enzyme reactions with their organism's optimal growth temperature (T_Growth) and analyzed trends of rate constants as a function of T_Growth. These data do not support a general increase in rate enhancement in cold adaptation. In the model enzyme ketosteroid isomerase (KSI), there is prior evidence for temperature adaptation from a change in an active site residue that results in a tradeoff between activity and stability. Nevertheless, we found that little of the rate constant variation for 20 KSI variants was accounted for by T_Growth. In contrast, and consistent with prior expectations, we observed a correlation between stability and T_Growth across 433 proteins. These results suggest that temperature exerts a weaker selection pressure on enzyme rate constants than stability and that evolutionary forces other than temperature are responsible for the majority of enzymatic rate constant variation.

Inferring microbiota functions from taxonomic genes: a review

Deciphering microbiota functions is crucial to predict ecosystem sustainability in response to global change. High-throughput sequencing at the individual or community level has revolutionized our understanding of microbial ecology, leading to the big data era and improving our ability to link microbial diversity with microbial functions. Recent advances in bioinformatics have been key for developing functional prediction tools based on DNA metabarcoding data and using taxonomic gene information. This cheaper approach in every aspect serves as an alternative to shotgun sequencing. Although these tools are increasingly used by ecologists, an objective evaluation of their modularity, portability, and robustness is lacking. Here, we reviewed 100 scientific papers on functional inference and ecological trait assignment to rank the advantages, specificities, and drawbacks of these tools, using a scientific benchmarking. To date, inference tools have been mainly devoted to bacterial functions, and ecological trait assignment tools, to fungal functions. A major limitation is the lack of reference genomes-compared with the human microbiota-especially for complex ecosystems such as soils. Finally, we explore applied research prospects. These tools are promising and already provide relevant information on ecosystem functioning, but standardized indicators and corresponding repositories are still lacking that would enable them to be used for operational diagnosis.

Kinetic Properties of Microbial Exoenzymes Vary With Soil Depth but Have Similar Temperature Sensitivities Through the Soil Profile

Current knowledge of the mechanisms driving soil organic matter (SOM) turnover and responses to warming is mainly limited to surface soils, although over 50% of global soil carbon is contained in subsoils. Deep soils have different physicochemical properties, nutrient inputs, and microbiomes, which may harbor distinct functional traits and lead to different SOM dynamics and temperature responses. We hypothesized that kinetic and thermal properties of soil exoenzymes, which mediate SOM depolymerization, vary with soil depth, reflecting microbial adaptation to distinct substrate and temperature regimes. We determined the Michaelis-Menten (MM) kinetics of three ubiquitous enzymes involved in carbon (C), nitrogen (N) and phosphorus (P) acquisition at six soil depths down to 90 cm at a temperate forest, and their temperature sensitivity based on Arrhenius/Q₁₀ and Macromolecular Rate Theory (MMRT) models over six temperatures between 4–50°C. Maximal enzyme velocity (V_max) decreased strongly with depth for all enzymes, both on a dry soil mass and a microbial biomass C basis, whereas their affinities increased, indicating adaptation to lower substrate availability. Surprisingly, microbial biomass-specific catalytic efficiencies also decreased with depth, except for the P-acquiring enzyme, indicating distinct nutrient demands at depth relative to microbial abundance. These results suggested that deep soil microbiomes encode enzymes with intrinsically lower turnover and/or produce less enzymes per cell, reflecting distinct life strategies. The relative kinetics between different enzymes also varied with depth, suggesting an increase in relative P demand with depth, or that phosphatases may be involved in C acquisition. V_max and catalytic efficiency increased consistently with temperature for all enzymes, leading to overall higher SOM-decomposition potential, but enzyme temperature sensitivity was similar at all depths and between enzymes, based on both Arrhenius/Q₁₀ and MMRT models. In a few cases, however, temperature affected differently the kinetic properties of distinct enzymes at discrete depths, suggesting that it may alter the relative depolymerization of different compounds. We show that soil exoenzyme kinetics may reflect intrinsic traits of microbiomes adapted to distinct soil depths, although their temperature sensitivity is remarkably uniform. These results improve our understanding of critical mechanisms underlying SOM dynamics and responses to changing temperatures through the soil profile.

Strategic traits of bacteria and archaea vary widely within substrate-use groups

Quantitative traits such as maximum growth rate and cell radial diameter are one facet of ecological strategy variation across bacteria and archaea. Another facet is substrate-use pathways, such as iron reduction or methylotrophy. Here, we ask how these two facets intersect, using a large compilation of data for culturable species and examining seven quantitative traits (genome size, signal transduction protein count, histidine kinase count, growth temperature, temperature-adjusted maximum growth rate, cell radial diameter and 16S rRNA operon copy number). Overall, quantitative trait variation within groups of organisms possessing a particular substrate-use pathway was very broad, outweighing differences between substrate-use groups. Although some substrate-use groups had significantly different means for some quantitative traits, standard deviation of quantitative trait values within each substrate-use pathway mostly averaged between 1.6 and 1.8 times larger than standard deviation across group means. Most likely, this wide variation reflects ecological strategy: for example, fast maximum growth rate is likely to express an early successional or copiotrophic strategy, and maximum growth varies widely within most substrate-use pathways. In general, it appears that these quantitative traits express different and complementary information about ecological strategy, compared with substrate use.

The Mutational Robustness of the Genetic Code and Codon Usage in Environmental Context: A Non-Extremophilic Preference?

The genetic code was evolved, to some extent, to minimize the effects of mutations. The effects of mutations depend on the amino acid repertoire, the structure of the genetic code and frequencies of amino acids in proteomes. The amino acid compositions of proteins and corresponding codon usages are still under selection, which allows us to ask what kind of environment the standard genetic code is adapted to. Using simple computational models and comprehensive datasets comprising genomic and environmental data from all three domains of Life, we estimate the expected severity of non-synonymous genomic mutations in proteins, measured by the change in amino acid physicochemical properties. We show that the fidelity in these physicochemical properties is expected to deteriorate with extremophilic codon usages, especially in thermophiles. These findings suggest that the genetic code performs better under non-extremophilic conditions, which not only explains the low substitution rates encountered in halophiles and thermophiles but the revealed relationship between the genetic code and habitat allows us to ponder on earlier phases in the history of Life.

A large-scale metagenomic study for enzyme profiles using the focused identification of the NGS-based definitive enzyme research (FINDER) strategy

Excavating the molecular details of many diverse enzymes from metagenomes remains challenging in agriculture, food, health, and environmental fields. We present a versatile method that accelerates metabolic enzyme discovery for highly selective gene capture in metagenomes using next-generation sequencing. Culture-independent enzyme mining of environmental DNA is based on a set of short identifying degenerate sequences specific for a wide range of enzyme superfamilies, followed by multiplexed DNA barcode sequencing. A strategy of 'focused identification of next-generation sequencing-based definitive enzyme research' enabled us to generate targeted enzyme datasets from metagenomes, resulting in minimal hands-on obtention of high-throughput biological diversity and potential function profiles, without being time-consuming. This method also provided a targeted inventory of predicted proteins and molecular features of metabolic activities from several metagenomic samples. We suggest that the efficiency and sensitivity of this method will accelerate the decryption of microbial diversity and the signature of proteins and their metabolism from environmental samples.

Performance of Regression Models as a Function of Experiment Noise

Background:

A challenge in developing machine learning regression models is that it is difficult to know whether maximal performance has been reached on the test dataset, or whether further model improvement is possible. In biology, this problem is particularly pronounced as sample labels (response variables) are typically obtained through experiments and therefore have experiment noise associated with them. Such label noise puts a fundamental limit to the metrics of performance attainable by regression models on the test dataset.

Results:

We address this challenge by deriving an expected upper bound for the coefficient of determination (R²) for regression models when tested on the holdout dataset. This upper bound depends only on the noise associated with the response variable in a dataset as well as its variance. The upper bound estimate was validated via Monte Carlo simulations and then used as a tool to bootstrap performance of regression models trained on biological datasets, including protein sequence data, transcriptomic data, and genomic data.

Conclusions:

The new method for estimating upper bounds for model performance on test data should aid researchers in developing ML regression models that reach their maximum potential. Although we study biological datasets in this work, the new upper bound estimates will hold true for regression models from any research field or application area where response variables have associated noise.

Trait dimensions in bacteria and archaea compared to vascular plants

Bacteria and archaea have very different ecology compared to plants. One similarity, though, is that much discussion of their ecological strategies has invoked concepts such as oligotrophy or stress tolerance. For plants, so-called 'trait ecology'-strategy description reframed along measurable trait dimensions-has made global syntheses possible. Among widely measured trait dimensions for bacteria and archaea three main axes are evident. Maximum growth rate in association with rRNA operon copy number expresses a rate-yield trade-off that is analogous to the acquisitive-conservative spectrum in plants, though underpinned by different trade-offs. Genome size in association with signal transduction expresses versatility. Cell size has influence on diffusive uptake and on relative wall costs. These trait dimensions, and potentially others, offer promise for interpreting ecology. At the same time, there are very substantial differences from plant trait ecology. Traits and their underpinning trade-offs are different. Also, bacteria and archaea use a variety of different substrates. Bacterial strategies can be viewed both through the facet of substrate-use pathways, and also through the facet of quantitative traits such as maximum growth rate. Preliminary evidence shows the quantitative traits vary widely within substrate-use pathways. This indicates they convey information complementary to substrate use.

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.

The temperature sensitivity of soil: microbial biodiversity, growth, and carbon mineralization

Microorganisms drive soil carbon mineralization and changes in their activity with increased temperature could feedback to climate change. Variation in microbial biodiversity and the temperature sensitivities (Q₁₀) of individual taxa may explain differences in the Q₁₀ of soil respiration, a possibility not previously examined due to methodological limitations. Here, we show phylogenetic and taxonomic variation in the Q₁₀ of growth (5-35 °C) among soil bacteria from four sites, one from each of Arctic, boreal, temperate, and tropical biomes. Differences in the temperature sensitivities of taxa and the taxonomic composition of communities determined community-assembled bacterial growth Q₁₀, which was strongly predictive of soil respiration Q₁₀ within and across biomes. Our results suggest community-assembled traits of microbial taxa may enable enhanced prediction of carbon cycling feedbacks to climate change in ecosystems across the globe.

Bayesian genome scale modelling identifies thermal determinants of yeast metabolism

The molecular basis of how temperature affects cell metabolism has been a long-standing question in biology, where the main obstacles are the lack of high-quality data and methods to associate temperature effects on the function of individual proteins as well as to combine them at a systems level. Here we develop and apply a Bayesian modeling approach to resolve the temperature effects in genome scale metabolic models (GEM). The approach minimizes uncertainties in enzymatic thermal parameters and greatly improves the predictive strength of the GEMs. The resulting temperature constrained yeast GEM uncovers enzymes that limit growth at superoptimal temperatures, and squalene epoxidase (ERG1) is predicted to be the most rate limiting. By replacing this single key enzyme with an ortholog from a thermotolerant yeast strain, we obtain a thermotolerant strain that outgrows the wild type, demonstrating the critical role of sterol metabolism in yeast thermosensitivity. Therefore, apart from identifying thermal determinants of cell metabolism and enabling the design of thermotolerant strains, our Bayesian GEM approach facilitates modelling of complex biological systems in the absence of high-quality data and therefore shows promise for becoming a standard tool for genome scale modeling.

While temperature impacts the function of all cellular components, it’s hard to rule out how the temperature dependence of cell phenotypes emerged from the dependence of individual components. Here, the authors develop a Bayesian genome scale modelling approach to identify thermal determinants of yeast metabolism.

Large-scale in silico mutagenesis experiments reveal optimization of genetic code and codon usage for protein mutational robustness

BACKGROUND

How, and the extent to which, evolution acts on DNA and protein sequences to ensure mutational robustness and evolvability is a long-standing open question in the field of molecular evolution. We addressed this issue through the first structurome-scale computational investigation, in which we estimated the change in folding free energy upon all possible single-site mutations introduced in more than 20,000 protein structures, as well as through available experimental stability and fitness data.

RESULTS

At the amino acid level, we found the protein surface to be more robust against random mutations than the core, this difference being stronger for small proteins. The destabilizing and neutral mutations are more numerous in the core and on the surface, respectively, whereas the stabilizing mutations are about 4% in both regions. At the genetic code level, we observed smallest destabilization for mutations that are due to substitutions of base III in the codon, followed by base I, bases I+III, base II, and other multiple base substitutions. This ranking highly anticorrelates with the codon-anticodon mispairing frequency in the translation process. This suggests that the standard genetic code is optimized to limit the impact of random mutations, but even more so to limit translation errors. At the codon level, both the codon usage and the usage bias appear to optimize mutational robustness and translation accuracy, especially for surface residues.

CONCLUSION

Our results highlight the non-universality of mutational robustness and its multiscale dependence on protein features, the structure of the genetic code, and the codon usage. Our analyses and approach are strongly supported by available experimental mutagenesis data.

A synthesis of bacterial and archaeal phenotypic trait data

A synthesis of phenotypic and quantitative genomic traits is provided for bacteria and archaea, in the form of a scripted, reproducible workflow that standardizes and merges 26 sources. The resulting unified dataset covers 14 phenotypic traits, 5 quantitative genomic traits, and 4 environmental characteristics for approximately 170,000 strain-level and 15,000 species-aggregated records. It spans all habitats including soils, marine and fresh waters and sediments, host-associated and thermal. Trait data can find use in clarifying major dimensions of ecological strategy variation across species. They can also be used in conjunction with species and abundance sampling to characterize trait mixtures in communities and responses of traits along environmental gradients.

Biodegradation of endocrine disruptor Bisphenol A by Pseudomonas putida strain YC-AE1 isolated from polluted soil, Guangdong, China

Background

Bisphenol A is an important organic chemical as an intermediate, final and inert ingredient in manufacturing of many important products like polycarbonate plastics, epoxy resins, flame retardants, food–drink packaging coating, and other. BPA is an endocrine disruptor compound that mimics the function of estrogen causing damage to reproductive organs. Bacterial degradation has been consider as a cost effective and eco-friendly method for BPA degradation compared with physical and chemical methods. This study aimed to isolate and identify bacterial strain capable to degrade and tolerate high concentrations of this pollutant, studying the factors affecting the degradation process and study the degradation mechanism of this strain.

Results

YC-AE1 is a Gram negative bacterial strain isolated from soil and identified as Pseudomonas putida by 16S rRNA gene sequence and BIOLOG identification system. This strain found to have a high capacity to degrade the endocrine disruptor Bisphenol A (BPA). Response surface methodology using central composite design was used to statistically optimize the environmental factors during BPA degradation and the results obtained by significant model were 7.2, 30 °C and 2.5% for optimum initial pH, temperature and inoculum size, respectively. Prolonged incubation period with low NaCl concentration improve the biodegradation of BPA. Analysis of variance (ANOVA) showed high coefficient of determination, R² and Adj-R² which were 0.9979 and 0.9935, respectively. Substrate analysis found that, strain YC-AE1 could degrade a wide variety of bisphenol A-related pollutants such as bisphenol B, bisphenol F, bisphenol S, Dibutyl phthalate, Diethylhexyl phthalate and Diethyl phthalate in varying proportion. Pseudomonas putida YC-AE1 showed high ability to degrade a wide range of BPA concentrations (0.5–1000 mg l^− 1) with completely degradation for 500 mg l^− 1 within 72 h. Metabolic intermediates detected in this study by HPLC-MS were identified as 4,4-dihydroxy-alpha-methylstilbene, p-hydroxybenzaldeyde, p-hydroxyacetophenone, 4-hydroxyphenylacetate, 4-hydroxyphenacyl alcohol, 2,2-bis(4-hydroxyphenyl)-1-propanol, 1,2-bis(4-hydroxyphenyl)-2-propanol and 2,2-bis(4-hydroxyphenyl) propanoate.

Conclusions

This study reports Pseudomonas putida YC-AE1 as BPA biodegrader with high performance in degradation and tolerance to high BPA concentration. It exhibited strong degradation capacity and prominent adaptability towards a wide range of environmental conditions. Moreover, it degrades BPA in a short time via two different degradation pathways.

Machine Learning Applied to Predicting Microorganism Growth Temperatures and Enzyme Catalytic Optima

Enzymes that catalyze chemical reactions at high temperatures are used for industrial biocatalysis, applications in molecular biology, and as highly evolvable starting points for protein engineering. The optimal growth temperature (OGT) of organisms is commonly used to estimate the stability of enzymes encoded in their genomes, but the number of experimentally determined OGT values are limited, particularly for thermophilic organisms. Here, we report on the development of a machine learning model that can accurately predict OGT for bacteria, archaea, and microbial eukaryotes directly from their proteome-wide 2-mer amino acid composition. The trained model is made freely available for reuse. In a subsequent step we use OGT data in combination with amino acid composition of individual enzymes to develop a second machine learning model-for prediction of enzyme catalytic temperature optima ( T_opt). The resulting model generates enzyme T_opt estimates that are far superior to using OGT alone. Finally, we predict T_opt for 6.5 million enzymes, covering 4447 enzyme classes, and make the resulting data set available to researchers. This work enables simple and rapid identification of enzymes that are potentially functional at extreme temperatures.