ThermoBase: A database of the phylogeny and physiology of thermophilic and hyperthermophilic organisms

Microbial host engineering for sustainable isobutanol production from renewable resources

Due to the limited resources and environmental problems associated with fossil fuels, there is a growing interest in utilizing renewable resources for the production of biofuels through microbial fermentation. Isobutanol is a promising biofuel that could potentially replace gasoline. However, its production efficiency is currently limited by the use of naturally isolated microorganisms. These naturally isolated microorganisms often encounter problems such as a limited range of substrates, low tolerance to solvents or inhibitors, feedback inhibition, and an imbalanced redox state. This makes it difficult to improve their production efficiency through traditional process optimization methods. Fortunately, recent advancements in genetic engineering technologies have made it possible to enhance microbial hosts for the increased production of isobutanol from renewable resources. This review provides a summary of the strategies and synthetic biology approaches that have been employed in the past few years to improve naturally isolated or non-natural microbial hosts for the enhanced production of isobutanol by utilizing different renewable resources. Furthermore, it also discusses the challenges that are faced by engineered microbial hosts and presents future perspectives to enhancing isobutanol production. KEY POINTS: • Promising potential of isobutanol to replace gasoline • Engineering of native and non-native microbial host for isobutanol production • Challenges and opportunities for enhanced isobutanol production.

Rapid hydrolysis rates of thio- and phosphate esters constrain the origin of metabolism to cool, acidic to neutral environments

Universal to all life is a reliance on energy carriers such as adenosine triphosphate (ATP) which connect energy-releasing reactions to energy-consuming processes. While ATP is ubiquitously used today, simpler molecules such as thioesters and polyphosphates are hypothesized to be primordial energy carriers. Investigating environmental constraints on the non-enzymatic emergence of metabolism, we find that hydrolysis rates-not hydrolysis energies-differentiate phosphate esters and thioesters. At temperatures consistent with thermophilic microbes, thioesters are favored at acidic pH and phosphate esters at basic pH. Thioacids have a high stability across pH 5-10. The planetary availability of sulfur and phosphate is coincident with these calculations, with phosphate being abundant in alkaline and sulfur in acidic environments. Since both sulfur esters and phosphate esters are uniquely required in metabolism, our results point to a non-thermophilic origin of early metabolism at cool, acidic to neutral environments.

Differential amino acid usage leads to ubiquitous edge effect in proteomes across domains of life that can be explained by amino acid secondary structure propensities

Amino acids are the building blocks of proteins and enzymes which are essential for life. Understanding amino acid usage offers insights into protein function and molecular mechanisms underlying life histories. However, genome-wide patterns of amino acid usage across domains of life remain poorly understood. Here, we analysed the proteomes of 5590 species across four domains and found that only a few amino acids are consistently the most and least used. This differential usage results in lower amino acid usage diversity at the most and least frequent ranks, creating a ubiquitous inverted U-shape pattern of amino acid diversity and rank which we call an 'edge effect' across proteomes and domains of life. This effect likely stems from protein secondary structural constraints, not the evolutionary chronology of amino acid incorporation into the genetic code, highlighting the functional rather than evolutionary influences on amino acid usage. We also tested other contemporary hypotheses regarding amino acid usage in proteomes and found that amino acid usage varies across life's domains and is only weakly influenced by growth temperature. Our findings reveal a novel and pervasive amino acid usage pattern across genomes with the potential to help us probe deep evolutionary relationships and advance synthetic biology.

Leveraging protein language model embeddings and logistic regression for efficient and accurate in-silico acidophilic proteins classification

The increasing demand for eco-friendly technologies in biotechnology necessitates effective and sustainable catalysts. Acidophilic proteins, functioning optimally in highly acidic environments, hold immense promise for various applications, including food production, biofuels, and bioremediation. However, limited knowledge about these proteins hinders their exploration. This study addresses this gap by employing in silico methods utilizing computational tools and machine learning. We propose a novel approach to predict acidophilic proteins using protein language models (PLMs), accelerating discovery without extensive lab work. Our investigation highlights the potential of PLMs in understanding and harnessing acidophilic proteins for scientific and industrial advancements. We introduce the ACE model, which combines a simple Logistic Regression model with embeddings derived from protein sequences processed by the ProtT5 PLM. This model achieves high performance on an independent test set, with accuracy (0.91), F1-score (0.93), and Matthew's correlation coefficient (0.76). To our knowledge, this is the first application of pre-trained PLM embeddings for acidophilic protein classification. The ACE model serves as a powerful tool for exploring protein acidophilicity, paving the way for future advancements in protein design and engineering.

A suite of designed protein cages using machine learning and protein fragment-based protocols

Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, but their creation remains challenging. Here, we apply computational approaches to design a suite of tetrahedrally symmetric, self-assembling protein cages. For the generation of docked conformations, we emphasize a protein fragment-based approach, while for sequence design of the de novo interface, a comparison of knowledge-based and machine learning protocols highlights the power and increased experimental success achieved using ProteinMPNN. An analysis of design outcomes provides insights for improving interface design protocols, including prioritizing fragment-based motifs, balancing interface hydrophobicity and polarity, and identifying preferred polar contact patterns. In all, we report five structures for seven protein cages, along with two structures of intermediate assemblies, with the highest resolution reaching 2.0 Å using cryo-EM. This set of designed cages adds substantially to the body of available protein nanoparticles, and to methodologies for their creation.

Molecular Characterization of the Iron-Containing Alcohol Dehydrogenase from the Extremely Thermophilic Bacterium Pseudothermotoga hypogea

Pseudothermotoga hypogea is an extremely thermophilic bacterium capable of growing at 90 °C and producing ethanol, which is catalyzed by an alcohol dehydrogenase (ADH). The gene encoding P. hypogea ADH (PhADH) was cloned, sequenced and over-expressed. The gene sequence (1164 bp) was obtained by sequencing all fragments of the gene, which were amplified from the genomic DNA. The deduced amino acid sequence showed high identity to iron-containing ADHs from other Thermotoga species and harbored typical iron- and NADP-binding motifs, Asp195His199His268His282 and Gly39Gly40Gly41Ser42, respectively. Structural modeling showed that the N-terminal domain of PhADH contains an α/β-dinucleotide-binding motif and that its C-terminal domain is an α-helix-rich region containing the iron-binding motif. The recombinant PhADH was soluble, active, and thermostable, with a subunit size of 43 ± 1 kDa revealed by SDS-PAGE analyses. The recombinant PhADH (69 ± 2 U/mg) was shown to have similar properties to the native enzyme. The optimal pH values for alcohol oxidation and aldehyde reduction were 11.0 and 8.0, respectively. It was also thermostable, with a half-life of 5 h at 70 °C. The successful expression of the recombinant PhADH in E. coli significantly enhanced the yield of enzyme production and thus will facilitate further investigation of the catalytic mechanisms of iron-containing ADHs.

Shotgun metagenomics and computational profiling of the plastisphere microbiome: unveiling the potential of enzymatic production and plastic degradation

Plastic pollution is one of the most resilient types of pollution and is considered a global environmental threat, particularly in the marine environment. This study aimed to identify plastic-degrading bacteria from the plastisphere and their pharmaceutical and therapeutic potential. We collected samples from soil and aquatic plastisphere to identify the bacterial communities using shotgun metagenomic sequencing and bioinformatic tools. Results showed that the microbiome comprised 93% bacteria, 0.29% archaea, and 3.87% unidentified microbes. Of these 93% of bacteria, 54% were Proteobacteria, 23.9% were Firmicutes, 13% were Actinobacteria, and 2.1% were other phyla. We found that the plastisphere microbiome was involved in degrading synthetic and polyhydroxy alkanoate (PHA) plastic, biosurfactant production, and can thrive under high temperatures. However, no association existed between thermophiles, synthetic plastic or PHA degraders, and biosurfactant-producing bacterial species except for Pseudomonas. Other plastisphere inhabiting plastic degrading microbes include Streptomyces, Bacillus, Achromobacter, Azospirillum, Bacillus, Brevundimonas, Clostridium, Paenibacillus, Rhodococcus, Serratia, Staphylococcus, Thermobifida, and Thermomonospora. However, the plastisphere microbiome showed potential for producing secondary metabolites that were found to act as anticancer, antitumor, anti-inflammatory, antimicrobial, and enzyme stabilizers. These results revealed that the plastisphere microbiome upholds clinical and environmental significance as it can open future portals in a multi-directional way.

A Suite of Designed Protein Cages Using Machine Learning Algorithms and Protein Fragment-Based Protocols

Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, while methods for their creation remain challenging and unpredictable. In the present study, we apply new computational approaches to design a suite of new tetrahedrally symmetric, self-assembling protein cages. For the generation of docked poses, we emphasize a protein fragment-based approach, while for de novo interface design, a comparison of computational protocols highlights the power and increased experimental success achieved using the machine learning program ProteinMPNN. In relating information from docking and design, we observe that agreement between fragment-based sequence preferences and ProteinMPNN sequence inference correlates with experimental success. Additional insights for designing polar interactions are highlighted by experimentally testing larger and more polar interfaces. In all, using X-ray crystallography and cryo-EM, we report five structures for seven protein cages, with atomic resolution in the best case reaching 2.0 Å. We also report structures of two incompletely assembled protein cages, providing unique insights into one type of assembly failure. The new set of designed cages and their structures add substantially to the body of available protein nanoparticles, and to methodologies for their creation.

Genes for highly abundant proteins in Escherichia coli avoid 5' codons that promote ribosomal initiation

In many species highly expressed genes (HEGs) over-employ the synonymous codons that match the more abundant iso-acceptor tRNAs. Bacterial transgene codon randomization experiments report, however, that enrichment with such "translationally optimal" codons has little to no effect on the resultant protein level. By contrast, consistent with the view that ribosomal initiation is rate limiting, synonymous codon usage following the 5' ATG greatly influences protein levels, at least in part by modifying RNA stability. For the design of bacterial transgenes, for simple codon based in silico inference of protein levels and for understanding selection on synonymous mutations, it would be valuable to computationally determine initiation optimality (IO) scores for codons for any given species. One attractive approach is to characterize the 5' codon enrichment of HEGs compared with the most lowly expressed genes, just as translational optimality scores of codons have been similarly defined employing the full gene body. Here we determine the viability of this approach employing a unique opportunity: for Escherichia coli there is both the most extensive protein abundance data for native genes and a unique large-scale transgene codon randomization experiment enabling objective definition of the 5' codons that cause, rather than just correlate with, high protein abundance (that we equate with initiation optimality, broadly defined). Surprisingly, the 5' ends of native genes that specify highly abundant proteins avoid such initiation optimal codons. We find that this is probably owing to conflicting selection pressures particular to native HEGs, including selection favouring low initiation rates, this potentially enabling high efficiency of ribosomal usage and low noise. While the classical HEG enrichment approach does not work, rendering simple prediction of native protein abundance from 5' codon content futile, we report evidence that initiation optimality scores derived from the transgene experiment may hold relevance for in silico transgene design for a broad spectrum of bacteria.