AIMC Topic: Archaea

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Deep Learning Exploration Expands the Natural Diversity of Metallothioneins in the Archaea Domain.

Journal of agricultural and food chemistry
The diversity and functions of metallothioneins (MTs) in Archaea remain poorly understood. This study identifies 180 archaeal MTs from 406 genomes, revealing distinct evolutionary lineages and structural diversity. Phylogenetic analysis suggests a no...

Unveiling the landscape of prokaryotic global regulators through deep protein language models.

mSystems
Global regulators (GRs) are key transcription factors that orchestrate the expression of multiple genes, playing essential roles in stress responses, virulence, secondary metabolism, and antibiotic resistance-traits that make them powerful tools for ...

Rubisco is slow across the tree of life.

Proceedings of the National Academy of Sciences of the United States of America
Rubisco is the main gateway through which inorganic carbon enters the biosphere, catalyzing the vast majority of carbon fixation on Earth. This pivotal enzyme has long been observed to be kinetically constrained. Yet, this impression is based on kine...

Digestibility, microbiome dynamics, and biogas generation in anaerobic digestion with integrated additives and artificial intelligence.

Environmental research
Addition of abiotic and biotic factors as single or combined in anaerobic digestion (AD) improves the substrate hydrolysis, microbial nexus, and enzymatic activity. The effect of a single abiotic (salinity, micronutrients, and conductive material) or...

LucaPCycle: Illuminating microbial phosphorus cycling in deep-sea cold seep sediments using protein language models.

Nature communications
Phosphorus is essential for life and critically influences marine productivity. Despite geochemical evidence of active phosphorus cycling in deep-sea cold seeps, the microbial processes involved remain poorly understood. Traditional sequence-based se...

Exploring species taxonomic kingdom using information entropy and nucleotide compositional features of coding sequences based on machine learning methods.

Methods (San Diego, Calif.)
The flow of genetic information from DNA to protein is governed by the central dogma of molecular biology. Genetic drift and mutations usually lead to changes in DNA composition, thereby affecting the coding sequences (CDS) that encode functional pro...

An atlas of protein homo-oligomerization across domains of life.

Cell
Protein structures are essential to understanding cellular processes in molecular detail. While advances in artificial intelligence revealed the tertiary structure of proteins at scale, their quaternary structure remains mostly unknown. We devise a s...

Explainable artificial intelligence as a reliable annotator of archaeal promoter regions.

Scientific reports
Archaea are a vast and unexplored cellular domain that thrive in a high diversity of environments, having central roles in processes mediating global carbon and nutrient fluxes. For these organisms to balance their metabolism, the appropriate regulat...

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

Nature communications
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...

Using neural networks to mine text and predict metabolic traits for thousands of microbes.

PLoS computational biology
Microbes can metabolize more chemical compounds than any other group of organisms. As a result, their metabolism is of interest to investigators across biology. Despite the interest, information on metabolism of specific microbes is hard to access. I...