Nitric Oxide, Nitric Oxide Formers and Their Physiological Impacts in Bacteria

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Abstract

Nitric oxide (NO) is an active and critical nitrogen oxide in the microbe-driven nitrogen biogeochemical cycle, and is of great interest to medicine and the biological sciences. As a gas molecule prior to oxygen, NO respiration represents an early form of energy generation via various reactions in prokaryotes. Major enzymes for endogenous NO formation known to date include two types of nitrite reductases in denitrification, hydroxylamine oxidoreductase in ammonia oxidation, and NO synthases (NOSs). While the former two play critical roles in shaping electron transport pathways in bacteria, NOSs are intracellular enzymes catalyzing metabolism of certain amino acids and have been extensively studied in mammals. NO interacts with numerous cellular targets, most of which are redox-active proteins. Doing so, NO plays harmful and beneficial roles by affecting diverse biological processes within bacterial physiology. Here, we discuss recent advances in the field, including NO-forming enzymes, the molecular mechanisms by which these enzymes function, physiological roles of bacterial NOSs, and regulation of NO homeostasis in bacteria.

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Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

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AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

Mihaly Varadi, Stephen Anyango, Mandar Deshpande … (2021)

The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk ) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.

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The microbial nitrogen-cycling network

Boran Kartal, Marcel M. M. Kuypers, Hannah Marchant (2018)

Nitrogen is an essential component of all living organisms and the main nutrient limiting life on our planet. By far, the largest inventory of freely accessible nitrogen is atmospheric dinitrogen, but most organisms rely on more bioavailable forms of nitrogen, such as ammonium and nitrate, for growth. The availability of these substrates depends on diverse nitrogen-transforming reactions that are carried out by complex networks of metabolically versatile microorganisms. In this Review, we summarize our current understanding of the microbial nitrogen-cycling network, including novel processes, their underlying biochemical pathways, the involved microorganisms, their environmental importance and industrial applications.