Broadening environmental research in the era of accurate protein structure determination and predictions

doi:10.1007/s11783-024-1851-0

Front. Environ. Sci. Eng.

2024, Vol. 18

Issue (7) : 91 https://doi.org/10.1007/s11783-024-1851-0

Broadening environmental research in the era of accurate protein structure determination and predictions

Mingda Zhou¹, Tong Wang¹, Ke Xu², Han Wang¹, Zibin Li¹, Wei-xian Zhang¹, Yayi Wang¹(

)

¹. State Key Laboratory of Pollution Control and Resources Reuse, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
². Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China

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Abstract

● The connections between protein structure and environmental research are proposed.

● Cryogenic electron microscopy facilitates studies of environmental protein dynamics.

● Protein structure predictions help understand unknown proteins in the environment.

● Environmental applications aided by protein structural research are anticipated.

The deep-learning protein structure prediction method AlphaFold2 has garnered enormous attention beyond the realm of structural biology, for its groundbreaking contribution to solving the “protein folding problem”. In this perspective, we explore the connection between protein structure studies and environmental research, delving into the potential for addressing specific environmental challenges. Proteins are promising for environmental applications because of the functional diversity endowed by their structural complexity. However, structural studies on proteins with environmental significance remain scarce. Here, we present the opportunity to study proteins by advancing experimental determination and deep-learning prediction methods. Specifically, the latest progress in environmental research via cryogenic electron microscopy is highlighted. It allows us to determine the structure of protein complexes in their native state within cells at molecular resolution, revealing environmentally-associated structural dynamics. With the remarkable advancements in computational power and experimental resolution, the study of protein structure and dynamics has reached unprecedented depth and accuracy. These advancements will undoubtedly accelerate the establishment of comprehensive environmental protein structural and functional databases. Tremendous opportunities for protein engineering exist to enable innovative solutions for environmental applications, such as the degradation of persistent contaminants, and the recovery of valuable metals as well as rare earth elements.

Keywords Environmental proteins Protein structure Cryogenic electron microscopy Protein structure prediction Protein engineering Artificial Intelligence

Corresponding Author(s): Yayi Wang

About author:

Li Liu and Yanqing Liu contributed equally to this work.

Issue Date: 22 April 2024

Cite this article:

Mingda Zhou,Tong Wang,Ke Xu, et al. Broadening environmental research in the era of accurate protein structure determination and predictions[J]. Front. Environ. Sci. Eng., 2024, 18(7): 91.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-024-1851-0
https://academic.hep.com.cn/fese/EN/Y2024/V18/I7/91

Fig.1 (a) Number of released Protein Data Bank (PDB) structures, classified by methods over the past 30 years. NMR, nuclear magnetic resonance spectroscopy; cryo-EM, cryogenic electron microscopy. (b) Percentage of resolution ranges in electron microscopy density maps between 2002 and 2023, from the Electron Microscopy Data Bank. Each color denotes a different resolution range of deposited structures. (c) Global distance test-total score (GDT-TS) trend line of the past 14 CASP ranges from easy to difficult. GDT-TS is the main accuracy metric for predictions. Bubble points denote the AlphaFold2 performance for each prediction project. (d) Structure of the ubiquinol oxidase subunit 1 (color) from Escherichia coli, determined by experiment (left) (image from the RCSB PDB of PDB ID 6WTI) (Su et al., 2021) and AlphaFold2 prediction (right).

Fig.2 (a) Typical workflow for single-particle analysis (SPA). (b) The difference in imaging methods between SPA and cryo-electron tomography.

Protein complex	Classification	Function	Organisms	Expression system	MW (kDa)	Reconstruction Method	Resolution (?)	Ref.
Methane monooxygenase	Oxidoreductase	Catalyze methane into methanol	Methylococcus capsulatus	n.a.	314.77	Single Particle	2.6	Chang et al. (2021)
Fdh-hdr-fmd hexamer	Oxidoreductase	Catalyze methanogenic electron-bifurcating and CO₂ fixing	Methanospirillum hungatei	n.a.	~3000	Single Particle	3.5	Watanabe et al. (2021)
F₄₂₀-reducing nife-hydrogenase	Oxidoreductase	Oxidation/reduction of F₄₂₀ in the methanogenesis pathway	Methanothermobacter marburgensis	n.a.	109.14	Single Particle	4.0	Mills et al. (2013)
Succinate dehydrogenase	Oxidoreductase	Catalyze succinate oxidation by menaquinone	Mycolicibacterium smegmatis	n.a.	423.47	Single Particle	2.84	Gong et al. (2020)
ketol-acid reductoisomerase	Isomerase	Catalyze the isomerism of ketol-acid	Saccharolobus solfataricus	Escherichia coli	447.05	Single Particle	3.0	Chen et al. (2019)
CYP102A1	Oxidoreductase	Catalyze hydroxylating long-chain fatty acids	Bacillus megaterium	Escherichia coli	238	Single Particle	6.7	Su et al. (2020)
N,N-dimethylformamidase	Hydrolase	Catalyze the formation of formate and dimethylamine	Paracoccus sp. SSG05	Escherichia coli	204.96	Single Particle	3.0	Arya et al. (2020)
Formate dehydrogenase	Oxidoreductase	Catalyze the reversible oxidation of formate to carbon dioxide	Rhodobacter capsulatus	n.a.	369.03	Single Particle	3.26	Radon et al. (2020)
Hydrazine dehydrogenase	Oxidoreductase	Catalyze hydrazine oxidation	Candidatus Kuenenia stuttgartiensis	n.a.	~1700	Single Particle	5.2	Akram et al. (2019)
Nitrite oxidoreductase	Oxidoreductase	Catalyze nitrite oxidation and reduction	Candidatus Kuenenia stuttgartiensis	n.a.	230.0/nm	Tomograph/Helical	22.0/6.2	Chicano et al. (2021)
Nitrite reductase	Oxidoreductase	Catalyze the nitrite into nitric oxide	Achromobacter cycloclastes	n.a.	110.25	Single Particle	2.99	Adachi et al. (2021)
Nitric oxide reductase	Oxidoreductase	Catalyze nitric oxide into nitrous oxide	Achromobacter xylosoxidans	Escherichia coli	171.85	Single Particle	3.3	Gopalasingam et al. (2019)
Sulfur oxygenase reductase	Oxidoreductase	Catalyze oxygenation and disproportionation of sulfur	Sulfurisphaera tokodaii	Escherichia coli	859.66	Single Particle	2.24	Sato et al., 2020)
Cytochrome s filament	Protein fibril	Nanowires for electron transport	Geobacter sulfurreducens	n.a.	46.72	Helical	3.4	Filman et al. (2019)
Ybtpq importer	Transport protein	Associated and retake the siderophores	Escherichia coli	n.a.	133.35	Single Particle	3.4	Wang et al. (2020)
Trkh-trka	Transport protein	Ion channel implicated in K⁺ uptake	Vibrio parahemolyticus	Escherichia coli	413.39	Single Particle	2.97	Zhang et al. (2020)

Tab.1 Advances in protein structures in environmental microorganisms via cryogenic electron microscopy

Fig.3 (a) Cryo-electron microscopy (cryo-EM) reconstruction of the CYP102A1 structure in the closed state (7.6 ?) (EMD-20785) and open state (8.3 ?) (EMD-20786) (Su et al., 2020). (b) Cryo-EM reconstruction of Cu-containing nitrite reductase structures at pH = 6.2 (2.99 ?) (EMD-0730) and pH = 8.1 (2.85 ?) (EMD-0731) (Adachi et al., 2021). (c) Structures of ketol-acid reductoisomerase over the temperature range 25–70 °C, overlaid with the 4 °C structure, showing the major change in the N-subdomain between 55 and 70 °C (Chen et al., 2019). (d) In situ nitrite oxidoreductase (NXR) complex anammoxosomal tubules via the tomogram of a K. stuttgartiensis cell (upper left) and the 22 ? resolution reconstruction of anammoxosomal tubules (upper right) (Chicano et al., 2021). NXR-A, -B, and -C subunit crystal structures (3.0 ?) (lower left).

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