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

Highlights

● 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.

Abstract

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.

Graphical abstract

Keywords

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

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Acknowledgements

Financial support from the National Natural Science Foundation of China (Grant Nos. 52225001 and 51978485), and the State Key Laboratory for Pollution Control (China) is acknowledged.

Conflict of Interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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