SpringerLink
Log in

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

  • Perspectives
  • Published:
Frontiers of Environmental Science & Engineering Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abola E, Kuhn P, Earnest T, Stevens R C (2000). Automation of X-ray crystallography. Nature Structural Biology, 7(11): 973–977

    Article  CAS  Google Scholar 

  • Adachi N, Yamaguchi T, Moriya T, Kawasaki M, Koiwai K, Shinoda A, Yamada Y, Yumoto F, Kohzuma T, Senda T (2021). 2.85 and 2.99 A resolution structures of 110 kDa nitrite reductase determined by 200 kV cryogenic electron microscopy. Journal of Structural Biology, 213(3): 107768

    Article  CAS  Google Scholar 

  • Aebersold R, Mann M (2016). Mass-spectrometric exploration of proteome structure and function. Nature, 537(7620): 347–355

    Article  CAS  Google Scholar 

  • Akram M, Dietl A, Mersdorf U, Prinz S, Maalcke W, Keltjens J, Ferousi C, De Almeida N M, Reimann J, Kartal B, Jetten M S M, Parey K, Barends T R M (2019). A 192-heme electron transfer network in the hydrazine dehydrogenase complex. Science Advances, 5(4): eaav4310

    Article  CAS  Google Scholar 

  • Anfinsen C B (1973). Principles that govern the folding of protein chains. Science, 181(4096): 223–230

    Article  CAS  Google Scholar 

  • Arya C K, Yadav S, Fine J, Casanal A, Chopra G, Ramanathan G, Vinothkumar K R, Subramanian R (2020). A 2-Tyr-1-carboxylate mononuclear iron center forms the active site of a paracoccus dimethylformamidase. Angewandte Chemie International Edition, 59(39): 16961–16966

    Article  CAS  Google Scholar 

  • Baek M, Dimaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee G R, Wang J, Cong Q, Kinch L N, Schaeffer R D, et al. (2021). Accurate prediction of protein structures and interactions using a three-track neural network. Science, 373(6557): 871–876

    Article  CAS  Google Scholar 

  • Bai X C, Mcmullan G, Scheres S H W (2015). How cryo-EM is revolutionizing structural biology. Trends in Biochemical Sciences, 40(1): 49–57

    Article  CAS  Google Scholar 

  • Bornscheuer U T, Huisman G W, Kazlauskas R J, Lutz S, Moore J C, Robins K (2012). Engineering the third wave of biocatalysis. Nature, 485(7397): 185–194

    Article  CAS  Google Scholar 

  • Bryant P, Pozzati G, Elofsson A (2022). Improved prediction of protein-protein interactions using AlphaFold2. Nature Communications, 13(1): 1265

    Article  CAS  Google Scholar 

  • Callaway E (2022). The entire protein universe’: AI predicts shape of nearly every known protein. Nature, 608(7921): 15–16

    Article  CAS  Google Scholar 

  • Chang W H, Lin H H, Tsai I K, Huang S H, Chung S C, Tu I P, Yu S S F, Chan S I (2021). Copper centers in the cryo-EM structure of particulate methane monooxygenase reveal the catalytic machinery of methane oxidation. Journal of the American Chemical Society, 143(26): 9922–9932

    Article  CAS  Google Scholar 

  • Chen C Y, Chang Y C, Lin B L, Huang C H, Tsai M D (2019). Temperature-resolved cryo-EM uncovers structural bases of temperature-dependent enzyme functions. Journal of the American Chemical Society, 141(51): 19983–19987

    Article  CAS  Google Scholar 

  • Chen K, Arnold F H (2020). Engineering new catalytic activities in enzymes. Nature Catalysis, 3(3): 203–213

    Article  CAS  Google Scholar 

  • Cheng Y (2018). Single-particle cryo-EM—How did it get here and where will it go? Science, 361(6405): 876–880

    Article  CAS  Google Scholar 

  • Chicano T M, Dietrich L, de Almeida N M, Akram M, Hartmann E, Leidreiter F, Leopoldus D, Mueller M, Sanchez R, Nuijten G H L, et al. (2021). Structural and functional characterization of the intracellular filament-forming nitrite oxidoreductase multiprotein complex. Nature Microbiology, 6(9): 1129–1139

    Article  CAS  Google Scholar 

  • Danev R, Yanagisawa H, Kikkawa M (2019). Cryo-electron microscopy methodology: current aspects and future directions. Trends in Biochemical Sciences, 44(10): 837–848

    Article  CAS  Google Scholar 

  • Danso D, Chow J, Streit W R (2019). Plastics: environmental and biotechnological perspectives on microbial degradation. Applied and Environmental Microbiology, 85(19): e01095–19

    Article  CAS  Google Scholar 

  • Devendrapandi G, Liu X, Balu R, Ayyamperumal R, Valan Arasu M, Lavanya M, Minnam Reddy V R, Kim W K, Karthika P C (2024). Innovative remediation strategies for persistent organic pollutants in soil and water: a comprehensive review. Environmental Research, 249: 118404

    Article  CAS  Google Scholar 

  • Durairaj J, Waterhouse A M, Mets T, Brodiazhenko T, Abdullah M, Studer G, Tauriello G, Akdel M, Andreeva A, Bateman A, et al. (2023). Uncovering new families and folds in the natural protein universe. Nature, 622(7983): 646–653

    Article  CAS  Google Scholar 

  • Edman P, Högfeldt E, Sillén L G, Kinell P O (1950). Method for determination of the amino acid sequence in peptides. Acta Chemica Scandinavica. Series A: Physical and Inorganic Chemistry, 4(7): 283–293

    Article  CAS  Google Scholar 

  • Eisenhaber F, Persson B, Argos P (1995). Protein structure prediction: recognition of primary, secondary, and tertiary structural features from amino acid sequence. Critical Reviews in Biochemistry and Molecular Biology, 30(1): 1–94

    Article  CAS  Google Scholar 

  • Fang X, Wang F, Liu L, He J, Lin D, **ang Y, Zhu K, Zhang X, Wu H, Li H, et al. (2023). A method for multiple-sequence-alignment-free protein structure prediction using a protein language model. Nature Machine Intelligence, 5(10): 1087–1096

    Article  Google Scholar 

  • Feynman R P (1992). There’s plenty of room at the bottom. Journal of microelectromechanical systems, 1(1): 60–66

    Article  Google Scholar 

  • Filman D J, Marino S F, Ward J E, Yang L, Mester Z, Bullitt E, Lovley D R, Strauss M (2019). Cryo-EM reveals the structural basis of long-range electron transport in a cytochrome-based bacterial nanowire. Communications Biology, 2(1): 219

    Article  Google Scholar 

  • Giri N, Roy R S, Cheng J (2023). Deep learning for reconstructing protein structures from cryo-EM density maps: recent advances and future directions. Current Opinion in Structural Biology, 79: 102536

    Article  CAS  Google Scholar 

  • Gong H, Gao Y, Zhou X, **ao Y, Wang W, Tang Y, Zhou S, Zhang Y, Ji W, Yu L, et al. (2020). Cryo-EM structure of trimeric Mycobacterium smegmatis succinate dehydrogenase with a membrane-anchor SdhF. Nature Communications, 11(1): 4245

    Article  CAS  Google Scholar 

  • Gopalasingam C C, Johnson R M, Chiduza G N, Tosha T, Yamamoto M, Shiro Y, Antonyuk S V, Muench S P, Hasnain S S (2019). Dimeric structures of quinol-dependent nitric oxide reductases (qNORs) revealed by cryo-electron microscopy. Science Advances, 5(8): eaax1803

    Article  CAS  Google Scholar 

  • Gouveia D, Chaumot A, Charnot A, Almunia C, François A, Navarro L, Armengaud J, Salvador A, Geffard O (2017). Ecotoxicoproteomics for aquatic environmental monitoring: first in situ application of a new proteomics-based multibiomarker assay using caged amphipods. Environmental Science & Technology, 51(22): 13417–13426

    Article  CAS  Google Scholar 

  • Huang P S, Boyken S E, Baker D (2016). The coming of age of de novo protein design. Nature, 537(7620): 320–327

    Article  CAS  Google Scholar 

  • Huang S, Kou X, Shen J, Chen G, Ouyang G (2020). “Armor-plating” enzymes with metal-organic frameworks (MOFs). Angewandte Chemie International Edition, 59(23): 8786–8798

    Article  CAS  Google Scholar 

  • Janssen D B, Schanstra J P (1994). Engineering proteins for environmental applications. Current Opinion in Biotechnology, 5(3): 253–259

    Article  CAS  Google Scholar 

  • Jiang R, Shang L, Wang R, Wang D, Wei N (2023). Machine learning based prediction of enzymatic degradation of plastics using encoded protein sequence and effective feature representation. Environmental Science & Technology Letters, 10(7): 557–564

    Article  CAS  Google Scholar 

  • Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Zidek A, Potapenko A, et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873): 583–589

    Article  CAS  Google Scholar 

  • Keller M, Hettich R (2009). Environmental proteomics: a paradigm shift in characterizing microbial activities at the molecular level. Microbiology and Molecular Biology Reviews, 73(1): 62–70

    Article  CAS  Google Scholar 

  • Kendrew J C, Bodo G, Dintzis H M, Parrish R G, Wyckoff H, Phillips D C (1958). A three-dimensional model of the myoglobin molecule obtained by X-ray analysis. Nature, 181(4610): 662–666

    Article  CAS  Google Scholar 

  • Kessel A N B T (2018). Introduction to Protein— Structure, Function, and Motion (2nd ed). New York: Chapman and Hall/CRC

    Book  Google Scholar 

  • Khakzad H, Igashov I, Schneuing A, Goverde C, Bronstein M, Correia B (2023). A new age in protein design empowered by deep learning. Cell Systems, 14(11): 925–939

    Article  CAS  Google Scholar 

  • Kincannon W M, Zahn M, Clare R, Lusty Beech J, Romberg A, Larson J, Bothner B, Beckham G T, Mcgeehan J E, Dubois J L (2022). Biochemical and structural characterization of an aromatic ring-hydroxylating dioxygenase for terephthalic acid catabolism. Proceedings of the National Academy of Sciences of the United States of America, 119(13): e2121426119

    Article  CAS  Google Scholar 

  • Kolata G (1986). Trying to crack the second half of the genetic code. Science, 233(4768): 1037–1039

    Article  CAS  Google Scholar 

  • Kühlbrandt W (2014). The resolution revolution. Science, 343(6178): 1443–1444

    Article  Google Scholar 

  • Lee D, Redfern O, Orengo C (2007). Predicting protein function from sequence and structure. Nature Reviews. Molecular Cell Biology, 8(12): 995–1005

    Article  CAS  Google Scholar 

  • Li P, Chen Q, Wang T C, Vermeulen N A, Mehdi B L, Dohnalkoya A, Browning N D, Shen D, Anderson R, Gomez-Gualdron D A, et al. (2018). Hierarchically engineered mesoporous metal-organic frameworks toward cell-free immobilized enzyme systems. Chem, 4(5): 1022–1034

    Article  CAS  Google Scholar 

  • Lin X M, Wang Y Y, Ma X, Yan Y, Wu M, Bond P L, Guo J H (2018). Evidence of differential adaptation to decreased temperature by anammox bacteria. Environmental Microbiology, 20(10): 3514–3528

    Article  CAS  Google Scholar 

  • Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, Smetanin N, Verkuil R, Kabeli O, Shmueli Y, et al. (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 379(6637): 1123–1130

    Article  CAS  Google Scholar 

  • Lu H, Diaz D J, Czarnecki N J, Zhu C, Kim W, Shroff R, Acosta D J, Alexander B R, Cole H O, Zhang Y, et al. (2022). Machine learning-aided engineering of hydrolases for PET depolymerization. Nature, 604(7907): 662–667

    Article  CAS  Google Scholar 

  • MacLeod M, Arp H P H, Tekman M B, Jahnke A (2021). The global threat from plastic pollution. Science, 373(6550): 61–65

    Article  CAS  Google Scholar 

  • Masrati G, Landau M, Ben-Tal N, Lupas A, Kosloff M, Kosinski J (2021). Integrative structural biology in the era of accurate structure prediction. Journal of Molecular Biology, 433(20): 167127

    Article  CAS  Google Scholar 

  • Merkx M, Smith B, Jewett M (2019). Engineering sensor proteins. ACS Sensors, 4(12): 3089–3091

    Article  CAS  Google Scholar 

  • Mills D J, Vitt S, Strauss M, Shima S, Vonck J (2013). De novo modeling of the F420-reducing [NiFe]-hydrogenase from a methanogenic archaeon by cryo-electron microscopy. eLife, 2: e00218

    Article  Google Scholar 

  • Nesvizhskii A I (2014). Proteogenomics: concepts, applications and computational strategies. Nature Methods, 11(11): 1114–1125

    Article  CAS  Google Scholar 

  • Ngo J T, Marks J, Karplus M (1994). Computational complexity, protein structure prediction, and the Levinthal paradox. In: Merz K M, Le Grand S M, eds. The Protein Folding Problem and Tertiary Structure Prediction. Boston: Birkhäuser Boston

    Google Scholar 

  • Oikonomou C M, Jensen G J (2017). The development of cryo-EM and how it has advanced microbiology. Nature Microbiology, 2(12): 1577–1579

    Article  CAS  Google Scholar 

  • Ovchinnikov S, Park H, Varghese N, Huang P S, Pavlopoulos G A, Kim D E, Kamisetty H, Kyrpides N C, Baker D (2017). Protein structure determination using metagenome sequence data. Science, 355(6322): 294–298

    Article  CAS  Google Scholar 

  • Pereira J, Simpkin A J, Hartmann M D, Rigden D J, Keegan R M, Lupas A N (2021). High-accuracy protein structure prediction in CASP14. Proteins, 89(12): 1687–1699

    Article  CAS  Google Scholar 

  • Pillai S, Behra R, Nestler H, Suter M J F, Sigg L, Schirmer K (2014). Linking toxicity and adaptive responses across the transcriptome, proteome, and phenotype of Chlamydomonas reinhardtii exposed to silver. Proceedings of the National Academy of Sciences of the United States of America, 111(9): 3490–3495

    Article  CAS  Google Scholar 

  • Radon C, Mittelstadt G, Duffus B R, Burger J, Hartmann T, Mielke T, Teutloff C, Leimkuhler S, Wendler P (2020). Cryo-EM structures reveal intricate Fe-S cluster arrangement and charging in Rhodobacter capsulatus formate dehydrogenase. Nature Communications, 11(1): 1912

    Article  CAS  Google Scholar 

  • Sato Y, Yabuki T, Adachi N, Moriya T, Arakawa T, Kawasaki M, Yamada C, Senda T, Fushinobu S, Wakagi T (2020). Crystallographic and cryogenic electron microscopic structures and enzymatic characterization of sulfur oxygenase reductase from Sulfurisphaera tokodaii. Journal of Structural Biology: X, 4: 100030

    CAS  Google Scholar 

  • Senior A W, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, Zídek A, Nelson A W R, Bridgland A, et al. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577(7792): 706–710

    Article  CAS  Google Scholar 

  • Sheldon R A, Pereira P C (2017). Biocatalysis engineering: the big picture. Chemical Society Reviews, 46(10): 2678–2691

    Article  CAS  Google Scholar 

  • Su C C, Lyu M, Morgan C E, Bolla J R, Robinson C V, Yu E W (2021). A ‘Build and Retrieve’ methodology to simultaneously solve cryo-EM structures of membrane proteins. Nature Methods, 18(1): 69–75

    Article  CAS  Google Scholar 

  • Su M, Chakraborty S, Osawa Y, Zhang H (2020). Cryo-EM reveals the architecture of the dimeric cytochrome P450 CYP102A1 enzyme and conformational changes required for redox partner recognition. Journal of Biological Chemistry, 295(6): 1637–1645

    Article  CAS  Google Scholar 

  • Tunyasuvunakool K, Adler J, Wu Z, Green T, Zielinski M, Zidek A, Bridgland A, Cowie A, Meyer C, Laydon A, et al. (2021). Highly accurate protein structure prediction for the human proteome. Nature, 596(7873): 590–596

    Article  CAS  Google Scholar 

  • Tüting C, Schmidt L, Skalidis I, Sinz A, Kastritis P L (2023). Enabling cryo-EM density interpretation from yeast native cell extracts by proteomics data and AlphaFold structures. Proteomics, 23(17): 2200096

    Article  Google Scholar 

  • Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, Yuan D, Stroe O, Wood G, Laydon A, et al. (2022). AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Research, 50(D1): D439–D444

    Article  CAS  Google Scholar 

  • Wang Z M, Hu W X, Zheng H J (2020). Pathogenic siderophore ABC importer YbtPQ adopts a surprising fold of exporter. Science Advances, 6(6): eaay7997

    Article  CAS  Google Scholar 

  • Watanabe T, Pfeil-Gardiner O, Kahnt J, Koch J, Murphy B J J S (2021). Three-megadalton complex of methanogenic electron-bifurcating and CO2-fixing enzymes. Science, 373(6559): 1151–1156

    Article  CAS  Google Scholar 

  • Wüthrich K (1990). Protein structure determination in solution by NMR spectroscopy. Journal of Biological Chemistry, 265(36): 22059–22062

    Article  Google Scholar 

  • Ye Q, Wang D, Wei N (2023). Engineering biomaterials for the recovery of rare earth elements. Trends in Biotechnology, 18: S0167–7799(23)00302–5

    Google Scholar 

  • Zhang H Z, Pan Y P, Hu L Y, Hudson M A, Hofstetter K S, Xu Z C, Rong M Q, Wang Z, Prasad B V V, Lockless S W, et al. (2020). TrkA undergoes a tetramer-to-dimer conversion to open TrkH which enables changes in membrane potential. Nature Communications, 11(1): 547

    Article  Google Scholar 

  • Zhu B, Chen Y, Wei N (2019). Engineering biocatalytic and biosorptive materials for environmental applications. Trends in Biotechnology, 37(6): 661–676

    Article  CAS  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  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

    Mingda Zhou, Tong Wang, Han Wang, Zibin Li, Wei-xian Zhang & Yayi Wang

  2. Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200434, China

    Ke Xu

Authors
  1. Mingda Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ke Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Han Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zibin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wei-xian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yayi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yayi Wang.

Ethics declarations

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.

Additional information

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.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, M., Wang, T., Xu, K. et al. Broadening environmental research in the era of accurate protein structure determination and predictions. Front. Environ. Sci. Eng. 18, 91 (2024). https://doi.org/10.1007/s11783-024-1851-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11783-024-1851-0

Keywords

Search

Navigation