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

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.

Graphical abstract

Keywords

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

References

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

[2] 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

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

[4] 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

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

[6] 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

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

[8] 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

[9] 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

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

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

[12] 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

[13] 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

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

[15] Cheng Y (2018). Single-particle cryo-EM—How did it get here and where will it go? Science, 361(6405): 876–880 10.1126/science.aat4346

[16] 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 . . (2021). Structural and functional characterization of the intracellular filament-forming nitrite oxidoreductase multiprotein complex. Nature Microbiology, 6(9): 1129–1139

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

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

[19] 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

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

[21] 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

[22] 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

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

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

[25] 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

[26] 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

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

[28] 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

[29] Gouveia D , Chaumot A , Charnot A , Almunia C , François A , Navarro L , Armengaud J , Salvador A , Geffard O . (2017). Ecotoxico-proteomics 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

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

[31] 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

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

[33] 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

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

[35] 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

[36] 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

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

[38] 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

[39] 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

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

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

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

[43] 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 . . (2018). Hierarchically engineered mesoporous metal-organic frameworks toward cell-free immobilized enzyme systems. Chem, 4(5): 1022–1034

[44] 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

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

[46] 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 . . (2022). Machine learning-aided engineering of hydrolases for PET depolymerization. Nature, 604(7907): 662–667

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

[48] 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

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

[50] 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

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

[52] 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

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

[54] 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

[55] 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

[56] 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

[57] 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

[58] 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

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

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

[61] 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

[62] 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

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

[64] 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

[65] Varadi M , Anyango S , Deshpande M , Nair S , Natassia C , Yordanova G , Yuan D , Stroe O , Wood G , Laydon A . . (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

[66] 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

[67] 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

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

[69] 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

[70] 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 . . (2020). TrkA undergoes a tetramer-to-dimer conversion to open TrkH which enables changes in membrane potential. Nature Communications, 11(1): 547

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

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