Reengineering of a flavin-binding fluorescent protein using ProteinMPNN.
flavin
fluorescent protein
ligand
machine learning
protein engineering
Journal
Protein science : a publication of the Protein Society
ISSN: 1469-896X
Titre abrégé: Protein Sci
Pays: United States
ID NLM: 9211750
Informations de publication
Date de publication:
Apr 2024
Apr 2024
Historique:
revised:
12
01
2024
received:
05
09
2023
accepted:
18
02
2024
medline:
19
3
2024
pubmed:
19
3
2024
entrez:
19
3
2024
Statut:
ppublish
Résumé
Recent advances in machine learning techniques have led to development of a number of protein design and engineering approaches. One of them, ProteinMPNN, predicts an amino acid sequence that would fold and match user-defined backbone structure. Its performance was previously tested for proteins composed of standard amino acids, as well as for peptide- and protein-binding proteins. In this short report, we test whether ProteinMPNN can be used to reengineer a non-proteinaceous ligand-binding protein, flavin-based fluorescent protein CagFbFP. We fixed the native backbone conformation and the identity of 20 amino acids interacting with the chromophore (flavin mononucleotide, FMN) while letting ProteinMPNN predict the rest of the sequence. The software package suggested replacing 36-48 out of the remaining 86 amino acids so that the resulting sequences are 55%-66% identical to the original one. The three designs that we tested experimentally displayed different expression levels, yet all were able to bind FMN and displayed fluorescence, thermal stability, and other properties similar to those of CagFbFP. Our results demonstrate that ProteinMPNN can be used to generate diverging unnatural variants of fluorescent proteins, and, more generally, to reengineer proteins without losing their ligand-binding capabilities.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e4958Subventions
Organisme : Russian Science Foundation
Organisme : Ministry of Science and Higher Education of the Russian Federation
Informations de copyright
© 2024 The Protein Society.
Références
Ai H, Baird MA, Shen Y, Davidson MW, Campbell RE. Engineering and characterizing monomeric fluorescent proteins for live-cell imaging applications. Nat Protoc. 2014;9(4):910-928. https://doi.org/10.1038/nprot.2014.054
Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR, et al. Accurate prediction of protein structures and interactions using a 3-track neural network. Science. 2021;373(6557):871-876. https://doi.org/10.1126/science.abj8754
Becke AD. Density-functional thermochemistry. III. The role of exact exchange. J Chem Phys. 1993;98(7):5648-5652. https://doi.org/10.1063/1.464913
Bennett NR, Coventry B, Goreshnik I, Huang B, Allen A, Vafeados D, et al. Improving de novo protein binder design with deep learning. Nat Commun. 2023;14:2625. https://doi.org/10.1038/s41467-023-38328-5
Breneman CM, Wiberg KB. Determining atom-centered monopoles from molecular electrostatic potentials. The need for high sampling density in formamide conformational analysis. J Comput Chem. 1990;11(3):361-373. https://doi.org/10.1002/jcc.540110311
Cranfill PJ, Sell BR, Baird MA, Allen JR, Lavagnino Z, de Gruiter HM, et al. Quantitative assessment of fluorescent proteins. Nat Methods. 2016;13(7):557-562. https://doi.org/10.1038/nmeth.3891
Dauparas J, Anishchenko I, Bennett N, Bai H, Ragotte RJ, Milles LF, et al. Robust deep learning based protein sequence design using ProteinMPNN. Science. 2022;378(6615):49-56. https://doi.org/10.1126/science.add2187
Dauparas J, Lee GR, Pecoraro R, An L, Anishchenko I, Glasscock C, et al. Atomic context-conditioned protein sequence design using LigandMPNN. bioRxiv 2023 https://doi.org/10.1101/2023.12.22.573103
de Haas RJ, Brunette N, Goodson A, Dauparas J, Yi SY, Yang EC, et al. Rapid and automated design of two-component protein nanomaterials using ProteinMPNN. bioRxiv 2023 https://doi.org/10.1101/2023.08.04.551935
Drepper T, Eggert T, Circolone F, Heck A, Krauß U, Guterl J-K, et al. Reporter proteins for in vivo fluorescence without oxygen. Nat Biotech. 2007;25(4):443-445. https://doi.org/10.1038/nbt1293
Dürr SL. ProteinMPNN Gradio Webapp (v0.3). Zenodo 2023.
Eastman P, Swails J, Chodera JD, McGibbon RT, Zhao Y, Beauchamp KA, et al. OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol. 2017;13(7):e1005659. https://doi.org/10.1371/journal.pcbi.1005659
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32(5):1792-1797. https://doi.org/10.1093/nar/gkh340
Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG. A smooth particle mesh Ewald method. J Chem Phys. 1995;103(19):8577-8593. https://doi.org/10.1063/1.470117
Feng C-Q, Zhang Z-Y, Zhu X-J, Lin Y, Chen W, Tang H, et al. iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators. Bioinformatics. 2019;35(9):1469-1477. https://doi.org/10.1093/bioinformatics/bty827
Glantz ST, Carpenter EJ, Melkonian M, Gardner KH, Boyden ES, Wong GK-S, et al. Functional and topological diversity of LOV domain photoreceptors. Proc Natl Acad Sci. 2016;113(11):E1442-E1451. https://doi.org/10.1073/pnas.1509428113
Goverde CA, Pacesa M, Dornfeld LJ, Goldbach N, Georgeon S, Rosset S, et al. Computational design of soluble analogues of integral membrane protein structures. bioRxiv 2023 https://doi.org/10.1101/2023.05.09.540044
He X, Man VH, Yang W, Lee T-S, Wang J. A fast and high-quality charge model for the next generation general AMBER force field. J Chem Phys. 2020;153(11):114502. https://doi.org/10.1063/5.0019056
Higgins SA, Ouonkap SVY, Savage DF. Rapid and programmable protein mutagenesis using plasmid recombineering. ACS Synth Biol. 2017;6(10):1825-1833. https://doi.org/10.1021/acssynbio.7b00112
Jarmoskaite I, AlSadhan I, Vaidyanathan PP, Herschlag D. How to measure and evaluate binding affinities. Elife. 2020;9:e57264. https://doi.org/10.7554/eLife.57264
Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML. Comparison of simple potential functions for simulating liquid water. J Chem Phys. 1983;79(2):926-935. https://doi.org/10.1063/1.445869
Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583-589. https://doi.org/10.1038/s41586-021-03819-2
Kazlauskas R. Engineering more stable proteins. Chem Soc Rev. 2018;47(24):9026-9045. https://doi.org/10.1039/C8CS00014J
Kim K-W, Kim J, Yun YD, Ahn H, Min B, Kim NH, et al. Small-angle x-ray scattering beamline BL4C SAXS at pohang light source II.Biodesign. 2017;5(1):24-29.
Ko S, Hwang B, Na J-H, Lee J, Jung ST. Engineered arabidopsis blue light receptor LOV domain variants with improved quantum yield, brightness, and thermostability. J Agric Food Chem. 2019;67(43):12037-12043. https://doi.org/10.1021/acs.jafc.9b05473
Konarev PV, Volkov VV, Sokolova AV, Koch MHJ, Svergun DI. PRIMUS: a windows PC-based system for small-angle scattering data analysis. J Appl Cryst. 2003;36(5):1277-1282. https://doi.org/10.1107/S0021889803012779
Korbeld KT, Fürst MJLJ. Curse and blessing of non-proteinogenic parts in computational enzyme engineering. Chembiochem. 2023;24(12):e202300192. https://doi.org/10.1002/cbic.202300192
Korendovych IV, DeGrado WF. De novo protein design, a retrospective. Q Rev Biophys. 2020;53:e3. https://doi.org/10.1017/S0033583519000131
Krapp LF, Meireles FA, Abriata LA, Dal Peraro M. Context-aware geometric deep learning for protein sequence design bioRxiv 2023. doi:10.1101/2023.06.19.545381.
Krishna R, Wang J, Ahern W, Sturmfels P, Venkatesh P, Kalvet I, et al. Generalized biomolecular modeling and design with RoseTTAFold all-atom. bioRxiv 2023 https://doi.org/10.1101/2023.10.09.561603
Lee C, Yang W, Parr RG. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys Rev B. 1988;37(2):785-789. https://doi.org/10.1103/PhysRevB.37.785
Lee J, Cheng X, Swails JM, Yeom MS, Eastman PK, Lemkul JA, et al. CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J Chem Theory Comput 2016;12(1):405-413. https://doi.org/10.1021/acs.jctc.5b00935
Liang G-T, Lai C, Yue Z, Zhang H, Li D, Chen Z, et al. Enhanced small green fluorescent proteins as a multisensing platform for biosensor development. Front Bioeng Biotechnol. 2022;10:1039317. https://doi.org/10.3389/fbioe.2022.1039317
Losi A, Gardner KH, Möglich A. Blue-light receptors for optogenetics. Chem Rev. 2018;118(21):10659-10709. https://doi.org/10.1021/acs.chemrev.8b00163
Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput. 2015;11(8):3696-3713. https://doi.org/10.1021/acs.jctc.5b00255
Manalastas-Cantos K, Konarev PV, Hajizadeh NR, Kikhney AG, Petoukhov MV, Molodenskiy DS, et al. ATSAS 3.0: expanded functionality and new tools for small-angle scattering data analysis. J Appl Cryst. 2021;54(1):343-355. https://doi.org/10.1107/S1600576720013412
Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: making protein folding accessible to all. Nat Methods. 2022;19(6):679-682. https://doi.org/10.1038/s41592-022-01488-1
Nazarenko VV, Remeeva A, Yudenko A, Kovalev K, Dubenko A, Goncharov IM, et al. A thermostable flavin-based fluorescent protein from chloroflexus aggregans: a framework for ultra-high resolution structural studies. Photochem Photobiol Sci. 2019;18(7):1793-1805. https://doi.org/10.1039/c9pp00067d
Neese F. The ORCA program system. WIREs Comput Mol Sci. 2012;2(1):73-78. https://doi.org/10.1002/wcms.81
Neese F. Software update: the ORCA program system, version 4.0. Wiley Interdiscip Rev Comput Mol Sci. 2017;8(1):73-78. https://doi.org/10.1002/wcms.81
Neese F, Wennmohs F, Becker U, Riplinger C. The ORCA quantum chemistry program package. J Chem Phys. 2020;152(22):224108. https://doi.org/10.1063/5.0004608
Nikolaev A, Yudenko A, Smolentseva A, Bogorodskiy A, Tsybrov F, Borshchevskiy V, et al. Fine spectral tuning of a flavin-binding fluorescent protein for multicolor imaging. J Biol Chem. 2023;299(3):102977. https://doi.org/10.1016/j.jbc.2023.102977
Ozbakir HF, Anderson NT, Fan K-C, Mukherjee A. Beyond the Green fluorescent protein: biomolecular reporters for anaerobic and deep-tissue imaging. Bioconjug Chem. 2020;31(2):293-302. https://doi.org/10.1021/acs.bioconjchem.9b00688
Park S-J, Kern N, Brown T, Lee J, Im W. CHARMM-GUI PDB manipulator: various PDB structural modifications for biomolecular modeling and simulation. J Mol Biol. 2023;435(14):167995. https://doi.org/10.1016/j.jmb.2023.167995
Price MN, Dehal PS, Arkin AP. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5(3):e9490. https://doi.org/10.1371/journal.pone.0009490
Rambaut A. FigTree v1.4. 2012.
Remeeva A, Yudenko A, Nazarenko VV, Semenov O, Smolentseva A, Bogorodskiy A, et al. Development and characterization of flavin-binding fluorescent proteins, part I: basic characterization. Methods Mol Biol. 2023;2564:121-141. https://doi.org/10.1007/978-1-0716-2667-2_6
Röllen K, Granzin J, Remeeva A, Davari MD, Gensch T, Nazarenko VV, et al. The molecular basis of spectral tuning in blue- and red-shifted flavin-binding fluorescent proteins. J Biol Chem. 2021;296:100662. https://doi.org/10.1016/j.jbc.2021.100662
Smith E, Metzler DE. The photochemical degradation of riboflavin. J Am Chem Soc. 1963;85(20):3285-3288. https://doi.org/10.1021/ja00903a051
Smolentseva A, Goncharov IM, Yudenko A, Bogorodskiy A, Semenov O, Nazarenko VV, et al. Extreme dependence of chloroflexus aggregans LOV domain thermo- and photostability on the bound flavin species. Photochem Photobiol Sci. 2021;20:1645-1656. https://doi.org/10.1007/s43630-021-00138-3
Song X, Wang Y, Shu Z, Hong J, Li T, Yao L. Engineering a more thermostable blue light photo receptor Bacillus subtilis YtvA LOV domain by a computer aided rational design method. PLoS Comput Biol. 2013;9(7):e1003129. https://doi.org/10.1371/journal.pcbi.1003129
Stephens PJ, Devlin FJ, Chabalowski CF, Frisch MJ. Ab initio calculation of vibrational absorption and circular dichroism spectra using Density functional force fields. J Phys Chem. 1994;98(45):11623-11627. https://doi.org/10.1021/j100096a001
Sumida KH, Núñez-Franco R, Kalvet I, Pellock SJ, Wicky BIM, Milles LF, et al. Improving protein expression, stability, and function with ProteinMPNN. J Am Chem Soc. 2024;146:2054-2061. https://doi.org/10.1021/jacs.3c10941
Svergun D, Barberato C, Koch MHJ. CRYSOL-a program to evaluate x-ray solution scattering of biological macromolecules from atomic coordinates. J Appl Cryst. 1995;28(6):768-773. https://doi.org/10.1107/S0021889895007047
Tamura K, Stecher G, Kumar S. MEGA11: molecular evolutionary genetics analysis version 11. Mol Biol Evol. 2021;38(7):3022-3027. https://doi.org/10.1093/molbev/msab120
Tareen A, Kinney JB. Logomaker: beautiful sequence logos in python. Bioinformatics. 2020;36(7):2272-2274. https://doi.org/10.1093/bioinformatics/btz921
Valle L, Coronel Y, Bravo G, Albarracín V, Farias ME, Abatedaga I. Archaeal LOV domains from lake diamante: first functional characterization of an halo-adapted photoreceptor. https://doi.org/10.21203/rs.3.rs-3073767/v1
Vosko SH, Wilk L, Nusair M. Accurate spin-dependent electron liquid correlation energies for local spin density calculations: a critical analysis. Can J Phys. 1980;58(8):1200-1211. https://doi.org/10.1139/p80-159
Watson JL, Juergens D, Bennett NR, Trippe BL, Yim J, Eisenach HE, et al. Novo Design of Protein Structure and Function with RFdiffusion. Nature. 2023;620:1089-1100. https://doi.org/10.1038/s41586-023-06415-8
Weigend F, Ahlrichs R. Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: design and assessment of accuracy. Phys Chem Chem Phys. 2005;7(18):3297-3305. https://doi.org/10.1039/B508541A
Westberg M, Etzerodt M, Ogilby PR. Rational design of genetically encoded singlet oxygen photosensitizing proteins. Curr Opin Struct Biol. 2019;57:56-62. https://doi.org/10.1016/j.sbi.2019.01.025
Wicky BIM, Milles LF, Courbet A, Ragotte RJ, Dauparas J, Kinfu E, et al. Hallucinating symmetric protein assemblies. Science. 2022;378(6615):56-61. https://doi.org/10.1126/science.add1964
Wingen M, Jaeger K-E, Gensch T, Drepper T. Novel thermostable flavin-binding fluorescent proteins from thermophilic organisms. Photochem Photobiol. 2017;93(3):849-856. https://doi.org/10.1111/php.12740
Woolfson DN. A brief history of de novo protein design: minimal, rational, and computational. J Mol Biol. 2021;433(20):167160. https://doi.org/10.1016/j.jmb.2021.167160
Yee EF, Diensthuber RP, Vaidya AT, Borbat PP, Engelhard C, Freed JH, et al. Signal transduction in light-oxygen-voltage receptors lacking the adduct-forming cysteine residue. Nat Commun. 2015;6(1):1-10. https://doi.org/10.1038/ncomms10079
Yeh AH-W, Norn C, Kipnis Y, Tischer D, Pellock SJ, Evans D, et al. De novo design of luciferases using deep learning. Nature. 2023;614(7949):774-780. https://doi.org/10.1038/s41586-023-05696-3
Yudenko A, Smolentseva A, Maslov I, Semenov O, Goncharov IM, Nazarenko VV, et al. Rational design of a split flavin-based fluorescent reporter. ACS Synth Biol. 2021;10(1):72-83. https://doi.org/10.1021/acssynbio.0c00454
Zheng J, Xu X, Truhlar DG. Minimally augmented Karlsruhe basis sets. Theor Chem Acc. 2011;128(3):295-305. https://doi.org/10.1007/s00214-010-0846-z