Heat-induced structural and chemical changes to a computationally designed miniprotein.

capillary electrophoresis deamidation dynamics mass spectrometry miniproteins nuclear magnetic resonance protein design

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:
Jun 2024
Historique:
revised: 22 03 2024
received: 11 12 2023
accepted: 28 03 2024
medline: 17 5 2024
pubmed: 17 5 2024
entrez: 17 5 2024
Statut: ppublish

Résumé

The de novo design of miniprotein inhibitors has recently emerged as a new technology to create proteins that bind with high affinity to specific therapeutic targets. Their size, ease of expression, and apparent high stability makes them excellent candidates for a new class of protein drugs. However, beyond circular dichroism melts and hydrogen/deuterium exchange experiments, little is known about their dynamics, especially at the elevated temperatures they seemingly tolerate quite well. To address that and gain insight for future designs, we have focused on identifying unintended and previously overlooked heat-induced structural and chemical changes in a particularly stable model miniprotein, EHEE_rd2_0005. Nuclear magnetic resonance (NMR) studies suggest the presence of dynamics on multiple time and temperature scales. Transiently elevating the temperature results in spontaneous chemical deamidation visible in the NMR spectra, which we validate using both capillary electrophoresis and mass spectrometry (MS) experiments. High temperatures also result in greatly accelerated intrinsic rates of hydrogen exchange and signal loss in NMR heteronuclear single quantum coherence spectra from local unfolding. These losses are in excellent agreement with both room temperature hydrogen exchange experiments and hydrogen bond disruption in replica exchange molecular dynamics simulations. Our analysis reveals important principles for future miniprotein designs and the potential for high stability to result in long-lived alternate conformational states.

Identifiants

pubmed: 38757381
doi: 10.1002/pro.4991
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e4991

Subventions

Organisme : NIH HHS
Pays : United States

Informations de copyright

© 2024 The Protein Society.

Références

Abraham, M., van der Spoel, D., Lindahl, E., Hess, B., The GROMACS Development Team, GROMACS user manual. Version 5.0.7. 2015b.
Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, et al. GROMACS: high performance molecular simulations through multi‐level parallelism from laptops to supercomputers. SoftwareX. 2015a;1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001
Akazawa‐Ogawa Y, Nagai H, Hagihara Y. Heat denaturation of the antibody, a multi‐domain protein. Biophys Rev. 2018;10(2):255–258. https://doi.org/10.1007/s12551-017-0361-8
Aliev AE, Kulke M, Khaneja HS, Chudasama V, Sheppard TD, Lanigan RM. Motional timescale predictions by molecular dynamics simulations: case study using proline and hydroxyproline sidechain dynamics. Proteins Struct Funct Bioinform. 2014;82(2):195–215. https://doi.org/10.1002/prot.24350
Andreakos E, Taylor PC, Feldmann M. Monoclonal antibodies in immune and inflammatory diseases. Curr Opin Biotechnol. 2002;13(6):615–620. https://doi.org/10.1016/S0958-1669(02)00355-5
Arkin MR, Tang Y, Wells J. Small‐molecule inhibitors of protein‐protein interactions: progressing towards the reality. Chem Biol. 2015;21(9):1102–1114. https://doi.org/10.1016/j.chembiol.2014.09.001
Berendsen HJC, Grigera JR, Straatsma TP. The missing term in effective pair potentials. J Phys Chem. 1987;91(24):6269–6271. https://doi.org/10.1021/j100308a038
Bonner WA, Hulett HR, Sweet RG, Herzenberg LA. Fluorescence activated cell sorting. Rev Sci Instrum. 1972;43(3):404–409. https://doi.org/10.1063/1.1685647
Cao L, Coventry B, Goreshnik I, Huang B, Sheffler W, Park JS, et al. Design of protein‐binding proteins from the target structure alone. Nature. 2022;605:551–560. https://doi.org/10.1038/s41586-022-04654-9
Cao L, Goreshnik I, Coventry B, Case JB, Miller L, Kozodoy L, et al. De novo design of picomolar SARS‐CoV‐2 miniprotein inhibitors. Science. 2020;370(6515):426–431. https://doi.org/10.1126/science.abd9909
Chames P, Van Regenmortel M, Weiss E, Baty D. Therapeutic antibodies: successes, limitations and hopes for the future. Br J Pharmacol. 2009;157(2):220–233. https://doi.org/10.1111/j.1476-5381.2009.00190.x
Chevalier A, Silva D‐A, Rocklin GJ, Hicks DR, Vergara R, Murapa P, et al. Massively parallel de novo protein design for targeted therapeutics. Nature. 2017;550(7674):74–79. https://doi.org/10.1038/nature23912
Chiu ML, Goulet DR, Teplyakov A, Gilliland GL. Antibody structure and function: the basis for engineering therapeutics. Antibodies. 2019;8(4):55. https://doi.org/10.3390/antib8040055
Clore GM, Szabo A, Bax A, Kay LE, Driscoll PC, Gronenborn AM. Deviations from the simple two‐parameter model‐free approach to the interpretation of nitrogen‐15 nuclear magnetic relaxation of proteins. J Am Chem Soc. 1990;112(12):4989–4991. https://doi.org/10.1021/ja00168a070
Cole R, Loria JP. FAST‐Modelfree: a program for rapid automated analysis of solution NMR spin‐relaxation data. J Biomol NMR. 2003;26(3):203–213. https://doi.org/10.1023/A:1023808801134
Crook ZR, Nairn NW, Olson JM. Miniproteins as a powerful modality in drug development. Trends Biochem Sci. 2020;45(4):332–346. https://doi.org/10.1016/j.tibs.2019.12.008
Delaglio F, Grzesiek S, Vuister GW, Zhu G, Pfeifer J, Bax A. NMRPipe: a multidimensional spectral processing system based on UNIX pipes. J Biomol NMR. 1995;6(3):277–293. https://doi.org/10.1007/BF00197809
Delmar JA, Wang J, Choi SW, Martins JA, Mikhail JP. Machine learning enables accurate prediction of asparagine deamidation probability and rate. Mol Ther Methods Clin Dev. 2019;15(December):264–274. https://doi.org/10.1016/j.omtm.2019.09.008
Dou J, Vorobieva AA, Sheffler W, Doyle LA, Park H, Bick MJ, et al. De novo design of a fluorescence‐activating β‐barrel. Nature. 2018;561(7724):485–491. https://doi.org/10.1038/s41586-018-0509-0
Dudley JA, Park S, MacDonald ME, Fetene E, Smith CA. Resolving overlapped signals with automated FitNMR analytical peak modeling. J Magn Reson. 2020;318:106773. https://doi.org/10.1016/j.jmr.2020.106773
Farías‐Rico JA, Selin FR, Myronidi I, Frühauf M, Von Heijne G. Effects of protein size, thermodynamic stability, and net charge on cotranslational folding on the ribosome. Proc Natl Acad Sci USA. 2018;115(40):E9280–E9287. https://doi.org/10.1073/pnas.1812756115
Farrow NA, Muhandiram R, Singer AU, Pascal SM, Kay CM, Gish IG, et al. Backbone dynamics of a free and a phosphopeptide‐complexed Src homology 2 domain studied by 15N NMR relaxation. Biochemistry. 1994;33:5984–6003. https://doi.org/10.1021/bi00185a040
Gapsys V, Michielssens S, Seeliger D, De Groot BL. pmx: automated protein structure and topology generation for alchemical perturbations. J Comput Chem. 2015;36(5):348–354. https://doi.org/10.1002/jcc.23804
Gervais D. Protein deamidation in biopharmaceutical manufacture: understanding, control and impact. J Chem Technol Biotechnol. 2016;91(3):569–575. https://doi.org/10.1002/jctb.4850
Gitlin I, Carbeck JD, Whitesides GM. Why are proteins charged? Networks of charge‐charge interactions in proteins measured by charge ladders and capillary electrophoresis. Angew Chem Int Ed. 2006;45(19):3022–3060. https://doi.org/10.1002/anie.200502530
Han B, Liu Y, Ginzinger SW, Wishart DS. SHIFTX2: significantly improved protein chemical shift prediction. J Biomol NMR. 2011;50(1):43–57. https://doi.org/10.1007/s10858-011-9478-4
Hansel TT, Kropshofer H, Singer T, Mitchell JA, George AJT. The safety and side effects of monoclonal antibodies. Nat Rev Drug Discov. 2010;9(4):325–338. https://doi.org/10.1038/nrd3003
Hostetter ER, Keyes JR, Poon I, Nguyen JP, Nite JM, Jimenez Hoyos CA, et al. Prediction of fluorophore brightness in designed mini fluorescence activating proteins. J Chem Theory Comput. 2022;18:3190–3203. https://doi.org/10.1021/acs.jctc.1c00748
Hukushima K, Nemoto K. Exchange Monte Carlo method and application to spin glass simulations. J Phys Soc Jpn. 1995;65:1604–1608. https://doi.org/10.1143/JPSJ.65.1604
Jia L, Sun Y. Protein asparagine deamidation prediction based on structures with machine learning methods. PloS One. 2017;12(7):1–17. https://doi.org/10.1371/journal.pone.0181347
Joshi A, Tripathi T, Singh SK, Padhi AK. Computational approaches for development of engineered therapeutics against SARS‐CoV‐2. Biochemistry. 2022;62:669–671. https://doi.org/10.1021/acs.biochem.2c00629
Joung IS, Cheatham TE. Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations. J Phys Chem B. 2008;112(30):9020–9041. https://doi.org/10.1021/jp8001614
Kato K, Nakayoshi T, Kurimoto E, Oda A. Mechanisms of deamidation of asparagine residues and effects of main‐chain conformation on activation energy. Int J Mol Sci. 2020;21(19):1–14. https://doi.org/10.3390/ijms21197035
Kay LE, Keifer P, Saarinen T. Pure absorption gradient enhanced heteronuclear single quantum correlation spectroscopy with improved sensitivity. J Am Chem Soc. 1992;114:10663–10665.
Kleckner IR, Foster MP. An introduction to NMR‐based approaches for measuring protein dynamics. Biochim Biophys Acta. 2011;8(1):942–968. https://doi.org/10.1016/j.bbapap.2010.10.012
Koga N, Tatsumi‐Koga R, Liu G, Xiao R, Acton TB, Montelione GT, et al. Principles for designing ideal protein structures. Nature. 2012;491(7423):222–227. https://doi.org/10.1038/nature11600
Lee W, Tonelli M, Markley JL. NMRFAM‐SPARKY: enhanced software for biomolecular NMR spectroscopy. Bioinformatics. 2015;31(8):1325–1327. https://doi.org/10.1093/bioinformatics/btu830
Lemak A, Gutmanas A, Chitayat S, Karra M, Farès C, Sunnerhagen M, et al. A novel strategy for NMR resonance assignment and protein structure determination. J Biomol NMR. 2011;49(1):27–38. https://doi.org/10.1007/s10858-010-9458-0
Li Q, Kang C. Mechanisms of action for small molecules revealed by structural biology in drug discovery. Int J Mol Sci. 2020;21(15):1–18. https://doi.org/10.3390/ijms21155262
Lipari G, Szabo A. Model‐free approach to the interpretation of nuclear magnetic resonance relaxation in macromolecules. 2. Analysis of experimental results. J Am Chem Soc. 1982;104(17):4559–4570. https://doi.org/10.1021/ja00381a010
Lorenzo JR, Alonso LG, Sánchez IE. Prediction of spontaneous protein deamidation from sequence‐derived secondary structure and intrinsic disorder. PloS One. 2015;10(12):1–14. https://doi.org/10.1371/journal.pone.0145186
Lu RM, Hwang YC, Liu IJ, Lee CC, Tsai HZ, Li HJ, et al. Development of therapeutic antibodies for the treatment of diseases. J Biomed Sci. 2020;27(1):1–30. https://doi.org/10.1186/s12929-019-0592-z
Lu X, Nobrega RP, Lynaugh H, Jain T, Barlow K, Boland T, et al. Deamidation and isomerization liability analysis of 131 clinical‐stage antibodies. MAbs. 2019;11(1):45–57. https://doi.org/10.1080/19420862.2018.1548233
Mohanty P, Agrata R, Habibullah BI, Arun GS, Das R. Deamidation disrupts native and transient contacts to weaken the interaction between UBC13 and RING‐finger E3 ligases. elife. 2019;8:1–32. https://doi.org/10.7554/eLife.49223
Nelson PN, Reynolds GM, Waldron EE, Ward E, Giannopoulos K, Murray PG. Monoclonal antibodies. J Clin Pathol. 2000;53:111–117.
Nord K, Gunneriusson E, Ringdahl J, Stahl S, Uhlen M, Nygren P. Binding proteins selected from combinatorial libraries of an alpha‐helical bacterial receptor domain. Nat Biotechnol. 1997;15(August):772–777.
Okabe T, Kawata M, Okamoto Y, Mikami M. Replica‐exchange Monte Carlo method for the isobaric‐isothermal ensemble. Chem Phys Lett. 2001;335(5–6):435–439. https://doi.org/10.1016/S0009-2614(01)00055-0
Patriksson A, Van Der Spoel D. A temperature predictor for parallel tempering simulations. Phys Chem Chem Phys. 2008;10(15):2073–2077. https://doi.org/10.1039/b716554d
Peng X, Baxa M, Faruk N, Sachleben JR, Pintscher S, Gagnon IA, et al. Prediction and validation of a protein's free energy surface using hydrogen exchange and (importantly) its denaturant dependence. J Chem Theory Comput. 2022;18(1):550–561. https://doi.org/10.1021/acs.jctc.1c00960
Phillips JJ, Buchanan A, Andrews J, Chodorge M, Sridharan S, Mitchell L, et al. Rate of asparagine deamidation in a monoclonal antibody correlating with hydrogen exchange rate at adjacent downstream residues. Anal Chem. 2017;89(4):2361–2368. https://doi.org/10.1021/acs.analchem.6b04158
Radkiewicz JL, Zipse H, Clarke S, Houk KN. Accelerated racemization of aspartic acid and asparagine residues via succinimide intermediates: an ab initio theoretical exploration of mechanism. J Am Chem Soc. 1996;118(38):9148–9155. https://doi.org/10.1021/ja953505b
Rasouli S, Abdolvahabi A, Croom CM, Plewman DL, Shi Y, Ayers JI, et al. Lysine acylation in superoxide dismutase‐1 electrostatically inhibits formation of fibrils with prion‐like seeding. J Biol Chem. 2017;292(47):19366–19380. https://doi.org/10.1074/jbc.M117.805283
Robinson NE. Protein deamidation. PNAS. 2002;99(8):5283–5288. https://doi.org/10.1073/pnas.082102799
Robinson NE, Robinson AB. Molecular clocks. Proc Natl Acad Sci USA. 2001a;98(3):944–949. https://doi.org/10.1073/pnas.98.3.944
Robinson NE, Robinson AB. Prediction of protein deamidation rates from primary and three‐dimensional structure. Proc Natl Acad Sci USA. 2001b;98(8):4367–4372. https://doi.org/10.1073/pnas.071066498
Robinson NE, Robinson ML, Schulze SES, Lai BT, Gray HB. Deamidation of α‐synuclein. Protein Sci. 2009;18(8):1766–1773. https://doi.org/10.1002/pro.183
Rocklin GJ, Chidyausiku TM, Goreshnik I, Ford A, Houliston S, Lemak A, et al. Global analysis of protein folding using massively parallel design, synthesis, and testing. Science. 2017;357(6347):168–175. https://doi.org/10.1126/science.aan0693
Shi Y, Rhodes NR, Abdolvahabi A, Kohn T, Cook NP, Marti AA, et al. Deamidation of asparagine to aspartate destabilizes Cu, Zn superoxide dismutase, accelerates fibrillization, and mirrors ALS‐linked mutations. J Am Chem Soc. 2013;135(42):15897–15908. https://doi.org/10.1021/ja407801x
Smith CA, Ban D, Pratihar S, Giller K, Paulat M, Becker S, et al. Allosteric switch regulates protein‐protein binding through collective motion. Proc Natl Acad Sci USA. 2016;113(12):3269–3274. https://doi.org/10.1073/pnas.1519609113
Smith CA, Ban D, Pratihar S, Giller K, Schwiegk C, De Groot BL, et al. Population shuffling of protein conformations. Angew Chem Int Ed. 2015;54(1):207–210. https://doi.org/10.1002/anie.201408890
Smith CA, Mazur A, Rout AK, Becker S, Lee D, de Groot BL, et al. Enhancing NMR derived ensembles with kinetics on multiple timescales. J Biomol NMR. 2020;74(1):27–43. https://doi.org/10.1007/s10858-019-00288-8
Soulby AJ, Heal JW, Barrow MP, Roemer RA, O'Connor PB. Does deamidation cause protein unfolding? A top‐down tandem mass spectrometry study. Protein Sci. 2015;24(5):850–860. https://doi.org/10.1002/pro.2659
Tregoning JS, Flight KE, Higham SL, Wang Z, Pierce BF. Progress of the COVID‐19 vaccine effort: viruses, vaccines and variants versus efficacy, effectiveness and escape. Nat Rev Immunol. 2021;21:626–636. https://doi.org/10.1038/s41577-021-00592-1
Tsuboyama AK, Dauparas J, Chen J, Mangan NM, Ovchinnikov S, Rocklin GJ. Mega‐scale experimental analysis of protein folding stability in biology and design. Nature. 2023;620(7973):434–444.
Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45:2615–2623. https://doi.org/10.1021/jm020017n
Wagner G, DeMarco A, Wüthrich K. Dynamics of the aromatic amino acid residues in the globular conformation of the basic pancreatic trypsin inhibitor (BPTI). I. 1H NMR studies. Biophys Struct Mech. 1976;2(2):139–158. https://doi.org/10.1007/BF00863706
Watson JL, Juergens D, Bennett NR. et al. De novo design of protein structure and function with RFdiffusion. Nature 620. 1089–1100 (2023). https://doi.org/10.1038/s41586-023-06415-8
Zahavi D, Weiner L. Monoclonal antibodies in cancer therapy. Antibodies. 2020;9(34):1–20. https://doi.org/10.3390/antib9030034

Auteurs

Joshua A Dudley (JA)

Department of Chemistry, Wesleyan University, Middletown, Connecticut, USA.

Sojeong Park (S)

Department of Chemistry, Wesleyan University, Middletown, Connecticut, USA.

Oliver Cho (O)

Department of Chemistry, Wesleyan University, Middletown, Connecticut, USA.

Nicholas G M Wells (NGM)

Department of Chemistry, Wesleyan University, Middletown, Connecticut, USA.

Meagan E MacDonald (ME)

Department of Chemistry, Wesleyan University, Middletown, Connecticut, USA.

Katerina M Blejec (KM)

Department of Chemistry, Wesleyan University, Middletown, Connecticut, USA.

Emmanuel Fetene (E)

Department of Chemistry, Wesleyan University, Middletown, Connecticut, USA.

Eric Zanderigo (E)

Department of Chemistry, Wesleyan University, Middletown, Connecticut, USA.

Scott Houliston (S)

Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada.

Jennifer C Liddle (JC)

Proteomics and Metabolomics Facility, University of Connecticut, Storrs, Connecticut, USA.

Chad M Dashnaw (CM)

Department of Chemistry and Biochemistry, Baylor University, Waco, Texas, USA.

T Michael Sabo (TM)

Department of Medicine and Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA.

Bryan F Shaw (BF)

Department of Chemistry and Biochemistry, Baylor University, Waco, Texas, USA.

Jeremy L Balsbaugh (JL)

Proteomics and Metabolomics Facility, University of Connecticut, Storrs, Connecticut, USA.

Gabriel J Rocklin (GJ)

Department of Pharmacology and Center for Synthetic Biology, Northwestern University, Evanston, Illinois, USA.

Colin A Smith (CA)

Department of Chemistry, Wesleyan University, Middletown, Connecticut, USA.

Articles similaires

Photosynthesis Ribulose-Bisphosphate Carboxylase Carbon Dioxide Molecular Dynamics Simulation Cyanobacteria
Databases, Protein Protein Domains Protein Folding Proteins Deep Learning
Silicon Dioxide Water Hot Temperature Compressive Strength X-Ray Diffraction
Fucosyltransferases Drug Repositioning Molecular Docking Simulation Molecular Dynamics Simulation Humans

Classifications MeSH