MELD-accelerated molecular dynamics help determine amyloid fibril structures.

Amyloid / chemistry Molecular Dynamics Simulation Nuclear Magnetic Resonance, Biomolecular

Journal

Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179

Informations de publication

Date de publication:
05 08 2021
Historique:
received: 09 04 2020
accepted: 15 07 2021
entrez: 6 8 2021
pubmed: 7 8 2021
medline: 19 11 2021
Statut: epublish

Résumé

It is challenging to determine the structures of protein fibrils such as amyloids. In principle, Molecular Dynamics (MD) modeling can aid experiments, but normal MD has been impractical for these large multi-molecules. Here, we show that MELD accelerated MD (MELD x MD) can give amyloid structures from limited data. Five long-chain fibril structures are accurately predicted from NMR and Solid State NMR (SSNMR) data. Ten short-chain fibril structures are accurately predicted from more limited restraints information derived from the knowledge of strand directions. Although the present study only tests against structure predictions - which are the most detailed form of validation currently available - the main promise of this physical approach is ultimately in going beyond structures to also give mechanical properties, conformational ensembles, and relative stabilities.

Identifiants

DOI: 10.1038/s42003-021-02461-y PMID: 34354239 PMC: PMC8342454
pubmed: 34354239
doi: 10.1038/s42003-021-02461-y
pii: 10.1038/s42003-021-02461-y
pmc: PMC8342454
doi:

Substances chimiques

Amyloid 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

942

Informations de copyright

© 2021. The Author(s).

Références

Selkoe, D. J. Folding proteins in fatal ways. Nature 426, 900–904 (2003).
doi: 10.1038/nature02264 pubmed: 14685251
Soto, C. Protein misfolding and disease; protein refolding and therapy. FEBS Lett. 498, 204–207 (2001).
pubmed: 11412858 doi: 10.1016/S0014-5793(01)02486-3
Soto, C. Unfolding the role of protein misfolding in neurodegenerative diseases. Nat. Rev. Neurosci. 4, 49–60 (2003).
doi: 10.1038/nrn1007 pubmed: 12511861
Dobson, C. M. Protein-misfolding diseases: getting out of shape. Nature 418, 729–730 (2002).
pubmed: 12181546 doi: 10.1038/418729a
Forman, M. S., Trojanowski, J. Q. & Lee, V. M. Neurodegenerative diseases: a decade of discoveries paves the way for therapeutic breakthroughs. Nat. Med. 10, 1055–1063 (2004).
pubmed: 15459709 doi: 10.1038/nm1113
Huang, H. C. & Jiang, Z. F. Accumulated amyloid-beta peptide and hyperphosphorylated Tau protein: relationship and links in Alzheimer’s disease. J. Alzheimers Dis. 16, 15–27 (2009).
pubmed: 19158417 doi: 10.3233/JAD-2009-0960
Iadanza, M. G., Jackson, M. P., Hewitt, E. W., Ranson, N. A. & Radford, S. E. A new era for understanding amyloid structures and disease. Nat. Rev. Mol. Cell Biol. 19, 755–773 (2018).
pubmed: 30237470 doi: 10.1038/s41580-018-0060-8
Loquet, A. et al. 3D structure determination of amyloid fibrils using solid-state NMR spectroscopy. Methods 138-139, 26–38 (2018).
pubmed: 29627359 doi: 10.1016/j.ymeth.2018.03.014
Jaroniec, C. P. Two decades of progress in structural and dynamic studies of amyloids by solid-state NMR. J. Magn. Reson. 306, 42–47 (2019).
pubmed: 31311708 pmcid: 6703944 doi: 10.1016/j.jmr.2019.07.015
Guo, J. T. & Xu, Y. Towards modeling of amyloid fibril structures. Front Biosci. 13, 4039–4050 (2008).
pubmed: 18508498 doi: 10.2741/2992
André, I. & Bradley, P. Prediction of the structure of symmetrical protein assemblies. Proc. Natl Acad. Sci. USA 104, 17656–17661 (2007).
pubmed: 17978193 pmcid: 2077069 doi: 10.1073/pnas.0702626104
Zhang, J., Gao, D. Y. & Yearwood, J. A novel canonical dual computational approach for prion AGAAAAGA amyloid fibril molecular modeling. J. Theor. Biol. 284, 149–157 (2011).
pubmed: 21723301 doi: 10.1016/j.jtbi.2011.06.024
Zhang, J. Optimal molecular structures of prion AGAAAAGA amyloid fibrils formatted by simulated annealing. J. Mol. Model. 17, 173–179 (2011).
pubmed: 20411399 doi: 10.1007/s00894-010-0691-y
Melckebeke, H. V. Atomic-resolution three-dimensional structure of HET-s(218-289) amyloid fibrils by solid-state NMR spectroscopy. J. Am. Chem. Soc. 132, 13765–13775 (2010).
pubmed: 20828131 doi: 10.1021/ja104213j
Qiang, W., Yau, W.-M., Luo, Y., Mattson, M. P. & Tycko, R. Anti-parallel β-sheet architecture in Iowa-mutant β-amyloid fibrils. Proc. Natl Acad. Sci. USA 109, 4443–4448 (2012).
pubmed: 22403062 pmcid: 3311365 doi: 10.1073/pnas.1111305109
Iwata, K. et al. 3D structure of amyloid protofilaments of β
pubmed: 17108084 pmcid: 1838716 doi: 10.1073/pnas.0607180103
Sgourakis, N. G., Yau, W.-M. & Qiang, W. Modeling an in-register, parallel Iowa Aβ fibril structure using solid-state NMR data from labeled samples with Rosetta. Structure 23, 216–227 (2015).
pubmed: 25543257 doi: 10.1016/j.str.2014.10.022
Matthes, D., Gapsys, V., Brennecke, J. T. & Groot, B. L. An atomistic view of amyloidogenic self-assembly: structure and dynamics of heterogeneous conformational states in the pre-nucleation phase. Sci. Rep. 6, 33156 (2016).
pubmed: 27616019 pmcid: 5018807 doi: 10.1038/srep33156
Punihaole, D., Workman, R. J., Hong, Z., Madura, J. D. & Asher, S. A. Polyglutamine fibrils: new insights into antiparallel β-Sheet conformational preference and side chain structure. J. Phys. Chem. B 120, 3012–3026 (2016).
pubmed: 26947327 doi: 10.1021/acs.jpcb.5b11380
MacCallum, J. L., Perez, A. & Dill, K. A. Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference. Proc. Natl Acad. Sci. USA 112, 6985–6990 (2015).
pubmed: 26038552 pmcid: 4460504 doi: 10.1073/pnas.1506788112
Perez, A., MacCallum, J. L. & Dill, K. A. Accelerating molecular simulations of proteins using Bayesian inference on weak information. Proc. Natl Acad. Sci. USA 112, 11846–11851 (2015).
pubmed: 26351667 pmcid: 4586851 doi: 10.1073/pnas.1515561112
Perez, A., Morrone, J. A. & Dill, K. A. Accelerating physical simulations of proteins by leveraging external knowledge. Wiley Interdiscip. Rev. Comput. Mol. Sci. 7, e1309 (2017).
pubmed: 28959358 pmcid: 5612641 doi: 10.1002/wcms.1309
Perez, A., Morrone, J. A., Brini, E., MacCallum, J. L. & Dill, K. A. Blind protein structure prediction using accelerated free-energy simulations. Sci. Adv. 2, e1601274 (2016).
pubmed: 27847872 pmcid: 5106196 doi: 10.1126/sciadv.1601274
Robertson, J. C. et al. NMR-assisted protein structure prediction with MELDxMD. Proteins 87, 1333–1340 (2019).
pubmed: 31350773 pmcid: 6851473 doi: 10.1002/prot.25788
Stankovic, I., Hall, M. B. & Zarić, S. D. Construction of amyloid PDB files database. IPSI BgD Tran. Int. Res. 13, 47–51 (2017).
Zemla, A. LGA: A method for finding 3D similarities in protein structures. Nucleic Acids Res. 31, 3370–3374 (2003).
pubmed: 12824330 pmcid: 168977 doi: 10.1093/nar/gkg571
Zhang, Y. & Skolnick, J. Scoring function for automated assessment of protein structure template quality. Proteins 57, 702–710 (2004).
pubmed: 15476259 doi: 10.1002/prot.20264
Sawaya, M. et al. Atomic structures of amyloid cross-β spines reveal varied steric zippers. Nature 447, 453–457 (2007).
pubmed: 17468747 doi: 10.1038/nature05695
Ghomi, T. H., Topp, E. M. & Lill, M. A. Fibpredictor: a computational method for rapid prediction of amyloid fibril structures. J. Mol. Model 22, 1–10 (2016).
Park, J., Kahng, B. & Hwang, W. Thermodynamic selection of steric zipper patterns in the amyloid cross-β spine. Plos Comput. Biol. 5, e1000492 (2009).
pubmed: 19730673 pmcid: 2723932 doi: 10.1371/journal.pcbi.1000492
Berhanu, W. M. & Masunov A. E. Alternative packing modes leading to amyloid polymorphism in five fragments studied with molecular dynamics. Pept. Sci. 98, 131–144 (2012).
doi: 10.1002/bip.21731
Berhanu, W. M. & Masunov, A. E. Molecular dynamic simulation of wild type and mutants of the polymorphic amyloid NNQNTF segments of elk prion: structural stability and thermodynamic of association. Biopolymers 95, 573–589 (2011).
pubmed: 21384336 doi: 10.1002/bip.21611
Wiltzius, J. J. W. et al. Molecular mechanisms for protein-encoded inheritance. Nat. Struct. Mol. Biol. 16, 973–978 (2009).
pubmed: 19684598 pmcid: 3640378 doi: 10.1038/nsmb.1643
Morrone, J. A., Perez, A., MacCallum, J. & Dill, K. A. Computed binding of peptides to proteins with MELD-accelerated molecular dynamics. J. Chem. Theory Comput. 13, 870–876 (2017).
pubmed: 28042966 doi: 10.1021/acs.jctc.6b00977
Morrone, J. A. et al. Molecular simulations identify binding poses and approximate affinities of stapled α-helical peptides to MDM2 and MDMX. J. Chem. Theory Comput. 13, 863–869 (2017).
pubmed: 28042965 doi: 10.1021/acs.jctc.6b00978
Brini, E., Kozakov, D. & Dill, K. A. Predicting protein dimer structures using MELD x MD. J. Chem. Theory Comp. 15, 3381–3389 (2019).
doi: 10.1021/acs.jctc.8b01208
Robertson, J. C., Perez, A. & Dill, K. A. MELD MD folds nonthreadables, giving native structures and populations. J. Chem. Theory Comput. 14, 6734–6740 (2018).
pubmed: 30407805 pmcid: 6705390 doi: 10.1021/acs.jctc.8b00886
Sugita, Y. & Okamoto, Y. Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 314, 141–151 (1999).
doi: 10.1016/S0009-2614(99)01123-9
Fukunishi, H., Watanabe, O. & Takada, S. On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: application to protein structure prediction. J. Chem. Phys. 116, 9058–9067 (2002).
doi: 10.1063/1.1472510
Eastman, P. et al. OpenMM 4: a reusable, extensible, hardware independent library for high performance molecular simulation. J. Chem. Theory Comput. 9, 461–469 (2013).
pubmed: 23316124 doi: 10.1021/ct300857j
Maier, J. A. et al. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 11, 3696–3713 (2015).
pubmed: 26574453 pmcid: 4821407 doi: 10.1021/acs.jctc.5b00255
Nguyen, H., Roe, D. R. & Simmerling, C. Improved generalized Born solvent model parameters for protein simulations. J. Chem. Theory Comput. 9, 2020–2034 (2013).
pubmed: 25788871 pmcid: 4361090 doi: 10.1021/ct3010485
Roe, D. R. & Cheatham T. E., III. PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J. Chem. Theory Comput. 9, 3084–3095 (2013).
MacCallum, J. L., Perez, A. & Dill, K. A. MELD-0.4.14. Zenodo (2021).

Auteurs

Bhanita Sharma (B)

Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.
ORCID: 0000-0001-6133-4221

Ken A Dill (KA)

Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA. dill@laufercenter.org.
Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA. dill@laufercenter.org.
Departments of Chemistry and Physics, Stony Brook University, Stony Brook, NY, USA. dill@laufercenter.org.
ORCID: 0000-0002-2390-2002

Articles similaires

Atomic view of photosynthetic metabolite permeability pathways and confinement in synthetic carboxysome shells.

Daipayan Sarkar, Christopher Maffeo, Markus Sutter et al.
1.00
Photosynthesis Ribulose-Bisphosphate Carboxylase Carbon Dioxide Molecular Dynamics Simulation Cyanobacteria

Mouse α-synuclein fibrils are structurally and functionally distinct from human fibrils associated with Lewy body diseases.

Arpine Sokratian, Ye Zhou, Meltem Tatli et al.
1.00
alpha-Synuclein Humans Animals Mice Lewy Body Disease

Drug repurposing against fucosyltransferase-2 via docking, STD-NMR, and molecular dynamic simulation studies.

Muhammad Atif, Humaira Zafar, Atia-Tul- Wahab et al.
1.00
Fucosyltransferases Drug Repositioning Molecular Docking Simulation Molecular Dynamics Simulation Humans

A head-to-head comparison of MM/PBSA and MM/GBSA in predicting binding affinities for the CB

Mei Qian Yau, Clarence W Y Liew, Jing Hen Toh et al.
1.00
Receptor, Cannabinoid, CB1 Ligands Molecular Dynamics Simulation Protein Binding Thermodynamics

Classifications MeSH

questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › Amyloïde › MELD-accelerated molecular dynamics help...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Simulation numérique › Simulation de dynamique moléculaire › MELD-accelerated molecular dynamics help...
questionsmedicales.fr › Techniques d'investigation › Techniques de chimie analytique › Analyse spectrale › Spectroscopie par résonance magnétique › Résonance magnétique nucléaire biomoléculaire › MELD-accelerated molecular dynamics help...