Novel insights into the structural changes induced by disease-associated mutations in TDP-43: a computational approach

References

• Alessandro, C., Francesca, C., & Alessandro, C. (2013). Molecular dynamic simulation of mGluR5 amino terminal domain: Essential dynamics analysis captures the agonist or antagonist behaviour of ligands. Journal of Molecular Graphics and Modelling, 41, 72–78.
   PubMed Web of Science ®Google Scholar
• Arai, T., Hasegawa, M., Akiyama, H., Ikeda, K., Nonaka, T., Mori, H., Mann, D., Tsuchiya, K., Yoshida, M., Hashizume, Y., & Oda, T. (2006). TDP-43 is a component of ubiquitin-positive tau-negative inclusions in frontotemporal lobar degeneration and amyotrophic lateral sclerosis. Biochemical and Biophysical Research Communications, 351(3), 602–611. https://doi.org/10.1016/j.bbrc.2006.10.093
   PubMed Web of Science ®Google Scholar
• Austin, J. A., Wright, G. S. A., Watanabe, S., Grossmann, J. G., Antonyuk, S. V., Yamanaka, K., & Hasnain, S. S. (2014). Disease causing mutants of TDP-43 nucleic acid binding domains are resistant to aggregation and have increased stability and half-life. Proceedings of the National Academy of Sciences of the United States of America, 111(11), 4309–4314. https://doi.org/10.1073/pnas.1317317111
   PubMed Web of Science ®Google Scholar
• Ayala, Y. M., Zago, P., D'Ambrogio, A., Xu, Y.-F., Petrucelli, L., Buratti, E., & Baralle, F. E. (2008). Structural determinants of the cellular localization and shuttling of TDP-43. Journal of Cell Science, 121(Pt 22), 3778–3785. https://doi.org/10.1242/jcs.038950
   PubMed Web of Science ®Google Scholar
• Böselt, L., Thürlemann, M., & Riniker, S. (2021). Machine learning in QM/MM molecular dynamics simulations of condensed-phase systems. Journal of Chemical Theory and Computation, 17(5), 2641–2658. https://doi.org/10.1021/acs.jctc.0c01112
   PubMed Web of Science ®Google Scholar
• Che, M. X., Jiang, L. L., Li, H. Y., Jiang, Y. J., & Hu, H. Y. (2015). TDP-35 sequesters TDP-43 into cytoplasmic inclusions through binding with RNA. FEBS Letters, 589(15), 1920–1928. https://doi.org/10.1016/j.febslet.2015.06.009
   PubMed Web of Science ®Google Scholar
• Chen, W., & Ferguson, A. L. (2018). Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape exploration. Journal of Computational Chemistry, 39(25), 2079–2102. https://doi.org/10.1002/jcc.25520
   PubMed Web of Science ®Google Scholar
• Chiang, C.-H., Grauffel, C., Wu, L.-S., Kuo, P.-H., Doudeva, L. G., Lim, C., Shen, C.-K J., & Yuan, H. S. (2016). Structural analysis of disease-related TDP-43 D169G mutation: Linking enhanced stability and caspase cleavage efficiency to protein accumulation. Scientific Reports, 6, 21581. https://doi.org/10.1038/srep21581
   PubMed Web of Science ®Google Scholar
• Chmiela, S., Sauceda, H. E., Müller, K.-R., & Tkatchenko, A. (2018). Towards exact molecular dynamics simulations with machine-learned force fields. Nature Communications, 9(1), 3887. https://doi.org/10.1038/s41467-018-06169-2
   PubMedGoogle Scholar
• Cicardi, M. E., Cristofani, R., Rusmini, P., Meroni, M., Ferrari, V., Vezzoli, G., Tedesco, B., Piccolella, M., Messi, E., Galbiati, M., Boncoraglio, A., Carra, S., Crippa, V., & Poletti, A. (2018). Tdp-25 routing to autophagy and proteasome ameliorates its aggregation in amyotrophic lateral sclerosis target cells. Scientific Reports, 8(1), 12390. https://doi.org/10.1038/s41598-018-29658-2
   PubMed Web of Science ®Google Scholar
• De Marco, G., Lomartire, A., Mandili, G., Lupino, E., Buccinnà, B., Ramondetti, C., Moglia, C., Novelli, F., Piccinini, M., Mostert, M., Rinaudo, M. T., Chiò, A., & Calvo, A. (2014). Reduced cellular Ca2+ availability enhances TDP-43 cleavage by apoptotic caspases. Biochimica et Biophysica Acta, 1843(4), 725–734. https://doi.org/10.1016/j.bbamcr.2014.01.010
   PubMed Web of Science ®Google Scholar
• Duan, Y., Wu, C., Chowdhury, S., Lee, M. C., Xiong, G., Zhang, W., Yang, R., Cieplak, P., Luo, R., Lee, T., Caldwell, J., Wang, J., & Kollman, P. (2003). A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. Journal of Computational Chemistry, 24(16), 1999–2012. https://doi.org/10.1002/jcc.10349
   PubMed Web of Science ®Google Scholar
• Dugger, B. N., & Dickson, D. W. (2017). Pathology of neurodegenerative diseases. Cold Spring Harbor Perspectives in Biology, 9(7), a028035. https://doi.org/10.1101/cshperspect.a028035
   PubMed Web of Science ®Google Scholar
• Facco, E., d'Errico, M., Rodriguez, A., & Laio, A. (2017). Estimating the intrinsic dimension of datasets by a minimal neighborhood information. Scientific Reports, 7(1), 12140. https://doi.org/10.1038/s41598-017-11873-y
   PubMed Web of Science ®Google Scholar
• Fleetwood, O., Kasimova, M. A., Westerlund, A. M., & Delemotte, L. (2020). Molecular insights from conformational ensembles via machine learning. Biophysical Journal, 118(3), 765–780. https://doi.org/10.1016/j.bpj.2019.12.016
   PubMed Web of Science ®Google Scholar
• Freibaum, B. D., Chitta, R. K., High, A. A., & Taylor, J. P. (2010). Global analysis of TDP-43 interacting proteins reveals strong association with RNA splicing and translation machinery. Journal of Proteome Research, 9(2), 1104–1120. https://doi.org/10.1021/pr901076y
   PubMed Web of Science ®Google Scholar
• Geuens, T., Bouhy, D., & Timmerman, V. (2016). The hnRNP family: Insights into their role in health and disease. Human Genetics, 135(8), 851–867. https://doi.org/10.1007/s00439-016-1683-5
   PubMed Web of Science ®Google Scholar
• Grear, T., Avery, C., Patterson, J., & Jacobs, D. J. (2021). Molecular function recognition by supervised projection pursuit machine learning. Scientific Reports, 11(1), 4247. https://doi.org/10.1038/s41598-021-83269-y
   PubMed Web of Science ®Google Scholar
• Hasegawa, M., Arai, T., Nonaka, T., Kametani, F., Yoshida, M., Hashizume, Y., Beach, T. G., Buratti, E., Baralle, F., Morita, M., Nakano, I., Oda, T., Tsuchiya, K., & Akiyama, H. (2008). Phosphorylated TDP-43 in frontotemporal lobar degeneration and amyotrophic lateral sclerosis. Annals of Neurology, 64(1), 60–70. https://doi.org/10.1002/ana.21425
   PubMed Web of Science ®Google Scholar
• Hashemian, B., Millán, D., & Arroyo, M. (2013). Modeling and enhanced sampling of molecular systems with smooth and nonlinear data-driven collective variables. The Journal of Chemical Physics, 139(21), 214101. https://doi.org/10.1063/1.4830403
   PubMed Web of Science ®Google Scholar
• Hess, B., Kutzner, C., van der Spoel, D., & Lindahl, E. (2008). GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation. Journal of Chemical Theory and Computation, 4(3), 435–447. https://doi.org/10.1021/ct700301q
   PubMed Web of Science ®Google Scholar
• Huang, Y.-C., Lin, K.-F., He, R.-Y., Tu, P.-H., Koubek, J., Hsu, Y.-C., & Huang, J. J.-T. (2013). Inhibition of TDP-43 aggregation by nucleic acid binding. Plos One, 8(5), e64002. https://doi.org/10.1371/journal.pone.0064002
   PubMed Web of Science ®Google Scholar
• Inukai, Y., Nonaka, T., Arai, T., Yoshida, M., Hashizume, Y., Beach, T. G., Buratti, E., Baralle, F. E., Akiyama, H., Hisanaga, S-i., & Hasegawa, M. (2008). Abnormal phosphorylation of Ser409/410 of TDP-43 in FTLD-U and ALS. FEBS Letters, 582(19), 2899–2904. https://doi.org/10.1016/j.febslet.2008.07.027
   PubMed Web of Science ®Google Scholar
• Jo, M., Lee, S., Jeon, Y.-M., Kim, S., Kwon, Y., & Kim, H.-J. (2020). The role of TDP-43 propagation in neurodegenerative diseases: Integrating insights from clinical and experimental studies. Experimental & Molecular Medicine, 52(10), 1652–1662. https://doi.org/10.1038/s12276-020-00513-7
   PubMed Web of Science ®Google Scholar
• Johnson, B. S., Snead, D., Lee, J. J., McCaffery, J. M., Shorter, J., & Gitler, A. D. (2009). TDP-43 Is Intrinsically Aggregation-prone, and Amyotrophic Lateral Sclerosis-linked Mutations Accelerate Aggregation and Increase Toxicity *. The Journal of Biological Chemistry, 284(30), 20329–20339. https://doi.org/10.1074/jbc.M109.010264
   PubMed Web of Science ®Google Scholar
• Jung, H., Covino, R., & Hummer, G. (2019). Artificial intelligence assists discovery of reaction coordinates and mechanisms from molecular dynamics simulations. arXiv Preprint Archive, 1901.04595
   Google Scholar
• Kamaraj, B., & Bogaerts, A. (2015). Structure and function of p53-DNA complexes with inactivation and rescue mutations: A molecular dynamics simulation study. Plos One, 10(8), e0134638. https://doi.org/10.1371/journal.pone.0134638
   PubMed Web of Science ®Google Scholar
• Kasahara, K., Fukuda, I., & Nakamura, H. (2014). A novel approach of dynamic cross correlation analysis on molecular dynamics simulations and its application to Ets1 dimer–DNA complex. Plos One, 9(11), e112419. https://doi.org/10.1371/journal.pone.0112419
   PubMed Web of Science ®Google Scholar
• Kitamura, A., Nakayama, Y., Shibasaki, A., Taki, A., Yuno, S., Takeda, K., Yahara, M., Tanabe, N., & Kinjo, M. (2016). Interaction of RNA with a C-terminal fragment of the amyotrophic lateral sclerosis-associated TDP43 reduces cytotoxicity. Scientific Reports, 6, 19230. https://doi.org/10.1038/srep19230
   PubMed Web of Science ®Google Scholar
• Kumar, V., Pandey, P., Idrees, D., Prakash, A., & Lynn, A. M. (2019). Delineating the effect of mutations on the conformational dynamics of N-terminal domain of TDP-43. Biophysical Chemistry, 250, 106174. https://doi.org/10.1016/j.bpc.2019.106174
   PubMed Web of Science ®Google Scholar
• Li, Q., Yokoshi, M., Okada, H., & Kawahara, Y. (2015). The cleavage pattern of TDP-43 determines its rate of clearance and cytotoxicity. Nature Communications, 6, 6183. https://doi.org/10.1038/ncomms7183
   PubMed Web of Science ®Google Scholar
• Ling, S.-C., Albuquerque, C. P., Han, J. S., Lagier-Tourenne, C., Tokunaga, S., Zhou, H., & Cleveland, D. W. (2010). ALS-associated mutations in TDP-43 increase its stability and promote TDP-43 complexes with FUS/TLS. Proceedings of the National Academy of Sciences of the United States of America, 107(30), 13318–13323. https://doi.org/10.1073/pnas.1008227107
   PubMed Web of Science ®Google Scholar
• Lundervold, A. S., & Lundervold, A. (2019). An overview of deep learning in medical imaging focusing on MRI. Zeitschrift Fur Medizinische Physik, 29(2), 102–127. https://doi.org/10.1016/j.zemedi.2018.11.002
   PubMed Web of Science ®Google Scholar
• Marx, T. C., Calmon, P. F., & Ustun, B. (2020). Predictive multiplicity in classification. In Proceedings of the 37 th International Conference on Machine Learning (Vol. 119, pp. 6765–6774).
   Google Scholar
• McAlary, L., Plotkin, S. S., Yerbury, J. J., & Cashman, N. R. (2019). Prion-like propagation of protein misfolding and aggregation in amyotrophic lateral sclerosis. Frontiers in Molecular Neuroscience, 12, 262. https://doi.org/10.3389/fnmol.2019.00262
   PubMed Web of Science ®Google Scholar
• Mittal, S., & Shukla, D. (2018). Recruiting machine learning methods for molecular simulations of proteins. Molecular Simulation, 44(11), 891–904. https://doi.org/10.1080/08927022.2018.1448976
   Web of Science ®Google Scholar
• Mutihac, R., Alegre-Abarrategui, J., Gordon, D., Farrimond, L., Yamasaki-Mann, M., Talbot, K., & Wade-Martins, R. (2015). TARDBP pathogenic mutations increase cytoplasmic translocation of TDP-43 and cause reduction of endoplasmic reticulum Ca2+ signaling in motor neurons. Neurobiology of Disease, 75, 64–77. https://doi.org/10.1016/j.nbd.2014.12.010
   PubMed Web of Science ®Google Scholar
• Nakielny, S., & Dreyfuss, G. (1997). Nuclear export of proteins and RNAs. Current Opinion in Cell Biology, 9(3), 420–429. https://doi.org/10.1016/S0955-0674(97)80016-6
   PubMed Web of Science ®Google Scholar
• Neumann, M., Sampathu, D. M., Kwong, L. K., Truax, A. C., Micsenyi, M. C., Chou, T. T., Bruce, J., Schuck, T., Grossman, M., Clark, C. M., McCluskey, L. F., Miller, B. L., Masliah, E., Mackenzie, I. R., Feldman, H., Feiden, W., Kretzschmar, H. A., Trojanowski, J. Q., & Lee, V. M.-Y. (2006). Ubiquitinated TDP-43 in frontotemporal lobar degeneration and amyotrophic lateral sclerosis. Science (New York, N.Y.), 314(5796), 130–133. https://doi.org/10.1126/science.1134108
   PubMed Web of Science ®Google Scholar
• Nichols, E., Szoeke, C. E. I., Vollset, S. E., Abbasi, N., Abd-Allah, F., Abdela, J., Aichour, M. T. E., Akinyemi, R. O., Alahdab, F., Asgedom, S. W., Awasthi, A., Barker-Collo, S. L., Baune, B. T., Béjot, Y., Belachew, A. B., Bennett, D. A., Biadgo, B., Bijani, A., Bin Sayeed, M. S., … Murray, C. J. L. (2019). Global, regional, and national burden of Alzheimer's disease and other dementias, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. The Lancet Neurology, 18(1), 88–106. https://doi.org/10.1016/S1474-4422(18)30403-4
   PubMed Web of Science ®Google Scholar
• Noé, F., & Clementi, C. (2017). Collective variables for the study of long-time kinetics from molecular trajectories: Theory and methods. Current Opinion in Structural Biology, 43, 141–147. https://doi.org/10.1016/j.sbi.2017.02.006
   PubMed Web of Science ®Google Scholar
• Nonaka, T., Watanabe, S. T., Iwatsubo, T., & Hasegawa, M. (2010). Seeded aggregation and toxicity of {alpha}-synuclein and tau: Cellular models of neurodegenerative diseases. The Journal of Biological Chemistry, 285(45), 34885–34898. https://doi.org/10.1074/jbc.M110.148460
   PubMed Web of Science ®Google Scholar
• Parrinello, M., & Rahman, A. (1980). Crystal structure and pair potentials: A molecular-dynamics study. Physical Review Letters, 45(14), 1196–1199. https://doi.org/10.1103/PhysRevLett.45.1196
   Web of Science ®Google Scholar
• Polymenidou, M., Lagier-Tourenne, C., Hutt, K. R., Bennett, C. F., Cleveland, D. W., & Yeo, G. W. (2012). Misregulated RNA processing in amyotrophic lateral sclerosis. Brain Research, 1462, 3–15. https://doi.org/10.1016/j.brainres.2012.02.059
   PubMed Web of Science ®Google Scholar
• Prakash, A., Kumar, V., Banerjee, A., Lynn, A. M., & Prasad, R. (2021). Structural heterogeneity in RNA recognition motif 2 (RRM2) of TAR DNA-binding protein 43 (TDP-43): Clue to amyotrophic lateral sclerosis. Journal of Biomolecular Structure & Dynamics, 39(1), 357–367. https://doi.org/10.1080/07391102.2020.1714481
   PubMed Web of Science ®Google Scholar
• Prakash, A., Kumar, V., Meena, N. K., Hassan, M. I., & Lynn, A. M. (2019). Comparative analysis of thermal unfolding simulations of RNA recognition motifs (RRMs) of TAR DNA-binding protein 43 (TDP-43). Journal of Biomolecular Structure & Dynamics, 37(1), 178–194. https://doi.org/10.1080/07391102.2017.1422026
   PubMed Web of Science ®Google Scholar
• Prasad, A., Bharathi, V., Sivalingam, V., Girdhar, A., & Patel, B. K. (2019). Molecular mechanisms of TDP-43 misfolding and pathology in amyotrophic lateral sclerosis. Frontiers in Molecular Neuroscience, 12, 25. https://doi.org/10.3389/fnmol.2019.00025
   PubMed Web of Science ®Google Scholar
• Schneider, E., Dai, L., Topper, R. Q., Drechsel-Grau, C., & Tuckerman, M. E. (2017). Stochastic neural network approach for learning high-dimensional free energy surfaces. Physical Review Letters, 119(15), 150601. https://doi.org/10.1103/PhysRevLett.119.150601
   PubMed Web of Science ®Google Scholar
• Scotter, E. L., Chen, H.-J., & Shaw, C. E. (2015). Erratum to: TDP-43 proteinopathy and ALS: Insights into disease mechanisms and therapeutic targets. Neurotherapeutics : The Journal of the American Society for Experimental NeuroTherapeutics, 12(2), 515–518. https://doi.org/10.1007/s13311-015-0351-0
   PubMed Web of Science ®Google Scholar
• Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., Penedones, H., Petersen, S., Simonyan, K., Crossan, S., Kohli, P., Jones, D. T., Silver, D., Kavukcuoglu, K., & Hassabis, D. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577(7792), 706–710. https://doi.org/10.1038/s41586-019-1923-7
   PubMed Web of Science ®Google Scholar
• Sharma, A., & Dey, P. (2021). A machine learning approach to unmask novel gene signatures and prediction of Alzheimer's disease within different brain regions. Genomics, 113(4), 1778–1789. https://doi.org/10.1016/j.ygeno.2021.04.028
   PubMed Web of Science ®Google Scholar
• Sittel, F., & Stock, G. (2018). Perspective: Identification of collective variables and metastable states of protein dynamics. The Journal of Chemical Physics, 149(15), 150901. https://doi.org/10.1063/1.5049637
   PubMed Web of Science ®Google Scholar
• Suk, T. R., & Rousseaux, M. W. C. (2020). The role of TDP-43 mislocalization in amyotrophic lateral sclerosis. Molecular Neurodegeneration, 15(1), 45. https://doi.org/10.1186/s13024-020-00397-1
   PubMed Web of Science ®Google Scholar
• Sultan, M. M., Kiss, G., Shukla, D., & Pande, V. S. (2014). Automatic selection of order parameters in the analysis of large scale molecular dynamics simulations. Journal of Chemical Theory and Computation, 10(12), 5217–5223. https://doi.org/10.1021/ct500353m
   PubMed Web of Science ®Google Scholar
• Suzuki, H., Lee, K., & Matsuoka, M. (2011). TDP-43-induced death is associated with altered regulation of BIM and Bcl-xL and attenuated by caspase-mediated TDP-43 cleavage. The Journal of Biological Chemistry, 286(15), 13171–13183. https://doi.org/10.1074/jbc.M110.197483
   PubMed Web of Science ®Google Scholar
• Wehmeyer, C., & Noé, F. (2018). Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics. The Journal of Chemical Physics, 148(24), 241703. https://doi.org/10.1063/1.5011399
   PubMed Web of Science ®Google Scholar
• Xiao, S., Sanelli, T., Chiang, H., Sun, Y., Chakrabartty, A., Keith, J., Rogaeva, E., Zinman, L., & Robertson, J. (2015). Low molecular weight species of TDP-43 generated by abnormal splicing form inclusions in amyotrophic lateral sclerosis and result in motor neuron death. Acta Neuropathologica, 130(1), 49–61. https://doi.org/10.1007/s00401-015-1412-5
   PubMed Web of Science ®Google Scholar
• Yamashita, T., Hideyama, T., Hachiga, K., Teramoto, S., Takano, J., Iwata, N., Saido, T. C., & Kwak, S. (2012). A role for calpain-dependent cleavage of TDP-43 in amyotrophic lateral sclerosis pathology. Nature Communications, 3, 1307. https://doi.org/10.1038/ncomms2303
   PubMed Web of Science ®Google Scholar
• Yesudhas, D., Anwar, M. A., Panneerselvam, S., Kim, H.-K., & Choi, S. (2017). Evaluation of Sox2 binding affinities for distinct DNA patterns using steered molecular dynamics simulation. FEBS Open Bio, 7(11), 1750–1767. https://doi.org/10.1002/2211-5463.12316
   PubMed Web of Science ®Google Scholar
• You, S., Lee, H.-G., Kim, K., & Yoo, J. (2020). Improved parameterization of protein–DNA interactions for molecular dynamics simulations of PCNA diffusion on DNA. Journal of Chemical Theory and Computation, 16(7), 4006–4013. https://doi.org/10.1021/acs.jctc.0c00241
   PubMed Web of Science ®Google Scholar
• Zhang, Y.-J., Xu, Y.-F., Cook, C., Gendron, T. F., Roettges, P., Link, C. D., Lin, W.-L., Tong, J., Castanedes-Casey, M., Ash, P., Gass, J., Rangachari, V., Buratti, E., Baralle, F., Golde, T. E., Dickson, D. W., & Petrucelli, L. (2009). Aberrant cleavage of TDP-43 enhances aggregation and cellular toxicity. Proceedings of the National Academy of Sciences of the United States of America, 106(18), 7607–7612. https://doi.org/10.1073/pnas.0900688106
   PubMed Web of Science ®Google Scholar