請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73755
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 林太家(Tai-Chia Lin) | |
dc.contributor.author | Wen-Hao Yang | en |
dc.contributor.author | 楊文皓 | zh_TW |
dc.date.accessioned | 2021-06-17T08:09:29Z | - |
dc.date.available | 2020-08-20 | |
dc.date.copyright | 2019-08-20 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-16 | |
dc.identifier.citation | Fosang, Amanda J., et al. 'Neutrophil collagenase (MMP-8) cleaves at the aggrecanase site E373A374 in the interglobular domain of cartilage aggrecan.' Biochemical Journal 304.2 (1994): 347-351.
Fosang, Amanda J., et al. 'Degradation of cartilage aggrecan by collagenase3 (MMP13).' FEBS letters 380.1-2 (1996): 17-20. Gers, Felix A., Jrgen Schmidhuber, and Fred Cummins. 'Learning to forget:Continual prediction with LSTM.' (1999): 850-855. Nakisa, Bahareh, et al. 'Long short term memory hyperparameter optimization for a neural network based emotion recognition framework.' IEEE Access 6 (2018): 49325-49338. Gers, Felix A., Douglas Eck, and Jrgen Schmidhuber. 'Applying LSTM to time series predictable through time-window approaches.' Neural Nets WIRN Vietri-01. Springer, London, 2002. 193-200. West, Daniel K., Peter D. Olmsted, and Emanuele Paci. 'Free energy for protein folding from nonequilibrium simulations using the Jarzynski equality.' The Journal of chemical physics 125.20 (2006): 204910. Dellago, Christoph, and Gerhard Hummer. 'Computing equilibrium free energies using non-equilibrium molecular dynamics.' Entropy 16.1 (2014):41-61. Cohen, E. G. D., and David Mauzerall. 'A note on the Jarzynski equality.' Journal of Statistical Mechanics: Theory and Experiment 2004.07 (2004):P07006. Bisgaard, Sren, and Murat Kulahci. Time series analysis and forecasting by example. John Wiley and Sons, 2011. Jordan, Michael. 'Attractor dynamics and parallelism in a connectionist sequential machine.' Proc. of the Eighth Annual Conference of the Cognitive Science Society (Erlbaum, Hillsdale, NJ), 1986. 1986. Pearlmutter, Barak A. 'Learning state space trajectories in recurrent neural networks.' Neural Computation 1.2 (1989): 263-269. Cleeremans, Axel, David Servan-Schreiber, and James L. McClelland. 'Finite state automata and simple recurrent networks.' Neural computation 1.3 (1989): 372-381. Hochreiter, Sepp, and Jrgen Schmidhuber. 'Long short-term memory.' Neural computation 9.8 (1997): 1735-1780. Werbos, Paul J. 'Backpropagation through time: what it does and how to do it.' Proceedings of the IEEE 78.10 (1990): 1550-1560. Williams, Ronald J., and David Zipser. 'Experimental analysis of the real time recurrent learning algorithm.' Connection science 1.1 (1989): 87-111. Aprodu, I., Redaelli, A., & Soncini, M. (2008). Actomyosin interaction: mechanical and energetic properties in di erent nucleotide binding ssates. International Journal of Molecular Sciences, 9(10), 1927-1943. Retrieved from <Go to ISI>: ==WOS:000260505000004. doi:10.3390/ijms9101927 Bertini, I., Calderone, V., Fragai, M., Luchinat, C., Maletta, M., & Yeo, K. J. (2006). Snapshots of the reaction mechanism of matrix metalloproteinases. Angewandte Chemie-International Edition, 45(47), 7952-7955. Retrieved from <Go to ISI>:==WOS:000242781200012. doi:10.1002/anie.200603100 Buckwalter, J. A. (2012). The role of mechanical forces in the initiation and progression of osteoarthritis. HSS Journal, 8(1), 37-38. Retrieved from http: ==www.ncbi.nlm.nih.gov/pmc/articles/PMC3295944/. doi:10.1007/s11420- 011-9251-y Buehler, M. J. (2010). Atomistic modeling of materials failure. New York: Springer. Dellago, C., & Hummer, G. (2013). Computing equilibrium free energies using non-equilibrium molecular dynamics. Entropy, 16(1), 41-61. doi:10.3390/e16010041 Durigova, M., Nagase, H., Mort, J. S., & Roughley, P. J. (2011). MMPs are less e cient than ADAMTS5 in cleaving aggrecan core protein. Matrix Biology, 30(2), 145-153. Retrieved from <Go to ISI>://WOS:000289011100008. doi:10.1016/j.matbio.2010.10.007 Felson, D. T. (2013). Osteoarthritis as a disease of mechanics. Osteoarthritis Cartilage, 21(1), 10-15. doi:10.1016/j.joca.2012.09.012 Ferreira, M. F. J., Franca, E. F., & Leite, F. L. (2017). Unbinding pathway energy of glyphosate from the EPSPs enzyme binding site characterized by Steered Molecular Dynamics and Potential of Mean Force. Journal of Molecular Graphics and Modelling, 72, 43-49. Retrieved from https: ==www.ncbi.nlm.nih.gov/pubmed/28033555. doi:10.1016/j.jmgm.2016.11.010 Fiser, A., & Sali, A. (2003). MODELLER: Generation and re nement of homology-based protein structure models. Macromolecular Crystallography, Pt D, 374, 461-491. Retrieved from <Go to ISI>://WOS:000188419600020. doi:Doi 10.1016/S0076-6879(03)74020-8 Huang, K., & Wu, L. D. (2008). Aggrecanase and aggrecan degradation in osteoarthritis: a review. Journal of International Medical Research, 36(6), 1149-1160. Retrieved from <Go to ISI>://WOS:000262024700001. doi:Doi 10.1177/147323000803600601 Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: Visual molecular dynamics. Journal of Molecular Graphics & Modelling, 14(1), 33-38. Retrieved from <Go to ISI>://WOS:A1996UH51500005. doi:Doi 10.1016/0263- 7855(96)00018-5 Jo, S., Cheng, X., Islam, S. M., Huang, L., Rui, H., Zhu, A.,. . . Im, W. (2014). CHARMM-GUI PDB manipulator for advanced modeling and simulations of proteins containing nonstandard residues. Advances in Protein Chemistry and Structural Biology, 96, 235-265. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/25443960. doi:10.1016/bs.apcsb.2014.06.002 MacKerell, A. D., Bashford, D., Bellott, M., Dunbrack, R. L., Evanseck, J. D., Field, M. J.,. . . Karplus, M. (1998). All-atom empirical potential for molecular modeling and dynamics studies of proteins. Journal of Physical Chemistry B, 102(18), 3586-3616. Retrieved from <Go to ISI>: ==WOS:000073632700037. Malemud, C. J. (2017). Matrix metalloproteinases and synovial joint pathology. Progress in Molecular Biology and Translational Science, 148, 305-325. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/28662824. doi:10.1016/bs.pmbts.2017.03.003 Patel, J. S., Berteotti, A., Ronsisvalle, S., Rocchia, W., & Cavalli, A. (2014). Steered molecular dynamics simulations for studying protein-ligand interaction in cyclin-dependent kinase 5. Journal of chemical information and modeling, 54(2), 470-480. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/24437446. doi:10.1021/ci4003574 Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E.,. . . Schulten, K. (2005). Scalable molecular dynamics with NAMD. Journal of Computational Chemistry, 26(16), 1781-1802. Retrieved from <Go to ISI>://WOS:000233021400007. doi:10.1002/jcc.20289 Renaux, A., & Consortium, U. (2018). UniProt: the universal protein knowledgebase (vol 45, pg D158, 2017). Nucleic Acids Research, 46(5), 2699-2699. Retrieved from <Go to ISI>: ==WOS:000427677100051. doi:10.1093/nar/gky092 Sanner, M. F., Olson, A. J., & Spehner, J. C. (1996). Reduced surface: An e cient way to compute molecular surfaces. Biopolymers, 38(3), 305-320. Retrieved from <Go to ISI>://WOS:A1996TX66700004. doi:Doi 10.1002/(Sici)1097-0282(199603)38:3<305::Aid-Bip4>3.0.Co;2-Y Shen, M., Guan, J., Xu, L., Yu, Y., He, J., Jones, G. W., & Song, Y. (2012). Steered molecular dynamics simulations on the binding of the appendant structure and helix-beta2 in domain-swapped human cystatin C dimer. Journal of Biomolecular Structure and Dynamics, 30(6), 652-661. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/22731964. doi:10.1080/07391102.2012.689698 Tekpinar, M. (2018). Steered molecular dynamics simulations of coumarin25Z/5E pulling reveal di erent interaction pro les for four human cytosolic carbonic anhydrases. Sleyman Demirel niversitesi Fen Bilimleri Enstits Dergisi, 22(2). doi:10.19113/sdufbed.47662 Vashisth, H., & Abrams, C. F. (2008). Ligand escape pathways and (un)binding free energy calculations for the hexameric insulin-phenol complex. Biophysical Journal, 95(9), 4193-4204. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/18676643. doi:10.1529/biophysj.108.139675 Venn, M., & Maroudas, A. (1977). Chemical composition and swelling of normal and osteoarthrotic femoral head cartilage. I. Chemical composition. Annals of the Rheumatic Diseases, 36(2), 121-129. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1006646/. Zhang, Z., Santos, A. P., Zhou, Q., Liang, L., Wang, Q., Wu, T., & Franzen, S. (2016). Steered molecular dynamics study of inhibitor binding in the internal binding site in dehaloperoxidase-hemoglobin. Biophys Chem, 211, 28-38. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/26824426. doi:10.1016/j.bpc.2016.01.003 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73755 | - |
dc.description.abstract | 時間尺度上的限制在分子動力模擬的領域上一直以來都是一個
具有挑戰的問題。龐大的分子系統,例如:蛋白質,在超級電腦的 運算幫助下,仍然無法突破奈秒尺度的時間長度。這樣不完整的模 擬系統,會導致不完全的分子反應過程和結果。一個機器學習的技 法:時間序列預測,會在這篇論文中被使用,作為解決時間尺度問 題的方法。分子動力模擬中的不同變數,例如總能量,總力合,或 是部分的應力,甚至是氫鍵的強度,都能作為一筆資料來看待,而 這些資料,由於是分子模擬而來,會存在強烈的時間相依性,在這 樣強烈的時間相依性下,我們能將其視為時間序列來處理。利用數 據本身的特性,創造出兩筆不同時間軸的資料,利用彼此做訓練, 我們能透過機器學習的方法來預測這些數據的未來走向。 | zh_TW |
dc.description.abstract | Time scale limitation has been and remains a challenging problem
in Molecular Dynamic(MD) simulations. Large systems such as proteins are still narrowed in scale of nano-second with the help of super computers. The limit of time may contribute to insu cient result and improper inference while the simulation is already time-consuming. A machine learning method: time series prediction is implemented in this paper to solve this problem. Since features (Total force, numbers of Hydrogen bond, etc.) of protein have strong time dependence during simulation, we can use this relationship to obtain a different data by shifting original data with time. Taking these two data, we can train the machine to catch up structure of systems and achieve predicting these features in the next time step. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T08:09:29Z (GMT). No. of bitstreams: 1 ntu-108-R06246004-1.pdf: 5229853 bytes, checksum: a0062f7d2427889643cabf364baf05cf (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | Certificate of Approval 3
Abstract(Chinese) 4 Abstract(English) 5 1 Introduction 10 2 Time-series Prediction 12 2.1 Stationarity 12 2.2 Auto-correlation 13 3 Training Model 16 3.1 RNN 16 3.2 LSTM 17 4 Data Set 20 4.1 Force Data Set and Pre-processing 20 4.2 Aggrecan 21 4.3 Simulations 22 4.4 Steered molecular dynamics simulation (SMD) 24 4.5 Two Particle System 25 5 Training Process 28 5.1 Articial Time Series 29 5.2 Example : Sine 30 6 Results and Discussion 31 6.1 Force Prediciton 31 6.2 Two Particle Prediction 31 7 Conclusions 31 8 Reference 35 | |
dc.language.iso | zh-TW | |
dc.title | 時間序列預測在拉伸分子動力模擬之應用 | zh_TW |
dc.title | Time-series Prediction in Steered Molecular Dynamics Simulation | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張書瑋(Shu-Wei Chang),洪子倫 | |
dc.subject.keyword | 分子動力模擬,拉伸分子動力,時間序列預測,雙質點系統,長短期記憶模型, | zh_TW |
dc.subject.keyword | Molecular Dynamic simulations,Steered Molecular Dynamics,time-series prediction,Aggrecan,LSTM,Two particle system, | en |
dc.relation.page | 38 | |
dc.identifier.doi | 10.6342/NTU201903615 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-16 | |
dc.contributor.author-college | 理學院 | zh_TW |
dc.contributor.author-dept | 應用數學科學研究所 | zh_TW |
顯示於系所單位: | 應用數學科學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-108-1.pdf 目前未授權公開取用 | 5.11 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。