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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/102967完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 郭哲來 | zh_TW |
| dc.contributor.advisor | Jer-Lai Kuo | en |
| dc.contributor.author | 同高孝 | zh_TW |
| dc.contributor.author | Dong Cao Hieu | en |
| dc.date.accessioned | 2026-08-04T07:52:51Z | - |
| dc.date.available | 2026-08-04 | - |
| dc.date.copyright | 2026-07-29 | - |
| dc.date.issued | 2026 | - |
| dc.date.submitted | 2026-07-22 | - |
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A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H–Pu. J. Chem. Phys., 132:154104, 2010 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/102967 | - |
| dc.description.abstract | 探索勝肽的構象空間對於理解其三維結構與分子穩定性之間的關係以及解讀光譜性質至關重要。然而，即使在低能量條件下，由於鏈長、質子化狀態、甲基化程度以及順反肽鍵異構體等因素的影響，應探索的結構數量龐大，因此窮舉式的搜尋在計算上仍然極具挑戰性。本論文發展了一種基於深度學習神經網路位能（DL-NNP）的迭代主動學習方法，旨在加速構象探索，同時保持接近密度泛函理論（DFT）的精確度。此框架結合了低成本採樣、NNP 引導的優化和 DFT 精修，能夠系統地研究甘氨酸寡聚體及甲基化多肽（三肽至六肽）的構象空間。在所有體系中，NNP 的能量預測誤差均低於 1 kcal/mol，同時與傳統基於 DFT 的工作流程相比，計算成本降低了 300 倍以上。一項關鍵進展在於證明，僅使用單點 DFT 能量和力即可訓練五肽和六肽的高精度 NNP，從而在不犧牲準確度的前提下降低訓練資料生成的成本。我們的結果表明，質子化和甲基化顯著穩定了順式胜肽構象，而甲基化亦會調節優先質子化位點。鏈長增加與 N-甲基化有助於促進兩性離子結構的穩定性，而隱式溶劑化則顯著增加了熱力學可及的低能構象異構體數量。總而言之，本研究顯示，基於主動學習的 DL-NNP 為探索胜肽構象空間提供了一個兼具高精度、可移植性與計算效率的框架，彌合了第一原理精度與大規模構象採樣之間的差距 | zh_TW |
| dc.description.abstract | Exploring peptide conformational landscapes is essential for understanding the relationships between molecular structure, stability, and spectroscopic properties. However, exhaustive conformational searches remain computationally challenging because of the large number of accessible structures arising from variations in chain length, protonation state, methylation pattern, and cis/trans peptide-bond isomerism. In this thesis, deep-learning neural network potentials (DL-NNPs) trained through iterative active learning were developed to accelerate conformational exploration while maintaining near-density-functional-theory (DFT) accuracy. The proposed framework combines low-cost conformational sampling, NNP-guided optimization, and DFT refinement, enabling systematic studies of glycine oligomers, methylated tripeptides, and polypeptides up to hexapeptides. Across all systems, the NNPs achieved energy prediction errors below 1 kcal mol⁻¹ while providing more than a 300-fold reduction in computational cost compared with conventional DFT-based workflows. A key methodological advance is the demonstration that accurate NNPs for penta- and hexapeptides can be trained using only single-point DFT energies and forces, substantially reducing the cost of training-data generation without sacrificing accuracy. The resulting conformational databases revealed that protonation and methylation significantly stabilize cis-peptide conformations, while methylation also modulates preferred protonation sites. Furthermore, chain elongation and N-methylation promote zwitterionic structures, whereas implicit solvation substantially increases the number of thermally accessible low-energy conformers. Overall, this work demonstrates that active-learning DL-NNPs provide an accurate, transferable, and computationally efficient framework for exploring peptide conformational landscapes, bridging the gap between first-principles accuracy and large-scale conformational sampling. | en |
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| dc.description.tableofcontents | Acknowledgements i
摘要 iii Abstract v Contents vii List of Figures xi List of Tables xvii Chapter 1 Introduction 1 1.1 Short peptides in gas-phase as model systems to study structure of biomolecules at molecular level . . . . . . . . . . . . . . . . . . . . 1 1.2 Traditional approaches to identify the low-energy structures of short peptides with DFT . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Neural Network Potentials (NNPs) assisted acceleration . . . . . . . 4 1.4 The SchNet architecture used in this work . . . . . . . . . . . . . . . 5 1.5 Thesis Organization and Chapter Overview . . . . . . . . . . . . . . 7 Chapter 2 Assessing Neural Network Potentials for Exploring the Energy Landscapes of Di- to Tetraglycine 11 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.1 Sampling at DFTB-3 level . . . . . . . . . . . . . . . . . . . . . . 14 2.2.2 Benchmark calculations at the B3LYP/M06-2X/MP2 levels of di- and tri-glycine . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.3 Training NNP models . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.4 Validating NNP models . . . . . . . . . . . . . . . . . . . . . . . . 21 2.2.5 Structural search using NNP and comparison with M06-2X . . . . . 22 2.2.6 Benchmark of NNP-2 and M06-2X minima versus DLPNO-CCSD(T)/AUG-cc-pVTZ . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.3.1 Influence of ZPE on relative energies . . . . . . . . . . . . . . . . . 26 2.3.2 Low-energy structures at the M06-2X level and comparison with structures found in the literature . . . . . . . . . . . . . . . . . . . 28 2.3.3 Calculated IR spectra of Gly2−4 between 2500 and 4000 cm−1 . . . 31 2.3.4 Calculated IR spectra of Gly2−4 between 1000 and 2000 cm−1 . . . 35 2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Chapter 3 Effects of Methylation on trans/cis Preferences and Protonation Stability in Triglycine 37 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.2.1 Generating Initial Configurations of Methylated Tripeptides from the Database of Triglycine . . . . . . . . . . . . . . . . . . . . . . 42 3.2.2 Generation of Test and Training Sets for NNP-0 and NNP-1 (Gas Phase) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.2.3 Generation of Test and Training Sets for NNP-0w and NNP-1w (PCM-Water) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2.4 Searching Low-Energy Minima of 26 Methylated Tripeptides Using NNP and M06-2X . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.3 Result and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.3.1 Evaluation of the Prediction Accuracy of NNP Models . . . . . . . 47 3.3.1.1 NNP-0 and NNP-1 Models for Methylated Tripeptides in the Gas Phase . . . . . . . . . . . . . . . . . . . . . . . 47 3.3.1.2 NNP-0w and NNP-1w Models for Methylated Tripeptides in PCM-Water Solvent . . . . . . . . . . . . . . . . 50 3.3.2 Comparison of the Low-Energy Minima of All 27 Tripeptides at M06-2X in the Gas Phase and PCM-Water . . . . . . . . . . . . . 52 3.3.3 Impact of Methylation on the Relative Stability of Protonation Sites and Trans/Cis Configuration . . . . . . . . . . . . . . . . . . . . 55 3.3.4 Vibrational Spectra of the Methylated Tripeptides in the Gas Phase . 59 3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Chapter 4 Structures of Polyglycine, Polyalanine, and Polysarcosine: A Neural Network-Assisted First-Principles Study of the Effects of Methylation and Chain Length 67 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.2.1 DFTB-3 Minima Generation from G2, G3, G4 data . . . . . . . . . 71 4.2.2 Active Learning of Neural Network Potentials and NNP-Based Structure Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.3 Result and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.3.1 Correlation between DFTB-3 and M06-2X/6-311+G(d,p) energies for 12 poly-peptides . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.3.2 Active Learning Driven Enhancement and M06-2X Validation of Final NNP Models . . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.3.3 Final-NNP Models and M06-2X Accuracy: CCSD(T)-DLPNO Benchmark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.3.4 Influence of Methylation and Chain Length on Peptide Bond Isomerization and Zwitterionic Stability: Insights from M06-2X Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.3.5 IR Spectra for A4 and A5 . . . . . . . . . . . . . . . . . . . . . . . 87 4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 Chapter 5 Peptide–Carbohydrate Complexes: NNP-Based Conformational Search and Thesis Conclusions 93 5.1 Peptide–Carbohydrate Complexes . . . . . . . . . . . . . . . . . . . 93 5.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 5.1.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 5.1.3 Validation and Test Set Construction . . . . . . . . . . . . . . . . . 98 5.1.4 NNP-Based Structural Search and DFT Refinement . . . . . . . . . 99 5.2 Conclusion and Future Perspectives . . . . . . . . . . . . . . . . . . 102 References 105 | - |
| dc.language.iso | en | - |
| dc.subject | 肽 | - |
| dc.subject | DFT | - |
| dc.subject | DL-NNPs | - |
| dc.subject | Peptides | - |
| dc.subject | DFT | - |
| dc.subject | DL-NNPs | - |
| dc.title | 神經網路位能加速之第一原理探索胜肽構形 | zh_TW |
| dc.title | A Neural Network Potential Accelerated First-Principles Study on the Conformational Landscapes of Peptide | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 包淳偉;余慈顏;羅世強;魏金明 | zh_TW |
| dc.contributor.oralexamcommittee | Chun-Wei Pao;Tsyr-Yan Yu;Shyh-Chyang Luo;Ching-Ming Wei | en |
| dc.subject.keyword | 肽; DFT; DL-NNPs | zh_TW |
| dc.subject.keyword | Peptides; DFT; DL-NNPs | en |
| dc.relation.page | 124 | - |
| dc.identifier.doi | 10.6342/NTU202602064 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2026-07-23 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 分子科學與技術國際研究生博士學位學程 | - |
| dc.date.embargo-lift | 2026-08-04 | - |
| 顯示於系所單位： | 分子科學與技術國際研究生博士學位學程 | |
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