整合蛋白質彈性網路模型與人工智慧方法預測蛋白質動態

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研究生：	張芫瑜 Chang, Yuan-Yu
論文名稱：	整合蛋白質彈性網路模型與人工智慧方法預測蛋白質動態 Integration of Elastic Network Model and AI/Machine Learning for Protein Dynamics Prediction
指導教授：	楊立威 Yang, Lee-Wei
口試委員:	林澤 Lin, Che 洪瑞鴻 Hung, Jui-Hung 楊進木 Yang, Jinn-Moon 蔡惠旭 Tsai, Hui-Hsu
學位類別：	博士 Doctor
系所名稱：	生命科學暨醫學院 - 生物資訊與結構生物研究所 Institute of Bioinformatics and Structural Biology
論文出版年：	2019
畢業學年度：	107
語文別：	英文
論文頁數：	171
中文關鍵詞：	彈力網路模型、高斯網路模型、非勻向網路模型、環境非勻向網路模型、動態組、固有動態域、固有動態、蛋白動態、深度神經網路模型、構型集合、對接、分子動力模擬、溫度因子、絕對振盪大小、頻譜熵、剽竊、BWT轉換、FM索引、隱私保護
外文關鍵詞：	ENM, GNM, ANM, envANM, DynOmics, Intrinsic dynamics domains, Intrinsic dynamics, Protein dynamics, Deep neural network, Native ensemble, Docking, MD, B-factor, Absolute fluctuations size, Spectral entropy, Plagiarism, BWT Burrows-Wheeler transform, FM index, privacy protection
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蛋白質動態(Protein dynamics)在蛋白-蛋白、蛋白-小分子交互作用及催化活性等調控扮演極為重要的角色，影響了蛋白巨分子在生物中的功能。蛋白質的序列以及三維結構直接決定了其固有動態(intrinsic dynamics)。彈力網路模型(Elastic network model, ENM)是用於從蛋白質或蛋白複合體的三維結構中解析其動態簡而有力的理論。而高斯網路模型(Gaussian network model, GNM)及非均向網路模型(Anisotropic network model, ANM)是其中兩個廣泛使用的粗粒化彈力網路模型。為了處理現今快速增長的蛋白質結構體(structural proteomics data)資料，我們建立一個具有良好使用者界面的蛋白質動力入口網站「DynOmics」 (http://dynomics.pitt.edu/)。DynOmics搜集整合四個用於從結構快速解析蛋白質動態的理論應用，其中ENM1.0及iGNM2.0是由我們設計並實作。在ENM1.0 (http://dyn.life.nthu.edu.tw/oENM/)中，主要是以ANM、GNM及新開發的envANM分析輸入的蛋白質結構，進而得到該蛋白質的動態資訊。envANM在分析動態時，將分子周遭環境一併考慮進而提升所提取動態的精確度，而這個環境可以是晶格周圍的其他蛋白(crystal contacts)、雙層磷脂質(lipid bilayer)、與該蛋白結合的受質(substrate)或配體(ligand)、多聚體結構中其他次單元。藉由分析以上這些模型所提取出的動態，我們進一步在服務中提供潛在功能位/催化中心分析(potentially functional sites/catalytic sites)、重建ANM預測之構型的全原子模型、濾除低可能性蛋白-蛋白或蛋白-DNA的對接模型(intrinsic dynamics domains, IDDs)等功能性分析。而iGNM 2.0 (https://dyn.life.nthu.edu.tw/gnmdb/)則是DynOmics裡最新的一個收集蛋白質動態的資料庫。該資料庫中的蛋白質動態訊息是我們預先利用GNM計算了大部分收錄在Protein Data Bank蛋白結構(95%)的結果。使用者可以在iGNM 2.0中取得殘基間或domain間的動態相關程度(cross-correlations)、每個運動模式的整體性(collectivity of modes)、蛋白中的樞紐點位(hinge sites)等進階分析結果。iGNM2.0的進階搜尋功能允許使用者依照這些預先分析的蛋白質動態訊息(如擺動亂度vibrational entropy、最大運動整體性largest collectivity等)來篩選想要的蛋白質。iGNM 2.0也提供巨分子蛋白質特別是生物功能性組裝下結構的動態資料，便於讓使用者了解蛋白結構、動態與功能的相互關係。
然而前述的彈力網路模型卻無法直接預估出蛋白質構型改變的絕對大小。為了解決這個問題，我們嘗試建立一個從蛋白質結構可高效預測每個殘基擺動絕對大小的深度神經網路模型(deep-neural-network, DNN)。該神經網路模型的訓練輸入包含了蛋白質的結構、動力學與化學特徵，而訓練目標則為從所有該蛋白質X-ray結構的平均B-factor換算而來的殘基擾動大小。這個模型預測6450個蛋白質的絕對擾動大小時，誤差僅有24% (平均絕對誤差為0.17 Å)；而預測12個有分子動力模擬估算的擾動大小是也僅有44%的誤差(平均絕對誤差為0.29Å)，相較於目前已知同目的最好軟體之143%。藉由分析模型的參數，我們發現39個特徵中其中與蛋白質形狀及正則模態密度頻譜熵(spectral entropy)相關的特徵是主要影響預測結果因子。利用神經網路模型預測的擾動大小及ENM模型給出殘基運動方向，可以由結合前結構推算結合後的結構。我們利用該結構作為蛋白-蛋白分子對接軟體的輸入，可以順利提高與真實結合方式相似對接結果的產出。
此外，我們以當代的生物資訊演算法為核心，開發了一個具有隱私功能剽竊偵測服務-「Sapiens Aperio Veritas Engine」 (S.A.V.E.)，該服務包含文件間比對或文件與網路資料間的比對。該核心整合了FM-index、Smith-Waterman 動態規劃及似BLAST索引算法，皆為現今常用與序列比對的算法。我們的剽竊比對方法在待測文件及比對目標資料庫皆被破壞性加密的狀況下,依然適用。S.A.V.E.的這個特性讓它在避免高機密文件洩露的情況下，還可以維持剽竊偵測精準。

Protein dynamics is essential to regulate protein-protein interaction, protein-ligand interaction and enzyme activity etc., which in turn affect the functions and efficiency of biological machines. Intrinsic dynamics of proteins are encoded in their sequence and structure. Elastic network model (ENM) is a simple yet powerful model to decode the dynamics of proteins and their complexes from their 3-dimensional structures. Gaussian network model (GNM) and Anisotropic network model (ANM) are two of the most popular coarse-grained ENM modes.
We build a user-friendly interface web portal named “DynOmics” (http://dynomics.pitt.edu/), which collects the applications that are designed to efficiently and accurately evaluate the dynamics of structurally resolved system for growing structural proteomics data. DynOmics allows users to access the conformational dynamics of biomolecular structures easily. There are currently 4 applications available on DynOmics and we contributed 2 of them, including ENM1.0 and iGNM 2.0.
ENM1.0 (http://dyn.life.nthu.edu.tw/oENM/) takes protein structure as the input and performs analysis on its dynamic information by using ANM, GNM and newly developed envANM, which also considers the molecular environment. By performing further advance analysis on the results provided by the models, we provide several types of output for users, including potentially functional sites, the extent of allosteric communication mechanisms, conformers reconstructed at atomic level by ANM predicted motions, protein-protein and protein-DNA interaction poses filter (intrinsic dynamics domains, IDDs). The definition of the ‘molecular environment’ of the envANM is wide enough to include crystal contacts of proteins in the crystalline environment, lipid bilayer, the substrate or ligands bound to a protein and surrounding subunits to a subunit of interest in a multimeric structure or assembly.
iGNM2.0 (https://dyn.life.nthu.edu.tw/gnmdb/) is a protein dynamic database which collects the dynamic information of more than 95% of the protein structures available on PDB. The dynamic information available on iGNM2.0 are pre-calculated by using GNM. Users can retrieve the dynamics information on inter-residue and inter-domain cross-correlations, cooperative modes of motion, the location of hinge sites from the server with plain text and customized 2D/3D visualization capabilities. The collections of protein dynamic on iGNM2.0 also allows users to carry out advance search by using protein dynamics features such as vibrational entropy, largest collectivity and more. The ability of iGNM 2.0 to provide structural dynamics data on large protein structures, especially their biological assemblies, makes it a powerful resource to establish the relation between their structure, dynamics and function.
The aforementioned ENMs however cannot provide the absolute size of conformational changes despite suggest their directionality. We further develop a deep-neural-network (DNN) prediction model that can efficiently estimate the absolute size of protein dynamics at the residue resolution, given a protein structure. The training features of this model includes the dynamics, structural and chemical properties of a protein structure while the training target is the fluctuation size obtained by the temperature factors averaged from x-ray crystallographic structures of the same family. The model is able to reproduce the experimentally characterized absolute fluctuations for 6450 proteins with 24% errors (Mean Absolute Error or MAE = 0.17Å) and also predicts MD-sampled per-residue fluctuations for 12 tested systems with a 44% error (MAE = 0.29Å). The most essential features in determining the absolute sizes of residue fluctuations are protein shape and spectral entropy derived from normal mode density of states. We are able to improve the enrichment of native poses in the protein-protein docking decoys relative to the poses generated using only the apo form structures by introducing both the AI predicted size of residues’ fluctuation and ENM-suggested deformation direction which can be obtained from DynOmics.
On the other hand, integrating state-of-the-art bioinformatics algorithms, we developed a plagiarism-detection software, “Sapiens Aperio Veritas Engine” (S.A.V.E.), which integrates FM-index, Smith-Waterman dynamics programming and BLAST-like indexing algorithms to detect copies of paragraphs in pairwise comparisons and/or across the internet. The algorithm works even when the text (in both the target database and queries) is destructively encrypted and compressed, so instead of searching online with queries in plain text, the sensitive text can all be protected locally, by which S.A.V.E. can achieve the highest privacy protection and the best search efficiency.

English Abstract    i
中文摘要    iv
致謝    vi
Table of Contents    1
List of Figures    4
List of Tables    6
Chapter 1.    iGNM 2.0: the Gaussian network model database for biomolecular structural dynamics    10
1.    Introduction    10
2.    Methods    14
2.1.    Gaussian network model and mode spectral    14
2.2.    Data set    17
2.3.    Inputs: query and searching functions    20
2.4.    Details about the iGNM 2.0 visualization    20
3.    Results    22
3.1.    X-ray crystallographic B-factors (3D/2D).    22
3.2.    Mode shapes (3D/2D).    27
3.3.    Domain separations by dynamics (3D/2D).    30
3.4.    GNM connectivity model (3D/2D)    30
3.5.    Cross-correlations (3D/2D)    30
3.6.    Collectivity (2D)    31
3.7.    Results in plain text.    31
3.8.    Database architecture of iGNM 2.0    31
4.    Discussion and Conclusion    34
Chapter 2.    DynOmics: dynamics of structural proteome and Beyond    44
1.    Introduction    44
2.    Methods    47
2.1.    Environment ANM/GNM (envANM/envGNM)    47
2.2.    Potential functional sites (PFSs) prediction: Conformational-mobility-based prediction of enzyme active sites (COMPACT) algorithm    48
2.3.    Intrinsic dynamics domains (IDDs)    52
2.4.    Construction of full-atomic structures from ANM-Driven conformers after regularization of C-C pseudo-bonds    55
2.5.    Hitting/commute time    59
2.6.    Perturbation-response scanning (PRS)    60
3.    Results    61
3.1.    Description of web server    61
3.2.    Effect of environment    65
3.3.    Identification of functional sites    73
4.    Conclusion    81
Chapter 3.    Deep Neural Network that Predicts Protein Dynamics in the Context of Facilitating Flexible Protein Docking    82
1.    Introduction    82
2.    Material and Methods    85
2.1.    Dataset    85
2.2.    Definition of fluctuation    87
2.3.    Molecular dynamic (MD) simulations    88
2.4.    Features    89
2.5.    Spectral entropy (SE) derived from the distribution of GNM mode eigenvalues (frequency square)    91
2.6.    Shape factor (SF)    92
2.7.    Optimization of hyperparameters in DNNs    93
2.8.    Extended Garson's algorithm    95
2.9.    Docking protocol    97
3.    Results    100
3.1.    Performance Comparison between different ML Models    100
3.2.    Performance of DNN model    100
3.3.    Applications    104
4.    Discussions    107
4.1.    Importance features    107
4.2.    The performance of DNN-B model    110
4.3.    Why use the average B-factor RMSF instead of using the B-factor from individual experiment directly?    110
Chapter 4.    SAVE - A Plagiarism Detection Tool with Enhanced Privacy Protection    113
1.    Introduction    113
2.    Methods    115
2.1.    Text extraction from documents    115
2.2.    Reference removing    115
2.3.    Word extraction    119
2.4.    Text cleansing    121
2.5.    Encoding readable words into pseudo-biological sequences (PBS)    121
2.6.    The central idea of in-private search    124
2.7.    Burrows-Wheeler transform and FM index (this program was built by Prof. Jui-Hung Hung/洪瑞鴻’s group)    124
2.8.    Building the PBS database    139
2.9.    Mutual plagiarism detection with privacy    141
2.10.    Plagiarism detection against the Internet    143
2.11.    Mutual plagiarism detection for plain texts    143
2.12.    Number of continuously copied words    145
2.13.    False positive rate    147
3.    Results    148
3.1.    SAVE website    148
3.2.    SAVE_APP    150
3.3.    Private + Internet mode    152
3.4.    Private + Pairwise mode    155
3.5.    Non-private + Internet mode    158
3.6.    Non-private + Pairwise mode    160
3.7.    False positive rate of a plagiarism detection with privacy    161
3.8.    Browser compatibility of the SAVE website    161
4.    Discussion    162
4.1.    Advantages of encoding text into PBSs    162
References    163


                                

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