高序生物巨分子的動態及功能解析：以穿膜蛋白，核醣體及蛋白-DNA複合體為例

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研究生：	陳偉順 Chan, Wai Soon
論文名稱：	高序生物巨分子的動態及功能解析：以穿膜蛋白，核醣體及蛋白-DNA複合體為例 Spatio-Temporal Characterization and Functional Elucidation of Higher-Order Biomacromolecules Exemplified with Transmembrane Proteins, Ribosome and DNA-Protein Complexes
指導教授：	楊立威 Yang, Lee-Wei
口試委員:	蔡惠旭 Tsai, Hui-Hsu 楊進木 Yang, Jinn-Moon 鄭惠春 Cheng, Hui-Chun 潘榮隆 Pan, Rong-Long
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：	210
中文關鍵詞：	環狀對稱、蛋白-蛋白對接、距離約束、單分子螢光共振能量轉移、傾斜角度、同源多聚體穿膜蛋白、分子動力模擬、阿拉伯芥磷酸轉運蛋白、磷酸轉運蛋白、質子化態、pH值、量子力學/分子動力學法、焦磷酸水解酶、配位數、親核性水分子、蛋白-DNA對接、彈力網路模型、高斯網路模型、固有動態域、動態域平面、時間尺度、動態大小、非勻向彈力網路模型、主成分分析、維納－辛欽定理、強度加權週期、核醣體、冪定律
外文關鍵詞：	cyclic symmetry (Cn symmetry), protein-protein docking, distance restraints, single molecule Förster resonance energy transfer (smFRET), tilt angle, homo-oligomeric transmembrane protein (HoTP), Cyclic Symmetry-Imposed Packing (SIP), molecular dynamics (MD) simulations, Arabidopsis PHT1;1 (AtPHT1;1), protonation state, pH, QM/MM, hydronium ion (H3O+), Vigna radiata H+-translocating pyrophosphatase (VrH+-PPase), protein-DNA docking, coordination number, nucleophilic water, elastic network model (ENM), gaussian network model (GNM), intrinsic dynamics domains (IDD), d-plane, motion timescale, motion size, anisotropic network model (ANM), principal component analysis (PCA), Wiener–Khintchine theorem (WKT), Intensity-Weighted Period (IWP), power law
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高效精確的生物代謝過程必須依賴多種生物分子包含蛋白質、DNA和RNA等的相互協作。而這些生物分子通常需要形成高序複合體(higher-order complexes)或稱生物巨分子(biomacromolecules)才能完整的執行其功能。要了解生物巨分子作用機制的第一步就是揭示它們的高序結構，但現今結構分析技術（如X-ray 晶體繞射、NMR、cryo-EM）解析這些巨大的多聚體分子結構仍然是一大挑戰，尤其是那些在膜中的多聚體(oligomer)。為了解決這個技術難題，許多以統計方法或物理生物化學為基礎的巨分子組裝算法被開發並應用於整合各種實驗結果來建構複雜的生物巨分子結構。
穿膜蛋白是其中一類相當重要的生物巨分子，它們參與了細胞訊息傳遞(cell signaling, transport)、運送膜內外的各種物質、維持膜內電位(electrochemical gradient)。從RCSB PDB中穿膜蛋白的統計資料發現有約66%的α-螺旋穿膜蛋白(α-helical transmembrane)是屬於同源多聚體的穿膜蛋白(homo-oligomeric transmembrane proteins, HoTPs)且這些HoTPs有約92%是環狀對稱(cyclic symmetric或Cn symmetric)。我們利用HoTPs環狀對稱與立體結構限制這兩個特性，分別開發兩個結構篩選器SIP (Symmetry-Imposed Packing)及DR (distance-restraints)，用於移除在分子對接結果中不符合前述條件的對接模型(docking poses)。SIP主要是比較同源二聚體(dimer)對接模型與由此對接模型推測的理想Cn對稱多聚體的差異作為篩選依據。這個理想Cn對稱多聚體推測過程不需要事先知道形成多聚體所需的單體個數(n)。僅使用SIP的狀況下，在7個HoTPs單體結構的對接結果中，有71%的可以在前10個對接模型中找到近似自然結構的模型(near-native poses)；以31個HoTP的同源建模結構(homology modeled structure)為基礎的對接結果，則有29%符合上述結果。而相同的對接結果但未經SIP處理前，分別只有14%及3%。當SIP再加上多聚體單體個數(n)給定時，近似自然結構出現在對接結果的前10個模型中在測試集佔有高達76%；比起SIP，其他算法如M-ZDOCK及Sam在相同條件下，分別僅有55%與47%。我們將3個已知部分殘基之間距離HoTPs的對接結果以DR及SIP處理後，發現3個HoTPs的近似自然結構在54,000個對接模型中皆被排名在第一名；而僅使用SIP處理，近似自然結構的排名分別是第一、一和十。我們將DR單獨用於一個有殘基距離資訊的水溶性蛋白(soluble protein)，其近似自然結構也出現在第一名。
我們分別研究了AtPht1;1 (transmembrane phosphate transporter)與H+-VrPPase (Vigna radiata pyrophosphatase) 兩個穿膜蛋白的分子運作機制。 AtPht1;1為利用質子(proton)濃度梯度將磷酸(phosphate)及氫離子從細胞膜外運送至膜內的共同轉運蛋白(symporter)。我們主要以磷酸結合inward-occluded態的AtPht1;1同源結構建模，研究可質子化殘基的質子化態(protonation state)如何誘導已結合於催化位磷酸的釋放。為了重現質子濃度梯度，AtPht1;1的可質子化殘基被區分為曝露於胞外及胞內，並分別以pH 4及pH 7質子化。在本研究中，我們依據PROPKA預測的pKa來指定殘基的質子化態。藉由觀察AtPht1;1的分子動力模擬(molecular dynamics simulations)中可質子化殘基pKa的變化，發現當ASP38 and ASP308的去質子化會導致催化位磷酸釋出。
H+-VrPPase以焦磷酸水解(hydrolysis)所釋出的能量為動力，可以抵抗濃度梯度運送質子穿過細胞膜。先前研究指出在高解析度的H+-VrPPase X-ray結構中，有一個直接與配體接觸的水分子參與焦磷酸水解過程，他們稱該分子為「親核性水分子」(nucleophilic water)。我們建立將H+-VrPPase鑲嵌於雙層POPC膜(POPC lipid bilayer)的全原子模型，並把X-ray解出的水分子放入相對應位置後進行模擬。然而我們發現原本「親核性水分子」很快地被附近的鉀離子取代，由此現象推測該位置適合帶正電的離子而非極性分子(如：水分子)。在這個位置附近有兩個帶負電的Asp殘基可以幫助穩定該位置上的鉀離子，而原本「親核性水分子」很可能是帶正電的H3O+ (hydronium ion)。我們進一步將原分子模擬系統的該水分子置換成H3O+後，在整個模擬過程中，該H3O+穩定存在此位置。
蛋白-DNA/RNA複合體(protein-DNA complex)負責起始及調控生物中心教條(轉錄transcription與轉譯translation)，對於生物的重要程度不亞於膜蛋白。我們發現蛋白-DNA結合的方位與蛋白質自身固有動態(intrinsic dynamics)有高度相關。因此我們基於蛋白質最慢的運動模式(slowest dynamics)建構了一個「動態域平面」(dynamics domain interfaces)，可將蛋白質分離成兩群運動方向相反的殘基群。從104個蛋白-DNA複合體的統計結果顯示有97%的DNA被其蛋白質的動態域平面切過。將該動態域平面運用於篩選DNA與DNA結合蛋白的剛體對接(rigid-body docking)的結果，比起使用隨機平面或立體互補性做篩選，近似自然結構的豐富度分別提升2.5倍及1.6倍之多。這個結果闡述了蛋白質的動態限制了蛋白質與DNA的結合方位(orientation)進而提高結合位(binding site)的搜尋精確度。
生物巨分子執行其功能時的構型動態(conformational dynamics)可以從結構被解析出來。分子動力模擬(molecular dynamics simulation, MD)是一個相當有力的理論工具來評估蛋白質的運動大小時間。但由於分子動力模擬非常消耗運算資源及時間，故本研究將計算成本低廉的彈力網路模型(elastic network model, ENM)轉換為估量分子運動時間及運動大小的工具並將其應用於核糖體(ribosome)上。
我們從分子模擬的軌跡分析了沿著主成分(principal components, PCs)的非諧波運動，接著以準諧波分析(Quasi-harmonic analysis)與WKT (Wiener–Khintchine theorem)評估每個PC運動模式的強度加權週期(Intensity-Weighted Period, IWP)，該週期即為本研究所定義的「週期t」。此「週期」的時間尺度與核磁共振(NMR)的order parameters相符。我們把每個PC運動模式的IWP與振動大小(fluctuation size)利用振動曲線映射法(fluctuation-profile mapping, FPM)對應到ENM的每個運動模式。我們發現ENM運動模式的特徵值(eigenvalues, λENM)可以透過利用冪定律(power law)轉換成振動週期及大小。最後我們分別得到用於評估蛋白質運動時間尺度及運動大小的兩個方程式t(ns) = 56.1λENM-1.6和σ2(Å2) = 32.7λENM-3.0。該方程式能推廣應用於評估NMR構型(NMR-resolved conformers)、X-Ray晶體繞射的ADP profile(anisotropic displacement parameters)與核糖體功能性運動的時間及大小。

Efficient and accurate biological processes require proper coordination of participating molecular components including but not limited to proteins, DNA and RNA. These molecules tend to form higher-order complexes (biomacromolecules) to perform their functions. To understand how these biomacromolecules work, we first need to elucidate their higher-order structures. The size and oligomeric state of these bio-macromolecules, especially for those in the membrane, pose a serious challenge to structural determination methods such as X-ray crystallography, NMR and cryo-EM. To fill in the gap, computational approaches that implement statistically or physicochemically derived rules governing molecular assemblage have become sophisticated enough in utilizing and integrating experimental data towards solving complicated biomacromolecules.
One very important group of biomacromolecules are transmembrane proteins which perform various functions such as cell signaling, transport of materials across membranes, maintenance of electrochemical gradient and others. Our statistical survey of transmembrane proteins in RCSB PDB show that ~66% of α-helical transmembrane proteins are homo-oligomeric transmembrane proteins (HoTPs). The survey also found that ~92% of these HoTPs are cyclic (Cn) symmetric. Given the prevalence of Cn symmetric HoTPs and the benefits of incorporating geometry restraints in aiding quaternary structure determination, we introduce two new filters, the distance-restraints (DR) and the Symmetry-Imposed Packing (SIP) filters. SIP relies on a new method that can rebuild the closest ideal Cn symmetric complex from docking poses containing a homo-dimer without prior knowledge of the number (n) of monomers. Using only the geometrical filter, SIP, near-native poses of 7 HoTPs in their monomeric states can be correctly identified in the top-10 for 71% of all cases, or 29% among 31 HoTP structures obtained through homology modeling, while ZDOCK alone returns 14% and 3%, respectively. When the n is given, the optional n-mer filter is applied with SIP and returns the near-native poses for 76% of the test set within the top-10, outperforming M-ZDOCK’s 55% and Sam’s 47%. While applying only SIP to three HoTPs that comes with distance restraints, we found the near-native poses were ranked 1st, 1st and 10th among 54,000 possible decoys. The results are further improved to 1st, 1st and 3rd when both DR and SIP filters are used. By applying only DR, a soluble system with distance restraints is recovered at the 1st-ranked pose.
We have studied two of these transmembrane proteins in mechanistic details. One is the transmembrane phosphate transporter, AtPht1;1 and the other is the transmembrane proton pump, Vigna radiata pyrophosphatase (H+-VrPPase). As for the phosphate transporter, AtPht1;1, it transports phosphates across the membrane using the proton gradient. Thus, it transports both protons and phosphates from the extra-cellular side to the intra-cellular side, acting as a symporter. There is only the homology modeled structure of AtPht1;1 in the inward-occluded state with a phosphate bound to the binding site. We wanted to study how the protonation states of protonizable residue sidechains could induce the release of the bound phosphate to the cytoplasmic side. To replicate the proton gradient across the membrane, the protonizable residues in the protein were split into those that are exposed to the extra-cellular side (with pH 4) and the others to the intra-cellular side (with pH 7). Their protonation states were assigned based on their predicted pKa by PROPKA. Monitoring the pKa changes of protonizable residues during MD simulations, we were able to identify two residues, ASP38 and ASP308, when deprotonated results in the release of phosphate to the cytoplasmic side.
H+-VrPPase catalyzes the hydrolysis of pyrophosphate into two phosphates, using the energy generated to pump protons against the proton gradient across the membrane. The high-resolution X-ray structure of H+-VrPPase has been solved showing waters near the binding site of the ligand. The authors proposed that one of the waters directly interacting with the ligand is involved in the catalysis of pyrophosphate into phosphate and thus called it the "nucleophilic water". To study this in more detail we performed all-atom MD simulations of H+-VrPPase embedded in a POPC lipid bilayer and solvated with water. However, the MD simulation results show that the position of the "nucleophilic water" does not favor waters, instead a nearby K+ ion moves and replaces the waters at this position. This implies that there is a preference for an extra positive charge at this position rather than a polar molecule like water. It is unsurprising considering that nearby this position are two aspartic acids which are negatively charged. It is possible that the "nucleophilic water" is a hydronium ion which is positive charged. We have performed MD simulations of "nucleophilic water" as a hydronium ion and showed that it is does not leave the site and the K+ ion does not move in and replace it.
Functional protein-DNA/RNA complexes that initiate and regulate the central dogma (transcription and translation) are of no less importance than membrane proteins. We find that protein–DNA docking orientation is a function of protein intrinsic dynamics, but the motions of the binding site itself does not necessarily display any unique patterns. We introduce a new technique that locates “dynamics interfaces” in proteins across which protein parts are anticorrelated in their slowest dynamics. The statistics show that such interfaces intersect the DNA in 97% of the 104 examined cases. These dynamic interfaces are then used to screen decoys generated by rigid-body docking of DNA molecules onto DNA-binding proteins. This enriches the near-native poses by 2.5- and 1.6-fold, as compared to a random guess and methods based on surface complementarity, respectively. Hence, dynamically permissible protein–DNA docking orientations can be used to filter and re-rank docking poses to enhance the prediction of DNA-binding sites although these sites do not have any distinct dynamics features.
Provided with the structures of the biomacromolecules, their conformational dynamics underlying the observed molecular functions can therefore be analyzed. Computationally expensive molecular dynamics (MD) simulation has been the only theoretical tool to gauge the time and sizes of these motions, though barely the slowest ones. Here, we convert a computationally cheap elastic network model (ENM) into a molecular timer and sizer to gauge the slowest functional motions of proteins and applied this to the ribosome. Quasi-harmonic analysis and the Wiener–Khintchine theorem (WKT) were used to define the Intensity-Weighted Period (IWP) which we define as the “time-period”, t, for the anharmonic motions along a principal components (PCs). We validated these timescales by NMR order parameters. The PCs with their respective IWPs and variances are then mapped to ENM modes using the newly introduced fluctuation-profile matching (FPM) method. We find that ENM mode eigenvalues (λENM) have a power law relationship with the assigned timescales and variances. Thus, the power laws t(ns) = 56.1λENM-1.6 and σ2(Å2) = 32.7λENM-3.0 are established allowing the characterization of the time scales of NMR-resolved conformers, crystallographic anisotropic displacement parameters, and important ribosomal motions, as well as motional sizes of the latter.

English Abstract    I
Chinese Abstract    IV
Acknowledgements    VII
Table of Contents    VIII
List of Figures    XIII
List of Tables    XVI
Chapter 1    DR-SIP: Protocols for Higher Order Structure Modeling with Distance Restraints- and Cyclic Symmetry-Imposed Packing    1
1    Introduction    1
2    Methods    4
2.1    Docking Protocols    5
2.2    Extracting Cn Symmetry Parameters from an Ideal Cn Symmetric Pose    7
2.3    Constructing the Ideal Cn Symmetric Pose that is Closest to a Non-Ideal Docking Pose or X-Ray Crystallographically Solved Structure    8
2.4    Cyclic Symmetry-Imposed Packing (SIP)    9
2.5    Distance-Restraints (DR) Filter    10
2.6    Shift Along Axis of Symmetry    10
2.7    Deviation from Ideal Rotation Angle    10
2.8    Clustering of Poses    10
2.9    Selecting the Representative for Each Cluster and Ranking of the Clusters    11
2.10    Evaluating the Quality of Docking Poses: Modified CAPRI Criteria for HoTPs    11
2.11    Data Sets    12
2.12    Optimizing the Cutoff Parameters for SIP    25
2.13    Sum of the Normalized M-ZDOCK Scores for the Top-100 Poses    25
2.14    Implementation of DR-SIP    26
3    Results    26
3.1    Statistical Analysis of 118 Structurally Solved α-Helical HoTPs    26
3.2    SIP Filter Performance without Distance Restraints    34
3.3    Performance of DR-SIP with Distance Restraints: Near-Native Poses Within the Top-3 Ranking Poses    44
4    Discussions    49
Chapter 2    Mechanochemical Coupling Found in PHT1;1 Facilitates Inorganic Phosphate Transport    54
1    Introduction    54
2    Results    57
2.1    Proton gradient across lipid membranes promotes phosphate fluctuations inside the channel.    57
2.2    Phosphate exits through a sequential deprotonation of Asp38 and Asp308 accompanied by proton release in two water channels.    63
2.3    QM/MM analysis reveals that it is more favorable to deprotonate Asp308 when Pi is further away from it    65
2.4    Pi releasing pathway gated by three transmembrane helices - TM1, TM4, and TM6    67
3    Discussion    68
3.1    Proposed model for Pi transport mechanism of PHT1;1    68
3.2    Use anisotropic network model (ANM) to describe conformational states of PHT1;1    69
3.3    Comparison of Pho84, PiPT and PHT1;1    71
3.4    Mutation data supports    71
4    Methods    73
4.1    Building the inward-occluded state of PHT1;1    73
4.2    Forcefield for H2PO4-    73
4.3    Building the initial protein-membrane system for performing MD simulations    74
4.4    Building protein-membrane systems with a proton gradient across the membrane    76
4.5    Starting structures for the simulations    77
4.6    Targeted MD (TMD) simulation for building the outward-facing state of PHT1;1    77
4.7    Root Mean Square Deviation (RMSD) calculations for simulation snapshots    80
4.8    Conformational transition of PHT1;1 during Pi releasing manifested in a 2D plane or 3D space spanned by dominant normal modes, derived from anisotropic network model (ANM) of outward-facing PHT1;1    80
4.9    QM/MM calculations    82
Chapter 3    Protonation State of Watnu in H+-VrPPase    84
1    Introduction    84
2    Results    85
2.1    Equilibration of the membrane-protein system.    85
2.2    Watnu is displaced by the K+ ion in the first 1.5 ns of simulations.    86
2.3    Coordination number analysis of Watnu in the crystal structure    87
2.4    Swapping K+ with water/H3O+.    88
2.5    Coordination number analysis of K+/H3O+ for the simulations with the swapped K+ and water/H3O+    89
2.6    Watnu interacts with ASP287 and ASP731.    90
3    Discussion    91
4    Methods    91
4.1    Equilibration Protocol for MD Simulation    91
4.2    Equilibration Analysis    92
4.3    Patching waters to hydronium ions (H3O+)    92
4.4    Computing the radius of the first hydration shell and coordination numbers (CNs)    95
4.5    Prediction of pKa’s    95
Chapter 4    Protein Dynamics and Contact Topology Reveal Protein-DNA Binding Orientation    97
1    Introduction    97
2    Materials and Methods    99
2.1    Dataset of DNA-bound and DNA-free proteins.    99
2.2    Definition of DNA-binding residues    106
2.3    Gaussian network model (GNM).    106
2.4    Anisotropic Network Model (ANM) and conformational changes upon DNA binding. The potential energy function of ANM penalizes changes of the Euclidean distance between two residues i and j from the equilibrium state:    107
2.5    Intrinsic dynamics domains, IDDs.    108
2.6    Determining D-planes    108
2.7    Determining GNM-based IDDs and D-plane.    110
2.8    Squared Displacement of CG nodes    113
2.9    Determining peaks and troughs in squared displacement profiles    113
2.10    Squared displacement of the distance between nodes i and j    113
2.11    Performance measures    114
2.12    Enrichment ratio for binding residues and enrichment factor for hits (EFH)    114
2.13    Generation of decoy sets.    115
2.14    Scoring decoys to determine native poses.    115
2.15    Enrichment factor of hits, EFH.    118
2.16    Random axis generation    118
2.17    Mean and variance of angle between two randomly oriented vectors    119
3    Results    120
3.1    Slow GNM modes predict conformational change upon DNA binding.    120
3.2    Folding core or flexible residues in a protein are not co-localized in DNA-binding sites.    120
3.3    D-planes of the free proteins intersect DNA from DNA-bound proteins.    123
3.4    Are DNA-binding residues close to D-planes?    134
3.5    IDD-based restraints enrich near-native poses.    135
4    Discussion    138
4.1    Predicting conformational change upon DNA binding by ENM models    138
4.2    Enthalpic considerations disfavor folding core residues from binding DNA.    139
4.3    DNA stays close to protein regions with minimal angular momentum and low vibrational entropy.    139
4.4    A plausible role for dynamics in determining binding orientations.    140
5    Conclusion    140
Chapter 5    An Efficient Timer and Sizer of Biomacromolecular Motions.    142
1    Introduction    142
2    Results    145
2.1    Overview    145
2.2    Determining the time scales of the anharmonic modes    147
2.3    Determining the time scales of experimentally observed dynamical variables    149
2.4    Determination of time scales of the ENM modes and inference of time scales from the ENM eigenvalues    157
2.5    ANM Mode Time Scale Assignment Through ANM-PCA Mode Mapping    157
2.6    Verification of the General ANM Power Law via the Dynamics Analysis of Ubiquitin, FGF2 and HPNAP    161
2.7    Derive ANM power law for the sizes of functional motions    161
2.8    Analysis of ribosomal motions    161
3    Discussion    166
3.1    Robustness Analysis    166
4    Methods    167
4.1    MD Simulations    167
4.2    Principal component analysis (PCA) of the covariance matrix computed from MD trajectories    168
4.3    Wiener–Khintchine theorem (WKT)    168
4.4    Intensity-Weighted Period (IWP), Relaxation Time and Characteristic Time    170
4.5    Theoretical Covariance Matrix Comprising a set of PC modes Derived from MD simulations    170
4.6    Computing the RMSF and ADP Profiles for ubiquitin derived from X-ray/NMR-determined Structures and MD    171
4.7    Order Parameter Profiles derived from NMR-determined structural ensemble and MD snapshots    171
4.8    Derivation of General Elastic Network Model (ENM) – the relation between covariance and Hessian    172
4.9    The Anisotropic Network Model (ANM)    176
4.10    Theoretical Covariance Matrix, RMSF and ADP from ANM With Removed Modes    176
4.11    Fitting the Time Power Laws    177
4.12    Fitting the Variance Power Law    177
4.13    Identifying the Functional Modes Corresponding to Ribosomal Body Rotation (Ratcheting) and Head Swiveling Motions    177
4.14    Predicting the Conformation of the Ribosome After the Ratcheting and Head Swiveling Motions    179
4.15    Identifying the Functional Modes and Conformations Corresponding to the L1 Stalk Closing Motion    179
4.16    Perturbation of the ANM for ubiquitin    179
Chapter 6    References    181
                                

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