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研究生： 鄭宇翔
Cheng, Yu-Hsiang
論文名稱： 將計算工具整合至統一平台以研究蛋白質相分離與小分子藥物篩選
Integrating computational tools into unified platforms for studying protein phase separation and screening small molecule drugs
指導教授： 王淑鶯
Wang, Shu-Ying
學位類別： 碩士
Master
系所名稱： 醫學院 - 微生物及免疫學研究所
Department of Microbiology & Immunology
論文出版年： 2025
畢業學年度： 113
語文別： 英文
論文頁數： 123
中文關鍵詞： 圖形使用者介面(GUI) 、液-液相分離(LLPS) 、光漂白後螢光回復(FRAP) 、神經網路 、虛擬藥物篩選
外文關鍵詞： graphical user interface (GUI), liquid-liquid phase separation (LLPS), fluorescent recovery after photobleaching (FRAP), neural network, virtual drug screening
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    • 雖然計算方法能加速並簡化各種實驗流程，但是由於將實驗問題轉化為電腦方法較困難，這些方法在以實驗為主的實驗室中常常未被充分利用。因此，本研究開發了三個計算工具，用以針對特定的實驗需求來填補實驗與計算之間的代溝。這些工具以簡便易用為設計原則，鼓勵其在實驗室流程中被廣泛應用，特別是在液-液相分離（LLPS）—一種新興且參與多種細胞反應調控的蛋白質機制—以及藥物開發。這三個程式分別為：1. FRAPy：一款安全且一站式的工具用於分析光漂白後螢光回復（FRAP）數據；2. PhaseNet：一個快速且高通量的神經網絡，用於預測蛋白質進行LLPS的傾向；3. MolDocker：一個簡化分子對接流程的整合平台，用於加速從電腦運算到實驗證實的藥物發現過程。這些程式展示了專門化的計算工具如何無縫的整合進實驗研究中，提升實驗效率與洞察力。

    Although computational methods can accelerate and simplify various experimental workflows, they are often underutilized in experiment-focused labs due to the challenges in translating experimental problems into computational solutions. Therefore, three programs were developed in this study that aim to bridge this gap by addressing specific experimental needs. These tools were designed with simplicity in mind to encourage their adaptation into laboratory workflows, particularly in the contexts of liquid-liquid phase separation (LLPS)—an emerging protein mechanism involved in regulating diverse cellular responses—and drug development. The three programs are: 1.) FRAPy, a secure, end-to-end tool for analyzing fluorescence recovery after photobleaching data, 2.) PhaseNet, a fast and scalable neural network for predicting protein LLPS propensity, and 3.) MolDocker, a unified platform for streamlining molecular docking workflows to accelerate drug discovery from computation to experimentation. These programs demonstrate how specialized computational tools can be seamlessly integrated into experimental research to enhance efficiency and insight.

    中文摘要 I Abstract II Acknowledgement III List of Tables VII List of Figures VIII Abbreviation XI Chapter 1 Introduction 1 1.1 Machine learning and user-friendly GUI 1 1.2 LLPS of biomolecules 2 1.2.1 Overview of LLPS 2 1.2.2 Fluorescence Recovery After Photobleaching (FRAP) in LLPS characterization 2 1.2.3 Sequence and structural properties driving LLPS 3 1.3 Virtual drug screening 3 1.3.1 Overview of virtual screening 3 1.3.2 Preparing small molecules for vHTS 4 1.3.3 Processing the 3D structure of target protein 5 1.3.4 Performing molecular docking 6 1.3.5 Analyzing the predicted results 7 1.4 Rationale and specific aim 8 Chapter 2 Materials and Methods 12 2.1 Materials 12 2.1.1 FRAPy 12 2.1.2 PhaseNet 12 2.1.3 MolDocker 12 2.2 Methods 14 2.2.1 FRAPy 14 2.2.2 PhaseNet 16 2.2.3 MolDocker 19 Chapter 3 Results 28 3.1 FRAPy 28 3.1.1 FRAPy interface 28 3.1.2 Operating FRAPy 28 3.1.3 Analyzing real-world data with FRAPy 29 3.2 PhaseNet 30 3.2.1 PhaseNet interface on Google Colab 30 3.2.2 Operating PhaseNet on Colab 30 3.2.3 Testing PhaseNet and the Colab interface 31 3.3 MolDocker 32 3.3.1 MolDocker interface 32 3.3.2 Operating MolDocker 33 3.3.3 Validating MolDocker workflows 47 Chapter 4 Conclusion 52 Chapter 5 Discussion 53 5.1 FRAPy 53 5.1.1 Extracting pixel intensities 53 5.1.2 Pixel intensity analysis 54 5.2 PhaseNet 55 5.2.1 Training PhaseNet and comparing with other models 55 5.2.2 The imprecise definition of protein LLPS 56 5.3 MolDocker 57 5.3.1 Prepare molecules 57 5.3.2 Protein processing and docking 59 5.3.3 Post-docking analyses 62 5.3.4 Overall improvement for MolDocker 65 5.3.5 Validating the docking accuracy of MolDocker 65 References 70 Figures 81 Supplementary Data 102 Appendix 105

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