Development of a Protein-Ligand Docking Program Based on Global Optimization

학위논문 (박사)-- 서울대학교 대학원 : 화학부(물리화학전공), 2014. 2. 석차옥.Protein-ligand docking has become an essential tool for computer-aided drug discovery since docking programs were first developed in 1980s. The goals of docking are to predict 1) the binding mode and 2) the binding affinity of a given protein-ligand complex accurately. Accurate prediction of binding mode requires appropriate sampling of both protein and ligand conformations. Many available docking programs sample ligand structures successfully because ligand has a relatively small number of degrees of freedom. However, a lot of current docking programs treat receptor as a rigid molecule although receptor often adapts its shape to bound ligand because treating receptor flexibility is a very complicated problem. First of all, the large conformational space of receptor is a challenge for typical sampling methods. In addition, current energy functions such as empirical docking score functions or force field-based energy functions do not accurately describe flexible receptor-flexible ligand interactions yet. In this thesis, the development process of an efficient docking program that treats receptor flexible, called GalaxyDock, is described. A powerful global optimization technique, called conformational space annealing, was employed for simultaneous sampling of the conformational space of protein and ligand. In addition, a new energy function for flexible-receptor docking was designed by combining the AutoDock energy function and a knowledge-based ROTA potential. With these components for sampling and scoring, GalaxyDock shows high performances in the binding pose prediction and virtual screening benchmark tests when compared to other state-of-art docking programs. This result suggests that the GalaxyDock program can provide a firm basis for further method developments and for practical applications to in sillico drug discovery processes.Abstract ............................................................................................. i Contents ............................................................................................. iii List of Figures ................................................................................. vi List of Tables ................................................................................... viii 1. Introduction .................................................................................. 1 1.1. Overview of protein-ligand docking .................................... 1 1.2. Sampling methods of protein-ligand docking ..................... 2 1.3. Scoring problem of protein-ligand docking ........................ 3 1.4. Flexble-receptor docking ........................................................ 5 1.5. Outline of this thesis ............................................................. 7 2. LigDockCSA: a rigid-receptor docking program ..................... 9 2.1. Overview of this section ....................................................... 9 2.2. Methods ................................................................................... 10 2.2.1. Benchmark set and AutoDock calculations .................... 10 2.2.2. Energy function for protein-ligand docking ................... 11 2.2.3. Application of conformational space annealing to protein-ligand docking ..................................................... 13 2.3. Results and discussion ........................................................... 16 2.3.1. Performance of CSA when combined with the AutoDock scoring function ............................................. 16 2.3.2. Energy function for LigDockCSA .................................. 20 2.3.3. Performance of LigDockCSA .......................................... 25 2.4. Conclusion of this section .................................................... 32 3. GalaxyDock: a flexible-receptor docking program .................. 33 3.1. Overview of this section ....................................................... 33 3.2. Methods ................................................................................... 34 3.2.1. Energy function for flexible protein-ligand docking ..... 34 3.2.2. GalaxyDock sampling that incorporates side-chain flexibility ........................................................................... 37 3.2.3. Cross-docking benchmark test ......................................... 39 3.2.3.1. HIV protease ................................................................ 40 3.2.3.2. LXRβ ............................................................................. 41 3.2.3.3. cAPK ............................................................................. 41 3.2.3.4. Diverse set .................................................................... 42 3.3. Results and discussion ........................................................... 43 3.3.1. Test results on the HIV protease set ............................. 48 3.3.2. Test results on the LXRβ set ......................................... 50 3.3.3. Test results on the cAPK set .......................................... 54 3.3.4. Test results on the diverse set ........................................ 55 3.3.5. Effect of using rotamers .................................................. 58 3.4. Conclusion of this section .................................................... 60 4. GalaxyDock2: improving GalaxyDock using beta-complex and binding affinity prediction ................................................. 61 4.1. Overview of this section ....................................................... 61 4.2. Methods ................................................................................... 63 4.2.1. Initial bank generation using Voronoi diagrams ........... 63 4.2.2. Benchmark test sets for binding mode prediction ........ 66 4.2.3. Development of binding affinity function ...................... 67 4.2.4. Virtual screening benchmark set ..................................... 73 4.2.4.1. Virtual screening using GalaxyDock2 ....................... 74 4.2.4.2. Virtual screening using AutoDock4 ........................... 74 4.2.4.3. Virtual screening using UCSF DOCK6 .................... 75 4.2.4.4. Measures for assessing virtual screening results ...... 75 4.2.5. Protein and ligand preparation ........................................ 77 4.3. Results and discussion ........................................................... 77 4.3.1. Binding mode prediction .................................................. 77 4.3.2. Binding affinity prediction ............................................... 86 4.3.3. Virtual screening ............................................................... 92 4.4. Conclusion of this section .................................................... 97 5. Conclusion .................................................................................... 99 Appendix ........................................................................................... 102 Bibliography ...................................................................................... 111 국문초록 ........................................................................................... 121Docto