1. 1 Computational Biophysics and Drug Design Jung-Hsin Lin ( 林榮信 ) Division of Mechanics, Research Center for Applied Sciences & Institute of Biomedical Sciences, Academia Sinica School of Pharmacy, National Taiwan University http://rx.mc.ntu.edu.tw/~jlin/ 2007/3/8 NCTU IoP Seminar

2. 2 Many roles of computation in drug discovery ־ better efficiency ־ lower cost ־ better affinity to the target ־ better selectivity ־ better solubility ־ better oral availability ־ better permeability ־ better bioavailability ־ better metabolites ־ no conflict of interests Computation can be helpful for discovering new drugs with

3. 3 Integrated Ligand-Based & Structure- Based Virtual Screening of Therapeutic Agents for Huntington Disease Min-Wei Liu (劉明暐) An-Liang Cheng (鄭安良)

4. 2007/3/8 NCTU IoP Seminar 4 Attenuation of GPCR Signaling

5. 2007/3/8 NCTU IoP Seminar 5 Signaling Pathways from GPCR Families

6. 2007/3/8 NCTU IoP Seminar 6 Sequence Alignment for A 2A Adenosine Receptors CLUSTALW score AA2AR_MOUSE 410, AA2AR_RAT 410 = 95 CLUSTALW score AA2AR_HUMAN 412 2 AA2AR_MOUSE 410 = 81 CLUSTALW score AA2AR_HUMAN 412 2 AA2AR_RAT 410 = 81

7. 2007/3/8 NCTU IoP Seminar 7 Training compounds 1 2 3 4 56 78 9 10 11 12

8. 2007/3/8 NCTU IoP Seminar 8 Training compounds 13 141516 171819 20 222123 24

9. 2007/3/8 NCTU IoP Seminar 9 Structural Alignment of General Molecules Verapamil Carvedilol

10. 2007/3/8 NCTU IoP Seminar 10 Verapamil

11. 2007/3/8 NCTU IoP Seminar 11 Carvedilol

12. 2007/3/8 NCTU IoP Seminar 12 Pharmacophore model for A 2A antagonists Best HypoGen pharmacophore model Hypo1 aligned to compound 1

13. 2007/3/8 NCTU IoP Seminar 13 Correlation Plot

14. 2007/3/8 NCTU IoP Seminar 14 Pharmacophore model for A 2A agonists Best HypoGen pharmacophore model Hypo2 aligned to compound 33

15. 2007/3/8 NCTU IoP Seminar 15 Correlation Table

16. 2007/3/8 NCTU IoP Seminar 16 Correlation Plot

17. 2007/3/8 NCTU IoP Seminar 17 Model from GPCR DB

18. 2007/3/8 NCTU IoP Seminar 18 Model from ModBase

19. 19 A Novel Global Optimization Algorithm for Protein-Ligand Interactions Jung-Hsin Lin (林榮信) Tien-Hao Chang (張天豪) Yen-Jen Oyang (歐陽彥正)

20. 2007/3/8 NCTU IoP Seminar 20 Characteristics of Biological Complex Problems The potential energy function is extremely rugged. The potential energy surface is usually highly asymmetric. The true global minimum is often surrounded by many deceptive local minima. The biological complex problems are mostly in the space of high dimensionality.

21. 2007/3/8 NCTU IoP Seminar 21 The Flexible Docking Problem

22. 2007/3/8 NCTU IoP Seminar 22 Thermodynamic Process of Docking

23. 2007/3/8 NCTU IoP Seminar 23 AutoDock Scoring Function A free energy-based empirical approach. J. Comput. Chem. 19: 1639-1662 (1998)

24. 2007/3/8 NCTU IoP Seminar 24 Searching is Generally a Global Optimization Problem Usually there is no general solution. Most heuristics cannot guarantee the optimal solution. Some of them have been classified as NP-complete or NP-hard problem.

25. 2007/3/8 NCTU IoP Seminar 25 How to explore the phase space? (Or, how to find a needle in a haystack?) ---Importance sampling We should only explore the important region of the phase space, not the entire phase space. Stochastic methods usually outperform deterministic approaches in higher dimensional space.

26. 2007/3/8 NCTU IoP Seminar 26 Genetic Algorithm 1. [Start] Generate random population of n chromosomes (suitable solutions for the problem) 2. [Fitness] Evaluate the fitness f(x) of each chromosome x in the population 3. [New population] Create a new population by repeating following steps until the new population is complete a. [Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected) b. [Crossover] With a crossover probability cross over the parents to form new offspring (children). If no crossover was performed, offspring is the exact copy of parents. c. [Mutation] With a mutation probability mutate new offspring at each locus (position in chromosome). d. [Accepting] Place new offspring in the new population 4. [Replace] Use new generated population for a further run of the algorithm 5. [Test] If the end condition is satisfied, stop, and return the best solution in current population 6. [Loop] Go to step 2

27. 2007/3/8 NCTU IoP Seminar 27 Chromosomes for Flexible Docking Crossover operation Leach, 2001.

28. 2007/3/8 NCTU IoP Seminar 28 Lamarckian Genetic Algorithm LGA is a hybrid of the Genetic Algorithm with the adaptive local search method. As in the GA scheme, energy is regarded as the phenotype, and the compound conformation and location are regarded as the genotype. In the LGA scheme, phenotype is modified by the local searcher, and then the genotype is modified by the locally optimized phenotype. In AutoDock, the so-called Solis-Wet algorithm is used (basically energy-based random move).

29. 2007/3/8 NCTU IoP Seminar 29 The Rank-based Adaptive Mutation Evolutionary Algorithm n individuals, denoted by s 1, s 2, …, s n, are generated. Each s i is a vector corresponding to a point in the domain of the objective function f. In order to achieve a scale-free representation, each component of s i is linearly mapped to the numerical range of [0,1]. The individuals in each generation of population are then sorted in the ascending order based on the values of the energy function on evaluated on these individuals. Let t 1, t 2, … t n denote the ordered individuals and we have f(t 1 )＜f(t 2 )＜f(t n ). n Gaussian distributions, denoted by G 1, G 2, … G n, are generated before the new generation of population is created. The center of each Gaussian distribution is selected randomly and independently from t 1, t 2, … t n, where the probability is not uniform but instead follows a discrete diminishing distribution, n : n-1 : … : 1. Nucleic Acids Research 33: W233-W238 (2005)

30. 2007/3/8 NCTU IoP Seminar 30 The RAME Algorithm

31. 2007/3/8 NCTU IoP Seminar 31 LGA versus RAME

32. 2007/3/8 NCTU IoP Seminar 32

33. 2007/3/8 NCTU IoP Seminar 33

34. 2007/3/8 NCTU IoP Seminar 34 http://bioinfo.mc.ntu.edu.tw/medock/, Nucleic Acids Research 33: W233-W238 (2005)

35. 2007/3/8 NCTU IoP Seminar 35 Randomized Benchmark Functions m: dimensionality

36. 2007/3/8 NCTU IoP Seminar 36 Performance of LGA vs. ME for a Random Benchmark Function Number of runs Probability of finding the global minima

37. 2007/3/8 NCTU IoP Seminar 37 Summary for the RAME Algorithm Our new RAME algorithm can find out the global minima for complex potential functions below dimensionality of 30 with substantial finite probability, which is suitable for most docking applications. The RAME algorithm avoids the “purification” effect inherent in the genetic algorithm and its derivatives, and therefore reduce the over-compression of information in the searching process.