Computational Biophysics and Drug Design

1. 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/ Computational Biophysics and Drug Design 2007/3/8 NCTU IoP Seminar

2. Many roles of computation in drug discovery Computation can be helpful for discovering new drugs with • 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

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

4. Attenuation of GPCR Signaling

5. Signaling Pathways from GPCR Families

6. Sequence Alignment for A2A 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. Training compounds 3 2 4 1 7 8 5 6 11 10 12 9

8. Training compounds 14 15 16 13 17 18 19 20 21 22 23 24

9. Structural Alignment of General Molecules Carvedilol Verapamil

10. Verapamil

11. Carvedilol

12. Pharmacophore model for A2A antagonists Best HypoGen pharmacophore model Hypo1 aligned to compound 1

13. Correlation Plot

14. Pharmacophore model for A2A agonists Best HypoGen pharmacophore model Hypo2 aligned to compound 33

15. Correlation Table

16. Correlation Plot

17. Model from GPCR DB

18. Model from ModBase

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

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. The Flexible Docking Problem

22. Thermodynamic Process of Docking

23. AutoDock Scoring Function J. Comput. Chem. 19: 1639-1662 (1998) • A free energy-based empirical approach.

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. 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. Genetic Algorithm • [Start]Generate random population of n chromosomes (suitable solutions for the problem) • [Fitness]Evaluate the fitness f(x) of each chromosome x in the population • [New population]Create a new population by repeating following steps until the new population is complete • [Selection]Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected) • [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. • [Mutation]With a mutation probability mutate new offspring at each locus (position in chromosome). • [Accepting]Place new offspring in the new population • [Replace]Use new generated population for a further run of the algorithm • [Test]If the end condition is satisfied, stop, and return the best solution in current population • [Loop]Go to step 2

27. Chromosomes for Flexible Docking Crossover operation Leach, 2001.

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. The Rank-based Adaptive Mutation Evolutionary Algorithm Nucleic Acids Research 33: W233-W238 (2005) • n individuals, denoted bys1, s2, …, sn,are generated. Each si 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 si 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 t1, t2, … tn denote the ordered individuals and we have f(t1)＜f(t2)＜f(tn). • nGaussian distributions, denoted by G1, G2, … Gn, are generated before the new generation of population is created. The center of each Gaussian distribution is selected randomly and independently from t1,t2, … tn, where the probability is not uniform but instead follows a discrete diminishing distribution, n : n-1 : … : 1.

30. The RAME Algorithm

31. LGA versus RAME

34. http://bioinfo.mc.ntu.edu.tw/medock/,Nucleic Acids Research 33: W233-W238 (2005)

35. Randomized Benchmark Functions m: dimensionality

36. Performance of LGA vs. ME for a Random Benchmark Function Probability of finding the global minima Number of runs

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.