# Deploying a Pharmacoinformatics Grid for Integrative Biomedical - PowerPoint PPT Presentation

## Deploying a Pharmacoinformatics Grid for Integrative Biomedical Researches Jung-Hsin Lin () School of Pharmacy, National Taiwan University & Institute of Biomedical Sciences, Academia Sinica http://rx.mc.ntu.edu.tw/~jlin/

1. Deploying a Pharmacoinformatics Grid for Integrative Biomedical Researches Jung-Hsin Lin (林榮信) School of Pharmacy, National Taiwan University & Institute of Biomedical Sciences, Academia Sinica http://rx.mc.ntu.edu.tw/~jlin/

2. Pharmacoinformatics integrates Bioinformatics and Chemoinformatics for Drug Discovery

3. Identifying Drug Targets using Microarray

4. http://bioinfo.mc.ntu.edu.tw:8080/GenePathway/

5. Constructing Biological Pathways and Networks

6. Mathematical Modeling of Signaling Pathways Am. J. Phys. Endo. Metab . 283: E1084 (2002)

7. Coupled Differential Equations for Biological Pathways dx = − 12 k x x k x dx = + − + − − 8 10 11 8 12 2 k x k [ PTP ] x k x x k x k x dt − − − 1 3 3 5 1 1 2 4 6 4 2 dt dx = + − + 13 dx k x k x ( k [ PTEN ] k [ SHIP ]) x = − − − 3 9 14 10 15 9 10 13 k x x k x k x dt − 1 1 2 1 3 3 3 dt dx = − dx 14 k [ PTEN ] x k x = + − + − 4 k x x k x k x k x 9 13 9 14 − − dt 2 1 5 2 4 4 7 4 4 dt dx = − dx 15 k [ SHIP ] x k x = + − + − − 5 [ PTP ] − k x k x k x x k x k x k x 10 13 10 15 − − − dt 3 3 2 4 2 1 5 3 5 4 8 4 5 dt dx dx = − 16 = + + + + − k x k x 6 k k x k [ PTP ]( x x ) k x k x − 11 17 11 16 − − 5 5 6 6 7 8 4 2 4 6 dt dt dx dx = − = − − 17 k x k x 7 k x k x k [ PTP ] x − − 11 16 11 17 4 4 4 7 6 7 dt dt dx dx = − = − − 8 18 k x k x k x k x k [ PTP ] x − − 4 5 4 8 6 8 12 19 12 18 dt dt dx dx = − + = − 9 k [ PTP ] x k x ( x x ) /( IR ) 19 k x k x − − 7 10 7 9 4 5 p 12 18 12 19 dt dt dx dx = + + − + 10 = − + + − k x ( x x ) /( IR ) k x ( k [ PTP ] k x ) x 20 k x ( k k ) x k k x − − 7 9 4 5 p 8 12 7 8 11 10 − − dt 13 21 13 13 ' 20 14 14 20 dt dx = − dx 11 k x k x x = + − 21 − ( k k ) x k x 8 12 8 10 11 − dt 13 13 ' 20 13 21 dt

8. Virtual Screening after Target Identified 1. 1. Start with crystal coordinates of target 2. Generate the molecular surface for the receptor 2. receptor and locate the active site 3. 3. Search for the optimal position and location 4. Pick up the conformations (or compounds) with best 4. based on some scoring function scores

9. State Vector in the Flexible Docking Problem φ θ ψ χ χ χ L ( x , y , z , , , , , , , ) CM CM CM 1 2 k

10. 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.

11. 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.

12. 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

13. Chromosomes for GA Docking Crossover operation Leach, 2001.

14. 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).

15. A Maximum Entropy Evolutionary Algorithm for the Docking Problem Nucleic Acids Research 33: W233-W238 (2005 ) • 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. β − α ( ) ( ) i ⎛ ⎞ ⎛ ⎞ − 2 s μ σ = α + 1 ' 2 ⎜ ⎟ ⎜ ⎟ = − s i i ( ) exp p ' ⎜ ⎟ − ⎜ ⎟ i σ n 1 i π ⋅ σ 2 2 ⎝ ⎠ ⎝ ⎠ 2 i i

16. LGA versus ME • The ME 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.

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

18. Overexpression of P-glycoprotein is the major cause for multidrug resistance problems in cancer chemotherapies • Multidrug resistance (MDR) has posed a serious clinical problem in cancer chemotherapy. • MDR will cause the reduction of bioavailability of drugs. • P-gp, product of the mdr1 gene in humans, localization on chromosome 7q21, is a member of the large ATP binding cassette (ABC) family of proteins. • P-gp is 1280 amino acids long and is very dynamic inside membranes.

19. Structure Prediction the MDR Protein Pgp 1. Predicting the structure of Pgp using homology modeling 2. Molecular dynamics simulation in a lipid bilayer

20. Paclitaxel (Taxol) • THR-199 [ TM3 ] • PHE-303,TYR-307,PHE-314 [ TM5 ] • SER-344, VAL-345, GLN-347 [ TM6 ]

21. Summary • Grid is the ideal computing architecture that enables integrative biomedical and pharmaceutical researches, which often require access to heterogeneous computing resources. • The GenePathway Viewer allows conversion of microarray data into colored pathway information, which uses the Web Service technology that can update the pathway database from KEGG in a real-time and automatic fashion. • Molecular dynamics simulations of biomacromolecules usually generate huge amount of data in a very high speed, and therefore good archiving facilities like DataGrid is important to ensure data security and integrity. • The ME algorithm is intrinsically parallel, and therefore straightforward to be implemented on the Grid architecture.

22. Acknowledgement Computer Science and Information Engineering, NTU • Prof. Yen-Jen Oyang • Tien-Hao Chang School of Pharmacy, NTU • Pei-Hua Lo • Hui-Hsuan Tu National Science Council of Taiwan

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