AMP-activated protein kinase (AMPK) is an energy sensor, play key role in the regu-lation of critical cellular pathways. When activated AMPK increase ATP generating catabolic process and decrease ATP consuming anabolic process. Studies confirmed reduced AMP-acti¬vated protein kinase (AMPK) activity in metabolic disease-induced diabetic chronic kidney diseases. In the kidney as a metabolic sensor activate tubular transport and helps renal cells to survive low energy states. The specific AMPK activator A-769662, leading to improvement in energy metabolism and kidney fibrosis. Thus becomes a therapeutic target, the discovery of potent and specific AMPK activators independent of the AMP sensing mechanism, for the pre¬vention and treatment of metabolic disorders appears to be more important.
Phosphorylation of Thr172 in α subunit is require to maintain AMPK activity. Under ATP Depletion AMPK kinase undergo allosteric activation, the first direct allosteric activator of AMPK, A-769662, was reported by Cool and colleagues in 2006 that activates AMPK by modulating the β subunit. The preclinical and clinical evidence supporting pharmacological activation of AMPK particularly, a direct activator PF-06409577 was reported to activate AMPK β1 complexes represents a potent strategy for the treatment of DN in humans. Among the two known β subunits, β1 subunit is highly abundant in kidney as suggested by mRNA levels. Thus aim of present proposal to screen natural compounds against β1 selective AMPK, through docking and highlights the computer-aided drug design in silico approaches and their limitations for screening natural compounds as activators target in the treatment of kidney dis¬eases.

Abstract i
Table of Contents ii
1. Introduction 1
1.1 Critical Amino Acids 3
1.2 Activation from the ADaM site: 5
1.3 Dual activation of AMPK (nucleotide and Adam site) 6
1.4 Docking studies - Adam site 6
1.5 Molecular docking based Virtual Screening and Machine Learning 7
1.6 Structure Based - Molecular Docking 8
2. Materials and Methods 10
2.1 Protein preparation for molecular docking study 10
2.2 Protein similarity study for molecular docking study. 10
2.3 Ligand preparation 11
2.4 Active Site Prediction for molecular docking 11
2.5 Molecular Docking using GOLD package 12
2.6 Targeted Scoring Functions 12
2.7 Application to test cases 13
3. Results 14
3.1 Sampling of molecular docking 14
3.2 Selectivity 14
3.3 Docking for Selectivity study. 15
3.4 Pose Selection 15
4. Conclusion 16
Ranking Efficiency 16
5. References 17

Figures
Figure 1. Depicts the structure of AMPK domains 28
Figure 2. Superposition of Prepared Proteins 29
Figure 3. Comparing the Active site residues between A Castp and B Ligplot 30
Figure 4. Ramachandran plot protein 4QFR with respect to their pdb structure 31
Figure 5. Process of molecular docking 32
Figure 7. Pose Selcetion 34
Figure 8. Pose Selection 35
Figure 9. Ranking Efficiency 36

Tables
Table 1. RMSD distributions of AMPK proteins 37
Table 2. Structure file of four known allosteric modulators 38
Table 3. Active Site Residues and Atoms 39
Table 4. Success Rates of 3 Scoring Functions 43
Table 5. Pocket dynamics among proteins. 43
Table 6. Refined data set combination of top 15 highest score and lowest RMSD 44
Table 7. Hydrogen bonding and hydrophobic contacting residues between Native protein and prepared protein with compound A-769662 45

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