Molecular docking and QSAR based studies for designing and development of β-secretase BACE-1 inhibitor Swapnil Kumar *, Shruti Kushwaha#, Shikha Agarwal** & Durg Vijay Singh* *Centre for Biological Science (Bioinformatics), SEBES Central University of Bihar, Patna – 800014 # UIET CSJM University, Kanpur - β080β4 . ** IBM-Gurgaon India Mail ID: dvsbiotech@gmail.com , Contact No.74880γ1175 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- INTRODUCTION ➢ Alzheimer’s disease (AD), has become the most common cause of dementia in the elderly, affecting approximately γ% of the population between the ages of 65 to 74, and nearly 50 percent of those 85 years and older. ➢ A major neuropathological hallmark of the AD is Plaques formation. Plaques are mainly consist of amyloid-ȕ (Aȕ) peptides; predominantly of 40 or 4β amino acids in length (Aȕ 40, Aȕ 4β). ➢ Aȕ 40 and Aȕ 4β are the result of the proteolytic cleavage of a larger membrane bound precursor protein, known as Amyloid precursor protein (APP). ➢ Two proteolytic cleavage events are required to generate Aȕ from its precursor , one at the N-terminus by an enzyme termed as “ȕ-secretase” and one at the C-terminus by an enzyme termed as “Ȗ-secretase”. ➢ ȕ-secretase was identified as a type 1 transmembrane, 501 amino acids protein containing aspartyl protease activity. OBJECTIVE Objective of the work was to design and development of new drug candidate for ȕ-secretase (BACE-1) to encounter progression of Alzheimer’s disease. RESULTS ➢Molecular Docking and QSAR MATERIALS AND METHODS MOLECULAR DOCKING ➢ 1β7 BACE-1 inhibitors were fetched from literature and prepared for docking. ➢ Protein molecule was prepared through Molegro Virtual Docker (MVD). ➢ Each chain of BACE-1 having cofactor and water were removed for docking, water molecules were removed prior to docking because they were not found to play any important roles in BACE1-ligand interaction. ➢ Obtained best docked poses were analyzed based on MoleDock Score and other thermodynamically calculated values. ➢ After docking, we found the frequency of BACE-1 inhibitors based on their binding affinity. QSAR MODELING ➢ Calculation of the molecular descriptors for the subset of 107 inhibitors of BACE-1 using QikProp. ➢ Before processing the molecules for model generation, the complete dataset containing 107 inhibitors are divided into training set and test set. ➢ Importing molecular data into Maestro for use by Strike. ➢ Generation, validation and application of QSAR models. ➢ Performing similarity analysis. PHARMACOPHORE MODELING ➢ The POCKET module of Ligbuilder was used for receptor-based pharmacophore modeling for the best docked conformation of EVγ. ➢ Ligbuilder is a multi-purpose program developed for receptor based de novo drug design and optimization. ➢ Based on γD structure of the target protein, it can automatically build ligand molecules within the binding pocket and subsequently screen them. S.no. Molecule Experimental pIC50 values Predicted pIC50 values 1. 5HA_400 7.8βγ9 7.51β6 2. γ5A 5.7447 7.γ51β1 3. 46β_1 6.ββ91 6.5β7β1 4. 47β_1 7.ββ18 6.9819 5. 569_1 7.6989 7.11098 6. 71β_401 7.5686 7.9477γ 7. AXQ 6.8068 9.49γ4β 8. AYH 5.4γ17 6.γ6γ96 9. C8C_1γ89 4.0655 5.498β7 10 CM7_β000 8.6989 7.β1958 11. EV5_γ96 5.1549 6.16γ19 12. FβI 6.5016 6.75011 13. SC7 8.0969 8.58446 14. VG7 6.7447 9.0β69γ S.no. Molecule Predicted pIC50 values 1. γHF_γ94 8.514γ6 2. γHH_A_γ94 8.γ6641 3. C44 8.ββ116 4. MV7 8.9γ481 5. PB7_γ94 7.9β119 6. RVI_40β 8.58114 7. S8Z 7.8γ456 8. ZPQ 8.44β14 Feature symbol H D H D A D H D H ----- 5.15 γ.54 7.β1 8.7γ γ.50 4.50 γ.50 D 5.15 ----- 5.10 γ.54 6.54 6.γ4 γ.50 γ.50 H γ.54 5.10 ----- 5.15 5.7β 6.87 5.0β 5.ββ D 7.β1 γ.54 5.15 ----- γ.50 9.γ9 5.50 6.γ4 A 8.7γ 6.54 5.7β γ.50 ----- 11.γ4 6.96 9.08 D γ.50 6,γ4 6.87 9.γ9 11.γ4 ----- 5.15 4.47 H 4.50 γ.50 5.0β 5.50 6.96 5.15 ----- 5.15 D γ.50 γ.50 5.ββ 6.γ4 9.08 4.47 5.15 ----- Figure 1. Plot of Correlation between the Frequencies and Energies of the ligands Table 1. Showing the predicted pIC50 values for the test set. Table 2. Showing the predicted pIC50 values for the external data set. Figure 2. Plot of Predicted Activity vs. IC50 for the training set. Figure 3. Showing pharmacophore of EVγ (PDB compound γMSJ). Sky blue (hydrophobic sites), Blue-(H-bond donor), Red(H-bond acceptor). Table 3. Feature internal distance of pharmacophore H – Hydrophobic site D – H-bond donar site A – H-bond acceptor site CONCLUSION ➢ 1β7 BACE-1 inhibitor were taken from literature and docked separately BACE-1 proteins having resolution between 1.5 to β Å to find out inhibitor which docked preferably at its active site. ➢ Receptor based drug design module was used for de novo drug design approach. ➢ A subset of seventy molecules of known IC 50 values were used for QSAR model generation ➢ QSAR model has been established and tested for internal and external data set. Possible synthesizable molecule is under study. REFERENCES ➢ Vassar R and Kandalepas PC.(β011) The ȕ-secretase enzyme BACE1 as a therapeutic target for Alzheimer’s disease. BioMed Central γ: β-6 ➢ René Thomsen and Mikael H. Christensen MolDock: A New Technique for High-Accuracy Molecular Docking J. Med. Chem., β006, 49(11), pp γγ15 - der ➢ Yaxia Yuan, Jianfeng Pei, and Luhua Lai, LigBuilder β: A Practical de Novo Drug Design Approach J. Chem. Inf. Model., β011, 51 (5), pp 108γ–1091 ➢ Phase, version γ.4, Schrödinger, LLC, New York, NY, β01β.