10.6084/m9.figshare.4546567 Prabu Manoharan Prabu Manoharan Kiranmai Chennoju Kiranmai Chennoju Nanda Ghoshal Nanda Ghoshal Computational analysis of BACE1-ligand complex crystal structures and linear discriminant analysis for identification of BACE1 inhibitors with anti P-glycoprotein binding property Taylor & Francis Group 2017 BACE1 hydrogen bond structure-based drug design weak interactions Alzheimer’s disease intraligand hydrogen bond protein–ligand interactions 2017-01-13 04:00:23 Journal contribution https://tandf.figshare.com/articles/journal_contribution/Computational_analysis_of_BACE1-ligand_complex_crystal_structures_and_linear_discriminant_analysis_for_identification_of_BACE1_inhibitors_with_anti_P-glycoprotein_binding_property/4546567 <p>More than 100 years of research on Alzheimer’s disease didn’t yield a potential cure for this dreadful disease. Poor Blood Brain Barrier (BBB) permeability and P-glycoprotein binding of BACE1 inhibitors are the major causes for the failure of these molecules during clinical trials. The design of BACE1 inhibitors with a balance of sufficient affinity to the binding site and little or no interaction with P-glycoproteins is indispensable. Identification and understanding of protein–ligand interactions are essential for ligand optimization process. Structure-based drug design (SBDD) efforts led to a steady accumulation of BACE1-ligand crystal complexes in the PDB. This study focuses on analyses of 153 BACE1-ligand complexes for the direct contacts (hydrogen bonds and weak interactions) observed between protein and ligand and indirect contacts (water-mediated hydrogen bonds), observed in BACE1-ligand complex crystal structures. Intraligand hydrogen bonds were analyzed, with focus on ligand P-glycoprotein efflux. The interactions are dissected specific to subsites in the active site and discussed. The observed protein-ligand and intraligand interactions were used to develop the linear discriminant model for the identification of BACE1 inhibitors with less or no P-glycoprotein binding property. Excellent statistical results and model’s ability to correctly predict a new data-set with an accuracy of 92% is achieved. The results are retrospectively analyzed to give input for the design of potential BACE1 inhibitors.</p>