Rationalizing fragment based drug discovery for BACE1: insights from FB-QSAR, FB-QSSR, multi objective (MO-QSPR) and MIF studies (original) (raw)
Related papers
Computational biology and chemistry, 2016
The inhibition of β-secretase (BACE1) is currently the main pharmacological strategy available for Alzheimer's disease (AD). 2D QSAR and 3D QSAR analysis on some cyclic sulfone hydroxyethylamines inhibitors against β-secretase (IC50: 0.002-2.75μM) were carried out using hologram QSAR (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods. The best model based on the training set was generated with a HQSAR q(2) value of 0.693 and r(2) value of 0.981; a CoMFA q(2) value of 0.534 and r(2) value of 0.913; and a CoMSIA q(2) value of 0.512 and r(2) value of 0.973. In order to gain further understand of the vital interactions between cyclic sulfone hydroxyethylamines and the protease, the analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the BACE1. The final QSAR models could be helpful in the design and development of novel active BACE1 inhibitors.
QSAR Studies as Strategic Approach in Drug Discovery
International Journal of Scientific Research in Chemistry, 2019
The QSAR models are useful for various purposes including the prediction of activities of untested chemicals. It helps in the rational design of drugs by computer aided tools via molecular modeling, simulation and virtual screening of promising candidates prior to synthesis. In order to achieve a reliable statistical model for predicting the behaviors of new chemical entities, quantitative structure activity relationship (QSAR) have been used for decades to establish connections between the physicochemical properties of chemicals and their biological activities. The fundamental concept of formalism is that the biological differences in the compounds have a difference in structural properties. The atom, groups or molecular characteristics of ligands affinity to its sites, inhibition constants, frequency constants, and more biological endpoints have been linked with the classic QSAR studies such as lipophilicity, polarization, electronic and steric properties (hansch analysis) and basic structural characteristics (free wilson analysis).
Applications of QSAR Study in Drug Design
Applications of QSAR Study in Drug Design
Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) studies are important in silico methods in rational drug design. The aim of this methods are to optimize the existing leads in order to improve their biological activities and physico-chemical properties. Also, to predict the biological activities of untested and sometimes yet unavailable compounds. This article is a general review of different QSAR/QSPR studies in different previous researches. R2 and Q2 parameters are used in some studies to predict the predictability and robustness of the constructed models. In all mentioned articles QSAR study were good prediction tool for investigation drug activity or binding mode on specific receptors.
Fragment-based discovery and optimization of BACE1 inhibitors
Bioorganic & Medicinal Chemistry Letters, 2010
A novel series of 2-aminobenzimidazole inhibitors of BACE1 has been discovered using fragment-based drug discovery (FBDD) techniques. The rapid optimization of these inhibitors using structure-guided medicinal chemistry is discussed.
Multi-dimensional QSAR in drug discovery
Drug Discovery Today, 2007
Quantitative structure-activity relationships (QSAR) is an area of computational research that builds virtual models to predict quantities such as the binding affinity or the toxic potential of existing or hypothetical molecules. Although a wealth of experimental data emphasizes the active role of the target protein in the binding process, QSAR studies are frequently restricted to the properties of the smallmolecule ligand. This review aims at discussing recent QSAR concepts exploring higher dimensions (simulation of induced fit, simultaneous exploration of alternative binding modes, and solvation scenarios), and their benefit for the drug-discovery process.
Hybrid Structure-Based Virtual Screening Protocol for the Identification of Novel BACE1 Inhibitors
Journal of Chemical Information and Modeling, 2009
BACE1, also called-secretase or memapsin 2, is an extensively studied aspartic protease, involved in etiopathogenesis and progression of Alzheimer's disease (AD). We report herein a modified structure-based virtual screening protocol that augments the lead identification process against BACE1 during virtual screening endeavors. A hybrid structure-based virtual screening protocol that incorporates elements from both ligandbased and structure-based techniques was used for the identification of prospective small molecule inhibitors. Virtual screening, using an active-site-derived pharmacophore, followed by ROCS (rapid overlay of chemical structures)-based GOLD (genetic optimization in ligand docking) docking was used to identify a library of focused candidates. The efficacy of the ROCS-based GOLD docking method together with our customized weighted consensus scoring function was evaluated against conventional docking methods for its ability to discern true positives from a screening library. An in-depth structural analysis of the binding mode of the top-ranking molecules reveals that emulation of the curial interaction patterns deemed necessary for BACE1 inhibition. The results obtained from our validation study ensure the superiority of our docking methodology over conventional docking methods in yielding higher enrichment rates.
3D-QSAR in Drug Design -A Review
Quantitative structure-activity relationships (QSAR) have been applied for decades in the development of relationships between physicochemical properties of chemical substances and their biological activities to obtain a reliable statistical model for prediction of the activities of new chemical entities. The fundamental principle underlying the formalism is that the difference in structural properties is responsible for the variations in biological activities of the compounds. In the classical QSAR studies, affinities of ligands to their binding sites, inhibition constants, rate constants, and other biological end points, with atomic, group or molecular properties such as lipophilicity, polarizability, electronic and steric properties (Hansch analysis) or with certain structural features (Free-Wilson analysis) have been correlated. However such an approach has only a limited utility for designing a new molecule due to the lack of consideration of the 3D structure of the molecules. 3D-QSAR has emerged as a natural extension to the classical Hansch and Free-Wilson approaches, which exploits the three-dimensional properties of the ligands to predict their biological activities using robust chemometric techniques such as PLS, G/PLS, ANN etc. It has served as a valuable predictive tool in the design of pharmaceuticals and agrochemicals. Although the trial and error factor involved in the development of a new drug cannot be ignored completely, QSAR certainly decreases the number of compounds to be synthesized by facilitating the selection of the most promising candidates. Several success stories of QSAR have attracted the medicinal chemists to investigate the relationships of structural properties with biological activity. This review seeks to provide a bird's eye view of the different 3D-QSAR approaches employed within the current drug discovery community to construct predictive structureactivity relationships and also discusses the limitations that are fundamental to these approaches, as well as those that might be overcome with the improved strategies. The components involved in building a useful 3D-QSAR model are discussed, including the validation techniques available for this purpose.
Computer-Aided Linear Modeling Employing Qsar for Drug Discovery
Scientific Research and Essays, 2009
Quantitative structure-activity relationship (QSAR) is a computational process that relates the chemical structure of compounds with their activities, especially biologic activities or effects. It employs series of computer-based processes to analyze quantitative experimental data of the activities of given compounds with known chemical structures in order to predict a relationship, model or equation that will help to propose the activity of known compounds with unknown activities or unknown compounds and their activities. Commonly used computer softwares in QSAR analysis include HYPERCHEM, MATLAB, DRAGON and RECKON. Key words: QSAR, biological activity, prediction, computer software.
Biomolecules
The treatment options for a patient diagnosed with Alzheimer’s disease (AD) are currently limited. The cerebral accumulation of amyloid-β (Aβ) is a critical molecular event in the pathogenesis of AD. When the amyloidogenic β-secretase (BACE1) is inhibited, the production of Aβ peptide is reduced. Henceforth, the main goal of this study is the discovery of new small bioactive molecules that potentially reach the brain and inhibit BACE1. The work was conducted by a customized molecular modelling protocol, including pharmacophore-based and molecular docking-based virtual screening (VS). Structure-based (SB) and ligand-based (LB) pharmacophore models were designed to accurately screen several drug-like compound databases. The retrieved hits were subjected to molecular docking and in silico filtered to predict their ability to cross the blood–brain barrier (BBB). Additionally, 34 high-scoring compounds structurally distinct from known BACE1 inhibitors were selected for in vitro screening...