Novel Approach for Efficient Pharmacophore-Based Virtual Screening: Method and Applications

Abstract

Virtual screening is emerging as a productive and cost-effective technology in rational drug design for the identification of novel lead compounds. An important model for virtual screening is the pharmacophore. Pharmacophore is the spatial configuration of essential features that enable a ligand molecule to interact with a specific target receptor. In the absence of a known receptor structure, a pharmacophore can be identified from a set of ligands that have been observed to interact with the target receptor. Here, we present a novel computational method for pharmacophore detection and virtual screening. The pharmacophore detection module is able to: (i) align multiple flexible ligands in a deterministic manner without exhaustive enumeration of the conformational space, (ii) detect subsets of input ligands that may bind to different binding sites or have different binding modes, (iii) address cases where the input ligands have different affinities by defining weighted pharmacophores based on the number of ligands that share them, and (iv) automatically select the most appropriate pharmacophore candidates for virtual screening. The algorithm is highly efficient, allowing a fast exploration of the chemical space by virtual screening of huge compound databases. The performance of PharmaGist was successfully evaluated on a commonly used dataset of G-Protein Coupled Receptor alpha1A. Additionally, a large-scale evaluation using the DUD (directory of useful decoys) dataset was performed. DUD contains 2950 active ligands for 40 different receptors, with 36 decoy compounds for each active ligand. PharmaGist enrichment rates are comparable with other state-of-the-art tools for virtual screening. Availability The software is available for download. A user-friendly web interface for pharmacophore detection is available at http://bioinfo3d.cs.tau.ac.il/PharmaGist.

COMPUTACIONAL STUDY OF 1H-IMIDAZOL-2-YL-PYRIMIDINE-4,6-DIAMINES FOR IDENTIFICATION OF POTENTIAL PARENT COMPOUNDS OF NEW ANTIMALARIAL AGENTS

“…Este programa gera como arquivos de saída candidatos a farmacóforos computados com base em múltiplos alinhamentos flexíveis dos compostos de entrada. [31][32][33] Como este servidor opera com conjuntos de dados de até 32 molé-culas, foram selecionados os 32 derivados mais ativos do conjunto para formar o sub-conjunto desta etapa. O derivado mais ativo (40) foi escolhido como a molécula pivô, a qual o programa sobrepõe cada estrutura a este e alinha o maior número possível de características em comum com o pivô, resultando em diversos farmacóforos.…”

Discovery of Novel Mycobacterial DNA Gyrase B Inhibitors: In Silico and In Vitro Biological Evaluation

“…Thus, its value was expected always to be greater than 1 and the higher it was, the better the enrichment performance of the virtual screening. [26] A second enrichment metric, the Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC) was also used as a way to ensure that the results and conclusions were significant and generalized the receiver operating characteristic (ROC) that addressed the "early scoring problem" by Boltzmann weighting the hits based on how early they were retrieved. [22] Based on the recovery rate of actives against the total 1048 compounds in which 48 were known inhibitors of mycobacterial GyrB inhibitors and 1000 were the decoy set which represented inactives.…”

Flexibility and binding affinity in protein–ligand, protein–protein and multi-component protein interactions: limitations of current computational approaches

“…Biophysical methods including nuclear magnetic resonance (NMR), mass spectrometry and fluorescence-based techniques allow the qualitative detection of a small molecule binding to a target and the quantitative determination of physical parameters associated with binding [1]. Drug design methods include structure-based virtual screening, where the three-dimensional protein structure is known [2,3], and ligand/pharmacophore-based virtual screening in the absence of a known receptor structure in order to identify and exploit the spatial configuration of essential features that enable a ligand to interact with a specific receptor [4,5]. Recent years have also seen the emergence of chemogenomics with the aim of understanding the recognition between all possible ligands and the full space of proteins by using traditional ligand-based approaches and biological information on drug targets [6] or by requiring only protein sequence and chemical structure data [7].…”