Protein-Ligand Docking in Drug Design: Performance Assessment and Binding-Pose Selection

2018 ◽  
pp. 67-88 ◽  
Author(s):  
Flavio Ballante
Keyword(s):  
Drug Design ◽  
Performance Assessment ◽  
Ligand Docking ◽  
Design Performance

Novel drug design and bioinformatics: an introduction

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohammad Kalim Ahmad Khan ◽  
Salman Akhtar
Keyword(s):  
Drug Design ◽  
Target Identification ◽  
Homology Modelling ◽  
3D Structure ◽  
Cost Effective ◽  
Biological Data ◽  
Combinatorial Design ◽  
Ligand Docking ◽  
Structure Based Drug Design ◽  
Biological Targets

Abstract In the current era of high-throughput technology, where enormous amounts of biological data are generated day by day via various sequencing projects, thereby the staggering volume of biological targets deciphered. The discovery of new chemical entities and bioisosteres of relatively low molecular weight has been gaining high momentum in the pharmacopoeia, and traditional combinatorial design wherein chemical structure is used as an initial template for enhancing efficacy pharmacokinetic selectivity properties. Once the compound is identified, it undergoes ADMET filtration to ensure whether it has toxic and mutagenic properties or not. If the compound has no toxicity and mutagenicity is either considered a potential lead molecule. Understanding the mechanism of lead molecules with various biological targets is imperative to advance related functions for drug discovery and development. Notwithstanding, a tedious and costly process, taking around 10–15 years and costing around $4 billion, cascaded approached of Bioinformatics and Computational biology viz., structure-based drug design (SBDD) and cognate ligand-based drug design (LBDD) respectively rely on the availability of 3D structure of target biomacromolecules and vice versa has made this process easy and approachable. SBDD encompasses homology modelling, ligand docking, fragment-based drug design and molecular dynamics, while LBDD deals with pharmacophore mapping, QSAR, and similarity search. All the computational methods discussed herein, whether for target identification or novel ligand discovery, continuously evolve and facilitate cost-effective and reliable outcomes in an era of overwhelming data.

scholarly journals Structure-Based Drug Design for Cytochrome P450 Family 1 Inhibitors

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Zbigniew Dutkiewicz ◽  
Renata Mikstacka
Keyword(s):  
Drug Design ◽  
Active Sites ◽  
Wide Spectrum ◽  
Cytochromes P450 ◽  
Cancer Chemoprevention ◽  
Ligand Docking ◽  
Current Status ◽  
Structure Based Drug Design ◽  
Cytochrome P450 Family ◽  
Distinctive Role

Cytochromes P450 are a class of metalloproteins which are responsible for electron transfer in a wide spectrum of reactions including metabolic biotransformation of endogenous and exogenous substrates. The superfamily of cytochromes P450 consists of families and subfamilies which are characterized by a specific structure and substrate specificity. Cytochromes P450 family 1 (CYP1s) play a distinctive role in the metabolism of drugs and chemical procarcinogens. In recent decades, these hemoproteins have been intensively studied with the use of computational methods which have been recently developed remarkably to be used in the process of drug design by the virtual screening of compounds in order to find agents with desired properties. Moreover, the molecular modeling of proteins and ligand docking to their active sites provide an insight into the mechanism of enzyme action and enable us to predict the sites of drug metabolism. The review presents the current status of knowledge about the use of the computational approach in studies of ligand-enzyme interactions for CYP1s. Research on the metabolism of substrates and inhibitors of CYP1s and on the selectivity of their action is particularly valuable from the viewpoint of cancer chemoprevention, chemotherapy, and drug-drug interactions.

scholarly journals Affinity Prediction using Mutated Protein-Ligand Docking with Regression Techniques of SCA

2019 ◽  
Vol 8 (2) ◽  
pp. 3642-3648
Keyword(s):  
Drug Design ◽  
Support Vector Regression ◽  
Spinocerebellar Ataxia ◽  
Genetic Disorder ◽  
Ligand Docking ◽  
Support Vector ◽  
Autodock Vina ◽  
Affinity Prediction ◽  
Energy Profiles ◽  
Regression Techniques

Drug discovery for rare genetic disorder like spinocerebellar ataxia is very complicated in biomedical research. Numerous approaches are available for drug design in clinical labs, but it is time consuming. There is a need for affinity prediction of spinocerebellar ataxia, which will help in facilitating the drug design. In this work, the proteins are mutated with the information available from HGMD database. The repeat mutations are induced manually, and that mutated proteins are docked with ligand. The model is trained with extricated features such as energy profiles, rf-score, autodock vina scores, cyscore and sequence descriptors. Regression techniques like linear, polynomial, ridge, SVM and neural network regression are implemented. The predictive models are built with various regression techniques and the predictive model implemented with support vector regression is compared with support vector regression kernel. Among all regression techniques, SVR performs well than the other regression models.

scholarly journals Ligand docking and binding site analysis with pymol and autodock/vina

2015 ◽  
Vol 4 (2) ◽  
pp. 168 ◽  
Author(s):  
Mohd. Ahmar Rauf ◽  
Swaleha Zubair ◽  
Asim Azhar
Keyword(s):  
Molecular Docking ◽  
Drug Design ◽  
Three Dimensional ◽  
Structural Features ◽  
Docking Studies ◽  
Ligand Docking ◽  
Structure Based Drug Design ◽  
Site Analysis ◽  
User Friendly ◽  
Autodock 4.2

<p>Docking of various therapeutically important chemical entities to the specific target sites offers a meaningful strategy that may have tremendous scope in a drug design process. For a thorough understanding of the structural features that determine the strength of bonding between a ligand with its receptor, an insight to visualize binding geometries and interaction is mandatory. Bioinformatical as well as graphical software ‘PyMOL’ in combination with the molecular docking suites Autodock and Vina allows the study of molecular combination to visualize and understand the structure-based drug design efforts. In the present study, we outlined a user friendly method to perform molecular docking using vina and finally the results were analyzed in pymol in both two as well as three-dimensional orientation. The operation bypasses the steps that are involved in docking using cygwin terminal like formation of gpf and dpf files. The simple and straight-forward operation method does not require formal bioinformatics training to apprehend molecular docking studies using AutoDock 4.2 program.</p>

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