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 Identification of protein pockets and cavities by Euclidean Distance Transform

2018 ◽  
Author(s):  
Sebastian Daberdaku
Keyword(s):  
Ligand Binding ◽  
Active Sites ◽  
Euclidean Distance ◽  
Protein Structures ◽  
Ligand Docking ◽  
Distance Transform ◽  
Structure Based Drug Design ◽  
Distance Map ◽  
Euclidean Distance Transform ◽  
Protein Pockets

Protein pockets and cavities usually coincide with the active sites of biological processes, and their identification is significant since it constitutes an important step for structure-based drug design and protein-ligand docking applications. This paper presents a novel purely geometric algorithm for the detection of ligand binding protein pockets and cavities based on the Euclidean Distance Transform (EDT). The EDT can be used to compute the Solvent-Excluded surface for any given probe sphere radius value at high resolutions and in a timely manner. The algorithm is adaptive to the specific candidate ligand: it computes two voxelised protein surfaces using two different probe sphere radii depending on the shape of the candidate ligand. The pocket regions consist of the voxels located between the two surfaces, which exhibit a certain minimum depth value from the outer surface. The distance map values computed by the EDT algorithm during the second surface computation can be used to elegantly determine the depth of each candidate pocket and to rank them accordingly. Cavities on the other hand, are identified by scanning the inside of the protein for voids. The algorithm determines and outputs the best k candidate pockets and cavities, i.e. the ones that are more likely to bind to the given ligand. The method was applied to a representative set of protein-ligand complexes and their corresponding unbound protein structures to evaluate its ligand binding site prediction capabilities, and was shown to outperform most of the previously developed purely geometric pocket and cavity search methods.

Bacterial Lipoprotein Biosynthetic Pathway as a Potential Target for Structure-based Design of Antibacterial Agents

2020 ◽  
Vol 27 (7) ◽  
pp. 1132-1150 ◽  
Author(s):  
Jie Xia ◽  
Bo Feng ◽  
Gang Wen ◽  
Wenjie Xue ◽  
Guixing Ma ◽  
...  
Keyword(s):  
Drug Design ◽  
Active Sites ◽  
Biosynthetic Pathway ◽  
Antibacterial Agents ◽  
Biochemical Characterization ◽  
Global Public Health ◽  
Structure Based Drug Design ◽  
Key Enzymes ◽  
Potential Target ◽  
Bacterial Lipoprotein

Background: Antibiotic resistance is currently a serious problem for global public health. To this end, discovery of new antibacterial drugs that interact with novel targets is important. The biosynthesis of lipoproteins is vital to bacterial survival and its inhibitors have shown efficacy against a range of bacteria, thus bacterial lipoprotein biosynthetic pathway is a potential target. Methods: At first, the literature that covered the basic concept of bacterial lipoprotein biosynthetic pathway as well as biochemical characterization of three key enzymes was reviewed. Then, the recently resolved crystal structures of the three enzymes were retrieved from Protein Data Bank (PDB) and the essential residues in the active sites were analyzed. Lastly, all the available specific inhibitors targeting this pathway and their Structure-activity Relationship (SAR) were discussed. Results: We briefly introduce the bacterial lipoprotein biosynthetic pathway and describe the structures and functions of three key enzymes in detail. In addition, we present much knowledge on ligand recognition that may facilitate structure-based drug design. Moreover, we focus on the SAR of LspA inhibitors and discuss their potency and drug-likeness. Conclusion: This review presents a clear background of lipoprotein biosynthetic pathway and provides practical clues for structure-based drug design. In particular, the most up-to-date knowledge on the SAR of lead compounds targeting this pathway would be a good reference for discovery of a novel class of antibacterial agents.

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.

Discovery of Phosphodiesterase-4 Inhibitors: Serendipity and Rational Drug Design

2014 ◽  
Vol 67 (12) ◽  
pp. 1780 ◽  
Author(s):  
Susanne C. Feil ◽  
Jessica K. Holien ◽  
Craig J. Morton ◽  
Nancy C. Hancock ◽  
Philip E. Thompson ◽  
...  
Keyword(s):  
Drug Design ◽  
Active Sites ◽  
Inflammatory Diseases ◽  
Critical Role ◽  
Rational Drug Design ◽  
Chronic Obstructive ◽  
Structure Based Drug Design ◽  
Obstructive Pulmonary Disease ◽  
Phosphodiesterase 4 ◽  
Wide Range

Phosphodiesterase 4 (PDE4), the primary cyclic AMP-hydrolysing enzyme in cells, is a promising drug target for a wide range of mental disorders including Alzheimer's and Huntington's diseases, schizophrenia, and depression, plus a range of inflammatory diseases including chronic obstructive pulmonary disease, asthma, and rheumatoid arthritis. However, targeting PDE4 is complicated by the fact that the enzyme is encoded by four very closely related genes, together with 20 distinct isoforms as a result of mRNA splicing, and inhibition of some of these isoforms leads to intolerable side effects in clinical trials. With almost identical active sites between the isoforms, X-ray crystallography has played a critical role in the discovery and development of safer PDE4 inhibitors. Here we describe our discovery of a novel class of highly potent PDE4 via a ‘virtuous’ cycle of structure-based drug design and serendipity.

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>

scholarly journals Predicting the structure and energetics of protein-ligand interaction

2019 ◽  
Author(s):  
◽  
Zhiwei Ma
Keyword(s):  
Molecular Docking ◽  
Drug Design ◽  
Large Scale ◽  
Kinase Inhibitors ◽  
Ligand Docking ◽  
Pose Prediction ◽  
Computer Algorithms ◽  
Ligand Interaction ◽  
Structure Based Drug Design ◽  
Ligand Selectivity

Molecular docking has been a crucial component and remains a highly active area in computer-aided drug design (CADD). In simple terms, molecular docking uses computer algorithms to identify the "best" match between two molecules, a process analogous to solving three-dimensional jigsaw puzzles. In more rigorous terms, the molecular docking problem can be defined as predicting the "correct" bound association state for the given atomic coordinates of two molecules. Docking is an important tool for structure and affinity predictions of molecular association, which would lead to the mechanistic understanding of the physicochemical interactions at the atomic level. Protein-small molecule (referred to as "ligand") docking, in particular, has broad application to structure-based drug design, as drug compounds are usually small molecules. In this dissertation, I present my studies on protein-ligand docking. In the background introduction, I reviewed the docking methodology and the key recent developments in the field. Next, I applied an ensemble docking algorithm onto 14 protein kinases to study ligand selectivity, a major issue for the development of kinase inhibitors as anticancer drugs. In Chapter 3, I developed a web server for automated, in silico screening of multiple targets for a given ligand query. Finally, I integrated the new methods for protein-ligand binding mode prediction and applied the integrated method to a large-scale, blind prediction competition named Continuous Evaluation of Ligand Pose Prediction (CELPP).

Sign in / Sign up

Export Citation Format

Share Document