scholarly journals LiP-Quant, an automated chemoproteomic approach to identify drug targets in complex proteomes

2019 ◽  
Author(s):  
Ilaria Piazza ◽  
Nigel Beaton ◽  
Roland Bruderer ◽  
Thomas Knobloch ◽  
Crystel Barbisan ◽  
...  
Keyword(s):  
Small Molecule ◽  
Binding Sites ◽  
Drug Target ◽  
Drug Targets ◽  
Target Identification ◽  
Limited Proteolysis ◽  
Drug Binding ◽  
Homologous Proteins ◽  
Protein Targets ◽  
Drug Target Identification

Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in developing optimized small-molecule compounds. Current unbiased approaches cannot directly pinpoint the interaction surfaces between ligands and protein targets. To address his limitation we have developed a new drug target deconvolution approach based on limited proteolysis coupled with mass spectrometry that works across species including human cells (LiP-Quant). LiP-Quant features an automated data analysis pipeline and peptide-level resolution for the identification of any small-molecule binding sites, Here we demonstrate drug target identification by LiP-Quant across compound classes, including compounds targeting kinases and phosphatases. We demonstrate that LiP-Quant estimates the half maximal effective concentration (EC50) of compound binding sites in whole cell lysates. LiP-Quant identifies targets of both selective and promiscuous drugs and correctly discriminates drug binding to homologous proteins. We finally show that the LiP-Quant technology identifies targets of a novel research compound of biotechnological interest.

scholarly journals Rationalizing Viral Drug Target Identification Using Computational Approaches : The SARS-CoV-2 Spike Glycoprotein S Case Study

2020 ◽  
Author(s):  
Petr Popov ◽  
Pavel Buslaev ◽  
Igor Kozlovskii ◽  
Mark Zaretskii ◽  
Dmitry Karlov ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Binding Site ◽  
Binding Sites ◽  
Drug Target ◽  
Conformational Transition ◽  
Target Identification ◽  
Drug Binding ◽  
Spike Glycoprotein ◽  
Important Amino Acid ◽  
Drug Target Identification

<div><div><div><p>COVID-19 emphasized the need for fast reaction tools to fight global biological threats such as viruses. Rapid drug discovery is one of the strategies for efficient social response. The success of a drug discovery campaign critically depends on the selected drug target, and the wrong target nullifies all the efforts to develop a drug. Viral drug target identification is a challenging problem, and computational methods can reduce the number of candidate targets. Here we present a structure-based approach to identify vulnerable regions in viral proteins that comprise drug binding sites. To detect promising binding sites, we take into account protein dynamics, accessibility, and mutability of the binding site, coupled with the putative mechanism of action of a drug. Applying to the SARS-CoV-2 Spike Glycoprotein S, we observed conformation- and oligomer-specific glycan-free binding site that is proximal to the receptor binding domain and comprises topologically important amino acid residues. Molecular dynamics simulations of Spike in complex with drug-like molecules docked into the binding sites revealed shifted equilibrium towards the inactive conformation compared to the ligand-free simulations. Small molecules targeting this binding site could prevent the closed-to-open conformational transition of the Spike protein, thus, allosterically inhibit the interaction with the human angiotensin-converting enzyme 2 receptor.</p></div></div></div>

scholarly journals Rationalizing Viral Drug Target Identification Using Computational Approaches : The SARS-CoV-2 Spike Glycoprotein S Case Study

2020 ◽  
Author(s):  
Petr Popov ◽  
Pavel Buslaev ◽  
Igor Kozlovskii ◽  
Mark Zaretskii ◽  
Dmitry Karlov ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Binding Site ◽  
Binding Sites ◽  
Drug Target ◽  
Conformational Transition ◽  
Target Identification ◽  
Drug Binding ◽  
Spike Glycoprotein ◽  
Important Amino Acid ◽  
Drug Target Identification

<div><div><div><p>COVID-19 emphasized the need for fast reaction tools to fight global biological threats such as viruses. Rapid drug discovery is one of the strategies for efficient social response. The success of a drug discovery campaign critically depends on the selected drug target, and the wrong target nullifies all the efforts to develop a drug. Viral drug target identification is a challenging problem, and computational methods can reduce the number of candidate targets. Here we present a structure-based approach to identify vulnerable regions in viral proteins that comprise drug binding sites. To detect promising binding sites, we take into account protein dynamics, accessibility, and mutability of the binding site, coupled with the putative mechanism of action of a drug. Applying to the SARS-CoV-2 Spike Glycoprotein S, we observed conformation- and oligomer-specific glycan-free binding site that is proximal to the receptor binding domain and comprises topologically important amino acid residues. Molecular dynamics simulations of Spike in complex with drug-like molecules docked into the binding sites revealed shifted equilibrium towards the inactive conformation compared to the ligand-free simulations. Small molecules targeting this binding site could prevent the closed-to-open conformational transition of the Spike protein, thus, allosterically inhibit the interaction with the human angiotensin-converting enzyme 2 receptor.</p></div></div></div>

scholarly journals Drug Target Identification with Machine Learning: How to Choose Negative Examples

2021 ◽  
Vol 22 (10) ◽  
pp. 5118
Author(s):  
Matthieu Najm ◽  
Chloé-Agathe Azencott ◽  
Benoit Playe ◽  
Véronique Stoven
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Target Identification ◽  
Target Prediction ◽  
False Positives ◽  
Machine Learning Algorithms ◽  
Statistical Bias ◽  
Protein Targets ◽  
Drug Target Identification ◽  
Approved Drugs

Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target Interactions databases used for training present high statistical bias, leading to a high number of false positives, thus increasing time and cost of experimental validation campaigns. To minimize the number of false positives among predicted targets, we propose a new scheme for choosing negative examples, so that each protein and each drug appears an equal number of times in positive and negative examples. We artificially reproduce the process of target identification for three specific drugs, and more globally for 200 approved drugs. For the detailed three drug examples, and for the larger set of 200 drugs, training with the proposed scheme for the choice of negative examples improved target prediction results: the average number of false positives among the top ranked predicted targets decreased, and overall, the rank of the true targets was improved.Our method corrects databases’ statistical bias and reduces the number of false positive predictions, and therefore the number of useless experiments potentially undertaken.

A Review of Recent Advances and Research on Drug Target Identification Methods

2019 ◽  
Vol 20 (3) ◽  
pp. 209-216 ◽  
Author(s):  
Yang Hu ◽  
Tianyi Zhao ◽  
Ningyi Zhang ◽  
Ying Zhang ◽  
Liang Cheng
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Drug Target ◽  
Drug Targets ◽  
Target Identification ◽  
Machine Learning Algorithms ◽  
Topological Features ◽  
Drug Target Identification ◽  
Incomplete Datasets ◽  
Optimal Set

Background:From a therapeutic viewpoint, understanding how drugs bind and regulate the functions of their target proteins to protect against disease is crucial. The identification of drug targets plays a significant role in drug discovery and studying the mechanisms of diseases. Therefore the development of methods to identify drug targets has become a popular issue.Methods:We systematically review the recent work on identifying drug targets from the view of data and method. We compiled several databases that collect data more comprehensively and introduced several commonly used databases. Then divided the methods into two categories: biological experiments and machine learning, each of which is subdivided into different subclasses and described in detail.Results:Machine learning algorithms are the majority of new methods. Generally, an optimal set of features is chosen to predict successful new drug targets with similar properties. The most widely used features include sequence properties, network topological features, structural properties, and subcellular locations. Since various machine learning methods exist, improving their performance requires combining a better subset of features and choosing the appropriate model for the various datasets involved.Conclusion:The application of experimental and computational methods in protein drug target identification has become increasingly popular in recent years. Current biological and computational methods still have many limitations due to unbalanced and incomplete datasets or imperfect feature selection methods

scholarly journals A Systems-Level Framework for Drug Discovery Identifies Csf1R As A Novel Anti-Epileptic Drug Target

2017 ◽  
Author(s):  
Prashant K Srivastava ◽  
Jonathan van Eyll ◽  
Patrice Godard ◽  
Manuela Mazzuferi ◽  
Benedicte Danis ◽  
...  
Keyword(s):  
Causal Reasoning ◽  
Drug Target ◽  
Drug Targets ◽  
Target Identification ◽  
Analytical Framework ◽  
Membrane Receptors ◽  
New Drugs ◽  
Tyrosine Kinase Receptor ◽  
Drug Target Identification ◽  
Novel Therapeutic

ABSTRACTThe identification of mechanistically novel drug targets is highly challenging, particularly for diseases of the central nervous system. To address this problem we developed and experimentally validated a new computational approach to drug target identification that combines gene-regulatory information with a causal reasoning framework (“causal reasoning analytical framework for target discovery” – CRAFT). Starting from gene expression data, CRAFT provides a predictive functional genomics framework for identifying membrane receptors with a direction-specified influence over network expression. As proof-of-concept we applied CRAFT to epilepsy, and predicted the tyrosine kinase receptor Csf1R as a novel therapeutic target for epilepsy. The predicted therapeutic effect of Csf1R blockade was validated in two pre-clinical models of epilepsy using a small molecule inhibitor of Csf1R. These results suggest Csf1R blockade as a novel therapeutic strategy in epilepsy, and highlight CRAFT as a systems-level framework for predicting mechanistically new drugs and targets. CRAFT is applicable to disease settings other than epilepsy.

Photo-cross-linked small-molecule affinity matrix as a tool for target identification of bioactive small molecules

2016 ◽  
Vol 33 (5) ◽  
pp. 709-718 ◽  
Author(s):  
Naoki Kanoh
Keyword(s):  
Small Molecules ◽  
Small Molecule ◽  
Drug Target ◽  
Target Identification ◽  
Affinity Matrix ◽  
Industrial Chemical ◽  
Drug Target Identification ◽  
The Status

This review describes the status of the photo-cross-linked small-molecule affinity matrix while providing a useful tutorial for academic and industrial chemical biologists who are involved or interested in drug target identification.

Drug-target identification in Drosophila cells: combining high-throughout RNAi and small-molecule screens

2007 ◽  
Vol 12 (1-2) ◽  
pp. 28-33 ◽  
Author(s):  
Norbert Perrimon ◽  
Adam Friedman ◽  
Bernard Mathey-Prevot ◽  
Ulrike S. Eggert
Keyword(s):  
Small Molecule ◽  
Drug Target ◽  
Target Identification ◽  
Drug Target Identification ◽  
Drosophila Cells

scholarly journals Comparative analysis of Rosetta stone events in Klebsiella pneumoniae and Streptococcus pneumoniae for drug target identification

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Poornima Ramesh ◽  
Jayashree Honnebailu Nagendrappa ◽  
Santosh Kumar Hulikal Shivashankara
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Functional Role ◽  
Target Identification ◽  
Protein Domain ◽  
Model Parameters ◽  
Signaling Mechanism ◽  
Rosetta Stone ◽  
A Genome ◽  
Drug Target Identification

Abstract Background Drug target identification is a fast-growing field of research in many human diseases. Many strategies have been devised in the post-genomic era to identify new drug targets for infectious diseases. Analysis of protein sequences from different organisms often reveals cases of exon/ORF shuffling in a genome. This results in the fusion of proteins/domains, either in the same genome or that of some other organism, and is termed Rosetta stone sequences. They help link disparate proteins together describing local and global relationships among proteomes. The functional role of proteins is determined mainly by domain-domain interactions and leading to the corresponding signaling mechanism. Putative proteins can be identified as drug targets by re-annotating their functional role through domain-based strategies. Results This study has utilized a bioinformatics approach to identify the putative proteins that are ideal drug targets for pneumonia infection by re-annotating the proteins through position-specific iterations. The putative proteome of two pneumonia-causing pathogens was analyzed to identify protein domain abundance and versatility among them. Common domains found in both pathogens were identified, and putative proteins containing these domains were re-annotated. Among many druggable protein targets, the re-annotation of EJJ83173 (which contains the GFO_IDH_MocA domain) showed that its probable function is glucose-fructose oxidoreduction. This protein was found to have sufficient interactor proteins and homolog in both pathogens but no homolog in the host (human), indicating it as an ideal drug target. 3D modeling of the protein showed promising model parameters. The model was utilized for virtual screening which revealed several ligands with inhibitory activity. These ligands included molecules documented in traditional Chinese medicine and currently marketed drugs. Conclusions This novel strategy of drug target identification through domain-based putative protein re-annotation presents a prospect to validate the proposed drug target to confer its utility as a typical protein targeting both pneumonia-causing species studied herewith.

scholarly journals Elucidating drug targets and mechanisms of action by genetic screens in mammalian cells

2017 ◽  
Vol 53 (53) ◽  
pp. 7162-7167 ◽  
Author(s):  
Martin Kampmann
Keyword(s):  
Mammalian Cells ◽  
Drug Target ◽  
Drug Targets ◽  
Drug Response ◽  
Target Identification ◽  
Mechanisms Of Action ◽  
Genetic Screens ◽  
Crispr Interference ◽  
Genome Wide ◽  
Drug Target Identification

Genome-wide CRISPR interference (CRISPRi) screens in mammalian cells enable drug target identification and uncover genes controlling drug response.

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