scholarly journals Small molecule photocatalysis enables drug target identification via energy transfer

2021 ◽  
Author(s):  
Aaron D Trowbridge ◽  
Ciaran P Seath ◽  
Frances P Rodriguez-Rivera ◽  
Beryl X Li ◽  
Barbara E Dul ◽  
...  
Keyword(s):  
Energy Transfer ◽  
Drug Discovery ◽  
Small Molecule ◽  
Drug Target ◽  
Target Identification ◽  
High Energy ◽  
Therapeutic Benefit ◽  
Cellular Targets ◽  
Cellular Environment ◽  
Drug Target Identification

The identification of cellular targets that can be exploited for therapeutic benefit, broadly known as target ID, remains a fundamental goal in drug discovery. In recent years, the application of new chemical and biological technologies that accelerate target ID has become commonplace within drug discovery programs, as a complete understanding of how molecules react in a cellular environment can lead to increased binding selectivity, improved safety profiles, and clinical efficacy. Established approaches using photoaffinity labelling (PAL) are often costly and time-consuming due to poor signal-to-noise coupled with extensive probe optimization. Such challenges are exacerbated when dealing with low abundance membrane proteins or multiple protein target engagement, typically rendering target ID unfeasible. Herein, we describe a general platform for photocatalytic small molecule target ID, which hinges upon the generation of high-energy carbene intermediates via visible light-mediated Dexter energy transfer. By decoupling the reactive warhead from the drug, catalytic signal amplification results in multiple labelling events per drug, leading to unprecedented levels of target enrichment. Through the development of cell permeable photocatalyst conjugates, this method has enabled the quantitative target and off target identification of several drugs including (+)-JQ1, paclitaxel, and dasatinib. Moreover, this methodology has led to the target ID of two GPCRs, ADORA2A and GPR40m, a class of drug target seldom successfully uncovered in small molecule PAL campaigns.

scholarly journals A New Big-Data Paradigm for Target Identification and Drug Discovery

2017 ◽  
Author(s):  
Neel S. Madhukar ◽  
Prashant K. Khade ◽  
Linda Huang ◽  
Kaitlyn Gayvert ◽  
Giuseppe Galletti ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecules ◽  
Clinical Application ◽  
Small Molecule ◽  
Target Identification ◽  
Clinical Development ◽  
Data Types ◽  
Public Data ◽  
Drug Target Identification ◽  
Approved Drugs

AbstractDrug target identification is one of the most important aspects of pre-clinical development yet it is also among the most complex, labor-intensive, and costly. This represents a major issue, as lack of proper target identification can be detrimental in determining the clinical application of a bioactive small molecule. To improve target identification, we developed BANDIT, a novel paradigm that integrates multiple data types within a Bayesian machine-learning framework to predict the targets and mechanisms for small molecules with unprecedented accuracy and versatility. Using only public data BANDIT achieved an accuracy of approximately 90% over 2000 different small molecules – substantially better than any other published target identification platform. We applied BANDIT to a library of small molecules with no known targets and generated ∼4,000 novel molecule-target predictions. From this set we identified and experimentally validated a set of novel microtubule inhibitors, including three with activity on cancer cells resistant to clinically used anti-microtubule therapies. We next applied BANDIT to ONC201 – an active anti- cancer small molecule in clinical development – whose target has remained elusive since its discovery in 2009. BANDIT identified dopamine receptor 2 as the unexpected target of ONC201, a prediction that we experimentally validated. Not only does this open the door for clinical trials focused on target-based selection of patient populations, but it also represents a novel way to target GPCRs in cancer. Additionally, BANDIT identified previously undocumented connections between approved drugs with disparate indications, shedding light onto previously unexplained clinical observations and suggesting new uses of marketed drugs. Overall, BANDIT represents an efficient and highly accurate platform that can be used as a resource to accelerate drug discovery and direct the clinical application of small molecule therapeutics with improved precision.

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 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>

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