Reversible Covalent Inhibition of a Protein Target

2012 ◽  
Vol 51 (35) ◽  
pp. 8699-8700 ◽  
Author(s):  
Chang-Uk Lee ◽  
Tom N. Grossmann
Keyword(s):  
Protein Target ◽  
Covalent Inhibition ◽  
Reversible Covalent

ChemInform Abstract: Reversible Covalent Inhibition of a Protein Target

ChemInform ◽  
2012 ◽  
Vol 43 (49) ◽  
pp. no-no
Author(s):  
Chang-Uk Lee ◽  
Tom N. Grossmann
Keyword(s):  
Protein Target ◽  
Covalent Inhibition ◽  
Reversible Covalent

scholarly journals Reversible Covalent Inhibition of eEF-2K by Carbonitriles

ChemBioChem ◽  
2014 ◽  
Vol 15 (16) ◽  
pp. 2435-2442 ◽  
Author(s):  
Ashwini K. Devkota ◽  
Ramakrishna Edupuganti ◽  
Chunli Yan ◽  
Yue Shi ◽  
Jiney Jose ◽  
...  
Keyword(s):  
Covalent Inhibition ◽  
Reversible Covalent

scholarly journals The Meisenheimer Complex as a Paradigm in Drug Discovery: Reversible Covalent Inhibition through C67 of the ATP Binding Site of PLK1

2018 ◽  
Vol 25 (9) ◽  
pp. 1107-1116.e4 ◽  
Author(s):  
Russell J. Pearson ◽  
David G. Blake ◽  
Mokdad Mezna ◽  
Peter M. Fischer ◽  
Nicholas J. Westwood ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Binding Site ◽  
Atp Binding ◽  
Atp Binding Site ◽  
Covalent Inhibition ◽  
Reversible Covalent

ONIOM calculations on serotonin degradation by monoamine oxidase B: insight into the oxidation mechanism and covalent reversible inhibition

2016 ◽  
Vol 14 (39) ◽  
pp. 9239-9252 ◽  
Author(s):  
Kubra Cakir ◽  
Safiye Sag Erdem ◽  
Vildan Enisoglu Atalay
Keyword(s):  
Monoamine Oxidase ◽  
Oxidation Mechanism ◽  
Monoamine Oxidase B ◽  
Reversible Inhibition ◽  
Covalent Inhibition ◽  
Hybrid Mechanism ◽  
Reversible Covalent ◽  
Insight Into ◽  
Oniom Calculations

We propose a hybrid mechanism for MAO where the formation of FAD-N5-ylide causes a reversible covalent inhibition, which can be modulated for designing superior therapeutics.

Reversible covalent inhibition of a phenol sulfotransferase by coenzyme A

2007 ◽  
Vol 457 (2) ◽  
pp. 197-204 ◽  
Author(s):  
Sundari Chodavarapu ◽  
Heather Hertema ◽  
Tien Huynh ◽  
Jessica Odette ◽  
Rachel Miller ◽  
...  
Keyword(s):  
Coenzyme A ◽  
Covalent Inhibition ◽  
Phenol Sulfotransferase ◽  
Reversible Covalent

scholarly journals Efficient targeted degradation via reversible and irreversible covalent PROTACs

2020 ◽  
Author(s):  
Ronen Gabizon ◽  
Amit Shraga ◽  
Paul Gehrtz ◽  
Ella Livnah ◽  
Neta Gurwicz ◽  
...  
Keyword(s):  
Cell Activation ◽  
Covalent Bond ◽  
Model System ◽  
Proof Of Concept ◽  
B Cell Activation ◽  
Irreversible Binding ◽  
Relevant Model ◽  
Single Digit ◽  
Protein Target ◽  
Reversible Covalent

<p>PROteolysis Targeting Chimeras (PROTACs) represent an exciting inhibitory modality with many advantages, including sub-stoichiometric degradation of targets. Their scope, though, is still limited to-date by the requirement for a sufficiently potent target binder. A solution that proved useful in tackling challenging targets is the use of electrophiles to allow irreversible binding to the target. However, such binding will negate the catalytic nature of PROTACs. Reversible covalent PROTACs offer the best of both worlds. They possess the potency and selectivity associated with the formation of the covalent bond, while being able to dissociate and regenerate once the protein target is degraded. Using Bruton’s tyrosine kinase (BTK) as a clinically relevant model system, we present a proof-of concept for the first in class cyanoacrylamide reversible covalent PROTACs. We show efficient degradation with reversible covalent PROTACs, as well as their non-covalent and irreversible counterparts. The latter are amongst the most efficient PROTACs reported for BTK. They display single digit nM DC50, full degradation within 2-4 hours, proteome wide selectivity and show ~10-fold better inhibition of B cell activation than Ibrutinib. These examples refute the notion that covalent binders are not suitable as the basis for PROTACs, and may pave the way for the design of covalent PROTACs for a wide variety of challenging targets.</p>

A Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Ligand-Target Predictions

2020 ◽  
Author(s):  
Lewis Mervin ◽  
Avid M. Afzal ◽  
Ola Engkvist ◽  
Andreas Bender
Keyword(s):  
Target Prediction ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Protein Target ◽  
Bioactivity Prediction ◽  
Vector Machines ◽  
Scaling Methods ◽  
Data Points ◽  
Compound Target

In the context of bioactivity prediction, the question of how to calibrate a score produced by a machine learning method into reliable probability of binding to a protein target is not yet satisfactorily addressed. In this study, we compared the performance of three such methods, namely Platt Scaling, Isotonic Regression and Venn-ABERS in calibrating prediction scores for ligand-target prediction comprising the Naïve Bayes, Support Vector Machines and Random Forest algorithms with bioactivity data available at AstraZeneca (40 million data points (compound-target pairs) across 2112 targets). Performance was assessed using Stratified Shuffle Split (SSS) and Leave 20% of Scaffolds Out (L20SO) validation.

Sign in / Sign up

Export Citation Format

Share Document