Calculating the Full Free Energy Profile for Covalent Modification of a Druggable Cysteine in Bruton’s Tyrosine Kinase

Targeted Covalent Inhibitors bind to their targets both covalent and non-covalent modes, providing exceptionally high affinity and selectivity. These inhibitors have been effectively employed as inhibitors of protein kinases, with Taunton and coworkers (Nat. Chem. Biol. 2015, 11 (7), 525–531) reporting a notable example of a TCI with a cyanoacrylamide warhead that forms a covalent thioether linkage to an active-site cysteine (Cys481) of Bruton's tyrosine kinase. The specific mechanism of the binding and the relative importance of the covalent and non-covalent interactions is difficult to determine experimentally, but established simulation methods for calculating the absolute binding affinity of an inhibitor cannot describe the covalent bond forming steps. Here, an integrated approach using alchemical free energy perturbationand QM/MM molecular dynamics methods was employed to model the complete Gibbs energy profile for the covalent inhibition of BTK by a cyanoacrylamide TCI. These calculations provide a rigorous and complete absolute Gibbs energy profile of the covalent modification binding process. The mechanism is ionic, where the target cysteine is deprotonated to form a nucleophilic thiolate, which then undergoes a facile conjugate addition to the electrophilic functional group to form a bond with the non covalently bound ligand. This model predicts that the formation of the covalent linkage makes binding 19.3 kcal/mol more exergonic than the non-covalent binding alone. Nevertheless, non-covalent interactions between the ligand and individual amino acid residues in the binding pocket of the enzyme are also essential for ligand binding,particularly, van der Waals dispersion forces that have a larger contribution to the binding energy than the covalent component in absolute terms. This model also shows that the mechanism of covalent modification of a protein occurs through a complex series of steps and that entropy, conformational flexibility, non-covalent interactions, and the formation of covalent linkage are all significant factors in the ultimatebinding affinity of a covalent drug to its target.

Mechanism of Covalent Binding of Ibrutinib to Bruton’s Tyrosine Kinase revealed by QM/MM Calculations

10.26434/chemrxiv.13149893 ◽

2020 ◽

Author(s):

Angus Voice ◽

Gary Tresadern ◽

Rebecca Twidale ◽

Herman Van Vlijmen ◽

Adrian Mulholland

Keyword(s):

Tyrosine Kinase ◽

Covalent Binding ◽

Bond Formation ◽

Covalent Bond ◽

Covalent Modification ◽

Mechanistic Study ◽

Bruton’S Tyrosine Kinase ◽

Bruton's Tyrosine Kinase ◽

Covalent Inhibitors ◽

Covalent Bond Formation

Ibrutinib is the first covalent inhibitor of Bruton’s tyrosine kinase (BTK) to be used in the treatment of B-cell cancers. Understanding the mechanism of covalent inhibition is crucial for the design of safer and more selective covalent inhibitors that target BTK. There are questions surrounding the precise mechanism of covalent bond formation in BTK as there is no appropriate active site residue that can act as a base to deprotonate the cysteine thiol prior to covalent bond formation. To address this, we have investigated several mechanistic pathways of covalent modification of C481 in BTK by ibrutinib using QM/MM reaction simulations. The lowest energy pathway we identified involves a direct proton transfer from C481 to the acrylamide warhead in ibrutinib, followed by covalent bond formation to form an enol intermediate. There is a subsequent rate-limiting keto-enol tautomerisation step (DG‡=10.5 kcal mol-1) to reach the inactivated BTK/ibrutinib complex. Our results represent the first mechanistic study of BTK inactivation by ibrutinib to consider multiple mechanistic pathways. These findings should aid in the design of covalent drugs that target BTK and related proteins.

Oligomerization in vitro of the extracellular domain of the insulin receptor by non-covalent interactions is accompanied by increased binding affinity, decreased binding capacity and curvilinear Scatchard plots

Peptides ◽

10.1007/978-94-010-9066-7_36 ◽

1993 ◽

pp. 125-129

Author(s):

Jan Markussen ◽

Lauge Schäffer ◽

Mogens Christensen

Keyword(s):

Insulin Receptor ◽

Binding Affinity ◽

Binding Capacity ◽

Extracellular Domain ◽

Covalent Interactions ◽

Scatchard Plots

Functional group interaction profiles: a general treatment of solvent effects on non-covalent interactions

Chemical Science ◽

10.1039/d0sc01288b ◽

2020 ◽

Vol 11 (17) ◽

pp. 4456-4466 ◽

Cited By ~ 4

Author(s):

Mark D. Driver ◽

Mark J. Williamson ◽

Joanne L. Cook ◽

Christopher A. Hunter

Keyword(s):

Free Energy ◽

Solvent Effects ◽

Functional Group ◽

Group Interaction ◽

General Treatment ◽

Effect Of Solvent ◽

Covalent Interactions ◽

Quantitative Tool

Functional group interaction profiles are a quantitative tool for predicting the effect of solvent on the free energy changes associated with non-covalent interactions.

Philosophical Transactions of the Royal Society of London Series A Physical and Engineering Sciences ◽

Destabilization is as important as binding

10.1098/rsta.1993.0112 ◽

1993 ◽

Vol 345 (1674) ◽

pp. 3-10 ◽

Cited By ~ 3

Keyword(s):

Binding Energy ◽

Gibbs Energy ◽

Active Site ◽

Electrostatic Interactions ◽

Large Fraction ◽

Geometric Distortion ◽

Substrate Complex ◽

Enzyme Substrate ◽

Enzymes make use of non-covalent interactions with their substrates to bring about a large fraction of their catalytic activity. These interactions must destabilize, or increase the Gibbs energy, of the substrate in the active site in order that the transition state can be reached easily. This destabilization may be brought about by utilization of the intrinsic binding energy between the active site and the bound substrate by desolvation of charged groups, geometric distortion, electrostatic interactions and, especially, loss of entropy in the enzyme-substrate complex. These mechanisms are described by interaction energies and require utilization of the intrinsic binding energy that is realized from non-covalent interactions between the enzyme and substrate. Receptors and coupled vectorial processes, such as muscle contraction and active transport, utilize binding energy similarly to avoid large peaks and valleys along the Gibbs energy profile of the reaction under physiological conditions.

Mechanism of Covalent Binding of Ibrutinib to Bruton’s Tyrosine Kinase revealed by QM/MM Calculations

10.26434/chemrxiv.13149893.v1 ◽

2020 ◽

Author(s):

Angus Voice ◽

Gary Tresadern ◽

Rebecca Twidale ◽

Herman Van Vlijmen ◽

Adrian Mulholland

Keyword(s):

Tyrosine Kinase ◽

Covalent Binding ◽

Bond Formation ◽

Covalent Bond ◽

Covalent Modification ◽

Mechanistic Study ◽

Bruton’S Tyrosine Kinase ◽

Bruton's Tyrosine Kinase ◽

Covalent Inhibitors ◽

Covalent Bond Formation

Ibrutinib is the first covalent inhibitor of Bruton’s tyrosine kinase (BTK) to be used in the treatment of B-cell cancers. Understanding the mechanism of covalent inhibition is crucial for the design of safer and more selective covalent inhibitors that target BTK. There are questions surrounding the precise mechanism of covalent bond formation in BTK as there is no appropriate active site residue that can act as a base to deprotonate the cysteine thiol prior to covalent bond formation. To address this, we have investigated several mechanistic pathways of covalent modification of C481 in BTK by ibrutinib using QM/MM reaction simulations. The lowest energy pathway we identified involves a direct proton transfer from C481 to the acrylamide warhead in ibrutinib, followed by covalent bond formation to form an enol intermediate. There is a subsequent rate-limiting keto-enol tautomerisation step (DG‡=10.5 kcal mol-1) to reach the inactivated BTK/ibrutinib complex. Our results represent the first mechanistic study of BTK inactivation by ibrutinib to consider multiple mechanistic pathways. These findings should aid in the design of covalent drugs that target BTK and related proteins.

Importance of protein intrinsic conformational dynamics and transient nature of non-covalent interactions in ligand binding affinity

International Journal of Biological Macromolecules ◽

10.1016/j.ijbiomac.2021.10.045 ◽

2021 ◽

Author(s):

Mario Kekez ◽

Vladimir Zanki ◽

Ivan Antičević ◽

Jasmina Rokov-Plavec ◽

Aleksandra Maršavelski

Keyword(s):

Ligand Binding ◽

Binding Affinity ◽

Conformational Dynamics ◽

Transient Nature ◽

MONN: a Multi-Objective Neural Network for Predicting Pairwise Non-Covalent Interactions and Binding Affinities between Compounds and Proteins

10.1101/2019.12.30.891515 ◽

2019 ◽

Author(s):

Shuya Li ◽

Fangping Wan ◽

Hantao Shu ◽

Tao Jiang ◽

Dan Zhao ◽

...

Keyword(s):

Neural Network ◽

Binding Affinity ◽

Protein Interactions ◽

Prediction Models ◽

Benchmark Dataset ◽

Binding Affinities ◽

Multi Objective ◽

Interaction Sites ◽

AbstractComputational approaches for inferring the mechanisms of compound-protein interactions (CPIs) can greatly facilitate drug development. Recently, although a number of deep learning based methods have been proposed to predict binding affinities and attempt to capture local interaction sites in compounds and proteins through neural attentions, they still lack a systematic evaluation on the interpretability of the identified local features. In addition, in these previous approaches, the exact matchings between interaction sites from compounds and proteins, which are generally important for understanding drug mechanisms of action, still remain unknown. Here, we compiled the first benchmark dataset containing the inter-molecular non-covalent interactions for more than 10,000 compound-protein pairs, and used it to systematically evaluate the interpretability of neural attentions in existing prediction models. We developed a multi-objective neural network, called MONN, to predict both non-covalent interactions and binding affinity for a given compound-protein pair. MONN uses convolution neural networks on molecular graphs of compounds and primary sequences of proteins to effectively capture the intrinsic features from both inputs, and also takes advantage of the predicted non-covalent interactions to further boost the accuracy of binding affinity prediction. Comprehensive evaluation demonstrated that while the previous neural attention based approaches fail to exhibit satisfactory interpretability results without extra supervision, MONN can successfully predict non-covalent interactions on our benchmark dataset as well as another independent dataset derived from the Protein Data Bank (PDB). Moreover, MONN can outperform other state-of-the-art methods in predicting compound-protein binding affinities. In addition, the pairwise interactions predicted by MONN displayed compatible and accordant patterns in chemical properties, which provided another evidence to support the strong predictive power of MONN. These results suggested that MONN can offer a powerful tool in predicting binding affinities of compound-protein pairs and also provide useful insights into understanding the molecular mechanisms of compound-protein interactions, which thus can greatly advance the drug discovery process. The source code of the MONN model and the dataset creation process can be downloaded from https://github.com/lishuya17/MONN.