Diversity of Long-Lived Intermediates along the Binding Pathway of Imatinib to Abl Kinase Revealed by MD Simulations

Abstract

Imatinib, a drug used for the treatment of chronic myeloid leukemia and other cancers, works by blocking the catalytic site of pathological constitutively active Abl kinase. While the binding pose is known from X-ray crystallography, the different steps leading to the formation of the complex are not well understood. The results from extensive molecular dynamics simulations show that imatinib can primarily exit the known crystallographic binding pose through the cleft of the binding site or by sliding under the αC helix. Once displaced from the crystallographic binding pose, imatinib becomes trapped in intermediate states. These intermediates are characterized by a high diversity of ligand orientations and conformations, and relaxation timescales within this region may exceed 3−4 ms. Analysis indicates that the metastable intermediate states should be spectroscopically indistinguishable from the crystallographic binding pose, in agreement with tryptophan stopped-flow fluorescence experiments.

Dasatinib–SIK2 Binding Elucidated by Homology Modeling, Molecular Docking, and Dynamics Simulations

“…Kinase activation loops have two conformers that differ in terms of their ability to recognize various inhibitors. Imatinib could bind only the inactive ABL1 conformation, whereas dasatinib could also bind the active ABL1 conformation. ,− ,, Therefore, dasatinib targets imatinib-resistant ABL and increases the binding affinity. HM and MD revealed the closed ( SIK2-I ) and open ( SIK2-II , SIK2-III , and SIK2-IV ) conformations of T-loop.…”

Tyrosine kinases: complex molecular systems challenging computational methodologies

“…At the present time, Markov state models (MSM) perhaps represent the richest and most powerful computational framework to characterize the dynamics of complex biomolecular systems [12][13][14]. Yet, in spite of a huge amount of aggregate MD data, our own efforts with kinase proteins were only partial successes [94,[115][116][117]. One of the fundamental difficulties and an important requirement for the construction of a reliable and accurate MSM is the definition of "states", dynamically meaningful regions of conformational space that must correctly map out the behavior of the system.…”

“…J. B (2021) 94 :203 to characterize the binding of a specific ligand such as imatinib to Abl kinase [117].…”

“…The authors interpreted this as evidence for an induced-fit conformational change of the protein kinase that is directly responsible for the binding specificity. However, an extensive MSM study indicates that there exists a large number of metastable states corresponding to non-specific association of imatinib with Abl kinase along two main pathways leading imatinib to the binding pocket [117]. The MSM analysis based on more than 500 μs of aggregate MD data strongly supports the notion that the interconversion from the metastable binding modes of the ligand to the fully bound state is extremely slow, suggesting that the set of metastable binding modes of imatinib could reasonably give rise to the long-lived intermediate state revealed by the tryptophan fluorescence data [75].…”

“…VAMPnets [26] combine the optimization of hyperparameters with the featurization, dimensionality reduction, and clustering of the system into a single machine learning pipeline, thereby eliminating many sources of error. We have applied this methodology to the imatinib-Abl system [117], and demonstrated its ability to identify metastable states of the protein, although it does not solve undersampling problems. Ung et al used machine learning to classify the conformation of all kinase structures in the PDB and a similar approach could be applied to simulation data [148].…”