Qualitative Prediction of Ligand Dissociation Kinetics from Focal Adhesion Kinase Using Steered Molecular Dynamics

Spiriti, Justin; Wong, Chung F.

doi:10.3390/life11020074

Cited by 18 publications

(32 citation statements)

References 71 publications

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“…The one‐dimensional potential of mean force shown in Figure 3C contains a large well at 2 Å, corresponding to the bound state, but does not show a distinct transition state, unlike the one‐dimensional PMFs calculated for the exit of various ligands from FAK 50 . This is likely because this PMF results from integrating out the other two degrees of freedom and combining the two pathways, which have transition states with the ligand at different distances from its original position.…”

Section: Resultsmentioning

confidence: 88%

“…Furthermore, free energy surfaces for dissociation were derived for three of the ligands using umbrella sampling, with the center of mass of the ligand relative to the protein as a reaction coordinate. Although the free energy surfaces further supported the two‐step mechanism of dissociation, the estimated barrier heights did not rank‐order the experimental dissociation rates properly, and absolute dissociation rates estimated from Eyring's equation were approximately six orders of magnitude too high 50 …”

Section: Introductionmentioning

confidence: 83%

“…In the present work, initial configurations for longer MD simulations were extracted from these trajectories using a protocol similar to that used for our previous simulations of FAK in complex with ligands 50 . The first frame from the first trajectory was identified as an initial configuration, and subsequently additional frames were identified if the ligand center of mass was at least 1 Å from any previous center of mass and less than 15 Å from the initial configuration.…”

Section: Methodsmentioning

confidence: 99%

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Simulation of ligand dissociation kinetics from the protein kinase PYK2

2022

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Early-stage drug discovery projects often focus on equilibrium binding affinity to the target alongside selectivity and other pharmaceutical properties. The kinetics of drug binding are ignored but can have significant influence on drug efficacy. Therefore, increasing attention has been paid on evaluating drug-binding kinetics early in a drug discovery process. Simulating drug-binding kinetics at the atomic level is challenging for the long time scale involved. Here, we used the transition-based reweighting analysis method (TRAM) with the Markov state model to study the dissociation of a ligand from the protein kinase PYK2. TRAM combines biased and unbiased simulations to reduce computational costs. This work used the umbrella sampling technique for the biased simulations. Although using the potential of mean force from umbrella sampling simulations with the transition-state theory over-estimated the dissociation rate by three orders of magnitude, TRAM gave a dissociation rate within an order of magnitude of the experimental value.

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Section: Resultsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 83%

Section: Methodsmentioning

confidence: 99%

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Simulation of ligand dissociation kinetics from the protein kinase PYK2

2022

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“…The maximum value of GQ oxidation peak current was observed following incubation of 10 days and plateaued after 17 days. The combination of AFM-DFS (Strunz et al, 1999), theory of stochastic system, and steered molecular dynamics (Zhang et al, 2010;Wong 2018;Spiriti and Wong 2021) have created a new research area that explores mechanical aspects of ligand-receptor interactions (unbinding/binding) and muscle proteins as they stretch. This hybrid method has been utilized in generating information about the energy landscape that regulates mechanical functions and the multiple components of proteins that carry forces.…”

Section: Characterization Of Biomolecules and Biosensors Using Afmmentioning

confidence: 99%

Biosensing, Characterization of Biosensors, and Improved Drug Delivery Approaches Using Atomic Force Microscopy: A Review

Sarkar

2022

Front. Nanotechnol.

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Since its invention, atomic force microscopy (AFM) has come forth as a powerful member of the “scanning probe microscopy” (SPM) family and an unparallel platform for high-resolution imaging and characterization for inorganic and organic samples, especially biomolecules, biosensors, proteins, DNA, and live cells. AFM characterizes any sample by measuring interaction force between the AFM cantilever tip (the probe) and the sample surface, and it is advantageous over other SPM and electron micron microscopy techniques as it can visualize and characterize samples in liquid, ambient air, and vacuum. Therefore, it permits visualization of three-dimensional surface profiles of biological specimens in the near-physiological environment without sacrificing their native structures and functions and without using laborious sample preparation protocols such as freeze-drying, staining, metal coating, staining, or labeling. Biosensors are devices comprising a biological or biologically extracted material (assimilated in a physicochemical transducer) that are utilized to yield electronic signal proportional to the specific analyte concentration. These devices utilize particular biochemical reactions moderated by isolated tissues, enzymes, organelles, and immune system for detecting chemical compounds via thermal, optical, or electrical signals. Other than performing high-resolution imaging and nanomechanical characterization (e.g., determining Young’s modulus, adhesion, and deformation) of biosensors, AFM cantilever (with a ligand functionalized tip) can be transformed into a biosensor (microcantilever-based biosensors) to probe interactions with a particular receptors of choice on live cells at a single-molecule level (using AFM-based single-molecule force spectroscopy techniques) and determine interaction forces and binding kinetics of ligand receptor interactions. Targeted drug delivery systems or vehicles composed of nanoparticles are crucial in novel therapeutics. These systems leverage the idea of targeted delivery of the drug to the desired locations to reduce side effects. AFM is becoming an extremely useful tool in figuring out the topographical and nanomechanical properties of these nanoparticles and other drug delivery carriers. AFM also helps determine binding probabilities and interaction forces of these drug delivery carriers with the targeted receptors and choose the better agent for drug delivery vehicle by introducing competitive binding. In this review, we summarize contributions made by us and other researchers so far that showcase AFM as biosensors, to characterize other sensors, to improve drug delivery approaches, and to discuss future possibilities.

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“… 1 These techniques include, among others, scaled MD ( Sinko et al, 2013 ; Bernetti et al, 2018 ), steered MD ( Paci and Karplus, 2000 ; Potterton et al, 2019 ; Spiriti and Wong, 2021 ), targeted MD ( Schlitter et al, 1994 ; Wolf et al, 2019 ), GAMBES ( Debnath and Parrinello, 2020 ) path-reweighting methods ( Chodera et al, 2011 ; Donati et al, 2017 ; Kieninger and Keller, 2021 ) metadynamics of paths ( Mandelli et al, 2020 ) and many transition path sampling-derived methods ( Pratt, 1986 ; Dellago et al, 1998 ; Van Erp et al, 2003 ). A brief review of some of these methods (namely scaled MD, targeted MD and GAMBES) is given in the supplementary material.…”

mentioning

confidence: 99%

Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective

Ahmad

Rizzi

Capelli

et al. 2022

Front. Mol. Biosci.

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The dissociation rate (koff) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction ofkoff. Next, we discuss the impact of the potential energy function models on the accuracy of calculatedkoffvalues. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.

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Qualitative Prediction of Ligand Dissociation Kinetics from Focal Adhesion Kinase Using Steered Molecular Dynamics

Cited by 18 publications

References 71 publications

Simulation of ligand dissociation kinetics from the protein kinase PYK2

Simulation of ligand dissociation kinetics from the protein kinase PYK2

Biosensing, Characterization of Biosensors, and Improved Drug Delivery Approaches Using Atomic Force Microscopy: A Review

Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective

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