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

Quantitative Prediction of Dissociation Rates of PYK2 Ligands Using Umbrella Sampling and Milestoning

We used umbrella sampling and the milestoning simulation method to study the dissociation of multiple ligands from protein kinase PYK2. The activation barriers obtained from the potential of mean force of the umbrella sampling simulations correlated well with the experimental dissociation rates. Using the zero-temperature string method, we obtained optimized paths along the free-energy surfaces for milestoning simulations of three ligands with a similar structure. The milestoning simulations gave an absolute dissociation rate within 2 orders of magnitude of the experimental value for two ligands but at least 3 orders of magnitude too high for the third. Despite the similarity in their structures, the ligands took different pathways to exit from the binding site of PYK2, making contact with different sets of residues. In addition, the protein experienced different conformational changes for dissociation of the three ligands.

High-throughput virtual screening of potential inhibitors of GPR52 using docking and biased sampling method for Huntington's disease therapy

Huntington's disease (HD) is a rare and progressive neurodegenerative disorder caused by polyglutamine (poly-Q) mutations of the huntingtin (HTT) gene resulting in chorea, cognitive, and psychiatric dysfunctions. Being a monogenic condition, reducing the levels of the mutated huntingtin protein (mHTT) holds promise as an effective therapeutic approach. GPR52, an orphan G-protein coupled receptor (GPCR), enriched in the striatum, is a novel target for slowing down the progression of HD by lowering the mHTT levels. Therefore, the study focuses on identifying potent small-molecule inhibitors for GPR52 using a combination of robust high-throughput virtual screening (HTVS) and pharmacokinetics profiling followed by fast pulling of ligand (FPL) and umbrella sampling (US) simulations. Initially, screening a library of 2,36,545 compounds was done against the binding pocket of GPR52. Based on binding affinity, stereochemical and non-bonded interactions, and pharmacokinetic profiling, 50 compounds were shortlisted. Selected hit compounds 1, 2, and 3 were subjected to FPL simulations with applied external bias potential to investigate their unique dissociation pathways and intermolecular interactions over time. Subsequently, the US simulations were performed on the selected hit compounds to estimate their binding free energy (ΔG). The analysis of the trajectories obtained from simulations revealed that the residues TYR34, TYR185, GLY187, ASP188, ILE189, SER299, PHE300, and THR303 within the active site of GPR52 were significant for efficient ligand binding through the formation of various hydrogen bond interactions and hydrophobic contacts. Out of the three hit compounds, compound 3 had the lowest ΔG of - 20.82 ± 0.44 kcal/mol. The study identified compounds 1, 2, and 3 as potential molecules that can be developed as GPR52 inhibitors holding promise for lowering mHTT levels.

Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness

Computational techniques applied to drug discovery have gained considerable popularity for their ability to filter potentially active drugs from inactive ones, reducing the time scale and costs of preclinical investigations. The main focus of these studies has historically been the search for compounds endowed with high affinity for a specific molecular target to ensure the formation of stable and long-lasting complexes. Recent evidence has also correlated the in vivo drug efficacy with its binding kinetics, thus opening new fascinating scenarios for ligand/protein binding kinetic simulations in drug discovery. The present article examines the state of the art in the field, providing a brief summary of the most popular and advanced ligand/protein binding kinetics techniques and evaluating their current limitations and the potential solutions to reach more accurate kinetic models. Particular emphasis is put on the need for a paradigm change in the present methodologies toward ligand and protein parametrization, the force field problem, characterization of the transition states, the sampling issue, and algorithms' performance, user-friendliness, and data openness.

15 Years of molecular simulation of drug-binding kinetics

INTRODUCTION

Drug-binding kinetics has been increasingly recognized as an important factor to be considered in drug discovery. Long residence time could prolong the action of some drugs while produce toxicity on others. Early evaluation of the binding kinetics of drug candidates could reduce attrition rate late in the drug discovery process. Computational prediction of drug-binding kinetics is useful as compounds can be evaluated even before they are made. However, simulation of drug-binding kinetics is a challenging problem because of the long-time scale involved. Nevertheless, significant progress has been made.

AREAS COVERED

This review illustrates the rapid evolution of qualitative to quantitative molecular dynamics-based methods that have been developed over the last 15 years.

EXPERT OPINION

The development of new methods based on molecular dynamics simulations now enables computation of absolute association/dissociation rate constants. Cheaper methods capable of identifying candidates with fast or slow binding kinetics, or rank-ordering rate constants are also available. Together, these methods have generated useful insights into the molecular mechanisms of drug-binding kinetics, and the design of drug candidates with therapeutically favorable kinetics. Although predicting absolute rate constants is still expensive and challenging, rapid improvement is expected in the coming years with the continuing refinement of current technologies, development of new methodologies, and the utilization of machine learning.

Structure-Kinetic Relationship for Drug Design Revealed by a PLS Model with Retrosynthesis-Based Pre-Trained Molecular Representation and Molecular Dynamics Simulation

Drug design based on kinetic properties is growing in application. Here, we applied retrosynthesis-based pre-trained molecular representation (RPM) in machine learning (ML) to train 501 inhibitors of 55 proteins and successfully predicted the dissociation rate constant (k_off) values of 38 inhibitors from an independent dataset for the N-terminal domain of heat shock protein 90α (N-HSP90). Our RPM molecular representation outperforms other pre-trained molecular representations such as GEM, MPG, and general molecular descriptors from RDKit. Furthermore, we optimized the accelerated molecular dynamics to calculate the relative retention time (RT) for the 128 inhibitors of N-HSP90 and obtained the protein-ligand interaction fingerprints (IFPs) on their dissociation pathways and their influencing weights on the k_off value. We observed a high correlation among the simulated, predicted, and experimental -log(k_off) values. Combining ML, molecular dynamics (MD) simulation, and IFPs derived from accelerated MD helps design a drug for specific kinetic properties and selectivity profiles to the target of interest. To further validate our k_off predictive ML model, we tested our model on two new N-HSP90 inhibitors, which have experimental k_off values and are not in our ML training dataset. The predicted k_off values are consistent with experimental data, and the mechanism of their kinetic properties can be explained by IFPs, which shed light on the nature of their selectivity against N-HSP90 protein. We believe that the ML model described here is transferable to predict k_off of other proteins and will enhance the kinetics-based drug design endeavor.

Advances in computational methods for ligand binding kinetics

Binding kinetic parameters can be correlated with drug efficacy, which in recent years led to the development of various computational methods for predicting binding kinetic rates and gaining insight into protein-drug binding paths and mechanisms. In this review, we introduce and compare computational methods recently developed and applied to two systems, trypsin-benzamidine and kinase-inhibitor complexes. Methods involving enhanced sampling in molecular dynamics simulations or machine learning can be used not only to predict kinetic rates, but also to reveal factors modulating the duration of residence times, selectivity, and drug resistance to mutations. Methods which require less computational time to make predictions are highlighted, and suggestions to reduce the error of computed kinetic rates are presented.

Estimation of Drug-Target Residence Time by Targeted Molecular Dynamics Simulations

Drug-target residence time has emerged as a key selection factor in drug discovery since the binding duration of a drug molecule to its protein target can significantly impact its in vivo efficacy. The challenge in studying the residence time, in early drug discovery stages, lies in how to cost-effectively determine the residence time for the systematic assessment of compounds. Currently, there is still a lack of computational protocols to quickly estimate such a measure, particularly for large and flexible protein targets and drugs. Here, we report an efficient computational protocol, based on targeted molecular dynamics, to rank drug candidates by their residence time and to obtain insights into ligand-target dissociation mechanisms. The method was assessed on a dataset of 10 arylpyrazole inhibitors of CDK8, a large, flexible, and clinically important target, for which the experimental residence time of the inhibitors ranges from minutes to hours. The compounds were correctly ranked according to their estimated residence time scores compared to their experimental values. The analysis of protein-ligand interactions along the dissociation trajectories highlighted the favorable contribution of hydrophobic contacts to residence time and revealed key residues that strongly affect compound residence time.

Simulation of ligand dissociation kinetics from the protein kinase PYK2

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.

Bell-Evans model and steered molecular dynamics in uncovering the dissociation kinetics of ligands targeting G-protein-coupled receptors

Recently, academic and industrial scientific communities involved in kinetics-based drug development have become immensely interested in predicting the drug target residence time. Screening drug candidates in terms of their computationally predicted residence times, which is a measure of drug efficacy in vivo, and simultaneously assessing computational binding affinities are becoming inevitable. Non-equilibrium molecular simulation approaches are proven to be useful in this purpose. Here, we have implemented an optimized approach of combining the data derived from steered molecular dynamics simulations and the Bell-Evans model to predict the absolute residence times of the antagonist ZMA241385 and agonist NECA that target the A2A adenosine receptor of the G-protein-coupled receptor (GPCR) protein family. We have predicted the absolute ligand residence times on the timescale of seconds. However, our predictions were many folds shorter than those determined experimentally. Additionally, we calculated the thermodynamics of ligand binding in terms of ligand binding energies and the per-residue contribution of the receptor. Subsequently, binding pocket hotspot residues that would be important for further computational mutagenesis studies were identified. In the experiment, similar sets of residues were found to be in significant contact with both ligands under study. Our results build a strong foundation for further improvement of our approach by rationalizing the kinetics of ligand unbinding with the thermodynamics of ligand binding.

Molecular Docking, Molecular Dynamics Simulations, and Free Energy Calculation Insights into the Binding Mechanism between VS-4718 and Focal Adhesion Kinase

Focal adhesion kinase (FAK) is a 125 kDa nonreceptor tyrosine kinase that plays an important role in many carcinomas. Thus, the targeting of FAK by small molecules is considered to be promising for cancer therapy. Some FAK inhibitors have been reported as potential anticancer drugs and have entered into clinical development; for example, VS-4718 is currently undergoing clinical trials. However, the lack of crystal structural data for the binding of VS-4718 with FAK has hindered the optimization of this anticancer agent. In this work, the VS-4718/FAK interaction model was obtained by molecular docking and molecular dynamics simulations. The binding free energies of VS-4718/FAK were also calculated using the molecular mechanics generalized Born surface area method. It was found that the aminopyrimidine group formed hydrogen bonds with the C502 residue of the hinge loop, while the D564 residue of the T-loop interacted with the amide group. In addition, I428, A452, V484, M499, G505, and L553 residues formed hydrophobic interactions with VS-4718. The obtained results therefore provide an improved understanding of the interaction between human FAK and VS-4718. Based on the obtained binding mechanism, 47 novel compounds were designed to target the adenosine 5'-triphosphate-binding pocket of human FAK, and ensemble docking was performed to assess the effects of these modifications on the inhibitor binding affinity. This work is also expected to provide additional insights into potential future target design strategies based on VS-4718.

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

The dissociation rate (k off) 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 of k off. Next, we discuss the impact of the potential energy function models on the accuracy of calculated k off values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.

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

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.