Ligand Desolvation Steers On-Rate and Impacts Drug Residence Time of Heat Shock Protein 90 (Hsp90) Inhibitors

Understanding the impact of binding free energy and kinetics calculations in modern drug discovery

INTRODUCTION

For rational drug design, it is crucial to understand the receptor-drug binding processes and mechanisms. A new era for the use of computer simulations in predicting drug-receptor interactions at an atomic level has begun with remarkable advances in supercomputing and methodological breakthroughs.

AREAS COVERED

End-point free energy calculation methods such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) or Molecular-Mechanics/Generalized Born Surface Area (MM/GBSA), free energy perturbation (FEP), and thermodynamic integration (TI) are commonly used for binding free energy calculations in drug discovery. In addition, kinetic dissociation and association rate constants (k off and k on ) play critical roles in the function of drugs. Nowadays, Molecular Dynamics (MD) and enhanced sampling simulations are increasingly being used in drug discovery. Here, the authors provide a review of the computational techniques used in drug binding free energy and kinetics calculations.

EXPERT OPINION

The applications of computational methods in drug discovery and design are expanding, thanks to improved predictions of the binding free energy and kinetic rates of drug molecules. Recent microsecond-timescale enhanced sampling simulations have made it possible to accurately capture repetitive ligand binding and dissociation, facilitating more efficient and accurate calculations of ligand binding free energy and kinetics.

Accurate Characterization of Binding Kinetics and Allosteric Mechanisms for the HSP90 Chaperone Inhibitors Using AI-Augmented Integrative Biophysical Studies

The binding kinetics of drugs to their targets are gradually being recognized as a crucial indicator of the efficacy of drugs in vivo, leading to the development of various computational methods for predicting the binding kinetics in recent years. However, compared with the prediction of binding affinity, the underlying structure and dynamic determinants of binding kinetics are more complicated. Efficient and accurate methods for predicting binding kinetics are still lacking. In this study, quantitative structure-kinetics relationship (QSKR) models were developed using 132 inhibitors targeting the ATP binding domain of heat shock protein 90α (HSP90α) to predict the dissociation rate constant (koff), enabling a direct assessment of the drug-target residence time. These models demonstrated good predictive performance, where hydrophobic and hydrogen bond interactions significantly influence the koff prediction. In subsequent applications, our models were used to assist in the discovery of new inhibitors for the N-terminal domain of HSP90α (N-HSP90α), demonstrating predictive capabilities on an experimental validation set with a new scaffold. In X-ray crystallography experiments, the loop-middle conformation of apo N-HSP90α was observed for the first time (previously, the loop-middle conformation had only been observed in holo-N-HSP90α structures). Interestingly, we observed different conformations of apo N-HSP90α simultaneously in an asymmetric unit, which was also observed in a holo-N-HSP90α structure, suggesting an equilibrium of conformations between different states in solution, which could be one of the determinants affecting the binding kinetics of the ligand. Different ligands can undergo conformational selection or alter the equilibrium of conformations, inducing conformational rearrangements and resulting in different effects on binding kinetics. We then used molecular dynamics simulations to describe conformational changes of apo N-HSP90α in different conformational states. In summary, the study of the binding kinetics and molecular mechanisms of N-HSP90α provides valuable information for the development of more targeted therapeutic approaches.

QMrebind: incorporating quantum mechanical force field reparameterization at the ligand binding site for improved drug-target kinetics through milestoning simulations

Understanding the interaction of ligands with biomolecules is an integral component of drug discovery and development. Challenges for computing thermodynamic and kinetic quantities for pharmaceutically relevant receptor-ligand complexes include the size and flexibility of the ligands, large-scale conformational rearrangements of the receptor, accurate force field parameters, simulation efficiency, and sufficient sampling associated with rare events. Our recently developed multiscale milestoning simulation approach, SEEKR2 (Simulation Enabled Estimation of Kinetic Rates v.2), has demonstrated success in predicting unbinding (koff) kinetics by employing molecular dynamics (MD) simulations in regions closer to the binding site. The MD region is further subdivided into smaller Voronoi tessellations to improve the simulation efficiency and parallelization. To date, all MD simulations are run using general molecular mechanics (MM) force fields. The accuracy of calculations can be further improved by incorporating quantum mechanical (QM) methods into generating system-specific force fields through reparameterizing ligand partial charges in the bound state. The force field reparameterization process modifies the potential energy landscape of the bimolecular complex, enabling a more accurate representation of the intermolecular interactions and polarization effects at the bound state. We present QMrebind (Quantum Mechanical force field reparameterization at the receptor-ligand binding site), an ORCA-based software that facilitates reparameterizing the potential energy function within the phase space representing the bound state in a receptor-ligand complex. With SEEKR2 koff estimates and experimentally determined kinetic rates, we compare and interpret the receptor-ligand unbinding kinetics obtained using the newly reparameterized force fields for model host-guest systems and HSP90-inhibitor complexes. This method provides an opportunity to achieve higher accuracy in predicting receptor-ligand koff rate constants.

The translational value of ligand-receptor binding kinetics in drug discovery

The translation of in vitro potency of a candidate drug, as determined by traditional pharmacology metrics (such as EC50 /IC50 and KD /Ki values), to in vivo efficacy and safety is challenging. Residence time, which represents the duration of drug-target interaction, can be part of a more comprehensive understanding of the dynamic nature of drug-target interactions in vivo, thereby enabling better prediction of drug efficacy and safety. As a consequence, a prolonged residence time may help in achieving sustained pharmacological activity, while transient interactions with shorter residence times may be favourable for targets associated with side effects. Therefore, integration of residence time into the early stages of drug discovery and development has yielded a number of clinical candidates with promising in vivo efficacy and safety profiles. Insights from residence time research thus contribute to the translation of in vitro potency to in vivo efficacy and safety. Further research and advances in measuring and optimizing residence time will bring a much-needed addition to the drug discovery process and the development of safer and more effective drugs. In this review, we summarize recent research progress on residence time, highlighting its importance from a translational perspective.

The Role of Allylic Strain for Conformational Control in Medicinal Chemistry

It is axiomatic in medicinal chemistry that optimization of the potency of a small molecule at a macromolecular target requires complementarity between the ligand and target. In order to minimize the conformational penalty on binding, both enthalpically and entropically, it is therefore preferred to have the ligand preorganized in the bound conformation. In this Perspective, we highlight the role of allylic strain in controlling conformational preferences. Allylic strain was originally described for carbon-based allylic systems, but the same principles apply to other types of structure with sp² or pseudo-sp² arrangements. These systems include benzylic (including heteroaryl methyl) positions, amides, N-aryl groups, aryl ethers, and nucleotides. We have derived torsion profiles from small molecule X-ray structures for these systems. Through multiple examples, we show how these effects have been applied in drug discovery and how they can be used prospectively to influence conformation in the design process.

Predicting Protein-Ligand Binding and Unbinding Kinetics with Biased MD Simulations and Coarse-Graining of Dynamics: Current State and Challenges

The prediction of drug-target binding and unbinding kinetics that occur on time scales between milliseconds and several hours is a prime challenge for biased molecular dynamics simulation approaches. This Perspective gives a concise summary of the theory and the current state-of-the-art of such predictions via biased simulations, of insights into the molecular mechanisms defining binding and unbinding kinetics as well as of the extraordinary challenges predictions of ligand kinetics pose in comparison to binding free energy predictions.

In silico identification of potential Hsp90 inhibitors via ensemble docking, DFT and molecular dynamics simulations

The molecular chaperone heat shock protein 90 (Hsp90) has emerged as one of the most exciting targets for anticancer drug development and Hsp90 inhibitors are potentially useful chemotherapeutic agents in cancer. Within the current study, Hsp90 inhibitors that entered different phases of clinical trials were subjected to Zinc15 structure query to find similar compounds (≥ 78%). Obtained small molecules (1-29) with defined similarity cut-off were docked into ensemble of Hsp90-α NTDs. Docked complexes were ranked on the basis of binding modes and Gibbs free energies as Hsp90 binders (cut-off point; ΔG_b ≤ -12 kcal/mol). Top-ranked compounds were subjected to energy decomposition analysis per residue of binding pocket via density functional theory (DFT) calculations in B3LYP level of theory. Subsequent MD simulations of the top-ranked complexes were performed for 100 ns to explore the stable binding modes during a reasonable period in explicit water. Results of molecular docking and intermolecular binding analysis indicated that H-bond, hydrophobic and salt bridge interactions were determinant forces in complex formation. Compounds 19 and 20 were well accommodated in binding pocket of Hsp90 via relatively varied conformations. It was revealed that Asn51 and Phe138 were key residues that interacted stably to 19 and 20. Although primary mechanism of action for proposed molecules are unknown and yet to be explored, results of the present study revealed key structural features for future structure-guided optimization toward potent inhibitors of Hsp90-α NTD. HighlightsHsp90 inhibitors that entered different phases of clinical trials were subjected to Zinc15 based structure query to afford potential enzyme inhibitors 19 and 20.Quantum chemical calculations confirmed docking results and verified pivotal role of a conserved residues (Asn51, Leu103, Phe138 and Tyr139) in making effective hydrogen bonds.MD simulations of top-ranked docked derivatives revealed the achievement of stable binding modes with less conformational variation of 20 than 19 in the active site of Hsp90-α NTD.H-bond, hydrophobic contacts and salt bridge interactions were determinant forces in binding interactions of in silico hits.Resorcinol and isoxazole were important structural motifs of in silico hits in binding to the active site of Hsp90-α NTD.Communicated by Ramaswamy H. Sarma.

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.

Ligand Unbinding Pathway and Mechanism Analysis Assisted by Machine Learning and Graph Methods

We present two methods to reveal protein-ligand unbinding mechanisms in biased unbinding simulations by clustering trajectories into ensembles representing unbinding paths. The first approach is based on a contact principal component analysis for reducing the dimensionality of the input data, followed by identification of unbinding paths and training a machine learning model for trajectory clustering. The second approach clusters trajectories according to their pairwise mean Euclidean distance employing the neighbor-net algorithm, which takes into account input data bias in the distances set and is superior to dendrogram construction. Finally, we describe a more complex case where the reaction coordinate relevant for path identification is a single intraligand hydrogen bond, highlighting the challenges involved in unbinding path reaction coordinate detection.

Insight into Thermodynamic and Kinetic Profiles in Small-Molecule Optimization

Structure-activity relationships (SARs) and structure-property relationships (SPRs) have been considered the most important factors during the drug optimization process. For medicinal chemists, improvements in the potencies and druglike properties of small molecules are regarded as their major goals. Among them, the binding affinity and selectivity of small molecules on their targets are the most important indicators. In recent years, there has been growing interest in using thermodynamic and kinetic profiles to analyze ligand-receptor interactions, which could provide not only binding affinities but also detailed binding parameters for small-molecule optimization. In this perspective, we are trying to provide an insight into thermodynamic and kinetic profiles in small-molecule optimization. Through a highlight of strategies on the small-molecule optimization with specific cases, we aim to put forward the importance of structure-thermodynamic relationships (STRs) and structure-kinetic relationships (SKRs), which could provide more guidance to find safe and effective small-molecule drugs.

Design, synthesis, and biological evaluation of novel dual inhibitors of heat shock protein 90/mammalian target of rapamycin (Hsp90/mTOR) against bladder cancer cells

In this study, a novel class of thieno [2,3-d] pyrimidine derivatives containing resorcinol and morpholine fragments as Hsp90/mTOR dual inhibitors was designed, synthesized, and evaluated. In vitro anti-tumor assay results: the obtained compounds demonstrated effectiveness in suppressing the enzymatic activities of the Hsp90 and mTOR and inhibiting the proliferation of J82, T24, and SW780 cancer cell lines. Among these dual inhibitors, the most potent compound 17o, confirmed remarkable inhibitory activities on Hsp90, mTOR, and SW780 cell. Furthermore, the molecular dynamics simulation and a panel of mechanism studies revealed that inhibitor 17o suppressed the proliferation of SW780 cells through the over-activation of the PI3K/AKT/mTOR pathway regulated by mTOR inhibition and apoptosis regulated by the mitochondrial pathway. In subcutaneous J82 xenograft models, the compound 17o also presented considerable in vivo anti-tumor activity. Therefore, our investigations highlight that a new-found dual Hsp90/mTOR inhibitor by rational drug design strategies could be a promising lead compound for targeted bladder cancer therapy and deserves further studies.

Drug Discovery by Automated Adaptation of Chemical Structure and Identity

Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.

The prediction of protein-ligand unbinding for modern drug discovery

INTRODUCTION

Drug-target thermodynamic and kinetic information have perennially important roles in drug design. The prediction of protein-ligand unbinding, which can provide important kinetic information, in experiments continues to face great challenges. Uncovering protein-ligand unbinding through molecular dynamics simulations has become efficient and inexpensive with the progress and enhancement of computing power and sampling methods.

AREAS COVERED

In this review, various sampling methods for protein-ligand unbinding and their basic principles are firstly briefly introduced. Then, their applications in predicting aspects of protein-ligand unbinding, including unbinding pathways, dissociation rate constants, residence time and binding affinity, are discussed.

EXPERT OPINION

Although various sampling methods have been successfully applied in numerous systems, they still have shortcomings and deficiencies. Most enhanced sampling methods require researchers to possess a wealth of prior knowledge of collective variables or reaction coordinates. In addition, most systems studied at present are relatively simple, and the study of complex systems in real drug research remains greatly challenging. Through the combination of machine learning and enhanced sampling methods, prediction accuracy can be further improved, and some problems encountered in complex systems also may be solved.

Contact Map Fingerprints of Protein-Ligand Unbinding Trajectories Reveal Mechanisms Determining Residence Times Computed from Scaled Molecular Dynamics

The binding kinetic properties of potential drugs may significantly influence their subsequent clinical efficacy. Predictions of these properties based on computer simulations provide a useful alternative to their expensive and time-consuming experimental counterparts, even at an early drug discovery stage. Herein, we perform scaled molecular dynamics (ScaledMD) simulations on a set of 27 ligands of HSP90 belonging to more than seven chemical series to estimate their relative residence times. We introduce two new techniques for the analysis and the classification of the simulated unbinding trajectories. The first technique, which helps in estimating the limits of the free energy well around the bound state, and the second one, based on a new contact map fingerprint, allow the description and the comparison of the paths that lead to unbinding. Using these analyses, we find that ScaledMD's relative residence time generally enables the identification of the slowest unbinders. We propose an explanation for the underestimation of the residence times of a subset of compounds, and we investigate how the biasing in ScaledMD can affect the mechanistic insights that can be gained from the simulations.

Dual Targeting Strategies On Histone Deacetylase 6 (HDAC6) And Heat Shock Protein 90 (Hsp90)

The design of multi-target drugs acting simultaneously on multiple signaling pathways is a growing field in medicinal chemistry, especially for the treatment of complex diseases such as cancer. Histone deacetylase 6 (HDAC6) is an established anticancer drug target involved in tumor cells transformation. Being an epigenetic enzyme at the interplay of many biological processes, HDAC6 has become an attractive target for polypharmacology studies aimed at improving therapeutic efficacy of anticancer drugs. For example, the molecular chaperone Heat shock protein 90 (Hsp90) is a substrate of HDAC6 deacetylation, and several lines of evidence demonstrate that simultaneous inhibition of HDAC6 and Hsp90 promote synergistic antitumor effects on different cancer cell lines, highlighting the potential benefits of developing a single molecule endowed with multi-target activity. This review will summarize the complex interplay between HDAC6 and Hsp90, providing also useful hints for multi-target drug design and discovery approaches in this field. To this end, crystallographic structures of HDAC6 and Hsp90 complexes will be extensively reviewed in the light of discussing binding pockets features and pharmacophore requirements and providing useful guidelines for the design of dual inhibitors. The few examples of multi-target inhibitors obtained so far, mostly based on chimeric approaches, will be summarized and put into context. Finally, the main features of HDAC6 and Hsp90 inhibitors will be compared, and ligand- and structure-based strategies potentially useful for the development of small molecular weight dual inhibitors will be proposed and discussed.

A structure-kinetic relationship study using matched molecular pair analysis

The lifetime of a binary drug–target complex is increasingly acknowledged as an important parameter for drug efficacy and safety. With a better understanding of binding kinetics and better knowledge about kinetic parameter optimization, intentionally induced prolongation of the drug–target residence time through structural changes of the ligand could become feasible. In this study we assembled datasets from 21 publications and the K4DD (Kinetic for Drug Discovery) database to conduct large scale data analysis. This resulted in 3812 small molecules annotated to 78 different targets from five protein classes (GPCRs: 273, kinases: 3238, other enzymes: 240, HSPs: 160, ion channels: 45). Performing matched molecular pair (MMP) analysis to further investigate the structure–kinetic relationship (SKR) in this data collection allowed us to identify a fundamental contribution of a ligand's polarity to its association rate, and in selected cases, also to its dissociation rate. However, we furthermore observed that the destabilization of the transition state introduced by increased polarity is often accompanied by simultaneous destabilization of the ground state resulting in an unaffected or even worsened residence time. Supported by a set of case studies, we provide concepts on how to alter ligands in ways to trigger on-rates, off-rates, or both.

A large-scale study employing matched molecular pair (MMP) analysis to uncover the contribution of a compound's polarity to its association and dissociation rates.

Recent progress in molecular simulation methods for drug binding kinetics

Due to the contribution of drug-target binding kinetics to drug efficacy, there is a high level of interest in developing methods to predict drug-target binding kinetic parameters. During the review period, a wide range of enhanced sampling molecular dynamics simulation-based methods has been developed for computing drug-target binding kinetics and studying binding and unbinding mechanisms. Here, we assess the performance of these methods considering two benchmark systems in detail: mutant T4 lysozyme-ligand complexes and a large set of N-HSP90-inhibitor complexes. The results indicate that some of the simulation methods can already be usefully applied in drug discovery or lead optimization programs but that further studies on more high-quality experimental benchmark datasets are necessary to improve and validate computational methods.

Revisit ligand-receptor interaction at the human vasopressin V2 receptor: A kinetic perspective

The vasopressin V₂ receptor belongs to the superfamily of G protein-coupled receptors (GPCRs) and is a potential drug target for water balance disorders such as polycystic kidney disease. Traditionally, the discovery of novel agents for the vasopressin V₂ receptor has been guided by evaluating their receptor affinity, largely ignoring the binding kinetics. However, the latter is receiving increasing attention in the drug research community and has been proved to be a more complete descriptor of the dynamic process of ligand-receptor interaction. Herein we aim to revisit the molecular basis of ligand-vasopressin V₂ receptor interaction from the less-investigated kinetic perspective. A homogenous time-resolved fluorescence resonance energy transfer (TR-FRET) assay was set up and optimized, which enabled accurate kinetic profiling of unlabeled vasopressin V₂ receptor ligands. Receptor occupancy profiles of two representative antagonists with distinct target residence time were simulated. Their functional effects were further explored in cAMP assays. Our results showed that the antagonist with longer receptor residence time (lixivaptan) displayed sustained target occupancy than the antagonist with shorter receptor residence time (mozavaptan). In accordance, lixivaptan displayed insurmountable antagonism and wash-resistant inhibitory effect on the cellular cAMP level, while not so for mozavaptan. Together, our data provide evidence that binding kinetics, next to their affinity, offers additional information for the dynamic process of ligand-receptor interaction. Hopefully, this study may lead to more kinetics-directed medicinal chemistry efforts and aid the design and discovery of different-in-class of vasopressin V₂ receptor ligands for clinical applications.

In Silico Prediction of the Dissociation Rate Constants of Small Chemical Ligands by 3D-Grid-Based VolSurf Method

Accumulated evidence suggests that binding kinetic properties—especially dissociation rate constant or drug-target residence time—are crucial factors affecting drug potency. However, quantitative prediction of kinetic properties has always been a challenging task in drug discovery. In this study, the VolSurf method was successfully applied to quantitatively predict the k_off values of the small ligands of heat shock protein 90α (HSP90α), adenosine receptor (AR) and p38 mitogen-activated protein kinase (p38 MAPK). The results showed that few VolSurf descriptors can efficiently capture the key ligand surface properties related to dissociation rate; the resulting models demonstrated to be extremely simple, robust and predictive in comparison with available prediction methods. Therefore, it can be concluded that the VolSurf-based prediction method can be widely applied in the ligand-receptor binding kinetics and de novo drug design researches.

Conformational Changes in Alkyl Chains Determine the Thermodynamic and Kinetic Binding Profiles of Carbonic Anhydrase Inhibitors

Thermodynamics and kinetics of protein-ligand binding are both important aspects for the design of novel drug molecules. Presently, thermodynamic data are collected with isothermal titration calorimetry, while kinetic data are mostly derived from surface plasmon resonance. The new method of kinITC provides both thermodynamic and kinetic data from calorimetric titration measurements. The present study demonstrates the convenient collection of calorimetric data suitable for both thermodynamic and kinetic analysis for two series of congeneric ligands of human carbonic anhydrase II and correlates these findings with structural data obtained by macromolecular crystallography to shed light on the importance of shape complementarity for thermodynamics and kinetics governing a protein-ligand binding event. The study shows how minute chemical alterations change preferred ligand conformation and can be used to manipulate thermodynamic and kinetic signatures of binding. They give rise to the observation that analogous n-alkyl and n-alkyloxy derivatives of identical chain length swap their binding kinetic properties at unchanged binding affinity.

STACKED - Solvation Theory of Aromatic Complexes as Key for Estimating Drug Binding

The use of fragments to biophysically characterize a protein binding pocket and determine the strengths of certain interactions is a computationally and experimentally commonly applied approach. Almost all drug like molecules contain at least one aromatic moiety forming stacking interactions in the binding pocket. In computational drug design, the strength of stacking and the resulting optimization of the aromatic core or moiety is usually calculated using high level quantum mechanical approaches. However, as these calculations are performed in a vacuum, solvation properties are neglected. We close this gap by using Grid Inhomogeneous Solvation Theory (GIST) to describe the properties of individual heteroaromatics and complexes and thereby estimate the desolvation penalty. In our study, we investigated the solvation free energies of heteroaromatics frequently occurring in drug design projects in complex with truncated side chains of phenylalanine, tyrosine, and tryptophan. Furthermore, we investigated the properties of drug-fragments crystallized in a fragment-based lead optimization approach investigating PDE-10-A. We do not only find good correlation for the estimated desolvation penalty and the experimental binding free energy, but our calculations also allow us to predict prominent interaction sites. We highlight the importance of including the desolvation penalty of the respective heteroaromatics in stacked complexes to explain the gain or loss in affinity of potential lead compounds.

[Ligand-receptor binding kinetics in drug design]

Traditionally, the thermodynamic values of affinity are considered as the main criterion for the development of new drugs. Usually, these values for drugs are measured <i>in vitro</i> at steady concentrations of the receptor and ligand, which are differed from <i>in vivo</i> environment. Recent studies have shown that the kinetics of the process of drug binding to its receptor make significant contribution in the drug effectiveness. This has increased attention in characterizing and predicting the rate constants of association and dissociation of the receptor ligand at the stage of preclinical studies of drug candidates. A drug with a long residence time can determine ligand-receptor selectivity (kinetic selectivity), maintain pharmacological activity of the drug at its low concentration in vivo. The paper discusses the theoretical basis of protein-ligand binding, molecular determinants that control the kinetics of the drug-receptor binding. Understanding the molecular features underlying the kinetics of receptor-ligand binding will contribute to the rational design of drugs with desired properties.

Estimation of Protein-Ligand Unbinding Kinetics Using Non-Equilibrium Targeted Molecular Dynamics Simulations

We here report on nonequilibrium targeted molecular dynamics simulations as a tool for the estimation of protein-ligand unbinding kinetics. Correlating simulations with experimental data from SPR kinetics measurements and X-ray crystallography on two small molecule compound libraries bound to the N-terminal domain of the chaperone Hsp90, we show that the mean nonequilibrium work computed in an ensemble of trajectories of enforced ligand unbinding is a promising predictor for ligand unbinding rates. We furthermore investigate the molecular basis determining unbinding rates within the compound libraries. We propose ligand conformational changes and protein-ligand nonbonded interactions to impact on unbinding rates. Ligands may remain longer at the protein if they exhibit strong electrostatic and/or van der Waals interactions with the target. In the case of ligands with a rigid chemical scaffold that exhibit longer residence times, transient electrostatic interactions with the protein appear to facilitate unbinding. Our results imply that understanding the unbinding pathway and the protein-ligand interactions along this path is crucial for the prediction of small molecule ligands with defined unbinding kinetics.

An investigation of structural stability in protein-ligand complexes reveals the balance between order and disorder

The predominant view in structure-based drug design is that small-molecule ligands, once bound to their target structures, display a well-defined binding mode. However, structural stability (robustness) is not necessary for thermodynamic stability (binding affinity). In fact, it entails an entropic penalty that counters complex formation. Surprisingly, little is known about the causes, consequences and real degree of robustness of protein-ligand complexes. Since hydrogen bonds have been described as essential for structural stability, here we investigate 469 such interactions across two diverse structure sets, comprising of 79 drug-like and 27 fragment ligands, respectively. Completely constricted protein-ligand complexes are rare and may fulfill a functional role. Most complexes balance order and disorder by combining a single anchoring point with looser regions. 25% do not contain any robust hydrogen bond and may form loose structures. Structural stability analysis reveals a hidden layer of complexity in protein-ligand complexes that should be considered in ligand design.

Machine Learning Analysis of τRAMD Trajectories to Decipher Molecular Determinants of Drug-Target Residence Times

Drug-target residence times can impact drug efficacy and safety, and are therefore increasingly being considered during lead optimization. For this purpose, computational methods to predict residence times, τ, for drug-like compounds and to derive structure-kinetic relationships are desirable. A challenge for approaches based on molecular dynamics (MD) simulation is the fact that drug residence times are typically orders of magnitude longer than computationally feasible simulation times. Therefore, enhanced sampling methods are required. We recently reported one such approach: the τRAMD procedure for estimating relative residence times by performing a large number of random acceleration MD (RAMD) simulations in which ligand dissociation occurs in times of about a nanosecond due to the application of an additional randomly oriented force to the ligand. The length of the RAMD simulations is used to deduce τ. The RAMD simulations also provide information on ligand egress pathways and dissociation mechanisms. Here, we describe a machine learning approach to systematically analyze protein-ligand binding contacts in the RAMD trajectories in order to derive regression models for estimating τ and to decipher the molecular features leading to longer τ values. We demonstrate that the regression models built on the protein-ligand interaction fingerprints of the dissociation trajectories result in robust estimates of τ for a set of 94 drug-like inhibitors of heat shock protein 90 (HSP90), even for the compounds for which the length of the RAMD trajectories does not provide a good estimation of τ. Thus, we find that machine learning helps to overcome inaccuracies in the modeling of protein-ligand complexes due to incomplete sampling or force field deficiencies. Moreover, the approach facilitates the identification of features important for residence time. In particular, we observed that interactions of the ligand with the sidechain of F138, which is located on the border between the ATP binding pocket and a hydrophobic transient sub-pocket, play a key role in slowing compound dissociation. We expect that the combination of the τRAMD simulation procedure with machine learning analysis will be generally applicable as an aid to target-based lead optimization.

Drug-Target Association Kinetics in Drug Discovery

The important role of ligand-receptor binding kinetics in drug design and discovery is increasingly recognized by the drug research community. Over the past decade, accumulating evidence has shown that optimizing the ligand's dissociation rate constant can lead to desirable duration of in vivo target occupancy and, hence, improved pharmacodynamic properties. However, the association rate constant as a pharmacological principle remains less investigated, whereas it can play an equally important role in the selection of drug candidates. This review provides a compilation and discussion of otherwise scarce and dispersed information on this topic, bringing to light the importance of drug-target association in kinetics-directed drug design and discovery.

Binding kinetics of ligands acting at GPCRs

The influence of drug-receptor binding kinetics has often been overlooked during the development of new therapeutics that target G protein-coupled receptors (GPCRs). Over the last decade there has been a growing understanding that an in-depth knowledge of binding kinetics at GPCRs is required to successfully target this class of proteins. Ligand binding to a GPCR is often not a simple single step process with ligand freely diffusing in solution. This review will discuss the experiments and equations that are commonly used to measure binding kinetics and how factors such as allosteric regulation, rebinding and ligand interaction with the plasma membrane may influence these measurements. We will then consider the molecular characteristics of a ligand and if these can be linked to association and dissociation rates.

Structure-kinetic relationships that control the residence time of drug-target complexes: insights from molecular structure and dynamics

Time-dependent target occupancy is a function of both the thermodynamics and kinetics of drug-target interactions. However, while the optimization of thermodynamic affinity through approaches such as structure-based drug design is now relatively straight forward, less is understood about the molecular interactions that control the kinetics of drug complex formation and breakdown since this depends on both the ground and transition state energies on the binding reaction coordinate. In this opinion we highlight several recent examples that shed light on current approaches that are elucidating the factors that control the life-time of the drug-target complex.

Estimation of Drug-Target Residence Times by τ-Random Acceleration Molecular Dynamics Simulations

Drug-target residence time (τ), one of the main determinants of drug efficacy, remains highly challenging to predict computationally and, therefore, is usually not considered in the early stages of drug design. Here, we present an efficient computational method, τ-random acceleration molecular dynamics (τRAMD), for the ranking of drug candidates by their residence time and obtaining insights into ligand-target dissociation mechanisms. We assessed τRAMD on a data set of 70 diverse drug-like ligands of the N-terminal domain of HSP90α, a pharmaceutically important target with a highly flexible binding site, obtaining computed relative residence times with an accuracy of about 2.3τ for 78% of the compounds and less than 2.0τ within congeneric series. Analysis of dissociation trajectories reveals features that affect ligand unbinding rates, including transient polar interactions and steric hindrance. These results suggest that τRAMD will be widely applicable as a computationally efficient aid to improving drug residence times during lead optimization.