Understanding ligand-receptor non-covalent binding kinetics using molecular modeling

Computer aided design of inhibitor molecules against Vpr protein from different HIV-1 subtypes

HIV-1 is a retrovirus that affects the human immune system and consequently leads to the development of AIDS. The high mutation rate in HIV-1 produces different subtypes which underscores the development of new therapeutics against it. This study aims to develop a novel small molecule that can be used as a potential inhibitor against the Vpr protein of all the subtypes of HIV-1. The druggable pockets of the Vpr protein of each subtype were identified and the conformational stability of these pockets was studied. The structure-based Drug Design method was used to design small molecules against the high-scoring pocket from each subtype individually using AutoGrow4 software. Molecules with strong binding affinity were selected from each subtype individually and binding affinity was checked for all the subtypes. Considering druggability and ADMET properties, we have identified two novel molecules that act as potential Vpr protein inhibitors. Both the molecules were shown to form stable complexes with the Vpr proteins of all the subtypes. The biological activity of both molecules was examined using DFT calculation. This study may provide some insight into developing of new therapies in HIV-1 treatment by interrupting protein-protein interaction.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-025-00318-4.

Advances and Challenges in Milestoning Simulations for Drug-Target Kinetics

Molecular dynamics simulations have become indispensable for exploring complex biological processes, yet their limitations in capturing rare events hinder our understanding of drug-target kinetics. In this Perspective, we investigate the domain of milestoning simulations to understand this challenge. The milestoning approach divides the phase space of the drug-target complex into discrete cells, offering extended time scale insights. This Perspective traces the history, applications, and future potential of milestoning simulations in the context of drug-target kinetics. It explores the fundamental principles of milestoning, highlighting the importance of probabilistic transitions and transition time independence. Markovian milestoning with Voronoi tessellations is revisited to address the traditional milestoning challenges. While observing the advancements in this field, this Perspective also addresses impending challenges in estimating drug-target unbinding rate constants through milestoning simulations, paving the way for more effective drug design strategies.

Prediction of Threonine-Tyrosine Kinase Receptor-Ligand Unbinding Kinetics with Multiscale Milestoning and Metadynamics

Accurately describing protein-ligand binding and unbinding kinetics remains challenging. Computational calculations are difficult and costly, while experimental measurements often lack molecular detail and can be unobtainable. Here, we extend our multiscale milestoning method, Simulation-Enabled Estimation of Kinetics Rates (SEEKR), with metadynamics molecular dynamics simulations to yield accurate small molecule drug residence times. Using the pharmaceutically relevant threonine-tyrosine kinase (TTK) and eight long-residence-time (tens of seconds to hours) inhibitors, we demonstrate accurate prediction of absolute and rank-ordered ligand residence times and free energies of binding.

Potential (co-)contamination of dairy milk with AFM1 and MC-LR and their synergistic interaction in inducing mitochondrial dysfunction in HepG2 cells

Several toxic metabolites, such as aflatoxin M1 (AFM1), are known to contaminate dairy milk. However, as mentioned in an external EFSA report, there is a knowledge gap regarding the carry-over of certain emerging toxins such as microcystin-LR (MC-LR). Therefore, this work aimed to develop an LC-MS/MS method for MC-LR quantification in dairy milk. Also, the method included AFM1 as a common fungal metabolite and applied to analyze 113 dairy milk samples collected directly after the end of the summer peak. Both toxins were below their LODs, keeping the question on MC-LR carry-over still unanswered. Moreover, an in silico analysis, using a 3D molecular modeling was performed, pointing to a possible interaction between MC-LR and milk proteins, especially β-lactoglobulin. Since AFM1 and MC-LR are hepatotoxic, their interaction in inducing mitochondrial dysfunction in HepG2 cells was investigated at low (subcytotoxic) concentrations. Live cell imaging-based assays showed an inhibition in cell viability, without involvement of caspase-3/7, and a hyperpolarization in the mitochondrial membrane potential after the exposure to a mixture of 100 ng mL-1 AFM1 and 1000 ng mL-1 MC-LR for 48h. Extracellular flux analysis revealed inhibitions of several key parameters of mitochondrial function (basal respiration, ATP-linked respiration, and spare respiratory capacity).

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.

Predicting Biomolecular Binding Kinetics: A Review

Biomolecular binding kinetics including the association (k_on) and dissociation (k_off) rates are critical parameters for therapeutic design of small-molecule drugs, peptides, and antibodies. Notably, the drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling, and Machine Learning has been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.

Computational Evaluation of Azadirachta indica-Derived Bioactive Compounds as Potential Inhibitors of NLRP3 in the Treatment of Alzheimer's Disease

BACKGROUND

The development of therapeutic agents against Alzheimer's disease (AD) has stalled recently. Drug candidates targeting amyloid-β (Aβ) deposition have often failed clinical trials at different stages, prompting the search for novel targets for AD therapy. The NLRP3 inflammasome is an integral part of innate immunity, contributing to neuroinflammation and AD pathophysiology. Thus, it has become a promising new target for AD therapy.

OBJECTIVE

The study sought to investigate the potential of bioactive compounds derived from Azadirachta-indica to inhibit the NLRP3 protein implicated in the pathophysiology of AD.

METHODS

Structural bioinformatics via molecular docking and density functional theory (DFT) analysis was utilized for the identification of novel NLRP3 inhibitors from A. indica bioactive compounds. The compounds were further subjected to pharmacokinetic and drug-likeness analysis. Results obtained from the compounds were compared against that of oridonin, a known NLRP3 inhibitor.

RESULTS

The studied compounds optimally saturated the binding site of the NLRP3 NACHT domain, forming principal interactions with the different amino acids at its binding site. The studied compounds also demonstrated better bioactivity and chemical reactivity as ascertained by DFT analysis and all the compounds except 7-desacetyl-7-benzoylazadiradione, which had two violations, conformed to Lipinski's rule of five.

CONCLUSION

In silico studies show that A. indica derived compounds have better inhibitory potential against NLRP3 and better pharmacokinetic profiles when compared with the reference ligand (oridonin). These compounds are thus proposed as novel NLRP3 inhibitors for the treatment of AD. Further wet-lab studies are needed to confirm the potency of the studied compounds.

The investigation into the effect of the length of RGD peptides and temperature on the interaction with the αIIbβ3 integrin: a molecular dynamic study

The tripeptide Arg-Gly-Asp acid (RGD) is a protein sequence in the binding of proteins to cell surfaces, and is involved in various biological processes such as cell adhesion to the extracellular matrix, platelet activation, hemostasis, etc. The C2 domain of the Von Willebrand Factor (VWF), containing the RGD motif, plays an important role in the initial homeostasis process. It binds to the αIIbβ3 integrin and stimulates platelet aggregation. We have investigated, using the molecular Dynamic (MD) simulation method, the effect of the RGD-peptide length, and temperature variation, on the binding to the αIIbβ3 integrin receptor. We examined 10 different structural modes of the αIIbβ3 at three different temperatures; 237 K, 310 K and 318 K. Our findings show that the amino acids that form a binding pocket include Asp₂₂₄, Tyr₂₃₄, Ser₂₂₆, Tyr₁₉₀, Tyr₁₈₉, Trp₂₆₀, Trp₂₆₂, Asp_259, Lys₂₅₃, Arg₂₁₄, Asp₂₁₇, Ser₁₆₁ and Ala₂₁₈ and that the ligand-receptor interaction was increased at higher temperatures. It was also found that the increase in the number of ligands' amino acids and their types (% glycine) plays an important role in the stability, conformation, and ligand-receptor interaction.Communicated by Ramaswamy H. Sarma.

Computational Methods for the Interaction between Cyclodextrins and Natural Compounds: Technology, Benefits, Limitations, and Trends

Cyclodextrins (CDs) have a hollow structure with a hydrophobic interior and hydrophilic exterior. Forming inclusion complexes with CDs will maximize the bioavailability of natural compounds and enable active components to be processed into functional foods, medicines, additives, and so forth. However, experimental methods cannot explain CD-guest binding at the atomic level. Different models have been recently developed to simulate the interaction between CDs and guests to study the binding conformation and analyze noncovalent forces. This review paper summarizes modeling methods of CD-natural compound complexes. The methods include quantitative structure-activity relationships, molecular docking, molecular dynamics simulations, and quantum-chemical calculations. The applications of these methods to enhance the solubility and bioactivities of guest molecules, assist material transportation, and promote compound extraction are also discussed. The purpose of this review is to explore interaction mechanisms of CDs and guests and to help expand new applications of CDs.

Ritonavir and xk263 Binding-Unbinding with HIV-1 Protease: Pathways, Energy and Comparison

Understanding non-covalent biomolecular recognition, which includes drug-protein bound states and their binding/unbinding processes, is of fundamental importance in chemistry, biology, and medicine. Fully revealing the factors that govern the binding/unbinding processes can further assist in designing drugs with desired binding kinetics. HIV protease (HIVp) plays an integral role in the HIV life cycle, so it is a prime target for drug therapy. HIVp has flexible flaps, and the binding pocket can be accessible by a ligand via various pathways. Comparing ligand association and dissociation pathways can help elucidate the ligand-protein interactions such as key residues directly involved in the interaction or specific protein conformations that determine the binding of a ligand under certain pathway(s). Here, we investigated the ligand unbinding process for a slow binder, ritonavir, and a fast binder, xk263, by using unbiased all-atom accelerated molecular dynamics (aMD) simulation with a re-seeding approach and an explicit solvent model. Using ritonavir-HIVp and xk263-HIVp ligand-protein systems as cases, we sampled multiple unbinding pathways for each ligand and observed that the two ligands preferred the same unbinding route. However, ritonavir required a greater HIVp motion to dissociate as compared with xk263, which can leave the binding pocket with little conformational change of HIVp. We also observed that ritonavir unbinding pathways involved residues which are associated with drug resistance and are distal from catalytic site. Analyzing HIVp conformations sampled during both ligand-protein binding and unbinding processes revealed significantly more overlapping HIVp conformations for ritonavir-HIVp rather than xk263-HIVp. However, many HIVp conformations are unique in xk263-HIVp unbinding processes. The findings are consistent with previous findings that xk263 prefers an induced-fit model for binding and unbinding, whereas ritonavir favors a conformation selection model. This study deepens our understanding of the dynamic process of ligand unbinding and provides insights into ligand-protein recognition mechanisms and drug discovery.

Cholinesterase inhibitory activity of tinosporide and 8-hydroxytinosporide isolated from Tinospora cordifolia: In vitro and in silico studies targeting management of Alzheimer's disease

Tinosporide and 8-hydroxytinosporide isolated from Tinospora cordifolia were evaluated for acetylcholinesterase (AChE) and butylcholinesterase (BuChE) inhibitory activities. The structure of the compound was confirmed by spectroscopic analysis, whereas cholinesterase inhibition was investigated by Ellman method using donepezil as standard drug and the data were presented as IC50 (μg/ml ± SEM). Furthermore, donepezil, tinosporide and 8-hydroxytinosporide were executed for docking analysis. The results from the isolated compounds TC-16R confirmed as tinosporide promisingly inhibited AChE with IC50 value of 13.45 ± 0.144, whereas TC-19R confirmed as 8-hydroxytinosporide moderately inhibited AChE with IC50 value of 46.71 ± 0.511. In case of BuChE inhibition, the IC50 values were found to be 408.50 ± 17.197 and 317.26 ± 6.918 for tinosporide and 8-hydroxytinosporide, respectively. The in silico studies revealed that the ligand tinosporide fit with the binding sites and inhibited AChE. Overall, the study findings suggested that tinosporide would be a complementary noble molecule of donepezil which is correlated with its pharmacological activity through in vitro studies, while 8-hydroxytinosporide modestly inhibited BuChE and the results are very close to the standard donepezil.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

Transient States and Barriers from Molecular Simulations and the Milestoning Theory: Kinetics in Ligand-Protein Recognition and Compound Design

This study presents a novel computational approach to study molecular recognition and binding kinetics for drug-like compounds dissociating from a flexible protein system. The intermediates and their free energy profile during ligand association and dissociation processes control ligand-protein binding kinetics and bring a more complete picture of ligand-protein binding. The method applied the milestoning theory to extract kinetics and thermodynamics information from running short classical molecular dynamics (MD) simulations for frames from a given dissociation path. High-dimensional ligand-protein motions (3N-6 degrees of freedom) during ligand dissociation were reduced by use of principal component modes for assigning more than 100 milestones, and classical MD runs were allowed to travel multiple milestones to efficiently obtain ensemble distribution of initial structures for MD simulations and estimate the transition time and rate during ligand traveling between milestones. We used five pyrazolourea ligands and cyclin-dependent kinase 8 with cyclin C (CDK8/CycC) as our model system as well as metadynamics and a pathway search method to sample dissociation pathways. With our strategy, we constructed the free energy profile for highly mobile biomolecular systems. The computed binding free energy and residence time correctly ranked the pyrazolourea ligand series, in agreement with experimental data. Guided by a barrier of a ligand passing an αC helix and activation loop, we introduced one hydroxyl group to parent compounds to design our ligands with increased residence time and validated our prediction by experiments. This work provides a novel and robust approach to investigate dissociation kinetics of large and flexible systems for understanding unbinding mechanisms and designing new small-molecule drugs with desired binding kinetics.

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities

The drug development is generally arduous, costly, and success rates are low. Thus, the identification of drug-target interactions (DTIs) has become a crucial step in early stages of drug discovery. Consequently, developing computational approaches capable of identifying potential DTIs with minimum error rate are increasingly being pursued. These computational approaches aim to narrow down the search space for novel DTIs and shed light on drug functioning context. Most methods developed to date use binary classification to predict if the interaction between a drug and its target exists or not. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If that strength is not sufficiently strong, such DTI may not be useful. Therefore, the methods developed to predict drug-target binding affinities (DTBA) are of great value. In this study, we provide a comprehensive overview of the existing methods that predict DTBA. We focus on the methods developed using artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches, as well as related benchmark datasets and databases. Furthermore, guidance and recommendations are provided that cover the gaps and directions of the upcoming work in this research area. To the best of our knowledge, this is the first comprehensive comparison analysis of tools focused on DTBA with reference to AI/ML/DL.

Potential Mean Force from Umbrella Sampling Simulations: What Can We Learn and What Is Missed?

Changes in free energy provide valuable information for molecular recognition, including both ligand-receptor binding thermodynamics and kinetics. Umbrella sampling (US), a widely used free energy calculation method, has long been used to explore the dissociation process of ligand-receptor systems and compute binding free energy. In existing publications, the binding free energy computed from the potential of mean force (PMF) with US simulation mostly yielded "ball park" values with experimental data. However, the computed PMF values are highly influenced by factors such as initial conformations and/or trajectories provided, the reaction coordinate, and sampling of conformational space in each US window. These critical factors have rarely been carefully studied. Here we used US to study the guest aspirin and 1-butanol dissociation processes of β-cyclodextrin (β-CD) and an inhibitor SB2 dissociation from a p38α mitogen-activated protein kinase (MAPK) complex. For β-CD, we used three different β-CD conformations to generate the dissociation path with US windows. For p38α, we generated the dissociation pathway by using accelerated molecular dynamics followed by conformational relaxing with short conventional MD, steered MD, and manual pulling. We found that, even for small β-CD complexes, different β-CD conformations altered the height of the PMF, but the pattern of PMF was not affected if the MD sampling in each US window was well-converged. Because changing the macrocyclic ring conformation needs to rotate dihedral angles in the ring, a bound ligand largely restrains the motion of cyclodextrin. Therefore, once a guest is in the binding site, cyclodextrin cannot freely change its initial conformation, resulting in different absolute heights of the PMF, which cannot be overcome by running excessively long MD simulations for each US window. Moreover, if the US simulations were not converged, the important barrier and minimum were missed. For ligand-protein systems, our studies also suggest that the dissociation trajectories modeled by an enhanced sampling method must maintain a natural molecular movement to avoid biased PMF plots when using US simulations.

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.

Escape of a Small Molecule from Inside T4 Lysozyme by Multiple Pathways

The T4 lysozyme L99A mutant is often used as a model system to study small-molecule binding to proteins, but pathways for ligand entry and exit from the buried binding site and the associated protein conformational changes have not been fully resolved. Here, molecular dynamics simulations were employed to model benzene exit from its binding cavity using the weighted ensemble (WE) approach to enhance sampling of low-probability unbinding trajectories. Independent WE simulations revealed four pathways for benzene exit, which correspond to transient tunnels spontaneously formed in previous simulations of apo T4 lysozyme. Thus, benzene unbinding occurs through multiple pathways partially created by intrinsic protein structural fluctuations. Motions of several α-helices and side chains were involved in ligand escape from metastable microstates. WE simulations also provided preliminary estimates of rate constants for each exit pathway. These results complement previous works and provide a semiquantitative characterization of pathway heterogeneity for binding of small molecules to proteins.

Role of Molecular Interactions and Protein Rearrangement in the Dissociation Kinetics of p38α MAP Kinase Type-I/II/III Inhibitors

Understanding the governing factors of fast or slow inhibitor binding/unbinding assists in developing drugs with preferred kinetic properties. For inhibitors with the same binding affinity targeting different binding sites of the same protein, the kinetic behavior can profoundly differ. In this study, we investigated unbinding kinetics and mechanisms of fast (type-I) and slow (type-II/III) binders of p38α mitogen-activated protein kinase, where the crystal structures showed that type-I and type-II/III inhibitors bind to pockets with different conformations of the Asp-Phe-Gly (DFG) motif. The work used methods that combine conventional molecular dynamics (MD), accelerated molecular dynamics (AMD) simulations, and the newly developed pathway search guided by internal motions (PSIM) method to find dissociation pathways. The study focuses on revealing key interactions and molecular rearrangements that hinder ligand dissociation by using umbrella sampling and post-MD processing to examine changes in free energy during ligand unbinding. As anticipated, the initial dissociation steps all require breaking interactions that appeared in crystal structures of the bound complexes. Interestingly, for type-I inhibitors such as SB2, p38α keeps barrier-free conformational fluctuation in the ligand-bound complex and during ligand dissociation. In contrast, with a type-II/III inhibitor such as BIRB796, with the rearrangements of p38α in its bound state, ligand unbinding features energetically unfavorable protein-ligand concerted movement. Our results also show that the type-II/III inhibitors preferred dissociation pathways through the allosteric channel, which is consistent with an existing publication. The study suggests that the level of required protein rearrangement is one major determining factor of drug binding kinetics in p38α systems, providing useful information for development of inhibitors.