Metadynamics-Based Approaches for Modeling the Hypoxia-Inducible Factor 2α Ligand Binding Process

Molecular dynamics simulation reveals structural insights into isozyme selectivity of carbonic anhydrase XII inhibitors in hypoxic tumor microenvironment

Human carbonic anhydrase (CA) isoenzymes IX and XII are overexpressed in cancer cells, contributing to tumor microenvironment acidification and representing important targets for cancer therapy. In this study, we identified compound V35 (ZINC09419065) as a selective inhibitor of CA IX and CA XII with enhanced binding stability and selectivity compared to standard inhibitors. We analyzed conserved regions in CA I, CA II, CA IX, and CA XII to investigate their isozyme selectivity, revealing critical selectivity determinants at positions 95, 141, and 203. Molecular docking results indicated that V35 interacts robustly with CA XII, forming a metal ion coordination complex with Zn via HIS94, HIS96, HIS119, and THR199, similar to the interaction pattern of standard inhibitor SLC-0111. Molecular dynamics (MD) simulations conducted over 500 ns under hypoxic conditions showed that V35 has high binding stability, with root mean square deviation (RMSD) and fluctuation (RMSF) values comparable to SLC-0111, demonstrating its conformational stability in CA XII. Binding free energy calculations using the MMGBSA method showed that V35 achieves binding free energy of -44.17 kcal/mol with CA XII, closely matching SLC-0111 (-49.41 kcal/mol). Density functional theory (DFT) calculations further highlighted V35's electrostatic potential distribution, supporting its isozyme selectivity. Post-dynamics analysis indicated that the ester functional groups and the inward movement of HIS64 stabilize V35's interactions in CA XII, a feature absent in CA I.

Unveiling the drug delivery mechanism of graphene oxide dots at the atomic scale

Graphene oxide (GO) is an amphiphilic and versatile graphene-based nanomaterial that is extremely promising for targeted drug delivery, which aims to administer drugs in a spatially and temporally controlled manner. A typical GO nanocarrier features a polyethylene glycol coating and conjugation to an active targeting ligand. However, it is challenging to accurately model GO dots, because of their intrinsically complex and not unique structure. Here, realistic atomistic GO models are designed as homogeneously/inhomogeneously oxidized flakes and then coated with stealth polymeric chains conjugated to an active targeting ligand (PEG-cRGD). Doxorubicin (DOX) adsorption is investigated by metadynamics simulations for accelerated loading/release events. The presence of PEG and cRGD are found not to affect the DOX adsorption, whereas the homogeneity of oxidation plays a crucial role. We also proved that a change in pH towards acidic conditions causes a reduction in the GO/DOX affinity in line with a pH-triggered release mechanism. Based on this study, the ideal graphene-based DOX carrier is identified as a homogeneously highly oxidized GO where graphitic regions with strong DOX π-π stacking are limited. Such interactions excessively stabilize DOX and are not weakened by a pH-change. On the contrary, DOX interactions with surface oxidized groups are H-bonding and electrostatic, which can effectively be modified by a pH reduction. Our findings are useful to the experimental community to further develop successful drug delivery systems.

Gating residues govern ligand unbinding kinetics from the buried cavity in HIF-2α PAS-B

While transcription factors have been generally perceived as "undruggable," an exception is the HIF-2 hypoxia-inducible transcription factor, which contains an internal cavity that is sufficiently large to accommodate a range of small-molecules, including the therapeutically used inhibitor belzutifan. Given the relatively long ligand residence times of these small molecules and the lack of any experimentally observed pathway connecting the cavity to solvent, there has been great interest in understanding how these drug ligands exit the buried receptor cavity. Here, we focus on the relevant PAS-B domain of hypoxia-inducible factor 2α (HIF-2α) and examine how one such small molecule (THS-017) exits from the buried cavity within this domain on the seconds-timescale using atomistic simulations and ZZ-exchange NMR. To enable the simulations, we applied the weighted ensemble path sampling strategy, which generates continuous pathways for a rare-event process [e.g., ligand (un)binding] with rigorous kinetics in orders of magnitude less computing time compared to conventional simulations. Results reveal the formation of an encounter complex intermediate and two distinct classes of pathways for ligand exit. Based on these pathways, we identified two pairs of conformational gating residues in the receptor: one for the major class (N288 and S304) and another for the minor class (L272 and M309). ZZ-exchange NMR validated the kinetic importance of N288 for ligand unbinding. Our results provide an ideal simulation dataset for rational manipulation of ligand unbinding kinetics.

Self-organizing maps of unbiased ligand-target binding pathways and kinetics

The interpretation of ligand-target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, with the employment of both network-based approaches and Markov state models. We demonstrate the method by comprehensively mapping a large dataset of 640 μs of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand-target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design.

Discovery and optimisation of pyrazolo[1,5-a]pyrimidines as aryl hydrocarbon receptor antagonists

The aryl hydrocarbon receptor (AHR) is a versatile ligand-dependent transcription factor involved in diverse biological processes, from metabolic adaptations to immune system regulation. Recognising its pivotal role in cancer immunology, AHR has become a promising target for cancer therapy. Here we report the discovery and structure-activity relationship studies of novel AHR antagonists. The potential AHR antagonists were identified via homology model-based high-throughput virtual screening and were experimentally verified in a luciferase reporter gene assay. The identified pyrazolo[1,5-a]pyrimidine-based AHR antagonist 7 (IC50 = 650 nM) was systematically optimised to elucidate structure-activity relationships and reach low nanomolar AHR antagonistic potency (7a, IC50 = 31 nM). Overall, the findings presented here provide new starting points for AHR antagonist development and offer insightful information on AHR antagonist structure-activity relationships.

Exploring Product Release from Yeast Cytosine Deaminase with Metadynamics

The yeast cytosine deaminase (yCD) enzyme/5-fluorocytosine prodrug system is a promising candidate for targeted chemotherapeutics. After conversion of the prodrug into the toxic chemotherapeutic drug, 5-fluorouracil (5-FU), the slow product release from the enzyme limits the overall catalytic efficiency of the enzyme/prodrug system. Here, we present a computational study of the product release of the anticancer drug, 5-FU, from yCD using metadynamics. We present a comparison of the 5-FU drug to the natural enzyme product, uracil. We use volume-based metadynamics to compute the free energy landscape for product release and show a modest binding affinity for the product to the enzyme, consistent with experiments. Next, we use infrequent metadynamics to estimate the unbiased release rate from Kramers time-dependent rate theory and find a favorable comparison to experiment with a slower rate of product release for the 5-FU system. Our work demonstrates how adaptive sampling methods can be used to study the protein-ligand unbinding process for engineering enzyme/prodrug systems and gives insights into the molecular mechanism of product release for the yCD/5-FU system.

Investigating the Aryl Hydrocarbon Receptor Agonist/Antagonist Conformational Switch Using Well-Tempered Metadynamics Simulations

The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that mediates biological signals to control various complicated cellular functions. It plays a crucial role in environmental sensing and xenobiotic metabolism. Dysregulation of AhR is associated with health concerns, including cancer and immune system disorders. Upon binding to AhR ligands, AhR, along with heat shock protein 90 and other partner proteins undergoes a transformation in the nucleus, heterodimerizes with the aryl hydrocarbon receptor nuclear translocator (ARNT), and mediates numerous biological functions by inducing the transcription of various AhR-responsive genes. In this manuscript, the 3-dimensional structure of the entire human AhR is obtained using an artificial intelligence tool, and molecular dynamics (MD) simulations are performed to study different structural conformations. These conformations provide insights into the protein's function and movement in response to ligand binding. Understanding the dynamic behavior of AhR will contribute to the development of targeted therapies for associated health conditions. Therefore, we employ well-tempered metadynamics (WTE-metaD) simulations to explore the conformational landscape of AhR and obtain a better understanding of its functional behavior. Our computational results are in excellent agreement with previous experimental findings, revealing the closed and open states of helix α1 in the basic helix-loop-helix (bHLH domain) in the cytoplasm at the atomic level. We also predict the inactive form of AhR and identify Arginine 42 as a key residue that regulates switching between closed and open conformations in existing AhR modulators.

Assessing Metadynamics and Docking for Absolute Binding Free Energy Calculations Using Severe Acute Respiratory Syndrome Coronavirus 2 Main Protease Inhibitors

Absolute binding free energy (ABFE) calculations can be an important part of the drug discovery process by identifying molecules that have the potential to be strong binders for a biomolecular target. Recent work has used free energy perturbation (FEP) theory for these calculations, focusing on a set of 16 inhibitors of the severe acute respiratory syndrome coronavirus 2 main protease (Mpro). Herein, the same data set is evaluated by metadynamics (MetaD), four different docking programs, and molecular mechanics with generalized Born and surface area solvation. MetaD yields a Kendall τ distance of 0.28 and Pearson r2 of 0.49, which reflect somewhat less accuracy than that from the ABFE FEP results. Notably, it is demonstrated that an ensemble docking protocol by which each ligand is docked into the 13 crystal structures in this data set provides improved performance, particularly when docking is carried out with Glide XP (Kendall τ distance = 0.20, Pearson r2 = 0.71), Glide SP (Kendall τ distance = 0.19, Pearson r2 = 0.66), or AutoDock 4 (Kendall τ distance = 0.21, Pearson r2 = 0.55). The best results are obtained with "superconsensus" docking by averaging the 52 results for each compound using the 4 docking protocols and all 13 crystal structures (Kendall τ distance = 0.18, Pearson r2 = 0.73).

Exploring the Binding Pathway of Novel Nonpeptidomimetic Plasmepsin V Inhibitors

Predicting the interaction modes and binding affinities of virtual compound libraries is of great interest in drug development. It reduces the cost and time of lead compound identification and selection. Here we apply path-based metadynamics simulations to characterize the binding of potential inhibitors to the Plasmodium falciparum aspartic protease plasmepsin V (plm V), a validated antimalarial drug target that has a highly mobile binding site. The potential plm V binders were identified in a high-throughput virtual screening (HTVS) campaign and were experimentally verified in a fluorescence resonance energy transfer (FRET) assay. Our simulations allowed us to estimate compound binding energies and revealed relevant states along binding/unbinding pathways in atomistic resolution. We believe that the method described allows the prioritization of compounds for synthesis and enables rational structure-based drug design for targets that undergo considerable conformational changes upon inhibitor binding.

Metadynamics simulations for the investigation of drug loading on functionalized inorganic nanoparticles

Inorganic nanoparticles show promising properties that allow them to be efficiently used as drug carriers. The main limitation in this type of application is currently the drug loading capacity, which can be overcome with a proper functionalization of the nanoparticle surface. In this study, we present, for the first time, a computational approach based on metadynamics to estimate the binding free energy of the doxorubicin drug (DOX) to a functionalized TiO₂ nanoparticle under different pH conditions. On a thermodynamic basis, we demonstrate the robustness of our approach to capture the overall mechanism behind the pH-triggered release of DOX due to environmental pH changes. Notably, binding free energy estimations align well with what is expected for a pH-sensitive drug delivery system. Based on our results, we envision the use of metadynamics as a promising computational tool for the rational design and in silico optimization of organic ligands with improved drug carrier properties.

Arginine transportation mechanism through cationic amino acid transporter 1: insights from molecular dynamics studies

Metabolic and signaling mechanisms in mammalian cells are facilitated by the transportation of L-arginine (Arg) across the plasma membrane through cationic amino acid transporter (CAT) proteins. Due to a lack of argininosuccinate synthase (ASS) activity in various tumor cells such as acute myeloid leukemia, acute lymphocytic leukemia, and chronic lymphocytic leukemia, these tumor entities are arginine-auxotrophic and therefore depend on the uptake of the amino acid arginine. Cationic amino acid transporter-1 (CAT-1) is the leading arginine importer expressed in the aforementioned tumor entities. Hence, in the present study, to investigate the transportation mechanism of arginine in CAT-1, we performed molecular dynamics (MD) simulation methods on the modeled human CAT-1. The MM-PBSA approach was conducted to determine the critical residues interacting with arginine within the corresponding binding site of CAT-1. In addition, we found out that the water molecules have the leading role in forming the transportation channel within CAT-1. The conductive structure of CAT-1 was formed only when the water molecules were continuously distributed across the channel. Steered molecular dynamics (SMD) simulation approach showed various energy barriers against arginine transportation through CAT-1, especially while crossing the bottlenecks of the related channel. These findings at the molecular level might shed light on identifying the crucial amino acids in the binding of arginine to eukaryotic CATs and also provide fundamental insights into the arginine transportation mechanisms through CAT-1. Understanding the transportation mechanism of arginine is essential to developing CAT-1 blockers, which can be potential medications for some types of cancers.Communicated by Ramaswamy H. Sarma.

Hypoxia modeling techniques: A review

Hypoxia is the main cause and effect of a large number of diseases, including the most recent one facing the world, the coronavirus disease (COVID-19). Hypoxia is divided into short-term, long-term, and periodic, it can be the result of diseases, climate change, or living and traveling in the high mountain regions of the world. Since each type of hypoxia can be a cause and a consequence of various physiological changes, the methods for modeling these hypoxias are also different. There are many techniques for modeling hypoxia under experimental conditions. The most common animal for modeling hypoxia is a rat. Hypoxia models (hypoxia simulations) in rats are a tool to study the effect of various conditions on the oxygen supply of the body. These models can provide a necessary information to understand hypoxia and also provide effective treatment, highlighting the importance of various reactions of the body to hypoxia. The main parameters when choosing a model should be reproducibility and the goal that the scientist wants to achieve. Hypoxia in rats can be reproduced both ways exogenously and endogenously. The reason for writing this review was the aim to systematize the models of rats available in the literature in order to facilitate their selection by scientists. The relative strengths and limitations of each model need to be identified and understood in order to evaluate the information obtained from these models and extrapolate these results to humans to develop the necessary generalizations. Despite these problems, animal models have been and remain vital to understanding the mechanisms involved in the development and progression of hypoxia. The eligibility criteria for the selected studies was a comprehensive review of the methods and results obtained from the studies. This made it possible to make generalizations and give recommendations on the application of these methods. The review will assist scientists in choosing an appropriate hypoxia simulation method, as well as assist in interpreting the results obtained with these methods.

The Conformational Transition Pathways and Hidden Intermediates in DFG-Flip Process of c-Met Kinase Revealed by Metadynamics Simulations

Protein kinases intrinsically translate their conformations between active and inactive states, which is key to their enzymatic activities. The conformational flipping of the three-residue conservative motif, Asp-Phe-Gly (DFG), is crucial for many kinases' biological functions. Obtaining a detailed demonstration of the DFG flipping process and its corresponding dynamical and thermodynamical features could broaden our understanding of kinases' conformation-activity relationship. In this study, we employed metadynamics simulation, a widely used enhanced sampling technique, to analyze the conformational transition pathways of the DFG flipping for the c-Met kinase. The corresponding free energy landscape suggested two distinct transition pathways between the "DFG-in" and "DFG-out" states of the DFG-flip from c-Met. On the basis of the orientation direction of the F1223 residue, we correspondingly named the two pathways the "DFG-up" path, featuring forming a commonly discovered "DFG-up" transition state, and the "DFG-down" path, a unique transition pathway with F1223 rotating along the opposite direction away from the hydrophobic cavity. The free energies along the two pathways were then calculated using the Path Collective Variable (PCV) metadynamics simulation. The simulation results showed that, though having similar free energy barriers, the free energy cuve for the DFG-down path suggested a two-step conformational transition mechanism, while that for the DFG-up path showed the one-step transition feature. The c-Met DFG flipping mechanism and the new intermediate state discovered in this work could provide a deeper understanding of the conformation-activity relationship for c-Met and, possibly, reveal a new conformational state as the drug target for c-Met and other similar kinases.

Exploring Aspartic Protease Inhibitor Binding to Design-Selective Antimalarials

Selectivity is a major issue in the development of drugs targeting pathogen aspartic proteases. Here, we explore the selectivity-determining factors by studying specifically designed malaria aspartic protease (plasmepsin) open-flap inhibitors. Metadynamics simulations are used to uncover the complex binding/unbinding pathways of these inhibitors and describe the critical transition states in atomistic resolution. The simulation results are compared with experimentally determined enzymatic activities. Our findings demonstrate that plasmepsin inhibitor selectivity can be achieved by targeting the flap loop with hydrophobic substituents that enable ligand binding under the flap loop, as such a behavior is not observed for several other aspartic proteases. The ability to estimate the selectivity of compounds before they are synthesized is of considerable importance in drug design; therefore, we expect that our approach will be useful in selective inhibitor designs against not only aspartic proteases but also other enzyme classes.

PathDetect-SOM: A Neural Network Approach for the Identification of Pathways in Ligand Binding Simulations

Understanding the process of ligand–protein recognition is important to unveil biological mechanisms and to guide drug discovery and design. Enhanced-sampling molecular dynamics is now routinely used to simulate the ligand binding process, resulting in the need for suitable tools for the analysis of large data sets of binding events. Here, we designed, implemented, and tested PathDetect-SOM, a tool based on self-organizing maps to build concise visual models of the ligand binding pathways sampled along single simulations or replicas. The tool performs a geometric clustering of the trajectories and traces the pathways over an easily interpretable 2D map and, using an approximate transition matrix, it can build a graph model of concurrent pathways. The tool was tested on three study cases representing different types of problems and simulation techniques. A clear reconstruction of the sampled pathways was derived in all cases, and useful information on the energetic features of the processes was recovered. The tool is available at https://github.com/MottaStefano/PathDetect-SOM.