Model For Ligand Binding Research Articles

Model of P-Glycoprotein Ligand Binding and Validation with Efflux Substrate Matched Pairs.

The blood-brain barrier (BBB) poses a significant obstacle in developing therapeutics for neurodegenerative diseases and central nervous system (CNS) disorders. P-glycoprotein (P-gp), a multidrug resistance protein, is a critical gatekeeper in the BBB and plays a role in cancer chemoresistance. This paper uses cryo-EM P-gp structures as starting points with an induced fit docking (IFD) model to evaluate 19 pairs of compounds with known P-gp efflux data. The study reveals significant differences in binding energy and sheds light on structural modifications' impact on efflux properties. In the cases examined, fluorine incorporation influences the efflux by altering the molecular conformation rather than proximal heteroatom basicity. Although there are limitations in addressing covalent interactions or when binding extends into the more flexible vestibule region of the protein, the results provide valuable insights and potential strategies to overcome P-gp efflux, contributing to the advancement of drug development for both CNS disorders and cancer therapies.

Effect of Ligand Binding on Polymer Diffusiophoresis

Diffusiophoresis is the migration of a macromolecule in response to a concentration gradient of a cosolute in liquids. Diffusiophoresis of polyethylene glycol (PEG) in water occurs from high to low concentration of trimethylamine-N-oxide (TMAO). This is consistent with the preferential hydration of PEG observed in the presence of TMAO. In other words, PEG migrates in the direction in which it lowers its chemical potential. On the other hand, although PEG is found to preferentially bind urea in water, PEG diffusiophoresis still occurs from high to low urea concentration. Thus, PEG migrates in the direction that increases its chemical potential in the urea case. Here, a ligand-binding model for polymer diffusiophoresis in the presence of a cosolute that preferentially binds to polymer is developed. It includes both actual polymer–ligand binding and the effect of the polymer on cosolute thermodynamic activity. This model shows that polymer–cosolute binding has a marginal effect on polymer diffusiophoresis and indicates that weak repulsive interactions, such as hard-core exclusion forces, are the main factor responsible for the observed PEG diffusiophoresis from high to low urea concentration. This work contributes to a better understanding of diffusiophoresis of macromolecules in response to gradients of nonelectrolytes.

A nonequilibrium allosteric model for receptor-kinase complexes: The role of energy dissipation in chemotaxis signaling

The Escherichia coli chemotaxis signaling pathway has served as a model system for the adaptive sensing of environmental signals by large protein complexes. The chemoreceptors control the kinase activity of CheA in response to the extracellular ligand concentration and adapt across a wide concentration range by undergoing methylation and demethylation. Methylation shifts the kinase response curve by orders of magnitude in ligand concentration while incurring a much smaller change in the ligand binding curve. Here, we show that the disproportionate shift in binding and kinase response is inconsistent with equilibrium allosteric models. To resolve this inconsistency, we present a nonequilibrium allosteric model that explicitly includes the dissipative reaction cycles driven by adenosine triphosphate (ATP) hydrolysis. The model successfully explains all existing joint measurements of ligand binding, receptor conformation, and kinase activity for both aspartate and serine receptors. Our results suggest that the receptor complex acts as an enzyme: Receptor methylation modulates the ON-state kinetics of the kinase (e.g., phosphorylation rate), while ligand binding controls the equilibrium balance between kinase ON/OFF states. Furthermore, sufficient energy dissipation is responsible for maintaining and enhancing the sensitivity range and amplitude of the kinase response. We demonstrate that the nonequilibrium allosteric model is broadly applicable to other sensor-kinase systems by successfully fitting previously unexplained data from the DosP bacterial oxygen-sensing system. Overall, this work provides a nonequilibrium physics perspective on cooperative sensing by large protein complexes and opens up research directions for understanding their microscopic mechanisms through simultaneous measurements and modeling of ligand binding and downstream responses.

Arsenic-dissolved organic matter complexation in water soluble extracts from lignite

Arsenic in groundwater is a global threat to public health. Recently, As mobility has been tied to the concentration and chemical characteristics of dissolved organic matter (DOM) through formation of As-DOM complexes. To date, there has been a wide range of DOM types studied to understand As-DOM interactions, but most of these have focused on surface water derived materials and not groundwater DOM. We address this gap in knowledge by simulating groundwater DOM using water extractable organic matter (WEOM) from two lignite deposits and treating the extracts with increasing concentrations of As. As-DOM complexes were measured using size-exclusion chromatography coupled to multiple detectors including an inductively coupled plasma mass spectrometer (ICPMS) for As detection as well as fluorescence and variable wave detectors for organic matter detection. First, we found two different size fractions of As-DOM, one of ∼1 kDa and another of ∼15 kDa, depending on the DOM types. The smaller As-DOM complex (∼1 kDa) was approximately 10 times more abundant than the larger complex (∼15 kDa). Second, we found that the lignite derived DOMs showed higher conditional distribution coefficients than did the surface water reference material (Suwanee River Natural Organic Matter, SRNOM). Finally, the data showed good fit (R2 > 0.92) to one-site ligand binding models, and the lignite derived DOMs showed higher maximum sorbate concentrations (Bmax) compared to SRNOM. Together, this study shows that As-DOM complexation is an important control on As speciation, even in groundwater systems.

Classical structural identifiability methodology applied to low-dimensional dynamic systems in receptor theory.

Mathematical modelling has become a key tool in pharmacological analysis, towards understanding dynamics of cell signalling and quantifying ligand-receptor interactions. Ordinary differential equation (ODE) models in receptor theory may be used to parameterise such interactions using timecourse data, but attention needs to be paid to the theoretical identifiability of the parameters of interest. Identifiability analysis is an often overlooked step in many bio-modelling works. In this paper we introduce structural identifiability analysis (SIA) to the field of receptor theory by applying three classical SIA methods (transfer function, Taylor Series and similarity transformation) to ligand-receptor binding models of biological importance (single ligand and Motulsky-Mahan competition binding at monomers, and a recently presented model of a single ligand binding at receptor dimers). New results are obtained which indicate the identifiable parameters for a single timecourse for Motulsky-Mahan binding and dimerised receptor binding. Importantly, we further consider combinations of experiments which may be performed to overcome issues of non-identifiability, to ensure the practical applicability of the work. The three SIA methods are demonstrated through a tutorial-style approach, using detailed calculations, which show the methods to be tractable for the low-dimensional ODE models.

Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA.

Ribonucleic acids (RNAs) play crucial roles in living organisms and some of them, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, whereas others, e.g. bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA-small molecule interactions. We recently developed fingeRNAt-a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions and encodes them as structural interaction fingerprint (SIFt). Here, we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We also employed Explainable Artificial Intelligence (XAI)-the SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations and other methods to help understand the decision-making process behind the predictive models. We conducted a case study in which we applied XAI on a predictive model of ligand binding to human immunodeficiency virus type 1 trans-activation response element RNA to distinguish between residues and interaction types important for binding. We also used XAI to indicate whether an interaction has a positive or negative effect on binding prediction and to quantify its impact. Our results obtained using all XAI methods were consistent with the literature data, demonstrating the utility and importance of XAI in medicinal chemistry and bioinformatics.

Confinement of unliganded EGFR by tetraspanin nanodomains gates EGFR ligand binding and signaling

The epidermal growth factor receptor (EGFR) is a central regulator of cell physiology. EGFR is activated by ligand binding, triggering receptor dimerization, activation of kinase activity, and intracellular signaling. EGFR is transiently confined within various plasma membrane nanodomains, yet how this may contribute to regulation of EGFR ligand binding is poorly understood. To resolve how EGFR nanoscale compartmentalization gates ligand binding, we developed single-particle tracking methods to track the mobility of ligand-bound and total EGFR, in combination with modeling of EGFR ligand binding. In comparison to unliganded EGFR, ligand-bound EGFR is more confined and distinctly regulated by clathrin and tetraspanin nanodomains. Ligand binding to unliganded EGFR occurs preferentially in tetraspanin nanodomains, and disruption of tetraspanin nanodomains impairs EGFR ligand binding and alters the conformation of the receptor’s ectodomain. We thus reveal a mechanism by which EGFR confinement within tetraspanin nanodomains regulates receptor signaling at the level of ligand binding.

Towards Computational Modeling of Ligand Binding to the ILPR G-Quadruplex.

Using a combination of unconstrained and constrained molecular dynamics simulations, we have evaluated the binding affinities between two porphyrin derivatives (TMPyP4 and TEGPy) and the G-quadruplex (G4) of a DNA fragment modeling the insulin-linked polymorphic region (ILPR). Refining a well-established potential of mean force (PMF) approach to selections of constraints based on root-mean-square fluctuations results in an excellent agreement between the calculated and observed absolute free binding energy of TMPyP4. The binding affinity of IPLR-G4 toward TEGPy is predicted to be higher than that toward TMPyP4 by 2.5 kcal/mol, which can be traced back to stabilization provided by the polyether side chains of TMPyP4 that can nestle into the grooves of the quadruplex and form hydrogen bonds through the ether oxygen atoms. Because our refined methodology can be applied to large ligands with high flexibility, the present research opens an avenue for further ligand design in this important area.

Effects of PTH glandular and external dosing patterns on bone cell activity using a two-state receptor model-Implications for bone disease progression and treatment.

Temporal aspects of ligand specificity have been shown to play a significant role in the case of pulsatile hormone secretion, as exemplified by parathyroid hormone (PTH) binding to its receptor (PTH1R), a G-protein-coupled receptor expressed on surfaces of osteoblasts and osteocytes. The latter binding reaction regulates intracellular signalling and subsequently modulates skeletal homeostasis via bone remodelling. PTH glandular secretion patterns dictate bone cellular activity. In healthy humans, 70% of PTH is secreted in a tonic fashion, whereas 30% is secreted in low-amplitude and high-frequency bursts occurring every 10-20 min, superimposed on the tonic secretion. Changes in the PTH secretion patterns have been associated with various bone diseases. In this paper, we analyse PTH glandular secretion patterns for healthy and pathological states and their link to bone cellular responsiveness (αR). We utilise a two-state receptor ligand binding model of PTH to PTH1R together with a cellular activity function which is able to distinguish various aspects of the stimulation signal including peak dose, time of ligand exposure, and exposure period. Formulating and solving several constrained optimisation problems, we investigate the potential of pharmacological manipulation of the diseased glandular secretion and via clinical approved external PTH injections to restore healthy bone cellular responsiveness. Based on the mean experimentally reported data, our simulation results indicate cellular responsiveness in healthy subjects is sensitive to the tonic baseline stimulus and it is 28% of the computed maximum responsiveness. Simulation results for pathological cases of glucocorticoid-induced osteoporosis, hyperparathyroidism, initial and steady state hypocalcemia clamp tests indicate αR values significantly larger than the healthy baseline (1.7, 2.2, 4.9 and 1.9-times, respectively). Manipulation of the pulsatile glandular secretion pattern, while keeping the mean PTH concentration constant, allowed restoration of healthy baseline values from these catabolic bone diseases. Conversely, PTH glandular diseases that led to maximum bone cellular responsiveness below the healthy baseline value can't be restored to baseline via glandular manipulation. However, external PTH injections allowed restoration of these latter cases.

Exploring Human 5HT1A/2A Receptor Through Homology Modeling and Flexible Docking Studies for the Binding Hotspot of Substituted 2,4-imidazolidinedione and Oxazolidinedione Derivatives

Introduction: To obtain the binding site of a model of the human 5-HT1A/2A receptor, a series of substituted 2,4-imidazolidinediones and oxazolidinediones were subjected to flexible docking using GLIDE. Methods: The docking scores that were generated are correlated with the in-vivo affinity data that had already been collected. Results: When combined with a homology model of 5HT1A/2A, the GLIDE docking approach was based on a template for 2-adrenergic receptors. Conclusion: A model for ligand binding in the hydrophobic portion of the binding site was proposed after discussing the impact of the structure and hydrophobic characteristics of the aryl moiety on binding affinities.

Kinetic Model of Adsorption of Aqueous Solutes onto Lipid Bilayers: Modulation of the Activity of Membrane Proteins.

It has been argued that membrane proteins that are activated by agonist binding and whose activity depends on conformational transitions are sensitive to membrane adsorption of agonists as well as other solutes such as anesthetics. Ligand-gated ion channels such as GABAAR have been observed electrophysiologically to exhibit remarkable temporal complexity, with multiple time scales of desensitization and deactivation that depend on concentration over a very broad range. Earlier theoretical work was able to predict much of this complexity for GABAAR using a model that incorporates a simple Langmuir approximation of adsorption and desorption of neurotransmitters and solutes such as anesthetics, along with classical modeling of ligand binding and conformational transitions among the three canonical protein states. Here, a simple kinetic model is developed that improves on the Langmuir approach by incorporating the energetics of adsorbate-adsorbate (and adsorbate-bilayer) interactions. Predicted equilibrium isotherms are compared to experimental results, along with the time-course of adsorption/desorption, over a range of values of energetic parameters. Initial predictions of effects of adsorbate energetics on ion current traces involving long and short pulses of agonists show remarkably large effects on the time scale(s) of desensitization and deactivation.

Structural model for ligand binding and channel opening of an insect gustatory receptor

Insect gustatory receptors play roles in sensing tastants, such as sugars and bitter substances. We previously demonstrated that the BmGr9 silkworm gustatory receptor is a d-fructose-gated ion channel receptor. However, the molecular mechanism of how d-fructose could initiate channel opening were unclear. Herein, we present a structural model for a channel pore and a d-fructose-binding site in BmGr9. Since the membrane topology and oligomeric state of BmGr9 appeared to be similar to those of an insect odorant receptor coreceptor, Orco, we constructed a structural model of BmGr9 based on the cryo-EM Orco structure. Our site-directed mutagenesis data suggested that the transmembrane region 7 forms channel pore and controls channel gating. This model also suggested that a pocket formed by transmembrane helices 2 to 4 and 6 binds d-fructose. Using mutagenesis experiments in combination with docking simulations, we were able to determine the potent binding mode of d-fructose. Finally, based on these data, we propose a conformational change that leads to channel opening upon d-fructose binding. Taken together, these findings detail the molecular mechanism by which an insect gustatory receptor can be activated by its ligand molecule.

A New Structural Model of Apolipoprotein B100 Based on Computational Modeling and Cross Linking.

ApoB-100 is a member of a large lipid transfer protein superfamily and is one of the main apolipoproteins found on low-density lipoprotein (LDL) and very low-density lipoprotein (VLDL) particles. Despite its clinical significance for the development of cardiovascular disease, there is limited information on apoB-100 structure. We have developed a novel method based on the “divide and conquer” algorithm, using PSIPRED software, by dividing apoB-100 into five subunits and 11 domains. Models of each domain were prepared using I-TASSER, DEMO, RoseTTAFold, Phyre2, and MODELLER. Subsequently, we used disuccinimidyl sulfoxide (DSSO), a new mass spectrometry cleavable cross-linker, and the known position of disulfide bonds to experimentally validate each model. We obtained 65 unique DSSO cross-links, of which 87.5% were within a 26 Å threshold in the final model. We also evaluated the positions of cysteine residues involved in the eight known disulfide bonds in apoB-100, and each pair was measured within the expected 5.6 Å constraint. Finally, multiple domains were combined by applying constraints based on detected long-range DSSO cross-links to generate five subunits, which were subsequently merged to achieve an uninterrupted architecture for apoB-100 around a lipoprotein particle. Moreover, the dynamics of apoB-100 during particle size transitions was examined by comparing VLDL and LDL computational models and using experimental cross-linking data. In addition, the proposed model of receptor ligand binding of apoB-100 provides new insights into some of its functions.

Effects of organic sulfur and arsenite/dissolved organic matter ratios on arsenite complexation with dissolved organic matter.

The speciation and fate of arsenic (As) in soil-water systems is a topic of great interest, in part due to growing awareness of As uptake into rice as an important human exposure pathway to As. Rice paddy and other wetland soils are rich in dissolved organic matter (DOM), leading to As/DOM ratios that are typically lower than those in groundwater aquifers or that have been used in many laboratory studies of As-DOM interactions. In this contribution, we evaluate arsenite (As(III)) binding to seven different DOM samples at As/DOM ratios relevant for wetland pore waters, and explore the chemical properties of the DOM samples associated with high levels of As(III)-DOM complexation. We integrate data from wet chemical analysis of DOM chemical properties, dialysis equilibrium experiments, and two-site ligand binding models to show that in some DOM samples, 15-60% of As(III) can be bound to DOM at environmentally-relevant As/DOM ratios of 0.0032-0.016μmol As/mmol C. Binding decreases as the As(III)/DOM ratio increases. The organic sulfur (Sorg) content of the DOM samples was strongly correlated with levels of As(III)-DOM complexation and "strong" binding site densities, consistent with theories that thiols are strong binding ligands for As(III) in natural organic matter. Finally, a whole-cell E. coli biosensor assay was used to show that DOM samples most effective at complexing As(III) also led to decreased microbial As(III) uptake at low As/DOC ratios. This work demonstrates that naturally-occurring variations in the Sorg content of DOM has a significant impact on As(III) binding to DOM, and has implications for As(III) availability to microorganisms.

Ligand binding remodels protein side-chain conformational heterogeneity.

While protein conformational heterogeneity plays an important role in many aspects of biological function, including ligand binding, its impact has been difficult to quantify. Macromolecular X-ray diffraction is commonly interpreted with a static structure, but it can provide information on both the anharmonic and harmonic contributions to conformational heterogeneity. Here, through multiconformer modeling of time- and space-averaged electron density, we measure conformational heterogeneity of 743 stringently matched pairs of crystallographic datasets that reflect unbound/apo and ligand-bound/holo states. When comparing the conformational heterogeneity of side chains, we observe that when binding site residues become more rigid upon ligand binding, distant residues tend to become more flexible, especially in non-solvent-exposed regions. Among ligand properties, we observe increased protein flexibility as the number of hydrogen bonds decreases and relative hydrophobicity increases. Across a series of 13 inhibitor-bound structures of CDK2, we find that conformational heterogeneity is correlated with inhibitor features and identify how conformational changes propagate differences in conformational heterogeneity away from the binding site. Collectively, our findings agree with models emerging from nuclear magnetic resonance studies suggesting that residual side-chain entropy can modulate affinity and point to the need to integrate both static conformational changes and conformational heterogeneity in models of ligand binding.

Creating Maps of the Ligand Binding Landscape for Kinetics-Based Drug Discovery.

Simulations of ligand-protein interactions can be very useful for drug design and to gain biological insight. Full pathways of ligand-protein binding can be used to get information about ligand binding transition states, which form the rate-limiting step of the binding and release processes. However, these simulations are typically limited by the presence of large energy barriers that separate stable poses of interest. Here we describe a simulation protocol for exploring and analyzing landscapes of ligand-protein interactions that makes use of molecular docking, enhanced molecular simulation with the weighted ensemble algorithm, and network analysis. It can be accomplished using a modest cluster of graphics processing units and freely accessible software. This protocol focuses on the construction and analysis of a network model of ligand binding poses and provides links to resources that describe the other steps in more detail. The end result of this protocol is a map of the ligand-protein binding landscape that identifies transition states of the ligand binding pathway, as well as alternative bound poses that could be stabilized with modifications to the ligand.

Complexation of arsenate to humic acid with different molecular weight fractions in aqueous solution

Natural organic matter (NOM) has been considered a critical substance in the transport and transformation of arsenic. NOM is a complex mixture of multifunctional organic components with a wide molecular weight (MW) distribution, and it is necessary to understand the complexation of arsenic with MW-dependent NOM fractions. In this study, humic acid (HA) was chosen as the representative fraction of NOM to investigate the complexation mechanism with arsenic. The bulk HA sample was fractionated to five fractions by ultrafiltration technology, and the complexing property of HA fractions with arsenic was analyzed by the dialysis method. We observed that the acidic and neutral conditions favor the complexation of HA fractions with arsenate (As(V)). The HA fractions with molecular weight > 100 kDa, 1-10 kDa, and <1 kDa have the stronger complexing capacity of As(V) than the other HA fractions. The bound As(V) percentage was positively associated with carboxyl content, phenolic content, and especially total acidity. A two-site ligand-binding model can describe the complexing capacity of arsenic onto HA fractions. The results can provide some fundamental information about the complexation of arsenic with MW-dependent HA fractions quantitatively.

Autoinhibition of Mouse Von Willebrand Factor: Addressing the Species Compatibility between VWF and Gpibα in Humans and Mice

Introduction: The usage of mouse models to investigate thrombosis and hemostasis has now spanned several decades, with the advent of genetic manipulation elucidating the roles of platelet receptors, secondary messengers, and coagulation factors to name a few. While there are many similarities between humans and mice, the vast differences in physical parameters of blood circulation and coagulation should not be ignored. The incompatibility of mouse platelet receptor glycoprotein (GP)Ibα with human von Willebrand factor (VWF) has been well documented, yet human GPIbα can interact with mouse plasma VWF under shear. In light of the recently discovered autoinhibitory module (AIM) in modulating the activity of human VWF, here we seek to address the role of the AIM in species compatibility between these two interacting proteins.Methods: Recombinant human and mouse VWF fragments containing the A1 domain and either intact or truncated AIM, followed by a poly-histidine tag, were expressed from stably transfected mammalian cells. Recombinant human and mouse GPIbα ligand-binding domain (LBD) (residues 1-290, mature protein numbering) were similarly expressed bearing a biotin acceptor sequence and poly-histidine tag. Stable cell lines co-expressing E. coli biotin ligase were used to express and biotinylate the LBD protein before purification. All proteins were purified by nickel-affinity chromatography followed by size exclusion chromatography. Bio-layer interferometry was performed on an Octet QK e using streptavidin biosensors to capture biotinylated GPIbα LBD fragments. Data was analyzed using a 2:1 heterogeneous ligand binding model for kinetic parameters, and steady state affinity (K D) was calculated either from binding responses at the end of the association phase, or from predicted equilibria from the aforementioned model. Human or mouse washed platelets were isolated by centrifugation, followed by pelleting the PRP in the presence of PGE-1. Platelets were resuspended to 150,000/ μL in modified Tyrode's buffer, and aggregation was monitored in a light transmission aggregometer.Results: Consistent with earlier reports, human AIM-A1 protein (VWF residues 1238-1493), at concentrations below 1 μM, showed no appreciable binding to human GPIbα LBD. Conversely, human truncated AIM-A1 protein (tAIM-A1, residues 1261-1472) exhibited a K D of 348 nM towards the LBD. Mouse tAIM-A1 proteins showed enhanced binding to mouse LBD (K D = 214 nM), but mouse AIM-A1 is able to bind to mouse LBD with a K D of 716 nM, which is considerably higher than the affinity of the human-human pair of tens of μM (Figure 1).Given the increased affinity of mouse AIM-A1 to mouse LBD, we reasoned that the mouse AIM may be less stable than the human counterpart. A chimeric AIM-A1 protein, in which human AIM sequences flank mouse A1 (human residues 1238-1271, mouse 1272-1458, and human 1459-1493), was generated. The chimeric AIM-A1 showed little binding to mouse LBD. However, this protein can still aggregate washed platelets from both species and bind to gain-of-function human GPIbα, indicating it still is a folded domain capable to recognizing its cognate receptor, mouse GPIbα (Figure 2).The isolated mouse AIM-A1 fragments are capable of inducing robust mouse and human platelet aggregation for washed platelets in a dose dependent manner. It had been previously demonstrated that in platelet rich plasma, ristocetin has no effect on mouse VWF. However, washed mouse platelets incubated with 60 nM of mouse AIM-A1 followed by addition of 1.5 mg/mL ristocetin was able to agglutinate the platelets (Figure 2). These results suggest that these proteins may be used to help elucidate the signaling pathways of GPIb-IX-V complex in mice.Conclusion: In agreement with previous reports, we observe that in general mouse AIM-A1 is more active than human AIM-A1 in its propensity to bind to GPIbα. On the aspect that human VWF in incompatible with mouse platelets, we observed that mouse LBD has lower affinity for human tAIM-A1 than human LBD does for mouse tAIM-A1. These findings altogether suggest that there are likely differences in activation forces for each species AIM, and that the GPIbα-A1 interactions, in terms of their mechanical and thermodynamic properties, are somewhat unique, and perhaps optimized, for each species. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Assessing the affinity of oxazolidinedione/hydantoin derivatives for the human 5HT1A/2A receptor through homology modeling and docking studies

A flexible docking of a series of heteroaryl compounds to the binding site of a model of human 5-HT1A/2A receptor was exercised using GLIDE docking methods. The resultant docking scores were used to correlate the in vivo affinity data. The GLIDE docking algorithm when used with a homology model of 5HT1A/2A was based on β2- adrenergic receptor template. The influence of structure and hydrophobic properties of aryl moiety on binding affinities was discussed and a model for ligand binding in the hydrophobic part of the binding site was proposed.

RNA-ligand molecular docking: advances and challenges.

With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.