Structure Of Ligand Binding Research Articles

Interaction and dynamics of chemokine receptor CXCR4 binding with CXCL12 and hBD-3

Chemokine receptor CXCR4 is involved in diverse diseases. A comparative study was conducted on CXCR4 embedded in a POPC lipid bilayer binding with CXCL12 in full and truncated forms, hBD-3 in wildtype, analog, and mutant forms based on in total 63 µs all-atom MD simulations. The initial binding structures of CXCR4 with ligands were predicted using HADDOCK docking or random-seed method, then μs-long simulations were performed to refine the structures. CXCR4&ligand binding structures predicted agree with available literature data. Both kinds of ligands bind stably to the N-terminus, extracellular loop 2 (ECL2), and ECL3 regions of CXCR4; the C2-C3 (K32-R38) region and occasionally the head of hBD-3 bind stably with CXCR4. hBD-3 analogs with Cys11-Cys40 disulfide bond can activate CXCR4 based on the Helix3-Helix6 distance calculation, but not other analogs or mutant. The results provide insight into understanding the dynamics and activation mechanism of CXCR4 receptor binding with different ligands.

Structure and dynamics of differential ligand binding in the human rho-type GABAA receptor

Structure and dynamics of differential ligand binding in the human rho-type GABAA receptor

Design and Discovery of Genistein-based Drugs as a Potential Tyrosine Kinase Inhibitor for Lung Adenocarcinoma through Hybrid In-silico Methods

This research employs a comprehensive approach to investigate potential therapeutic candidates for lung adenocarcinoma through drug-drug transcriptomic similarity analysis and molecular docking simulations. Using the Connectivity Map Touchstone tool, we identified compounds with high transcriptomic similarity to genistein, revealing potential shared mechanisms of action. The selected compounds, including avrainvillamide-analog-2, were further assessed through molecular docking simulations against the tyrosine kinase inhibitor (TKI) enzyme. Avrainvillamide-analog-2 exhibited a remarkable binding affinity in pocket C2, interacting with key amino acids. The results provide valuable insights into the pharmacological properties of the identified compounds, laying the groundwork for future experimental validations and drug development initiatives. Additionally, cavities detection by CB Dock server and structural refinement by PDB REDO contribute to the overall understanding of ligand binding and protein structure. This integrative approach offers a holistic perspective for identifying potential lead compounds and understanding their molecular interactions, facilitating the rational design of novel therapeutics for lung adenocarcinoma.

Structure and dynamics of differential ligand binding in the human ρ-type GABAA receptor

The neurotransmitter γ-aminobutyric acid (GABA) drives critical inhibitory processes in and beyond the nervous system, partly via ionotropic type-A receptors (GABAARs). Pharmacological properties of ρ-type GABAARs are particularly distinctive, yet the structural basis for their specialization remains unclear. Here, we present cryo-EM structures of a lipid-embedded human ρ1 GABAAR, including a partial intracellular domain, under apo, inhibited, and desensitized conditions. An apparent resting state, determined first in the absence of modulators, was recapitulated with the specific inhibitor (1,2,5,6-tetrahydropyridin-4-yl)methylphosphinic acid and blocker picrotoxin and provided a rationale for bicuculline insensitivity. Comparative structures, mutant recordings, and molecular simulations with and without GABA further explained the sensitized but slower activation of ρ1 relative to canonical subtypes. Combining GABA with picrotoxin also captured an apparent uncoupled intermediate state. This work reveals structural mechanisms of gating and modulation with applications to ρ-specific pharmaceutical design and to our biophysical understanding of ligand-gated ion channels.

Structure and dynamics of differential ligand binding in the human ρ-type GABAA receptor

Structure and dynamics of differential ligand binding in the human ρ-type GABAA receptor

Machine-Learning- and Knowledge-Based Scoring Functions Incorporating Ligand and Protein Fingerprints.

We propose a novel machine-learning-based scoring function for drug discovery that incorporates ligand and protein structural information into a knowledge-based PMF score. Molecular docking, a simulation method for structure-based drug design (SBDD), is expected to reduce the enormous costs associated with conventional experimental methods in terms of rational drug discovery. Molecular docking has two main purposes: to predict ligand-binding structures for target proteins and to predict protein–ligand binding affinity. Currently available programs of molecular docking offer an accurate prediction of ligand binding structures for many systems. However, the accurate prediction of binding affinity remains challenging. In this study, we developed a new scoring function that incorporates fingerprints representing ligand and protein structures as descriptors in the PMF score. Here, regression analysis of the scoring function was performed using the following machine learning techniques: least absolute shrinkage and selection operator (LASSO) and light gradient boosting machine (LightGBM). The results on a test data set showed that the binding affinity delivered by the newly developed scoring function has a Pearson correlation coefficient of 0.79 with the experimental value, which surpasses that of the conventional scoring functions. Further analysis provided a chemical understanding of the descriptors that contributed significantly to the improvement in prediction accuracy. Our approach and findings are useful for rational drug discovery.

Exploration of Cytochrome P450 Structure and Function in In Silico Assays for Toxicity

Cytochrome P450 (CYP or P450) enzymes are known for their role in detoxifying xenobiotics and may at times cause toxicity. CYP1A1gene polymorphisms have been linked to susceptibility risks of various cancers and neoplasms, including bladder cancer. The aim of the study was to use bioinformatics tools to examine structural differences in CYP1A1 wild type and isoforms 2 and 3. Protein sequences of selected CYPs were retrieved from the NCBI protein databank and GenPept. Sequence homologies were searched for by BLAST, and by multiple sequence alignments. Protein structural characteristics such as ligand binding and secondary structure prediction were analyzed by Phyre. The CYPIA1 wild type and isoforms had 33 % variation in their amino acid residues and binding sites. The predicted binding sites for the first 6 amino acid residues were similar between the wild type and isoform two; however, the average distances from the ligands differed. The predicted secondary structure of the CYP1A1 isoform 3 exhibited the widest difference from the wild type in the percentage of disordered helix (17%), alpha helix (66%), beta strand (3%) and trans‐membrane domains (12%). Differences in nucleotide sequence between the CYP1A1 wild type and isoforms caused a change in the protein structure which further altered the trans‐membrane topology and ligand binding potential of the protein. These findings indicate structural deficits among the three Cytochrome P450 isoforms, suggesting the possible conferment of risks to toxicity and associated diseases.

Combining classical molecular docking with self-consistent charge density-functional tight-binding computations for the efficient and quality prediction of ligand binding structure

To improve the successful prediction rate of the existing molecular docking methods, a new docking approach is proposed that consists of three steps: generating an ensemble of docked poses with a conventional docking method, performing clustering analysis of the ensemble to select the representative poses, and optimizing the representative structures with a low-cost quantum mechanics method. Three quantum mechanics methods, self-consistent charge density-functional tight-binding, ONIOM(DFT:PM6), and ONIOM(SCC-DFTB:PM6), are tested on 18 ligand-receptor bio-complexes. The rate of successful binding pose predictions by the proposed self-consistent charge density-functional tight-binding docking method is the highest, at 67%. The self-consistent charge density-functional tight-binding docking method should be useful for the structure-based drug design.

Structure-based substrate specificity analysis of GH11 xylanase from Streptomyces olivaceoviridis E-86.

Although many xylanases have been studied, many of the characteristics of xylanases toward branches in xylan remain unclear. In this study, the substrate specificity of a GH11 xylanase from Streptomyces olivaceoviridis E-86 (SoXyn11B) was elucidated based on its three-dimensional structure. Subsite mapping suggests that SoXyn11B has seven subsites (four subsites on the - side and three subsites on the + side), and it is one longer than the GH10 xylanase from S. olivaceoviridis (SoXyn10A). SoXyn11B has no affinity for the subsites at either end of the scissile glycosidic bond, and the sugar-binding energy at subsite - 2 was the highest, followed by subsite + 2. These properties were very similar to those of SoXyn10A. In contrast, SoXyn11B produced different branched oligosaccharides from bagasse compared with those of SoXyn10A. These branched oligosaccharides were identified as O-β-D-xylopyranosyl-(1→4)-[O-α-L-arabinofuranosyl-(1→3)]-O-β-D-xylopyranosyl-(1→4)-β-D-xylopyranosyl-(1→4)-β-D-xylopyranose (Ara3Xyl4) and O-β-D-xylopyranosyl-(1→4)-[O-4-O-methyl-α-D-glucuronopyranosyl-(l→2)]-β-D-xylopyranosyl-(1→4)-β-D-xylopyranosyl-(1→4)-β-D-xylopyranose (MeGlcA3Xyl4) by nuclear magnetic resonance (NMR) and electrospray ionization mass spectrometry (ESI-MS) and confirmed by crystal structure analysis of SoXyn11B in complex with these branched xylooligosaccharides. SoXyn11B has a β-jerryroll fold structure, and the catalytic cleft is located on the inner β-sheet of the fold. The ligand-binding structures revealed seven subsites of SoXyn11B. The 2- and 3-hydroxy groups of xylose at the subsites + 3, + 2, and - 3 face outwards, and an arabinose or a glucuronic acid side chain can be linked to these positions. These subsite structures appear to cause the limited substrate specificity of SoXyn11B for branched xylooligosaccharides. KEY POINTS: • Crystal structure of family 11 β-xylanase from Streptomyces olivaceoviridis was determined. • Topology of substrate-binding cleft of family 11 β-xylanase from Streptomyces olivaceoviridis was characterized. • Mode of action of family 11 β-xylanase from Streptomyces olivaceoviridis for substitutions in xylan was elucidated.

ITScore-NL: An Iterative Knowledge-Based Scoring Function for Nucleic Acid-Ligand Interactions.

Nucleic acid-ligand complexes underlie numerous cellular processes, such as gene function expression and regulation, in which their three-dimensional structures are important to understand their functions and thus to develop therapeutic interventions. Given the high cost and technical difficulties in experimental methods, computational methods such as molecular docking have been actively used to investigate nucleic acid-ligand interactions in which an accurate scoring function is crucial. However, because of the limited number of experimental nucleic acid-ligand binding data and structures, the scoring function development for nucleic acid-ligand interactions falls far behind that for protein-protein and protein-ligand interactions. Here, based on our statistical mechanics-based iterative approach, we have developed an iterative knowledge-based scoring function for nucleic acid-ligand interactions, named as ITScore-NL, by explicitly including stacking and electrostatic potentials. Our ITScore-NL scoring function was extensively evaluated for its ability in the binding mode and binding affinity predictions on three diverse test sets and compared with state-of-the-art scoring functions. Overall, ITScore-NL obtained significantly better performance than the other 12 scoring functions and predicted near-native poses with rmsd ≤ 1.5 Å for 71.43% of the cases when the top three binding modes were considered and a good correlation of R = 0.64 in binding affinity prediction on the large test set of 77 nucleic acid-ligand complexes. These results suggested the accuracy of ITScore-NL and the necessity of explicitly including stacking and electrostatic potentials.

Alchemical free energy calculations via metadynamics: Application to the theophylline-RNA aptamer complex.

We propose a computational workflow for robust and accurate prediction of both binding poses and their affinities at early stage in designing drug candidates. Small, rigid ligands with few intramolecular degrees of freedom, for example, fragment-like molecules, have multiple binding poses, even at a single binding site, and their affinities are often close to each other. We explore various structures of ligand binding to a target through metadynamics using a small number of collective variables, followed by reweighting to obtain the atomic coordinates. After identifying each binding pose by cluster analysis, we perform alchemical free energy calculations on each structure to obtain the overall value. We applied this protocol in computing free energy of binding for the theophylline-RNA aptamer complex. Of the six (meta)stable structures found, the most favorable binding structure is consistent with the structure obtained by NMR. The overall free energy of binding reproduces the experimental values very well.

DeepMalaria: Artificial Intelligence Driven Discovery of Potent Antiplasmodials.

Antimalarial drugs are becoming less effective due to the emergence of drug resistance. Resistance has been reported for all available malaria drugs, including artemisinin, thus creating a perpetual need for alternative drug candidates. The traditional drug discovery approach of high throughput screening (HTS) of large compound libraries for identification of new drug leads is time-consuming and resource intensive. While virtual in silico screening is a solution to this problem, however, the generalization of the models is not ideal. Artificial intelligence (AI), utilizing either structure-based or ligand-based approaches, has demonstrated highly accurate performances in the field of chemical property prediction. Leveraging the existing data, AI would be a suitable alternative to blind-search HTS or fingerprint-based virtual screening. The AI model would learn patterns within the data and help to search for hit compounds efficiently. In this work, we introduce DeepMalaria, a deep-learning based process capable of predicting the anti-Plasmodium falciparum inhibitory properties of compounds using their SMILES. A graph-based model is trained on 13,446 publicly available antiplasmodial hit compounds from GlaxoSmithKline (GSK) dataset that are currently being used to find novel drug candidates for malaria. We validated this model by predicting hit compounds from a macrocyclic compound library and already approved drugs that are used for repurposing. We have chosen macrocyclic compounds as these ligand-binding structures are underexplored in malaria drug discovery. The in silico pipeline for this process also consists of additional validation of an in-house independent dataset consisting mostly of natural product compounds. Transfer learning from a large dataset was leveraged to improve the performance of the deep learning model. To validate the DeepMalaria generated hits, we used a commonly used SYBR Green I fluorescence assay based phenotypic screening. DeepMalaria was able to detect all the compounds with nanomolar activity and 87.5% of the compounds with greater than 50% inhibition. Further experiments to reveal the compounds’ mechanism of action have shown that not only does one of the hit compounds, DC-9237, inhibits all asexual stages of Plasmodium falciparum, but is a fast-acting compound which makes it a strong candidate for further optimization.

Facile synthesis, X-Ray structure of new multi-substituted aryl imidazole ligand, biological screening and DNA binding of its Cr(III), Fe(III) and Cu(II) coordination compounds as potential antibiotic and anticancer drugs

A simple highly adjustable and effective synthesis of aryl imidazole ligand HL namely (2-(1-butyl-4,5-diphenyl-1H-imidazole-2-yl) (4-bromophenol) was discussed where it was prepared by cyclo condensation of 5-bromo-2-hydroxybenzaldehyde, benzil and butan-1-amine. Three new Cr(III), Fe(III) and Cu(II) coordination compounds of aryl imidazole ligand were synthesized. The multi-substituted aryl imidazole ligand (HL) and its coordination compounds were characterized via a wide range of spectroscopic and analytical tools such as 1H NMR and 13C NMR, infrared (IR) and UV–Vis spectrophotometry, conductivity and magnetic measurements. The crystal and molecular structure of aryl imidazole ligand HL were discussed by using maXus. The structure of the titled aryl imidazole ligand HL and its metal coordination compounds were discussed theoretically by using Gaussian 09 program at the B3LYP/LANL2DZ level of theory. The obtained data showed that the new compounds have 1:1 M ratio (metal: ligand) and non-electrolytes in nature. The newly prepared [Cr(L)Cl2(H2O)2], [Fe(L)(NO3)2(H2O)2] and [Cu(L)Cl(H2O)3] coordination compounds have a distorted-octahedral geometry. Density Functional Theory (DFT) calculations have been carried out to investigate the equilibrium geometry of the ligands and its coordination compound using Gaussian 09 program at the B3LYP/LANL2DZ level. Moreover, the new compounds were tested against the selected species of microorganism namely Staphylococcus aureus (+ve), Pseudomonas aeruginosa (-ve), Escherichia coli (-ve) and also against Candida albicans, Aspergillus flavus and Trichophyton Rubrumin. The result revealed that new compounds showed high efficacy towards the growth inhibition of the selected pathogenic microorganism. Moreover, the interaction of the new coordination compounds with CT-DNA was studied by absorption spectra, gel electrophoresis and viscosity techniques. The result showed that the interaction of the new coordination compounds with CT-DNA is an intercalative binding mode. Furthermore, the growth inhibitory effects of the new compounds were tested against Hep-G2, MCF-7 and HCT-116 cell lines. Moreover, all complexes exhibited stronger cytotoxicity effect on the outgrowth of different types of carcinoma cells, than their corresponding imidazole ligand and follow the order: CuL > CrL > FeL > HL.

Pharmaceutical property interpretation and biological property study on Ethionamide using quantum spectroscopic and computational tools

ABSTRACTIn this work, the molecular spectroscopic technique was used in a nonconventional way to predict the drug mechanism action of the drug compound, Ethionamide. The formatted molecular charge levels, due to the injection of substitutions in suitable positions, were measured. The molecular geometry distortion by the adoption of ligands in the ring system was noted at the nodal points of the core and the change of force constant of the heterogeneous intra-molecular assembly. The activity of the compositional parts of the molecule was purposively monitored from which the entire core and allied parts of the molecule were evaluated by the vibrational characteristics. The molecular reaction mechanism for saturating drug potential was observed from the chemical shift of core and substituted carbons. The fabricated charge transfer complex was identified and recognized from the base and ligand-binding structure. The major part of the exchange of electro-chemical quantized energy in non-bonding molecular orbitals for arranging biological activity in the compound was measured. The rule of five and Lipinski parameters to recognize the drug quality were checked and estimated.

Conformation Search Across Multiple-Level Potential-Energy Surfaces (CSAMP): A Strategy for Accurate Prediction of Protein–Ligand Binding Structures

Accurate protein binding structure determination presents a great challenge to both experiment and theory. Here, in this work, we propose a new DOX protocol which combines the ensemble molecular Docking as the coarse-level, structure Optimization with the semiempirical quantum mechanics methods as the medium level, and the eXtended ONIOM ( XO) calculations as the fine level. The fundamental of the DOX protocol relies on the Conformation Search Across Multiple-level Potential-energy surfaces (CSAMP) strategy, where the conformation spaces of a funnel-like structure are searched from the coarse level with hundreds of candidates to the medium level with around 10 top candidates to the fine level with the final top 1 or 2 binding modes. An in-depth test for the protocol set up against 28 crystallographic data consisting of HMGR-statins, SDase-inhibitors, 3HNRase-inhibitors, and NA-inhibitors yielded a satisfactory result with ∼0.5 Å root-mean-square deviations (RMSDs) on geometries and ∼0.8 kcal/mol absolute error of relative binding energies on average. A further larger scale validation on the Astex test set (including 85 diverse structures) revealed an impressive performance with a RMSD < 2 Å success rate of 99%, suggesting DOX is a promising computational route toward accurate prediction of the protein-ligand binding structures.

Measuring the structural impact of mutations on cytochrome P450 21A2, the major steroid 21-hydroxylase related to congenital adrenal hyperplasia

Congenital adrenal hyperplasia is an inherited autosomal recessive disorder related to deficient cortisol synthesis. The deficiency of steroid 21-hydroxylase (cytochrome P450 21A2), an enzyme involved in cortisol synthesis, is responsible for ∼95% of cases of congenital adrenal hyperplasia. This metabolic disease exhibits three clinical forms: salt-wasting, simple virilizing, and non-classical form, which are divided according to the degree of severity. In the present study, structural and mutational analyses were performed in order to identify the structural impact of mutations on cytochrome P450 21A2 and correlate them with patient clinical severity. The following mutations were selected: arginine-356 to tryptophan (R356W), proline-30 to leucine (P30L), isoleucine-172 to asparagine (I172N), valine-281 to leucine (V281L), and the null mutation glutamine-318 (Q318X). Our computational approach mapped the location of residues on P450 and identified their implications on enzyme electrostatic potential mapping to progesterone and heme binding pockets. Using molecular dynamics simulations, we analyzed the structural stability of ligand binding and protein structure, as well as possible conformational changes at the catalytic pocket that leads to impairment of enzymatic activity. Our study sheds light on the impact structural mutations have over steroid 21-hydroxylase structure-function in the cell.Communicated by Ramaswamy H. Sarma

Surface Chemistry of Atomically Precise Coinage-Metal Nanoclusters: From Structural Control to Surface Reactivity and Catalysis.

A comprehensive understanding of chemical bonding and reactions at the surface of nanomaterials is of great importance in the rational design of their functional properties and applications. With the rapid development in cluster science, it has become clear that atomically precise metal clusters represent ideal models for resolving various important and/or unsolved issues related to surface science. This Account highlights our recent efforts on the fabrication of ligand-stabilized coinage nanoclusters with atomic precision from the viewpoint ofsurface coordination chemistry in particular. The successful synthesis of a large variety of metal clusters in our group has greatly benefitted from the development of an effective amine-assisted NaBH4 reduction method. First discussed in this Account is how the introduction of amines in the synthetic protocol enhances the long-term stability and high-yield production of Ag/Cu-based metals in air. Such a method allows the utilization of different organic ligands as surface stabilizing agents to manipulate both the core and surface structures of metal nanoclusters, helping to understand the role of surface ligands in determining the structures of metal nanoclusters. The coordination chemistry of ligands used in the synthesis of metal nanoclusters is crucial in determining their overall shape, metal arrangement, surface ligand binding structure, chirality and also metal exposure. Detailed discussions are given in the following four different systems: (1) The co-use of phosphines and thiolates with rich coordination structures (2 to 4-coordinated) helps to control the formation of a sequence of Ag nanoclusters with a near-perfectly cubic shape; (2) The metal arrangements and surface structures of AuCu clusters highly depend on metal precursors and counter cations used in the synthesis; (3) Metal clusters with intrinsic chirality are readily prepared by introducing chiral ligands or counterions, making it possible to obtain optically active enantiomers and understand the origin of chirality of metal nanoclusters; (4) The variation of metal exposure of the inner metal core of metal nanocluster can be controlled by the surface ligand coordination structure. Such capabilities to manipulate the surface structure of metal nanoclusters allow the creation of model systems for investigating the structure-reactivity relationship of metal nanomaterials. Several important examples are then discussed to highlight the importance of ligand coordination chemistry in tuning the surface reactivity and catalysis of metal nanoclusters. For example, bulky thiolates on Agare demonstrated to be more labile than small thiolates for making metal nanoclusters with both enhanced ligand exchange capability and catalysis. Alkynyl ligands can be thermally released from metal nanoclusters more easily than thiolates and halides while maintaining the overall structure, thereby serving as ideal systems for understanding the promoting effect of surface stabilizers on catalysis. Finally, we provide a perspective on the principles of surface coordination chemistry of metal nanoclusters and their potential applications with regards to catalysis of protected metal clusters.

Bioinspired dynamic microcapsules.

There is an increasing interest in bioinspired dynamic materials. Abundant illustrations of protein domains exist in nature, with remarkable ligand binding characteristics and structures that undergo conformational changes. For example, calmodulin (CaM) can have three conformational states, which are the unstructured Apo-state, Ca2+-bound ligand-exposed binding state, and compact ligand-bound state. CaM's mechanical response to biological cues is highly suitable for engineering dynamic materials. The distance between CaM globular terminals in the Ca2+-bound state is 5 nm and in the ligand-bound state is 1.5 nm. CaM's nanoscale conformational changes have been used to develop dynamic hydrogel microspheres that undergo reversible volume changes. The current work presents the fabrication and preliminary results of layer-by-layer (LbL) self-assembled Dynamic MicroCapsules (DynaMicCaps) whose multilayered shell walls are composed of polyelectrolytes and CaM. Quasi-dynamic perfusion results show that the DynaMicCaps undergo drastic volume changes, with up to ∼1500% increase, when exposed to a biochemical ligand trifluoperazine (TFP) at pH 6.3. Under similar test conditions, microcapsules without CaM also underwent volume changes, with only up to ∼290% increase, indicating that CaM's bio-responsiveness was retained within the shell walls of the DynaMicCaps. Furthermore, DynaMicCaps exposed to 0.1 M NaOH underwent volume changes, with only up to ∼580% volume increase. Therefore, DynaMicCaps represent a new class of polyelectrolyte multilayer (PEM) capsules that can potentially be used to release their payload at near physiological pH. With over 200 proteins that undergo marked, well-characterized conformational changes in response to specific biochemical triggers, several other versions of DynaMicCaps can potentially be developed.

Machine learning accelerates MD-based binding pose prediction between ligands and proteins.

MotivationFast and accurate prediction of protein–ligand binding structures is indispensable for structure-based drug design and accurate estimation of binding free energy of drug candidate molecules in drug discovery. Recently, accurate pose prediction methods based on short Molecular Dynamics (MD) simulations, such as MM-PBSA and MM-GBSA, among generated docking poses have been used. Since molecular structures obtained from MD simulation depend on the initial condition, taking the average over different initial conditions leads to better accuracy. Prediction accuracy of protein–ligand binding poses can be improved with multiple runs at different initial velocity.ResultsThis paper shows that a machine learning method, called Best Arm Identification, can optimally control the number of MD runs for each binding pose. It allows us to identify a correct binding pose with a minimum number of total runs. Our experiment using three proteins and eight inhibitors showed that the computational cost can be reduced substantially without sacrificing accuracy. This method can be applied for controlling all kinds of molecular simulations to obtain best results under restricted computational resources.Availability and implementationCode and data are available on GitHub at https://github.com/tsudalab/bpbi.Supplementary information Supplementary data are available at Bioinformatics online.

Concentration-Dependent Binding of Small Ligands to Multiple Saturable Sites in Membrane Proteins

Membrane proteins are primary targets for most therapeutic indications in cancer and neurological diseases, binding over 50% of all known small molecule drugs. Understanding how such ligands impact membrane proteins requires knowledge on the molecular structure of ligand binding, a reasoning that has driven relentless efforts in drug discovery and translational research. Binding of small ligands appears however highly complex involving interaction to multiple transmembrane protein sites featuring single or multiple occupancy states. Within this scenario, looking for new developments in the field, we investigate the concentration-dependent binding of ligands to multiple saturable sites in membrane proteins. The study relying on docking and free-energy perturbation provides us with an extensive description of the probability density of protein-ligand states that allows for computation of thermodynamic properties of interest. It also provides one- and three-dimensional spatial descriptions for the ligand density across the protein-membrane system which can be of interest for structural purposes. Illustration and discussion of the results are shown for binding of the general anesthetic sevoflurane against Kv1.2, a mammalian ion channel for which experimental data are available.