Showing papers on "Docking (molecular) published in 2013"
TL;DR: The optimized polypeptide protocol is most accurate for extended peptides of limited size and number of formal charges, defining a domain of applicability for this approach.
Abstract: Predicting the binding mode of flexible polypeptides to proteins is an important task that falls outside the domain of applicability of most small molecule and protein–protein docking tools. Here, we test the small molecule flexible ligand docking program Glide on a set of 19 non-α-helical peptides and systematically improve pose prediction accuracy by enhancing Glide sampling for flexible polypeptides. In addition, scoring of the poses was improved by post-processing with physics-based implicit solvent MM-GBSA calculations. Using the best RMSD among the top 10 scoring poses as a metric, the success rate (RMSD ≤ 2.0 A for the interface backbone atoms) increased from 21% with default Glide SP settings to 58% with the enhanced peptide sampling and scoring protocol in the case of redocking to the native protein structure. This approaches the accuracy of the recently developed Rosetta FlexPepDock method (63% success for these 19 peptides) while being over 100 times faster. Cross-docking was performed for a su...
1,134 citations
TL;DR: The main developments in docking in this period, covered in this review, are receptor flexibility, solvation, fragment docking, postprocessing, docking into homology models, and docking comparisons.
Abstract: The aim of docking is to accurately predict the structure of a ligand within the constraints of a receptor binding site and to correctly estimate the strength of binding. We discuss, in detail, methodological developments that occurred in the docking field in 2010 and 2011, with a particular focus on the more difficult, and sometimes controversial, aspects of this promising computational discipline. The main developments in docking in this period, covered in this review, are receptor flexibility, solvation, fragment docking, postprocessing, docking into homology models, and docking comparisons. Several new, or at least newly invigorated, advances occurred in areas such as nonlinear scoring functions, using machine-learning approaches. This review is strongly focused on docking advances in the context of drug design, specifically in virtual screening and fragment-based drug design. Where appropriate, we refer readers to exemplar case studies.
285 citations
TL;DR: A high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network is presented.
Abstract: Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate.
225 citations
TL;DR: This work presents a relatively straightforward method for improving the probability of identifying accurately docked poses, similar in concept to consensus scoring schemes, but combines information about predicted binding modes rather than predicted binding affinities.
Abstract: Structure-based virtual screening relies on scoring the predicted binding modes of compounds docked into the target. Because the accuracy of this scoring relies on the accuracy of the docking, methods that increase docking accuracy are valuable. Here, we present a relatively straightforward method for improving the probability of identifying accurately docked poses. The method is similar in concept to consensus scoring schemes, which have been shown to increase ranking power and thus hit rates, but combines information about predicted binding modes rather than predicted binding affinities. The pose prediction success rate of each docking program alone was found in this trial to be 55% for Autodock, 58% for DOCK, and 64% for Vina. By using more than one docking program to predict the binding pose, correct poses were identified in 82% or more of cases, a significant improvement. In a virtual screen, these more reliably posed compounds can be preferentially advanced to subsequent scoring stages to improve hi...
185 citations
TL;DR: A staged study to address the effects of various aspects of protein flexibility and inclusion of active site water molecules on docking effectiveness to retrieve (and to be able to predict) correct ligand poses and to rank docked ligands in relation to their biological activity for CHK1, ERK2, LpxC, and UPA.
Abstract: Computational tools are essential in the drug design process, especially in order to take advantage of the increasing numbers of solved X-ray and NMR protein–ligand structures. Nowadays, molecular docking methods are routinely used for prediction of protein–ligand interactions and to aid in selecting potent molecules as a part of virtual screening of large databases. The improvements and advances in computational capacity in the past decade have allowed for further developments in molecular docking algorithms to address more complicated aspects such as protein flexibility. The effects of incorporation of active site water molecules and implicit or explicit solvation of the binding site are other relevant issues to be addressed in the docking procedures. Using the right docking algorithm at the right stage of virtual screening is most important. We report a staged study to address the effects of various aspects of protein flexibility and inclusion of active site water molecules on docking effectiveness to ...
180 citations
TL;DR: A structure-based virtual screen of a compound library containing ∼2 million small molecular entities led to the identification of a novel N'-(1-phenylethylidene)-benzohydrazide series of LSD1 inhibitors with hits showing biochemical IC50s in the 200-400 nM range.
Abstract: Lysine specific demethylase 1 (LSD1) plays an important role in regulating histone lysine methylation at residues K4 and K9 on histone H3 and is an attractive therapeutic target in multiple malignancies. Here we report a structure-based virtual screen of a compound library containing ∼2 million small molecular entities. Computational docking and scoring followed by biochemical screening led to the identification of a novel N'-(1-phenylethylidene)-benzohydrazide series of LSD1 inhibitors with hits showing biochemical IC50s in the 200-400 nM range. Hit-to-lead optimization and structure-activity relationship studies aided in the discovery of compound 12, with a Ki of 31 nM. Compound 12 is reversible and specific for LSD1 as compared to the monoamine oxidases shows minimal inhibition of CYPs and hERG and inhibits proliferation and survival in several cancer cell lines, including breast and colorectal cancer. Compound 12 may be used to probe LSD1's biological role in these cancers.
169 citations
TL;DR: An ensemble, flexible protein-peptide docking protocol that combines conformational selection and induced fit mechanisms that should open the route to the modeling of challenging systems such as disorder-order transitions taking place upon binding, significantly expanding the applicability limit of biomolecular interaction modeling by docking.
Abstract: Protein-peptide interactions are vital for the cell. They mediate, inhibit or serve as structural components in nearly 40% of all macromolecular interactions, and are often associated with diseases, making them interesting leads for protein drug design. In recent years, large-scale technologies have enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. Yet, the paucity of data regarding their molecular binding mechanisms together with their inherent flexibility makes the structural prediction of protein-peptide interactions very challenging. This leaves flexible docking as one of the few amenable computational techniques to model these complexes. We present here an ensemble, flexible protein-peptide docking protocol that combines conformational selection and induced fit mechanisms. Starting from an ensemble of three peptide conformations (extended, a-helix, polyproline-II), flexible docking with HADDOCK generates 79.4% of high quality models for bound/unbound and 69.4% for unbound/unbound docking when tested against the largest protein-peptide complexes benchmark dataset available to date. Conformational selection at the rigid-body docking stage successfully recovers the most relevant conformation for a given protein-peptide complex and the subsequent flexible refinement further improves the interface by up to 4.5 A interface RMSD. Cluster-based scoring of the models results in a selection of near-native solutions in the top three for ∼75% of the successfully predicted cases. This unified conformational selection and induced fit approach to protein-peptide docking should open the route to the modeling of challenging systems such as disorder-order transitions taking place upon binding, significantly expanding the applicability limit of biomolecular interaction modeling by docking.
166 citations
TL;DR: It is suggested that the phenylacetyl type of substituents and cyclohexyl moiety make the favorable interactions with a number of residues in the active site, and show better inhibitory activity to improve the pharmacokinetic profile of compounds against CDK2.
144 citations
TL;DR: Pyrazolealdehydes, Knoevenagel's condensates, and Schiff's bases of curcumin-I were synthesized, purified and characterized and it was observed that DNA-compound adducts were stabilized by three governing forces (Van der Wall's, H-bonding and electrostatic attractions).
127 citations
TL;DR: The modulator site (M-site) is characterized by cross interactions between both Pgp halves herein defined for the first time, having an important role in impairing conformational changes leading to substrate efflux.
Abstract: P-Glycoprotein (Pgp) is one of the best characterized ABC transporters, often involved in the multidrug-resistance phenotype overexpressed by several cancer cell lines. Experimental studies contributed to important knowledge concerning substrate polyspecificity, efflux mechanism, and drug-binding sites. This information is, however, scattered through different perspectives, not existing a unifying model for the knowledge available for this transporter. Using a previously refined structure of murine Pgp, three putative drug-binding sites were hereby characterized by means of molecular docking. The modulator site (M-site) is characterized by cross interactions between both Pgp halves herein defined for the first time, having an important role in impairing conformational changes leading to substrate efflux. Two other binding sites, located next to the inner leaflet of the lipid bilayer, were identified as the substrate-binding H and R sites by matching docking and experimental results. A new classification model with the ability to discriminate substrates from modulators is also proposed, integrating a vast number of theoretical and experimental data.
123 citations
TL;DR: The utility of structure-guided functional predictions for predicting the substrate specificities of several enzymes encoded by a bacterial gene cluster is established to enable the discovery of new metabolic pathways.
Abstract: Assigning valid functions to proteins identified in genome projects is challenging: overprediction and database annotation errors are the principal concerns. We and others are developing computation-guided strategies for functional discovery with 'metabolite docking' to experimentally derived or homology-based three-dimensional structures. Bacterial metabolic pathways often are encoded by 'genome neighbourhoods' (gene clusters and/or operons), which can provide important clues for functional assignment. We recently demonstrated the synergy of docking and pathway context by 'predicting' the intermediates in the glycolytic pathway in Escherichia coli. Metabolite docking to multiple binding proteins and enzymes in the same pathway increases the reliability of in silico predictions of substrate specificities because the pathway intermediates are structurally similar. Here we report that structure-guided approaches for predicting the substrate specificities of several enzymes encoded by a bacterial gene cluster allowed the correct prediction of the in vitro activity of a structurally characterized enzyme of unknown function (PDB 2PMQ), 2-epimerization of trans-4-hydroxy-L-proline betaine (tHyp-B) and cis-4-hydroxy-D-proline betaine (cHyp-B), and also the correct identification of the catabolic pathway in which Hyp-B 2-epimerase participates. The substrate-liganded pose predicted by virtual library screening (docking) was confirmed experimentally. The enzymatic activities in the predicted pathway were confirmed by in vitro assays and genetic analyses; the intermediates were identified by metabolomics; and repression of the genes encoding the pathway by high salt concentrations was established by transcriptomics, confirming the osmolyte role of tHyp-B. This study establishes the utility of structure-guided functional predictions to enable the discovery of new metabolic pathways.
TL;DR: The structure of the cytb5-cytP450 complex allows us to propose an interprotein electron transfer pathway involving the highly conserved Arg-125 on cytB5 serving as a salt bridge between the heme propionates of cytP450 and cytb 5.
TL;DR: Experimental results based on spectroscopy, isothermal calorimetry, electrochemistry aided with DNA-melting, and circular dichroism studies unambiguously established the formation of a groove binding network between the NPOS and ctDNA.
Abstract: The present study embodies a detailed investigation of the binding modes of a potential anticancer and neuroprotective fluorescent drug, 2-(5-selenocyanato-pentyl)-6-chloro benzo[de]isoquinoline-1,3-dione (NPOS) with calf thymus DNA (ctDNA). Experimental results based on spectroscopy, isothermal calorimetry, electrochemistry aided with DNA-melting, and circular dichroism studies unambiguously established the formation of a groove binding network between the NPOS and ctDNA. Molecular docking analysis ascertained a hydrogen bonding mediated ‘A-T rich region of B-DNA’ as the preferential docking site for NPOS. The cellular uptake and binding of NPOS with DNA from “Ehrlich Ascites Carcinoma” cells confirmed its biocompatibility within tumor cells. Experimental and ex vivo cell imaging studies vividly signify the importance of NPOS as a potential prerequisite for its use in therapeutic purposes.
TL;DR: This work has developed a case‐based reasoning approach called KBDOCK which systematically identifies and reuses domain family binding sites from the authors' database of nonredundant DDIs and provides a near‐perfect way to model single‐domain protein complexes when full‐homology templates are available.
Abstract: Protein docking algorithms aim to calculate the three-dimensional (3D) structure of a protein complex starting from its unbound components. Although ab initio docking algorithms are improving, there is a growing need to use homology modeling techniques to exploit the rapidly increasing volumes of structural information that now exist. However, most current homology modeling approaches involve finding a pair of complete single-chain structures in a homologous protein complex to use as a 3D template, despite the fact that protein complexes are often formed from one or more domain-domain interactions (DDIs). To model 3D protein complexes by domain-domain homology, we have developed a case-based reasoning approach called KBDOCK which systematically identifies and reuses domain family binding sites from our database of nonredundant DDIs. When tested on 54 protein complexes from the Protein Docking Benchmark, our approach provides a near-perfect way to model single-domain protein complexes when full-homology templates are available, and it extends our ability to model more difficult cases when only partial or incomplete templates exist. These promising early results highlight the need for a new and diverse docking benchmark set, specifically designed to assess homology docking approaches.
TL;DR: The docking study of the most potent compound 4m, indicated that Phe330 is responsible for ligand recognition and trafficking by forming π-cation interaction with benzylpiperidine moiety, which could stabilize the ligand in the active site resulting in more potent inhibition of the enzyme.
TL;DR: A prospective, large library virtual screen against an activated β2-adrenergic receptor (β2AR) structure returned potent agonists to the exclusion of inverse-agonists, providing the first complement to the previous virtual screening campaigns against inverse-agonist-bound G protein coupled receptor (GPCR) structures.
Abstract: A prospective, large library virtual screen against an activated β2-adrenergic receptor (β2AR) structure returned potent agonists to the exclusion of inverse-agonists, providing the first complement to the previous virtual screening campaigns against inverse-agonist-bound G protein coupled receptor (GPCR) structures, which predicted only inverse-agonists. In addition, two hits recapitulated the signaling profile of the co-crystal ligand with respect to the G protein and arrestin mediated signaling. This functional fidelity has important implications in drug design, as the ability to predict ligands with predefined signaling properties is highly desirable. However, the agonist-bound state provides an uncertain template for modeling the activated conformation of other GPCRs, as a dopamine D2 receptor (DRD2) activated model templated on the activated β2AR structure returned few hits of only marginal potency.
TL;DR: The ideal fusion location for exchanging class 1 and class 2 docking domains is determined and effective polyketide chain transfer in heterologous modules is demonstrated, indicating class 2 docks are tools for rational bioengineering of a broad range of PKSs containing either class 1 or 2 dockingdomain.
TL;DR: An evaluation of 115 scoring functions on an unbound docking decoy benchmark covering 118 complexes for which a near-native solution can be found, yielding top 10 success rates of up to 58%.
Abstract: Protein-protein docking, which aims to predict the structure of a protein-protein complex from its unbound components, remains an unresolved challenge in structural bioinformatics. An important step is the ranking of docked poses using a scoring function, for which many methods have been developed. There is a need to explore the differences and commonalities of these methods with each other, as well as with functions developed in the fields of molecular dynamics and homology modelling. We present an evaluation of 115 scoring functions on an unbound docking decoy benchmark covering 118 complexes for which a near-native solution can be found, yielding top 10 success rates of up to 58%. Hierarchical clustering is performed, so as to group together functions which identify near-natives in similar subsets of complexes. Three set theoretic approaches are used to identify pairs of scoring functions capable of correctly scoring different complexes. This shows that functions in different clusters capture different aspects of binding and are likely to work together synergistically. All functions designed specifically for docking perform well, indicating that functions are transferable between sampling methods. We also identify promising methods from the field of homology modelling. Further, differential success rates by docking difficulty and solution quality suggest a need for flexibility-dependent scoring. Investigating pairs of scoring functions, the set theoretic measures identify known scoring strategies as well as a number of novel approaches, indicating promising augmentations of traditional scoring methods. Such augmentation and parameter combination strategies are discussed in the context of the learning-to-rank paradigm.
TL;DR: A series of 2-(2-hydrazinyl)thiazole derivatives with a wide range of substitutions at 2-, 4- and 5-positions were designed by considering Lipinski rule to discover new potent inhibitors for Mycobacterium tuberculosis.
TL;DR: High-throughput docking into the induced-fit conformation of ZAP70 generated by molecular dynamics has revealed 10 low-micromolar inhibitors which correspond to six distinct chemotype which has an IC50 of 110 nM for JAK2.
TL;DR: The results suggest that EGCg inhibits the major function of porin proteins, namely the passive transport of small hydrophilic molecules such as glucose, leading to growth inhibition of E. coli.
TL;DR: Homology modeling studies were performed to obtain a three-dimensional structure of VEB-1 β-lactamase, and virtual screening of a large chemical ligand library with docking simulations was performed using AutoDock software with the ZINC database.
Abstract: blaVEB-1 is an integron-located extended-spectrum β-lactamase gene initially detected in Escherichia coli and Pseudomonas aeruginosa strains from south-east Asia. Several recent studies have reported that VEB-1-positive strains are highly resistant to ceftazidime, cefotaxime and aztreonam antibiotics. One strategy to overcome resistance involves administering antibiotics together with β-lactamase inhibitors during the treatment of infectious diseases. During this study, four VEB-1 β-lactamase inhibitors were identified using computer-aided drug design. The SWISS-MODEL tool was utilized to generate three dimensional structures of VEB-1 β-lactamase, and the 3D model VEB-1 was verified using PROCHECK, ERRAT and VERIFY 3D programs. Virtual screening was performed by docking inhibitors obtained from the ZINC Database to the active site of the VEB-1 protein using AutoDock Vina software. Homology modeling studies were performed to obtain a three-dimensional structure of VEB-1 β-lactamase. The generated model was validated, and virtual screening of a large chemical ligand library with docking simulations was performed using AutoDock software with the ZINC database. On the basis of the dock-score, four molecules were subjected to ADME/TOX analysis, with ZINC4085364 emerging as the most potent inhibitor of the VEB-1 β-lactamase.
TL;DR: The 1-(4-fluorobenzyl)pyridinium derivative 6g showed the most potent anti-AChE activity and the highest AChE/BuChE selectivity (SI>48).
TL;DR: Forescence resonance energy transfer (FRET) analysis proved high probability of energy transfer from Trp residue to the drug molecule and alterations of protein secondary structure in the presence of gemcitabine were assessed by CD UV-vis and FT-IR spectroscopy.
TL;DR: A series of quinoline derivatives (4a-4o) have been synthesized and their biological activities were evaluated as potential telomerase inhibitors and compounds 4d and 4i with potent inhibitory activity in tumor growth inhibition may be potential anticancer agents.
TL;DR: Insight is provided to elucidate the binding pattern of SIRT2 inhibitors and help in the rational structure-based design of novel Sirtuin 2 inhibitors with improved potency and better resistance profile.
Abstract: The ability to identify the site of a protein that can bind with high affinity to small, drug-like compounds has been an important goal in drug design. Sirtuin 2 (SIRT2), histone deacetylase protein family, plays a central role in the regulation of various pathways. Hence, identification of drug for SIRT2 has attracted great interest in the drug discovery community. To elucidate the molecular basis of the small molecules interactions to inhibit the SIRT2 function we employed the molecular docking, molecular dynamics simulations, and the molecular mechanism Poisson-Boltzmann/surface area (MM-PBSA) calculations. Five well know inhibitors such as suramin, mol-6, sirtinol, 67, and nf675 were selected to establish the nature of the binding mode of the inhibitors in the SIRT2 active site. The molecular docking and dynamics simulations results revealed that the hydrogen bonds between Arg97 and Gln167 are crucial to inhibit the function of SIRT2. In addition, the MM-PBSA calculations revealed that binding of inhibitors to SIRT2 is mainly driven by van der Waals/non-polar interactions. Although the five inhibitors are very different in structure, shape, and electrostatic potential, they are able to fit in the same binding pocket. These findings from this study provide insights to elucidate the binding pattern of SIRT2 inhibitors and help in the rational structure-based design of novel SIRT2 inhibitors with improved potency and better resistance profile.
TL;DR: A large flexible pocket in the Pgp transmembrane domains is able to bind chemically diverse compounds and homology modeling of human Pgp and substrate and modulator docking studies support the biochemical and transport data.
Abstract: P-glycoprotein (Pgp, ABCB1) is an ATP-Binding Cassette (ABC) transporter that is associated with the development of multidrug resistance in cancer cells. Pgp transports a variety of chemically dissimilar amphipathic compounds using the energy from ATP hydrolysis. In the present study, to elucidate the binding sites on Pgp for substrates and modulators, we employed site-directed mutagenesis, cell- and membrane-based assays, molecular modeling and docking. We generated single, double and triple mutants with substitutions of the Y307, F343, Q725, F728, F978 and V982 residues at the proposed drug-binding site with cys in a cysless Pgp, and expressed them in insect and mammalian cells using a baculovirus expression system. All the mutant proteins were expressed at the cell surface to the same extent as the cysless wild-type Pgp. With substitution of three residues of the pocket (Y307, Q725 and V982) with cysteine in a cysless Pgp, QZ59S-SSS, cyclosporine A, tariquidar, valinomycin and FSBA lose the ability to inhibit the labeling of Pgp with a transport substrate, [125I]-Iodoarylazidoprazosin, indicating these drugs cannot bind at their primary binding sites. However, the drugs can modulate the ATP hydrolysis of the mutant Pgps, demonstrating that they bind at secondary sites. In addition, the transport of six fluorescent substrates in HeLa cells expressing triple mutant (Y307C/Q725C/V982C) Pgp is also not significantly altered, showing that substrates bound at secondary sites are still transported. The homology modeling of human Pgp and substrate and modulator docking studies support the biochemical and transport data. In aggregate, our results demonstrate that a large flexible pocket in the Pgp transmembrane domains is able to bind chemically diverse compounds. When residues of the primary drug-binding site are mutated, substrates and modulators bind to secondary sites on the transporter and more than one transport-active binding site is available for each substrate.
TL;DR: Nine different charge-assigning methods were sufficiently explored for molecular docking performance by using AutoDock4.2 to demonstrate that the empirical Gasteiger-Hückel charge is the most applicable in virtual screening for large database; meanwhile, the semiempirical AM1-BCC charge is practicable in lead compound optimization as well as accuratevirtual screening for small databases.
Abstract: Molecular docking, which is the indispensable emphasis in predicting binding conformations and energies of ligands to receptors, constructs the high-throughput virtual screening available. So far, increasingly numerous molecular docking programs have been released, and among them, AutoDock 4.2 is a widely used docking program with exceptional accuracy. It has heretofore been substantiated that the calculation of partial charge is very fundamental for the accurate conformation search and binding energy estimation. However, no systematic comparison of the significances of electrostatic potentials on docking accuracy of AutoDock 4.2 has been determined. In this paper, nine different charge-assigning methods, including AM1-BCC, Del-Re, formal, Gasteiger–Huckel, Gasteiger–Marsili, Huckel, Merck molecular force field (MMFF), and Pullman, as well as the ab initio Hartree–Fock charge, were sufficiently explored for their molecular docking performance by using AutoDock4.2. The results clearly demonstrated that the...
TL;DR: 3g, 3s, and 3d were more potent inhibitors of platelet COX-1 and aggregation than P6 (named 6) for their tighter binding to the enzyme, and the pharmacological results were supported by docking simulations.
Abstract: 3-(5-Chlorofuran-2-yl)-5-methyl-4-phenylisoxazole (P6), a known selective cyclooxygenase-1 (COX-1) inhibitor, was used to design a new series of 3,4-diarylisoxazoles in order to improve its biochemical COX-1 selectivity and antiplatelet efficacy Structure–activity relationships were studied using human whole blood assays for COX-1 and COX-2 inhibition in vitro, and results showed that the simultaneous presence of 5-methyl (or -CF3), 4-phenyl, and 5-chloro(-bromo or -methyl)furan-2-yl groups on the isoxazole core was essential for their selectivity toward COX-1 3g, 3s, 3d were potent and selective COX-1 inhibitors that affected platelet aggregation in vitro through the inhibition of COX-1-dependent thromboxane (TX) A2 Moreover, we characterized their kinetics of COX-1 inhibition 3g, 3s, and 3d were more potent inhibitors of platelet COX-1 and aggregation than P6 (named 6) for their tighter binding to the enzyme The pharmacological results were supported by docking simulations The oral administration
TL;DR: Simulations, molecular docking and experimental data reciprocally supported each other and suggested that this flavonoid can interact with β-casein, without affecting the secondary structure of β- casein.
Abstract: The interaction of quercetin with β-casein nanoparticle micelle was studied at various temperatures in order to do a complete thermodynamic and molecular analysis on the binding process. The results of fluorescence studies showed the possibility of fluorescence energy transfer between excited tryptophan and quercetin. The determined values of critical transfers distance and the mean distance of ligand from Trp-143 residues in β-casein micelle represents a non-radiative energy transfer mechanism for quenching and the existence of a significant interaction between this flavonoid and β-casein nanoparticle. The equilibrium binding of quercetin with β-casein micelle at different temperatures was studied by using UV-Vis absorption spectroscopy. The chemometric analysis (principal component analysis (PCA) and multivariate curve resolution-alternating least squares (MCR-ALS) methods) on spectrophotometric data revealed the existence of two components in solution (quercetin and β-casein-quercetin complex) and resolved their pure concentration and spectral profiles. This information let us to calculate the equilibrium binding constant at various temperatures and the relevant thermodynamic parameters of interaction (enthalpy, entropy and Gibbs free energy) with low uncertainty. The negative values of entropy and enthalpy changes represent the predominate role of hydrogen binding and van der Waals interactions in the binding process. Docking calculations showed the probable binding site of quercetin is located in the hydrophobic core of β-casein where the quercetin molecule is lined by hydrophobic residues and make five hydrogen bonds and several van der Waals contacts with them. Moreover, molecular dynamic (MD) simulation results suggested that this flavonoid can interact with β-casein, without affecting the secondary structure of β-casein. Simulations, molecular docking and experimental data reciprocally supported each other.