Experimental Binding Constants Research Articles

A Hybrid Structure-Based Machine Learning Approach for Predicting Kinase Inhibition by Small Molecules.

Kinases have been the focus of drug discovery programs for three decades leading to over 70 therapeutic kinase inhibitors and biophysical affinity measurements for over 130,000 kinase-compound pairs. Nonetheless, the precise target spectrum for many kinases remains only partly understood. In this study, we describe a computational approach to unlocking qualitative and quantitative kinome-wide binding measurements for structure-based machine learning. Our study has three components: (i) a Kinase Inhibitor Complex (KinCo) data set comprising in silico predicted kinase structures paired with experimental binding constants, (ii) a machine learning loss function that integrates qualitative and quantitative data for model training, and (iii) a structure-based machine learning model trained on KinCo. We show that our approach outperforms methods trained on crystal structures alone in predicting binary and quantitative kinase-compound interaction affinities; relative to structure-free methods, our approach also captures known kinase biochemistry and more successfully generalizes to distant kinase sequences and compound scaffolds.

In silico prediction of protein binding affinities onto core-shell PEGylated noble metal nanoparticles for rational design of drug nanocarriers.

Polymer-coated nanoparticles (NP) are commonly used as drug carriers or theranostic agents. Their uptake rates are modulated by the interactions with essential serum proteins such as transferrin and albumin. Understanding the control parameters of these interactions is crucial for improving the efficiency of these nanoscale devices. In this work, we perform a multiscale computational study of protein adsorption onto polyethylene glycol (PEG) coated gold and silver NPs, producing protein-NP adsorption rankings as a function of PEG grafting density, which are validated against previously reported experimental protein-NP binding constants. Furthermore, the applied nano-docking method provides information on the preferred orientation of proteins immobilised on the surface of NPs. We propose a method of construction of model core-shell NPs in silico. The presented protocol can provide molecular level insights for the experimental development of biosensors, nanocarriers, or other nanoplatforms where information on the preferred orientation of protein at the bio-nano interface is crucial, and enables fast in silico prescreening of assays of various nanocarriers, i.e., combinations of proteins, NPs, and coatings.

DNA/protein binding and anticancer activity of ruthenium (II) arene complexes based on quinoline dipyrrin

Arene ruthenium complexes [(η6–arene)Ru(L)Cl] (η6–arene = benzene, R1; p-cymene, R2) containing the chelating ligand (2–chloro–3–(di(1H–pyrrol–2–yl)methyl)quinoline) (L) have been synthesized and carefully characterized by various studies (1H and 13C NMR, IR, ESI–MS, UV–vis). The structure of R1 has been verified by X-ray single crystal analyses. Binding of the complexes with calf thymus DNA (CT–DNA) has been investigated by absorption titration, ethidium bromide (EB) displacement and viscosity measurements. Experimental binding constants (5.1 × 104 M−1, R1; 5.7 × 104 M−1, R2) suggested appreciable bonding of the complexes with CT–DNA. Fluorescence, synchronous and 3D fluorescence spectroscopic studies showed that complexes strongly bind with bovine serum albumin. Intercalative interaction of the complexes with DNA has been further supported by DFT studies. Our studies have shown potential anticancer activity of both the complexes R1 and R2 against T cell lymphoma. Among these complexes R2 showed better anticancer activity relative to R1 (IC50, R1, 30 μM and R2, 20 μM).

Investigation of Binding behaviour of a water-soluble gallium (III) phthalocyanine with double-stranded and G-quadruplex DNA via experimental and computational methods

DNA is an important target for the development of chemotherapeutic drug design due to increased and uncontrolled cell proliferation of cancer cells. In the present study, the affinity of water-soluble gallium(III) phthalocyanine chloride (GaPc) non-peripherally substituted with 2-mercapto-N-methylpyridinium groups to different types of DNA (double-stranded ctDNA and G-quadruplex DNA) was investigated. The interaction mechanisms were evaluated by UV-Vis, fluorescence and circular dichroism spectroscopic techniques, competitive dialysis assay, and capillary gel electrophoresis (CGE) system integrated with a high sensitivity LED-induced fluorescence (LIF) detector. Binding mode studies were carried out by molecular docking and molecular dynamic simulations. The experimental binding constants were determined as 1.9 × 106 M−1, 1.1 × 106 M−1, 8.5 × 105 M−1, 2.9 × 104 M−1 for c-MYC, AS1411, Tel21, and ctDNA, respectively. Competitive binding assay confirmed the higher affinity towards G-quadruplex structure rather than ctDNA. The GaPc-G-quadruplex DNA conjugates showed higher Kb constants and a strong absorption band closer to NIR window which is a desired feature for the photodynamic therapy applications. Besides, experimental data show that the formation of GaPc-G-quadruplex DNA conjugates reduced the aggregation of GaPc. Reduction of aggregation of phthalocyanine is important. Because aggregation decreases the photoactivity of most photosensitizers. The experimental results suggested that both intercalative and non‑intercalative binding contributions occurred in the case of double-stranded ctDNA. Confirming experiments, molecular docking studies showed that the GaPc can bind from two different major groove regions in double-stranded DNA while only one binding mode was observed in the G-quadruplex DNA. Both experimental and computational studies suggested that the conformational change and unfolding mechanism in G-quadruplex DNA structure were determined upon interaction with GaPc.

Computational Studies of Glycosylated Photosensitizers with Plasma Proteins

Photodynamic therapy (PDT) is a promising treatment against cancer, it utilizes photosensitizers that when excited by light undergo photochemical reactions that can kill abnormal cancer cells. However, in order for photosensitizers to reach their target, they must effectively bind serum albumin, the most abundant plasma protein circulated in an organism’s blood stream. In our previous study, we synthesized three glycosylated photosensitizers (PGlu, CGlu and IGlu) and performed in vitro spectroscopy studies that revealed each glycosylated photosensitizer effectively binding bovine serum albumin and human serum albumin (BSA and HSA). The aim of this work was to implement an in silico approach to study how PGlu, CGlu, and IGlu are binding onto BSA and HSA. We performed molecular docking simulations to predict a favorable mode of binding between each of our glycosylated photosensitizers to BSA and HSA. Further, we performed a 5 ns molecular dynamics (MD) simulation for each of our predicted systems; consequently, we calculated the RMSD of the systems to attest for stability, performed binding free energy calculations to correlate with experimental binding constant results and analyzed residue interactions critical to each binding event. We observed PGlu, CGlu and IGlu favorably binding the hemin site located in subdomain IB of BSA, forming strong interactions with the Trp134 residue, as well as favorably binding Sudlow Site 1 located in subdomain IIA of HSA, forming strong interactions to Trp214. RMSD studies revealed the BSA‐IB and HSA‐IIA systems plateauing after a short time indicating stable binding events; in addition, a Pearson correlation coefficient of R=0.87 between the experimental binding constants and computational binding free energies showed a strong correlation between in vitro and in silico results. Overall, our computational results corroborated our experimental results, revealing that our glycosylated photosensitizers can bind serum albumin effectively through critical tryptophan residue interactions and can potentially aid in the progression of drug development for PDT.Support or Funding InformationThis research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE‐AC02‐05CH11231.

Host-guest complexation of curcumin and coumarin 6 with PAMAM-OH: Insight from fluorescence spectroscopy and molecular dynamics simulations

The 1:1 host-guest association between hydroxyl-terminated poly(amido amine) dendrimers of the fourth generation (G4-PAMAM-OH) and two model fluorescent dyes, curcumin (CUR) and coumarin 6 (CU6), was studied from fluorescence intensity experiments and fully atomistic molecular dynamics (MD) simulations. Our results account for experimental binding constants of 3.45 × 105 and 2.97 × 105 mol−1L for CUR-G4-PAMAM-OH and CU6-G4-PAMAM-OH systems, respectively. The potential of dendrimer complexation to increase dye photostability was evaluated from fluorescence intensity experiments in 5–100 μmol L−1 dye solutions containing 100 μmol L−1 of G4-PAMAM-OH under visible light irradiation. Our results revealed that G4-PAMAM-OH enhances the photostability of CUR at 1:1, 1:2 and 1:20 dye-dendrimer ratios, whereas CU6 is effectively protected only at 1:20 dendrimer excess conditions. MD simulations predicted a deep encapsulation of CUR within innermost dendrimer branches and an outer association of CU6 to surface dendrimer cavities, which can be related to the enhanced photoprotection of CUR at lower dye-dendrimer ratios. MM/GBSA binding free energy estimates suggest the predominance of van der Waals interactions between apolar dye segments and hydrophobic dendrimer pockets as the main binding forces driving dye-dendrimer supramolecular association in the systems under study.

In silico prediction of binding capacity and interaction forces of organic compounds with α- and β-cyclodextrins

As commonly-used cyclic oligomers, cyclodextrins are usually composed of 6 and 7 glucopyranose units (α-cyclodextrin and β-cyclodextrin). The binding constant is a significant parameter to estimate the association stability of inclusion complexes and evaluate the influence of cyclodextrins on guest compounds. In order to cover the shortage of experimental methods, we intended to predict the binding constants and interaction forces of cyclodextrin inclusion complexes by developing consensus models and calculating energy changes. 418 cyclodextrin inclusion complexes were collected with their experimental binding constants. By combining 6 description methods and 5 machine learning methods, we built 60 single models, which were selected to develop 104 consensus models. The coefficients of determination of the test sets in the best α-cyclodextrin consensus model and the best β-cyclodextrin consensus model were 0.84 and 0.89, respectively. The applicability domains of models were defined. After performing molecular docking and calculating energy changes, we could observe the binding poses, identify the interaction forces, and select the dominant forces. The consensus models and the calculation of energy changes can be applied to explore the binding capacity and interaction forces of inclusion complexes.

Investigation of indole chalcones encapsulation in β-cyclodextrin: determination of stoichiometry, binding constants and thermodynamic parameters

The study focuses on the formation of inclusion complexes of indole chalcone (IC) derivatives with β-cyclodextrin (β-CD), which involves absorption and steady state fluorescence spectroscopies. The formation of inclusion complexes is validated by increase in their absorbance and fluorescence intensity as well as the blue shift with increase in the concentration of β-CD in the aqueous solution. The stoichiometries and binding constants (Kin) of these complexes have been investigated by monitoring their absorbance and fluorescence spectral profiles. The data are analyzed by Benesi–Hildebrand plots as well as Job’s method, which indicate 1:1 stoichiometry of IC:β-CD complexes. Fluorescence measurements are also used to investigate the effect of temperature on the stability of inclusion complexes. Stability of IC:β-CD complexes is significantly affected with variation in substituents on the phenyl ring and temperature. It is observed that the stability of the inclusion complex decreases with increase in temperature; Kin(293 K) > Kin(298 K) > Kin(308 K) > Kin(318 K). All the experimental results and the geometrical data obtained using PM3 semiempirical method illustrate the partial inclusion of IC derivatives from the phenyl ring side in β-CD cavity. The binding process of IC derivatives with β-CD is found to be exothermic in nature and seems to be controlled by electrostatic and hydrophobic forces. The binding free energies calculated using semiemprical PM3 method for IC:β-CD complexes are found to be in the order: I < OH–I < Me–I < OMe–I < NH2–I, which largely supports the findings based on the experimental binding constants.

Design of oxophilic metalloporphyrins: an experimental and DFT study of methanol binding.

By systematic measurements we have evaluated a series of tetraphenyl metalloporphyrins and halogenated tetraphenyl metalloporphyrin derivatives for binding to ligands with oxygen containing functional groups, using methanol, acetic acid and acetone as examples. Experimental binding constants identified three metalloporphyrins with good binding to all three ligands: MgTPFPP, MgTPPBr8 and ZnTPPBr8 as well as a range of porphyrins binding to select ligands. Based on these results the optimal porphyrins can be selected for the desired binding interactions. We also show how to use DFT calculations to evaluate the potential binding between a metalloporphyrin and a ligand, which is deduced from free energies of binding ΔG, charge transfer ΔQ, and change of metal spin state. Computations on unsubstituted porphyrins in lieu of tetraphenyl porphyrin systems yield reliable predictions of binding interactions with good correlation to the corresponding experimental data. The calculations have also yielded interesting insights into the effect of halogenation in the β-position on the binding to ligands with oxygen containing functional groups.

Importance of aliphatic C–H hydrogen bonding on anion recognition

We have designed and synthesised new anion receptors 1 and 2, both of their C–H groups were at the α positions to carbonyl groups and further polarised by the attached polarising substituents. This enabled us to study hydrogen bonding donor ability of C–H bonds. The polarising substituents are electron withdrawing cyano group for host 1, while charged pyridinium group for host 2. As expected from charge effects, host 2 shows roughly an order of magnitude higher binding constants against various anion guests than those of receptor 1. Since the magnitude of polarisation change should be greatest for C–H group among various hydrogen bonding groups, this indicates the importance of C–H hydrogen bonding. In contrast, the relative order of binding constants was the same for both host 1 and 2. The order of association constants was found to be (CH3)2POO− > CH3COO− > C6H5COO− > Cl− > Br−. DFT calculation results were in good agreement with experimental binding constants and confirmed the importance of charged group substitution. In addition, receptor 1 showed the highest association constant for dimethyl phosphinate, which is implicated in many metabolic diseases.

Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity.

Calculation of ligand-binding affinity is an open problem in computational medicinal chemistry. The ability to computationally predict affinities has a beneficial impact in the early stages of drug development, since it allows a mathematical model to assess protein-ligand interactions. Due to the availability of structural and binding information, machine learning methods have been applied to generate scoring functions with good predictive power. Our goal here is to review recent developments in the application of machine learning methods to predict ligand-binding affinity. We focus our review on the application of computational methods to predict binding affinity for protein targets. In addition, we also describe the major available databases for experimental binding constants and protein structures. Furthermore, we explain the most successful methods to evaluate the predictive power of scoring functions. Association of structural information with ligand-binding affinity makes it possible to generate scoring functions targeted to a specific biological system. Through regression analysis, this data can be used as a base to generate mathematical models to predict ligandbinding affinities, such as inhibition constant, dissociation constant and binding energy. Experimental biophysical techniques were able to determine the structures of over 120,000 macromolecules. Considering also the evolution of binding affinity information, we may say that we have a promising scenario for development of scoring functions, making use of machine learning techniques. Recent developments in this area indicate that building scoring functions targeted to the biological systems of interest shows superior predictive performance, when compared with other approaches.

Density Functional Theory Study on the Complexation of NOTA as a Bifunctional Chelator with Radiometal Ions.

1,4,7-Triazacyclononane-1,4,7-triacetic acid (NOTA) is a key bifunctional chelator utilized for the complexation of metal ions in radiopharmaceutical applications; the ability of these chelators depends on the strength of their binding with ions. The focus of the present work is to evaluate the complexation of Cu2+, Ga3+, Sc3+, and In3+ radiometal ions with NOTA using density functional theory (B3LYP functional) and 6-311+G(2d,2p)/DGDZVP basis sets. The significant role of ion-water interactions in the chelation interaction energies in solution reflects the competition between ion-water and NOTA-ion interaction in the chelation process. There is reasonable agreement between experimental and theoretical binding constants, geometries, and 1H NMR chemical shifts. Chelation interaction energies, Gibbs free energies, and entropies in solution show that the NOTA-Ga3+ and NOTA-Cu2+ are the most and least stable complexes, respectively. The natural atomic charges and second order perturbation analysis reveal charge transfer between NOTA and radiometal ions. The theoretical 1H NMR chemical shifts of NOTA are in good agreement with experiment; these values are influenced by the presence of the ions, which have a deshielding effect on the protons of NOTA. Global scalar properties such as EHOMO/ELUMO, ΔELUMO-HOMO, and chemical hardness/softness confirm that the NOTA-Cu2+ complex, which has a singly occupied molecular orbital, has the lowest ΔELUMO-HOMO value, the least chemical hardness, and the highest chemical softness. The significant variation of the hardness and ΔELUMO-HOMO values of the complexes can be attributed to the different positions of the metal ions on the periodic table. This study affirms that, among the radiometal ions, Ga3+ can be used to effectively radiolabel NOTA chelator for radiopharmaceutical usage as it binds most stably with NOTA.

Functioning of drug-metabolizing microsomal cytochrome P450s: In silico probing of proteins suggests that the distal heme 'active site' pocket plays a relatively 'passive role' in some enzyme-substrate interactions.

PurposeThe currently held mechanistic understanding of microsomal cytochrome P450s (CYPs) seeks that diverse drug molecules bind within the deep-seated distal heme pocket and subsequently react at the heme centre. To explain a bevy of experimental observations and meta-analyses, we indulge a hypothesis that involves a “diffusible radical mediated” mechanism. This new hypothesis posits that many substrates could also bind at alternate loci on/within the enzyme and be reacted without the pertinent moiety accessing a bonding proximity to the purported catalytic Fe-O enzyme intermediate.MethodsThrough blind and heme-distal pocket centered dockings of various substrates and non-substrates (drug molecules of diverse sizes, classes, topographies etc.) of microsomal CYPs, we explored the possibility of access of substrates via the distal channels, its binding energies, docking orientations, distance of reactive moieties (or molecule per se) to/from the heme centre, etc. We investigated specific cases like- (a) large drug molecules as substrates, (b) classical marker drug substrates, (c) class of drugs as substrates (Sartans, Statins etc.), (d) substrate preferences between related and unrelated CYPs, (e) man-made site-directed mutants’ and naturally occurring mutants’ reactivity and metabolic disposition, (f) drug-drug interactions, (g) overall affinities of drug substrate versus oxidized product, (h) meta-analysis of in silico versus experimental binding constants and reaction/residence times etc.ResultsIt was found that heme-centered dockings of the substrate/modulator drug molecules with the available CYP crystal structures gave poor docking geometries and distances from Fe-heme centre. In conjunction with several other arguments, the findings discount the relevance of erstwhile hypothesis in many CYP systems. Consequently, the newly proposed hypothesis is deemed a viable alternate, as it satisfies Occam’s razor.ConclusionsThe new proposal affords expanded scope for explaining the mechanism, kinetics and overall phenomenology of CYP mediated drug metabolism. It is now understood that the heme-iron and the hydrophobic distal pocket of CYPs serve primarily to stabilize the reactive intermediate (diffusible radical) and the surface or crypts of the apoprotein bind to the xenobiotic substrate (and in some cases, the heme distal pocket could also serve the latter function). Thus, CYPs enhance reaction rates and selectivity/specificity via a hitherto unrecognized modality.Electronic supplementary materialThe online version of this article (doi:10.1186/s40203-016-0016-7) contains supplementary material, which is available to authorized users.

In Silico and in Vitro Study of Binding Affinity of Tripeptides to Amyloid β Fibrils: Implications for Alzheimer's Disease.

Self-assembly of Aβ peptides into amyloid aggregates has been suggested as the major cause of Alzheimer's disease (AD). Nowadays, there is no medication for AD, but experimental data indicate that reversion of the process of amyloid aggregation reduces the symptoms of disease. In this paper, all 8000 tripeptides were studied for their ability to destroy Aβ fibrils. The docking method and the more sophisticated MM-PBSA (molecular mechanics Poisson-Boltzmann surface area) method were employed to calculate the binding affinity and mode of tripeptides to Aβ fibrils. The ability of these peptides to depolymerize Aβ fibrils was also investigated experimentally using atomic force microscopy and fluorescence spectroscopy (Thioflavin T assay). It was shown that tripeptides prefer to bind to hydrophobic regions of 6Aβ9-40 fibrils. Tripeptides WWW, WWP, WPW and PWW were found to be the most potent binders. In vitro experiments showed that tight-binding tripeptides have significant depolymerizing activities and their DC50 values determined from dose-response curves were in micromolar range. The ability of nonbinding (GAM, AAM) and weak-binding (IVL and VLA) tripeptides to destroy Aβ fibrils was negligible. In vitro data of tripeptide depolymerizing activities support the predictions obtained by molecular docking and all-atom simulation methods. Our results suggest that presence of multiple complexes of heterocycles forming by tryptophan and proline residues in tripeptides is crucial for their tight binding to Aβ fibrils as well as for extensive fibril depolymerization. We recommend PWW for further studies as it has the lowest experimental binding constant.

Beware of machine learning-based scoring functions-on the danger of developing black boxes.

Training machine learning algorithms with protein-ligand descriptors has recently gained considerable attention to predict binding constants from atomic coordinates. Starting from a series of recent reports stating the advantages of this approach over empirical scoring functions, we could indeed reproduce the claimed superiority of Random Forest and Support Vector Machine-based scoring functions to predict experimental binding constants from protein-ligand X-ray structures of the PDBBind dataset. Strikingly, these scoring functions, trained on simple protein-ligand element-element distance counts, were almost unable to enrich virtual screening hit lists in true actives upon docking experiments of 10 reference DUD-E datasets; this is a a feature that, however, has been verified for an a priori less-accurate empirical scoring function (Surflex-Dock). By systematically varying ligand poses from true X-ray coordinates, we show that the Surflex-Dock scoring function is logically sensitive to the quality of docking poses. Conversely, our machine-learning based scoring functions are totally insensitive to docking poses (up to 10 Å root-mean square deviations) and just describe atomic element counts. This report does not disqualify using machine learning algorithms to design scoring functions. Protein-ligand element-element distance counts should however be used with extreme caution and only applied in a meaningful way. To avoid developing novel but meaningless scoring functions, we propose that two additional benchmarking tests must be systematically done when developing novel scoring functions: (i) sensitivity to docking pose accuracy, and (ii) ability to enrich hit lists in true actives upon structure-based (docking, receptor-ligand pharmacophore) virtual screening of reference datasets.

Employment of Electrodonating Capacity as an Index of Reactive Modulation by Substituent Effects: Application for Electron-Transfer-Controlled Hydrogen Bonding

Evaluation of the substituent effect in reaction series is an issue of interest, as it is fundamental for controlling chemical reactivity in molecules. Within the framework of density functional theory, employment of the chemical potential, μ, and the chemical hardness, η, leads to the calculation of properties of common use, such as the electrodonating (ω(-)) and electroaccepting (ω(+)) powers, in many chemical systems. In order to examine the predictive character of the substituent effect by these indexes, a comparison between these and experimental binding constants (Kb) for binding of a series of radical anions from para- and ortho-substituted nitrobenzenes with 1,3-diethylurea in acetonitrile was performed, and fair correlations were obtained; furthermore, this strategy was suitable for all of the studied compounds, even those for which empirical approximations, such as Hammett's model, are not valid. Visual representations of substituent effects are presented by considering the local electrodonating power ω(-)(r).

Structural Features of Copigmentation of Oenin with Different Polyphenol Copigments

The copigmentation binding constants (K) for the interaction of different copigments with oenin (major red wine anthocyanin) were determined. All tests were performed in a 12% ethanol citrate buffer solution (0.2 M) at pH 3.5, with an ionic strength adjusted to 0.5 M by the addition of sodium chloride. Over the past years, several copigmentation studies were made and many copigments were tested, but none of them included prodelphinidin B3 or a dimeric-type adduct like oenin-(O)-catechin, probably due to the difficulty in obtaining them. The data yielded from this study allowed concluding that (a) the presence of a pyrogallol group in the B ring of the flavan-3-ol structure slightly increases the copimentation potential and (b) within all copigments tested oenin-(O)-catechin was revealed to be the best. According to computational studies performed on epicatechin/oenin, epigallocatechin/oenin, procyanidin B3/oenin, and oenin-(O)-catechin/oenin complexes, the ΔGbinding energy of the oenin-(O)-catechin/oenin complex is the most negative compared to the other copigmentation complexes, hence being more stable and thermodynamically favored. All structural data show that oenin-(O)-catechin and epigallocatechin are closer to the pigment molecule, which is in accordance with these two copigments having the highest experimental copigmentation binding constants for oenin.

A consistent description of HYdrogen bond and DEhydration energies in protein–ligand complexes: methods behind the HYDE scoring function

The estimation of free energy of binding is a key problem in structure-based design. We developed the scoring function HYDE based on a consistent description of HYdrogen bond and DEhydration energies in protein-ligand complexes. HYDE is applicable to all types of protein targets since it is not calibrated on experimental binding affinity data or protein-ligand complexes. The comprehensible atom-based score of HYDE is visualized by applying a very intuitive coloring scheme, thereby facilitating the analysis of protein-ligand complexes in the lead optimization process. In this paper, we have revised several aspects of the former version of HYDE which was described in detail previously. The revised HYDE version was already validated in large-scale redocking and screening experiments which were performed in the course of the Docking and Scoring Symposium at 241st ACS National Meeting. In this study, we additionally evaluate the ability of the revised HYDE version to predict binding affinities. On the PDBbind 2007 coreset, HYDE achieves a correlation coefficient of 0.62 between the experimental binding constants and the predicted binding energy, performing second best on this dataset compared to 17 other well-established scoring functions. Further, we show that the performance of HYDE in large-scale redocking and virtual screening experiments on the Astex diverse set and the DUD dataset respectively, is comparable to the best methods in this field.

Complexation of Microcystins and Nodularin by Cyclodextrins in Aqueous Solution, a Potential Removal Strategy

Cyanotoxins are potent toxic compounds produced by cyanobacteria during algal blooms, which threaten drinking water supplies. These compounds can poison and kill animals and humans. The host-guest interactions of α-, β-, and γ-cyclodextrins (CD) with problematic cyanotoxins, including microcystins (MCs) and nodularin (NOD), were investigated to demonstrate the potential application of CDs for the removal of these toxins from drinking water or applications related to their separation or purification. MCs and NOD have a hydrophobic Adda chain, which contains diene and benzene functional groups. The complexation of these cyanotoxins with CDs was monitored by nuclear magnetic resonance (NMR). The (1)H NMR spectra for MCs are unchanged upon addition of α-CD (smallest host). However, addition of larger hosts, β-CD and γ-CD, leads to significant changes in chemical shifts of the benzene and diene resonances on the 3-amino-9-methoxy-2,6,8-trimethal-10-phenyldeca-4,6-dienoic acid (Adda) chain of MCs and NOD. Solution pH, natural organic matter, and salinity do not appreciably influence the host-guest complexation under our experimental conditions. The experimental binding constants for MCs and NOD with γ-CD are relatively strong, ranging from 1155 to 507 M(-1). The observed changes in chemical shifts for specific protons and competitive binding experiments demonstrate a 1:1 inclusion complex between γ-CD and MCs or NOD, with the Adda chains threading through the CD ring, resulting in an inclusion complex. Our results suggest that CD-type substrates are useful hosts for the complexation of MCs and NOD. CDs can be readily attached to a number of polymeric or solid supports and their functionality tailored to strengthen specific host-guest interactions. With further development of such materials, CD host-guest chemistry may find direct application in the removal and/or separation science of these compounds.

Energy Landscapes Associated with Macromolecular Conformational Changes from Endpoint Structures

Conformational changes modulate macromolecular function by promoting the specific binding of ligands (such as in antigen recognition) or the stabilization of transition states in enzymatic reactions. However, quantitative characterization of the energetics underlying dynamic structural interconversions is still challenging and lacks a unified method. Here, we introduce a novel in silico approach based on the combined use of essential dynamics sampling and nonequilibrium free-energy calculations to obtain quantitative data on conformational energy landscapes. This technique allows the unbiased investigation of highly complex rearrangements, and does not require the crucial definition of user-defined collective variables. We show that free-energy values derived from profiles connecting the unliganded and ligand-bound X-ray structures of a bacterial nucleoside hydrolase match the experimental binding constant. This approach also provides first evidence for a rate-limiting character of the conformational transition in this enzyme, and an unexpected role of the protonation state of a single residue in regulating substrate binding and product release.