Structural analysis of charge discrimination in the binding of inhibitors to human carbonic anhydrases I and II

Despite the similarity in the active site pockets of carbonic anhydrase (CA) isozymes I and II, the binding affinities of benzenesulfonamide inhibitors are invariably higher with CA II as compared to CA I. To explore the structural basis of this molecular recognition phenomenon, we have designed and synthesized simple benzenesulfonamide inhibitors substituted at the para position with positively charged, negatively charged, and neutral functional groups, and we have determined the affinities and X-ray crystal structures of their enzyme complexes. The para-substituents are designed to bind in the midsection of the 15 A deep active site cleft, where interactions with enzyme residues and solvent molecules are possible. We find that a para-substituted positively charged amino group is more poorly tolerated in the active site of CA I compared with CA II. In contrast, a para-substituted negatively charged carboxylate substituent is tolerated equally well in the active sites of both CA isozymes. Notably, enzyme-inhibitor affinity increases upon neutralization of inhibitor charged groups by amidation or esterification. These results inform the design of short molecular linkers connecting the benzenesulfonamide group and a para-substituted tail group in "two-prong" CA inhibitors: an optimal linker segment will be electronically neutral, yet capable of engaging in at least some hydrogen bond interactions with protein residues and/or solvent. Microcalorimetric data reveal that inhibitor binding to CA I is enthalpically less favorable and entropically more favorable than inhibitor binding to CA II. This contrasting behavior may arise in part from differences in active site desolvation and the conformational entropy of inhibitor binding to each isozyme active site.     

New computational strategy to analyze the interactions of ERalpha and ERbeta with different ERE sequences

The importance of computational methods for the simulation and analysis of biological systems has increased during the last years. In particular, methods to predict binding energies are developing not only with the aim of ranking the affinities between two or more complexes, but also to quantify the contribution of different types of interaction. In this work, we present the application of HINT, a non Newtonian force field, to rank the affinities of complexes formed by estrogen receptors (ER) alpha and beta and different estrogen responsive elements (ERE) near the estrogen-regulated genes. We used the crystallographic coordinates of the DNA binding domain of ERalpha complexed to a consensus ERE as a starting point to simulate several complexes in which some nucleotides in the ERE sequence were mutated. Moreover, we used homology modeling methods to create the structure of the complexes between the DNA binding domain of ERbeta (for which no experimental structures are currently available) and the same ERE sequences. Our results show that HINT is able to rank the affinities of ERalpha and ERbeta for different ERE sequences, and to correctly identify the positions on the DNA sequence that are most important for binding affinity. Moreover, the HINT output gives us the opportunity to identify and quantify the role played by each single atom of amino acids and nucleotides in the binding event, as well as to predict the effect on the binding affinity for other nucleotide mutations.     

Analysis of human carbonic anhydrase II: docking reliability and receptor-based 3D-QSAR study

The ability of Gold software to predict the binding disposition of carbonic anhydrase (CA) inhibitors was evaluated using CA II as a case study. The best procedure was subsequently used for docking almost 300 CA II ligands, and the best poses were used as an alignment tool for the development of a 3D quantitative structure-activity relationship (QSAR) study. Evaluation of the resulting 3D-QSAR model allowed us to indicate the ligand properties and residues important for CA II inhibition. Since CAs are an important target involved in many pathologies such as glaucoma, obesity, and tumors, the results obtained could accurately predict the binding affinity of newly designed CA II inhibitors. Furthermore, it is reasonable that this strategy could be profitably used also for the investigation of other CAs.     

Towards a rational spacer design for bivalent inhibition of estrogen receptor

Estrogen receptors are known drug targets that have been linked to several kinds of cancer. The structure of the estrogen receptor ligand binding domain is available and reveals a homodimeric layout. In order to improve the binding affinity of known estrogen receptor inhibitors, bivalent compounds have been developed that consist of two individual ligands linked by flexible tethers serving as spacers. So far, binding affinities of the bivalent compounds do not surpass their monovalent counterparts. In this article, we focus our attention on the molecular spacers that are used to connect the individual ligands to form bivalent compounds, and describe their thermodynamic contribution during the ligand binding process. We use computational methods to predict structural and entropic parameters of different spacer structures. We find that flexible spacers introduce a number of effects that may interfere with ligand binding and possibly can be connected to the low binding affinities that have been reported in binding assays. Based on these findings, we try to provide guidelines for the design of novel molecular spacers.     

An Activatable T1-Weighted MR Contrast Agent: A Noninvasive Tool for Tracking the Vicinal Thiol Motif of Thioredoxin in Live Cells

We have synthesized new magnetic resonance imaging (MRI) T₁ contrast agents (CA1 and CA2) that permit the activatable recognition of the cellular vicinal thiol motifs of the protein thioredoxin. The contrast agents showed MR relaxivities typical of gadolinium complexes with a single water molecule coordinated to a Gd³⁺ center (i.e., ~4.54 mM⁻¹s⁻¹) for both CA1 and CA2 at 60 MHz. The contrast agent CA1 showed a ~140% relaxivity enhancement in the presence of thioredoxin, a finding attributed to a reduction in the flexibility of the molecule after binding to thioredoxin. Support for this rationale, as opposed to one based on preferential binding, came from ¹H-¹⁵N-HSQC NMR spectral studies; these revealed that the binding affinities toward thioredoxin were almost the same for both CA1 and CA2. In the case of CA1, T₁-weighted phantom images of cancer cells (MCF-7, A549) could be generated based on the expression of thioredoxin. We further confirmed thioredoxin expression-dependent changes in the T₁-weighted contrast via knockdown of the expression of the thioredoxin using siRNA-transfected MCF-7 cells. The nontoxic nature of CA1, coupled with its relaxivity features, leads us to suggest that it constitutes a first-in-class MRI T₁ contrast agent that allows for the facile and noninvasive monitoring of vicinal thiol protein motif expression in live cells.     

3D QSAR selectivity analyses of carbonic anhydrase inhibitors: insights for the design of isozyme selective inhibitors

A 3D QSAR selectivity analysis of carbonic anhydrase (CA) inhibitors using a data set of 87 CA inhibitors is reported. After ligand minimization in the binding pockets of CA I, CA II, and CA IV isoforms, selectivity CoMFA and CoMSIA 3D QSAR models have been derived by taking the affinity differences (DeltapKi) with respect to two CA isozymes as independent variables. Evaluation of the developed 3D QSAR selectivity models allows us to determine amino acids in the respective CA isozymes that possibly play a crucial role for selective inhibition of these isozymes. We further combined the ligand-based 3D QSAR models with the docking program AUTODOCK in order to screen for novel CA inhibitors. Correct binding modes are predicted for various CA inhibitors with respect to known crystal structures. Furthermore, in combination with the developed 3D QSAR models we could successfully estimate the affinity of CA inhibitors even in cases where the applied scoring function failed. This novel strategy to combine AUTODOCK poses with CoMFA/CoMSIA 3D QSAR models can be used as a guideline to assess the relevance of generated binding modes and to accurately predict the binding affinity of newly designed CA inhibitors that could play a crucial role in the treatment of pathologies such as tumors, obesity, or glaucoma.     

Prediction and analysis of binding affinities for chemically diverse HIV-1 PR inhibitors by the modified SAFE_p approach

One of the biggest challenges in the "in silico" screening of enzyme ligands is to have a protocol that could predict the ligand binding free energies. In our group we have developed a very simple screening function (referred to as solvent accessibility free energy of binding predictor, SAFE_p) which we have applied previously to the study of peptidic HIV-1 protease (HIV-1 PR) inhibitors and later to cyclic urea type HIV-1 PR inhibitors. In this work, we have extended the SAFE_p protocol to a chemically diverse set of HIV-1 PR inhibitors with binding constants that differ by several orders of magnitude. The resulting function is able to reproduce the ranking and in many cases the value of the inhibitor binding affinities for the HIV-1 PR, with accuracy comparable with that of costlier protocols. We also demonstrate that the binding pocket SAFE_p analysis can contribute to the understanding of the physical forces that participate in ligand binding. The analysis tools afforded by our protocol have allowed us to identify an induced fit phenomena mediated by the inhibitor and have demonstrated that larger fragments do not necessarily contribute the most to the binding free energy, an outcome partially brought about by the substantial role the desolvation penalty plays in the energetics of binding. Finally, we have revisited the effect of the Asp dyad protonation state on the predicted binding affinities.     

凝血酶抑制剂的结构与活性的关系

应用了一种新的预测酶－配体复合物亲和性的方法来研究凝血酶抑制的结构－活性关系。凝血酶－抑制剂复合物的三维结构模板化合物的晶体结构进行搭建，然后使用程序ＳＣＯＲＥ计算复合物的亲和性。共分析了３个系列３４个抑制剂分子。计算所得的复合物的解离常数与实验值吻合得很好，大大优于用分子力学所给出的结果。通过比较其中两个抑制分子的结构和活性，说明了此方法能够定量给出配体分子中每个原子对结合过程的贡献大小，给出十     

Assessment and acceleration of binding energy calculations for protein–ligand complexes by the fragment molecular orbital method

In the field of drug discovery, it is important to accurately predict the binding affinities between target proteins and drug applicant molecules. Many of the computational methods available for evaluating binding affinities have adopted molecular mechanics‐based force fields, although they cannot fully describe protein–ligand interactions. A noteworthy computational method in development involves large‐scale electronic structure calculations. Fragment molecular orbital (FMO) method, which is one of such large‐scale calculation techniques, is applied in this study for calculating the binding energies between proteins and ligands. By testing the effects of specific FMO calculation conditions (including fragmentation size, basis sets, electron correlation, exchange‐correlation functionals, and solvation effects) on the binding energies of the FK506‐binding protein and 10 ligand complex molecule, we have found that the standard FMO calculation condition, FMO2‐MP2/6‐31G(d), is suitable for evaluating the protein–ligand interactions. The correlation coefficient between the binding energies calculated with this FMO calculation condition and experimental values is determined to be R = 0.77. Based on these results, we also propose a practical scheme for predicting binding affinities by combining the FMO method with the quantitative structure–activity relationship (QSAR) model. The results of this combined method can be directly compared with experimental binding affinities. The FMO and QSAR combined scheme shows a higher correlation with experimental data (R = 0.91). Furthermore, we propose an acceleration scheme for the binding energy calculations using a multilayer FMO method focusing on the protein–ligand interaction distance. Our acceleration scheme, which uses FMO2‐HF/STO‐3G:MP2/6‐31G(d) at R_int = 7.0 Å, reduces computational costs, while maintaining accuracy in the evaluation of binding energy. © 2015 Wiley Periodicals, Inc.     

偶氮氯膦Ⅲ与人血清白蛋白相互作用的电化学研究

偶氮氯膦Ⅲ是一种具有电化学活性的染料,在pH3.5的Britton-Robinson缓冲溶液中,它可以与人血清白蛋白发生相互作用形成一种生物超分子复合物,使溶液中游离的染料浓度降低.以线性扫描二阶导数极谱法对偶氮氯膦Ⅲ-人血清白蛋白的相互作用体系进行了详细的研究,复合物的形成使偶氮氯膦Ⅲ在-0.124V(vs.SCE)的还原峰电流降低,考察了结合反应的最佳条件和测定条件,求解了结合常数和结合比.     

An extensive test of 14 scoring functions using the PDBbind refined set of 800 protein-ligand complexes

Fourteen popular scoring functions, i.e., X-Score, DrugScore, five scoring functions in the Sybyl software (D-Score, PMF-Score, G-Score, ChemScore, and F-Score), four scoring functions in the Cerius2 software (LigScore, PLP, PMF, and LUDI), two scoring functions in the GOLD program (GoldScore and ChemScore), and HINT, were tested on the refined set of the PDBbind database, a set of 800 diverse protein-ligand complexes with high-resolution crystal structures and experimentally determined Ki or Kd values. The focus of our study was to assess the ability of these scoring functions to predict binding affinities based on the experimentally determined high-resolution crystal structures of proteins in complex with their ligands. The quantitative correlation between the binding scores produced by each scoring function and the known binding constants of the 800 complexes was computed. X-Score, DrugScore, Sybyl::ChemScore, and Cerius2::PLP provided better correlations than the other scoring functions with standard deviations of 1.8-2.0 log units. These four scoring functions were also found to be robust enough to carry out computation directly on unaltered crystal structures. To examine how well scoring functions predict the binding affinities for ligands bound to the same target protein, the performance of these 14 scoring functions were evaluated on three subsets of protein-ligand complexes from the test set: HIV-1 protease complexes (82 entries), trypsin complexes (45 entries), and carbonic anhydrase II complexes (40 entries). Although the results for the HIV-1 protease subset are less than desirable, several scoring functions are able to satisfactorily predict the binding affinities for the trypsin and the carbonic anhydrase II subsets with standard deviation as low as 1.0 log unit (corresponding to 1.3-1.4 kcal/mol at room temperature). Our results demonstrate the strengths as well as the weaknesses of current scoring functions for binding affinity prediction.     

Interaction of bioactive components caffeoylquinic Acid derivatives in Chinese medicines with bovine serum albumin

Five caffeoylquinic acid derivatives (CQAs), including methyl 3,4-di-O-caffeoylquinate (3,4-diCQM), methyl 3,5-di-O-caffeoylquinate (3,5-diCQM), 3,4-di-O-caffeoylquinic acid (3,4-diCQA), 3,5-di-O-caffeoylquinic acid (3,5-diCQA) and chlorogenic acid (CA), were isolated from Lonicera fulvotomentosa HSU et S. C. CHENG to be used as model compounds. The binding of these bioactive components to bovine serum albumin (BSA) was investigated by fluorescence quenching method. The results showed that there were binding affinities for CQAs with BSA, and the binding constants ranked in the following order: 3,4-diCQM>3,5-diCQM<3,4-diCQA>3,5-diCQA>CA, under the physiological conditions, which suggested that the numbers and the substituted positions of caffeoyl group as well as the esterification of carboxyl group in the molecular structures appeared to contribute moderate effects to the interaction processes. Furthermore, the Stern-Volmer curves demonstrated that CQAs caused the fluorescence quenching through a static quenching procedure. Thermodynamic analysis indicated that both hydrophobic and electrostatic interactions played major roles in stabilizing the complex. The binding distance for each binding reaction was also calculated by the F?ster theory.     

Prediction of SAMPL4 host–guest binding affinities using funnel metadynamics

Accurately predicting binding affinities between ligands and macromolecules has been a much sought-after goal. A tremendous amount of resources can be saved in the pharmaceutical industry through accurate binding-affinity prediction and hence correct decision-making for the drug discovery processes. Owing to the structural complexity of macromolecules, one of the issues in binding affinity prediction using molecular dynamics is the adequate sampling of the conformational space. Recently, the funnel metadynamics method (Limongelli et al. in Proc Natl Acad Sci USA 110:6358, 2013) was developed to enhance the sampling of the ligand at the binding site as well as in the solvated state, and offer the possibility to predict the absolute binding free energy. We apply funnel metadynamics to predict host–guest binding affinities for the cucurbit[7]uril host as part of the SAMPL4 blind challenge. Using total simulation times of 300–400 ns per ligand, we show that the errors due to inadequate sampling are below 1 kcal/mol. However, despite the large investment in terms of computational time, the results compared to experiment are not better than a random guess. As we obtain differences of up to 11 kcal/mol when switching between two commonly used force fields (with automatically generated parameters), we strongly believe that in the pursuit of accurate binding free energies a more careful force-field parametrization is needed to address this type of system.     

Blind prediction of SAMPL4 cucurbit[7]uril binding affinities with the mining minima method

Accurate methods for predicting protein–ligand binding affinities are of central interest to computer-aided drug design for hit identification and lead optimization. Here, we used the mining minima (M2) method to predict cucurbit[7]uril binding affinities from the SAMPL4 blind prediction challenge. We tested two different energy models, an empirical classical force field, CHARMm with VCharge charges, and the Poisson–Boltzmann surface area solvation model; and a semiempirical quantum mechanical (QM) Hamiltonian, PM6-DH+, coupled with the COSMO solvation model and a surface area term for nonpolar solvation free energy. Binding affinities based on the classical force field correlated strongly with the experiments with a correlation coefficient (R²) of 0.74. On the other hand, binding affinities based on the QM energy model correlated poorly with experiments (R² = 0.24), due largely to two major outliers. As we used extensive conformational search methods, these results point to possible inaccuracies in the PM6-DH+ energy model or the COSMO solvation model. Furthermore, the different binding free energy components, solute energy, solvation free energy, and configurational entropy showed significant deviations between the classical M2 and quantum M2 calculations. Comparison of different classical M2 free energy components to experiments show that the change in the total energy, i.e. the solute energy plus the solvation free energy, is the key driving force for binding, with a reasonable correlation to experiment (R² = 0.56); however, accounting for configurational entropy further improves the correlation.     

An integrated approach to ligand- and structure-based drug design: development and application to a series of serine protease inhibitors

A novel approach was developed to rationally interface structure- and ligand-based drug design through the rescoring of docking poses and automated generation of molecular alignments for 3D quantitative structure-activity relationship investigations. The procedure was driven by a genetic algorithm optimizing the value of a novel fitness function, accounting simultaneously for best regressions among binding-energy docking scores and affinities and for minimal geometric deviations from properly established crystal-based binding geometry. The GRID/CPCA method, as implemented in GOLPE, was used to feature molecular determinants of ligand binding affinity for each molecular alignment. In addition, unlike standard procedures, a novel multipoint equation was adopted to predict the binding affinity of ligands in the prediction set. Selectivity was investigated through square plots reporting experimental versus recalculated binding affinities on the targets under examination. The application of our approach to the modeling of affinity data of a large series of 3-amidinophenylalanine inhibitors of thrombin, trypsin, and factor Xa generated easily interpretable and independent models with robust statistics. As a further validation study, our approach was successfully applied to a series of 3,4,7-substituted coumarins, acting as selective MAO-B inhibitors.     

The F130L mutation in streptavidin reduces its binding affinity to biotin through electronic polarization effect

Recently, Baugh et al. discovered that a distal point mutation (F130L) in streptavidin causes no distinct variation to the structure of the binding pocket but a 1000‐fold reduction in biotin binding affinity. In this work, we carry out molecular dynamics simulations and apply an end‐state free energy method to calculate the binding free energies of biotin to wild type streptavidin and its F130L mutant. The absolute binding affinities based on AMBER charge are repulsive, and the mutation induced binding loss is underestimated. When using the polarized protein‐specific charge, the absolute binding affinities are significantly enhanced. In particular, both the absolute and relative binding affinities are in line with the experimental measurements. Further investigation indicates that polarization effect is indispensable in both the generation of structural ensembles and the calculation of interaction energies. This work verifies Baugh's conjecture that electrostatic polarization effect plays an essential role in modulating the binding affinity of biotin to the streptavidin through F130L mutation. © 2013 Wiley Periodicals, Inc.     

Towards predictive association theories

Association equations of state like SAFT, CPA and NRHB have been previously applied to many complex mixtures. In this work we focus on two of these models, the CPA and the NRHB equations of state and the emphasis is on the analysis of their predictive capabilities for a wide range of applications. We use the term predictive in two situations: (i) with no use of binary interaction parameters, and (ii) multicomponent calculations using binary interaction parameters based solely on binary data. It is shown that the CPA equation of state can satisfactorily predict CO₂-water-glycols-alkanes VLE and water-MEG-aliphatic hydrocarbons LLE using interaction parameters obtained from the binary data alone. Moreover, it is demonstrated that the NRHB equation of state is a versatile tool which can be employed equally well to mixtures with pharmaceuticals and solvents, including mixed solvents, as well as phase equilibria in mixtures containing glycols. The importance of considering the solvation of CO₂-water (in CPA) when the model is applied to multicomponent mixtures as well as of the multiple associations in heavy glycol-water mixtures (in NRHB) is investigated.     

Three-dimensional distribution function theory for the prediction of protein-ligand binding sites and affinities: application to the binding of noble gases to hen egg-white lysozyme in aqueous solution

The three-dimensional distribution function theory of molecular liquids is applied to lysozyme in mixtures of water and noble gases. The results indicate that the theory has the capability of predicting the protein-ligand binding sites and affinities. First, it is shown that the theory successfully reproduces the binding sites of xenon found by X-ray crystallography. Then, the ability of the theory to predict the size selectivity of noble gases is demonstrated. The effect of water on the selectivity is clarified by a theoretical analysis. Finally, it is demonstrated that the dose-response curve, which is employed in experiments for examining the binding affinity, is realized by the theory.     

Large scale free energy calculations for blind predictions of protein–ligand binding: the D3R Grand Challenge 2015

We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. The Hsp90 ATP binding site is known to be a challenging target for accurate calculations of ligand binding affinities because of the ligand-dependent conformational changes in the binding site, the presence of ordered waters and the broad chemical diversity of ligands that can bind at this site. Our primary focus here is to distinguish binders from nonbinders. Large scale absolute binding free energy calculations that cover over 3000 protein–ligand complexes were performed using the BEDAM method starting from docked structures generated by Glide docking. Although the ligand dataset in this study resembles an intermediate to late stage lead optimization project while the BEDAM method is mainly developed for early stage virtual screening of hit molecules, the BEDAM binding free energy scoring has resulted in a moderate enrichment of ligand screening against this challenging drug target. Results show that, using a statistical mechanics based free energy method like BEDAM starting from docked poses offers better enrichment than classical docking scoring functions and rescoring methods like Prime MM-GBSA for the Hsp90 data set in this blind challenge. Importantly, among the three methods tested here, only the mean value of the BEDAM binding free energy scores is able to separate the large group of binders from the small group of nonbinders with a gap of 2.4 kcal/mol. None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds. We discuss the possible sources of errors in the binding free energy calculations. The study suggests that BEDAM can be used strategically to discriminate binders from nonbinders in virtual screening and to more accurately predict the ligand binding modes prior to the more computationally expensive FEP calculations of binding affinity.