Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation

The 2015 D3R Grand Challenge provided an opportunity to test our new model for the binding free energy of small molecules, as well as to assess our protocol to predict binding poses for protein-ligand complexes. Our pose predictions were ranked 3–9 for the HSP90 dataset, depending on the assessment metric. For the MAP4K dataset the ranks are very dispersed and equal to 2–35, depending on the assessment metric, which does not provide any insight into the accuracy of the method. The main success of our pose prediction protocol was the re-scoring stage using the recently developed Convex-PL potential. We make a thorough analysis of our docking predictions made with AutoDock Vina and discuss the effect of the choice of rigid receptor templates, the number of flexible residues in the binding pocket, the binding pocket size, and the benefits of re-scoring. However, the main challenge was to predict experimentally determined binding affinities for two blind test sets. Our affinity prediction model consisted of two terms, a pairwise-additive enthalpy, and a non pairwise-additive entropy. We trained the free parameters of the model with a regularized regression using affinity and structural data from the PDBBind database. Our model performed very well on the training set, however, failed on the two test sets. We explain the drawback and pitfalls of our model, in particular in terms of relative coverage of the test set by the training set and missed dynamical properties from crystal structures, and discuss different routes to improve it.     

Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2

Computer-aided drug design has become an integral part of drug discovery and development in the pharmaceutical and biotechnology industry, and is nowadays extensively used in the lead identification and lead optimization phases. The drug design data resource (D3R) organizes challenges against blinded experimental data to prospectively test computational methodologies as an opportunity for improved methods and algorithms to emerge. We participated in Grand Challenge 2 to predict the crystallographic poses of 36 Farnesoid X Receptor (FXR)-bound ligands and the relative binding affinities for two designated subsets of 18 and 15 FXR-bound ligands. Here, we present our methodology for pose and affinity predictions and its evaluation after the release of the experimental data. For predicting the crystallographic poses, we used docking and physics-based pose prediction methods guided by the binding poses of native ligands. For FXR ligands with known chemotypes in the PDB, we accurately predicted their binding modes, while for those with unknown chemotypes the predictions were more challenging. Our group ranked #1st (based on the median RMSD) out of 46 groups, which submitted complete entries for the binding pose prediction challenge. For the relative binding affinity prediction challenge, we performed free energy perturbation (FEP) calculations coupled with molecular dynamics (MD) simulations. FEP/MD calculations displayed a high success rate in identifying compounds with better or worse binding affinity than the reference (parent) compound. Our studies suggest that when ligands with chemical precedent are available in the literature, binding pose predictions using docking and physics-based methods are reliable; however, predictions are challenging for ligands with completely unknown chemotypes. We also show that FEP/MD calculations hold predictive value and can nowadays be used in a high throughput mode in a lead optimization project provided that crystal structures of sufficiently high quality are available.     

Computational and experimental investigations of mono-septanoside binding by Concanavalin A: correlation of ligand stereochemistry to enthalpies of binding

Structure-energy relationships for a small group of pyranose and septanose mono-saccharide ligands are developed for binding to Concanavalin A (ConA). The affinity of ConA for methyl "manno"β-septanoside 7 was found to be higher than any of the previously reported mono-septanoside ligands. Isothermal titration calorimetry (ITC) in conjunction with docking simulations and quantum mechanics/molecular mechanics (QM/MM) modeling established the specific role of binding enthalpy in the structure-energy relations of ConA bound to natural mono-saccharides and unnatural mono-septanosides. An important aspect in the differential binding among ligands is the deformation energy required to reorganize internal hydroxyl groups upon binding of the ligand to ConA.     

Mixed nitrogen/oxygen ligand affinities for bipositive metal ions and dioxygen binding to cobalt(II) complexes

The complex formation of Co(II) and Cd(II) with mixed N/O ligands in dimethylsulfoxide (dmso) at 298 K is investigated by means of potentiometric, UV-Vis, calorimetric, FT-IR, NMR and electrochemical techniques. The linear and cyclic ligands investigated are: 2,2'-oxydiethylamine (NON), N-(2-hydroxyethyl)-ethylenediamine (NNO), 1,4,10-trioxa-7,13-diaza-cyclopentadecane (2,1) and 1,4,7,10-tetraoxa-13-azacyclopentadecane (2,2). The results are discussed by taking into account the donor strength of donor atoms, strain effects in the formation of chelate rings, steric and inductive effects and the size of the cavity when macrocyclic ligands are concerned. DFT studies are also performed to gain an insight into the stabilization of the lower oxidation state of the CoL2(2+/3+) couple in order to understand the different reactivity of the Co(II) complexes towards dioxygen. The kinetics of dioxygen uptake has been studied by UV-Vis measurements and the results reveal an interesting strong solvent effect, which is active in lowering the rate constant for formation of the initial superoxo complex.     

Structure-based approach for the study of estrogen receptor binding affinity and subtype selectivity

Estrogens exert important physiological effects through the modulation of two human estrogen receptor (hER) subtypes, alpha (hERalpha) and beta (hERbeta). Because the levels and relative proportion of hERalpha and hERbeta differ significantly in different target cells, selective hER ligands could target specific tissues or pathways regulated by one receptor subtype without affecting the other. To understand the structural and chemical basis by which small molecule modulators are able to discriminate between the two subtypes, we have applied three-dimensional target-based approaches employing a series of potent hER-ligands. Comparative molecular field analysis (CoMFA) studies were applied to a data set of 81 hER modulators, for which binding affinity values were collected for both hERalpha and hERbeta. Significant statistical coefficients were obtained (hERalpha, q(2) = 0.76; hERbeta, q(2) = 0.70), indicating the internal consistency of the models. The generated models were validated using external test sets, and the predicted values were in good agreement with the experimental results. Five hER crystal structures were used in GRID/PCA investigations to generate molecular interaction fields (MIF) maps. hERalpha and hERbeta were separated using one factor. The resulting 3D information was integrated with the aim of revealing the most relevant structural features involved in hER subtype selectivity. The final QSAR and GRID/PCA models and the information gathered from 3D contour maps should be useful for the design of novel hER modulators with improved selectivity.     

Multiple-site ligand binding to flexible macromolecules: Separation of global and local conformational change and an iterative mobile clustering approach

This article concerns the calculation of equilibria of ligand binding to multiple sites in macromolecules in the presence of conformational flexibility and conformation-dependent interaction among the sites. A formulation of this problem is presented in which global conformational changes are distinguished from conformational changes that are confined to “locally flexible regions.” The formalism is quite general in that ligands of different types, multivalent binding sites, tautomeric binding sites, and sites that bind more than one type of ligand can be accommodated. Strictly speaking, the separation of the conformational problem into global and local parts does not impose any loss of generality, although in practice it is necessary to restrict the number of global and local conformers. Because of the combinatorics of binding and conformational states, the computational complexity of a problem having only local conformational flexibility grows exponentially with the number of sites and the number of locally flexible regions. An iterative mobile clustering method for cutting off this exponential growth and obtaining approximate solutions with low computational cost is presented and tested. In this method, a binding site is selected, and a “cluster” of strongly interacting sites is set up around it; within the cluster, the binding and conformational states are fully enumerated, whereas the influences of sites outside the cluster on the sites inside are treated by a mean field approximation. The procedure then moves to the next site around which another (possibly overlapping) cluster is formed and the calculation is repeated. The procedure iterates through the list of sites in this way, using the results of previous iterations for the mean-field terms of current iterations until a convergence criterion is met. The method is tested on a large set of randomly generated problems of varying size, whose geometries are chosen to have protein-like statistical properties. It is found that the method is accurate and rapid with the computational cost scaling linearly to quadratically with the number of sites, except for a minority of cases in which large clusters occur by chance. The new method is more accurate than a Monte Carlo method, and may be faster or slower depending on the clustering criteria and details of the macromolecule. ©1999 John Wiley & Sons, Inc. J Comput Chem 20: 1091–1111, 1999     

计算受体和配体结合自由能的新方法

利用ABEEMσπ浮动电荷力场与连续介质模型相结合的方法,计算了受体和配体的结合自由能.将结合自由能分解为真空中的力场作用项、溶剂化能量以及熵效应.由于ABEEMσπ/MM方法充分考虑了外界环境发生变化引起的体系中各个位点之间的电荷极化,因而极大地提高了结合自由能的计算精度.利用该方法计算的2个复合物的结合自由能与实验值的偏差均小于0.5kJ/mol.     

Functionalization of the alicyclic skeleton of epibatidine: synthesis and nicotinic acetylcholine receptor binding affinities of epibatidine analogues

A novel method for the epimerization of endo-2-(6-chloro-3-pyridyl)-7-azabicyclo[2.2.1]heptan-3-one (12) on silica gel was developed and used as the key step to synthesize functionalized analogues of epibatidine which were evaluated for their nicotine receptor subtype selectivity in binding studies.     

Combining 4D pharmacophore generation and multidimensional QSAR: modeling ligand binding to the bradykinin B2 receptor

We recently reported the development of two receptor-modeling concepts (software Quasar and Raptor) based on multidimensional quantitative structure-activity relationships (QSAR) and allowing for the explicit simulation of induced fit. As the identification of the bioactive configuration of ligand molecules in such studies is all but unambiguous, each compound may be represented by an ensemble of different conformations, orientations, stereoisomers, and protonation states, leading to a 4D data set. In this account, we present a novel technology (software Symposar) allowed to automatically generate a 4D pharmacophore as input for multidimensional QSAR. Symposar aligns ligands utilizing fuzzylike 2D-subfeature mapping and, subsequently, a Monte Carlo search on a 3D similarity grid. The two-step concept (4D pharmacophore generation and quantification of ligand binding by multidimensional QSAR) was applied to 186 compounds binding to the bradykinin B2 receptor. The prediction of their binding affinity by means of the Quasar and Raptor technologies allowed for consensus scoring and generated topologically and quantitatively consistent receptor models. These converged at a cross-validated r2 of 0.752 and 0.815 and yielded a predictive r2 of 0.784 and 0.853 for a test set (for Quasar and Raptor, respectively).     

Fluorescein labeling of estrogen for application of fluorescence polarization binding assays for antibody and receptor

The fluorescence polarization binding assay (FPBA) using fluorescein-labeled estrogen tracer is a homogeneous assay applicable to both estrogen antibody and estrogen receptor-binding assays. Two estrogen-ethylendiamine fluoresceinthiobamyl (E-EDF) tracers were synthesized; estrogen-6-EDF (E-6-F) derived from 6-ketoestradiol 6-(o-carboxymethyl) oxime and estrogen-17-EDF (E-17-F) was from 17β-estradiol 17-hemisuccinate. In both FPBAs using antibody and receptor, E-6-F tracer (Rf_365nm=0.58) showed a better binding response than E-17-F (Rf_365nm=0.70) indicating that the 17-position of estrogen seems to play an essential role as a binding site for antibody or receptor. In the optimized conditions of FPBA for E₂ using E-6-F tracer, antibody binding (K_d=9.4×10⁻⁹ M) is 50 times sensitive than receptor binding (K_d=4.6×10⁻⁸ M). Binding responses of estrogen and its related chemicals by FPBA indicate that antibody binding assay is able to screen the structural similarity of estrogen showing some response with methyltestosterone (K_i=2.1×10⁻⁵ M). On the other hand, the receptor assay is able to screen for estrogenic chemicals such as tamoxifen (K_i=4.5×10⁻⁹ M) and diethylstilbesterol (K_i=8.1×10⁻⁷ M). Therefore, E-6-F tracer is useful as a tracer for FPBA that is able to screen for chemicals structurally similar to estrogen using antibody, and that is able to screen for chemicals functionally similar to estrogen using receptor binding assay.     

Molecular modeling for Cu(II)‐aminopolycarboxylate complexes: Structures,conformational energies,and ligand binding affinities

A ligand field molecular mechanics (LFMM) force field (FF) has been developed for d⁹ copper(II) complexes of aminopolycarboxylate ligands. Training data were derived from density functional theory (DFT) geometry optimizations of 14 complexes comprising potentially hexadentate N₂O₄, tetrasubstituted ethylenediamine (ed), and propylenediamine cores with various combinations of acetate and propionate side arms. The FF was validated against 13 experimental structures from X‐ray crystallography including hexadentate N₂O₄ donors where the nitrogens donors are forced to be cis and bis‐tridentate ONO ligands which generate complexes with trans nitrogen donors. Stochastic conformational searches for [Cu{ed(acetate)_n (propionate)_4‐n}]^2?, n = 0–4, were carried out and the lowest conformers for each system reoptimized with DFT. In each case, both DFT and LFMM predict the same lowest‐energy conformer and the structures and energies of the higher‐energy conformers are also in satisfactory agreement. The relative interaction energies for n = 0, 2, and 4 computed by molecular mechanics correlate with the experimental log β binding affinities. Adding in the predicted log β values for n = 1 and 3 suggest for this set of complexes a monotonic decrease in log β as the number of propionate arms increases. © 2013 Wiley Periodicals, Inc.     

A computational model of the 5-HT3 receptor extracellular domain: search for ligand binding sites

A three-dimensional model of the 5-HT₃ receptor extarcellular domain has been derived on the basis of the nicotinic acetylcholine receptor model recently published by Tsigelny et al. Maximum complementarity between the position and characteristics of mutated residues putatively involved in ligand interaction and the pharmacophoric elements derived by the indirect approach applied on several series of 5-HT₃ ligands have been exploited to gain insights into the ligand binding modalities and to speculate on the mechanistic role of the structural components. The analysis of the three-dimensional model allows one to distinguish among amino acids that exert key roles in ligand interactions, subunit architecture, receptor assembly and receptor dynamics. For some of these, alternative roles with respect to the ones hypothesized by experimentalists are assigned. Different binding modalities for agonists and antagonists are highlighted, and residues which probably play a role in the transduction of binding into a change in conformational state of the receptor are suggested. Received: 27 July 2000 / Accepted: 15 September 2000 / Published online: 21 December 2000     

De novo ligand design to partially flexible active sites: application of the ReFlex algorithm to carboxypeptidase A, acetylcholinesterase, and the estrogen receptor

Reflex is a recent algorithm in the de novo ligand design software, SkelGen, that allows the flexibility of amino acid side chains in a protein to be taken into account during the drug-design process. In this paper the impact of flexibility on the solutions generated by the de novo design algorithm, when applied to carboxypeptidase A, acetylcholinesterase, and the estrogen receptor (ER), is investigated. The results for each of the targets indicate that when allowing side-chain movement in the active site, solutions are generated that were not accessible from the multiple static protein conformations available for these targets. Furthermore, an analysis of structures generated in a flexible versus a static ER active site suggests that these additional solutions are not merely noise but contain many interesting chemotypes.     

Steroid binding by antibodies and artificial receptors: Exploration of theoretical methods to determine the origins of binding affinities and specificities

Binding mode calculations for complexes between an artificial paracyclophane receptor and digoxins, cholic acids as well as cortisone steroids show encapsulation of different ring combinations. Docking experiments were performed between the 26-10 antibody and digoxins. Coordination affinity arises from hydrophobic desolvation and van der Waals interactions rather than from hydrogen bonds. The specificity and affinity arises mainly from shape complementarity. Computed binding free energies and Kohonen neural network computations both point to physicochemical and structural similarities of natural antibodies and artificial receptors.     

The correlation of proton affinities with atomic charges and electronegativities for the group 14 to 17 hydrides

Proton affinities for hydrides of formula $\mathrm{AH}^{-}_{n-1}$ containing the elements A from the second to the fifth period of the periodic table and groups 14 to 17 are predicted at the Hartree–Fock, MP2 and B3LYP levels of theory employing both core potential basis sets and the 3‐21G basis set. The core potential methods perform well when compared with all electron calculations using the 3‐21++G** basis set. The proton affinities of the hydrides containing elements from groups 15 and 16 of the periodic table are more accurate than those with elements from groups 14 and 17. A cancellation of errors appears to occur more completely if the protonated and nonprotonated molecules contain both bond and lone pairs before and after the protonation reaction. Proton affinities correlate nearly linearly with the atomic charges on the hydrogen atoms when these charges are determined by the generalized atomic polar tensor (GAPT) method. This tendency can be associated, in principle, with the group electronegativities as introduced by Iczkowski and Margrave. © 2000 John Wiley & Sons, Inc. J Comput Chem 21: 1119–1131, 2000     

Toward quantitative estimates of binding affinities for protein–ligand systems involving large inhibitor compounds: A steered molecular dynamics simulation route

Understanding binding mechanisms between enzymes and potential inhibitors and quantifying protein – ligand affinities in terms of binding free energy is of primary importance in drug design studies. In this respect, several approaches based on molecular dynamics simulations, often combined with docking techniques, have been exploited to investigate the physicochemical properties of complexes of pharmaceutical interest. Even if the geometric properties of a modeled protein – ligand complex can be well predicted by computational methods, it is still challenging to rank with chemical accuracy a series of ligand analogues in a consistent way. In this article, we face this issue calculating relative binding free energies of a focal adhesion kinase, an important target for the development of anticancer drugs, with pyrrolopyrimidine‐based ligands having different inhibitory power. To this aim, we employ steered molecular dynamics simulations combined with nonequilibrium work theorems for free energy calculations. This technique proves very powerful when a series of ligand analogues is considered, allowing one to tackle estimation of protein – ligand relative binding free energies in a reasonable time. In our cases, the calculated binding affinities are comparable with those recovered from experiments by exploiting the Michaelis – Menten mechanism with a competitive inhibitor.