Estimating binding affinities by docking/scoring methods using variable protonation states

To investigate the effects of multiple protonation states on protein-ligand recognition, we generated alternative protonation states for selected titratable groups of ligands and receptors. The selection of states was based on the predicted pK(a) of the unbound receptor and ligand and the proximity of titratable groups of the receptor to the binding site. Various ligand tautomer states were also considered. An independent docking calculation was run for each state. Several protocols were examined: using an ensemble of all generated states of ligand and receptor, using only the most probable state of the unbound ligand/receptor, and using only the state giving the most favorable docking score. The accuracies of these approaches were compared, using a set of 176 protein-ligand complexes (15 receptors) for which crystal structures and measured binding affinities are available. The best agreement with experiment was obtained when ligand poses from experimental crystal structures were used. For 9 of 15 receptors, using an ensemble of all generated protonation states of the ligand and receptor gave the best correlation between calculated and measured affinities.     

Molecular dynamics study of small molecule inhibitors of the Bcl-2 family

We carried out docking and molecular dynamics simulations on ABT-737 and obatoclax, which are inhibitors of the Bcl-2 family of proteins. We modeled the binding mode of ABT-737 with Bcl-x(L) , Bcl-2, and Mcl-1 and examined their dynamical behavior. We found that the binding of the chlorobiphenyl end of ABT-737 was quite stable across all three proteins. However, the phenylpiperazine linker group was dramatically more mobile in Mcl-1 compared to either Bcl-x(L) or Bcl-2. The S-phenyl group at the p4 binding site was well-anchored in Bcl-x(L) and Bcl-2 but was somewhat more mobile in Mcl-1 although the phenyl ring itself on average stayed close to the p4 binding site in Mcl-1. This greater mobility is likely due to the greater openness of the p3 and p4 binding sites on Mcl-1. The calculated binding free energies were consistent with the much weaker binding affinity of ABT-737 for Mcl-1. Obatoclax was predicted to bind at the p1 and p2 binding sites of Mcl-1 and the binding mode was quite stable during the molecular dynamics simulation with Mcl-1 wrapping around the molecule. The modeled binding mode suggests that obatoclax is able to inhibit all three proteins because it makes use of the p1 and p2 binding sites alone, which is a fairly narrow groove in all three proteins unlike the p4 binding site, which is much broader in Mcl-1.     

Predicting large-scale conformational changes in proteins using energy-weighted normal modes

We report the development of a method to improve the sampling of protein conformational space in molecular simulations. It is shown that a principal component analysis of energy-weighted normal modes in Cartesian coordinates can be used to extract vectors suitable for describing the dynamics of protein substructures. The method can operate with either atomistic or user-defined coarse-grained models of protein structure. An implicit reverse coarse-graining allows the dynamics of all-atoms to be recovered when a coarse-grained model is used. For an external test set of four proteins, it is shown that the new method is more successful than normal mode analysis in describing the large-scale conformational changes observed on ligand binding. The method has potential applications in protein-ligand and protein-protein docking and in biasing molecular dynamics simulations.     

Fast approximations of the rotational diffusion tensor and their application to structural assembly of molecular complexes

We present and evaluate a rigid-body, deterministic, molecular docking method, called ELMDOCK, that relies solely on the three-dimensional structure of the individual components and the overall rotational diffusion tensor of the complex, obtained from nuclear spin-relaxation measurements. We also introduce a docking method, called ELMPATIDOCK, derived from ELMDOCK and based on the new concept of combining the shape-related restraints from rotational diffusion with those from residual dipolar couplings, along with ambiguous contact/interface-related restraints obtained from chemical shift perturbations. ELMDOCK and ELMPATIDOCK use two novel approximations of the molecular rotational diffusion tensor that allow computationally efficient docking. We show that these approximations are accurate enough to properly dock the two components of a complex without the need to recompute the diffusion tensor at each iteration step. We analyze the accuracy, robustness, and efficiency of these methods using synthetic relaxation data for a large variety of protein-protein complexes. We also test our method on three protein systems for which the structure of the complex and experimental relaxation data are available, and analyze the effect of flexible unstructured tails on the outcome of docking. Additionally, we describe a method for integrating the new approximation methods into the existing docking approaches that use the rotational diffusion tensor as a restraint. The results show that the proposed docking method is robust against experimental errors in the relaxation data or structural rearrangements upon complex formation and is computationally more efficient than current methods. The developed approximations are accurate enough to be used in structure refinement protocols.     

New insights into the GABA(A) receptor structure and orthosteric ligand binding: receptor modeling guided by experimental data

GABA(A) receptors (GABA(A)Rs) are ligand gated chloride ion channels that mediate overall inhibitory signaling in the CNS. A detailed understanding of their structure is important to gain insights in, e.g., ligand binding and functional properties of this pharmaceutically important target. Homology modeling is a necessary tool in this regard because experimentally determined structures are lacking. Here we present an exhaustive approach for creating a high quality model of the α(1)β(2)γ(2) subtype of the GABA(A)R ligand binding domain, and we demonstrate its usefulness in understanding details of orthosteric ligand binding. The model was constructed by using multiple templates and by incorporation of knowledge from biochemical/pharmacological experiments. It was validated on the basis of objective energy functions, its ability to account for available residue specific information, and its stability in molecular dynamics (MD) compared with that of the two homologous crystal structures. We then combined the model with extensive structure-activity relationships available from two homologous series of orthosteric GABA(A)R antagonists to create a detailed hypothesis for their binding modes. Excellent agreement with key experimental data was found, including the ability of the model to accommodate and explain a previously developed pharmacophore model. A coupling to agonist binding was thereby established and discussed in relation to activation mechanisms. Our results highlight the importance of critical evaluation and optimization of each step in the homology modeling process. The approach taken here can greatly aid in increasing the understanding of GABA(A)Rs and related receptors where structural insight is limited and reliable models are difficult to obtain.     

Protein structure prediction using a docking-based hierarchical folding scheme

The pathways by which proteins fold into their specific native structure are still an unsolved mystery. Currently, many methods for protein structure prediction are available, and most of them tackle the problem by relying on the vast amounts of data collected from known protein structures. These methods are often not concerned with the route the protein follows to reach its final fold. This work is based on the premise that proteins fold in a hierarchical manner. We present FOBIA, an automated method for predicting a protein structure. FOBIA consists of two main stages: the first finds matches between parts of the target sequence and independently folding structural units using profile-profile comparison. The second assembles these units into a 3D structure by searching and ranking their possible orientations toward each other using a docking-based approach. We have previously reported an application of an initial version of this strategy to homology based targets. Since then we have considerably enhanced our method's abilities to allow it to address the more difficult template-based target category. This allows us to now apply FOBIA to the template-based targets of CASP8 and to show that it is both very efficient and promising. Our method can provide an alternative for template-based structure prediction, and in particular, the docking-basedranking technique presented here can be incorporated into any profile-profile comparison based method.     

New azoles with antifungal activity: Design, synthesis, and molecular docking

In order to search for many target compounds with excellent activities, a series of 1-(1H-1,2,4-triazol-1-yl)-2-(2,4-difluoro-phenyl)-3-[(4-substituted phenyl)-piperazin-1-yl]-propan-2-ols were designed, synthesized, and evaluated as antifungal agents. Results of preliminary antifungal tests against eight human pathogenic fungi in vitro showed that all the title compounds exhibited excellent activities with broad spectrum. Moreover, a molecular model for the binding between 5a and the active site of CACYP51 was provided based on the computational docking results.     

Lignans as food constituents with estrogen and antiestrogen activity

Phytoestrogens are plant-derived food ingredients assumed to contribute to the prevention of hormone-dependent cancers, osteoporosis, cardiovascular disease, and menopausal symptoms. Lignans occur in numerous food plants and various structures; they are common constituents of human diet, and estrogen activity has been assessed for lignan metabolites formed in the mammalian intestine. We examined natural lignans and semisynthetic norlignans for estrogen and antiestrogen activity. A transformed yeast strain (Saccharomyces cerevisiae) expressing the estrogen receptor alpha and a reporter system was applied as test system. Some plant lignans showed estrogen activity while others and the semisynthetic norlignans were moderately active antiestrogens. Docking of lignans to protein models of estrogen receptor alpha in the active and inactive form sustained the results of the yeast estrogen assay and supported the concept of plant lignans as phytoestrogens.     

Inhibition of monoamine oxidase by selected C5- and C6-substituted isatin analogues

Previous studies have shown that (E)-5-styrylisatin and (E)-6-styrylisatin are reversible inhibitors of human monoamine oxidase (MAO) A and B. Both homologues are reported to exhibit selective binding to the MAO-B isoform with (E)-5-styrylisatin being the most potent inhibitor. To further investigate these structure-activity relationships (SAR), in the present study, additional C5- and C6-substituted isatin analogues were synthesized and evaluated as inhibitors of recombinant human MAO-A and MAO-B. With the exception of 5-phenylisatin, all of the analogues examined were selective MAO-B inhibitors. The C5-substituted isatins exhibited higher binding affinities to MAO-B than the corresponding C6-substituted homologues. The most potent MAO-B inhibitor, 5-(4-phenylbutyl)isatin, exhibited an IC₅₀ value of 0.66 nM, approximately 13-fold more potent than (E)-5-styrylisatin and 18,500-fold more potent than isatin. The most potent MAO-A inhibitor was found to be 5-phenylisatin with an IC₅₀ value of 562 nM. The results document that substitution at C5 with a variety of substituents is a general strategy for enhancing the MAO-B inhibition potency of isatin. Possible binding orientations of selected isatin analogues within the active site cavities of MAO-A and MAO-B are proposed.     

Mutation and docking studies on NS2b-NS3 complex from yellow fever virus towards drug discovery

Yellow fever virus is the causative agent of Yellow fever. The genome of the virus contains three structural and seven non-structural proteins. Of these seven nonstructural proteins, NS2B-NS3 protein complex has protease activity required for viral replication. Predicting the 3D structure of this complex and studying the interaction of residues at the recognized catalytic triad of the complex is an integral part to understand the virus replication mechanism. In the present study, the structure was determined for NS2B-NS3 complex by Homology modeling and modeled structure was validated for its stability. Mutation studies at the residues His94, Asp118 and Ser176 revealed that Asp118-His94 bond played an important role in the structural stability of NS2B-NS3 complex. This indicates site-directed mutagenesis, controlling YFV replication, as one mechanism to design vaccine strains. Docking studies of the bioactive compounds at the active site of NS2B-NS3 complex also indicated 4-hydroxypanduratin A as potential lead compound for drug development. The theoretical models will further pave way to experimentally verify our mutation and docking studies, thus taking a lead in pharmacogenomics and drug development.

Abbreviations

YFV - Yellow Fever Virus, WNV - West Nile Virus, H-bonds - hydrogen bonds, SNP - Single nucleotide polymorphism.