Computational Methodologies for Compound Database Searching that Utilize Experimental Protein–Ligand Interaction Information

Ligand- and target structure-based methods are widely used in virtual screening, but there is currently no methodology available that fully integrates these different approaches. Herein, we provide an overview of various attempts that have been made to combine ligand- and structure-based computational screening methods. We then review different types of approaches that utilize protein–ligand interaction information for database screening and filtering. Interaction-based approaches make use of a variety of methodological concepts including pharmacophore modeling and direct or indirect encoding of protein–ligand interactions in fingerprint formats. These interaction-based methods have been successfully applied to tackle different tasks related to virtual screening including postprocessing of docking poses, prioritization of binding modes, selectivity analysis, or similarity searching. Furthermore, we discuss the recently developed interacting fragment approach that indirectly incorporates 3D interaction information into 2D similarity searching and bridges between ligand- and structure-based methods.     

Insight into the molecular mechanism of P-glycoprotein mediated drug toxicity induced by bioflavonoids: an integrated computational approach

In this work, molecular docking, pharmacophore modeling and molecular dynamics (MD) simulation were rendered for the mouse P-glycoprotein (P-gp) (code: 4Q9H) and bioflavonoids; amorphigenin, chrysin, epigallocatechin, formononetin and rotenone including a positive control; verapamil to identify protein–ligand interaction features including binding affinities, interaction characteristics, hot-spot amino acid residues and complex stabilities. These flavonoids occupied the same binding site with high binding affinities and shared the same key residues for their binding interactions and the binding region of the flavonoids was revealed that overlapped the ATP binding region with hydrophobic and hydrophilic interactions suggesting a competitive inhibition mechanism of the compounds. Root mean square deviations (RMSDs) analysis of MD trajectories of the protein–ligand complexes and NBD2 residues, and ligands pointed out these residues were stable throughout the duration of MD simulations. Thus, the applied preliminary structure-based molecular modeling approach of interactions between NBD2 and flavonoids may be gainful to realize the intimate inhibition mechanism of P-gp at NBD2 level and on the basis of the obtained data, it can be concluded that these bioflavonoids have the potential to cause herb–drug interactions or be used as lead molecules for the inhibition of P-gp (as anti-multidrug resistance agents) via the NBD2 blocking mechanism in future.     

维A类化合物的构效关系研究.I.维A类与受体结合作用的三维构效关系

准确地预测配体 受体的结合常数是基于受体结构设计(structure-based design)的一个重要方面。目前几乎所有的全新设计(de novo design)或3D数据库搜寻的方法都侧重于结构的生成而忽视了对结构的定量评价。本文以副睾维A酸结合蛋白(ERABP)为模板,用DOCK程序研究了一组维A类化合物与受体的相互作用,得到一个预测受体结合常数的方程。另外,对DOCK后的分子构象进行了CoMFA分析,得到这类化合物的作用模型。     

Computational models for prediction of interactions with ABC-transporters

The polyspecific ligand recognition pattern of ATB-binding cassette (ABC)-transporters, combined with the limited knowledge on the molecular basis of their multispecificity, makes it difficult to apply traditional molecular modelling and quantitative structure-activity relationships (QSAR) methods for identification of new ligands. Recent advances relied mainly on pharmacophore modelling and machine learning methods. Structure-based design studies suffer from the lack of available protein structures at atomic resolution. The recently published protein homology models of P-glycoprotein structure, based on the high-resolution structure of the bacterial ABC-transporter of Sav1866, may open a new chapter for structure-based studies. Last, but not least, molecular dynamics simulations have already proved their high potential for structure-function modelling of ABC-transporter. Because of the recognition of several ABC-transporters as antitargets, algorithms for predicting substrate properties are of increasing interest.     

Developing a dynamic pharmacophore model for HIV-1 integrase

We present the first receptor-based pharmacophore model for HIV-1 integrase. The development of "dynamic" pharmacophore models is a new method that accounts for the inherent flexibility of the active site and aims to reduce the entropic penalties associated with binding a ligand. Furthermore, this new drug discovery method overcomes the limitation of an incomplete crystal structure of the target protein. A molecular dynamics (MD) simulation describes the flexibility of the uncomplexed protein. Many conformational models of the protein are saved from the MD simulations and used in a series of multi-unit search for interacting conformers (MUSIC) simulations. MUSIC is a multiple-copy minimization method, available in the BOSS program; it is used to determine binding regions for probe molecules containing functional groups that complement the active site. All protein conformations from the MD are overlaid, and conserved binding regions for the probe molecules are identified. Those conserved binding regions define the dynamic pharmacophore model. Here, the dynamic model is compared to known inhibitors of the integrase as well as a three-point, ligand-based pharmacophore model from the literature. Also, a "static" pharmacophore model was determined in the standard fashion, using a single crystal structure. Inhibitors thought to bind in the active site of HIV-1 integrase fit the dynamic model but not the static model. Finally, we have identified a set of compounds from the Available Chemicals Directory that fit the dynamic pharmacophore model, and experimental testing of the compounds has confirmed several new inhibitors.     

Docking studies on alphavbeta3 integrin ligands: pharmacophore refinement and implications for drug design

Starting from the first crystal structure of the extracellular segment of the alpha(v)beta(3) integrin receptor with a cyclic RGD ligand bound to the active site, structural models for the interactions of known ligands with the alpha(v)beta(3) integrin receptor were generated by automated computational docking. The obtained complexes were evaluated for their consistency with structure-activity relationships and site-directed mutagenesis data. A comparison between the calculated interaction free energies and the experimental biological activities was also made. All the possible interactions of the investigated compounds at the active site and the probable ligand binding conformations provide an improved basis for structure-based rational ligand design. Additionally, our docking results allow a further validation and refinement of the pharmacophore model previously postulated by us.     

Molecular modeling of melanocortin receptors

Molecular modeling of G-protein coupled receptors (GPCRs) remains a challenge due to the limited availability of structural information for the receptors. Molecular modeling approaches for melanocortin receptors (MCRs) fall into three categories: structure-based, ligand-based, and proteochemometric. Homology modeling combined with the information obtained from site-directed mutagenesis of receptors, recombined chimeric mutations of receptors and the structures of melanocortin type 4 receptor (MC4R) peptide ligands, has provided insights on detailed ligand-receptor interactions. Still, homology models based on the structures of bacteriorhodopsin (bR) or bovine rhodopsin as templates have not reached atomic level accuracy, making them unsuitable for rational drug design. On the other hand, availability of a large number of potent ligands of MCRs, especially those for the therapeutically important MC4R, has fueled ligand-based approaches, including automated pharmacophore query optimization and pharmacophore-based virtual screening. Proteochemometrics, a novel technology for the analysis of intermolecular interactions between ligand and receptor, has also shown great value in obtaining detailed information on molecular recognition and providing guidance to ligand design. In this review, the strengths and limitations of homology modeling, pharmacophore modeling and proteochemometrics modeling of MCRs are evaluated.     

维甲类化合物的构效关系研究维甲类与受体结合作用的三维构效关系

准确地预测配体－受体的结合常数是基于受体结构设计（structure-based design）的一个重要方面。目前的全新设计（de novo design）或3D数据库搜寻的方法大都侧重于结构的生成而对结构的定量评价有所忽视，本文以附睾维甲酸结合蛋白（ERABP）为模板，用DOCK程序研究了一组维甲类化合物与受体的相互作用，得到一个预测受体结合常数的方程。另外，对DOCK后的分子构象进行了CoMFA分析，得到这类化合物的作用模型。     

Integrating Pharmacophore into Membrane Molecular Dynamics Simulations to Improve Homology Modeling of G Protein‐coupled Receptors with Ligand Selectivity: A2A Adenosine Receptor as an Example

Homology modeling has been applied to fill in the gap in experimental G protein‐coupled receptors structure determination. However, achievement of G protein‐coupled receptors homology models with ligand selectivity remains challenging due to structural diversity of G protein‐coupled receptors. In this work, we propose a novel strategy by integrating pharmacophore and membrane molecular dynamics (MD) simulations to improve homology modeling of G protein‐coupled receptors with ligand selectivity. To validate this integrated strategy, the A_2A adenosine receptor (A_2AAR), whose structures in both active and inactive states have been established, has been chosen as an example. We performed blind predictions of the active‐state A_2AAR structure based on the inactive‐state structure and compared the performance of different refinement strategies. The blind prediction model combined with the integrated strategy identified ligand–receptor interactions and conformational changes of key structural elements related to the activation of A_2AAR, including (i) the movements of intracellular ends of TM3 and TM5/TM6; (ii) the opening of ionic lock; (iii) the movements of binding site residues. The integrated strategy of pharmacophore with molecular dynamics simulations can aid in the optimization in the identification of side chain conformations in receptor models. This strategy can be further investigated in homology modeling and expand its applicability to other G protein‐coupled receptor modeling, which should aid in the discovery of more effective and selective G protein‐coupled receptor ligands.     

Crystal structure-based virtual screening for fragment-like ligands of the human histamine H(1) receptor

The recent crystal structure determinations of druggable class A G protein-coupled receptors (GPCRs) have opened up excellent opportunities in structure-based ligand discovery for this pharmaceutically important protein family. We have developed and validated a customized structure-based virtual fragment screening protocol against the recently determined human histamine H(1) receptor (H(1)R) crystal structure. The method combines molecular docking simulations with a protein-ligand interaction fingerprint (IFP) scoring method. The optimized in silico screening approach was successfully applied to identify a chemically diverse set of novel fragment-like (≤22 heavy atoms) H(1)R ligands with an exceptionally high hit rate of 73%. Of the 26 tested fragments, 19 compounds had affinities ranging from 10 μM to 6 nM. The current study shows the potential of in silico screening against GPCR crystal structures to explore novel, fragment-like GPCR ligand space.     

A prospective cross-screening study on G-protein-coupled receptors: lessons learned in virtual compound library design

We present the systematic prospective evaluation of a protein-based and a ligand-based virtual screening platform against a set of three G-protein-coupled receptors (GPCRs): the β-2 adrenoreceptor (ADRB2), the adenosine A(2A) receptor (AA2AR), and the sphingosine 1-phosphate receptor (S1PR1). Novel bioactive compounds were identified using a consensus scoring procedure combining ligand-based (frequent substructure ranking) and structure-based (Snooker) tools, and all 900 selected compounds were screened against all three receptors. A striking number of ligands showed affinity/activity for GPCRs other than the intended target, which could be partly attributed to the fuzziness and overlap of protein-based pharmacophore models. Surprisingly, the phosphodiesterase 5 (PDE5) inhibitor sildenafil was found to possess submicromolar affinity for AA2AR. Overall, this is one of the first published prospective chemogenomics studies that demonstrate the identification of novel cross-pharmacology between unrelated protein targets. The lessons learned from this study can be used to guide future virtual ligand design efforts.     

Combining pharmacophore search, automated docking, and molecular dynamics simulations as a novel strategy for flexible docking. Proof of concept: docking of arginine-glycine-aspartic acid-like compounds into the alphavbeta3 binding site

A novel and highly efficient flexible docking approach is presented where the conformations (internal degrees of freedom) and orientations (external degrees of freedom) of the ligands are successively considered. This hybrid method takes advantage of the synergistic effects of structure-based and ligand-based drug design techniques. Preliminary antagonist-derived pharmacophore determination provides the postulated bioactive conformation. Subsequent docking of this pharmacophore to the receptor crystal structure results in a postulated pharmacophore/receptor binding mode. Pharmacophore-oriented docking of antagonists is subsequently achieved by matching ligand interacting groups with pharmacophore points. Molecular dynamics in water refines the proposed complexes. To validate the method, arginine-glycine-aspartic acid (RGD) containing peptides, pseudopeptides, and RGD-like antagonists were docked to the crystal structure of alphavbeta3 holoprotein and apoprotein. The proposed directed docking was found to be more accurate, faster, and less biased with respect to the protein structure (holo and apoprotein) than DOCK, Autodock, and FlexX docking methods. The successful docking of an antagonist recently cocrystallized with the receptor to both apo and holoprotein is particularly appealing. The results summarized in this report illustrated the efficiency of our light CoMFA/rigid body docking hybrid method.     

Tanshinone-I for the treatment of uterine fibroids: Molecular docking,simulation, and density functional theory investigations

Uterine fibroids (UF), most prevalent gynecological disorder, require surgery when symptomatic. It is estimated that between 25 and 35 percent of women wait until the symptoms have worsened like extended heavy menstrual bleeding and severe pelvic pain. These UF may be reduced in size through various methods such as medical or surgical intervention. Progesterone (prog) is a crucial hormone that restores the endometrium and controls uterine function. In the current study, 28 plant-based molecules are identified from previous literature and docked onto the prog receptors with 1E3K and 2OVH. Tanshinone-I has shown the best docking score against both proteins. The synthetic prog inhibitor Norethindrone Acetate is used as a standard to evaluate the docking outcomes. The best compound, tanshinone-I, was analyzed using molecular modeling and DFT. The RMSD for the 1E3K protein–ligand complex ranged from 0.10 to 0.42 Å, with an average of 0.21 Å and a standard deviation (SD) of 0.06, while the RMSD for the 2OVH protein–ligand complex ranged from 0.08 to 0.42 Å, with an average of 0.20 Å and a SD of 0.06 showing stable interaction. In principal component analysis, the observed eigen values of HPR-Tanshinone-I fluctuate between −1.11 to 1.48 and −1.07 to 1.25 for PC1 and PC2, respectively (1E3K), and the prog-tanshinone-I complex shows eigen values of −38.88 to −31.32 and −31.32 to 35.87 for PC1 and PC2, respectively (2OVH), which shows Tanshinone-I forms a stable protein–ligand complex with 1E3K in comparison to 2OVH. The Free Energy Landscape (FEL) analysis shows the Gibbs free energy in the range of 0 to 8 kJ/mol for Tanshinone-I with 1E3K and 0 to 14 kJ/mol for Tanshinone-I with the 2OVH complex. The DFT calculation reveals ΔE value of 2.8070 eV shows tanshinone-I as a stable compound. 1E3K modulates the prog pathway, it may have either an agonistic or antagonistic effect on hPRs. Tanshinone-I can cause ROS, apoptosis, autophagy (p62 accumulation), up-regulation of inositol requiring protein-1, enhancer-binding protein homologous protein, p-c-Jun N-terminal kinase (p-JNK), and suppression of MMPs. Bcl-2 expression can change LC3I to LC3II and cause apoptosis through Beclin-1 expression.     

Identification and mapping of small-molecule binding sites in proteins: computational tools for structure-based drug design

The number of protein structures is currently increasing at an impressive rate. The growing wealth of data calls for methods to efficiently exploit structural information for medicinal and pharmaceutical purposes. Given the three-dimensional (3D) structure of a validated protein target, the identification of functionally relevant binding sites and the analysis ('mapping') of these sites with respect to molecular recognition properties are important initial tasks in structure-based drug design. To address these tasks, a variety of computational tools have been developed. Approaches to identify binding pockets include geometric analyses of protein surfaces, comparisons of protein structures, similarity searches in databases of protein cavities, and docking scans to reveal areas of high ligand complementarity. In the context of binding-site analysis, powerful data mining tools help to retrieve experimental information about related protein-ligand complexes. To identify interaction hot spots, various potential functions and knowledge-based approaches are available for mapping binding regions. The results may subsequently be used to guide virtual screenings for new ligands via pharmacophore searches or docking simulations.     

Binding affinity prediction for ligands and receptors forming tautomers and ionization species: inhibition of mitogen-activated protein kinase-activated protein kinase 2 (MK2)

Treatment of ionization and tautomerism of ligands and receptors is one of the unresolved issues in structure-based prediction of binding affinities. Our solution utilizes the thermodynamic master equation, expressing the experimentally observed association constant as the sum of products, each valid for a specific ligand-receptor species pair, consisting of the association microconstant and the fractions of the involved ligand and receptor species. The microconstants are characterized by structure-based simulations, which are run for individual species pairs. Here we incorporated the multispecies approach into the QM/MM linear response method and used it for structural correlation of published inhibition data on mitogen-activated protein kinase (MAPK)-activated protein kinase (MK2) by 66 benzothiophene and pyrrolopyridine analogues, forming up to five tautomers and seven ionization species under experimental conditions. Extensive cross-validation showed that the resulting models were stable and predictive. Inclusion of all tautomers and ionization ligand species was essential: the explained variance increased to 90% from 66% for the single-species model.     

A common binding mode that may facilitate the design of novel broad-spectrum inhibitors against metallo-β-lactamases

Multidrug resistance caused by metallo-β-lactamases (MBLs) remains a therapeutic challenge owing to their broad hydrolytic spectrum on β-lactams, and current β-lactamase inhibitors are ineffective against MBL-carrying superbugs. In this study, we discovered a series of potential inhibitors against various MBLs through a hybrid in silico protocol that combines support vector machine, protein–ligand interaction fingerprint (PLIF) analysis, and molecular docking. These compounds were shown to have a common binding mode. PLIF analysis and molecular docking demonstrated that the sulfonyl oxygens and the amide oxygen coordinated to zinc ions in a similar fashion, indicating the importance of such structural characteristics in the design of broad-spectrum inhibitors against MBLs. In addition, the carbon atom connecting the sulfonyl amide and the nitrogen heterocycle played an important role in ligand flexibility, allowing the rotation of the aromatic ring to accommodate the hydrophobic pockets in the binding site of different subclasses of MBLs. These findings may pave the way to the design of novel broad-spectrum inhibitors against MBLs.     

The application of quantum mechanics in structure-based drug design

Introduction: Computational chemistry has become an established and valuable component in structure-based drug design. However the chemical complexity of many ligands and active sites challenges the accuracy of the empirical potentials commonly used to describe these systems. Consequently, there is a growing interest in utilizing electronic structure methods for addressing problems in protein–ligand recognition.

Areas covered: In this review, the authors discuss recent progress in the development and application of quantum chemical approaches to modeling protein–ligand interactions. The authors specifically consider the development of quantum mechanics (QM) approaches for studying large molecular systems pertinent to biology, focusing on protein–ligand docking, protein–ligand binding affinities and ligand strain on binding.

Expert opinion: Although computation of binding energies remains a challenging and evolving area, current QM methods can underpin improved docking approaches and offer detailed insights into ligand strain and into the nature and relative strengths of complex active site interactions. The authors envisage that QM will become an increasingly routine and valued tool of the computational medicinal chemist.