Receptor-ligand binding sites and virtual screening

Within the pharmaceutical industry, the ultimate source of continuing profitability is the unremitting process of drug discovery. To be profitable, drugs must be marketable: legally novel, safe and relatively free of side effects, efficacious, and ideally inexpensive to produce. While drug discovery was once typified by a haphazard and empirical process, it is now increasingly driven by both knowledge of the receptor-mediated basis of disease and how drug molecules interact with receptors and the wider physiome. Medicinal chemistry postulates that to understand a congeneric ligand series, or set thereof, is to understand the nature and requirements of a ligand binding site. Likewise, structural molecular biology posits that to understand a binding site is to understand the nature of ligands bound therein. Reality sits somewhere between these extremes, yet subsumes them both. Complementary to rules of ligand design, arising through decades of medicinal chemistry, structural biology and computational chemistry are able to elucidate the nature of binding site-ligand interactions, facilitating, at both pragmatic and conceptual levels, the drug discovery process.     

Receptor-ligand interaction-based virtual screening for novel Eg5/kinesin spindle protein inhibitors

Eg5/KSP is a promising mitotic spindle target for drug discovery in cancer chemotherapy and the development of agents against fungal diseases. A range of Eg5 targeting compounds identified by in vitro or cell-based screening is currently in development. We employed structure-based virtual screening of a database of 700,?000 compounds to identify three novel Eg5 inhibitors bearing quinazoline (24) or thioxoimidazolidine (30 and 37) scaffolds. The new compounds inhibit Eg5 ATPase activity, show growth inhibition in proliferation assays, and induce monoastral spindles in cells, the characteristic phenotype for Eg5 inhibiting agents. This is the first successful reported procedure for the identification of Eg5 inhibitors via receptor-ligand interaction-based virtual screening.     

Decoys for docking

Molecular docking is widely used to predict novel lead compounds for drug discovery. Success depends on the quality of the docking scoring function, among other factors. An imperfect scoring function can mislead by predicting incorrect ligand geometries or by selecting nonbinding molecules over true ligands. These false-positive hits may be considered "decoys". Although these decoys are frustrating, they potentially provide important tests for a docking algorithm; the more subtle the decoy, the more rigorous the test. Indeed, decoy databases have been used to improve protein structure prediction algorithms and protein-protein docking algorithms. Here, we describe 20 geometric decoys in five enzymes and 166 "hit list" decoys-i.e., molecules predicted to bind by our docking program that were tested and found not to do so-for beta-lactamase and two cavity sites in lysozyme. Especially in the cavity sites, which are very simple, these decoys highlight particular weaknesses in our scoring function. We also consider the performance of five other widely used docking scoring functions against our geometric and hit list decoys. Intriguingly, whereas many of these other scoring functions performed better on the geometric decoys, they typically performed worse on the hit list decoys, often highly ranking molecules that seemed to poorly complement the model sites. Several of these "hits"from the other scoring functions were tested experimentally and found, in fact, to be decoys. Collectively, these decoys provide a tool for the development and improvement of molecular docking scoring functions. Such improvements may, in turn, be rapidly tested experimentally against these and related experimental systems, which are well-behaved in assays and for structure determination.     

Analyzing for co-localization of proteins at a cell membrane

Cell-to-cell communication is mediated by molecular interactions at the surface of the cell by soluble ligands released from distant cells or by cell surface molecules on adjacent cells. These interactions lead to activation of intracellular signaling pathways that subsequently can lead to activation of specific genes. This signal transduction process controls cellular activities as diverse as proliferation, differentiation and apoptosis, so we must understand the underlying molecular events in detail in order to understand broader questions related to development, uncontrolled growth in tumors, tissue regeneration and use of stem cells to name a few. Binding of a ligand in the extracellular space to a transmembrane receptor constitutes the first crucial step for activation of a signaling pathway within the cell. This binding can either lead to oligomerization of individual receptors, to reorganization of existing clusters of receptors or to changes in the protein conformations, which in turn results in recruitment of signaling molecules in the cytoplasm. While different membrane receptors activate different downstream signaling pathways, some receptors can activate more than one pathway and a particular pathway can be activated by different receptors. It appears that these processes are regulated either by agonists and antagonists in the extracellular medium, by receptor-receptor interactions in the membrane or by a number of signaling mediators in the cytoplasm of the cell. Our work has focused on understanding how the intermolecular interactions in the membrane can control the signal transduction process: Are there specialized structures on the surface that facilitate receptor-receptor interactions? Do the receptors exist as monomers or pre-existing complexes that enhance the probability of activation? Do different receptors associate in the same domains or are there distinct organizational principles for each receptor type. In order to address these questions, we seek to develop tools that allow us to examine intermolecular interactions and reactions directly on the cell surface, particularly on live cells in culture or in tissue. This review discusses some of the approaches that are currently available and highlights some of the key advantages and disadvantages they represent with particular focus on image cross correlation spectroscopy as a relatively new quantitative tool developed by us to address some of these issues.     

膜蛋白与膜蛋白组学

膜蛋白是指能够结合或整合到细胞或细胞器的膜上的蛋白质的总称。现在市场上销售的药物80％以上都是作用在膜蛋白上的,膜蛋白是理想的药物靶点,因此,对膜蛋白的研究具有十分重要意义。但由于膜蛋白的溶解、分离、鉴定十分困难,造成膜蛋白组学研究的滞后。本文就膜蛋白的结构,膜蛋白在细胞生理和人类疾病中扮演的重要角色,膜蛋白作为药物靶点的潜力以及膜蛋白组学研究技术作一综述。     

Three-dimensional models for integral membrane proteins: Possibilities and pitfalls

Summary Some three-dimensional model-building techniques applicable to membrane proteins are presented. Requirements for a successful modeling project are outlined, and methodological limitations are discussed. One example of a modeling exercise for the ₂ adrenergic receptor is reviewed briefly, and the utility of such studies is explored. The explosion of data for integral membrane protein sequences and properties make 3D modeling studies increasingly feasible and necessary. Model-building exercises should enable us to take better advantage of new protein sequence data, and studies of this type are likely to proliferate in the future.     

Benchmarking sets for molecular docking

Ligand enrichment among top-ranking hits is a key metric of molecular docking. To avoid bias, decoys should resemble ligands physically, so that enrichment is not simply a separation of gross features, yet be chemically distinct from them, so that they are unlikely to be binders. We have assembled a directory of useful decoys (DUD), with 2950 ligands for 40 different targets. Every ligand has 36 decoy molecules that are physically similar but topologically distinct, leading to a database of 98,266 compounds. For most targets, enrichment was at least half a log better with uncorrected databases such as the MDDR than with DUD, evidence of bias in the former. These calculations also allowed 40x40 cross-docking, where the enrichments of each ligand set could be compared for all 40 targets, enabling a specificity metric for the docking screens. DUD is freely available online as a benchmarking set for docking at http://blaster.docking.org/dud/.     

Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins

We present the results of an extensive computational study in which we show that combining scoring functions in an intersection-based consensus approach results in an enhancement in the ability to discriminate between active and inactive enzyme inhibitors. This is illustrated in the context of docking collections of three-dimensional structures into three different enzymes of pharmaceutical interest: p38 MAP kinase, inosine monophosphate dehydrogenase, and HIV protease. An analysis of two different docking methods and thirteen scoring functions provides insights into which functions perform well, both singly and in combination. Our data shows that consensus scoring further provides a dramatic reduction in the number of false positives identified by individual scoring functions, thus leading to a significant enhancement in hit-rates.     

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.     

Atomistic models for free energy evaluation of drug binding to membrane proteins

The binding of various molecules to integral membrane proteins with optimal affinity and specificity is central to normal function of cell. While membrane proteins represent about one third of the whole cell proteome, they are a majority of common drug targets. The quest for the development of computational models capable of accurate evaluation of binding affinities, decomposition of the binding into its principal components and thus mapping molecular mechanisms of binding remains one of the main goals of modern computational biophysics and related drug development. The primary scope of this review will be on the recent extension of computational methods for the study of drug binding to membrane proteins. Several examples of such applications will be provided ranging from secondary transporters to voltage gated channels. In this mini-review, we will provide a short summary on the breadth of different methods for binding affinity evaluation. These methods include molecular docking with docking scoring functions, molecular dynamics (MD) simulations combined with post-processing analysis using Molecular Mechanics/Poisson Boltzmann (Generalized Born) Surface Area (MM/PB(GB)SA), as well as direct evaluation of free energies from Free Energy Perturbation (FEP) with constraining schemes, and Potential of Mean Force (PMF) computations. We will compare advantages and shortcomings of popular techniques and provide discussion on the integrative strategies for drug development aimed at targeting membrane proteins.     

基于分子对接仿真技术研究头孢地尼杂质分离机制

目的结合分子对接仿真技术,研究反相离子对色谱法(reversed phase ion chromatography,RPIC)对头孢地尼立体异构体杂质I、J、K、L、T、U的分离机制。方法采用RPIC对各杂质进行分离,考察在流动相中加入四甲基氢氧化铵(tetramethylammonium hydroxide,TMAH),体积分数分别为0.01%、0.05%、0.1%、0.25%、0.3%、0.5%的色谱条件下以及流动相pH在4.5～6.5内变化时,各杂质间的分离度以及保留时间的变化。采用分子对接仿真技术,研究各杂质与TMAH分子的结合能力的差异,阐明了RPIC洗脱过程中,在TMAH作用下,各杂质的分离度及保留行为产生差异的原因。结果 TMAH与杂质I、J、K、L的结合能力存在差异。与TMAH结合后,杂质I形成3个分子内氢键,杂质J形成2个分子内氢键,且与TMAH之间存在1个分子间氢键,杂质K形成1个分子内氢键,杂质L无分子内氢键,杂质I、J、K、L与TMAH分子之间均存在静电作用力和疏水作用力。TMAH与杂质T、U的结合能力无显著性差异。杂质T、U均可以形成3个分子内氢键,与TMAH之间均存在静电作用力和疏水作用力。随着流动相中的TMAH浓度的增大,杂质I、J、K、L的保留时间延长,分离度逐渐增大,杂质T、U的色谱行为无显著变化。结论采用分子对接仿真技术,在分析各杂质与TMAH自身理化性质的基础上,研究各杂质在流动相环境中与TMAH之间的相互作用能够更加精确地阐述RPIC法中头孢地尼立体异构体杂质的分离机制。     

Two-stage method for protein-ligand docking.

A two-stage method for the computational prediction of the structure of protein-ligand complexes is proposed. Given an experimentally determined structure of the protein, in the first stage a large number of plausible ligand conformations is generated using the fast docking algorithm FlexX. In the second stage these conformations are minimized and reranked using a method based on a classical force field. The two-stage method is tested for 10 different protein-ligand complexes. For 9 of them experimentally determined structures are known. It turns out that the two-stage method strongly improves the predictive power as compared to that of the fast docking stage alone. The tenth case is a bona fide prediction of a complex of thrombin with a new inhibitor for which no experimentally determined structure is available so far.     

肠道紧密连接跨膜蛋白研究进展

紧密连接(Tight junction,TJ)是广泛存在于所有上皮或内皮细胞间连接最顶端的多种蛋白质复合物,是相邻细胞之间的离子、溶质和水经细胞旁通道转运的结构和功能基础。Claudins、Occludin、Tricellulin与JAMs属于TJ跨膜蛋白,共同形成抵抗肠腔内有害分子如病原体、毒素和过敏原的物理屏障,在多种肠道疾病的发生、发展和转归中都发挥着重要作用。本文重点综述Claudins、Occludin、Tricellulin及JAMs的组成、结构、调控机制及其在肠道疾病中的作用,在与TJ跨膜蛋白改变相关的肠道疾病的诊断、治疗及预后方面具有重要的临床意义和广泛的应用价值。     

Automated docking for novel drug discovery

Introduction: The volume of three-dimensional structural information of macromolecules and the number of computational tools to predict binding modes and affinities of molecular complexes are increasing daily. Molecular docking is a rational structural approach employed to predict thermodynamic parameters based on molecular recognition between two or more molecules. In addition, docking studies have become very important for therapeutic applications in modern structure-based drug design because this computational tool uses few economic resources. However, they omit many biological conditions that critically influence small and macromolecular structural motions. To mimic physiological conditions, it is necessary to consider other environmental factors, such as the presence of water molecules and the flexibility of ligands and side chain residues of proteins. Furthermore, molecular dynamics simulations have been coupled with docking procedures to expand the boundaries and obtain more reliable information.

Areas covered: In this article, we review current advances in protein-small molecule docking and possible future directions.

Expert opinion: Docking studies include many conformations to predict binding free energies (scoring functions) and to search (scoring sampling) for the most representative binding conformations. Therefore, several biological properties, from side chain residues to complete protein motions, have been included in docking studies to improve theoretical predictions.     

Flexible receptor docking for drug discovery

Introduction: Molecular docking has become a popular method for virtual screening. Docking small molecules to a rigid biological receptor is fast but could produce many false negatives and identify less diverse compounds. Flexible receptor docking has alleviated this problem.

Areas covered: This article focuses on reviewing ensemble docking as an approximate but inexpensive method to incorporate receptor flexibility in molecular docking. It outlines key features and recent advances of this method and points out problem areas that need to be addressed to make it even more useful in drug discovery.

Expert opinion: Among the different methods introduced for flexible receptor docking, ensemble docking represents one of the most popular approaches, especially for high-throughput virtual screening. One can generate structural ensembles by using experimental structures, by structural modeling and by various types of molecular simulations. In building a structural ensemble, a judicious choice of the structures to be included can improve performance. Furthermore, reducing the size of the structural ensemble can cut computational costs, and removing the structures that can bind few ligands well could enrich the number of true actives identified by ensemble docking. The ability of ensemble docking to identify more true positives at the top of a rank-ordered list also depends on the choice of the methods to score and rank compounds, an area that needs further research.