Using silico methods predicting ligands for orphan GPCRs

The G-protein coupled receptor (GPCR) superfamily is one of the most important drug target classes for the pharmaceutical industry. The completion of the human genome project has revealed that there are more than 300 potential GPCR targets of interest. The identification of their natural ligands can gain significant insights into regulatory mechanisms of cellular signaling networks and provide unprecedented opportunities for drug discovery. Much effort has been directed towards the GPCR ligand discovery study by both academic institutions and pharmaceutical industries. However, the endogenous ligands still remain unknown for about 150 GPCRs in the human genome. It is necessary to develop new strategies to predict candidate ligands for these so-called orphan receptors. Computational techniques are playing an increasingly important role in finding and validating novel ligands for orphan GPCRs (oGPCRs). In this paper, we focus on recent development in applying bioinformatics approaches for the discovery of GPCR ligands. In addition, some of the data resources for ligand identification are also provided.     

Assay Related Target Similarity (ARTS) - chemogenomics approach for quantitative comparison of biological targets

Computer-based chemogenomics approaches compare macromolecular drug targets based on their amino acid sequences or derived properties, by similarity of their ligands, or according to ligand-target interaction models. Here we present ARTS (Assay Related Target Similarity) as a quantitative index that estimates target similarity directly from measured affinities of a set of probe compounds. This approach reduces the risk of deducing artificial target relationships from mutually inactive compounds. ARTS implements a scoring scheme that matches intertarget similarity based on dose-response measurements. While all experimentally derived target similarities have a tendency to be data set-dependent, we demonstrate that ARTS depends less on the used data set than the commonly used Pearson correlation or Tanimoto index.     

Detecting drug promiscuity using gaussian ensemble screening

Polypharmacology describes the binding of a ligand to multiple protein targets (a promiscuous ligand) or multiple diverse ligands binding to a given target (a promiscuous target). Pharmaceutical companies are discovering increasing numbers of both promiscuous drugs and drug targets. Hence, polypharmacology is now recognized as an important aspect of drug design. Here, we describe a new and fast way to predict polypharmacological relationships between drug classes quantitatively, which we call Gaussian Ensemble Screening (GES). This approach represents a cluster of molecules with similar spherical harmonic surface shapes as a Gaussian distribution with respect to a selected center molecule. Calculating the Gaussian overlap between pairs of such clusters allows the similarity between drug classes to be calculated analytically without requiring thousands of bootstrap comparisons, as in current promiscuity prediction approaches. We find that such cluster similarity scores also follow a Gaussian distribution. Hence, a cluster similarity score may be transformed into a probability value, or "p-value", in order to quantify the relationships between drug classes. We present results obtained when using the GES approach to predict relationships between drug classes in a subset of the MDL Drug Data Report (MDDR) database. Our results indicate that GES is a useful way to study polypharmacology relationships, and it could provide a novel way to propose new targets for drug repositioning.     

化学信息学与生物信息学开放性比较

通过对化学信息学与生物信息学在公共资源、数据文件格式、软件编程语言、科学文献与教材等诸方面的比较与分析,指出生物信息学的成功因素在于其开放性;与之相比,虽然高等学校、研究所等教育科研领域和商业公司（特别是药物研发公司）都发布了不少化学信息学开源或自由软件以及一些开放数据,但化学信息学仍然由于各种原因使得开放性不足。有鉴于此,国内建立了化学化工资源导航系统,启动了《计算化学e-science研究以及示范应用》等国家自然科学基金重大项目。在教育、教学方面也应继续改革,使化学信息学能真正适应和促进当前药物发现研究。     

The relationship between the sequence identities of alpha helical proteins in the PDB and the molecular similarities of their ligands.

This paper considers the relationship between the percentage sequence identities of protein chains and the molecular similarities of the ligands they bind. Among a set of alpha helical proteins from the PDB, it is found that related proteins tend to bind similar ligands. Furthermore, the property of binding similar ligands can be used to define the categories of "like" and "unlike" pairs of protein chains, separated by an approximate cutoff at a sequence identity of, or somewhat above, 45%. Similarly, the property of binding related protein chains can be used to define "low" and "high" similarity pairs of ligand residues, with a cutoff at a Tanimoto score of 0.70. The ligands bound to two "like" protein chains are five times more likely to be of high similarity than would be expected if protein sequence identity and ligand molecular similarity were independent variables. Nonetheless, the nature of the PDB means that it is unclear whether the same conclusions would be reached with a data set representing an unbiased sample of all protein-ligand complexes in a living cell. The construction of an appropriate data set for such a study represents a significant challenge.     

Design of a tripartite network for the prediction of drug targets

Drug-target networks have aided in many target prediction studies aiming at drug repurposing or the analysis of side effects. Conventional drug-target networks are bipartite. They contain two different types of nodes representing drugs and targets, respectively, and edges indicating pairwise drug-target interactions. In this work, we introduce a tripartite network consisting of drugs, other bioactive compounds, and targets from different sources. On the basis of analog relationships captured in the network and so-called neighbor targets of drugs, new drug targets can be inferred. The tripartite network was found to have a stable structure and simulated network growth was accompanied by a steady increase in assortativity, reflecting increasing correlation between degrees of connected nodes leading to even network connectivity. Local drug environments in the tripartite network typically contained neighbor targets and revealed interesting drug-compound-target relationships for further analysis. Candidate targets were prioritized. The tripartite network design extends standard drug-target networks and provides additional opportunities for drug target prediction.     

New insights from molecular dynamic simulation studies of the multiple binding modes of a ligand with G-quadruplex DNA

G-quadruplexes are higher-order DNA and RNA structures formed from guanine-rich sequences. These structures have recently emerged as a new class of potential molecular targets for anticancer drugs. An understanding of the three-dimensional interactions between small molecular ligands and their G-quadruplex targets in solution is crucial for rational drug design and the effective optimization of G-quadruplex ligands. Thus far, rational ligand design has been focused mainly on the G-quartet platform. It should be noted that small molecules can also bind to loop nucleotides, as observed in crystallography studies. Hence, it would be interesting to elucidate the mechanism underlying how ligands in distinct binding modes influence the flexibility of G-quadruplex. In the present study, based on a crystal structure analysis, the models of a tetra-substituted naphthalene diimide ligand bound to a telomeric G-quadruplex with different modes were built and simulated with a molecular dynamics simulation method. Based on a series of computational analyses, the structures, dynamics, and interactions of ligand-quadruplex complexes were studied. Our results suggest that the binding of the ligand to the loop is viable in aqueous solutions but dependent on the particular arrangement of the loop. The binding of the ligand to the loop enhances the flexibility of the G-quadruplex, while the binding of the ligand simultaneously to both the quartet and the loop diminishes its flexibility. These results add to our understanding of the effect of a ligand with different binding modes on G-quadruplex flexibility. Such an understanding will aid in the rational design of more selective and effective G-quadruplex binding ligands.     

An ontology for pharmaceutical ligands and its application for in silico screening and library design

Annotation efforts in biosciences have focused in past years mainly on the annotation of genomic sequences. Only very limited effort has been put into annotation schemes for pharmaceutical ligands. Here we propose annotation schemes for the ligands of four major target classes, enzymes, G protein-coupled receptors (GPCRs), nuclear receptors (NRs), and ligand-gated ion channels (LGICs), and outline their usage for in silico screening and combinatorial library design. The proposed schemes cover ligand functionality and hierarchical levels of target classification. The classification schemes are based on those established by the EC, GPCRDB, NuclearDB, and LGICDB. The ligands of the MDL Drug Data Report (MDDR) database serve as a reference data set of known pharmacologically active compounds. All ligands were annotated according to the schemes when attribution was possible based on the activity classification provided by the reference database. The purpose of the ligand-target classification schemes is to allow annotation-based searching of the ligand database. In addition, the biological sequence information of the target is directly linkable to the ligand, hereby allowing sequence similarity-based identification of ligands of next homologous receptors. Ligands of specified levels can easily be retrieved to serve as comprehensive reference sets for cheminformatics-based similarity searches and for design of target class focused compound libraries. Retrospective in silico screening experiments within the MDDR01.1 database, searching for structures binding to dopamine D2, all dopamine receptors and all amine-binding class A GPCRs using known dopamine D2 binding compounds as a reference set, have shown that such reference sets are in particular useful for the identification of ligands binding to receptors closely related to the reference system. The potential for ligand identification drops with increasing phylogenetic distance. The analysis of the focus of a tertiary amine based combinatorial library compared to known amine binding class A GPCRs, peptide binding class A GPCRs, and LGIC ligands constitutes a second application scenario which illustrates how the focus of a combinatorial library can be treated quantitatively. The provided annotation schemes, which bridge chem- and bioinformatics by linking ligands to sequences, are expected to be of key utility for further systematic chemogenomics exploration of previously well explored target families.     

Schrödinger药物虚拟筛选流程模块在大学生物和化学信息学教学中的应用

介绍了Schr?dinger药物虚拟筛选的基本原理和流程,结合大学生物和化学信息学课程的相关教学内容,分别描述了蛋白受体的预处理、类药性五原则、毒药物动力学(ADME)、泛筛选干扰化合物(PAINS)、高通量虚拟筛选、标准精度筛选、高精度筛选和MM/GBSA的打分排序原理和使用方法。该软件可以在大学生物和化学信息学的教学中演示,有助于提高学生对蛋白结构、分子构象、药物虚拟筛选和计算机辅助分子设计的理解,该软件有很好的图形界面,可以给学生直观的体验,大大丰富了大学课堂的教学内容。此外,该软件在药物设计领域里面也有很好的应用价值,大大节约了药物筛选的成本,提高了药物发现的效率。     

Functional drift of sequence attributes in the FK506-binding proteins (FKBPs)

Diverse members of the FK506-binding proteins (FKBPs) group and their complexes with different macrocyclic ligands of fungal origins such as FK506, rapamycin, ascomycin, and their immunosuppressive and nonimmunosuppressive derivatives display a variety of cellular and biological activities. The functional relatedness of the FKBPs was estimated from the following attributes of their aligned sequences: 1 degrees conservation of the consensus sequence; 2 degrees sequence similarity; 3 degrees pI; 4 degrees hydrophobicity; 5 degrees amino acid hydrophobicity and bulkiness profiles. Analyses of the multiple sequence alignments and intramolecular interaction networks calculated from a series of structures of the FKBPs revealed some variations in the interaction clusters formed by the AA residues that are crucial for sustaining peptidylprolyl cis/trans isomerases (PPIases) activity and binding capacity of the FKBPs. Fine diversification of the sequences of the multiple paralogues and orthologues of the FKBPs encoded in different genomes alter the intramolecular interaction patterns of their structures and allowed them to gain some selectivity in binding to diverse targets (functional drift).     

A comprehensive docking study on the selectivity of binding of aromatic compounds to proteins

Generally, computer-aided drug design is focused on screening of ligand molecules for a single protein target. The screening of several proteins for a ligand is a relatively new application of molecular docking. In the present study, complexes from the Brookhaven Protein Databank were used to investigate a docking approach of protein screening. Automated molecular docking calculations were applied to reproduce 44 protein-aromatic ligand complexes (31 different proteins and 39 different ligand molecules) of the databank. All ligands were docked to all different protein targets in altogether 12090 docking runs. Based on the results of the extensive docking simulations, two relative measures, the molecular interaction fingerprint (MIF) and the molecular affinity fingerprint (MAF), were introduced to describe the selectivity of aromatic ligands to different proteins. MIF and MAF patterns are in agreement with fragment and similarity considerations. Limitations and future extension of our approach are discussed.     

Mathematical correction for fingerprint similarity measures to improve chemical retrieval

In many modern chemoinformatics systems, molecules are represented by long binary fingerprint vectors recording the presence or absence of particular features or substructures, such as labeled paths or trees, in the molecular graphs. These long fingerprints are often compressed to much shorter fingerprints using a simple modulo operation. As the length of the fingerprints decreases, their typical density and overlap tend to increase, and so does any similarity measure based on overlap, such as the widely used Tanimoto similarity. Here we show that this correlation between shorter fingerprints and higher similarity can be thought of as a systematic error introduced by the fingerprint folding algorithm and that this systematic error can be corrected mathematically. More precisely, given two molecules and their compressed fingerprints of a given length, we show how a better estimate of their uncompressed overlap, hence of their similarity, can be derived to correct for this bias. We show how the correction can be implemented not only for the Tanimoto measure but also for all other commonly used measures. Experiments on various data sets and fingerprint sizes demonstrate how, with a negligible computational overhead, the correction noticeably improves the sensitivity and specificity of chemical retrieval.     

Similarity metrics for ligands reflecting the similarity of the target proteins

In this study we evaluate how far the scope of similarity searching can be extended to identify not only ligands binding to the same target as the reference ligand(s) but also ligands of other homologous targets without initially known ligands. This "homology-based similarity searching" requires molecular representations reflecting the ability of a molecule to interact with target proteins. The Similog keys, which are introduced here as a new molecular representation, were designed to fulfill such requirements. They are based only on the molecular constitution and are counts of atom triplets. Each triplet is characterized by the graph distances and the types of its atoms. The atom-typing scheme classifies each atom by its function as H-bond donor or acceptor and by its electronegativity and bulkiness. In this study the Similog keys are investigated in retrospective in silico screening experiments and compared with other conformation independent molecular representations. Studied were molecules of the MDDR database for which the activity data was augmented by standardized target classification information from public protein classification databases. The MDDR molecule set was split randomly into two halves. The first half formed the candidate set. Ligands of four targets (dopamine D2 receptor, opioid delta-receptor, factor Xa serine protease, and progesterone receptor) were taken from the second half to form the respective reference sets. Different similarity calculation methods are used to rank the molecules of the candidate set by their similarity to each of the four reference sets. The accumulated counts of molecules binding to the reference target and groups of targets with decreasing homology to it were examined as a function of the similarity rank for each reference set and similarity method. In summary, similarity searching based on Unity 2D-fingerprints or Similog keys are found to be equally effective in the identification of molecules binding to the same target as the reference set. However, the application of the Similog keys is more effective in comparison with the other investigated methods in the identification of ligands binding to any target belonging to the same family as the reference target. We attribute this superiority to the fact that the Similog keys provide a generalization of the chemical elements and that the keys are counted instead of merely noting their presence or absence in a binary form. The second most effective molecular representation are the occurrence counts of the public ISIS key fragments, which like the Similog method, incorporates key counting as well as a generalization of the chemical elements. The results obtained suggest that ligands for a new target can be identified by the following three-step procedure: 1. Select at least one target with known ligands which is homologous to the new target. 2. Combine the known ligands of the selected target(s) to a reference set. 3. Search candidate ligands for the new targets by their similarity to the reference set using the Similog method. This clearly enlarges the scope of similarity searching from the classical application for a single target to the identification of candidate ligands for whole target families and is expected to be of key utility for further systematic chemogenomics exploration of previously well explored target families.     

Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder

Protein–protein interactions (PPIs) play essential roles in many biological processes. In protein–protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (IDPs) with unstable structures can promote the promiscuity of hubs and also involve in many disease pathways, so they also could serve as potential drug targets. Moreover, proteins with similar functions measured by semantic similarity of gene ontology (GO) terms tend to interact with each other. Here, the relationship between hub proteins and drug targets based on GO terms and intrinsic disorder was explored. The semantic similarities of GO terms and genes between two proteins, and the rate of intrinsic disorder residues of each protein were extracted as features to characterize the functional similarity between two interacting proteins. Only using 8 feature variables, prediction models by support vector machine (SVM) were constructed to predict PPIs. The accuracy of the model on the PPI data from human hub proteins is as high as 83.72%, which is very promising compared with other PPI prediction models with hundreds or even thousands of features. Then, 118 of 142 PPIs between hubs are correctly predicted that the two interacting proteins are targets of the same drugs. The results indicate that only 8 functional features are fully efficient for representing PPIs. In order to identify new targets from IDP dataset, the PPIs between hubs and IDPs are predicted by the SVM model and the model yields a prediction accuracy of 75.84%. Further research proves that 3 of 5 PPIs between hubs and IDPs are correctly predicted that the two interacting proteins are targets of the same drugs. All results demonstrate that the model with only 8-dimensional features from GO terms and intrinsic disorder still gives a good performance in predicting PPIs and further identifying drug targets.