Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates

MOTIVATION: To gather information about available databases and chemoinformatics methods for prediction of properties relevant to the drug discovery and optimization process. RESULTS: We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability of chemical compounds as potential drugs, as well as for predicting their physico-chemical and ADMET properties have been proposed in recent years. These methods are discussed, and some possible future directions in this rapidly developing field are described.     

Improving Compound Quality through in vitro and in silico Physicochemical Profiling

Many compounds entering clinical studies do not survive the numerous hurdles for a good pharmacological lead to a drug on the market. The reasons for attrition have been widely studied which resulted in more early attention to compound quality related to physical chemistry, drug metabolism and pharmacokinetics (DMPK), and toxicology/safety. This paper will briefly review current physicochemical in vitro assays and in silico predictions to support compound and library design through to lead optimization. The most important physicochemical properties include lipophilicity (log P/D), pK_a, solubility, and permeability. These drive key ADMET properties such as absorption, cell penetration, access to the brain, volume of distribution, plasma protein binding, metabolism, and toxicity, as well as biopharmaceutical behavior. Much data are now available from medium‐ to high‐throughput physchem and ADMET in vitro assays, either in the public domain (see, e.g., PubChem, PubMed) or in drug companies' in‐house databases. Such data are increasingly being computer‐modelled and used in predictive chemistry. New pipelining technology makes it easier to build and update QSAR models so that such models can use the latest available data to produce robust local and global predictive in silico ADMET models.     

化合物ADMET性质预测平台的构建

在药物研发早期阶段对化合物成药性和安全性进行评估,对于提高药物研发成功率、降低研发成本具有十分重要的意义。为了能够帮助药物研究工作者快速准确地判断候选化合物的成药性与安全性,开发了一个基于计算机方法的化合物ADMET性质预测平台。首先,通过文本挖掘的方法收集了化合物药代动力学性质和毒性(ADMET)的高质量实验数据。然后,根据原始文献复原了13个预测模型,同时采用支持向量机方法自建了15个具有较高预测能力的计算模型。最后,基于分布式架构,结合高性能计算集群优势,开发了化合物ADMET性质预测平台(http://www.vslead.com/?r=admet/index),用于预测28种重要的化合物ADMET性质。研究者可以使用这一平台快捷方便地对药物研究中比较重要的ADMET性质进行预测,在药物研发早期对候选化合物进行成药性评价和风险评估,有助于提高药物研的成功率,节省研发时间和经费的投入。     

In vitro methods in the prediction of kinetics of drugs: focus on drug metabolism

The absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of a candidate drug influence its final clinical success. These properties have traditionally been evaluated by using various in vivo animal approaches, but recently, a number of in vitro and in silico methods have been introduced to determine key ADMET features. Basic events, such as absorption through the gut wall, binding to plasma proteins, active and passive transfer through the blood-brain barrier, and various metabolic parameters, can now be screened with rapid in vitro and computer modelling methods. The focus in this short review is on the basic in vitro and in silico methods that are used for studying the metabolism properties of new drug molecules.     

PAQ: Partition Analysis of Quasispecies

MOTIVATION: The complexities of genetic data may not be accurately described by any single analytical tool. Phylogenetic analysis is often used to study the genetic relationship among different sequences. Evolutionary models and assumptions are invoked to reconstruct trees that describe the phylogenetic relationship among sequences. Genetic databases are rapidly accumulating large amounts of sequences. Newly acquired sequences, which have not yet been characterized, may require preliminary genetic exploration in order to build models describing the evolutionary relationship among sequences. There are clustering techniques that rely less on models of evolution, and thus may provide nice exploratory tools for identifying genetic similarities. Some of the more commonly used clustering methods perform better when data can be grouped into mutually exclusive groups. Genetic data from viral quasispecies, which consist of closely related variants that differ by small changes, however, may best be partitioned by overlapping groups. RESULTS: We have developed an intuitive exploratory program, Partition Analysis of Quasispecies (PAQ), which utilizes a non-hierarchical technique to partition sequences that are genetically similar. PAQ was used to analyze a data set of human immunodeficiency virus type 1 (HIV-1) envelope sequences isolated from different regions of the brain and another data set consisting of the equine infectious anemia virus (EIAV) regulatory gene rev. Analysis of the HIV-1 data set by PAQ was consistent with phylogenetic analysis of the same data, and the EIAV rev variants were partitioned into two overlapping groups. PAQ provides an additional tool which can be used to glean information from genetic data and can be used in conjunction with other tools to study genetic similarities and genetic evolution of viral quasispecies.     

Beyond Profiling: Using ADMET Models to Guide Decisions

ADMET Models, whether in silico or in vitro, are commonly used to ‘profile’ molecules, to identify potential liabilities or filter out molecules expected to have undesirable properties. While useful, this is the most basic application of such models. Here, we will show how models may be used to go ‘beyond profiling’ to guide key decisions in drug discovery. For example, selection of chemical series to focus resources with confidence or design of improved molecules targeting structural modifications to improve key properties. To prioritise molecules and chemical series, the success criteria for properties and their relative importance to a project's objective must be defined. Data from models (experimental or predicted) may then be used to assess each molecule's balance of properties against those requirements. However, to make decisions with confidence, the uncertainties in all of the data must also be considered. In silico models encode information regarding the relationship between molecular structure and properties. This is used to predict the property value of a novel molecule. However, further interpretation can yield information on the contributions of different groups in a molecule to the property and the sensitivity of the property to structural changes. Visualising this information can guide the redesign process. In this article, we describe methods to achieve these goals and drive drug‐discovery decisions and illustrate the results with practical examples.     

FAF-Drugs2: Free ADME/tox filtering tool to assist drug discovery and chemical biology projects

Background  

Drug discovery and chemical biology are exceedingly complex and demanding enterprises. In recent years there are been increasing awareness about the importance of predicting/optimizing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small chemical compounds along the search process rather than at the final stages. Fast methods for evaluating ADMET properties of small molecules often involve applying a set of simple empirical rules (educated guesses) and as such, compound collections' property profiling can be performedin silico. Clearly, these rules cannot assess the full complexity of the human body but can provide valuable information and assist decision-making.     

<Emphasis Type="Italic">ADDME – Avoiding Drug Development Mistakes Early</Emphasis>: central nervous system drug discovery perspective

The advent of early absorption, distribution, metabolism, excretion, and toxicity (ADMET) screening has increased the attrition rate of weak drug candidates early in the drug-discovery process, and decreased the proportion of compounds failing in clinical trials for ADMET reasons. This paper reviews the history of ADMET screening and its place in pharmaceutical development, and central nervous system drug discovery in particular. Assays that have been developed in response to specific needs and improvements in technology that result in higher throughput and greater accuracy of prediction of human mechanisms of absorption and toxicity are discussed. The paper concludes with the authors' forecast of new models that will better predict human efficacy and toxicity.     

Pharmacophore and structure-based drug design,molecular dynamics and admet/tox studies to design novel potential pad4 inhibitors

We have used docking (GLIDE), pharmacophore modeling (Discovery Studio), long trajectory molecular dynamics (Discovery Studio) and ADMET/Tox (QikProp and DEREK) to investigate PAD4 in order to determine potential novel inhibitors and hits. We have carried out virtual screening in the ZINC natural compounds database. Pharmacokinetics and Toxicity of the best hits were assessed using databases implemented in softwares that create models based on chemical structures taking into account consideration about the toxicophoric groups. A wide variety of pharmaceutical relevant properties are determined in order to make decisions about molecular suitability. After screening and analysis, the 6 most promising PAD4 inhibitors are suggested, with strong interactions (pi-stacking, hydrogen bonds, hydrophobic contacts) and suitable pharmacotherapeutic profile as well.     

An integrated approach for the analysis of biological pathways using mixed models

Gene class, ontology, or pathway testing analysis has become increasingly popular in microarray data analysis. Such approaches allow the integration of gene annotation databases, such as Gene Ontology and KEGG Pathway, to formally test for subtle but coordinated changes at a system level. Higher power in gene class testing is gained by combining weak signals from a number of individual genes in each pathway. We propose an alternative approach for gene-class testing based on mixed models, a class of statistical models that: a) provides the ability to model and borrow strength across genes that are both up and down in a pathway, b) operates within a well-established statistical framework amenable to direct control of false positive or false discovery rates, c) exhibits improved power over widely used methods under normal location-based alternative hypotheses, and d) handles complex experimental designs for which permutation resampling is difficult. We compare the properties of this mixed models approach with nonparametric method GSEA and parametric method PAGE using a simulation study, and illustrate its application with a diabetes data set and a dose-response data set.     

Discover potential inhibitors for PFKFB3 using 3D-QSAR,virtual screening,molecular docking and molecular dynamics simulation

Abstract

The 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3) is a master regulator of glycolysis in cancer cells by synthesizing fructose-2,6-bisphosphate (F-2,6-BP), a potent allosteric activator of phosphofructokinase-1 (PFK-1), which is a rate-limiting enzyme of glycolysis. PFKFB3 is an attractive target for cancer treatment. It is valuable to discover promising inhibitors by using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking and molecular dynamics simulation. Twenty molecules with known activity were used to build 3D-QSAR pharmacophore models. The best pharmacophore model was ADHR called Hypo1, which had the highest correlation value of 0.98 and the lowest RMSD of 0.82. Then, the Hypo1 was validated by cost value method, test set method and decoy set validation method. Next, the Hypo1 combined with Lipinski's rule of five and ADMET properties were employed to screen databases including Asinex and Specs, total of 1,048,159 molecules. The hits retrieved from screening were docked into protein by different procedures including HTVS, SP and XP. Finally, nine molecules were picked out as potential PFKFB3 inhibitors. The stability of PFKFB3-lead complexes was verified by 40?ns molecular dynamics simulation. The binding free energy and the energy contribution of per residue to the binding energy were calculated by MM-PBSA based on molecular dynamics simulation.     

Virtual screening of natural inhibitors targeting ornithine decarboxylase with pharmacophore scaffolding of DFMO and validation by molecular dynamics simulation studies

Ornithine decarboxylase (ODC) is an enzyme that initiates polyamine synthesis in human. Polyamines play key roles in cell–cell adhesion, cell motility and cell cycle regulation. Higher synthesis of polyamines also occurs in rapidly proliferating cancer cells are mediated by ODC. As per earlier studies, di-flouro-methyl-orninthine (DFMO) is a proven efficient inhibitor ODC targeting the catalytic activity, however, its usage is limited due to side effects. Targeting ODC is considered as a potential therapeutic modality in the treatment of cancer. In this study, it is attempted to use DFMO scaffold to build a ligand-based pharmocophore query using MOE to screen similar active compounds from Universal Natural Products Database with better ADMET properties. The identified compounds were virtually screened against the active cavity of ODC using Glide. Further, potential natural hits targeting ODC were shortlisted based on Molecular Mechanics/Generalized-Born/Surface Area (MM-GBSA) score. Finally, molecular dynamics simulations were performed for the natural molecule hit and DFMO in complex with ODC using Desmond. Among the hits shortlisted, 2-amino-5, 9, 13, 17-tetramethyloctadeca-8, 16-diene-1, 3, 14-triol (UNPD208110) was found to be highly potential, as it showed a higher binding affinity in terms of interactions with key active cavity residues, and also showed better ADMET property, HUMO–LUMO gap energy and more stable complex formation with ODC compared to DFMO. Hence, the proposed molecule (UNPD208110) shall be favourably considered as a potential natural inhibitor targeting ODC-mediated disease conditions.     

The ARKdb: genome databases for farmed and other animals

The ARKdb genome databases provide comprehensive public repositories for genome mapping data from farmed species and other animals (http://www.thearkdb.org) providing a resource similar in function to that offered by GDB or MGD for human or mouse genome mapping data, respectively. Because we have attempted to build a generic mapping database, the system has wide utility, particularly for those species for which development of a specific resource would be prohibitive. The ARKdb genome database model has been implemented for 10 species to date. These are pig, chicken, sheep, cattle, horse, deer, tilapia, cat, turkey and salmon. Access to the ARKdb databases is effected via the World Wide Web using the ARKdb browser and Anubis map viewer. The information stored includes details of loci, maps, experimental methods and the source references. Links to other information sources such as PubMed and EMBL/GenBank are provided. Responsibility for data entry and curation is shared amongst scientists active in genome research in the species of interest. Mirror sites in the United States are maintained in addition to the central genome server at Roslin.     

Fantastic databases and where to find them: Web applications for researchers in a rush

Public databases are essential to the development of multi-omics resources. The amount of data created by biological technologies needs a systematic and organized form of storage, that can quickly be accessed, and managed. This is the objective of a biological database. Here, we present an overview of human databases with web applications. The databases and tools allow the search of biological sequences, genes and genomes, gene expression patterns, epigenetic variation, protein-protein interactions, variant frequency, regulatory elements, and comparative analysis between human and model organisms. Our goal is to provide an opportunity for exploring large datasets and analyzing the data for users with little or no programming skills. Public user-friendly web-based databases facilitate data mining and the search for information applicable to healthcare professionals. Besides, biological databases are essential to improve biomedical search sensitivity and efficiency and merge multiple datasets needed to share data and build global initiatives for the diagnosis, prognosis, and discovery of new treatments for genetic diseases. To show the databases at work, we present a a case study using ACE2 as example of a gene to be investigated. The analysis and the complete list of databases is available in the following website <https://kur1sutaru.github.io/fantastic_databases_and_where_to_find_them/>.     

低心血管疾病风险选择性环氧合酶-2抑制剂的虚拟筛选

摘要 目的：寻找具有血栓素A2受体（Thromboxane A2 receptor,TP）抑制作用的选择性环氧合酶-2（Cyclooxygenase-2,COX-2）抑制剂,以期降低其心血管疾病风险。方法：本研究从公开数据库中获取了512种TP抑制剂,通过分子对接、分子动力学模拟和ADMET预测,筛选出化合物TP84。结果：分子对接结果显示,与先前获批的选择性COX-2抑制剂罗非昔布相比,TP84对COX-2的亲和力更高,对环氧合酶-1（Cyclooxygenase-1,COX-1）的亲和力更低;分子动力学模拟进一步表明,模拟过程中TP84与COX-1的结合不稳定,而TP84能稳定结合COX-2,与COX-2的结合自由能是COX-1的3倍;此外,根据ADMET预测,TP84的药物化学、吸收、分布、代谢、排泄和毒性处于类药物候选物的可接受范围内。结论：TP84是一种潜在的低心血管疾病风险选择性COX-2抑制剂。     

JXP4BIGI: a generalized, Java XML-based approach for biological information gathering and integration

MOTIVATION: In the post-genomic era, biologists interested in systems biology often need to import data from public databases and construct their own system-specific or subject-oriented databases to support their complex analysis and knowledge discovery. To facilitate the analysis and data processing, customized and centralized databases are often created by extracting and integrating heterogeneous data retrieved from public databases. A generalized methodology for accessing, extracting, transforming and integrating the heterogeneous data is needed. RESULTS: This paper presents a new data integration approach named JXP4BIGI (Java XML Page for Biological Information Gathering and Integration). The approach provides a system-independent framework, which generalizes and streamlines the steps of accessing, extracting, transforming and integrating the data retrieved from heterogeneous data sources to build a customized data warehouse. It allows the data integrator of a biological database to define the desired bio-entities in XML templates (or Java XML pages), and use embedded extended SQL statements to extract structured, semi-structured and unstructured data from public databases. By running the templates in the JXP4BIGI framework and using a number of generalized wrappers, the required data from public databases can be efficiently extracted and integrated to construct the bio-entities in the XML format without having to hard-code the extraction logics for different data sources. The constructed XML bio-entities can then be imported into either a relational database system or a native XML database system to build a biological data warehouse. AVAILABILITY: JXP4BIGI has been integrated and tested in conjunction with the IKBAR system (http://www.ikbar.org/) in two integration efforts to collect and integrate data for about 200 human genes related to cell death from HUGO, Ensembl, and SWISS-PROT (Bairoch and Apweiler, 2000), and about 700 Drosophila genes from FlyBase (FlyBase Consortium, 2002). The integrated data has been used in comparative genomic analysis of x-ray induced cell death. Also, as explained later, JXP4BIGI is a middleware and framework to be integrated with biological database applications, and cannot run as a stand-alone software for end users. For demonstration purposes, a demonstration version is accessible at (http://www.ikbar.org/jxp4bigi/demo.html).     

Protein‐based virtual screening of chemical databases. II. Are homology models of g‐protein coupled receptors suitable targets?

The aim of the current study is to investigate whether homology models of G-Protein-Coupled Receptors (GPCRs) that are based on bovine rhodopsin are reliable enough to be used for virtual screening of chemical databases. Starting from the recently described 2.8 A-resolution X-ray structure of bovine rhodopsin, homology models of an "antagonist-bound" form of three human GPCRs (dopamine D3 receptor, muscarinic M1 receptor, vasopressin V1a receptor) were constructed. The homology models were used to screen three-dimensional databases using three different docking programs (Dock, FlexX, Gold) in combination with seven scoring functions (ChemScore, Dock, FlexX, Fresno, Gold, Pmf, Score). Rhodopsin-based homology models turned out to be suitable, indeed, for virtual screening since known antagonists seeded in the test databases could be distinguished from randomly chosen molecules. However, such models are not accurate enough for retrieving known agonists. To generate receptor models better suited for agonist screening, we developed a new knowledge- and pharmacophore-based modeling procedure that might partly simulate the conformational changes occurring in the active site during receptor activation. Receptor coordinates generated by this new procedure are now suitable for agonist screening. We thus propose two alternative strategies for the virtual screening of GPCR ligands, relying on a different set of receptor coordinates (antagonist-bound and agonist-bound states).     

Analysis and comparison of metabolic pathway databases

Enormous amounts of data result from genome sequencing projects and new experimental methods. Within this tremendous amount of genomic data 30-40 per cent of the genes being identified in an organism remain unknown in terms of their biological function. As a consequence of this lack of information the overall schema of all the biological functions occurring in a specific organism cannot be properly represented. To understand the functional properties of the genomic data more experimental data must be collected. A pathway database is an effort to handle the current knowledge of biochemical pathways and in addition can be used for interpretation of sequence data. Some of the existing pathway databases can be interpreted as detailed functional annotations of genomes because they are tightly integrated with genomic information. However, experimental data are often lacking in these databases. This paper summarises a list of pathway databases and some of their corresponding biological databases, and also focuses on information about the content and the structure of these databases, the organisation of the data and the reliability of stored information from a biological point of view. Moreover, information about the representation of the pathway data and tools to work with the data are given. Advantages and disadvantages of the analysed databases are pointed out, and an overview to biological scientists on how to use these pathway databases is given.