PROLIX: rapid mining of protein-ligand interactions in large crystal structure databases

A central problem in structure-based drug design is understanding protein-ligand interactions quantitatively and qualitatively. Several recent studies have highlighted from a qualitative perspective the nature of these interactions and their utility in drug discovery. However, a common limitation is a lack of adequate tools to mine these interactions comprehensively, since exhaustive searches of the protein data bank are time-consuming and difficult to perform. Consequently, fundamental questions remain unanswered: How unique or how common are the protein-ligand interactions observed in a given drug design project when compared to all complexed structures in the protein data bank? Which interaction patterns might explain the affinity of a tool compound toward unwanted targets? To answer these questions and to enable the systematic and comprehensive study of protein-ligand interactions, we introduce PROLIX (Protein Ligand Interaction Explorer), a tool that uses sophisticated fingerprint representations of protein-ligand interaction patterns for rapid data mining in large crystal structure databases. Our implementation strategy pursues a branch-and-bound technique that enables mining against thousands of complexes within a few seconds. Key elements of PROLIX include (i) an intuitive interface that enables users to formulate complex queries easily, (ii) exceptional speed for results retrieval, and (iii) a sophisticated results summarization. Herein we describe the algorithms developed to enable complex queries and fast retrieval of search results, as well as the intuitive aspects of the user interface and summarization viewer.     

A novel search engine for virtual screening of very large databases

Virtual screening of large chemical databases using the structure of the receptor can be computationally very demanding. We present a novel strategy that combines exhaustive similarity searches directly in SMILES format with the docking of flexible ligands, whose 3D structure is generated on the fly from the SMILES representation. Our strategy makes use of the recently developed LINGO tools to extract implicit chemical information from SMILES strings and integrates LINGO similarities into a pseudo-evolutionary algorithm. The algorithm represents a combination of a fast target-independent similarity method with a slower but information richer target-focused method. A virtual search of FactorXa ligands provided 62% of the potential hits after docking only 6.5% of a database of nearly 1 million molecules. The set of solutions showed good diversity, indicating that the method shows good scaffold hopping capabilities.     

Fast 3D shape screening of large chemical databases through alignment-recycling

Background  

Large chemical databases require fast, efficient, and simple ways of looking for similar structures. Although such tasks are now fairly well resolved for graph-based similarity queries, they remain an issue for 3D approaches, particularly for those based on 3D shape overlays. Inspired by a recent technique developed to compare molecular shapes, we designed a hybrid methodology, alignment-recycling, that enables efficient retrieval and alignment of structures with similar 3D shapes.     

Fluorine-NMR competition binding experiments for high-throughput screening of large compound mixtures

High-throughput ligand-based NMR screening with competition binding experiments is extended to (19)F detection. Fluorine is a favorable nucleus for these experiments because of the significant contribution of the Chemical Shift Anisotropy (CSA) to the (19)F transverse relaxation of the ligand signal when bound to a macromolecular target. A low to moderate affinity ligand containing a fluorine atom is used as a reference molecule for the detection and characterization of new ligands. Titration NMR experiments with the selected reference compound are performed for finding the optimal set-up conditions for HTS and for deriving the binding constants of the identified NMR hits. Rapid HTS of large chemical mixtures and plant or fungi extracts against the receptor of interest is possible due to the high sensitivity of the (19)F nucleus and the absence of overlap with the signals of the mixtures to be screened. Finally, a novel approach for HTS using a reference molecule in combination with a control molecule is presented.     

LeadScope: software for exploring large sets of screening data

Modern approaches to drug discovery have dramatically increased the speed and quantity of compounds that are made and tested for potential potency. The task of collecting, organizing, and assimilating this information is a major bottleneck in the discovery of new drugs. We have developed LeadScope a novel, interactive computer program for visualizing, browsing, and interpreting chemical and biological screening data that can assist pharmaceutical scientists in finding promising drug candidates. The software organizes the chemical data by structural features familiar to medicinal chemists. Graphs are used to summarize the data, and structural classes are highlighted that are statistically correlated with biological activity.     

Using high-throughput screening data to discriminate compounds with single-target effects from those with side effects

The most desirable compound leads from high-throughput assays are those with novel biological activities resulting from their action on a single biological target. Valuable resources can be wasted on compound leads with significant 'side effects' on additional biological targets; therefore, technical refinements to identify compounds that primarily have effects resulting from a single target are needed. This study explores the use of multiple assays of a chemical library and a statistic based on entropy to identify lead compound classes that have patterns of assay activity resulting primarily from small molecule action on a single target. This statistic, called the coincidence score, discriminates with 88% accuracy compound classes known to act primarily on a single target from compound classes with significant side effects on nonhomologous targets. Furthermore, a significant number of the compound classes predicted to have primarily single-target effects contain known bioactive compounds. We also show that a compound's known biological target or mechanism of action can often be suggested by its pattern of activities in multiple assays.     

An informatic pipeline for managing high-throughput screening experiments and analyzing data from stereochemically diverse libraries

Integration of flexible data-analysis tools with cheminformatics methods is a prerequisite for successful identification and validation of “hits” in high-throughput screening (HTS) campaigns. We have designed, developed, and implemented a suite of robust yet flexible cheminformatics tools to support HTS activities at the Broad Institute, three of which are described herein. The “hit-calling” tool allows a researcher to set a hit threshold that can be varied during downstream analysis. The results from the hit-calling exercise are reported to a database for record keeping and further data analysis. The “cherry-picking” tool enables creation of an optimized list of hits for confirmatory and follow-up assays from an HTS hit list. This tool allows filtering by computed chemical property and by substructure. In addition, similarity searches can be performed on hits of interest and sets of related compounds can be selected. The third tool, an “S/SAR viewer,” has been designed specifically for the Broad Institute’s diversity-oriented synthesis (DOS) collection. The compounds in this collection are rich in chiral centers and the full complement of all possible stereoisomers of a given compound are present in the collection. The S/SAR viewer allows rapid identification of both structure/activity relationships and stereo-structure/activity relationships present in HTS data from the DOS collection. Together, these tools enable the prioritization and analysis of hits from diverse compound collections, and enable informed decisions for follow-up biology and chemistry efforts.     

Analysis of a high-throughput screening data set using potency-scaled molecular similarity algorithms

Molecular similarity methods for ligand-based virtual screening (VS) generally do not take compound potency as a variable or search parameter into account. We have incorporated a logarithmic potency scaling function into two conceptually distinct VS algorithms to account for relative compound potency during search calculations. A high-throughput screening (HTS) data set containing cathepsin B inhibitors was analyzed to evaluate the effects of potency scaling. Sets of template compounds were randomly selected from the HTS data and used to search for hits having varying potency levels in the presence or absence of potency scaling. Enrichment of potent compounds in small subsets of the HTS data set was observed as a consequence of potency scaling. In part, observed enrichments could be rationalized as a result of recentering chemical reference space on a subspace populated by potent compounds. Our findings suggest that VS calculations using multiple reference compounds can be directed toward the preferential detection of potent database hits by scaling compound contributions according to potency differences.     

Retrospective analysis of an experimental high-throughput screening data set by recursive partitioning

With the emergence of combinatorial chemistry, whether based on parallel, mixture, solution, or solid phase chemistry, it is now possible to generate large numbers of diverse or focused compound libraries. In this paper we aim to demonstrate that it is possible to design targeted libraries by applying nonparametric statistical methods, recursive partitioning in particular, to large data sets containing thousands of compounds and their associated biological data. Moreover, when applied to an experimental high-throughput screening (HTS) data set, our data strongly suggest that this method can improve the hit rate of our primary screens (about 4- to 5-fold) while increasing screening efficiency: less than one-fifth of the complete selection needs to be screened in order to identify about 75% of all actives present.     

Computation of the physio-chemical properties and data mining of large molecular collections

Very large data sets of molecules screened against a broad range of targets have become available due to the advent of combinatorial chemistry. This information has led to the realization that ADME (absorption, distribution, metabolism, and excretion) and toxicity issues are important to consider prior to library synthesis. Furthermore, these large data sets provide a unique and important source of information regarding what types of molecular shapes may interact with specific receptor or target classes. Thus, the requirement for rapid and accurate data mining tools became paramount. To address these issues Pharmacopeia, Inc. formed a computational research group, The Center for Informatics and Drug Discovery (CIDD).* In this review we cover the work done by this group to address both in silico ADME modeling and data mining issues faced by Pharmacopeia because of the availability of a large and diverse collection (over 6 million discrete compounds) of drug-like molecules. In particular, in the data mining arena we discuss rapid docking tools and how we employ them, and we describe a novel data mining tool based on a ID representation of a molecule followed by a molecular sequence alignment step. For the ADME area we discuss the development and application of absorption, blood-brain barrier (BBB) and solubility models. Finally, we summarize the impact the tools and approaches might have on the drug discovery process.     

Comparative study of machine-learning and chemometric tools for analysis of in-vivo high-throughput screening data

High-throughput screening (HTS) has become a central tool of many pharmaceutical and crop-protection discovery operations. If HTS screening is carried out at the level of the intact organism, as is commonly done in crop protection, this strategy has the potential of uncovering a completely new mechanism of actions. The challenge in running a cost-effective HTS operation is to identify ways in which to improve the overall success rate in discovering new biologically active compounds. To this end, we describe our efforts directed at making full use of the data stream arising from HTS. This paper describes a comparative study in which several machine learning and chemometric methodologies were used to develop classifiers on the same data sets derived from in vivo HTS campaigns and their predictive performances compared in terms of false negative and false positive error profiles.     

Receptor-based computational screening of compound databases: the main docking-scoring engines

The processes used by academic and industrial scientists to discover new drugs have recently experienced a true renaissance with many new and exciting techniques. The number of protein structures and/or chemical ligands is constantly growing, through the use of parallel chemistry, X-ray crystallography, NMR or homology modeling methods and so is the theoretical understanding of protein-ligand interactions. As such, structure-based approaches to drug-design and in silico screening are becoming routine part of most modern lead discovery programs. Prioritization of compound libraries is an extremely important task that aims at the rapid identification of tight-binding ligands and ultimately new therapeutic compounds. These in silico approaches combined with other experimental methods facilitate the design of new medicines to treat cardiovascular, degenerative, infectious, and neoplastic diseases, among others. Here, we review key concepts and specific features of several selected ligand-receptor docking/scoring methods while several other topics pertaining to the field of in silico screening are reviewed in the following articles of this special issue of Current Protein and Peptide Science.     

Polymorphism - integrated approach from high-throughput screening to crystallization optimization

Crystal structure (polymorphism) as well as crystal shape (morphology) and size have a huge practical and commercial impact on active substances all the way from research to manufacture of the final product. For an optimal development process, it is important to have an integrated approach to these issues ranging from a systematic polymorphism screening to a controlled scale-up of the crystallization process. The polymorphism program has to be tailored according to the development stage. Particularly suitable for an early development stage is a high-throughput polymorphism screening, which is the basis for a more thorough investigation if the product proceeds further in development. Such a comprehensive polymorphism investigation involves further crystallization experiments and extensive physicochemical characterization of the various forms. In this article the high-throughput polymorphism screening method that we have developed is described. Using carbamazepine as an example, the power of this high-throughput polymorphism screening system is demonstrated. Not only were all published forms found, but also new forms were identified. In the second part of the article, important considerations for crystallization optimization are discussed, again using the example of carbamazepine. This revised version was published online in July 2006 with corrections to the Cover Date.     

Integration of NMR and high-throughput screening

NMR-based screening has become a powerful method for the identification and analysis of low-molecular weight organic compounds that bind to protein targets and can be utilized in drug discovery programs. In particular, heteronuclear NMR-based screening can yield information about both the affinity and binding location of potential lead compounds. In addition, heteronuclear NMR-based screening has wide applications in complementing and facilitating conventional high-throughout screening programs. This article will describe several strategies for the integration of NMR-based screening and high-throughput screening. The marriage of these two techniques promises to be of tremendous benefit in the triage of hits that come from HTS, and can aid the medicinal chemist in the identification of quality leads that have high potential for further optimization.     

Chip-based high-throughput screening of herbal medicines

At present, high-throughput screening (HTS) programs in drug discovery rely mainly on compound libraries from combinational chemistry. Similarly, natural flora has been used as a prominent origin for new and potent herbal drugs. Herbal medicines have been used worldwide for thousands of years to cure many diseases. As such, herbal secondary metabolites show a remarkable structural diversity that supplements chemically synthesized compound analogs in drug discovery screening. Unfortunately, there is often a considerable deterioration in the quality of herbal drugs in such screening programs as there are time-consuming manual processes involved in the isolation of active ingredients from the highly complex mixtures of herbal plant products. The quality and quantity of herbal samples are critical for the success of HTS programs. In the recent past, there have been substantial improvements in HTS due to the miniaturization and integration of microchip (e.g., Herbochip(?), DNA chip, protein chip, cell chip, etc.)-based technologies so as to design herbal drugs that compete with synthetic drug analogs. Here we will review various technologies used for HTS of herbal medicines. Finally, we will summarize our efforts to develop a novel chip-based HTS assay to explore the antioxidant and radioprotective properties of herbal plants.     

高通量药物筛选现代检测技术研究进展

高通量药物筛选是发现创新药物的重要技术途径.高通量筛选结果必须通过适当的检测方法才能反映出来,检测技术是实现高通量药物筛选的基础.本文综述了近年来有关光学分析、色谱分析、热分析、电化学分析、质谱、核磁共振等现代检测技术在高通量药物筛选研究中的进展.