MayaChemTools: An Open Source Package for Computational Drug Discovery.

MayaChemTools is a growing collection of Perl scripts, modules, and classes to support a variety of computational drug discovery needs, such as manipulation and analysis of data, generation of two-dimensional (2D) fingerprints, similarity searching, and calculation of physicochemical properties. MayaChemTools provides command line scripts for the following tasks: manipulation and analysis of data in SD, CSV/TSV, sequence/alignments, and PDB files; calculation of a key set of physicochemical properties, such as molecular weight, hydrogen bond donors and acceptors, logP, and topological polar surface area; generation of 2D fingerprints corresponding to atom neighborhoods, atom types, E-state indices, extended connectivity, MACCS keys, path lengths, topological atom pairs, topological atom triplets, topological atom torsions, topological pharmacophore atom pairs, and topological pharmacophore atom triplets; similarity searching and calculation of similarity matrices using available 2D fingerprints; listing properties of elements in the periodic table, amino acids, and nucleic acids; and exporting data from relational databases. An extensive set of modules and classes are also available for custom development.

Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools.

The Metabolomics Workbench, available at www.metabolomicsworkbench.org, is a public repository for metabolomics metadata and experimental data spanning various species and experimental platforms, metabolite standards, metabolite structures, protocols, tutorials, and training material and other educational resources.

Template-based combinatorial enumeration of virtual compound libraries for lipids.

A variety of software packages are available for the combinatorial enumeration of virtual libraries for small molecules, starting from specifications of core scaffolds with attachments points and lists of R-groups as SMILES or SD files. Although SD files include atomic coordinates for core scaffolds and R-groups, it is not possible to control 2-dimensional (2D) layout of the enumerated structures generated for virtual compound libraries because different packages generate different 2D representations for the same structure. We have developed a software package called LipidMapsTools for the template-based combinatorial enumeration of virtual compound libraries for lipids.

LIPID MAPS-Nature Lipidomics Gateway: An Online Resource for Students and Educators Interested in Lipids.

The LIPID MAPS-Nature Lipidomics Gateway is a free, comprehensive online resource providing tutorials and instructional material, experimental data for lipids and genes along with protocols and standards, databases of lipid structures and lipid-associated genes or proteins, and a variety of lipidomics tools.

Lipid classification, structures and tools.

The study of lipids has developed into a research field of increasing importance as their multiple biological roles in cell biology, physiology and pathology are becoming better understood. The Lipid Metabolites and Pathways Strategy (LIPID MAPS) consortium is actively involved in an integrated approach for the detection, quantitation and pathway reconstruction of lipids and related genes and proteins at a systems-biology level. A key component of this approach is a bioinformatics infrastructure involving a clearly defined classification of lipids, a state-of-the-art database system for molecular species and experimental data and a suite of user-friendly tools to assist lipidomics researchers.

Computational Modeling of Kinase Inhibitor Selectivity.

An exhaustive computational exercise on a comprehensive set of 15 therapeutic kinase inhibitors was undertaken to identify as to which compounds hit which kinase off-targets in the human kinome. Although the kinase selectivity propensity of each inhibitor against ∼480 kinase targets is predicted, we compared our predictions to ∼280 kinase targets for which consistent experimental data are available and demonstrate an overall average prediction accuracy and specificity of ∼90%. A comparison of the predictions was extended to an additional ∼60 kinases for sorafenib and sunitinib as new experimental data were reported recently with similar prediction accuracy.

A mouse macrophage lipidome.

We report the lipidomic response of the murine macrophage RAW cell line to Kdo(2)-lipid A, the active component of an inflammatory lipopolysaccharide functioning as a selective TLR4 agonist and compactin, a statin inhibitor of cholesterol biosynthesis. Analyses of lipid molecular species by dynamic quantitative mass spectrometry and concomitant transcriptomic measurements define the lipidome and demonstrate immediate responses in fatty acid metabolism represented by increases in eicosanoid synthesis and delayed responses characterized by sphingolipid and sterol biosynthesis. Lipid remodeling of glycerolipids, glycerophospholipids, and prenols also take place, indicating that activation of the innate immune system by inflammatory mediators leads to alterations in a majority of mammalian lipid categories, including unanticipated effects of a statin drug.

Bioinformatics for lipidomics.

Lipids are recognized as key participants in the regulation and control of cellular function, having important roles in signal transduction processes. The diversity in lipid chemical structure presents a challenge for establishing practical methods to generate and manage high volumes of complex data that translate into a snapshot of cellular lipid changes. The need for high-quality bioinformatics to manage and integrate experimental data becomes imperative at several levels: (1) definition of lipid classification and ontologies, (2) relational database design, (3) capture and automated pipelining of experimental data, (4) efficient management of metadata, (5) development of lipid-centric search tools, (6) analysis and visual display of results, and (7) integration of the lipid knowledge base into biochemical pathways and interactive maps.

LIPID MAPS online tools for lipid research.

The LIPID MAPS consortium has developed a number of online tools for performing tasks such as drawing lipid structures and predicting possible structures from mass spectrometry (MS) data. A simple online interface has been developed to enable an end-user to rapidly generate a variety of lipid chemical structures, along with corresponding systematic names and ontological information. The structure-drawing tools are available for six categories of lipids: (i) fatty acyls, (ii) glycerolipids, (iii) glycerophospholipids, (iv) cardiolipins, (v) sphingolipids and (vi) sterols.

LMSD: LIPID MAPS structure database.

The LIPID MAPS Structure Database (LMSD) is a relational database encompassing structures and annotations of biologically relevant lipids. Structures of lipids in the database come from four sources: (i) LIPID MAPS Consortium's core laboratories and partners; (ii) lipids identified by LIPID MAPS experiments; (iii) computationally generated structures for appropriate lipid classes; (iv) biologically relevant lipids manually curated from LIPID BANK, LIPIDAT and other public sources. All the lipid structures in LMSD are drawn in a consistent fashion.