LipidPedia: a comprehensive lipid knowledgebase

Novel Small Molecule Matrix Screening for Simultaneous MALDI Mass Spectrometry Imaging of Multiple Lipids and Phytohormones

Most of the traditional matrices cannot simultaneously image multiple lipids and phytohormones, so screening and discovery of novel matrices stand as essential approaches for broadening the application scope of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). In this work, 12 organic small molecule compounds were comprehensively screened and investigated as potential MALDI matrices for simultaneous imaging analysis of various lipids and phytohormones. In the positive ionization mode, p-nitroaniline, m-nitroaniline, and 2-aminoterephthalic acid displayed good performance for the highly sensitive detection of lysophosphatidylcholines (LPCs), phosphatidylcholines (PCs), and triacylglycerols (TGs). Furthermore, p-nitroaniline possessed excellent characteristics of strong ultraviolet absorption and homogeneous cocrystallization, making it a desirable matrix for MALDI-MSI analysis of eight plant hormones. Compared with conventional matrices (2,5-dihydroxybenzoic acid (DHB), α-cyano-4-hydroxycinnamic acid (CHCA), and 9-aminoacridine (9-AA), the use of p-nitroaniline resulted in higher ionization efficiency, superior sensitivity, and clearer imaging images in dual polarity mode. Our research offers valuable guidance and new ideas for future endeavors in matrix screening.

Hallmarks of the metabolic secretome

The identification of novel secreted factors is advancing at an unprecedented pace. However, there is a critical need to consolidate and integrate this knowledge to provide a framework of their diverse mechanisms, functional significance, and inter-relationships. Complicating this effort are challenges related to nonstandardized methods, discrepancies in sample handling, and inconsistencies in the annotation of unknown molecules. This Review aims to synthesize the rapidly expanding field of the metabolic secretome, encompassing the five major types of secreted factors: proteins, peptides, metabolites, lipids, and extracellular vesicles. By systematically defining the functions and detection of the components within the metabolic secretome, this Review provides a primer into the advances of the field, and how integration of the techniques discussed can provide a deeper understanding of the mechanisms underlying metabolic homeostasis and its disorders.

Isomer-Resolved Mass Spectrometry Imaging of Acidic Phospholipids

The biological functions of lipids are entirely dependent on their molecular structures with even small changes in structure─such as different sites of unsaturation─providing critical markers for changes in the underlying metabolism. Conventional mass spectrometry imaging (MSI) approaches, however, face the twin challenges of mixture and structural complexity and are typically unable to differentiate lipid isomers that differ only in the position(s) of carbon-carbon double bonds. Recent coupling of ozone-induced dissociation (OzID) with matrix-assisted laser desorption/ionization (MALDI)-MSI has demonstrated the potential to map changes in individual double-bond isomers, thus enabling visualization of the modulation in lipid desaturation in adjacent tissue types. This has, to date, only been performed in positive-ion mode due to a generally higher abundance of phosphatidylcholines (PC) in mammalian tissues and the efficient desorption/ionization of this lipid subclass. Many other glycerophospholipids (GPLs), however, are better detected in negative-ion mode as deprotonated anions. Recently, OzID has been implemented on a traveling-wave ion-mobility mass spectrometer (Waters, SYNAPT G2-Si) that provides a 50-fold increase in the rate of the gas-phase reaction between ionized lipids and ozone and a commensurate increase in sensitivity for isomer-resolved mass spectrometry. These gains are exploited here to interrogate the distributions of anionic GPL isomers in biological tissues, covering the subclasses phosphatidylserine (PS), phosphatidylethanolamine (PE), phosphatidylinositol (PI), phosphatidylglycerol (PG), and phosphatidic acid (PA). Exploiting both ozone- and collision-induced dissociation in a single acquisition simultaneously identifies sites of unsaturation and acyl chain composition from the same mass spectrum.

DBLiPro: A Database for Lipids and Proteins in Human Lipid Metabolism

To help researchers in the field of biology, medicine, chemistry, and materials science to use lipidomic data conveniently, there is an urgent need to develop a platform that provides a systematic knowledgebase of human lipid metabolism and lipidome-centric omics analysis tools. DBLiPro is a user-friendly webserver allowing for access to human metabolism-related lipids and proteins knowledge database and an interactive bioinformatics integrative analysis workflow for lipidomics, transcriptomics, and proteomics data. In DBLiPro, there are 3109 lipid-associated proteins (LAPs) and 2098 lipid metabolites in the knowledge base section, which were obtained from Uniprot, Kyoto Encyclopedia of Genes and Genomes (KEGG) and were further annotated by information from other public resources in the knowledge base section, such as RaftProt and PubChem. DBLiPro offers a step-by-step interactive analysis workflow for lipidomics, transcriptomics, proteomics, and their integrating multi-omics analysis focusing on the human lipid metabolism. In summary, DBLiPro is capable of helping users discover key molecules (lipids and proteins) in human lipid metabolism and investigate lipid-protein functions underlying mechanisms based on their own omics data. The DBLiPro is freely available at http://lipid.cloudna.cn/home.

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

Regulation mechanism of lipids for extracellular yellow pigments production by Monascus purpureus BWY-5

The biosynthesis and secretion of Monascus pigments are closely related to the integrity of the cell membrane, which determines the composition of lipids and its content in cell membrane. The present study aimed to thoroughly describe the changes of lipid profiling in Monascus purpureus BWY-5, which was screened by carbon ion beam irradiation (¹²C⁶⁺) to almost single yield extracellular Monascus yellow pigments (extra-MYPs), by absolute quantitative lipidomics and tandem mass tags (TMT) based quantitative proteomic. ¹²C⁶⁺ irradiation caused non-lipid oxidation damage to Monascus cell membrane, leading to an imbalance in cell membrane lipid homeostasis. This imbalance was attributed to significant changes not only in the composition but also in the content of lipids in Monascus, especially the inhibition of glycerophospholipid biosynthesis. Integrity of plasma membrane was maintained by the increased production of ergosterol, monogalactosylmonoacylglycerol (MGMG) and sulfoquinovosylmonoacylglycerol (SQMG), while mitochondrial membrane homeostasis was maintained by the increase of cardiolipin production. The growth and extra-MYPs production of Monascus BWY-5 have been regulated by the promotion of sphingolipids (ceramide and sulfatide) biosynthesis. Simultaneous, energy homeostasis may be achieved by increase of TG synthesis and Ca²⁺/Mg²⁺-ATPase activity. These finding suggest ergosterol, cardiolipin, sphingolipids, MGMG and SQMG play a key facilitating role in cytomembrane lipid homeostasis maintaining for Monascus purpureus BWY-5, and then it is closely related to cell growth and extra-MYPs production. KEY POINTS: 1. Energy homeostasis in Monascus purpureus BWY-5 was achieved by increase of TG synthesis and Ca²⁺/Mg²⁺-ATPase activity. 2. Integrity of plasma membrane in Monascus purpureus BWY-5 was maintained by the increased production of ergosterol. 3. Mitochondrial membrane homeostasis in Monascus purpureus BWY-5 was maintaed by the increase of cardiolipin synthesis.

Molecular Biophysics of Class A G Protein Coupled Receptors-Lipids Interactome at a Glance-Highlights from the A2A Adenosine Receptor

G protein-coupled receptors (GPCRs) are embedded in phospholipid membrane bilayers with cholesterol representing 34% of the total lipid content in mammalian plasma membranes. Membrane lipids interact with GPCRs structures and modulate their function and drug-stimulated signaling through conformational selection. It has been shown that anionic phospholipids form strong interactions between positively charged residues in the G protein and the TM5-TM6-TM 7 cytoplasmic interface of class A GPCRs stabilizing the signaling GPCR-G complex. Cholesterol with a high content in plasma membranes can be identified in more specific sites in the transmembrane region of GPCRs, such as the Cholesterol Consensus Motif (CCM) and Cholesterol Recognition Amino Acid Consensus (CRAC) motifs and other receptor dependent and receptor state dependent sites. Experimental biophysical methods, atomistic (AA) MD simulations and coarse-grained (CG) molecular dynamics simulations have been applied to investigate these interactions. We emphasized here the impact of phosphatidyl inositol-4,5-bisphosphate (PtdIns(4,5)P₂ or PIP₂), a minor phospholipid component and of cholesterol on the function-related conformational equilibria of the human A_2A adenosine receptor (A_2AR), a representative receptor in class A GPCR. Several GPCRs of class A interacted with PIP₂ and cholesterol and in many cases the mechanism of the modulation of their function remains unknown. This review provides a helpful comprehensive overview for biophysics that enter the field of GPCRs-lipid systems.

Caenorhabditis elegans deep lipidome profiling by using integrative mass spectrometry acquisitions reveals significantly altered lipid networks

Lipidomics coverage improvement is essential for functional lipid and pathway construction. A powerful approach to discovering organism lipidome is to combine various data acquisitions, such as full scan mass spectrometry (full MS), data-dependent acquisition (DDA), and data-independent acquisition (DIA). Caenorhabditis elegans (C. elegans) is a useful model for discovering toxic-induced metabolism, high-throughput drug screening, and a variety of human disease pathways. To determine the lipidome of C. elegans and investigate lipid disruption from the molecular level to the system biology level, we used integrative data acquisition. The methyl-tert-butyl ether method was used to extract L4 stage C. elegans after exposure to triclosan (TCS), perfluorooctanoic acid, and nanopolystyrene (nPS). Full MS, DDA, and DIA integrations were performed to comprehensively profile the C. elegans lipidome by Q-Exactive Plus MS. All annotated lipids were then analyzed using lipid ontology and pathway analysis. We annotated up to 940 lipids from 20 lipid classes involved in various functions and pathways. The biological investigations revealed that when C. elegans were exposed to nPS, lipid droplets were disrupted, whereas plasma membrane-functionalized lipids were likely to be changed in the TCS treatment group. The nPS treatment caused a significant disruption in lipid storage. Triacylglycerol, glycerophospholipid, and ether class lipids were those primarily hindered by toxicants. Finally, toxicant exposure frequently involved numerous lipid-related pathways, including the phosphoinositide 3-kinase/protein kinase B pathway. In conclusion, an integrative data acquisition strategy was used to characterize the C. elegans lipidome, providing valuable biological insights into hypothesis generation and validation.

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Multiple data acquisitions were used to profile the lipidome of C. elegans.

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940 detected lipids of 20 main classes involved in various pathways.

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Relevant hypotheses were generated using high-coverable lipidomics and pathways analysis.

Cognitive improvement effect of nervonic acid and essential fatty acids on rats ingesting Acer truncatum Bunge seed oil revealed by lipidomics approach

Acer truncatum Bunge seed oil (ASO) is rich in ω-9 (53.93%) and ω-6 (30.7%) fatty acids (FAs) and characterized by 3-7% nervonic acid (NA, C24:1ω-9). Evidence suggests that ω-9 FAs such as NA participate in processes of cognitive improvement; however, their mechanism remains ambiguous. In this study, we investigated the effect of ASO on rat memory and the change in lipid profiling and underlying metabolism. After ASO was administrated to rats for one, three and seven days, their capacity for learning and memory significantly increased via the MWM test. Lipid profiling showed alterations in a wide range of metabolic features after ASO was administrated to the rats, in which sphingolipids (SP) in the serum and glycerophospholipids (GP) in the brain were regulated significantly. The changes in the fatty acids in the serum and brain showed the synergetic effects of NA, EA, OA and DHA, where NA, EA and OA exhibited similar change trends. The enrichment analysis based on KEGG indicated that ASO supplementation evoked the pathways of neurotrophin signaling, glycerophospholipid metabolism and sphingolipid metabolism, which are related to memory and cognition improvement. Among the metabolites with different molecular forms, the biomarkers with C24:1ω-9 chains exhibited a positive correlation with others both in the serum SP and brain GP. These results suggest the synergistic effects of ω-9 FAs and that their conversion into each other may result in enhanced cognition in rats ingesting Acer truncatum Bunge seed oil.

LINT-Web: A Web-Based Lipidomic Data Mining Tool Using Intra-Omic Integrative Correlation Strategy

Lipidomics is a younger member of the "omics" family. It aims to profile lipidome alterations occurring in biological systems. Similar to the other "omics", lipidomic data is highly dimensional and contains a massive amount of information awaiting deciphering and data mining. Currently, the available bioinformatic tools targeting lipidomic data processing and lipid pathway analysis are limited. A few tools designed for lipidomic analysis perform only basic statistical analyses, and lipid pathway analyses rely heavily on public databases (KEGG, Reactome, and HMDB). Due to the inadequate understanding of lipid signaling and metabolism, the use of public databases for lipid pathway analysis can be biased and misleading. Instead of using public databases to interpret lipidomic ontology, the authors introduce an intra-omic integrative correlation strategy for lipidomic data mining. Such an intra-omic strategy allows researchers to unscramble and predict lipid biological functions from correlated genomic ontological results using statistical approaches. To simplify and improve the lipidomic data processing experience, they designed an interactive web-based tool: LINT-web (http://www.lintwebomics.info/) to perform the intra-omic analysis strategy, and validated the functions of LINT-web using two biological systems. Users without sophisticated statistical experience can easily process lipidomic datasets and predict the potential lipid biological functions using LINT-web.

LipiDisease: associate lipids to diseases using literature mining

Summary

Lipids exhibit an essential role in cellular assembly and signaling. Dysregulation of these functions has been linked with many complications including obesity, diabetes, metabolic disorders, cancer and more. Investigating lipid profiles in such conditions can provide insights into cellular functions and possible interventions. Hence the field of lipidomics is expanding in recent years. Even though the role of individual lipids in diseases has been investigated, there is no resource to perform disease enrichment analysis considering the cumulative association of a lipid set. To address this, we have implemented the LipiDisease web server. The tool analyzes millions of records from the PubMed biomedical literature database discussing lipids and diseases, predicts their association and ranks them according to false discovery rates generated by random simulations. The tool takes into account 4270 diseases and 4798 lipids. Since the tool extracts the information from PubMed records, the number of diseases and lipids will be expanded over time as the biomedical literature grows.

Availability and implementation

The LipiDisease webserver can be freely accessed at http://cbdm-01.zdv.uni-mainz.de:3838/piyusmor/LipiDisease/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Unravelling the structural complexity of protein-lipid interactions with neutron reflectometry

Neutron reflectometry (NR) is a large-facility technique used to examine structure at interfaces. In this brief review an introduction to the utilisation of NR in the study of protein-lipid interactions is given. Cold neutron beams penetrate matter deeply, have low energies, wavelengths in the Ångstrom regime and are sensitive to light elements. High differential hydrogen sensitivity (between protium and deuterium) enables solution and sample isotopic labelling to be utilised to enhance or diminish the scattering signal of individual components within complex biological structures. The combination of these effects means NR can probe buried structures such as those at the solid-liquid interface and encode molecular level structural information on interfacial protein-lipid complexes revealing the relative distribution of components as well as the overall structure. Model biological membrane sample systems can be structurally probed to examine phenomena such as antimicrobial mode of activity, as well as structural and mechanistic properties peripheral/integral proteins within membrane complexes. Here, the example of the antimicrobial protein α1-purothionin binding to a model Gram negative bacterial outer membrane is used to highlight the utilisation of this technique, detailing how changes in the protein/lipid distributions across the membrane before and after the protein interaction can be easily encoded using hydrogen isotope labelling.

Using machine learning approaches for multi-omics data analysis: A review

With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.

Limits of temperature adaptation and thermopreferendum

Background

Managing the limits of temperature adaptation is relevant both in medicine and in biotechnology. There are numerous scattered publications on the identification of the temperature limits of existence for various organisms and using different methods. Dmitry Petrovich Kharakoz gave a general explanation for many of these experimental results. The hypothesis implied that each cycle of synaptic exocytosis includes reversible phase transitions of lipids of the presynaptic membrane due to the entry and subsequent removal of calcium ions from the synaptic terminal. The correspondence of the times of phase transitions has previously been experimentally shown on isolated lipids in vitro. In order to test the hypothesis of D.P. Kharakoz in vivo, we investigated the influence of the temperature of long-term acclimatization on the temperature of heat and cold shock, as well as on the kinetics of temperature adaptation in zebrafish. Testing the hypothesis included a comparison of our experimental results with the results of other authors obtained on various models from invertebrates to humans.

Results

The viability polygon for Danio rerio was determined by the minimum temperature of cold shock (about 6 °C), maximum temperature of heat shock (about 43 °C), and thermopreferendum temperature (about 27 °C). The ratio of the temperature range of cold shock to the temperature range of heat shock was about 1.3. These parameters obtained for Danio rerio describe with good accuracy those for the planarian Girardia tigrina, the ground squirrel Sermophilus undulatus, and for Homo sapiens.

Conclusions

The experimental values of the temperatures of cold shock and heat shock and the temperature of the thermal preferendum correspond to the temperatures of phase transitions of the lipid-protein composition of the synaptic membrane between the liquid and solid states. The viability range for zebrafish coincides with the temperature range, over which enzymes function effectively and also coincides with the viability polygons for the vast majority of organisms. The boundaries of the viability polygon are characteristic biological constants. The viability polygon of a particular organism is determined not only by the genome, but also by the physicochemical properties of lipids that make up the membrane structures of synaptic endings. The limits of temperature adaptation of any biological species are determined by the temperature range of the functioning of its nervous system.

Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview

Hepatic biopsy is the gold standard for staging nonalcoholic fatty liver disease (NAFLD). Unfortunately, accessing the liver is invasive, requires a multidisciplinary team and is too expensive to be conducted on large segments of the population. NAFLD starts quietly and can progress until liver damage is irreversible. Given this complex situation, the search for noninvasive alternatives is clinically important. A hallmark of NAFLD progression is the dysregulation in lipid metabolism. In this context, recent advances in the area of machine learning have increased the interest in evaluating whether multi-omics data analysis performed on peripheral blood can enhance human interpretation. In the present review, we show how the use of machine learning can identify sets of lipids as predictive biomarkers of NAFLD progression. This approach could potentially help clinicians to improve the diagnosis accuracy and predict the future risk of the disease. While NAFLD has no effective treatment yet, the key to slowing the progression of the disease may lie in predictive robust biomarkers. Hence, to detect this disease as soon as possible, the use of computational science can help us to make a more accurate and reliable diagnosis. We aimed to provide a general overview for all readers interested in implementing these methods.

High-coverage lipidomics for functional lipid and pathway analyses

Rapid advances in front-end separation approaches and analytical technologies have accelerated the development of lipidomics, particularly in terms of increasing analytical coverage to encompass an expanding repertoire of lipids within a single analytical approach. Developments in lipid pathway analysis, however, have somewhat lingered behind, primarily due to (1) the lack of coherent alignment between lipid identifiers in common databases versus that generated from experiments, owing to the differing structural resolution of lipids at molecular level that is specific to the analytical approaches adopted by various laboratories; (2) the immense complexity of lipid metabolic relationships that may entail head group changes, fatty acyls modifications of various forms (e.g. elongation, desaturation, oxidation), as well as active remodeling that demands a multidimensional, panoramic view to take into account all possibilities in lipid pathway analyses. Herein, we discuss current efforts undertaken to address these challenges, as well as alternative form of "pathway analyses" that may be particularly useful for uncovering functional lipid interactions under different biological contexts. Consolidating lipid pathway analyses will be indispensable in facilitating the transition of lipidomics from its prior role of phenotype validation to a hypothesis-generating tool that uncovers novel molecular targets to drive downstream mechanistic pursuits under biomedical settings.

Human Breathomics Database

Breathomics is a special branch of metabolomics that quantifies volatile organic compounds (VOCs) from collected exhaled breath samples. Understanding how breath molecules are related to diseases, mechanisms and pathways identified from experimental analytical measurements is challenging due to the lack of an organized resource describing breath molecules, related references and biomedical information embedded in the literature. To provide breath VOCs, related references and biomedical information, we aim to organize a database composed of manually curated information and automatically extracted biomedical information. First, VOCs-related disease information was manually organized from 207 literature linked to 99 VOCs and known Medical Subject Headings (MeSH) terms. Then an automated text mining algorithm was used to extract biomedical information from this literature. In the end, the manually curated information and auto-extracted biomedical information was combined to form a breath molecule database—the Human Breathomics Database (HBDB). We first manually curated and organized disease information including MeSH term from 207 literatures associated with 99 VOCs. Then, an automatic pipeline of text mining approach was used to collect 2766 literatures and extract biomedical information from breath researches. We combined curated information with automatically extracted biomedical information to assemble a breath molecule database, the HBDB. The HBDB is a database that includes references, VOCs and diseases associated with human breathomics. Most of these VOCs were detected in human breath samples or exhaled breath condensate samples. So far, the database contains a total of 913 VOCs in relation to human exhaled breath researches reported in 2766 publications. The HBDB is the most comprehensive HBDB of VOCs in human exhaled breath to date. It is a useful and organized resource for researchers and clinicians to identify and further investigate potential biomarkers from the breath of patients.

Database URL: https://hbdb.cmdm.tw

Methods of the Analysis of Oxylipins in Biological Samples

Oxylipins are derivatives of polyunsaturated fatty acids and due to their important and diverse functions in the body, they have become a popular subject of studies. The main challenge for researchers is their low stability and often very low concentration in samples. Therefore, in recent years there have been developments in the extraction and analysis methods of oxylipins. New approaches in extraction methods were described in our previous review. In turn, the old analysis methods have been replaced by new approaches based on mass spectrometry (MS) coupled with liquid chromatography (LC) and gas chromatography (GC), and the best of these methods allow hundreds of oxylipins to be quantitatively identified. This review presents comparative and comprehensive information on the progress of various methods used by various authors to achieve the best results in the analysis of oxylipins in biological samples.

The Fusion of Lipid and DNA Nanotechnology

Lipid membranes form the boundary of many biological compartments, including organelles and cells. Consisting of two leaflets of amphipathic molecules, the bilayer membrane forms an impermeable barrier to ions and small molecules. Controlled transport of molecules across lipid membranes is a fundamental biological process that is facilitated by a diverse range of membrane proteins, including ion-channels and pores. However, biological membranes and their associated proteins are challenging to experimentally characterize. These challenges have motivated recent advances in nanotechnology towards building and manipulating synthetic lipid systems. Liposomes-aqueous droplets enclosed by a bilayer membrane-can be synthesised in vitro and used as a synthetic model for the cell membrane. In DNA nanotechnology, DNA is used as programmable building material for self-assembling biocompatible nanostructures. DNA nanostructures can be functionalised with hydrophobic chemical modifications, which bind to or bridge lipid membranes. Here, we review approaches that combine techniques from lipid and DNA nanotechnology to engineer the topography, permeability, and surface interactions of membranes, and to direct the fusion and formation of liposomes. These approaches have been used to study the properties of membrane proteins, to build biosensors, and as a pathway towards assembling synthetic multicellular systems.

PubChem and ChEMBL beyond Lipinski

Seven million of the currently 94 million entries in the PubChem database break at least one of the four Lipinski constraints for oral bioavailability, 183,185 of which are also found in the ChEMBL database. These non-Lipinski PubChem (NLP) and ChEMBL (NLC) subsets are interesting because they contain new modalities that can display biological properties not accessible to small molecule drugs. Unfortunately, the current search tools in PubChem and ChEMBL are designed for small molecules and are not well suited to explore these subsets, which therefore remain poorly appreciated. Herein we report MXFP (macromolecule extended atom-pair fingerprint), a 217-D fingerprint tailored to analyze large molecules in terms of molecular shape and pharmacophores. We implement MXFP in two web-based applications, the first one to visualize NLP and NLC interactively using Faerun (http://faerun.gdb.tools/), the second one to perform MXFP nearest neighbor searches in NLP and NLC (http://similaritysearch.gdb.tools/). We show that these tools provide a meaningful insight into the diversity of large molecules in NLP and NLC. The interactive tools presented here are publicly available at http://gdb.unibe.ch and can be used freely to explore and better understand the diversity of non-Lipinski molecules in PubChem and ChEMBL.

Emerging Diversity in Lipid-Protein Interactions

Membrane lipids interact with proteins in a variety of ways, ranging from providing a stable membrane environment for proteins to being embedded in to detailed roles in complicated and well-regulated protein functions. Experimental and computational advances are converging in a rapidly expanding research area of lipid–protein interactions. Experimentally, the database of high-resolution membrane protein structures is growing, as are capabilities to identify the complex lipid composition of different membranes, to probe the challenging time and length scales of lipid–protein interactions, and to link lipid–protein interactions to protein function in a variety of proteins. Computationally, more accurate membrane models and more powerful computers now enable a detailed look at lipid–protein interactions and increasing overlap with experimental observations for validation and joint interpretation of simulation and experiment. Here we review papers that use computational approaches to study detailed lipid–protein interactions, together with brief experimental and physiological contexts, aiming at comprehensive coverage of simulation papers in the last five years. Overall, a complex picture of lipid–protein interactions emerges, through a range of mechanisms including modulation of the physical properties of the lipid environment, detailed chemical interactions between lipids and proteins, and key functional roles of very specific lipids binding to well-defined binding sites on proteins. Computationally, despite important limitations, molecular dynamics simulations with current computer power and theoretical models are now in an excellent position to answer detailed questions about lipid–protein interactions.

Tools and resources for metabolomics research community: A 2017-2018 update

The scale at which MS- and NMR-based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences-ranging from biomedical to agricultural-is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017-2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.