A novel approach to identify shared fragments in drugs and natural products

Fragment-based approaches have now become an important component of the drug discovery process. At the same time, pharmaceutical chemists are more often turning to the natural world and its extremely large and diverse collection of natural compounds to discover new leads that can potentially be turned into drugs. In this study we introduce and discuss a computational pipeline to automatically extract statistically overrepresented chemical fragments in therapeutic classes, and search for similar fragments in a large database of natural products. By systematically identifying enriched fragments in therapeutic groups, we are able to extract and focus on few fragments that are likely to be active or structurally important as scaffolds. We show that several therapeutic classes (including antibacterial, antineoplastic, and drugs active on the cardiovascular system, among others) have enriched fragments that are also found in many natural compounds. Further, our method is able to detect fragments shared by a drug and a natural product even when the global similarity between the two molecules is generally low. A further development of this computational pipeline is to help predict putative therapeutic activities of natural compounds, and to help identify novel leads for drug discovery

Identifying enriched drug fragments as possible candidates for metabolic engineering

Background: Fragment-based approaches have now become an important component of the drug discovery process. At the same time, pharmaceutical chemists are more often turning to the natural world and its extremely large and diverse collection of natural compounds to discover new leads that can potentially be turned into drugs. In this study we introduce and discuss a computational pipeline to automatically extract statistically overrepresented chemical fragments in therapeutic classes, and search for similar fragments in a large database of natural products. By systematically identifying enriched fragments in therapeutic groups, we are able to extract and focus on few fragments that are likely to be active or structurally important. Results: We show that several therapeutic classes (including antibacterial, antineoplastic, and drugs active on the cardiovascular system, among others) have enriched fragments that are also found in many natural compounds. Further, our method is able to detect fragments shared by a drug and a natural product even when the global similarity between the two molecules is generally low. Conclusions: A further development of this computational pipeline is to help predict putative therapeutic activities of natural compounds, and to help identify novel leads for drug discovery

Multi-Omic Profiling of Melophlus Sponges Reveals Diverse Metabolomic and Microbiome Architectures that Are Non-overlapping with Ecological Neighbors.

Marine sponge holobionts, defined as filter-feeding sponge hosts together with their associated microbiomes, are prolific sources of natural products. The inventory of natural products that have been isolated from marine sponges is extensive. Here, using untargeted mass spectrometry, we demonstrate that sponges harbor a far greater diversity of low-abundance natural products that have evaded discovery. While these low-abundance natural products may not be feasible to isolate, insights into their chemical structures can be gleaned by careful curation of mass fragmentation spectra. Sponges are also some of the most complex, multi-organismal holobiont communities in the oceans. We overlay sponge metabolomes with their microbiome structures and detailed metagenomic characterization to discover candidate gene clusters that encode production of sponge-derived natural products. The multi-omic profiling strategy for sponges that we describe here enables quantitative comparison of sponge metabolomes and microbiomes to address, among other questions, the ecological relevance of sponge natural products and for the phylochemical assignment of previously undescribed sponge identities

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included

Biotechnological Potential of Bacteria Isolated from the Sea Cucumber Holothuria leucospilota and Stichopus vastus from Lampung, Indonesia

In order to minimize re-discovery of already known anti-infective compounds, we focused our screening approach on understudied, almost untapped marine environments including marine invertebrates and their associated bacteria. Therefore, two sea cucumber species, Holothuria leucospilota and Stichopus vastus, were collected from Lampung (Indonesia), and 127 bacterial strains were identified by partial 16S rRNA-gene sequencing analysis and compared with the NCBI database. In addition, the overall bacterial diversity from tissue samples of the sea cucumbers H. leucospilota and S. vastus was analyzed using the cultivation-independent Illumina MiSEQ analysis. Selected bacterial isolates were grown to high densities and the extracted biomass was tested against a selection of bacteria and fungi as well as the hepatitis C virus (HCV). Identification of putative bioactive bacterial-derived compounds were performed by analyzing the accurate mass of the precursor/parent ions (MS1) as well as product/daughter ions (MS2) using high resolution mass spectrometry (HRMS) analysis of all active fractions. With this attempt we were able to identify 23 putatively known and two previously unidentified precursor ions. Moreover, through 16S rRNA-gene sequencing we were able to identify putatively novel bacterial species from the phyla Actinobacteria, Proteobacteria and also Firmicutes. Our findings suggest that sea cucumbers like H. leucospilota and S. vastus are promising sources for the isolation of novel bacterial species that produce compounds with potentially high biotechnological potential

A Scientific Roadmap for Antibiotic Discovery: A Sustained and Robust Pipeline of New Antibacterial Drugs and Therapies is Critical to Preserve Public Health

In recent decades, the discovery and development of new antibiotics have slowed dramatically as scientific barriers to drug discovery, regulatory challenges, and diminishing returns on investment have led major drug companies to scale back or abandon their antibiotic research. Consequently, antibiotic discovery—which peaked in the 1950s—has dropped precipitously. Of greater concern is the fact that nearly all antibiotics brought to market over the past 30 years have been variations on existing drugs. Every currently available antibiotic is a derivative of a class discovered between the early 1900s and 1984.At the same time, the emergence of antibiotic-resistant pathogens has accelerated, giving rise to life-threatening infections that will not respond to available antibiotic treatment. Inevitably, the more that antibiotics are used, the more that bacteria develop resistance—rendering the drugs less effective and leading public health authorities worldwide to flag antibiotic resistance as an urgent and growing public health threat

Adverse Drug Reaction Classification With Deep Neural Networks

We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs

Accurate Dereplication of Bioactive Secondary Metabolites from Marine-Derived Fungi by UHPLC-DAD-QTOFMS and a MS/HRMS Library

In drug discovery, reliable and fast dereplication of known compounds is essential for identification of novel bioactive compounds. Here, we show an integrated approach using ultra-high performance liquid chromatography-diode array detection-quadrupole time of flight mass spectrometry (UHPLC-DAD-QTOFMS) providing both accurate mass full-scan mass spectrometry (MS) and tandem high resolution MS (MS/HRMS) data. The methodology was demonstrated on compounds from bioactive marine-derived strains of Aspergillus, Penicillium, and Emericellopsis, including small polyketides, non-ribosomal peptides, terpenes, and meroterpenoids. The MS/HRMS data were then searched against an in-house MS/HRMS library of ~1300 compounds for unambiguous identification. The full scan MS data was used for dereplication of compounds not in the MS/HRMS library, combined with ultraviolet/visual (UV/Vis) and MS/HRMS data for faster exclusion of database search results. This led to the identification of four novel isomers of the known anticancer compound, asperphenamate. Except for very low intensity peaks, no false negatives were found using the MS/HRMS approach, which proved to be robust against poor data quality caused by system overload or loss of lock-mass. Only for small polyketides, like patulin, were both retention time and UV/Vis spectra necessary for unambiguous identification. For the ophiobolin family with many structurally similar analogues partly co-eluting, the peaks could be assigned correctly by combining MS/HRMS data and m/z of the [M + Na]+ ions

Structural diversity of biologically interesting datasets: a scaffold analysis approach

ABSTRACT:The recent public availability of the human metabolome and natural product datasets has revitalized "metabolite-likeness" and "natural product-likeness" as a drug design concept to design lead libraries targeting specific pathways. Many reports have analyzed the physicochemical property space of biologically important datasets, with only a few comprehensively characterizing the scaffold diversity in public datasets of biological interest. With large collections of high quality public data currently available, we carried out a comparative analysis of current day leads with other biologically relevant datasets.In this study, we note a two-fold enrichment of metabolite scaffolds in drug dataset (42%) as compared to currently used lead libraries (23%). We also note that only a small percentage (5%) of natural product scaffolds space is shared by the lead dataset. We have identified specific scaffolds that are present in metabolites and natural products, with close counterparts in the drugs, but are missing in the lead dataset. To determine the distribution of compounds in physicochemical property space we analyzed the molecular polar surface area, the molecular solubility, the number of rings and the number of rotatable bonds in addition to four well-known Lipinski properties. Here, we note that, with only few exceptions, most of the drugs follow Lipinski's rule. The average values of the molecular polar surface area and the molecular solubility in metabolites is the highest while the number of rings is the lowest. In addition, we note that natural products contain the maximum number of rings and the rotatable bonds than any other dataset under consideration.Currently used lead libraries make little use of the metabolites and natural products scaffold space. We believe that metabolites and natural products are recognized by at least one protein in the biosphere therefore, sampling the fragment and scaffold space of these compounds, along with the knowledge of distribution in physicochemical property space, can result in better lead libraries. Hence, we recommend the greater use of metabolites and natural products while designing lead libraries. Nevertheless, metabolites have a limited distribution in chemical space that limits the usage of metabolites in library design.14 page(s