1
|
Stratton CA, Thompson Y, Zio K, Morrison WR, Murrell EG. uafR: An R package that automates mass spectrometry data processing. PLoS One 2024; 19:e0306202. [PMID: 38968199 PMCID: PMC11226021 DOI: 10.1371/journal.pone.0306202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 06/12/2024] [Indexed: 07/07/2024] Open
Abstract
Chemical information has become increasingly ubiquitous and has outstripped the pace of analysis and interpretation. We have developed an R package, uafR, that automates a grueling retrieval process for gas -chromatography coupled mass spectrometry (GC -MS) data and allows anyone interested in chemical comparisons to quickly perform advanced structural similarity matches. Our streamlined cheminformatics workflows allow anyone with basic experience in R to pull out component areas for tentative compound identifications using the best published understanding of molecules across samples (pubchem.gov). Interpretations can now be done at a fraction of the time, cost, and effort it would typically take using a standard chemical ecology data analysis pipeline. The package was tested in two experimental contexts: (1) A dataset of purified internal standards, which showed our algorithms correctly identified the known compounds with R2 values ranging from 0.827-0.999 along concentrations ranging from 1 × 10-5 to 1 × 103 ng/μl, (2) A large, previously published dataset, where the number and types of compounds identified were comparable (or identical) to those identified with the traditional manual peak annotation process, and NMDS analysis of the compounds produced the same pattern of significance as in the original study. Both the speed and accuracy of GC -MS data processing are drastically improved with uafR because it allows users to fluidly interact with their experiment following tentative library identifications [i.e. after the m/z spectra have been matched against an installed chemical fragmentation database (e.g. NIST)]. Use of uafR will allow larger datasets to be collected and systematically interpreted quickly. Furthermore, the functions of uafR could allow backlogs of previously collected and annotated data to be processed by new personnel or students as they are being trained. This is critical as we enter the era of exposomics, metabolomics, volatilomes, and landscape level, high-throughput chemotyping. This package was developed to advance collective understanding of chemical data and is applicable to any research that benefits from GC -MS analysis. It can be downloaded for free along with sample datasets from Github at github.org/castratton/uafR or installed directly from R or RStudio using the developer tools: 'devtools::install_github("castratton/uafR")'.
Collapse
Affiliation(s)
- Chase A. Stratton
- The Land Institute, Salina, KS, United States of America
- Department of Biology, Delaware State University, Dover, DE, United States of America
| | | | | | - William R. Morrison
- USDA-ARS, Agricultural Research Service, Center for Grain and Animal Health Research, Manhattan, KS, United States of America
| | | |
Collapse
|
2
|
Enokiya T, Ozaki K. Developing an AI-based prediction model for anaphylactic shock from injection drugs using Japanese real-world data and chemical structure-based analysis. Daru 2024; 32:253-262. [PMID: 38580799 PMCID: PMC11087410 DOI: 10.1007/s40199-024-00511-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 03/20/2024] [Indexed: 04/07/2024] Open
Abstract
BACKGROUND This study aims to develop an AI-based prediction model for injection drugs that cause anaphylactic shock using Japanese Real-World Data (JADER database) and chemical structure-based analysis. METHODS Data sourced from the JADER database included adverse drug reaction reports from April 2004 to December 2020. Only drugs with an adverse reaction named "anaphylactic shock" were selected for analysis. For model building, various models were constructed to predict anaphylactic shock-inducing drugs, such as logistic regression, LASSO, XGBoost, RF, SVM, and NNW. These models used chemical properties and structural similarities as feature variables. Dimension reduction was applied using principal component analysis. The dataset was split into training (80%) and validation (20%) sets. Six different models were trained and optimized through fivefold cross-validation. RESULTS From April 2004 to December 2020, 947 drugs with the adverse reaction name "anaphylactic shock" were extracted from the JADER database. 320 drugs were excluded due to analytical challenges, and another 400 were removed due to their administration route. 227 drugs were finalized as target medicines. For model validation, the performance of each model was evaluated based on metrics like AUCs of ROC curve, sensitivity, and specificity. Additionally, two ensemble models, constructed from the six models were assessed using bootstrap sampling. Interestingly, it was identified that mepivacaine structural similarity had the highest importance in the final model. CONCLUSIONS The study successfully developed an AI-based prediction model for anaphylactic shock inducing-injection drugs. The model would offer potential for drug safety evaluation and anaphylactic shock risk assessment.
Collapse
Affiliation(s)
- Tomoyuki Enokiya
- Laboratory of Pharmacoinformatics, Department of Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, Suzuka University of Medical Science, 3500-3 Minamitamagakichō, Suzuka, Mie Prefecture, 513-8670, Japan.
| | - Kaito Ozaki
- Laboratory of Pharmacoinformatics, Department of Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, Suzuka University of Medical Science, 3500-3 Minamitamagakichō, Suzuka, Mie Prefecture, 513-8670, Japan
| |
Collapse
|
3
|
Bhattacharjee S, Saha B, Saha S. Symptom-based drug prediction of lifestyle-related chronic diseases using unsupervised machine learning techniques. Comput Biol Med 2024; 174:108413. [PMID: 38608323 DOI: 10.1016/j.compbiomed.2024.108413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 02/13/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024]
Abstract
BACKGROUND AND OBJECTIVES Lifestyle-related diseases (LSDs) impose a substantial economic burden on patients and health care services. LSDs are chronic in nature and can directly affect the heart and lungs. Therapeutic interventions only based on symptoms can be crucial for prompt treatment initiation in LSDs, as symptoms are the first information available to clinicians. So, this work aims to apply unsupervised machine learning (ML) techniques for developing models to predict drugs from symptoms for LSDs, with a specific focus on pulmonary and heart diseases. METHODS The drug-disease and disease-symptom associations of 143 LSDs, 1271 drugs, and 305 symptoms were used to compute direct associations between drugs and symptoms. ML models with four different algorithms - K-Means, Bisecting K-Means, Mean Shift, and Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) - were developed to cluster the drugs using symptoms as features. The optimal model was saved in a server for the development of a web application. A web application was developed to perform the prediction based on the optimal model. RESULTS The Bisecting K-means model showed the best performance with a silhouette coefficient of 0.647 and generated 138 drug clusters. The drugs within the optimal clusters showed good similarity based on i) gene ontology annotations of the gene targets, ii) chemical ontology annotations, and iii) maximum common substructure of the drugs. In the web application, the model also provides a confidence score for each predicted drug while predicting from a new set of input symptoms. CONCLUSION In summary, direct associations between drugs and symptoms were computed, and those were used to develop a symptom-based drug prediction tool for LSDs with unsupervised ML models. The ML-based prediction can provide a second opinion to clinicians to aid their decision-making for early treatment of LSD patients. The web application (URL - http://bicresources.jcbose.ac.in/ssaha4/sdldpred) can provide a simple interface for all end-users to perform the ML-based prediction.
Collapse
Affiliation(s)
- Sudipto Bhattacharjee
- Department of Computer Science and Engineering, University of Calcutta, JD-2, Sector-III, Salt Lake, Kolkata, 700098, India.
| | - Banani Saha
- Department of Computer Science and Engineering, University of Calcutta, JD-2, Sector-III, Salt Lake, Kolkata, 700098, India.
| | - Sudipto Saha
- Department of Biological Sciences, Bose Institute, EN 80, Sector V, Bidhan Nagar, Kolkata, 700091, India.
| |
Collapse
|
4
|
Adams SA, Gurajapu A, Qiang A, Gerbaulet M, Schulz S, Tsutsui ND, Ramirez SR, Gillespie RG. Chemical species recognition in an adaptive radiation of Hawaiian Tetragnatha spiders (Araneae: Tetragnathidae). Proc Biol Sci 2024; 291:20232340. [PMID: 38593845 PMCID: PMC11003775 DOI: 10.1098/rspb.2023.2340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/05/2024] [Indexed: 04/11/2024] Open
Abstract
Studies of adaptive radiations have played a central role in our understanding of reproductive isolation. Yet the focus has been on human-biased visual and auditory signals, leaving gaps in our knowledge of other modalities. To date, studies on chemical signals in adaptive radiations have focused on systems with multimodal signalling, making it difficult to isolate the role chemicals play in reproductive isolation. In this study we examine the use of chemical signals in the species recognition and adaptive radiation of Hawaiian Tetragnatha spiders by focusing on entire communities of co-occurring species, and conducting behavioural assays in conjunction with chemical analysis of their silks using gas chromatography-mass spectrometry. Male spiders significantly preferred the silk extracts of conspecific mates over those of sympatric heterospecifics. The compounds found in the silk extracts, long chain alkyl methyl ethers, were remarkably species-specific in the combination and quantity. The differences in the profile were greatest between co-occurring species and between closely related sibling species. Lastly, there were significant differences in the chemical profile between two populations of a particular species. These findings provide key insights into the role chemical signals play in the attainment and maintenance of reproductive barriers between closely related co-occurring species.
Collapse
Affiliation(s)
- Seira A. Adams
- Department of Environmental Science, Policy, and Management, University of California, 130 Mulford Hall, #3114, Berkeley, CA 94720, USA
- Center for Population Biology, University of California, 2320 Storer Hall, Davis, CA 95616, USA
- Department of Evolution and Ecology, University of California, 2320 Storer Hall, Davis, CA 95616, USA
| | - Anjali Gurajapu
- Department of Environmental Science, Policy, and Management, University of California, 130 Mulford Hall, #3114, Berkeley, CA 94720, USA
| | - Albert Qiang
- Department of Environmental Science, Policy, and Management, University of California, 130 Mulford Hall, #3114, Berkeley, CA 94720, USA
| | - Moritz Gerbaulet
- Institute of Organic Chemistry, Technische Universität Braunschweig, Hagenring 30, Braunschweig 38106, Germany
| | - Stefan Schulz
- Institute of Organic Chemistry, Technische Universität Braunschweig, Hagenring 30, Braunschweig 38106, Germany
| | - Neil D. Tsutsui
- Department of Environmental Science, Policy, and Management, University of California, 130 Mulford Hall, #3114, Berkeley, CA 94720, USA
| | - Santiago R. Ramirez
- Center for Population Biology, University of California, 2320 Storer Hall, Davis, CA 95616, USA
- Department of Evolution and Ecology, University of California, 2320 Storer Hall, Davis, CA 95616, USA
| | - Rosemary G. Gillespie
- Department of Environmental Science, Policy, and Management, University of California, 130 Mulford Hall, #3114, Berkeley, CA 94720, USA
| |
Collapse
|
5
|
Bramwell LR, Frankum R, Harries LW. Repurposing Drugs for Senotherapeutic Effect: Potential Senomorphic Effects of Female Synthetic Hormones. Cells 2024; 13:517. [PMID: 38534362 DOI: 10.3390/cells13060517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 02/26/2024] [Accepted: 03/11/2024] [Indexed: 03/28/2024] Open
Abstract
Repurposing previously approved drugs may fast track the route to the clinic for potential senotherapeutics and improves the inefficiency of the clinical drug development pipeline. We performed a repurposing screen of 240 clinically approved molecules in human primary dermal fibroblasts for their effects on CDKN2A expression. Molecules demonstrating effects on CDKN2A expression underwent secondary screening for senescence-associated beta galactosidase (SAB) activity, based on effect size, direction, and/or molecule identity. Selected molecules then underwent a more detailed assessment of senescence phenotypes including proliferation, apoptosis, DNA damage, senescence-associated secretory phenotype (SASP) expression, and regulators of alternative splicing. A selection of the molecules demonstrating effects on senescence were then used in a new bioinformatic structure-function screen to identify common structural motifs. In total, 90 molecules displayed altered CDKN2A expression at one or other dose, of which 15 also displayed effects on SAB positivity in primary human dermal fibroblasts. Of these, 3 were associated with increased SAB activity, and 11 with reduced activity. The female synthetic sex hormones-diethylstilboestrol, ethynyl estradiol and levonorgestrel-were all associated with a reduction in aspects of the senescence phenotype in male cells, with no effects visible in female cells. Finally, we identified that the 30 compounds that decreased CDKN2A activity the most had a common substructure linked to this function. Our results suggest that several drugs licensed for other indications may warrant exploration as future senotherapies, but that different donors and potentially different sexes may respond differently to senotherapeutic compounds. This underlines the importance of considering donor-related characteristics when designing drug screening platforms.
Collapse
Affiliation(s)
- Laura R Bramwell
- RNA-Mediated Mechanisms of Disease Group, Department of Clinical and Biomedical Sciences (Medical School), Faculty of Health and Life Sciences, University of Exeter, Exeter EX2 5DW, UK
| | - Ryan Frankum
- RNA-Mediated Mechanisms of Disease Group, Department of Clinical and Biomedical Sciences (Medical School), Faculty of Health and Life Sciences, University of Exeter, Exeter EX2 5DW, UK
| | - Lorna W Harries
- RNA-Mediated Mechanisms of Disease Group, Department of Clinical and Biomedical Sciences (Medical School), Faculty of Health and Life Sciences, University of Exeter, Exeter EX2 5DW, UK
| |
Collapse
|
6
|
Wong F, Zheng EJ, Valeri JA, Donghia NM, Anahtar MN, Omori S, Li A, Cubillos-Ruiz A, Krishnan A, Jin W, Manson AL, Friedrichs J, Helbig R, Hajian B, Fiejtek DK, Wagner FF, Soutter HH, Earl AM, Stokes JM, Renner LD, Collins JJ. Discovery of a structural class of antibiotics with explainable deep learning. Nature 2024; 626:177-185. [PMID: 38123686 PMCID: PMC10866013 DOI: 10.1038/s41586-023-06887-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 11/21/2023] [Indexed: 12/23/2023]
Abstract
The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis1-9. Deep learning approaches have aided in exploring chemical spaces1,10-15; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We tested this hypothesis by developing an explainable, substructure-based approach for the efficient, deep learning-guided exploration of chemical spaces. We determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Using explainable graph algorithms, we identified substructure-based rationales for compounds with high predicted antibiotic activity and low predicted cytotoxicity. We empirically tested 283 compounds and found that compounds exhibiting antibiotic activity against Staphylococcus aureus were enriched in putative structural classes arising from rationales. Of these structural classes of compounds, one is selective against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci, evades substantial resistance, and reduces bacterial titres in mouse models of MRSA skin and systemic thigh infection. Our approach enables the deep learning-guided discovery of structural classes of antibiotics and demonstrates that machine learning models in drug discovery can be explainable, providing insights into the chemical substructures that underlie selective antibiotic activity.
Collapse
Affiliation(s)
- Felix Wong
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Integrated Biosciences, San Carlos, CA, USA
| | - Erica J Zheng
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Chemical Biology, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Jacqueline A Valeri
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Nina M Donghia
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Melis N Anahtar
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Satotaka Omori
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Integrated Biosciences, San Carlos, CA, USA
| | - Alicia Li
- Integrated Biosciences, San Carlos, CA, USA
| | - Andres Cubillos-Ruiz
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Aarti Krishnan
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Wengong Jin
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Abigail L Manson
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jens Friedrichs
- Leibniz Institute of Polymer Research and the Max Bergmann Center of Biomaterials, Dresden, Germany
| | - Ralf Helbig
- Leibniz Institute of Polymer Research and the Max Bergmann Center of Biomaterials, Dresden, Germany
| | - Behnoush Hajian
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dawid K Fiejtek
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Florence F Wagner
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Holly H Soutter
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ashlee M Earl
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jonathan M Stokes
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research and David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada
| | - Lars D Renner
- Leibniz Institute of Polymer Research and the Max Bergmann Center of Biomaterials, Dresden, Germany
| | - James J Collins
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
| |
Collapse
|
7
|
Chen YC, Wu HY, Wu WS, Hsu JY, Chang CW, Lee YH, Liao PC. Identification of Xenobiotic Biotransformation Products Using Mass Spectrometry-Based Metabolomics Integrated with a Structural Elucidation Strategy by Assembling Fragment Signatures. Anal Chem 2023; 95:14279-14287. [PMID: 37713273 PMCID: PMC10538286 DOI: 10.1021/acs.analchem.3c02419] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 09/01/2023] [Indexed: 09/17/2023]
Abstract
The identification of xenobiotic biotransformation products is crucial for delineating toxicity and carcinogenicity that might be caused by xenobiotic exposures and for establishing monitoring systems for public health. However, the lack of available reference standards and spectral data leads to the generation of multiple candidate structures during identification and reduces the confidence in identification. Here, a UHPLC-HRMS-based metabolomics strategy integrated with a metabolite structure elucidation approach, namely, FragAssembler, was proposed to reduce the number of false-positive structure candidates. biotransformation product candidates were filtered by mass defect filtering (MDF) and multiple-group comparison. FragAssembler assembled fragment signatures from the MS/MS spectra and generated the modified moieties corresponding to the identified biotransformation products. The feasibility of this approach was demonstrated by the three biotransformation products of di(2-ethylhexyl)phthalate (DEHP). Comprehensive identification was carried out, and 24 and 13 biotransformation products of two xenobiotics, DEHP and 4'-Methoxy-α-pyrrolidinopentiophenone (4-MeO-α-PVP), were annotated, respectively. The number of 4-MeO-α-PVP biotransformation product candidates in the FragAssembler calculation results was approximately 2.1 times lower than that generated by BioTransformer 3.0. Our study indicates that the proposed approach has great potential for efficiently and reliably identifying xenobiotic biotransformation products, which is attributed to the fact that FragAssembler eliminates false-positive reactions and chemical structures and distinguishes modified moieties on isomeric biotransformation products. The FragAssembler software and associated tutorial are freely available at https://cosbi.ee.ncku.edu.tw/FragAssembler/ and the source code can be found at https://github.com/YuanChihChen/FragAssembler.
Collapse
Affiliation(s)
- Yuan-Chih Chen
- Department
of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Hsin-Yi Wu
- Instrumentation
Center, National Taiwan University, Taipei 106, Taiwan
| | - Wei-Sheng Wu
- Department
of Electrical Engineering, National Cheng
Kung University, Tainan 701, Taiwan
| | - Jen-Yi Hsu
- Department
of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Chih-Wei Chang
- Department
of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Yuan-Han Lee
- Department
of Electrical Engineering, National Cheng
Kung University, Tainan 701, Taiwan
| | - Pao-Chi Liao
- Department
of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| |
Collapse
|
8
|
Petrén H, Köllner TG, Junker RR. Quantifying chemodiversity considering biochemical and structural properties of compounds with the R package chemodiv. THE NEW PHYTOLOGIST 2023; 237:2478-2492. [PMID: 36527232 DOI: 10.1111/nph.18685] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 12/11/2022] [Indexed: 06/17/2023]
Abstract
Plants produce large numbers of phytochemical compounds affecting plant physiology and interactions with their biotic and abiotic environment. Recently, chemodiversity has attracted considerable attention as an ecologically and evolutionary meaningful way to characterize the phenotype of a mixture of phytochemical compounds. Currently used measures of phytochemical diversity, and related measures of phytochemical dissimilarity, generally do not take structural or biosynthetic properties of compounds into account. Such properties can be indicative of the compounds' function and inform about their biosynthetic (in)dependence, and should therefore be included in calculations of these measures. We introduce the R package chemodiv, which retrieves biochemical and structural properties of compounds from databases and provides functions for calculating and visualizing chemical diversity and dissimilarity for phytochemicals and other types of compounds. Our package enables calculations of diversity that takes the richness, relative abundance and - most importantly - structural and/or biosynthetic dissimilarity of compounds into account. We illustrate the use of the package with examples on simulated and real datasets. By providing the R package chemodiv for quantifying multiple aspects of chemodiversity, we hope to facilitate investigations of how chemodiversity varies across levels of biological organization, and its importance for the ecology and evolution of plants and other organisms.
Collapse
Affiliation(s)
- Hampus Petrén
- Evolutionary Ecology of Plants, Department of Biology, Philipps-University Marburg, 35043, Marburg, Germany
| | - Tobias G Köllner
- Department of Natural Product Biosynthesis, Max Planck Institute for Chemical Ecology, 07745, Jena, Germany
| | - Robert R Junker
- Evolutionary Ecology of Plants, Department of Biology, Philipps-University Marburg, 35043, Marburg, Germany
- Department of Environment and Biodiversity, University of Salzburg, 5020, Salzburg, Austria
| |
Collapse
|
9
|
Kruse LH, Weigle AT, Irfan M, Martínez-Gómez J, Chobirko JD, Schaffer JE, Bennett AA, Specht CD, Jez JM, Shukla D, Moghe GD. Orthology-based analysis helps map evolutionary diversification and predict substrate class use of BAHD acyltransferases. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 111:1453-1468. [PMID: 35816116 DOI: 10.1111/tpj.15902] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 06/15/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
Large enzyme families catalyze metabolic diversification by virtue of their ability to use diverse chemical scaffolds. How enzyme families attain such functional diversity is not clear. Furthermore, duplication and promiscuity in such enzyme families limits their functional prediction, which has produced a burgeoning set of incompletely annotated genes in plant genomes. Here, we address these challenges using BAHD acyltransferases as a model. This fast-evolving family expanded drastically in land plants, increasing from one to five copies in algae to approximately 100 copies in diploid angiosperm genomes. Compilation of >160 published activities helped visualize the chemical space occupied by this family and define eight different classes based on structural similarities between acceptor substrates. Using orthologous groups (OGs) across 52 sequenced plant genomes, we developed a method to predict BAHD acceptor substrate class utilization as well as origins of individual BAHD OGs in plant evolution. This method was validated using six novel and 28 previously characterized enzymes and helped improve putative substrate class predictions for BAHDs in the tomato genome. Our results also revealed that while cuticular wax and lignin biosynthetic activities were more ancient, anthocyanin acylation activity was fixed in BAHDs later near the origin of angiosperms. The OG-based analysis enabled identification of signature motifs in anthocyanin-acylating BAHDs, whose importance was validated via molecular dynamic simulations, site-directed mutagenesis and kinetic assays. Our results not only describe how BAHDs contributed to evolution of multiple chemical phenotypes in the plant world but also propose a biocuration-enabled approach for improved functional annotation of plant enzyme families.
Collapse
Affiliation(s)
- Lars H Kruse
- Plant Biology Section, School of Integrative Plant Sciences, Cornell University, Ithaca, New York, 14853, USA
| | - Austin T Weigle
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Mohammad Irfan
- Plant Biology Section, School of Integrative Plant Sciences, Cornell University, Ithaca, New York, 14853, USA
| | - Jesús Martínez-Gómez
- Plant Biology Section, School of Integrative Plant Sciences, Cornell University, Ithaca, New York, 14853, USA
- L.H. Bailey Hortorium, Cornell University, Ithaca, New York, 14853, USA
| | - Jason D Chobirko
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Jason E Schaffer
- Department of Biology, Washington University in St. Louis, St. Louis, Missouri, 63130, USA
| | - Alexandra A Bennett
- Plant Biology Section, School of Integrative Plant Sciences, Cornell University, Ithaca, New York, 14853, USA
| | - Chelsea D Specht
- Plant Biology Section, School of Integrative Plant Sciences, Cornell University, Ithaca, New York, 14853, USA
- L.H. Bailey Hortorium, Cornell University, Ithaca, New York, 14853, USA
| | - Joseph M Jez
- Department of Biology, Washington University in St. Louis, St. Louis, Missouri, 63130, USA
| | - Diwakar Shukla
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Gaurav D Moghe
- Plant Biology Section, School of Integrative Plant Sciences, Cornell University, Ithaca, New York, 14853, USA
| |
Collapse
|
10
|
Ren K, Su G. Characteristic fragmentations of nitroaromatic compounds (NACs) in Orbitrap HCD and integrated strategy for recognition of NACs in environmental samples. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 834:155106. [PMID: 35398140 DOI: 10.1016/j.scitotenv.2022.155106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 03/28/2022] [Accepted: 04/03/2022] [Indexed: 06/14/2023]
Abstract
Nitroaromatic compounds (NACs) are high of concern due to their mutagenicity, and carcinogenicity to organisms. Here, we attempted to establish a novel searching-validation-evaluation workflow that is tailored to recognize unknown NACs in environmental samples using liquid chromatography coupled with quadrupole Orbitrap high-resolution mass spectrometry (LC-Orbitrap-HRMS). We studied the fragmentation process of NAC standards in Orbitrap higher-energy collision dissociation (HCD) cells and observed that the mass loss of NO was the most prevalent among all NAC standards at both low and medium levels of collision energy. Thus, neutral loss of NO was considered as a diagnostic fragment of nitro groups and was used to screen out NACs in environmental samples. This technique is mass-loss-dependent, which enhances the recognition efficiency of NACs. Candidates exported from the PubChem compound database were further evaluated to obtain possible structures. This strategy was applied for the analysis of 24 surface soil, and we tentatively discovered two novel NACs in the analyzed samples. The semi-quantification results demonstrated that the concentrations of novel NACs were comparable to those of the ten targeted NACs in soil samples. This study provides an integrated strategy for the recognition of known and unknown NACs, which could be extended to other environmental matrices.
Collapse
Affiliation(s)
- Kefan Ren
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China
| | - Guanyong Su
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China.
| |
Collapse
|
11
|
Vasileiadis S, Perruchon C, Scheer B, Adrian L, Steinbach N, Trevisan M, Plaza-Bolaños P, Agüera A, Chatzinotas A, Karpouzas DG. Nutritional inter-dependencies and a carbazole-dioxygenase are key elements of a bacterial consortium relying on a Sphingomonas for the degradation of the fungicide thiabendazole. Environ Microbiol 2022; 24:5105-5122. [PMID: 35799498 DOI: 10.1111/1462-2920.16116] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 06/20/2022] [Accepted: 06/21/2022] [Indexed: 11/28/2022]
Abstract
Thiabendazole (TBZ), is a persistent fungicide/anthelminthic and a serious environmental threat. We previously enriched a TBZ-degrading bacterial consortium and provided first evidence for a Sphingomonas involvement in TBZ transformation. Here, using a multi-omic approach combined with DNA-stable isotope probing (SIP) we verified the key degrading role of Sphingomonas and identify potential microbial interactions governing consortium functioning. SIP and amplicon sequencing analysis of the heavy and light DNA fraction of cultures grown on 13 C-labelled versus 12 C-TBZ showed that 66% of the 13 C-labelled TBZ was assimilated by Sphingomonas. Metagenomic analysis retrieved 18 metagenome-assembled genomes with the dominant belonging to Sphingomonas, Sinobacteriaceae, Bradyrhizobium, Filimonas and Hydrogenophaga. Meta-transcriptomics/-proteomics and non-target mass spectrometry suggested TBZ transformation by Sphingomonas via initial cleavage by a carbazole dioxygenase (car) to thiazole-4-carboxamidine (terminal compound) and catechol or a cleaved benzyl ring derivative, further transformed through an ortho-cleavage (cat) pathway. Microbial co-occurrence and gene expression networks suggested strong interactions between Sphingomonas and a Hydrogenophaga. The latter activated its cobalamin biosynthetic pathway and Sphingomonas its cobalamin salvage pathway to satisfy its B12 auxotrophy. Our findings indicate microbial interactions aligning with the 'black queen hypothesis' where Sphingomonas (detoxifier, B12 recipient) and Hydrogenophaga (B12 producer, enjoying detoxification) act as both helpers and beneficiaries.
Collapse
Affiliation(s)
- Sotirios Vasileiadis
- Laboratory of Plant and Environmental Biotechnology, Department of Biochemistry and Biotechnology, University of Thessaly, Larissa, Viopolis, Greece
| | - Chiara Perruchon
- Laboratory of Plant and Environmental Biotechnology, Department of Biochemistry and Biotechnology, University of Thessaly, Larissa, Viopolis, Greece
| | - Benjamin Scheer
- Department of Environmental Biotechnology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
| | - Lorenz Adrian
- Department of Environmental Biotechnology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany.,Chair of Geobiotechnology, Technische Universität Berlin, Berlin, Germany
| | - Nicole Steinbach
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
| | - Marco Trevisan
- Department of Sustainable Food Process, Universitá Cattolica del Sacro Cuore, Piacenza, Italy
| | - Patricia Plaza-Bolaños
- Solar Energy Research Centre (CIESOL), Joint Center University of Almería-CIEMAT, Almeria, Spain
| | - Ana Agüera
- Solar Energy Research Centre (CIESOL), Joint Center University of Almería-CIEMAT, Almeria, Spain
| | - Antonis Chatzinotas
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany.,Institute of Biology, Leipzig University, Leipzig, Germany.,German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | - Dimitrios G Karpouzas
- Laboratory of Plant and Environmental Biotechnology, Department of Biochemistry and Biotechnology, University of Thessaly, Larissa, Viopolis, Greece
| |
Collapse
|
12
|
Randhawa V, Pathania S, Kumar M. Computational Identification of Potential Multitarget Inhibitors of Nipah Virus by Molecular Docking and Molecular Dynamics. Microorganisms 2022; 10:microorganisms10061181. [PMID: 35744699 PMCID: PMC9227315 DOI: 10.3390/microorganisms10061181] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/28/2022] [Accepted: 05/11/2022] [Indexed: 02/04/2023] Open
Abstract
Nipah virus (NiV) is a recently emerged paramyxovirus that causes severe encephalitis and respiratory diseases in humans. Despite the severe pathogenicity of this virus and its pandemic potential, not even a single type of molecular therapeutics has been approved for human use. Considering the role of NiV attachment glycoprotein G (NiV-G), fusion glycoprotein (NiV-F), and nucleoprotein (NiV-N) in virus replication and spread, these are the most attractive targets for anti-NiV drug discovery. Therefore, to prospect for potential multitarget chemical/phytochemical inhibitor(s) against NiV, a sequential molecular docking and molecular-dynamics-based approach was implemented by simultaneously targeting NiV-G, NiV-F, and NiV-N. Information on potential NiV inhibitors was compiled from the literature, and their 3D structures were drawn manually, while the information and 3D structures of phytochemicals were retrieved from the established structural databases. Molecules were docked against NiV-G (PDB ID:2VSM), NiV-F (PDB ID:5EVM), and NiV-N (PDB ID:4CO6) and then prioritized based on (1) strong protein-binding affinity, (2) interactions with critically important binding-site residues, (3) ADME and pharmacokinetic properties, and (4) structural stability within the binding site. The molecules that bind to all the three viral proteins (NiV-G ∩ NiV-F ∩ NiV-N) were considered multitarget inhibitors. This study identified phytochemical molecules RASE0125 (17-O-Acetyl-nortetraphyllicine) and CARS0358 (NA) as distinct multitarget inhibitors of all three viral proteins, and chemical molecule ND_nw_193 (RSV604) as an inhibitor of NiV-G and NiV-N. We expect the identified compounds to be potential candidates for in vitro and in vivo antiviral studies, followed by clinical treatment of NiV.
Collapse
Affiliation(s)
- Vinay Randhawa
- Virology Discovery Unit and Bioinformatics Centre, CSIR-Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh 160036, India; (V.R.); (S.P.)
| | - Shivalika Pathania
- Virology Discovery Unit and Bioinformatics Centre, CSIR-Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh 160036, India; (V.R.); (S.P.)
| | - Manoj Kumar
- Virology Discovery Unit and Bioinformatics Centre, CSIR-Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh 160036, India; (V.R.); (S.P.)
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
- Correspondence: ; Tel.: +91-172-6665453
| |
Collapse
|
13
|
Lea I, Pham LL, Antonijevic T, Thompson C, Borghoff SJ. Assessment of the applicability of the threshold of toxicological concern for per- and polyfluoroalkyl substances. Regul Toxicol Pharmacol 2022; 133:105190. [PMID: 35662637 DOI: 10.1016/j.yrtph.2022.105190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 05/18/2022] [Accepted: 05/18/2022] [Indexed: 11/24/2022]
Abstract
While toxicity information is available for selected PFAS, little or no information is available for most, thereby necessitating a resource-effective approach to screen and prioritize those needing further safety assessment. The threshold of toxicological concern (TTC) approach proposes a de minimis exposure value based on chemical structure and toxicology of similar substances. The applicability of the TTC approach to PFAS was tested by incorporating a data set of no-observed-adverse-effect level (NOAEL) values for 27 PFAS into the Munro TTC data set. All substances were assigned into Cramer Class III and the cumulative distribution of the NOAELs evaluated. The TTC value for the PFAS-enriched data set was not statistically different compared to the Munro data set. Derived human exposure level for the PFAS-enriched data set was 1.3 μg/kg/day. Structural chemical profiles showed the PFAS-enriched data set had distinct chemotypes with lack of similarity to substances in the Munro data set using Maximum Common Structures. The incorporation of these 27 PFAS did not significantly change TTC Cramer Class III distribution and expanded the chemical space, supporting the potential use of the TTC approach for PFAS chemicals.
Collapse
Affiliation(s)
- Isabel Lea
- ToxStrategies, 1249 Kildaire Farm Road, #134, Cary, NC, 27511, USA
| | - Ly Ly Pham
- ToxStrategies Inc., 23123 Cinco Ranch Blvd, Katy, TX, 77494, USA
| | | | - Chad Thompson
- ToxStrategies Inc., 23123 Cinco Ranch Blvd, Katy, TX, 77494, USA
| | - Susan J Borghoff
- ToxStrategies, 1249 Kildaire Farm Road, #134, Cary, NC, 27511, USA.
| |
Collapse
|
14
|
Abstract
The study aims to analyze the degree of similarity of some molecules belonging to two subgroups of Aminoalkylindoles. After extracting the molecules’ characteristics using Cheminformatics methods, and the computation of the Tanimoto coefficients, dendrograms and heatmaps were built to reveal the degree of similarity of the analyzed drugs. Some atom-pair similarities between the molecules in the same group were detected. The clusters determined by the k-means method divided the Benzoylindoles into two subgroups but kept all the Phenylacetylindoles together in the same set. The activity spectrum of the elements in each group was also analyzed, and similarities have been emphasized. The clustering has been validated using the Kruskal–Wallis test on the series of computed probabilities of the main effects.
Collapse
|
15
|
Periwal V, Bassler S, Andrejev S, Gabrielli N, Patil KR, Typas A, Patil KR. Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs. PLoS Comput Biol 2022; 18:e1010029. [PMID: 35468126 PMCID: PMC9071136 DOI: 10.1371/journal.pcbi.1010029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 05/05/2022] [Accepted: 03/17/2022] [Indexed: 11/19/2022] Open
Abstract
Natural compounds constitute a rich resource of potential small molecule therapeutics. While experimental access to this resource is limited due to its vast diversity and difficulties in systematic purification, computational assessment of structural similarity with known therapeutic molecules offers a scalable approach. Here, we assessed functional similarity between natural compounds and approved drugs by combining multiple chemical similarity metrics and physicochemical properties using a machine-learning approach. We computed pairwise similarities between 1410 drugs for training classification models and used the drugs shared protein targets as class labels. The best performing models were random forest which gave an average area under the ROC of 0.9, Matthews correlation coefficient of 0.35, and F1 score of 0.33, suggesting that it captured the structure-activity relation well. The models were then used to predict protein targets of circa 11k natural compounds by comparing them with the drugs. This revealed therapeutic potential of several natural compounds, including those with support from previously published sources as well as those hitherto unexplored. We experimentally validated one of the predicted pair’s activities, viz., Cox-1 inhibition by 5-methoxysalicylic acid, a molecule commonly found in tea, herbs and spices. In contrast, another natural compound, 4-isopropylbenzoic acid, with the highest similarity score when considering most weighted similarity metric but not picked by our models, did not inhibit Cox-1. Our results demonstrate the utility of a machine-learning approach combining multiple chemical features for uncovering protein binding potential of natural compounds.
Collapse
Affiliation(s)
- Vinita Periwal
- European Molecular Biology Laboratory, Heidelberg, Germany
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Stefan Bassler
- European Molecular Biology Laboratory, Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | | | | | - Kaustubh Raosaheb Patil
- Institute of Neuroscience and Medicine (INM-7), Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | | | - Kiran Raosaheb Patil
- European Molecular Biology Laboratory, Heidelberg, Germany
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, United Kingdom
- * E-mail:
| |
Collapse
|
16
|
Li WX, Tong X, Yang PP, Zheng Y, Liang JH, Li GH, Liu D, Guan DG, Dai SX. Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods. Aging (Albany NY) 2022; 14:1448-1472. [PMID: 35150482 PMCID: PMC8876917 DOI: 10.18632/aging.203887] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 01/28/2022] [Indexed: 11/25/2022]
Abstract
Bacterial infection is one of the most important factors affecting the human life span. Elderly people are more harmed by bacterial infections due to their deficits in immunity. Because of the lack of new antibiotics in recent years, bacterial resistance has increasingly become a serious problem globally. In this study, an antibacterial compound predictor was constructed using the support vector machines and random forest methods and the data of the active and inactive antibacterial compounds from the ChEMBL database. The results showed that both models have excellent prediction performance (mean accuracy >0.9 and mean AUC >0.9 for the two models). We used the predictor to screen potential antibacterial compounds from FDA-approved drugs in the DrugBank database. The screening results showed that 1087 small-molecule drugs have potential antibacterial activity and 154 of them are FDA-approved antibacterial drugs, which accounts for 76.2% of the approved antibacterial drugs collected in this study. Through molecular fingerprint similarity analysis and common substructure analysis, we screened 8 predicted antibacterial small-molecule compounds with novel structures compared with known antibacterial drugs, and 5 of them are widely used in the treatment of various tumors. This study provides a new insight for predicting antibacterial compounds by using approved drugs, the predicted compounds might be used to treat bacterial infections and extend lifespan.
Collapse
Affiliation(s)
- Wen-Xing Li
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, Guangdong, China.,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Xin Tong
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Peng-Peng Yang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Yang Zheng
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Ji-Hao Liang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Gong-Hua Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, Yunnan, China
| | - Dahai Liu
- School of Medicine, Foshan University, Foshan 528000, Guangdong, China
| | - Dao-Gang Guan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, Guangdong, China.,Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Shao-Xing Dai
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| |
Collapse
|
17
|
Abstract
The appearance on the free market of synthetic cannabinoids raised the researchers’ interest in establishing their molecular similarity by QSAR analysis. A rigorous criterion for classifying drugs is their chemical structure. Therefore, this article presents the structural similarity of two groups of drugs: benzoylindoles and phenylacetylindoles. Statistical analysis and clustering of the molecules are performed based on their numerical characteristics extracted using Cheminformatics methods. Their similarities/dissimilarities are emphasized using the dendrograms and heat map. The highest discrepancies are found in the phenylacetylindoles group.
Collapse
|
18
|
Laguionie-Marchais C, Allcock AL, Baker BJ, Conneely EA, Dietrick SG, Kearns F, McKeever K, Young RM, Sierra CA, Soldatou S, Woodcock HL, Johnson MP. Not Drug-like, but Like Drugs: Cnidaria Natural Products. Mar Drugs 2021; 20:42. [PMID: 35049897 PMCID: PMC8779300 DOI: 10.3390/md20010042] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 12/23/2021] [Accepted: 12/24/2021] [Indexed: 12/26/2022] Open
Abstract
Phylum Cnidaria has been an excellent source of natural products, with thousands of metabolites identified. Many of these have not been screened in bioassays. The aim of this study was to explore the potential of 5600 Cnidaria natural products (after excluding those known to derive from microbial symbionts), using a systematic approach based on chemical space, drug-likeness, predicted toxicity, and virtual screens. Previous drug-likeness measures: the rule-of-five, quantitative estimate of drug-likeness (QED), and relative drug likelihoods (RDL) are based on a relatively small number of molecular properties. We augmented this approach using reference drug and toxin data sets defined for 51 predicted molecular properties. Cnidaria natural products overlap with drugs and toxins in this chemical space, although a multivariate test suggests that there are some differences between the groups. In terms of the established drug-likeness measures, Cnidaria natural products have generally lower QED and RDL scores than drugs, with a higher prevalence of metabolites that exceed at least one rule-of-five threshold. An index of drug-likeness that includes predicted toxicity (ADMET-score), however, found that Cnidaria natural products were more favourable than drugs. A measure of the distance of individual Cnidaria natural products to the centre of the drug distribution in multivariate chemical space was related to RDL, ADMET-score, and the number of rule-of-five exceptions. This multivariate similarity measure was negatively correlated with the QED score for the same metabolite, suggesting that the different approaches capture different aspects of the drug-likeness of individual metabolites. The contrasting of different drug similarity measures can help summarise the range of drug potential in the Cnidaria natural product data set. The most favourable metabolites were around 210-265 Da, quite often sesquiterpenes, with a moderate degree of complexity. Virtual screening against cancer-relevant targets found wide evidence of affinities, with Glide scores <-7 in 19% of the Cnidaria natural products.
Collapse
Affiliation(s)
- Claire Laguionie-Marchais
- School of Natural Sciences and Ryan Institute, National University of Ireland Galway, H91 TK33 Galway, Ireland; (C.L.-M.); (A.L.A.); (E.-A.C.); (K.M.); (R.M.Y.)
| | - A. Louise Allcock
- School of Natural Sciences and Ryan Institute, National University of Ireland Galway, H91 TK33 Galway, Ireland; (C.L.-M.); (A.L.A.); (E.-A.C.); (K.M.); (R.M.Y.)
| | - Bill J. Baker
- Department of Chemistry, University of South Florida, Tampa, FL 33620-5250, USA; (B.J.B.); (S.G.D.); (F.K.); (C.A.S.); (S.S.); (H.L.W.)
| | - Ellie-Ann Conneely
- School of Natural Sciences and Ryan Institute, National University of Ireland Galway, H91 TK33 Galway, Ireland; (C.L.-M.); (A.L.A.); (E.-A.C.); (K.M.); (R.M.Y.)
| | - Sarah G. Dietrick
- Department of Chemistry, University of South Florida, Tampa, FL 33620-5250, USA; (B.J.B.); (S.G.D.); (F.K.); (C.A.S.); (S.S.); (H.L.W.)
| | - Fiona Kearns
- Department of Chemistry, University of South Florida, Tampa, FL 33620-5250, USA; (B.J.B.); (S.G.D.); (F.K.); (C.A.S.); (S.S.); (H.L.W.)
| | - Kate McKeever
- School of Natural Sciences and Ryan Institute, National University of Ireland Galway, H91 TK33 Galway, Ireland; (C.L.-M.); (A.L.A.); (E.-A.C.); (K.M.); (R.M.Y.)
| | - Ryan M. Young
- School of Natural Sciences and Ryan Institute, National University of Ireland Galway, H91 TK33 Galway, Ireland; (C.L.-M.); (A.L.A.); (E.-A.C.); (K.M.); (R.M.Y.)
- School of Chemistry, National University of Ireland Galway, H91 TK33 Galway, Ireland
| | - Connor A. Sierra
- Department of Chemistry, University of South Florida, Tampa, FL 33620-5250, USA; (B.J.B.); (S.G.D.); (F.K.); (C.A.S.); (S.S.); (H.L.W.)
| | - Sylvia Soldatou
- Department of Chemistry, University of South Florida, Tampa, FL 33620-5250, USA; (B.J.B.); (S.G.D.); (F.K.); (C.A.S.); (S.S.); (H.L.W.)
- School of Chemistry, National University of Ireland Galway, H91 TK33 Galway, Ireland
| | - H. Lee Woodcock
- Department of Chemistry, University of South Florida, Tampa, FL 33620-5250, USA; (B.J.B.); (S.G.D.); (F.K.); (C.A.S.); (S.S.); (H.L.W.)
| | - Mark P. Johnson
- School of Natural Sciences and Ryan Institute, National University of Ireland Galway, H91 TK33 Galway, Ireland; (C.L.-M.); (A.L.A.); (E.-A.C.); (K.M.); (R.M.Y.)
| |
Collapse
|
19
|
Predicting drug-metagenome interactions: Variation in the microbial β-glucuronidase level in the human gut metagenomes. PLoS One 2021; 16:e0244876. [PMID: 33411719 PMCID: PMC7790408 DOI: 10.1371/journal.pone.0244876] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 12/17/2020] [Indexed: 12/17/2022] Open
Abstract
Characterizing the gut microbiota in terms of their capacity to interfere with drug metabolism is necessary to achieve drug efficacy and safety. Although examples of drug-microbiome interactions are well-documented, little has been reported about a computational pipeline for systematically identifying and characterizing bacterial enzymes that process particular classes of drugs. The goal of our study is to develop a computational approach that compiles drugs whose metabolism may be influenced by a particular class of microbial enzymes and that quantifies the variability in the collective level of those enzymes among individuals. The present paper describes this approach, with microbial β-glucuronidases as an example, which break down drug-glucuronide conjugates and reactivate the drugs or their metabolites. We identified 100 medications that may be metabolized by β-glucuronidases from the gut microbiome. These medications included morphine, estrogen, ibuprofen, midazolam, and their structural analogues. The analysis of metagenomic data available through the Sequence Read Archive (SRA) showed that the level of β-glucuronidase in the gut metagenomes was higher in males than in females, which provides a potential explanation for the sex-based differences in efficacy and toxicity for several drugs, reported in previous studies. Our analysis also showed that infant gut metagenomes at birth and 12 months of age have higher levels of β-glucuronidase than the metagenomes of their mothers and the implication of this observed variability was discussed in the context of breastfeeding as well as infant hyperbilirubinemia. Overall, despite important limitations discussed in this paper, our analysis provided useful insights on the role of the human gut metagenome in the variability in drug response among individuals. Importantly, this approach exploits drug and metagenome data available in public databases as well as open-source cheminformatics and bioinformatics tools to predict drug-metagenome interactions.
Collapse
|
20
|
Duan Y, Evans DS, Miller RA, Schork NJ, Cummings S, Girke T. signatureSearch: environment for gene expression signature searching and functional interpretation. Nucleic Acids Res 2020; 48:e124. [PMID: 33068417 PMCID: PMC7708038 DOI: 10.1093/nar/gkaa878] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 08/19/2020] [Accepted: 09/25/2020] [Indexed: 12/14/2022] Open
Abstract
signatureSearch is an R/Bioconductor package that integrates a suite of existing and novel algorithms into an analysis environment for gene expression signature (GES) searching combined with functional enrichment analysis (FEA) and visualization methods to facilitate the interpretation of the search results. In a typical GES search (GESS), a query GES is searched against a database of GESs obtained from large numbers of measurements, such as different genetic backgrounds, disease states and drug perturbations. Database matches sharing correlated signatures with the query indicate related cellular responses frequently governed by connected mechanisms, such as drugs mimicking the expression responses of a disease. To identify which processes are predominantly modulated in the GESS results, we developed specialized FEA methods combined with drug-target network visualization tools. The provided analysis tools are useful for studying the effects of genetic, chemical and environmental perturbations on biological systems, as well as searching single cell GES databases to identify novel network connections or cell types. The signatureSearch software is unique in that it provides access to an integrated environment for GESS/FEA routines that includes several novel search and enrichment methods, efficient data structures, and access to pre-built GES databases, and allowing users to work with custom databases.
Collapse
Affiliation(s)
- Yuzhu Duan
- Institute for Integrative Genome Biology, 1207F Genomics Building, University of California, Riverside, CA 92521, USA
| | - Daniel S Evans
- California Pacific Medical Center Research Institute, 550 16th Street, 2nd floor, San Francisco, CA 94158, USA
| | - Richard A Miller
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Nicholas J Schork
- Department of Quantitative Medicine and Systems Biology, The Translational Genomics Research Institute, 445 N. Fifth Street Phoenix, AZ 85004, USA
| | - Steven R Cummings
- California Pacific Medical Center Research Institute, 550 16th Street, 2nd floor, San Francisco, CA 94158, USA
| | - Thomas Girke
- Institute for Integrative Genome Biology, 1207F Genomics Building, University of California, Riverside, CA 92521, USA
| |
Collapse
|
21
|
Grimm M, Liu Y, Yang X, Bu C, Xiao Z, Cao Y. LigMate: A Multifeature Integration Algorithm for Ligand-Similarity-Based Virtual Screening. J Chem Inf Model 2020; 60:6044-6053. [DOI: 10.1021/acs.jcim.9b01210] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Maximilian Grimm
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Yang Liu
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Xiaocong Yang
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Chunya Bu
- College of Biological Science and Engineering, Beijing University of Agriculture, Beijing 102206, China
| | - Zhixiong Xiao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Yang Cao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| |
Collapse
|
22
|
Xing S, Hu Y, Yin Z, Liu M, Tang X, Fang M, Huan T. Retrieving and Utilizing Hypothetical Neutral Losses from Tandem Mass Spectra for Spectral Similarity Analysis and Unknown Metabolite Annotation. Anal Chem 2020; 92:14476-14483. [DOI: 10.1021/acs.analchem.0c02521] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Shipei Xing
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 BC, Canada
| | - Yan Hu
- Department of Computer Sciences, University of British Columbia, 2366 Main Mall, Vancouver, V6T 1Z1 BC, Canada
| | - Zixuan Yin
- Fortinet, 4190 Still Creek Dr, Burnaby, V5C 6C6 BC, Canada
| | - Min Liu
- School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore
| | - Xiaoyu Tang
- Institute of Chemical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Mingliang Fang
- School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore
| | - Tao Huan
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 BC, Canada
| |
Collapse
|
23
|
Lombard DB, Kohler WJ, Guo AH, Gendron C, Han M, Ding W, Lyu Y, Ching TT, Wang FY, Chakraborty TS, Nikolovska-Coleska Z, Duan Y, Girke T, Hsu AL, Pletcher SD, Miller RA. High-throughput small molecule screening reveals Nrf2-dependent and -independent pathways of cellular stress resistance. SCIENCE ADVANCES 2020; 6:6/40/eaaz7628. [PMID: 33008901 PMCID: PMC7852388 DOI: 10.1126/sciadv.aaz7628] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 08/14/2020] [Indexed: 05/03/2023]
Abstract
Aging is the dominant risk factor for most chronic diseases. Development of antiaging interventions offers the promise of preventing many such illnesses simultaneously. Cellular stress resistance is an evolutionarily conserved feature of longevity. Here, we identify compounds that induced resistance to the superoxide generator paraquat (PQ), the heavy metal cadmium (Cd), and the DNA alkylator methyl methanesulfonate (MMS). Some rescue compounds conferred resistance to a single stressor, while others provoked multiplex resistance. Induction of stress resistance in fibroblasts was predictive of longevity extension in a published large-scale longevity screen in Caenorhabditis elegans, although not in testing performed in worms and flies with a more restricted set of compounds. Transcriptomic analysis and genetic studies implicated Nrf2/SKN-1 signaling in stress resistance provided by two protective compounds, cardamonin and AEG 3482. Small molecules identified in this work may represent attractive tools to elucidate mechanisms of stress resistance in mammalian cells.
Collapse
Affiliation(s)
- David B Lombard
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
- Geriatrics Center, University of Michigan, Ann Arbor, MI, USA
| | - William J Kohler
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Angela H Guo
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Christi Gendron
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Melissa Han
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Weiqiao Ding
- Department of Internal Medicine, Division of Geriatric and Palliative Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Yang Lyu
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Tsui-Ting Ching
- Institute of Biopharmaceutical Sciences, National Yang Ming University, Taipei 112, Taiwan
| | - Feng-Yung Wang
- Institute of Biochemistry and Molecular Biology, National Yang Ming University, Taipei 112, Taiwan
| | - Tuhin S Chakraborty
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | | | - Yuzhu Duan
- Institute for Integrative Genome Biology, University of California Riverside, Riverside, CA, USA
| | - Thomas Girke
- Institute for Integrative Genome Biology, University of California Riverside, Riverside, CA, USA
| | - Ao-Lin Hsu
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, Division of Geriatric and Palliative Medicine, University of Michigan, Ann Arbor, MI, USA
- Research Center for Healthy Aging, China Medical University, Taichung, Taiwan
| | - Scott D Pletcher
- Geriatrics Center, University of Michigan, Ann Arbor, MI, USA
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Richard A Miller
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Geriatrics Center, University of Michigan, Ann Arbor, MI, USA
| |
Collapse
|
24
|
Punt A, Firman J, Boobis A, Cronin M, Gosling JP, Wilks MF, Hepburn PA, Thiel A, Fussell KC. Potential of ToxCast Data in the Safety Assessment of Food Chemicals. Toxicol Sci 2020; 174:326-340. [PMID: 32040188 PMCID: PMC7098372 DOI: 10.1093/toxsci/kfaa008] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Tox21 and ToxCast are high-throughput in vitro screening programs coordinated by the U.S. National Toxicology Program and the U.S. Environmental Protection Agency, respectively, with the goal of forecasting biological effects in vivo based on bioactivity profiling. The present study investigated whether mechanistic insights in the biological targets of food-relevant chemicals can be obtained from ToxCast results when the chemicals are grouped according to structural similarity. Starting from the 556 direct additives that have been identified in the ToxCast database by Karmaus et al. [Karmaus, A. L., Trautman, T. D., Krishan, M., Filer, D. L., and Fix, L. A. (2017). Curation of food-relevant chemicals in ToxCast. Food Chem. Toxicol. 103, 174-182.], the results showed that, despite the limited number of assays in which the chemical groups have been tested, sufficient results are available within so-called "DNA binding" and "nuclear receptor" target families to profile the biological activities of the defined chemical groups for these targets. The most obvious activity identified was the estrogen receptor-mediated actions of the chemical group containing parabens and structurally related gallates, as well the chemical group containing genistein and daidzein (the latter 2 being particularly active toward estrogen receptor β as a potential health benefit). These group effects, as well as the biological activities of other chemical groups, were evaluated in a series of case studies. Overall, the results of the present study suggest that high-throughput screening data could add to the evidence considered for regulatory risk assessment of food chemicals and to the evaluation of desirable effects of nutrients and phytonutrients. The data will be particularly useful for providing mechanistic information and to fill data gaps with read-across.
Collapse
Affiliation(s)
- Ans Punt
- Wageningen Food Safety Research, 6700 AE Wageningen, The Netherlands
| | - James Firman
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK
| | - Alan Boobis
- National Heart & Lung Institute, Imperial College London, London W12 0NN, UK
| | - Mark Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK
| | | | - Martin F Wilks
- Swiss Centre for Applied Human Toxicology, University of Basel, 4055 Basel, Switzerland
| | - Paul A Hepburn
- Unilever, Safety & Environmental Assurance Centre, Colworth Science Park, Sharnbrook MK44 1LQ, UK
| | - Anette Thiel
- DSM Nutritional Products, 4303 Kaiseraugst, Switzerland
| | | |
Collapse
|
25
|
Pathania S, Randhawa V, Kumar M. Identifying potential entry inhibitors for emerging Nipah virus by molecular docking and chemical-protein interaction network. J Biomol Struct Dyn 2019; 38:5108-5125. [DOI: 10.1080/07391102.2019.1696705] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Shivalika Pathania
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific & Industrial Research, Chandigarh, India
| | - Vinay Randhawa
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific & Industrial Research, Chandigarh, India
| | - Manoj Kumar
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific & Industrial Research, Chandigarh, India
| |
Collapse
|
26
|
Gupta V, Crudu A, Matsuoka Y, Ghosh S, Rozot R, Marat X, Jäger S, Kitano H, Breton L. Multi-dimensional computational pipeline for large-scale deep screening of compound effect assessment: an in silico case study on ageing-related compounds. NPJ Syst Biol Appl 2019; 5:42. [PMID: 31798962 PMCID: PMC6879499 DOI: 10.1038/s41540-019-0119-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 09/23/2019] [Indexed: 12/18/2022] Open
Abstract
Designing alternative approaches to efficiently screen chemicals on the efficacy landscape is a challenging yet indispensable task in the current compound profiling methods. Particularly, increasing regulatory restrictions underscore the need to develop advanced computational pipelines for efficacy assessment of chemical compounds as alternative means to reduce and/or replace in vivo experiments. Here, we present an innovative computational pipeline for large-scale assessment of chemical compounds by analysing and clustering chemical compounds on the basis of multiple dimensions-structural similarity, binding profiles and their network effects across pathways and molecular interaction maps-to generate testable hypotheses on the pharmacological landscapes as well as identify potential mechanisms of efficacy on phenomenological processes. Further, we elucidate the application of the pipeline on a screen of anti-ageing-related compounds to cluster the candidates based on their structure, docking profile and network effects on fundamental metabolic/molecular pathways associated with the cell vitality, highlighting emergent insights on compounds activities based on the multi-dimensional deep screen pipeline.
Collapse
Affiliation(s)
| | - Alina Crudu
- L’Oréal Research and Innovation, Aulnay-sous-Bois, France
| | | | | | - Roger Rozot
- L’Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Xavier Marat
- L’Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Sibylle Jäger
- L’Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Hiroaki Kitano
- The Systems Biology Institute, Tokyo, Japan
- Okinawa Institute of Science and Technology, Okinawa, Japan
| | - Lionel Breton
- L’Oréal Research and Innovation, Aulnay-sous-Bois, France
| |
Collapse
|
27
|
Coupling enhanced sampling of the apo-receptor with template-based ligand conformers selection: performance in pose prediction in the D3R Grand Challenge 4. J Comput Aided Mol Des 2019; 34:149-162. [DOI: 10.1007/s10822-019-00244-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 10/30/2019] [Indexed: 12/20/2022]
|
28
|
Stanstrup J, Broeckling CD, Helmus R, Hoffmann N, Mathé E, Naake T, Nicolotti L, Peters K, Rainer J, Salek RM, Schulze T, Schymanski EL, Stravs MA, Thévenot EA, Treutler H, Weber RJM, Willighagen E, Witting M, Neumann S. The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites 2019; 9:E200. [PMID: 31548506 PMCID: PMC6835268 DOI: 10.3390/metabo9100200] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 11/17/2022] Open
Abstract
Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.
Collapse
Affiliation(s)
- Jan Stanstrup
- Preventive and Clinical Nutrition, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark.
| | - Corey D Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO 80523, USA.
| | - Rick Helmus
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 1098 XH Amsterdam, The Netherlands.
| | - Nils Hoffmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Straße 6b, 44227 Dortmund, Germany.
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Thomas Naake
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany.
| | - Luca Nicolotti
- The Australian Wine Research Institute, Metabolomics Australia, PO Box 197, Adelaide SA 5064, Australia.
| | - Kristian Peters
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Johannes Rainer
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, 39100 Bolzano, Italy.
| | - Reza M Salek
- The International Agency for Research on Cancer, 150 cours Albert Thomas, CEDEX 08, 69372 Lyon, France.
| | - Tobias Schulze
- Department of Effect-Directed Analysis, Helmholtz Centre for Environmental Research-UFZ, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belvaux, Luxembourg.
| | - Michael A Stravs
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dubendorf, Switzerland.
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Sciences and Decision, MetaboHUB, Gif-Sur-Yvette F-91191, France.
| | - Hendrik Treutler
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Ralf J M Weber
- Phenome Centre Birmingham and School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
| | - Egon Willighagen
- Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands.
| | - Michael Witting
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
- Chair of Analytical Food Chemistry, Technische Universität München, 85354 Weihenstephan, Germany.
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher, Platz 5e, 04103 Leipzig, Germany.
| |
Collapse
|
29
|
Zhu C, Sawrey-Kubicek L, Beals E, Hughes RL, Rhodes CH, Sacchi R, Zivkovic AM. The HDL lipidome is widely remodeled by fast food versus Mediterranean diet in 4 days. Metabolomics 2019; 15:114. [PMID: 31422486 DOI: 10.1007/s11306-019-1579-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 08/12/2019] [Indexed: 11/24/2022]
Abstract
INTRODUCTION HDL is associated with increased longevity and protection from multiple chronic diseases. The major HDL protein ApoA-I has a half-life of about 4 days, however, the effects of diet on the composition of HDL particles at this time scale have not been studied. OBJECTIVES The objective of this study is to investigate the short term dietary effect on HDL lipidomic composition. METHODS In this randomized order cross-over study, ten healthy subjects consumed a Mediterranean (Med) and a fast food (FF) diet for 4 days, with a 4-day wash-out between treatments. Lipidomic composition was analyzed in isolated HDL fractions by an untargeted LC-MS method with 15 internal standards. RESULTS HDL phosphatidylethanolamine (PE) content was increased by FF diet, and 41 out of 170 lipid species were differentially affected by diet. Saturated fatty acids (FAs) and odd chain FA were enriched after FF diet, while very-long chain FA and unsaturated FA were enriched after Med diet. The composition of phosphatidylcholine (PC), triacylglycerol (TG) and cholesteryl ester (CE) were significantly altered to reflect the FA composition of the diet whereas the composition of sphingomyelin (SM) and ceramides were generally unaffected. CONCLUSION Results from this study indicate that the HDL lipidome is widely remodeled within 4 days of diet change and that certain lipid classes are more sensitive markers of diet whereas other lipid classes are better indicators of non-dietary factors.
Collapse
Affiliation(s)
- Chenghao Zhu
- Department of Nutrition, University of California, Davis, Davis, CA, 95616, USA
| | - Lisa Sawrey-Kubicek
- Department of Nutrition, University of California, Davis, Davis, CA, 95616, USA
| | - Elizabeth Beals
- Department of Nutrition, University of California, Davis, Davis, CA, 95616, USA
| | - Riley L Hughes
- Department of Nutrition, University of California, Davis, Davis, CA, 95616, USA
| | - Chris H Rhodes
- Department of Nutrition, University of California, Davis, Davis, CA, 95616, USA
| | - Romina Sacchi
- Department of Nutrition, University of California, Davis, Davis, CA, 95616, USA
| | - Angela M Zivkovic
- Department of Nutrition, University of California, Davis, Davis, CA, 95616, USA.
| |
Collapse
|
30
|
Koukos PI, Xue LC, Bonvin AMJJ. Protein-ligand pose and affinity prediction: Lessons from D3R Grand Challenge 3. J Comput Aided Mol Des 2019; 33:83-91. [PMID: 30128928 PMCID: PMC6373529 DOI: 10.1007/s10822-018-0148-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 08/09/2018] [Indexed: 12/30/2022]
Abstract
We report the performance of HADDOCK in the 2018 iteration of the Grand Challenge organised by the D3R consortium. Building on the findings of our participation in last year's challenge, we significantly improved our pose prediction protocol which resulted in a mean RMSD for the top scoring pose of 3.04 and 2.67 Å for the cross-docking and self-docking experiments respectively, which corresponds to an overall success rate of 63% and 71% when considering the top1 and top5 models respectively. This performance ranks HADDOCK as the 6th and 3rd best performing group (excluding multiple submissions from a same group) out of a total of 44 and 47 submissions respectively. Our ligand-based binding affinity predictor is the 3rd best predictor overall, behind only the two leading structure-based implementations, and the best ligand-based one with a Kendall's Tau correlation of 0.36 for the Cathepsin challenge. It also performed well in the classification part of the Kinase challenges, with Matthews Correlation Coefficients of 0.49 (ranked 1st), 0.39 (ranked 4th) and 0.21 (ranked 4th) for the JAK2, vEGFR2 and p38a targets respectively. Through our participation in last year's competition we came to the conclusion that template selection is of critical importance for the successful outcome of the docking. This year we have made improvements in two additional areas of importance: ligand conformer selection and initial positioning, which have been key to our excellent pose prediction performance this year.
Collapse
Affiliation(s)
- Panagiotis I Koukos
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
| | - Li C Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands.
| |
Collapse
|
31
|
Kurkcuoglu Z, Koukos PI, Citro N, Trellet ME, Rodrigues JPGLM, Moreira IS, Roel-Touris J, Melquiond ASJ, Geng C, Schaarschmidt J, Xue LC, Vangone A, Bonvin AMJJ. Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R Grand Challenge 2. J Comput Aided Mol Des 2018; 32:175-185. [PMID: 28831657 PMCID: PMC5767195 DOI: 10.1007/s10822-017-0049-y] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 08/18/2017] [Indexed: 10/28/2022]
Abstract
We present the performance of HADDOCK, our information-driven docking software, in the second edition of the D3R Grand Challenge. In this blind experiment, participants were requested to predict the structures and binding affinities of complexes between the Farnesoid X nuclear receptor and 102 different ligands. The models obtained in Stage1 with HADDOCK and ligand-specific protocol show an average ligand RMSD of 5.1 Å from the crystal structure. Only 6/35 targets were within 2.5 Å RMSD from the reference, which prompted us to investigate the limiting factors and revise our protocol for Stage2. The choice of the receptor conformation appeared to have the strongest influence on the results. Our Stage2 models were of higher quality (13 out of 35 were within 2.5 Å), with an average RMSD of 4.1 Å. The docking protocol was applied to all 102 ligands to generate poses for binding affinity prediction. We developed a modified version of our contact-based binding affinity predictor PRODIGY, using the number of interatomic contacts classified by their type and the intermolecular electrostatic energy. This simple structure-based binding affinity predictor shows a Kendall's Tau correlation of 0.37 in ranking the ligands (7th best out of 77 methods, 5th/25 groups). Those results were obtained from the average prediction over the top10 poses, irrespective of their similarity/correctness, underscoring the robustness of our simple predictor. This results in an enrichment factor of 2.5 compared to a random predictor for ranking ligands within the top 25%, making it a promising approach to identify lead compounds in virtual screening.
Collapse
Affiliation(s)
- Zeynep Kurkcuoglu
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Panagiotis I Koukos
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Nevia Citro
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Mikael E Trellet
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - J P G L M Rodrigues
- James H. Clark Center, Stanford University, 318 Campus Drive, S210, Stanford, CA, 94305, USA
| | - Irina S Moreira
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
- CNC - Center for Neuroscience and Cell Biology, FMUC, Universidade de Coimbra, Rua Larga, Polo I, 1ºandar, 3004-517, Coimbra, Portugal
| | - Jorge Roel-Touris
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Adrien S J Melquiond
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Cunliang Geng
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Jörg Schaarschmidt
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Li C Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Anna Vangone
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - A M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands.
| |
Collapse
|
32
|
Schollée JE, Schymanski EL, Stravs MA, Gulde R, Thomaidis NS, Hollender J. Similarity of High-Resolution Tandem Mass Spectrometry Spectra of Structurally Related Micropollutants and Transformation Products. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2017; 28:2692-2704. [PMID: 28952028 DOI: 10.1007/s13361-017-1797-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 08/23/2017] [Accepted: 08/23/2017] [Indexed: 06/07/2023]
Abstract
High-resolution tandem mass spectrometry (HRMS2) with electrospray ionization is frequently applied to study polar organic molecules such as micropollutants. Fragmentation provides structural information to confirm structures of known compounds or propose structures of unknown compounds. Similarity of HRMS2 spectra between structurally related compounds has been suggested to facilitate identification of unknown compounds. To test this hypothesis, the similarity of reference standard HRMS2 spectra was calculated for 243 pairs of micropollutants and their structurally related transformation products (TPs); for comparison, spectral similarity was also calculated for 219 pairs of unrelated compounds. Spectra were measured on Orbitrap and QTOF mass spectrometers and similarity was calculated with the dot product. The influence of different factors on spectral similarity [e.g., normalized collision energy (NCE), merging fragments from all NCEs, and shifting fragments by the mass difference of the pair] was considered. Spectral similarity increased at higher NCEs and highest similarity scores for related pairs were obtained with merged spectra including measured fragments and shifted fragments. Removal of the monoisotopic peak was critical to reduce false positives. Using a spectral similarity score threshold of 0.52, 40% of related pairs and 0% of unrelated pairs were above this value. Structural similarity was estimated with the Tanimoto coefficient and pairs with higher structural similarity generally had higher spectral similarity. Pairs where one or both compounds contained heteroatoms such as sulfur often resulted in dissimilar spectra. This work demonstrates that HRMS2 spectral similarity may indicate structural similarity and that spectral similarity can be used in the future to screen complex samples for related compounds such as micropollutants and TPs, assisting in the prioritization of non-target compounds. Graphical Abstract ᅟ.
Collapse
Affiliation(s)
- Jennifer E Schollée
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600, Dübendorf, Switzerland.
- Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, 8092, Zürich, Switzerland.
| | - Emma L Schymanski
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600, Dübendorf, Switzerland
| | - Michael A Stravs
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600, Dübendorf, Switzerland
- Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, 8092, Zürich, Switzerland
| | - Rebekka Gulde
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600, Dübendorf, Switzerland
| | - Nikolaos S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, 157 71, Athens, Greece
| | - Juliane Hollender
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600, Dübendorf, Switzerland
- Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, 8092, Zürich, Switzerland
| |
Collapse
|
33
|
The self-inhibitory nature of metabolic networks and its alleviation through compartmentalization. Nat Commun 2017; 8:16018. [PMID: 28691704 PMCID: PMC5508129 DOI: 10.1038/ncomms16018] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2016] [Accepted: 05/23/2017] [Indexed: 01/03/2023] Open
Abstract
Metabolites can inhibit the enzymes that generate them. To explore the general nature of metabolic self-inhibition, we surveyed enzymological data accrued from a century of experimentation and generated a genome-scale enzyme-inhibition network. Enzyme inhibition is often driven by essential metabolites, affects the majority of biochemical processes, and is executed by a structured network whose topological organization is reflecting chemical similarities that exist between metabolites. Most inhibitory interactions are competitive, emerge in the close neighbourhood of the inhibited enzymes, and result from structural similarities between substrate and inhibitors. Structural constraints also explain one-third of allosteric inhibitors, a finding rationalized by crystallographic analysis of allosterically inhibited L-lactate dehydrogenase. Our findings suggest that the primary cause of metabolic enzyme inhibition is not the evolution of regulatory metabolite–enzyme interactions, but a finite structural diversity prevalent within the metabolome. In eukaryotes, compartmentalization minimizes inevitable enzyme inhibition and alleviates constraints that self-inhibition places on metabolism. Metabolites act as enzyme inhibitors, but their global impact on metabolism has scarcely been considered. Here, the authors generate a human genome-wide metabolite-enzyme inhibition network, and find that inhibition occurs largely due to limited structural diversity of metabolites, leading to a global constraint on metabolism which subcellular compartmentalization minimizes.
Collapse
|
34
|
Ramakrishnan C, Mary Thangakani A, Velmurugan D, Anantha Krishnan D, Sekijima M, Akiyama Y, Gromiha MM. Identification of type I and type II inhibitors of c-Yes kinase using in silico and experimental techniques. J Biomol Struct Dyn 2017; 36:1566-1576. [DOI: 10.1080/07391102.2017.1329098] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Chandrasekaran Ramakrishnan
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600036, Tamilnadu, India
| | - Anthony Mary Thangakani
- Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai 600025, Tamilnadu, India
| | - Devadasan Velmurugan
- Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai 600025, Tamilnadu, India
| | - Dhanabalan Anantha Krishnan
- Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai 600025, Tamilnadu, India
| | - Masakazu Sekijima
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama 226-8501, Japan
- Advanced Computational Drug Discovery Unit (ACDD), Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama 226-8501, Japan
- Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Yutaka Akiyama
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama 226-8501, Japan
- Advanced Computational Drug Discovery Unit (ACDD), Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama 226-8501, Japan
- Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - M. Michael Gromiha
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600036, Tamilnadu, India
| |
Collapse
|
35
|
O'Hagan S, Kell DB. Analysis of drug-endogenous human metabolite similarities in terms of their maximum common substructures. J Cheminform 2017; 9:18. [PMID: 28316656 PMCID: PMC5344883 DOI: 10.1186/s13321-017-0198-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Accepted: 02/09/2017] [Indexed: 12/21/2022] Open
Abstract
In previous work, we have assessed the structural similarities between marketed drugs (‘drugs’) and endogenous natural human metabolites (‘metabolites’ or ‘endogenites’), using ‘fingerprint’ methods in common use, and the Tanimoto and Tversky similarity metrics, finding that the fingerprint encoding used had a dramatic effect on the apparent similarities observed. By contrast, the maximal common substructure (MCS), when the means of determining it is fixed, is a means of determining similarities that is largely independent of the fingerprints, and also has a clear chemical meaning. We here explored the utility of the MCS and metrics derived therefrom. In many cases, a shared scaffold helps cluster drugs and endogenites, and gives insight into enzymes (in particular transporters) that they both share. Tanimoto and Tversky similarities based on the MCS tend to be smaller than those based on the MACCS fingerprint-type encoding, though the converse is also true for a significant fraction of the comparisons. While no single molecular descriptor can account for these differences, a machine learning-based analysis of the nature of the differences (MACCS_Tanimoto vs MCS_Tversky) shows that they are indeed deterministic, although the features that are used in the model to account for this vary greatly with each individual drug. The extent of its utility and interpretability vary with the drug of interest, implying that while MCS is neither ‘better’ nor ‘worse’ for every drug–endogenite comparison, it is sufficiently different to be of value. The overall conclusion is thus that the use of the MCS provides an additional and valuable strategy for understanding the structural basis for similarities between synthetic, marketed drugs and natural intermediary metabolites.
Collapse
Affiliation(s)
- Steve O'Hagan
- School of Chemistry, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK.,Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK
| | - Douglas B Kell
- School of Chemistry, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK.,Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK.,Centre for the Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), The University of Manchester, 131 Princess St, Manchester, M1 7DN UK
| |
Collapse
|
36
|
Abstract
The success of molecular modeling and computational chemistry efforts are, by definition, dependent on quality software applications. Open source software development provides many advantages to users of modeling applications, not the least of which is that the software is free and completely extendable. In this review we categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry. An updated online version of this catalog can be found at https://opensourcemolecularmodeling.github.io.
Collapse
|
37
|
Abstract
![]()
Despite a large and
rapidly growing body of small molecule bioactivity
screens available in the public domain, systematic leverage of the
data to assess target druggability and compound selectivity has been
confounded by a lack of suitable cross-target analysis software. We
have developed bioassayR, a computational tool that enables simultaneous
analysis of thousands of bioassay experiments performed over a diverse
set of compounds and biological targets. Unique features include support
for large-scale cross-target analyses of both public and custom bioassays,
generation of high throughput screening fingerprints (HTSFPs), and
an optional preloaded database that provides access to a substantial
portion of publicly available bioactivity data. bioassayR is implemented
as an open-source R/Bioconductor package available from https://bioconductor.org/packages/bioassayR/.
Collapse
Affiliation(s)
- Tyler William H Backman
- Institute for Integrative Genome Biology, University of California, Riverside , Riverside, California 92521, United States
| | - Thomas Girke
- Institute for Integrative Genome Biology, University of California, Riverside , Riverside, California 92521, United States
| |
Collapse
|
38
|
Luna A, Rajapakse VN, Sousa FG, Gao J, Schultz N, Varma S, Reinhold W, Sander C, Pommier Y. rcellminer: exploring molecular profiles and drug response of the NCI-60 cell lines in R. Bioinformatics 2015; 32:1272-4. [PMID: 26635141 DOI: 10.1093/bioinformatics/btv701] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2015] [Accepted: 11/25/2015] [Indexed: 01/06/2023] Open
Abstract
PURPOSE The rcellminer R package provides a wide range of functionality to help R users access and explore molecular profiling and drug response data for the NCI-60. The package enables flexible programmatic access to CellMiner's unparalleled breadth of NCI-60 data, including gene and protein expression, copy number, whole exome mutations, as well as activity data for ∼21K compounds, with information on their structure, mechanism of action and repeat screens. Functions are available to easily visualize compound structures, activity patterns and molecular feature profiles. Additionally, embedded R Shiny applications allow interactive data exploration. AVAILABILITY AND IMPLEMENTATION rcellminer is compatible with R 3.2 and above on Windows, Mac OS X and Linux. The package, documentation, tutorials and Shiny-based applications are available through Bioconductor (http://www.bioconductor.org/packages/rcellminer); ongoing updates will occur according to the Bioconductor release schedule with new CellMiner data. The package is free and open-source (LGPL 3). CONTACT lunaa@cbio.mskcc.org or vinodh.rajapakse@nih.gov.
Collapse
Affiliation(s)
- Augustin Luna
- Computer Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA
| | - Vinodh N Rajapakse
- Developmental Therapeutic Branch, Center for Cancer Research, NCI, NIH, Bethesda, MD 20892, USA
| | - Fabricio G Sousa
- Centro De Estudos Em Células Tronco, Terapia Celular E Genética Toxicológica, Programa De Pós-Graduação Em Farmácia, Universidade Federal De Mato Grosso Do Sul, Campo Grande, MS 79070-900, Brazil and
| | - Jianjiong Gao
- Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA
| | - Nikolaus Schultz
- Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA
| | - Sudhir Varma
- Developmental Therapeutic Branch, Center for Cancer Research, NCI, NIH, Bethesda, MD 20892, USA
| | - William Reinhold
- Developmental Therapeutic Branch, Center for Cancer Research, NCI, NIH, Bethesda, MD 20892, USA
| | - Chris Sander
- Computer Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA
| | - Yves Pommier
- Developmental Therapeutic Branch, Center for Cancer Research, NCI, NIH, Bethesda, MD 20892, USA
| |
Collapse
|
39
|
Yang M, Chen J, Shi X, Xu L, Xi Z, You L, An R, Wang X. Development of in Silico Models for Predicting P-Glycoprotein Inhibitors Based on a Two-Step Approach for Feature Selection and Its Application to Chinese Herbal Medicine Screening. Mol Pharm 2015; 12:3691-713. [PMID: 26376206 DOI: 10.1021/acs.molpharmaceut.5b00465] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
P-glycoprotein (P-gp) is regarded as an important factor in determining the ADMET (absorption, distribution, metabolism, elimination, and toxicity) characteristics of drugs and drug candidates. Successful prediction of P-gp inhibitors can thus lead to an improved understanding of the underlying mechanisms of both changes in the pharmacokinetics of drugs and drug-drug interactions. Therefore, there has been considerable interest in the development of in silico modeling of P-gp inhibitors in recent years. Considering that a large number of molecular descriptors are used to characterize diverse structural moleculars, efficient feature selection methods are required to extract the most informative predictors. In this work, we constructed an extensive available data set of 2428 molecules that includes 1518 P-gp inhibitors and 910 P-gp noninhibitors from multiple resources. Importantly, a two-step feature selection approach based on a genetic algorithm and a greedy forward-searching algorithm was employed to select the minimum set of the most informative descriptors that contribute to the prediction of P-gp inhibitors. To determine the best machine learning algorithm, 18 classifiers coupled with the feature selection method were compared. The top three best-performing models (flexible discriminant analysis, support vector machine, and random forest) and their ensemble model using respectively only 3, 9, 7, and 14 descriptors achieve an overall accuracy of 83.2%-86.7% for the training set containing 1040 compounds, an overall accuracy of 82.3%-85.5% for the test set containing 1039 compounds, and a prediction accuracy of 77.4%-79.9% for the external validation set containing 349 compounds. The models were further extensively validated by DrugBank database (1890 compounds). The proposed models are competitive with and in some cases better than other published models in terms of prediction accuracy and minimum number of descriptors. Applicability domain then was addressed by developing an ensemble classification model to obtain more reliable predictions. Finally, we employed these models as a virtual screening tool for identifying potential P-gp inhibitors in Traditional Chinese Medicine Systems Pharmacology (TCMSP) database containing a total of 13 051 unique compounds from 498 herbs, resulting in 875 potential P-gp inhibitors and 15 inhibitor-rich herbs. These predictions were partly supported by a literature search and are valuable not only to develop novel P-gp inhibitors from TCM in the early stages of drug development, but also to optimize the use of herbal remedies.
Collapse
Affiliation(s)
- Ming Yang
- Department of Chemistry, College of Pharmacy, Shanghai University of Traditional Chinese Medicine , Shanghai 200444, People's Republic of China.,Department of Pharmacy, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine , Shanghai 200032, People's Republic of China
| | - Jialei Chen
- Department of Pharmacy, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine , Shanghai 200032, People's Republic of China
| | - Xiufeng Shi
- Department of Pharmacy, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine , Shanghai 200032, People's Republic of China
| | - Liwen Xu
- Department of Pharmacy, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine , Shanghai 200032, People's Republic of China
| | - Zhijun Xi
- Department of Pharmacy, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine , Shanghai 200032, People's Republic of China
| | - Lisha You
- Department of Chemistry, College of Pharmacy, Shanghai University of Traditional Chinese Medicine , Shanghai 200444, People's Republic of China
| | - Rui An
- Department of Chemistry, College of Pharmacy, Shanghai University of Traditional Chinese Medicine , Shanghai 200444, People's Republic of China
| | - Xinhong Wang
- Department of Chemistry, College of Pharmacy, Shanghai University of Traditional Chinese Medicine , Shanghai 200444, People's Republic of China
| |
Collapse
|
40
|
IMG-ABC: A Knowledge Base To Fuel Discovery of Biosynthetic Gene Clusters and Novel Secondary Metabolites. mBio 2015; 6:e00932. [PMID: 26173699 PMCID: PMC4502231 DOI: 10.1128/mbio.00932-15] [Citation(s) in RCA: 84] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
In the discovery of secondary metabolites, analysis of sequence data is a promising exploration path that remains largely underutilized due to the lack of computational platforms that enable such a systematic approach on a large scale. In this work, we present IMG-ABC (https://img.jgi.doe.gov/abc), an atlas of biosynthetic gene clusters within the Integrated Microbial Genomes (IMG) system, which is aimed at harnessing the power of “big” genomic data for discovering small molecules. IMG-ABC relies on IMG’s comprehensive integrated structural and functional genomic data for the analysis of biosynthetic gene clusters (BCs) and associated secondary metabolites (SMs). SMs and BCs serve as the two main classes of objects in IMG-ABC, each with a rich collection of attributes. A unique feature of IMG-ABC is the incorporation of both experimentally validated and computationally predicted BCs in genomes as well as metagenomes, thus identifying BCs in uncultured populations and rare taxa. We demonstrate the strength of IMG-ABC’s focused integrated analysis tools in enabling the exploration of microbial secondary metabolism on a global scale, through the discovery of phenazine-producing clusters for the first time in Alphaproteobacteria. IMG-ABC strives to fill the long-existent void of resources for computational exploration of the secondary metabolism universe; its underlying scalable framework enables traversal of uncovered phylogenetic and chemical structure space, serving as a doorway to a new era in the discovery of novel molecules. IMG-ABC is the largest publicly available database of predicted and experimental biosynthetic gene clusters and the secondary metabolites they produce. The system also includes powerful search and analysis tools that are integrated with IMG’s extensive genomic/metagenomic data and analysis tool kits. As new research on biosynthetic gene clusters and secondary metabolites is published and more genomes are sequenced, IMG-ABC will continue to expand, with the goal of becoming an essential component of any bioinformatic exploration of the secondary metabolism world.
Collapse
|
41
|
Discovery of LRRK2 inhibitors using sequential in silico joint pharmacophore space (JPS) and ensemble docking. Bioorg Med Chem Lett 2015; 25:2713-9. [DOI: 10.1016/j.bmcl.2015.04.027] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Revised: 04/08/2015] [Accepted: 04/10/2015] [Indexed: 11/22/2022]
|
42
|
Abstract
Maximum common substructure search is a computationally hard optimization problem with diverse applications in the field of cheminformatics, including similarity search, lead optimization, molecule alignment, and clustering. Most of these applications have strict constraints on running time, so heuristic methods are often preferred. However, the development of an algorithm that is both fast enough and accurate enough for most practical purposes is still a challenge. Moreover, in some applications, the quality of a common substructure depends not only on its size but also on various topological features of the one-to-one atom correspondence it defines. Two state-of-the-art heuristic algorithms for finding maximum common substructures have been implemented at ChemAxon Ltd., and effective heuristics have been developed to improve both their efficiency and the relevance of the atom mappings they provide. The implementations have been thoroughly evaluated and compared with existing solutions (KCOMBU and Indigo). The heuristics have been found to greatly improve the performance and applicability of the algorithms. The purpose of this paper is to introduce the applied methods and present the experimental results.
Collapse
Affiliation(s)
- Péter Englert
- †Department of Algorithms and Applications, Eötvös Loránd University, Budapest 1117, Hungary
| | | |
Collapse
|
43
|
Recent Advances in the Open Access Cheminformatics Toolkits, Software Tools, Workflow Environments, and Databases. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2014. [DOI: 10.1007/7653_2014_35] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
|