1
|
Youn J, Li F, Simmons G, Kim S, Tagkopoulos I. FoodAtlas: Automated knowledge extraction of food and chemicals from literature. Comput Biol Med 2024; 181:109072. [PMID: 39216404 DOI: 10.1016/j.compbiomed.2024.109072] [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: 05/27/2024] [Revised: 07/16/2024] [Accepted: 08/22/2024] [Indexed: 09/04/2024]
Abstract
Automated generation of knowledge graphs that accurately capture published information can help with knowledge organization and access, which have the potential to accelerate discovery and innovation. Here, we present an integrated pipeline to construct a large-scale knowledge graph using large language models in an active learning setting. We apply our pipeline to the association of raw food, ingredients, and chemicals, a domain that lacks such knowledge resources. By using an iterative active learning approach of 4120 manually curated premise-hypothesis pairs as training data for ten consecutive cycles, the entailment model extracted 230,848 food-chemical composition relationships from 155,260 scientific papers, with 106,082 (46.0 %) of them never been reported in any published database. To augment the knowledge incorporated in the knowledge graph, we further incorporated information from 5 external databases and ontology sources. We then applied a link prediction model to identify putative food-chemical relationships that were not part of the constructed knowledge graph. Validation of the 443 hypotheses generated by the link prediction model resulted in 355 new food-chemical relationships, while results show that the model score correlates well (R2 = 0.70) with the probability of a novel finding. This work demonstrates how automated learning from literature at scale can accelerate discovery and support practical applications through reproducible, evidence-based capture of latent interactions of diverse entities, such as food and chemicals.
Collapse
Affiliation(s)
- Jason Youn
- Department of Computer Science, University of California, Davis, Davis, CA, 95616, USA; Genome Center, University of California, Davis, Davis, CA, 95616, USA; USDA/NSF AI Institute for Next Generation Food Systems, Davis, CA, 95616, USA
| | - Fangzhou Li
- Department of Computer Science, University of California, Davis, Davis, CA, 95616, USA; Genome Center, University of California, Davis, Davis, CA, 95616, USA; USDA/NSF AI Institute for Next Generation Food Systems, Davis, CA, 95616, USA
| | - Gabriel Simmons
- Department of Computer Science, University of California, Davis, Davis, CA, 95616, USA; Genome Center, University of California, Davis, Davis, CA, 95616, USA; USDA/NSF AI Institute for Next Generation Food Systems, Davis, CA, 95616, USA
| | - Shanghyeon Kim
- Genome Center, University of California, Davis, Davis, CA, 95616, USA; USDA/NSF AI Institute for Next Generation Food Systems, Davis, CA, 95616, USA
| | - Ilias Tagkopoulos
- Department of Computer Science, University of California, Davis, Davis, CA, 95616, USA; Genome Center, University of California, Davis, Davis, CA, 95616, USA; USDA/NSF AI Institute for Next Generation Food Systems, Davis, CA, 95616, USA.
| |
Collapse
|
2
|
Nguyen QH, Nguyen H, Oh EC, Nguyen T. Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review. Brief Bioinform 2024; 25:bbae498. [PMID: 39397425 DOI: 10.1093/bib/bbae498] [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: 05/22/2024] [Revised: 09/03/2024] [Accepted: 10/02/2024] [Indexed: 10/15/2024] Open
Abstract
Metabolite profiling is a powerful approach for the clinical diagnosis of complex diseases, ranging from cardiometabolic diseases, cancer, and cognitive disorders to respiratory pathologies and conditions that involve dysregulated metabolism. Because of the importance of systems-level interpretation, many methods have been developed to identify biologically significant pathways using metabolomics data. In this review, we first describe a complete metabolomics workflow (sample preparation, data acquisition, pre-processing, downstream analysis, etc.). We then comprehensively review 24 approaches capable of performing functional analysis, including those that combine metabolomics data with other types of data to investigate the disease-relevant changes at multiple omics layers. We discuss their availability, implementation, capability for pre-processing and quality control, supported omics types, embedded databases, pathway analysis methodologies, and integration techniques. We also provide a rating and evaluation of each software, focusing on their key technique, software accessibility, documentation, and user-friendliness. Following our guideline, life scientists can easily choose a suitable method depending on method rating, available data, input format, and method category. More importantly, we highlight outstanding challenges and potential solutions that need to be addressed by future research. To further assist users in executing the reviewed methods, we provide wrappers of the software packages at https://github.com/tinnlab/metabolite-pathway-review-docker.
Collapse
Affiliation(s)
- Quang-Huy Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, United States
| | - Ha Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, United States
| | - Edwin C Oh
- Department of Internal Medicine, UNLV School of Medicine, University of Nevada, Las Vegas, NV 89154, United States
| | - Tin Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, United States
| |
Collapse
|
3
|
Li W, Jiang H, Zhang W, Sun Q, Zhang Q, Xu J, Huang J, Wan Y. Mechanisms of action of Sappan lignum for prostate cancer treatment: network pharmacology, molecular docking and experimental validation. Front Pharmacol 2024; 15:1407525. [PMID: 39318781 PMCID: PMC11420528 DOI: 10.3389/fphar.2024.1407525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 08/27/2024] [Indexed: 09/26/2024] Open
Abstract
Background Prostate cancer (PCa) is the most common non-cutaneous malignancy in men globally. Sappan lignum, which exists in the heartwood of Caesalpinia sappan L., has antitumor effects; however, its exact mechanism of action remains unclear. This study elucidated the underlying mechanisms of Sappan lignum in PCa through network pharmacology approaches and molecular docking techniques. Moreover, the therapeutic effects of Sappan lignum on PCa were verified through in vitro experiments. Methods The constituent ingredients of Sappan lignum were retrieved from the HERB database. Active plant-derived compounds of Sappan lignum were screened based on gastrointestinal absorption and gastric drug properties. Disease targets for PCa were screened using unpaired and paired case datasets from the Gene Expression Omnibus. Intersection targets were used for gene ontology and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. Core targets were identified through topological analysis parameters and their clinical relevance was validated through The Cancer Genome Atlas database. The affinity between the phytochemicals of Sappan lignum and core proteins was verified using the molecular docking technique. Validation experiments confirmed the significant potential of Sappan lignum in treating PCa. Results Twenty-one plant-derived compounds of Sappan lignum and 821 differentially expressed genes associated with PCa were collected. Among 32 intersection targets, 8 were screened according to topological parameters. KEGG analysis indicated that the antitumor effects of Sappan lignum on PCa were primarily associated with the p53 pathway. The molecular docking technique demonstrated a strong affinity between 3-deoxysappanchalcone (3-DSC) and core proteins, particularly cyclin B1 (CCNB1). CCNB1 expression correlated with clinicopathological features in patients with PCa. Experimental results revealed that 3-DSC exhibited anti-proliferative, anti-migratory, and pro-apoptotic effects on 22RV1 and DU145 cells while also causing G2/M phase cell cycle arrest, potentially through modulating the p53/p21/CDC2/CCNB1 pathway. Conclusion This research highlights the promising therapeutic potential of Sappan lignum in treating PCa, with a particular focus on targeting the p53 pathway.
Collapse
Affiliation(s)
- Wenna Li
- The Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing, China
- Institute of Acupuncture and Moxibustion in Cancer Care, Beijing University of Chinese Medicine, Beijing, China
| | - Honglin Jiang
- The Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Weina Zhang
- The Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Qiuyue Sun
- The Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Qiaoli Zhang
- The Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing, China
- Institute of Acupuncture and Moxibustion in Cancer Care, Beijing University of Chinese Medicine, Beijing, China
| | - Jingnan Xu
- The Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing, China
- Institute of Acupuncture and Moxibustion in Cancer Care, Beijing University of Chinese Medicine, Beijing, China
| | - Jinchang Huang
- The Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing, China
- Institute of Acupuncture and Moxibustion in Cancer Care, Beijing University of Chinese Medicine, Beijing, China
| | - Yuxiang Wan
- The Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing, China
- Institute of Acupuncture and Moxibustion in Cancer Care, Beijing University of Chinese Medicine, Beijing, China
| |
Collapse
|
4
|
Redway A, Spry C, Brown A, Wiedemann U, Fathoni I, Garnie LF, Qiu D, Egan TJ, Lehane AM, Jackson Y, Saliba KJ, Downer-Riley N. Discovery of antiplasmodial pyridine carboxamides and thiocarboxamides. Int J Parasitol Drugs Drug Resist 2024; 25:100536. [PMID: 38663046 PMCID: PMC11068522 DOI: 10.1016/j.ijpddr.2024.100536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 03/30/2024] [Accepted: 04/01/2024] [Indexed: 05/07/2024]
Abstract
Malaria continues to be a significant burden, particularly in Africa, which accounts for 95% of malaria deaths worldwide. Despite advances in malaria treatments, malaria eradication is hampered by insecticide and antimalarial drug resistance. Consequently, the need to discover new antimalarial lead compounds remains urgent. To help address this need, we evaluated the antiplasmodial activity of twenty-two amides and thioamides with pyridine cores and their non-pyridine analogues. Twelve of these compounds showed in vitro anti-proliferative activity against the intraerythrocytic stage of Plasmodium falciparum, the most virulent species of Plasmodium infecting humans. Thiopicolinamide 13i was found to possess submicromolar activity (IC50 = 142 nM) and was >88-fold less active against a human cell line. The compound was equally effective against chloroquine-sensitive and -resistant parasites and did not inhibit β-hematin formation, pH regulation or PfATP4. Compound 13i may therefore possess a novel mechanism of action.
Collapse
Affiliation(s)
- Alexa Redway
- Department of Chemistry, The University of the West Indies, Mona, Kingston 7, Jamaica; Chemistry Divison, University of Technology, 237 Old Hope Road, Kingston 6, Jamaica
| | - Christina Spry
- Research School of Biology, The Australian National University, Canberra, ACT, 2601, Australia
| | - Ainka Brown
- Department of Chemistry, The University of the West Indies, Mona, Kingston 7, Jamaica
| | - Ursula Wiedemann
- Research School of Biology, The Australian National University, Canberra, ACT, 2601, Australia
| | - Imam Fathoni
- Research School of Biology, The Australian National University, Canberra, ACT, 2601, Australia
| | - Larnelle F Garnie
- Department of Chemistry, University of Cape Town, Rondebosch, 7701, South Africa
| | - Deyun Qiu
- Research School of Biology, The Australian National University, Canberra, ACT, 2601, Australia
| | - Timothy J Egan
- Department of Chemistry, University of Cape Town, Rondebosch, 7701, South Africa; Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Observatory, 7925, South Africa
| | - Adele M Lehane
- Research School of Biology, The Australian National University, Canberra, ACT, 2601, Australia
| | - Yvette Jackson
- Department of Chemistry, The University of the West Indies, Mona, Kingston 7, Jamaica
| | - Kevin J Saliba
- Research School of Biology, The Australian National University, Canberra, ACT, 2601, Australia
| | - Nadale Downer-Riley
- Department of Chemistry, The University of the West Indies, Mona, Kingston 7, Jamaica.
| |
Collapse
|
5
|
Guzman-Pando A, Ramirez-Alonso G, Arzate-Quintana C, Camarillo-Cisneros J. Deep learning algorithms applied to computational chemistry. Mol Divers 2024; 28:2375-2410. [PMID: 38151697 DOI: 10.1007/s11030-023-10771-y] [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: 09/20/2023] [Accepted: 11/14/2023] [Indexed: 12/29/2023]
Abstract
Recently, there has been a significant increase in the use of deep learning techniques in the molecular sciences, which have shown high performance on datasets and the ability to generalize across data. However, no model has achieved perfect performance in solving all problems, and the pros and cons of each approach remain unclear to those new to the field. Therefore, this paper aims to review deep learning algorithms that have been applied to solve molecular challenges in computational chemistry. We proposed a comprehensive categorization that encompasses two primary approaches; conventional deep learning and geometric deep learning models. This classification takes into account the distinct techniques employed by the algorithms within each approach. We present an up-to-date analysis of these algorithms, emphasizing their key features and open issues. This includes details of input descriptors, datasets used, open-source code availability, task solutions, and actual research applications, focusing on general applications rather than specific ones such as drug discovery. Furthermore, our report discusses trends and future directions in molecular algorithm design, including the input descriptors used for each deep learning model, GPU usage, training and forward processing time, model parameters, the most commonly used datasets, libraries, and optimization schemes. This information aids in identifying the most suitable algorithms for a given task. It also serves as a reference for the datasets and input data frequently used for each algorithm technique. In addition, it provides insights into the benefits and open issues of each technique, and supports the development of novel computational chemistry systems.
Collapse
Affiliation(s)
- Abimael Guzman-Pando
- Computational Chemistry Physics Laboratory, Facultad de Medicina y Ciencias Biomédicas, Universidad Autónoma de Chihuahua, Campus II, 31125, Chihuahua, Mexico
| | - Graciela Ramirez-Alonso
- Faculty of Engineering, Universidad Autónoma de Chihuahua, Campus II, 31125, Chihuahua, Mexico
| | - Carlos Arzate-Quintana
- Computational Chemistry Physics Laboratory, Facultad de Medicina y Ciencias Biomédicas, Universidad Autónoma de Chihuahua, Campus II, 31125, Chihuahua, Mexico
| | - Javier Camarillo-Cisneros
- Computational Chemistry Physics Laboratory, Facultad de Medicina y Ciencias Biomédicas, Universidad Autónoma de Chihuahua, Campus II, 31125, Chihuahua, Mexico.
| |
Collapse
|
6
|
Hassanin SO, Hegab AMM, Mekky RH, Said MA, Khalil MG, Hamza AA, Amin A. Combining In Vitro, In Vivo, and Network Pharmacology Assays to Identify Targets and Molecular Mechanisms of Spirulina-Derived Biomolecules against Breast Cancer. Mar Drugs 2024; 22:328. [PMID: 39057437 PMCID: PMC11278317 DOI: 10.3390/md22070328] [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: 06/23/2024] [Revised: 07/15/2024] [Accepted: 07/20/2024] [Indexed: 07/28/2024] Open
Abstract
The current research employed an animal model of 7,12-dimethylbenz(a)anthracene (DMBA)-induced mammary gland carcinogenesis. The estrogen receptor-positive human breast adenocarcinoma cell line (MCF-7) was used for in vitro analysis. This was combined with a network pharmacology-based approach to assess the anticancer properties of Spirulina (SP) extract and understand its molecular mechanisms. The results showed that the administration of 1 g/kg of SP increased the antioxidant activity by raising levels of catalase (CAT) and superoxide dismutase (SOD), while decreasing the levels of malonaldehyde (MDA) and protein carbonyl. A histological examination revealed reduced tumor occurrence, decreased estrogen receptor expression, suppressed cell proliferation, and promoted apoptosis in SP protected animals. In addition, SP disrupted the G2/M phase of the MCF-7 cell cycle, inducing apoptosis and reactive oxygen species (ROS) accumulation. It also enhanced intrinsic apoptosis in MCF-7 cells by upregulating cytochrome c, Bax, caspase-8, caspase-9, and caspase-7 proteins, while downregulating Bcl-2 production. The main compounds identified in the LC-MS/MS study of SP were 7-hydroxycoumarin derivatives of cinnamic acid, hinokinin, valeric acid, and α-linolenic acid. These substances specifically targeted three important proteins: ERK1/2 MAPK, PI3K-protein kinase B (AKT), and the epidermal growth factor receptor (EGFR). Network analysis and molecular docking indicated a significant binding affinity between SP and these proteins. This was verified by Western blot analysis that revealed decreased protein levels of p-EGFR, p-ERK1/2, and p-AKT following SP administration. SP was finally reported to suppress MCF-7 cell growth and induce apoptosis by modulating the PI3K/AKT/EGFR and MAPK signaling pathways suggesting EGFR as a potential target of SP in breast cancer (BC) treatment.
Collapse
Affiliation(s)
- Soha Osama Hassanin
- Biochemistry Department, Faculty of Pharmacy, Modern University for Technology and Information, Cairo 11585, Egypt;
| | - Amany Mohammed Mohmmed Hegab
- Egyptian Drug Authority (EDA), Formerly National Organization of Drug Control and Research, Developmental Pharmacology and Acute Toxicity Department, Giza 12611, Egypt;
| | - Reham Hassan Mekky
- Department of Pharmacognosy, Faculty of Pharmacy, Egyptian Russian University, Badr City, Cairo-Suez Road, Cairo 11829, Egypt;
| | - Mohamed Adel Said
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Egyptian Russian University, Badr City, Cairo 11829, Egypt
| | - Mona G. Khalil
- Pharmacology and Toxicology Department, Faculty of Pharmacy, Modern University for Technology and Information, Cairo 11829, Egypt
| | - Alaaeldin Ahmed Hamza
- Biology Department, Egyptian Drug Authority (EDA), Formerly National Organization of Drug Control and Research (NODCAR), Giza 12611, Egypt
- Medical Research Council, Academy of Scientific Research and Technology, Cairo 11334, Egypt
| | - Amr Amin
- Basic Medical Sciences, College of Medicine, University of Sharjah, Sharjah 27272, United Arab Emirates
| |
Collapse
|
7
|
Wu H, Liu J, Zhang R, Lu Y, Cui G, Cui Z, Ding Y. A review of deep learning methods for ligand based drug virtual screening. FUNDAMENTAL RESEARCH 2024; 4:715-737. [PMID: 39156568 PMCID: PMC11330120 DOI: 10.1016/j.fmre.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/10/2024] [Accepted: 02/18/2024] [Indexed: 08/20/2024] Open
Abstract
Drug discovery is costly and time consuming, and modern drug discovery endeavors are progressively reliant on computational methodologies, aiming to mitigate temporal and financial expenditures associated with the process. In particular, the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic. Recently, the performance of deep learning methods in drug virtual screening has been particularly prominent. It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening, select different models for different drug screening problems, exploit the advantages of deep learning models, and further improve the capability of deep learning in drug virtual screening. This review first introduces the basic concepts of drug virtual screening, common datasets, and data representation methods. Then, large numbers of common deep learning methods for drug virtual screening are compared and analyzed. In addition, a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening. Finally, the existing challenges and future directions in the field of virtual screening are presented.
Collapse
Affiliation(s)
- Hongjie Wu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Junkai Liu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Runhua Zhang
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Yaoyao Lu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Guozeng Cui
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Zhiming Cui
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| |
Collapse
|
8
|
Kim S, Yu B, Li Q, Bolton EE. PubChem synonym filtering process using crowdsourcing. J Cheminform 2024; 16:69. [PMID: 38880887 PMCID: PMC11181558 DOI: 10.1186/s13321-024-00868-3] [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: 03/01/2024] [Accepted: 06/09/2024] [Indexed: 06/18/2024] Open
Abstract
PubChem ( https://pubchem.ncbi.nlm.nih.gov ) is a public chemical information resource containing more than 100 million unique chemical structures. One of the most requested tasks in PubChem and other chemical databases is to search chemicals by name (also commonly called a "chemical synonym"). PubChem performs this task by looking up chemical synonym-structure associations provided by individual depositors to PubChem. In addition, these synonyms are used for many purposes, including creating links between chemicals and PubMed articles (using Medical Subject Headings (MeSH) terms). However, these depositor-provided name-structure associations are subject to substantial discrepancies within and between depositors, making it difficult to unambiguously map a chemical name to a specific chemical structure. The present paper describes PubChem's crowdsourcing-based synonym filtering strategy, which resolves inter- and intra-depositor discrepancies in synonym-structure associations as well as in the chemical-MeSH associations. The PubChem synonym filtering process was developed based on the analysis of four crowd-voting strategies, which differ in the consistency threshold value employed (60% vs 70%) and how to resolve intra-depositor discrepancies (a single vote vs. multiple votes per depositor) prior to inter-depositor crowd-voting. The agreement of voting was determined at six levels of chemical equivalency, which considers varying isotopic composition, stereochemistry, and connectivity of chemical structures and their primary components. While all four strategies showed comparable results, Strategy I (one vote per depositor with a 60% consistency threshold) resulted in the most synonyms assigned to a single chemical structure as well as the most synonym-structure associations disambiguated at the six chemical equivalency contexts. Based on the results of this study, Strategy I was implemented in PubChem's filtering process that cleans up synonym-structure associations as well as chemical-MeSH associations. This consistency-based filtering process is designed to look for a consensus in name-structure associations but cannot attest to their correctness. As a result, it can fail to recognize correct name-structure associations (or incorrect ones), for example, when a synonym is provided by only one depositor or when many contributors are incorrect. However, this filtering process is an important starting point for quality control in name-structure associations in large chemical databases like PubChem.
Collapse
Affiliation(s)
- Sunghwan Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Bo Yu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Qingliang Li
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Evan E Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.
| |
Collapse
|
9
|
Saeed NM, Ramadan LA, El-Sabbagh WA, Said MA, Abdel-Rahman HM, Mekky RH. Exploring the anti-osteoporosis potential of Petroselinum crispum (Mill.) Fuss extract employing experimentally ovariectomized rat model and network pharmacology approach. Fitoterapia 2024; 175:105971. [PMID: 38663562 DOI: 10.1016/j.fitote.2024.105971] [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: 10/26/2023] [Revised: 03/11/2024] [Accepted: 04/21/2024] [Indexed: 04/30/2024]
Abstract
One of the most prevalent secondary osteoporosis is ovariectomy-induced osteoporosis. Parsley (Petroselinum crispum) has potent estrogenic and antioxidant properties and was used traditionally in the treatment of amenorrhea and dysmenorrhea. The present study aimed to characterize parsley leaf extract (PLE) employing RP-HPLC-MS-MS/MS-based method and possible protective effect in ovariectomized (OVX)-induced osteoporosis in rats was assessed. Rats were randomly assigned into SHAM group, OVX group, PLE + OVX group (150 mg/kg/day, p.o), and estradiol benzoate (E2) + OVX group (30 μg/kg/day, s.c). After eight weeks following ovariectomy, biomarkers of bone strength, bone resorption, oxidative stress and histopathology were carried out. A network pharmacology approach investigated the key targets and potential mechanisms by of PLE metabolites against osteoporosis using databases: PubChem, BindingDB server, DisGeNET, ShinyGO, and KEGG Pathway. Moreover, FunRich 3.1.3, Cytoscape 3.10.0, and MOE 2019.0102 softwares were used for network pharmacology analysis and molecular docking studies. Flavones and hydroxycinnamic acid derivatives were predominant among 38 metabolites in PLE. It significantly restored bone strength and bone resorption biomarkers, osteocalcin (OST), oxidative stress biomarkers and histopathological alterations. The employed network pharmacology approach revealed that 14 primary target genes were associated with decreasing the severity of osteoporosis. Molecular docking revealed that cGMP-PKG signaling pathway has the highest fold enrichment and its downstream PDE5A. Luteolin, diosmetin, and isorhamnetin derivatives affected mostly osteoporosis targets. PLE exhibited protective action against ovariectomy-induced osteoporosis in rats and may be a promising therapy for premenopausal bone loss. cGMP-PKG signaling pathway could be a promising target for PLE in treating osteoporosis.
Collapse
Affiliation(s)
- Noha M Saeed
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Egyptian Russian University, Badr City, Cairo-Suez Road, 11829 Cairo, Egypt.
| | - Laila A Ramadan
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Egyptian Russian University, Badr City, Cairo-Suez Road, 11829 Cairo, Egypt
| | - Walaa A El-Sabbagh
- Drug Radiation Research Department, National Centre for Radiation Research and Technology (NCRRT), Egyptian Atomic Energy Authority (EAEA), 11787 Cairo, Egypt
| | - Mohamed A Said
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Egyptian Russian University, Badr City, Cairo 11829, Egypt
| | - Hanaa M Abdel-Rahman
- Department of Pharmacy Practice, Faculty of Pharmacy, Egyptian Russian University, Cairo 11829, Egypt; Department of Forensic Medicine and Toxicology, Faculty of Medicine, Ain Shams University, Cairo 11562, Egypt
| | - Reham Hassan Mekky
- Department of Pharmacognosy, Faculty of Pharmacy, Egyptian Russian University, Badr City, Cairo-Suez Road, 11829, Cairo, Egypt..
| |
Collapse
|
10
|
Feng C, Wei H, Li X, Feng B, Xu C, Zhu X, Liu R. A stacking-based algorithm for antifreeze protein identification using combined physicochemical, pseudo amino acid composition, and reduction property features. Comput Biol Med 2024; 176:108534. [PMID: 38754217 DOI: 10.1016/j.compbiomed.2024.108534] [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: 02/04/2024] [Revised: 04/03/2024] [Accepted: 04/28/2024] [Indexed: 05/18/2024]
Abstract
Antifreeze proteins have wide applications in the medical and food industries. In this study, we propose a stacking-based classifier that can effectively identify antifreeze proteins. Initially, feature extraction was performed in three aspects: reduction properties, scalable pseudo amino acid composition, and physicochemical properties. A hybrid feature set comprised of the combined information from these three categories was obtained. Subsequently, we trained the training set based on LightGBM, XGBoost, and RandomForest algorithms, and the training outcomes were passed to the Logistic algorithm for matching, thereby establishing a stacking algorithm. The proposed algorithm was tested on the test set and an independent validation set. Experimental data indicates that the algorithm achieved a recognition accuracy of 98.3 %, and an accuracy of 98.5 % on the validation set. Lastly, we analyzed the reasons why numerical features achieved high recognition capabilities from multiple aspects. Data dimensionality reduction and the analysis from two-dimensional and three-dimensional views revealed separability between positive and negative samples, and the protein three-dimensional structure further demonstrated significant differences in related features between the two samples. Analysis of the classifier revealed that Hr*Hr, HrHr, and Sc-PseAAC_1, 188D(152,116,57,183) were among the seven most important numerical features affecting algorithm recognition. For Hr*Hr and HrHr, supportive sequence level evidence for the reduction dictionary was found in terms of conservation area analysis, multiple sequence alignment, and amino acid conservative substitution. Moreover, the importance of the reduction dictionary was recognized through a comparative analysis of importance before and after the reduction, realizing the effectiveness of the dictionary in improving feature importance. A decision tree model has been utilized to discern the distinctions between dipeptides associated with the physical and chemical properties of His(H), Iso(I), Leu(L), and Lys(K) and other dipeptides. We finally analyzed the other seven features of importance, and data analysis confirmed that hydrophobicity, secondary structure, charge properties, van der Waals forces, and solvent accessibility are also factors affecting the antifreeze capability of proteins.
Collapse
Affiliation(s)
- Changli Feng
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Haiyan Wei
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Xin Li
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Bin Feng
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Chugui Xu
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Xiaorong Zhu
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Ruijun Liu
- School of Software, Beihang University, Beijing, 100191, China.
| |
Collapse
|
11
|
Saadeldin IM, Ehab S, Noreldin AE, Swelum AAA, Bang S, Kim H, Yoon KY, Lee S, Cho J. Current strategies using 3D organoids to establish in vitro maternal-embryonic interaction. J Vet Sci 2024; 25:e40. [PMID: 38834510 PMCID: PMC11156602 DOI: 10.4142/jvs.24004] [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: 01/07/2024] [Revised: 03/14/2024] [Accepted: 03/28/2024] [Indexed: 06/06/2024] Open
Abstract
IMPORTANCE The creation of robust maternal-embryonic interactions and implantation models is important for comprehending the early stages of embryonic development and reproductive disorders. Traditional two-dimensional (2D) cell culture systems often fail to accurately mimic the highly complex in vivo conditions. The employment of three-dimensional (3D) organoids has emerged as a promising strategy to overcome these limitations in recent years. The advancements in the field of organoid technology have opened new avenues for studying the physiology and diseases affecting female reproductive tract. OBSERVATIONS This review summarizes the current strategies and advancements in the field of 3D organoids to establish maternal-embryonic interaction and implantation models for use in research and personalized medicine in assisted reproductive technology. The concepts of endometrial organoids, menstrual blood flow organoids, placental trophoblast organoids, stem cell-derived blastoids, and in vitro-generated embryo models are discussed in detail. We show the incorportaion of organoid systems and microfluidic technology to enhance tissue performance and precise management of the cellular surroundings. CONCLUSIONS AND RELEVANCE This review provides insights into the future direction of modeling maternal-embryonic interaction research and its combination with other powerful technologies to interfere with this dialogue either by promoting or hindering it for improving fertility or methods for contraception, respectively. The merging of organoid systems with microfluidics facilitates the creation of sophisticated and functional organoid models, enhancing insights into organ development, disease mechanisms, and personalized medical investigations.
Collapse
Affiliation(s)
- Islam Mohamed Saadeldin
- Comparative Medicine Department, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia
| | - Seif Ehab
- Biomedical Sciences Program, University of Science and Technology, Zewail City of Science and Technology, Giza 11341, Egypt
| | - Ahmed Elsayed Noreldin
- Department of Histology and Cytology, Faculty of Veterinary Medicine, Damanhour University, the Scientific Campus, Damanhour 22511, Egypt
| | - Ayman Abdel-Aziz Swelum
- Department of Animal Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia
- Department of Theriogenology, Faculty of Veterinary Medicine, Zagazig University, Zagazig 44519, Egypt
| | - Seonggyu Bang
- College of Veterinary Medicine, Chungnam National University, Daejeon 34134, Korea
- College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul 08826, Korea
| | - Hyejin Kim
- Division in Biomedical Art, Department of Fine Art, Incheon Catholic University Graduate School, Incheon 21986, Korea
| | - Ki Young Yoon
- Department of Companion Animal, Shingu College, Seongnam 13174, Korea
| | - Sanghoon Lee
- College of Veterinary Medicine, Chungnam National University, Daejeon 34134, Korea
| | - Jongki Cho
- College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul 08826, Korea.
| |
Collapse
|
12
|
Gurwitz D, Shomron N. Artificial intelligence utility for drug development: ChatGPT and beyond. Drug Dev Res 2024; 85:e22121. [PMID: 37815084 DOI: 10.1002/ddr.22121] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/20/2023] [Accepted: 10/02/2023] [Indexed: 10/11/2023]
Affiliation(s)
- David Gurwitz
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv, Israel
| | - Noam Shomron
- Sagol School of Neuroscience, Tel Aviv, Israel
- Department of Cell and Developmental Biology, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel
- Tel Aviv University Innovation Labs (TILabs), Tel Aviv, Israel
- Djerassi Institute of Oncology, Tel Aviv University, Tel Aviv, Israel
| |
Collapse
|
13
|
Chen Y, Lu M, Lin M, Gao Q. Network pharmacology and molecular docking to elucidate the common mechanism of hydroxychloroquine treatment in lupus nephritis and IgA nephropathy. Lupus 2024; 33:347-356. [PMID: 38285068 DOI: 10.1177/09612033241230377] [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] [Indexed: 01/30/2024]
Abstract
OBJECTIVE Hydroxychloroquine (HCQ), characterized by a broad effect on immune regulation, has been widely used in the treatment of autoimmune glomerulonephritis such as lupus nephritis (LN) and immunoglobulin A nephropathy (IgAN). The current research investigates whether HCQ plays a role in the treatment of LN and IgAN through common mechanisms since the pathogenesis of both LN and IgAN is closely related to immune complex deposition, complement activation, and ultimately inflammation. METHODS Seventy-two common targets were obtained related to the common mechanism of HCQ treatment of LN and IgAN. Targets associated with LN and IgAN were collected based on DisGeNET, GeneCards, and OMIM databases. Possible HCQ targets were obtained from the PubChem database and PharmMapper databases. The overlapping targets of HCQ ingredients, IgAN, and LN were discovered via the Venn 2.1.0 online platform. Through the DAVID database, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted. Cytoscape (v3.9.1) was used to build a protein-protein interaction (PPI) network. Molecular docking was performed by using AutoDockTools 1.5.6 software and PyMol software to match the binding activity between HCQ and the 10 core targets. RESULTS The results showed that core targets (including MMP 2, PPARG, IL-2, MAPK14, MMP 9, and SRC), three signaling pathways (including the PI3K-Akt, AGE-RAGE, and MAPK), and cell differentiation (including Th1, Th2, and Th17) might be related to the body's immunity and inflammation. These results suggested that HCQ might act on targets and pathways involved in inflammation and immune regulation to exert a common effect on the treatment of LN and IgAN. CONCLUSIONS The current study provided new evidence for the protective mechanism and clinical utility of HCQ against LN and IgAN.
Collapse
Affiliation(s)
- Yixuan Chen
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, China
| | - Meiqi Lu
- Department of Nephrology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Mengshu Lin
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, China
| | - Qing Gao
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, China
- Department of Nephrology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| |
Collapse
|
14
|
Talavera Andújar B, Mary A, Venegas C, Cheng T, Zaslavsky L, Bolton EE, Heneka MT, Schymanski EL. Can Small Molecules Provide Clues on Disease Progression in Cerebrospinal Fluid from Mild Cognitive Impairment and Alzheimer's Disease Patients? ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:4181-4192. [PMID: 38373301 PMCID: PMC10919072 DOI: 10.1021/acs.est.3c10490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/24/2024] [Accepted: 01/31/2024] [Indexed: 02/21/2024]
Abstract
Alzheimer's disease (AD) is a complex and multifactorial neurodegenerative disease, which is currently diagnosed via clinical symptoms and nonspecific biomarkers (such as Aβ1-42, t-Tau, and p-Tau) measured in cerebrospinal fluid (CSF), which alone do not provide sufficient insights into disease progression. In this pilot study, these biomarkers were complemented with small-molecule analysis using non-target high-resolution mass spectrometry coupled with liquid chromatography (LC) on the CSF of three groups: AD, mild cognitive impairment (MCI) due to AD, and a non-demented (ND) control group. An open-source cheminformatics pipeline based on MS-DIAL and patRoon was enhanced using CSF- and AD-specific suspect lists to assist in data interpretation. Chemical Similarity Enrichment Analysis revealed a significant increase of hydroxybutyrates in AD, including 3-hydroxybutanoic acid, which was found at higher levels in AD compared to MCI and ND. Furthermore, a highly sensitive target LC-MS method was used to quantify 35 bile acids (BAs) in the CSF, revealing several statistically significant differences including higher dehydrolithocholic acid levels and decreased conjugated BA levels in AD. This work provides several promising small-molecule hypotheses that could be used to help track the progression of AD in CSF samples.
Collapse
Affiliation(s)
- Begoña Talavera Andújar
- Luxembourg
Centre for Systems Biomedicine (LCSB), University
of Luxembourg, Avenue du Swing 6, L-4367 Belvaux, Luxembourg
| | - Arnaud Mary
- Luxembourg
Centre for Systems Biomedicine (LCSB), University
of Luxembourg, Avenue du Swing 6, L-4367 Belvaux, Luxembourg
| | - Carmen Venegas
- Luxembourg
Centre for Systems Biomedicine (LCSB), University
of Luxembourg, Avenue du Swing 6, L-4367 Belvaux, Luxembourg
| | - Tiejun Cheng
- National
Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States
| | - Leonid Zaslavsky
- National
Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States
| | - Evan E. Bolton
- National
Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States
| | - Michael T. Heneka
- Luxembourg
Centre for Systems Biomedicine (LCSB), University
of Luxembourg, Avenue du Swing 6, L-4367 Belvaux, Luxembourg
| | - Emma L. Schymanski
- Luxembourg
Centre for Systems Biomedicine (LCSB), University
of Luxembourg, Avenue du Swing 6, L-4367 Belvaux, Luxembourg
| |
Collapse
|
15
|
Wang S, Han H, Lei X, Ma J, Tao Z, Ren Y. Cellulose nanofibers produced from spaghetti squash peel by deep eutectic solvents and ultrasonication. Int J Biol Macromol 2024; 261:129777. [PMID: 38286364 DOI: 10.1016/j.ijbiomac.2024.129777] [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: 10/20/2023] [Revised: 01/13/2024] [Accepted: 01/24/2024] [Indexed: 01/31/2024]
Abstract
In this study, the cellulose nanofibers (CNFs) derived from spaghetti squash peel (SSP) were prepared using a novel approach involving deep eutectic solvent (DES) pretreatment coupled with ultrasonication. Molecular dynamics (MD) simulations revealed that the number of hydrogen bonds influences the viscosity and density of DES systems, and experimental viscosity (ηexp) confirmed consistency with the computed viscosity (ηMD) trends. After DES pretreatment and ultrasonication, the cellulose content of ChCl/oxalic acid (ChCl/OA) CNF (35.63%) and ChCl/formic acid (ChCl/FA) (32.46%) is higher than ChCl/Urea CNF (28.27%). The widths of ChCl/OA CNF, ChCl/FA CNF, and ChCl/Urea CNF were 19.83, 11.34, and 18.27 nm, respectively, showing a network-like fiber distribution. Compared with SSP (29.76%) and non-ultrasonic samples, the crystallinity index of ChCl/OA CNF, ChCl/FA CNF, and ChCl/Urea CNF was improved by ultrasonication. The thermal decomposition residue of ChCl/OA CNF (25.54%), ChCl/FA CNF (18.54%), and ChCl/Urea CNF (23.62%) was lower than that of SSP (29.57%). These results demonstrate that CNFs can be prepared from SSP via DES pretreatment combined with ultrasonication. The lowest viscosity observed in the formic acid DES group (ηexp of 18 mPa·s), the ChCl/FA CNF exhibits excellent stability (Zeta potential of -37.6 mV), which can provide a promising prospect for utilization in biomass by-products and applications in the materials field.
Collapse
Affiliation(s)
- Shuo Wang
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China
| | - Hui Han
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China
| | - Xiaoqing Lei
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China
| | - Jianxiang Ma
- College of Horticulture, Northwest A&F University, Yangling 712100, Shaanxi, China
| | - Ze Tao
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China
| | - Yamei Ren
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory for Food Nutrition and Safety Control of Xinjiang Production and Construction Corps, School of Food Science and Technology, Shihezi University, Shihezi, Xinjiang 832003, China.
| |
Collapse
|
16
|
Laha A, Sarkar A, Panja AS, Bandopadhyay R. Screening of Prospective Antiallergic Compound as FcεRI Inhibitors and Its Antiallergic Efficacy Through Immunoinformatics Approaches. Mol Biotechnol 2024; 66:26-33. [PMID: 36988875 DOI: 10.1007/s12033-023-00728-9] [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: 10/26/2022] [Accepted: 03/21/2023] [Indexed: 03/30/2023]
Abstract
The occurrence of allergy, a type I hypersensitivity reaction, is rising exponentially all over the world. Sometimes, allergy proves to be fatal for atopic patients, due to the occurrence of anaphylaxis. This study is aimed to find an anti-allergic agent that can inhibit the binding of IgE to Human High Affinity IgE Receptor (FCεRI), thereby preventing the degranulation of mast cells. A considerable number of potential anti-allergic compounds were assessed for their inhibitory strength through ADMET studies. AUTODOCK was used for estimating the binding energy between anti-allergic compounds and FCεRI, along with the interacting amino acids. The docked pose showing favorable binding energy was subjected to molecular dynamics simulation study. Marrubiin, a diterpenoid lactone from Lamiaceae, and epicatechin-3-gallate appears to be effective in blocking the Human High Affinity IgE Receptor (FCεRI). This in-silico study proposes the use of marrubiin and epicatechin-3-gallate, in the downregulation of allergic responses. Due to the better inhibition constant, future direction of this study is to analyze the safety and efficacy of marrubiin in anti-allergic activities through in-vivo clinical human trials.
Collapse
Affiliation(s)
- Anubhab Laha
- UGC Centre for Advanced Study, Department of Botany, The University of Burdwan, Golapbag, Burdwan, West Bengal, 713104, India
- Department of Botany, Chandernagore College, Chandernagore, Hooghly, West Bengal, 712136, India
| | - Aniket Sarkar
- Post-Graduate Department of Biotechnology, Oriental Institute of Science and Technology, Vidyasagar University, Midnapore, West Bengal, India
| | - Anindya Sundar Panja
- Department of Biotechnology, Molecular Informatics Laboratory, Oriental Institute of Science and Technology, Vidyasagar University, Midnapore, West Bengal, 721102, India
| | - Rajib Bandopadhyay
- UGC Centre for Advanced Study, Department of Botany, The University of Burdwan, Golapbag, Burdwan, West Bengal, 713104, India.
| |
Collapse
|
17
|
Khalifa NE, Noreldin AE, Khafaga AF, El-Beskawy M, Khalifa E, El-Far AH, Fayed AHA, Zakaria A. Chia seeds oil ameliorate chronic immobilization stress-induced neurodisturbance in rat brains via activation of the antioxidant/anti-inflammatory/antiapoptotic signaling pathways. Sci Rep 2023; 13:22409. [PMID: 38104182 PMCID: PMC10725506 DOI: 10.1038/s41598-023-49061-w] [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: 10/06/2023] [Accepted: 12/04/2023] [Indexed: 12/19/2023] Open
Abstract
Chronic immobilization stress plays a key role in several neuropsychiatric disorders. This investigation assessed the possible ameliorative effect of chia seed oil (CSO) against the neurodisturbance-induced in rats by chronic immobilization. Rats were randomly allocated into control, CSO (1 ml/kg b.wt./orally), restrained (6 h/day), CSO pre-restraint, and CSO post-restraint for 60 days. Results revealed a significant reduction in serum corticosterone level, gene expression of corticotrophin-releasing factor, pro-inflammatory cytokines, and oxidative biomarkers in restrained rats treated with CSO. The histopathological findings revealed restoring necrosis and neuronal loss in CSO-treated-restraint rats. The immunohistochemical evaluation revealed a significant reduction in the immuno-expression of caspase-3, nuclear factor kappa B, interleukin-6, and cyclooxygenase-2 (COX-2), and an elevation of calbindin-28k and synaptophysin expression compared to non-treated restraint rats. The molecular docking showed the CSO high affinity for several target proteins, including caspase-3, COX-2, corticotropin-releasing hormone binding protein, corticotropin-releasing factor receptors 1 and 2, interleukin-1 receptor types 1 and 2, interleukin-6 receptor subunits alpha and beta. In conclusion, CSO emerges as a promising candidate against stress-induced brain disruptions by suppressing inflammatory/oxidative/apoptotic signaling pathways due to its numerous antioxidant and anti-inflammatory components, mainly α-linolenic acid. Future studies are necessary to evaluate the CSO therapeutic impacts in human neurodisturbances.
Collapse
Affiliation(s)
- Norhan E Khalifa
- Department of Physiology, Faculty of Veterinary Medicine, Matrouh University, Matrouh, 51511, Egypt.
| | - Ahmed E Noreldin
- Department of Histology and Cytology, Faculty of Veterinary Medicine, Damanhour University, Damanhour, 22511, Egypt.
| | - Asmaa F Khafaga
- Department of Pathology, Faculty of Veterinary Medicine, Alexandria University, Edfina, 22758, Egypt
| | - Mohamed El-Beskawy
- Department of Animal Medicine, Faculty of Veterinary Medicine, Matrouh University, Matrouh, 51511, Egypt
| | - Eman Khalifa
- Department of Microbiology, Faculty of Veterinary Medicine, Matrouh University, Matrouh, 51511, Egypt
| | - Ali H El-Far
- Department of Biochemistry, Faculty of Veterinary Medicine, Damanhour University, Damanhour, 22511, Egypt
| | - Abdel-Hasseb A Fayed
- Department of Physiology, Faculty of Veterinary Medicine, Alexandria University, Edfina, 22758, Egypt
| | - Abdeldayem Zakaria
- Department of Physiology, Faculty of Veterinary Medicine, Alexandria University, Edfina, 22758, Egypt
| |
Collapse
|
18
|
Wu X, Cao S, Zou Y, Wu F. Traditional Chinese Medicine studies for Alzheimer's disease via network pharmacology based on entropy and random walk. PLoS One 2023; 18:e0294772. [PMID: 38019798 PMCID: PMC10686466 DOI: 10.1371/journal.pone.0294772] [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: 04/29/2023] [Accepted: 11/08/2023] [Indexed: 12/01/2023] Open
Abstract
Alzheimer's disease (AD) is a common neurodegenerative disease having complex pathogenesis, approved drugs can only alleviate symptoms of AD for a period of time. Traditional Chinese medicine (TCM) contains multiple active ingredients that can act on multiple targets simultaneously. In this paper, a novel algorithm based on entropy and random walk with the restart of heterogeneous network (RWRHE) is proposed for predicting active ingredients for AD and screening out the effective TCMs for AD. First, Six TCM compounds containing 20 herbs from the AD drug reviews in the CNKI (China National Knowledge Internet) are collected, their active ingredients and targets are retrieved from different databases. Then, comprehensive similarity networks of active ingredients and targets are constructed based on different aspects and entropy weight, respectively. A comprehensive heterogeneous network is constructed by integrating the known active ingredient-target association information and two comprehensive similarity networks. Subsequently, bi-random walks are applied on the heterogeneous network to predict active ingredient-target associations. AD related targets are selected as the seed nodes, a random walk is carried out on the target similarity network to predict the AD-target associations, and the associations of AD-active ingredients are inferred and scored. The effective herbs and compounds for AD are screened out based on their active ingredients' scores. The results measured by machine learning and bioinformatics show that the RWRHE algorithm achieves better prediction accuracy, the top 15 active ingredients may act as multi-target agents in the prevention and treatment of AD, Danshen, Gouteng and Chaihu are recommended as effective TCMs for AD, Yiqitongyutang is recommended as effective compound for AD.
Collapse
Affiliation(s)
- Xiaolu Wu
- School of Mathematical Sciences, Tiangong University, Tianjin, China
| | - Shujuan Cao
- School of Mathematical Sciences, Tiangong University, Tianjin, China
| | - Yongming Zou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
| | - Fangxiang Wu
- Division of Biomedical Engineering, Department of Mechanical Engineering and Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| |
Collapse
|
19
|
Smith E, Lewis A, Narine SS, Emery RJN. Unlocking Potentially Therapeutic Phytochemicals in Capadulla ( Doliocarpus dentatus) from Guyana Using Untargeted Mass Spectrometry-Based Metabolomics. Metabolites 2023; 13:1050. [PMID: 37887375 PMCID: PMC10608729 DOI: 10.3390/metabo13101050] [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: 08/30/2023] [Revised: 09/22/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023] Open
Abstract
Doliocarpus dentatus is thought to have a wide variety of therapeutic phytochemicals that allegedly improve libido and cure impotence. Although a few biomarkers have been identified with potential antinociceptive and cytotoxic properties, an untargeted mass spectrometry-based metabolomics approach has never been undertaken to identify therapeutic biofingerprints for conditions, such as erectile dysfunction, in men. This study executes a preliminary phytochemical screening of the woody vine of two ecotypes of D. dentatus with renowned differences in therapeutic potential for erectile dysfunction. Liquid chromatography-mass spectrometry-based metabolomics was used to screen for flavonoids, terpenoids, and other chemical classes found to contrast between red and white ecotypes. Among the metabolite chemodiversity found in the ecotype screens, using a combination of GNPS, MS-DIAL, and SIRIUS, approximately 847 compounds were annotated at levels 2 to 4, with the majority of compounds falling under lipid and lipid-like molecules, benzenoids and phenylpropanoids, and polyketides, indicative of the contributions of the flavonoid, shikimic acid, and terpenoid biosynthesis pathways. Despite the extensive annotation, we report on 138 tentative compound identifications of potentially therapeutic compounds, with 55 selected compounds at a level-2 annotation, and 22 statistically significant therapeutic biomarkers, the majority of which were polyphenols. Epicatechin methyl gallate, catechin gallate, and proanthocyanidin A2 had the greatest significant differences and were also relatively abundant among the red and white ecotypes. These putatively identified compounds reportedly act as antioxidants, neutralizing damaging free radicals, and lowering cell oxidative stress, thus aiding in potentially preventing cellular damage and promoting overall well-being, especially for treating erectile dysfunction (ED).
Collapse
Affiliation(s)
- Ewart Smith
- Environmental and Life Sciences Graduate Program, Trent University, Peterborough, ON K9J 0G2, Canada
| | - Ainsely Lewis
- Department of Biology, Trent University, Peterborough, ON K9J 0G2, Canada
| | - Suresh S. Narine
- Trent Centre for Biomaterials Research, Trent University, Peterborough, ON K9J 0G2, Canada
- Departments of Physics & Astronomy and Chemistry, Trent University, Peterborough, ON K9J 0G2, Canada
| | - R. J. Neil Emery
- Department of Biology, Trent University, Peterborough, ON K9J 0G2, Canada
| |
Collapse
|
20
|
Rajan K, Brinkhaus HO, Agea MI, Zielesny A, Steinbeck C. DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications. Nat Commun 2023; 14:5045. [PMID: 37598180 PMCID: PMC10439916 DOI: 10.1038/s41467-023-40782-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 08/09/2023] [Indexed: 08/21/2023] Open
Abstract
The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical IMagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai .
Collapse
Affiliation(s)
- Kohulan Rajan
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743, Jena, Germany
| | - Henning Otto Brinkhaus
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743, Jena, Germany
| | - M Isabel Agea
- Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technicka 5, 166 28, Prague, Czech Republic
| | - Achim Zielesny
- Institute for Bioinformatics and Chemoinformatics, Westphalian University of Applied Sciences, August-Schmidt-Ring 10, 45665, Recklinghausen, Germany
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743, Jena, Germany.
| |
Collapse
|
21
|
Polak I, Stryiński R, Majewska M, Łopieńska-Biernat E. Metabolomic analysis reveals a differential adaptation process of the larval stages of Anisakis simplex to the host environment. Front Mol Biosci 2023; 10:1233586. [PMID: 37520327 PMCID: PMC10373882 DOI: 10.3389/fmolb.2023.1233586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 07/04/2023] [Indexed: 08/01/2023] Open
Abstract
Introduction: Anisakis simplex are parasitic nematodes that cause anisakiasis. The possibility of infection with this parasite is through consumption of raw or undercooked fish products. A. simplex infections are often misdiagnosed, especially in subclinical cases that do not present with typical symptoms such as urticaria, angioedema, and gastrointestinal allergy. The resulting allergic reactions range from rapid-onset and potentially fatal anaphylactic reactions to chronic, debilitating conditions. While there have been numerous published studies on the genomes and proteomes of A. simplex, less attention has been paid to the metabolomes. Metabolomics is concerned with the composition of metabolites in biological systems. Dynamic responses to endogenous and exogenous stimuli are particularly well suited for the study of holistic metabolic responses. In addition, metabolomics can be used to determine metabolic activity at different stages of development or during growth. Materials and methods: In this study, we reveal for the first time the metabolomes of infectious stages (L3 and L4) of A. simplex using untargeted metabolomics by ultra-performance liquid chromatography-mass spectrometry. Results: In the negative ionization mode (ESI-), we identified 172 different compounds, whereas in the positive ionization mode (ESI+), 186 metabolites were found. Statistical analysis showed that 60 metabolites were found in the ESI- mode with different concentration in each group, of which 21 were more enriched in the L3 larvae and 39 in the L4 stage of A. simplex. Comparison of the individual developmental stages in the ESI + mode also revealed a total of 60 differential metabolites, but 32 metabolites were more enriched in the L3 stage larvae, and 28 metabolites were more concentrated in the L4 stage. Discussion: The metabolomics study revealed that the developmental stages of A. simplex differed in a number of metabolic pathways, including nicotinate and nicotinamide metabolism. In addition, molecules responsible for successful migration within their host, such as pyridoxine and prostaglandins (E1, E2, F1a) were present in the L4 stage. In contrast, metabolic pathways for amino acids, starch, and sucrose were mainly activated in the L3 stage. Our results provide new insights into the comparative metabolome profiles of two different developmental stages of A. simplex.
Collapse
Affiliation(s)
- Iwona Polak
- Department of Biochemistry, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
| | - Robert Stryiński
- Department of Biochemistry, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
| | - Marta Majewska
- Department of Human Physiology and Pathophysiology, School of Medicine, Collegium Medicum, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
| | - Elżbieta Łopieńska-Biernat
- Department of Biochemistry, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
| |
Collapse
|
22
|
Cheng T, Ono T, Shiota M, Yamada I, Aoki-Kinoshita KF, Bolton EE. Bridging glycoinformatics and cheminformatics: integration efforts between GlyCosmos and PubChem. Glycobiology 2023; 33:454-463. [PMID: 37129482 PMCID: PMC10284107 DOI: 10.1093/glycob/cwad028] [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: 11/11/2022] [Revised: 03/27/2023] [Accepted: 03/28/2023] [Indexed: 05/03/2023] Open
Abstract
The GlyCosmos Glycoscience Portal (https://glycosmos.org) and PubChem (https://pubchem.ncbi.nlm.nih.gov/) are major portals for glycoscience and chemistry, respectively. GlyCosmos is a portal for glycan-related repositories, including GlyTouCan, GlycoPOST, and UniCarb-DR, as well as for glycan-related data resources that have been integrated from a variety of 'omics databases. Glycogenes, glycoproteins, lectins, pathways, and disease information related to glycans are accessible from GlyCosmos. PubChem, on the other hand, is a chemistry-based portal at the National Center for Biotechnology Information. PubChem provides information not only on chemicals, but also genes, proteins, pathways, as well as patents, bioassays, and more, from hundreds of data resources from around the world. In this work, these 2 portals have made substantial efforts to integrate their complementary data to allow users to cross between these 2 domains. In addition to glycan structures, key information, such as glycan-related genes, relevant diseases, glycoproteins, and pathways, was integrated and cross-linked with one another. The interfaces were designed to enable users to easily find, access, download, and reuse data of interest across these resources. Use cases are described illustrating and highlighting the type of content that can be investigated. In total, these integrations provide life science researchers improved awareness and enhanced access to glycan-related information.
Collapse
Affiliation(s)
- Tiejun Cheng
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, United States
| | - Tamiko Ono
- Glycan and Life Systems Integration Center, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan
| | - Masaaki Shiota
- Glycan and Life Systems Integration Center, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan
| | - Issaku Yamada
- Laboratory of Glycoinformatics, The Noguchi Institute, 1-9-7 Kaga, Itabashi, Tokyo 173-0003, Japan
| | - Kiyoko F Aoki-Kinoshita
- Glycan and Life Systems Integration Center, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan
| | - Evan E Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, United States
| |
Collapse
|
23
|
Sun YL, Zhao PP, Zhu CB, Jiang MC, Li XM, Tao JL, Hu CC, Yuan B. Integrating metabolomics and network pharmacology to assess the effects of quercetin on lung inflammatory injury induced by human respiratory syncytial virus. Sci Rep 2023; 13:8051. [PMID: 37198253 DOI: 10.1038/s41598-023-35272-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 05/15/2023] [Indexed: 05/19/2023] Open
Abstract
Quercetin (QR) has significant anti-respiratory syncytial virus (RSV) effects. However, its therapeutic mechanism has not been thoroughly explored. In this study, a lung inflammatory injury model caused by RSV was established in mice. Untargeted lung tissue metabolomics was used to identify differential metabolites and metabolic pathways. Network pharmacology was used to predict potential therapeutic targets of QR and analyze biological functions and pathways modulated by QR. By overlapping the results of the metabolomics and the network pharmacology analyses, the common targets of QR that were likely to be involved in the amelioration of RSV-induced lung inflammatory injury by QR were identified. Metabolomics analysis identified 52 differential metabolites and 244 corresponding targets, while network pharmacology analysis identified 126 potential targets of QR. By intersecting these 244 targets with the 126 targets, hypoxanthine-guanine phosphoribosyltransferase (HPRT1), thymidine phosphorylase (TYMP), lactoperoxidase (LPO), myeloperoxidase (MPO), and cytochrome P450 19A1 (CYP19A1) were identified as the common targets. The key targets, HPRT1, TYMP, LPO, and MPO, were components of purine metabolic pathways. The present study demonstrated that QR effectively ameliorated RSV-induced lung inflammatory injury in the established mouse model. Combining metabolomics and network pharmacology showed that the anti-RSV effect of QR was closely associated with purine metabolism pathways.
Collapse
Affiliation(s)
- Ya-Lei Sun
- Department of Pediatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Key Laboratory of Pediatric Respiratory Disease, Institute of Pediatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Pei-Pei Zhao
- Department of Pediatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Key Laboratory of Pediatric Respiratory Disease, Institute of Pediatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Cheng-Bi Zhu
- Department of Pediatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Key Laboratory of Pediatric Respiratory Disease, Institute of Pediatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | | | - Xin-Min Li
- Henan University of Chinese Medicine, Zhengzhou, China
| | - Jia-Lei Tao
- Department of Pediatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China.
| | - Chan-Chan Hu
- Department of Pediatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China.
| | - Bin Yuan
- Department of Pediatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China.
| |
Collapse
|
24
|
Brinkhaus HO, Rajan K, Schaub J, Zielesny A, Steinbeck C. Open data and algorithms for open science in AI-driven molecular informatics. Curr Opin Struct Biol 2023; 79:102542. [PMID: 36805192 DOI: 10.1016/j.sbi.2023.102542] [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: 10/31/2022] [Revised: 01/10/2023] [Accepted: 01/13/2023] [Indexed: 02/19/2023]
Abstract
Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data as well as open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.
Collapse
Affiliation(s)
- Henning Otto Brinkhaus
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany
| | - Kohulan Rajan
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany
| | - Jonas Schaub
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany
| | - Achim Zielesny
- Institute for Bioinformatics and Chemoinformatics, Westphalian University of Applied Sciences, August-Schmidt-Ring 10, 45665 Recklinghausen, Germany
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany.
| |
Collapse
|
25
|
Naz A, Asif S, Alwutayd KM, Sarfaraz S, Abbasi SW, Abbasi A, Alenazi AM, Hasan ME. Repurposing FIASMAs against Acid Sphingomyelinase for COVID-19: A Computational Molecular Docking and Dynamic Simulation Approach. Molecules 2023; 28:molecules28072989. [PMID: 37049752 PMCID: PMC10096053 DOI: 10.3390/molecules28072989] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/16/2023] [Accepted: 03/24/2023] [Indexed: 03/30/2023] Open
Abstract
Over the past few years, COVID-19 has caused widespread suffering worldwide. There is great research potential in this domain and it is also necessary. The main objective of this study was to identify potential inhibitors against acid sphingomyelinase (ASM) in order to prevent coronavirus infection. Experimental studies revealed that SARS-CoV-2 causes activation of the acid sphingomyelinase/ceramide pathway, which in turn facilitates the viral entry into the cells. The objective was to inhibit acid sphingomyelinase activity in order to prevent the cells from SARS-CoV-2 infection. Previous studies have reported functional inhibitors against ASM (FIASMAs). These inhibitors can be exploited to block the entry of SARS-CoV-2 into the cells. To achieve our objective, a drug library containing 257 functional inhibitors of ASM was constructed. Computational molecular docking was applied to dock the library against the target protein (PDB: 5I81). The potential binding site of the target protein was identified through structural alignment with the known binding pocket of a protein with a similar function. AutoDock Vina was used to carry out the docking steps. The docking results were analyzed and the inhibitors were screened based on their binding affinity scores and ADME properties. Among the 257 functional inhibitors, Dutasteride, Cepharanthine, and Zafirlukast presented the lowest binding affinity scores of −9.7, −9.6, and −9.5 kcal/mol, respectively. Furthermore, computational ADME analysis of these results revealed Cepharanthine and Zafirlukast to have non-toxic properties. To further validate these findings, the top two inhibitors in complex with the target protein were subjected to molecular dynamic simulations at 100 ns. The molecular interactions and stability of these compounds revealed that these inhibitors could be a promising tool for inhibiting SARS-CoV-2 infection.
Collapse
Affiliation(s)
- Aliza Naz
- National Center for Bioinformatics, Quaid-i-Azam University, Islamabad 45320, Pakistan
- Department of Bioinformatics and Biotechnology, International Islamic University, Islamabad 44000, Pakistan
| | - Sumbul Asif
- Department of Bioinformatics and Biotechnology, International Islamic University, Islamabad 44000, Pakistan
- School of Interdisciplinary Engineering and Sciences, National University of Sciences and Technology, Islamabad 44000, Pakistan
| | - Khairiah Mubarak Alwutayd
- Department of Biology, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Sara Sarfaraz
- Department of Bioinformatics, Kohsar University Murree, Murree 47150, Pakistan
- Correspondence:
| | - Sumra Wajid Abbasi
- Department of Biological Sciences, National University of Medical Sciences, Rawalpindi 46000, Pakistan
| | - Asim Abbasi
- Department of Environmental Sciences, Kohsar University Murree, Murree 47150, Pakistan
| | - Abdulkareem M. Alenazi
- Pediatric Senior Registrar, King Salman Armed Forces Hospital in Northwestern Region (KSAFH), Tabuk 47512, Saudi Arabia
| | - Mohamed E. Hasan
- Bioinformatic Department, Genetic Engineering and Biotechnology Research Institute, University of Sadat City, Sadat City 32897, Egypt
| |
Collapse
|
26
|
Sobral PS, Luz VCC, Almeida JMGCF, Videira PA, Pereira F. Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors. Int J Mol Sci 2023; 24:ijms24065908. [PMID: 36982981 PMCID: PMC10054797 DOI: 10.3390/ijms24065908] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/16/2023] [Accepted: 03/19/2023] [Indexed: 03/30/2023] Open
Abstract
Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.
Collapse
Affiliation(s)
- Patrícia S Sobral
- LAQV and REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- UCIBIO, Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Vanessa C C Luz
- UCIBIO, Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - João M G C F Almeida
- UCIBIO, Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Paula A Videira
- UCIBIO, Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Florbela Pereira
- LAQV and REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| |
Collapse
|
27
|
Sayers EW, Bolton EE, Brister J, Canese K, Chan J, Comeau D, Farrell C, Feldgarden M, Fine AM, Funk K, Hatcher E, Kannan S, Kelly C, Kim S, Klimke W, Landrum M, Lathrop S, Lu Z, Madden T, Malheiro A, Marchler-Bauer A, Murphy T, Phan L, Pujar S, Rangwala S, Schneider V, Tse T, Wang J, Ye J, Trawick B, Pruitt K, Sherry S. Database resources of the National Center for Biotechnology Information in 2023. Nucleic Acids Res 2023; 51:D29-D38. [PMID: 36370100 PMCID: PMC9825438 DOI: 10.1093/nar/gkac1032] [Citation(s) in RCA: 135] [Impact Index Per Article: 135.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/11/2022] [Accepted: 11/09/2022] [Indexed: 11/15/2022] Open
Abstract
The National Center for Biotechnology Information (NCBI) provides online information resources for biology, including the GenBank® nucleic acid sequence database and the PubMed® database of citations and abstracts published in life science journals. NCBI provides search and retrieval operations for most of these data from 35 distinct databases. The E-utilities serve as the programming interface for most of these databases. New resources include the Comparative Genome Resource (CGR) and the BLAST ClusteredNR database. Resources receiving significant updates in the past year include PubMed, PMC, Bookshelf, IgBLAST, GDV, RefSeq, NCBI Virus, GenBank type assemblies, iCn3D, ClinVar, GTR, dbGaP, ALFA, ClinicalTrials.gov, Pathogen Detection, antimicrobial resistance resources, and PubChem. These resources can be accessed through the NCBI home page at https://www.ncbi.nlm.nih.gov.
Collapse
Affiliation(s)
- Eric W Sayers
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Evan E Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - J Rodney Brister
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Kathi Canese
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Jessica Chan
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Donald C Comeau
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Catherine M Farrell
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Michael Feldgarden
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Anna M Fine
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Kathryn Funk
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Eneida Hatcher
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Sivakumar Kannan
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Christopher Kelly
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Sunghwan Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - William Klimke
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Melissa J Landrum
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Stacy Lathrop
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Thomas L Madden
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Adriana Malheiro
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Aron Marchler-Bauer
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Terence D Murphy
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Lon Phan
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Shashikant Pujar
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Sanjida H Rangwala
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Valerie A Schneider
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Tony Tse
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Jiyao Wang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Jian Ye
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Barton W Trawick
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Kim D Pruitt
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Stephen T Sherry
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
| |
Collapse
|
28
|
Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, Zaslavsky L, Zhang J, Bolton EE. PubChem 2023 update. Nucleic Acids Res 2022; 51:D1373-D1380. [PMID: 36305812 PMCID: PMC9825602 DOI: 10.1093/nar/gkac956] [Citation(s) in RCA: 726] [Impact Index Per Article: 363.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 01/30/2023] Open
Abstract
PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves a wide range of use cases. In the past two years, a number of changes were made to PubChem. Data from more than 120 data sources was added to PubChem. Some major highlights include: the integration of Google Patents data into PubChem, which greatly expanded the coverage of the PubChem Patent data collection; the creation of the Cell Line and Taxonomy data collections, which provide quick and easy access to chemical information for a given cell line and taxon, respectively; and the update of the bioassay data model. In addition, new functionalities were added to the PubChem programmatic access protocols, PUG-REST and PUG-View, including support for target-centric data download for a given protein, gene, pathway, cell line, and taxon and the addition of the 'standardize' option to PUG-REST, which returns the standardized form of an input chemical structure. A significant update was also made to PubChemRDF. The present paper provides an overview of these changes.
Collapse
Affiliation(s)
- Sunghwan Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jie Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Tiejun Cheng
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Asta Gindulyte
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jia He
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Siqian He
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Qingliang Li
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Benjamin A Shoemaker
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Paul A Thiessen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Bo Yu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Leonid Zaslavsky
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jian Zhang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Evan E Bolton
- To whom correspondence should be addressed. Tel: +1 301 451 1811; Fax: +1 301 480 4559;
| |
Collapse
|
29
|
Mohammed Taha H, Aalizadeh R, Alygizakis N, Antignac JP, Arp HPH, Bade R, Baker N, Belova L, Bijlsma L, Bolton EE, Brack W, Celma A, Chen WL, Cheng T, Chirsir P, Čirka Ľ, D’Agostino LA, Djoumbou Feunang Y, Dulio V, Fischer S, Gago-Ferrero P, Galani A, Geueke B, Głowacka N, Glüge J, Groh K, Grosse S, Haglund P, Hakkinen PJ, Hale SE, Hernandez F, Janssen EML, Jonkers T, Kiefer K, Kirchner M, Koschorreck J, Krauss M, Krier J, Lamoree MH, Letzel M, Letzel T, Li Q, Little J, Liu Y, Lunderberg DM, Martin JW, McEachran AD, McLean JA, Meier C, Meijer J, Menger F, Merino C, Muncke J, Muschket M, Neumann M, Neveu V, Ng K, Oberacher H, O’Brien J, Oswald P, Oswaldova M, Picache JA, Postigo C, Ramirez N, Reemtsma T, Renaud J, Rostkowski P, Rüdel H, Salek RM, Samanipour S, Scheringer M, Schliebner I, Schulz W, Schulze T, Sengl M, Shoemaker BA, Sims K, Singer H, Singh RR, Sumarah M, Thiessen PA, Thomas KV, Torres S, Trier X, van Wezel AP, Vermeulen RCH, Vlaanderen JJ, von der Ohe PC, Wang Z, Williams AJ, Willighagen EL, Wishart DS, Zhang J, Thomaidis NS, Hollender J, Slobodnik J, Schymanski EL. The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry. ENVIRONMENTAL SCIENCES EUROPE 2022; 34:104. [PMID: 36284750 PMCID: PMC9587084 DOI: 10.1186/s12302-022-00680-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 09/24/2022] [Indexed: 06/16/2023]
Abstract
Background The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for "suspect screening" lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide. Results The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https://zenodo.org/communities/norman-sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA's CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101). Conclusions The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the "one substance, one assessment" approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-network.com/nds/SLE/). Supplementary Information The online version contains supplementary material available at 10.1186/s12302-022-00680-6.
Collapse
Affiliation(s)
- Hiba Mohammed Taha
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, 4367 Belvaux, Luxembourg
| | - Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Nikiforos Alygizakis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
- Environmental Institute, Okružná 784/42, 972 41 Koš, Slovak Republic
| | | | - Hans Peter H. Arp
- Norwegian Geotechnical Institute (NGI), Ullevål Stadion, P.O. Box 3930, 0806 Oslo, Norway
- Department of Chemistry, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
| | - Richard Bade
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, QLD 4102 Australia
| | | | - Lidia Belova
- Toxicological Centre, University of Antwerp, Antwerp, Belgium
| | - Lubertus Bijlsma
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, Spain
| | - Evan E. Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894 USA
| | - Werner Brack
- UFZ, Helmholtz Centre for Environmental Research, Leipzig, Germany
- Institute of Ecology, Evolution and Diversity, Goethe University, Frankfurt Am Main, Germany
| | - Alberto Celma
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, Spain
- Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
| | - Wen-Ling Chen
- Institute of Food Safety and Health, College of Public Health, National Taiwan University, 17 Xuzhou Rd., Zhongzheng Dist., Taipei, Taiwan
| | - Tiejun Cheng
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894 USA
| | - Parviel Chirsir
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, 4367 Belvaux, Luxembourg
| | - Ľuboš Čirka
- Environmental Institute, Okružná 784/42, 972 41 Koš, Slovak Republic
- Faculty of Chemical and Food Technology, Institute of Information Engineering, Automation, and Mathematics, Slovak University of Technology in Bratislava (STU), Radlinského 9, 812 37 Bratislava, Slovak Republic
| | - Lisa A. D’Agostino
- Science for Life Laboratory, Department of Environmental Science, Stockholm University, 10691 Stockholm, Sweden
| | | | - Valeria Dulio
- INERIS, National Institute for Environment and Industrial Risks, Verneuil en Halatte, France
| | - Stellan Fischer
- Swedish Chemicals Agency (KEMI), P.O. Box 2, 172 13 Sundbyberg, Sweden
| | - Pablo Gago-Ferrero
- Institute of Environmental Assessment and Water Research-Severo Ochoa Excellence Center (IDAEA), Spanish Council of Scientific Research (CSIC), Barcelona, Spain
| | - Aikaterini Galani
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Birgit Geueke
- Food Packaging Forum Foundation, Staffelstrasse 10, 8045 Zurich, Switzerland
| | - Natalia Głowacka
- Environmental Institute, Okružná 784/42, 972 41 Koš, Slovak Republic
| | - Juliane Glüge
- Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, 8092 Zurich, Switzerland
| | - Ksenia Groh
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Sylvia Grosse
- Thermo Fisher Scientific, Dornierstrasse 4, 82110 Germering, Germany
| | - Peter Haglund
- Department of Chemistry, Chemical Biological Centre (KBC), Umeå University, Linnaeus Väg 6, 901 87 Umeå, Sweden
| | - Pertti J. Hakkinen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894 USA
| | - Sarah E. Hale
- Norwegian Geotechnical Institute (NGI), Ullevål Stadion, P.O. Box 3930, 0806 Oslo, Norway
| | - Felix Hernandez
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, Spain
| | - Elisabeth M.-L. Janssen
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Tim Jonkers
- Department Environment and Health, Amsterdam Institute for Life and Environment, Vrije Universiteit, Amsterdam, The Netherlands
| | - Karin Kiefer
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Michal Kirchner
- Water Research Institute (WRI), Nábr. Arm. Gen. L. Svobodu 5, 81249 Bratislava, Slovak Republic
| | - Jan Koschorreck
- German Environment Agency (UBA), Wörlitzer Platz 1, Dessau-Roßlau, Germany
| | - Martin Krauss
- UFZ, Helmholtz Centre for Environmental Research, Leipzig, Germany
| | - Jessy Krier
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, 4367 Belvaux, Luxembourg
| | - Marja H. Lamoree
- Department Environment and Health, Amsterdam Institute for Life and Environment, Vrije Universiteit, Amsterdam, The Netherlands
| | - Marion Letzel
- Bavarian Environment Agency, 86179 Augsburg, Germany
| | - Thomas Letzel
- Analytisches Forschungsinstitut Für Non-Target Screening GmbH (AFIN-TS), Am Mittleren Moos 48, 86167 Augsburg, Germany
| | - Qingliang Li
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894 USA
| | - James Little
- Mass Spec Interpretation Services, 3612 Hemlock Park Drive, Kingsport, TN 37663 USA
| | - Yanna Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (SKLECE, RCEES, CAS), No. 18 Shuangqing Road, Haidian District, Beijing, 100086 China
| | - David M. Lunderberg
- Hope College, Holland, MI 49422 USA
- University of California, Berkeley, CA USA
| | - Jonathan W. Martin
- Science for Life Laboratory, Department of Environmental Science, Stockholm University, 10691 Stockholm, Sweden
| | - Andrew D. McEachran
- Agilent Technologies, Inc., 5301 Stevens Creek Blvd, Santa Clara, CA 95051 USA
| | - John A. McLean
- Department of Chemistry, Center for Innovative Technology, Vanderbilt-Ingram Cancer Center, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN 37235 USA
| | - Christiane Meier
- German Environment Agency (UBA), Wörlitzer Platz 1, Dessau-Roßlau, Germany
| | - Jeroen Meijer
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands
| | - Frank Menger
- Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
| | - Carla Merino
- University Rovira i Virgili, Tarragona, Spain
- Biosfer Teslab, Reus, Spain
| | - Jane Muncke
- Food Packaging Forum Foundation, Staffelstrasse 10, 8045 Zurich, Switzerland
| | | | - Michael Neumann
- German Environment Agency (UBA), Wörlitzer Platz 1, Dessau-Roßlau, Germany
| | - Vanessa Neveu
- Nutrition and Metabolism Branch, International Agency for Research On Cancer (IARC), 150 Cours Albert Thomas, 69372 Lyon Cedex 08, France
| | - Kelsey Ng
- Environmental Institute, Okružná 784/42, 972 41 Koš, Slovak Republic
- RECETOX, Faculty of Science, Masaryk University, Kotlářská 2, Brno, Czech Republic
| | - Herbert Oberacher
- Institute of Legal Medicine and Core Facility Metabolomics, Medical University of Innsbruck, Muellerstrasse 44, Innsbruck, Austria
| | - Jake O’Brien
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, QLD 4102 Australia
| | - Peter Oswald
- Environmental Institute, Okružná 784/42, 972 41 Koš, Slovak Republic
| | - Martina Oswaldova
- Environmental Institute, Okružná 784/42, 972 41 Koš, Slovak Republic
| | - Jaqueline A. Picache
- Department of Chemistry, Center for Innovative Technology, Vanderbilt-Ingram Cancer Center, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN 37235 USA
| | - Cristina Postigo
- Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
- Technologies for Water Management and Treatment Research Group, Department of Civil Engineering, University of Granada, Campus de Fuentenueva S/N, 18071 Granada, Spain
| | - Noelia Ramirez
- University Rovira i Virgili, Tarragona, Spain
- Institute of Health Research Pere Virgili, Tarragona, Spain
| | | | - Justin Renaud
- Agriculture and Agri-Food Canada/Agriculture et Agroalimentaire Canada, 1391 Sandford Street, London, ON N5V 4T3 Canada
| | | | - Heinz Rüdel
- Fraunhofer Institute for Molecular Biology and Applied Ecology (Fraunhofer IME), Schmallenberg, Germany
| | - Reza M. Salek
- Nutrition and Metabolism Branch, International Agency for Research On Cancer (IARC), 150 Cours Albert Thomas, 69372 Lyon Cedex 08, France
| | - Saer Samanipour
- Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, P.O. Box 94157, Amsterdam, 1090 GD The Netherlands
| | - Martin Scheringer
- Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, 8092 Zurich, Switzerland
- RECETOX, Faculty of Science, Masaryk University, Kotlářská 2, Brno, Czech Republic
| | - Ivo Schliebner
- German Environment Agency (UBA), Wörlitzer Platz 1, Dessau-Roßlau, Germany
| | - Wolfgang Schulz
- Laboratory for Operation Control and Research, Zweckverband Landeswasserversorgung, Am Spitzigen Berg 1, 89129 Langenau, Germany
| | - Tobias Schulze
- UFZ, Helmholtz Centre for Environmental Research, Leipzig, Germany
| | - Manfred Sengl
- Bavarian Environment Agency, 86179 Augsburg, Germany
| | - Benjamin A. Shoemaker
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894 USA
| | - Kerry Sims
- Environment Agency, Horizon House, Deanery Road, Bristol, BS1 5AH UK
| | - Heinz Singer
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Randolph R. Singh
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, 4367 Belvaux, Luxembourg
- Chemical Contamination of Marine Ecosystems (CCEM) Unit, Institut Français de Recherche pour l’Exploitation de la Mer (IFREMER), Rue de l’Ile d’Yeu, BP 21105, 44311 Cedex 3, Nantes France
| | - Mark Sumarah
- Agriculture and Agri-Food Canada/Agriculture et Agroalimentaire Canada, 1391 Sandford Street, London, ON N5V 4T3 Canada
| | - Paul A. Thiessen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894 USA
| | - Kevin V. Thomas
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, QLD 4102 Australia
| | | | - Xenia Trier
- Section for Environmental Chemistry and Physics, Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
| | - Annemarie P. van Wezel
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands
| | - Roel C. H. Vermeulen
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands
| | - Jelle J. Vlaanderen
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands
| | | | - Zhanyun Wang
- Technology and Society Laboratory, Empa-Swiss Federal Laboratories for Materials Science and Technology, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland
| | - Antony J. Williams
- Computational Chemistry and Cheminformatics Branch (CCCB), Chemical Characterization and Exposure Division (CCED), Center for Computational Toxicology and Exposure (CCTE), United States Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711 USA
| | - Egon L. Willighagen
- Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands
| | | | - Jian Zhang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894 USA
| | - Nikolaos S. Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Juliane Hollender
- Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, 8092 Zurich, Switzerland
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | | | - Emma L. Schymanski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, 4367 Belvaux, Luxembourg
| |
Collapse
|
30
|
Computational Resources for Molecular Biology 2022. J Mol Biol 2022; 434:167625. [PMID: 35569508 DOI: 10.1016/j.jmb.2022.167625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|