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Chen L, Gu R, Li Y, Liu H, Han W, Yan Y, Chen Y, Zhang Y, Jiang Y. Epigenetic target identification strategy based on multi-feature learning. J Biomol Struct Dyn 2024; 42:5946-5962. [PMID: 37827992 DOI: 10.1080/07391102.2023.2259511] [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: 01/16/2023] [Accepted: 06/20/2023] [Indexed: 10/14/2023]
Abstract
The identification of potential epigenetic targets for a known bioactive compound is essential and promising as more and more epigenetic drugs are used in cancer clinical treatment and the availability of chemogenomic data related to epigenetics increases. In this study, we introduce a novel epigenetic target identification strategy (ETI-Strategy) that integrates a multi-task graph convolutional neural network prior model and a protein-ligand interaction classification discriminating model using large-scale bioactivity data for a panel of 55 epigenetic targets. Our approach utilizes machine learning techniques to achieve an AUC value of 0.934 for the prior model and 0.830 for the discriminating model, outperforming inverse docking in predicting protein-ligand interactions. When comparing with other open-source target identification tools, it was found that only our tool was able to accurately predict all the targets corresponding to each compound. This further demonstrates the ability of our strategy to take full advantage of molecular-level information as well as protein-level information in molecular activity prediction. Our work highlights the contribution of machine learning in the identification of potential epigenetic targets and offers a novel approach for epigenetic drug discovery and development.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Lingfeng Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Rui Gu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yuanyuan Li
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Weijie Han
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yingchao Yan
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yulei Jiang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
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2
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Upton C, Healey J, Rothnie AJ, Goddard AD. Insights into membrane interactions and their therapeutic potential. Arch Biochem Biophys 2024; 755:109939. [PMID: 38387829 DOI: 10.1016/j.abb.2024.109939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/31/2024] [Accepted: 02/19/2024] [Indexed: 02/24/2024]
Abstract
Recent research into membrane interactions has uncovered a diverse range of therapeutic opportunities through the bioengineering of human and non-human macromolecules. Although the majority of this research is focussed on fundamental developments, emerging studies are showcasing promising new technologies to combat conditions such as cancer, Alzheimer's and inflammatory and immune-based disease, utilising the alteration of bacteriophage, adenovirus, bacterial toxins, type 6 secretion systems, annexins, mitochondrial antiviral signalling proteins and bacterial nano-syringes. To advance the field further, each of these opportunities need to be better understood, and the therapeutic models need to be further optimised. Here, we summarise the knowledge and insights into several membrane interactions and detail their current and potential uses therapeutically.
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Affiliation(s)
- Calum Upton
- School of Biosciences, Health & Life Science, Aston University, Birmingham, B4 7ET, UK
| | - Joseph Healey
- Nanosyrinx, The Venture Centre, University of Warwick Science Park, Coventry, CV4 7EZ, UK
| | - Alice J Rothnie
- School of Biosciences, Health & Life Science, Aston University, Birmingham, B4 7ET, UK
| | - Alan D Goddard
- School of Biosciences, Health & Life Science, Aston University, Birmingham, B4 7ET, UK.
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3
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Zdrazil B, Felix E, Hunter F, Manners EJ, Blackshaw J, Corbett S, de Veij M, Ioannidis H, Lopez DM, Mosquera J, Magarinos M, Bosc N, Arcila R, Kizilören T, Gaulton A, Bento A, Adasme M, Monecke P, Landrum G, Leach A. The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Res 2024; 52:D1180-D1192. [PMID: 37933841 PMCID: PMC10767899 DOI: 10.1093/nar/gkad1004] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 10/09/2023] [Accepted: 10/23/2023] [Indexed: 11/08/2023] Open
Abstract
ChEMBL (https://www.ebi.ac.uk/chembl/) is a manually curated, high-quality, large-scale, open, FAIR and Global Core Biodata Resource of bioactive molecules with drug-like properties, previously described in the 2012, 2014, 2017 and 2019 Nucleic Acids Research Database Issues. Since its introduction in 2009, ChEMBL's content has changed dramatically in size and diversity of data types. Through incorporation of multiple new datasets from depositors since the 2019 update, ChEMBL now contains slightly more bioactivity data from deposited data vs data extracted from literature. In collaboration with the EUbOPEN consortium, chemical probe data is now regularly deposited into ChEMBL. Release 27 made curated data available for compounds screened for potential anti-SARS-CoV-2 activity from several large-scale drug repurposing screens. In addition, new patent bioactivity data have been added to the latest ChEMBL releases, and various new features have been incorporated, including a Natural Product likeness score, updated flags for Natural Products, a new flag for Chemical Probes, and the initial annotation of the action type for ∼270 000 bioactivity measurements.
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Affiliation(s)
- Barbara Zdrazil
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Eloy Felix
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Fiona Hunter
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Emma J Manners
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - James Blackshaw
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Sybilla Corbett
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Marleen de Veij
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Harris Ioannidis
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - David Mendez Lopez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Juan F Mosquera
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Maria Paula Magarinos
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Nicolas Bosc
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Ricardo Arcila
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Tevfik Kizilören
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Anna Gaulton
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - A Patrícia Bento
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Melissa F Adasme
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Peter Monecke
- Sanofi, R&D, Preclinical Safety, Industriepark Höchst, 65926 Frankfurt am Main, Germany
| | - Gregory A Landrum
- Department of Chemistry and Applied Biosciences, ETH Zürich, 8093 Zürich, Switzerland
| | - Andrew R Leach
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
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Majellaro M, Bondar A. Editorial: Advanced biophysical and biochemical technologies to study GPCR signal transduction. Front Endocrinol (Lausanne) 2024; 14:1354689. [PMID: 38239976 PMCID: PMC10795064 DOI: 10.3389/fendo.2023.1354689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 12/20/2023] [Indexed: 01/22/2024] Open
Affiliation(s)
| | - Alexey Bondar
- Faculty of Science, University of South Bohemia in Ceské Budejovice, Ceské Budejovice, Czechia
- Laboratory of Microscopy and Histology, Institute of Entomology, Biology Centre of the Czech Academy of Sciences, Ceské Budejovice, Czechia
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5
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Masand VH, Al-Hussain S, Alzahrani AY, El-Sayed NNE, Yeo CI, Tan YS, Zaki MEA. Leveraging nitrogen occurrence in approved drugs to identify structural patterns. Expert Opin Drug Discov 2024; 19:111-124. [PMID: 37811790 DOI: 10.1080/17460441.2023.2266990] [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: 07/27/2023] [Accepted: 10/02/2023] [Indexed: 10/10/2023]
Abstract
BACKGROUND The process of drug development and discovery is costly and slow. Although an understanding of molecular design principles and biochemical processes has progressed, it is essential to minimize synthesis-testing cycles. An effective approach is to analyze key heteroatoms, including oxygen and nitrogen. Herein, we present an analysis focusing on the utilization of nitrogen atoms in approved drugs. RESEARCH DESIGN AND METHODS The present work examines the frequency, distribution, prevalence, and diversity of nitrogen atoms in a dataset comprising 2,049 small molecules approved by different regulatory agencies (FDA and others). Various types of nitrogen atoms, such as sp3-, sp2-, sp-hybridized, planar, ring, and non-ring are included in this investigation. RESULTS The results unveil both previously reported and newly discovered patterns of nitrogen atom distribution around the center of mass in the majority of drug molecules. CONCLUSIONS This study has highlighted intriguing trends in the role of nitrogen atoms in drug design and development. The majority of drugs contain 1-3 nitrogen atoms within 5Å from the center of mass (COM) of a molecule, with a higher preference for the ring and planar nitrogen atoms. The results offer invaluable guidance for the multiparameter optimization process, thus significantly contributing toward the conversion of lead compounds into potential drug candidates.
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Affiliation(s)
- Vijay H Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati, India
| | - Sami Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Abdullah Y Alzahrani
- Department of Chemistry, Faculty of Science and Arts, King Khalid University, Mohail Assir, Saudi Arabia
| | - Nahed N E El-Sayed
- National Organization for Drug Control and Research, Egyptian Drug Authority (EDA), Giza, Egypt
| | - Chien Ing Yeo
- Sunway Biofunctional Molecules Discovery Centre, School of Medical and Life Sciences, Sunway University, Sunway City, Malaysia
| | - Yee Seng Tan
- Sunway Biofunctional Molecules Discovery Centre, School of Medical and Life Sciences, Sunway University, Sunway City, Malaysia
| | - Magdi E A Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
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6
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Singh P, Mohsin M, Sultan A, Jha P, Khan MM, Syed MA, Chopra M, Serajuddin M, Rahmani AH, Almatroodi SA, Alrumaihi F, Dohare R. Combined Multiomics and In Silico Approach Uncovers PRKAR1A as a Putative Therapeutic Target in Multi-Organ Dysfunction Syndrome. ACS OMEGA 2023; 8:9555-9568. [PMID: 36936296 PMCID: PMC10018728 DOI: 10.1021/acsomega.3c00020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Despite all epidemiological, clinical, and experimental research efforts, therapeutic concepts in sepsis and sepsis-induced multi-organ dysfunction syndrome (MODS) remain limited and unsatisfactory. Currently, gene expression data sets are widely utilized to discover new biomarkers and therapeutic targets in diseases. In the present study, we analyzed MODS expression profiles (comprising 13 sepsis and 8 control samples) retrieved from NCBI-GEO and found 359 differentially expressed genes (DEGs), among which 170 were downregulated and 189 were upregulated. Next, we employed the weighted gene co-expression network analysis (WGCNA) to establish a MODS-associated gene co-expression network (weighted) and identified representative module genes having an elevated correlation with age. Based on the results, a turquoise module was picked as our hub module. Further, we constructed the PPI network comprising 35 hub module DEGs. The DEGs involved in the highest-confidence PPI network were utilized for collecting pathway and gene ontology (GO) terms using various libraries. Nucleotide di- and triphosphate biosynthesis and interconversion was the most significant pathway. Also, 3 DEGs within our PPI network were involved in the top 5 significantly enriched ontology terms, with hypercortisolism being the most significant term. PRKAR1A was the overlapping gene between top 5 significant pathways and GO terms, respectively. PRKAR1A was considered as a therapeutic target in MODS, and 2992 ligands were screened for binding with PRKAR1A. Among these ligands, 3 molecules based on CDOCKER score (molecular dynamics simulated-based score, which allows us to rank the binding poses according to their quality and to identify the best pose for each system) and crucial interaction with human PRKAR1A coding protein and protein kinase-cyclic nucleotide binding domains (PKA RI alpha CNB-B domain) via active site binding residues, viz. Val283, Val302, Gln304, Val315, Ile327, Ala336, Ala337, Val339, Tyr373, and Asn374, were considered as lead molecules.
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Affiliation(s)
- Prithvi Singh
- Centre
for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Mohd Mohsin
- Department
of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Armiya Sultan
- Department
of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Prakash Jha
- Laboratory
of Molecular Modeling and Anticancer Drug Development, Dr. B. R. Ambedkar
Center for Biomedical Research, University
of Delhi, New Delhi 110007, India
| | - Mohd Mabood Khan
- Department
of Zoology, University of Lucknow, Lucknow, Uttar Pradesh, 226007, India
| | - Mansoor Ali Syed
- Department
of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Madhu Chopra
- Laboratory
of Molecular Modeling and Anticancer Drug Development, Dr. B. R. Ambedkar
Center for Biomedical Research, University
of Delhi, New Delhi 110007, India
| | - Mohammad Serajuddin
- Department
of Zoology, University of Lucknow, Lucknow, Uttar Pradesh, 226007, India
| | - Arshad Husain Rahmani
- Department
of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia
| | - Saleh A. Almatroodi
- Department
of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia
| | - Faris Alrumaihi
- Department
of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia
| | - Ravins Dohare
- Centre
for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
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7
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Zhai Z, Zhu Z, Kong F, Xie D, Cai J, Dai J, Zhong Y, Gan Y, Zheng S, Xu Y, Sun T. Distinguish the Characteristic Mechanism of 3 Drug Pairs of Corydalis Rhizome in Ameliorating Angina Pectoris: Network Pharmacology and Meta-Analysis. Nat Prod Commun 2023. [DOI: 10.1177/1934578x231152309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Objective: Angina pectoris (AP), affecting over 523 million people, can be alleviated by corydalis rhizome (CR), usually combined with chuanxiong rhizome (CXR), angelica dahuricae radix (ADR), or astragali radix (AR) to enhance the effect. This study aims to distinguish the different mechanisms among 3 drug pairs to treat AP. Methods: The drug pair-disease intersection targets, compound targets, protein–protein interaction (PPI), and herb-compound-target-pathway network were obtained by Cytoscape, STRING, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses ( http://www.kegg.jp/ or http://www.genome.jp/kegg/ ). Importantly, with principal component analysis (PCA), the key point of KEGG and GO were explored and supported, while by meta-analysis, the different mechanisms of the drug pairs on AP were discovered. Results: JUN, SRC, PIK3CA, and MAPK1 as PPI core network of CR-AP, (CR-CXR)-AP, (CR-ADR)-AP, and (CR-AR)-AP. (highest confidence > 0.9). 10, 45, 35, and 21 key compounds, and 68, 123, 117, and 97 core targets were obtained from CR-AP, (CR-CXR)-AP, (CR-ADR)-AP, and (CR-AR)-AP based on more than 2-fold median value for degree and betweenness centrality, more than the median of closeness centrality. The core pathways of (CR-CXR)-AP and (CR-AR)-AP cover “fluid shear stress and atherosclerosis” and the “pathways in cancer”, while (CR-ADR)-AP was found as the “pathways in cancer” by PCA and KEGG ( P < .01). The core biological processes of (BP) (CR-CXR)-AP, (CR-ADR)-AP, and (CR-AR)-AP were all enriched in the “circulatory system process” by PCA and GO ( P < .01). Moreover, meta-analysis indicated the significant differences ( P < .05) of the 3 drug pairs. Conclusion: CR-CXR, CR-ADR, or CR-AR outperformed CR-AP in AP mitigation. Furthermore, meta-analysis revealed, CR-CXR was superior to alleviating AP by affecting “circulatory system process” and “fluid shear stress and atherosclerosis”, particularly the targets PTGS1, PTGS2, ADRB2, ADRA2C, and NOS, when compared with the drug pair of CR-ADR and the CR-AR.
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Affiliation(s)
- Zhenwei Zhai
- School of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zhishan Zhu
- School of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Fanjing Kong
- School of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Danni Xie
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jie Cai
- School of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jingyi Dai
- School of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yanmei Zhong
- School of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yanxiong Gan
- School of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Shichao Zheng
- School of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Ying Xu
- TCM Regulating Metabolic Diseases Key Laboratory of Sichuan Province, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Tao Sun
- School of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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8
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Down the membrane hole: Ion channels in protozoan parasites. PLoS Pathog 2022; 18:e1011004. [PMID: 36580479 PMCID: PMC9799330 DOI: 10.1371/journal.ppat.1011004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Parasitic diseases caused by protozoans are highly prevalent around the world, disproportionally affecting developing countries, where coinfection with other microorganisms is common. Control and treatment of parasitic infections are constrained by the lack of specific and effective drugs, plus the rapid emergence of resistance. Ion channels are main drug targets for numerous diseases, but their potential against protozoan parasites is still untapped. Ion channels are membrane proteins expressed in all types of cells, allowing for the flow of ions between compartments, and regulating cellular functions such as membrane potential, excitability, volume, signaling, and death. Channels and transporters reside at the interface between parasites and their hosts, controlling nutrient uptake, viability, replication, and infectivity. To understand how ion channels control protozoan parasites fate and to evaluate their suitability for therapeutics, we must deepen our knowledge of their structure, function, and modulation. However, methodological approaches commonly used in mammalian cells have proven difficult to apply in protozoans. This review focuses on ion channels described in protozoan parasites of clinical relevance, mainly apicomplexans and trypanosomatids, highlighting proteins for which molecular and functional evidence has been correlated with their physiological functions.
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9
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Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets. Comput Struct Biotechnol J 2022; 21:46-57. [PMID: 36514341 PMCID: PMC9732000 DOI: 10.1016/j.csbj.2022.11.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/21/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022] Open
Abstract
Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.
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10
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Veríssimo GC, Serafim MSM, Kronenberger T, Ferreira RS, Honorio KM, Maltarollo VG. Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern. Expert Opin Drug Discov 2022; 17:929-947. [PMID: 35983695 DOI: 10.1080/17460441.2022.2114451] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Modern drug discovery generally is accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed. AREAS COVERED One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future. EXPERT OPINION Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.
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Affiliation(s)
- Gabriel C Veríssimo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Mateus Sá M Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Thales Kronenberger
- Department of Medical Oncology and Pneumology, Internal Medicine VIII, University Hospital of Tübingen, Tübingen, Germany.,School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Rafaela S Ferreira
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Kathia M Honorio
- Escola de Artes, Ciências e Humanidades, Universidade de São Paulo (USP), São Paulo, Brazil.,Centro de Ciências Naturais e Humanas, Universidade Federal do ABC (UFABC), Santo André, Brazil
| | - Vinícius G Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
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11
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Lushington GH, Zgurzynski MI. Can the Written Word Fuel Pharmaceutical Innovation? Part 1. An Emerging Vista from von Economo to COVID-19. Comb Chem High Throughput Screen 2022; 25:1237-1238. [PMID: 35466871 DOI: 10.2174/1386207325666220422135755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/12/2022] [Accepted: 03/12/2022] [Indexed: 11/22/2022]
Affiliation(s)
- Gerald H Lushington
- Qnapsyn Biosciences, Inc. 16 Dekalb Pike, Suite 248, Blue Bell, PA 19422, USA
| | - Mary I Zgurzynski
- Boston College, Communication Dept., 140 Commonwealth Ave. Chestnut Hill, MA 02467, USA
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12
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Saldívar-González FI, Medina-Franco JL. Approaches for enhancing the analysis of chemical space for drug discovery. Expert Opin Drug Discov 2022; 17:789-798. [PMID: 35640229 DOI: 10.1080/17460441.2022.2084608] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
INTRODUCTION Chemical space is a powerful, general, and practical conceptual framework in drug discovery and other areas in chemistry that addresses the diversity of molecules and it has various applications. Moreover, chemical space is a cornerstone of chemoinformatics as a scientific discipline. In response to the increase in the set of chemical compounds in databases, generators of chemical structures, and tools to calculate molecular descriptors, novel approaches to generate visual representations of chemical space in low dimensions are emerging and evolving. Such approaches include a wide range of commercial and free applications, software, and open-source methods. AREAS COVERED The current state of chemical space in drug design and discovery is reviewed. The topics discussed herein include advances for efficient navigation in chemical space, the use of this concept in assessing the diversity of different data sets, exploring structure-property/activity relationships for one or multiple endpoints, and compound library design. Recent advances in methodologies for generating visual representations of chemical space have been highlighted, thereby emphasizing open-source methods. EXPERT OPINION Quantitative and qualitative generation and analysis of chemical space require novel approaches for handling the increasing number of molecules and their information available in chemical databases (including emerging ultra-large libraries). In addition, it is of utmost importance to note that chemical space is a conceptual framework that goes beyond visual representation in low dimensions. However, the graphical representation of chemical space has several practical applications in drug discovery and beyond.
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Affiliation(s)
- Fernanda I Saldívar-González
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico
| | - José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico
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Dey V, Machiraju R, Ning X. Improving Compound Activity Classification via Deep Transfer and Representation Learning. ACS OMEGA 2022; 7:9465-9483. [PMID: 35350358 PMCID: PMC8945064 DOI: 10.1021/acsomega.1c06805] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 02/23/2022] [Indexed: 06/14/2023]
Abstract
Recent advances in molecular machine learning, especially deep neural networks such as graph neural networks (GNNs), for predicting structure-activity relationships (SAR) have shown tremendous potential in computer-aided drug discovery. However, the applicability of such deep neural networks is limited by the requirement of large amounts of training data. In order to cope with limited training data for a target task, transfer learning for SAR modeling has been recently adopted to leverage information from data of related tasks. In this work, in contrast to the popular parameter-based transfer learning such as pretraining, we develop novel deep transfer learning methods TAc and TAc-fc to leverage source domain data and transfer useful information to the target domain. TAc learns to generate effective molecular features that can generalize well from one domain to another and increase the classification performance in the target domain. Additionally, TAc-fc extends TAc by incorporating novel components to selectively learn feature-wise and compound-wise transferability. We used the bioassay screening data from PubChem and identified 120 pairs of bioassays such that the active compounds in each pair are more similar to each other compared to their inactive compounds. Overall, TAc achieves the best performance with an average ROC-AUC of 0.801; it significantly improves the ROC-AUC of 83% of target tasks with an average task-wise performance improvement of 7.102%, compared to the best baseline dmpna. Our experiments clearly demonstrate that TAc achieves significant improvement over all baselines across a large number of target tasks. Furthermore, although TAc-fc achieves slightly worse ROC-AUC on average compared to TAc (0.798 vs 0.801), TAc-fc still achieves the best performance on more tasks in terms of PR-AUC and F1 compared to other methods. In summary, TAc-fc is also found to be a strong model with competitive or even better performance than TAc on a notable number of target tasks.
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Affiliation(s)
- Vishal Dey
- Department
of Computer Science and Engineering, The
Ohio State University, Columbus, Ohio 43210, United States
| | - Raghu Machiraju
- Department
of Computer Science and Engineering, The
Ohio State University, Columbus, Ohio 43210, United States
- Biomedical
Informatics, The Ohio State University, Columbus, Ohio 43210, United States
- Translational
Data Analytics Institute, The Ohio State
University, Columbus, Ohio 43210, United
States
| | - Xia Ning
- Department
of Computer Science and Engineering, The
Ohio State University, Columbus, Ohio 43210, United States
- Biomedical
Informatics, The Ohio State University, Columbus, Ohio 43210, United States
- Translational
Data Analytics Institute, The Ohio State
University, Columbus, Ohio 43210, United
States
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14
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Ventouri IK, Loeber S, Somsen GW, Schoenmakers PJ, Astefanei A. Field-flow fractionation for molecular-interaction studies of labile and complex systems: A critical review. Anal Chim Acta 2022; 1193:339396. [DOI: 10.1016/j.aca.2021.339396] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 11/11/2021] [Accepted: 12/22/2021] [Indexed: 12/11/2022]
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15
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You J, Hsing M, Cherkasov A. Deep Modeling of Regulating Effects of Small Molecules on Longevity-Associated Genes. Pharmaceuticals (Basel) 2021; 14:948. [PMID: 34681172 PMCID: PMC8539656 DOI: 10.3390/ph14100948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/17/2021] [Accepted: 09/18/2021] [Indexed: 11/16/2022] Open
Abstract
Aging is considered an inevitable process that causes deleterious effects in the functioning and appearance of cells, tissues, and organs. Recent emergence of large-scale gene expression datasets and significant advances in machine learning techniques have enabled drug repurposing efforts in promoting longevity. In this work, we further developed our previous approach-DeepCOP, a quantitative chemogenomic model that predicts gene regulating effects, and extended its application across multiple cell lines presented in LINCS to predict aging gene regulating effects induced by small molecules. As a result, a quantitative chemogenomic Deep Model was trained using gene ontology labels, molecular fingerprints, and cell line descriptors to predict gene expression responses to chemical perturbations. Other state-of-the-art machine learning approaches were also evaluated as benchmarks. Among those, the deep neural network (DNN) classifier has top-ranked known drugs with beneficial effects on aging genes, and some of these drugs were previously shown to promote longevity, illustrating the potential utility of this methodology. These results further demonstrate the capability of "hybrid" chemogenomic models, incorporating quantitative descriptors from biomarkers to capture cell specific drug-gene interactions. Such models can therefore be used for discovering drugs with desired gene regulatory effects associated with longevity.
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Affiliation(s)
| | | | - Artem Cherkasov
- Vancouver Prostate Centre, Department of Urologic Sciences, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3Z6, Canada; (J.Y.); (M.H.)
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16
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Serrano Nájera G, Narganes Carlón D, Crowther DJ. TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery. Sci Rep 2021; 11:15747. [PMID: 34344904 PMCID: PMC8333311 DOI: 10.1038/s41598-021-94897-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 07/08/2021] [Indexed: 02/07/2023] Open
Abstract
Target identification and prioritisation are prominent first steps in modern drug discovery. Traditionally, individual scientists have used their expertise to manually interpret scientific literature and prioritise opportunities. However, increasing publication rates and the wider routine coverage of human genes by omic-scale research make it difficult to maintain meaningful overviews from which to identify promising new trends. Here we propose an automated yet flexible pipeline that identifies trends in the scientific corpus which align with the specific interests of a researcher and facilitate an initial prioritisation of opportunities. Using a procedure based on co-citation networks and machine learning, genes and diseases are first parsed from PubMed articles using a novel named entity recognition system together with publication date and supporting information. Then recurrent neural networks are trained to predict the publication dynamics of all human genes. For a user-defined therapeutic focus, genes generating more publications or citations are identified as high-interest targets. We also used topic detection routines to help understand why a gene is trendy and implement a system to propose the most prominent review articles for a potential target. This TrendyGenes pipeline detects emerging targets and pathways and provides a new way to explore the literature for individual researchers, pharmaceutical companies and funding agencies.
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Affiliation(s)
- Guillermo Serrano Nájera
- Division of Cell and Developmental Biology, School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK
| | - David Narganes Carlón
- Division of Cell and Developmental Biology, School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK
- Division of Population Health and Genomics, Ninewells Hospital, School of Medicine, University of Dundee, Dundee, DD1 9SY, UK
- Exscientia Ltd, Dundee One, River Court, 5 West Victoria Dock Road, Dundee, DD1 3JT, UK
| | - Daniel J Crowther
- Exscientia Ltd, Dundee One, River Court, 5 West Victoria Dock Road, Dundee, DD1 3JT, UK.
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Sánchez-Cruz N, Medina-Franco JL. Epigenetic Target Fishing with Accurate Machine Learning Models. J Med Chem 2021; 64:8208-8220. [PMID: 33770434 DOI: 10.1021/acs.jmedchem.1c00020] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents many structure-activity relationships that have not been exploited thus far to develop predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26 318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. We built predictive models with high accuracy for small molecules' epigenetic target profiling through a systematic comparison of the machine learning models trained on different molecular fingerprints. The models were thoroughly validated, showing mean precisions of up to 0.952 for the epigenetic target prediction task. Our results indicate that the models reported herein have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as a freely accessible web application.
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Affiliation(s)
- Norberto Sánchez-Cruz
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
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18
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Sánchez-Cruz N, Medina-Franco JL. Epigenetic Target Profiler: A Web Server to Predict Epigenetic Targets of Small Molecules. J Chem Inf Model 2021; 61:1550-1554. [PMID: 33729791 DOI: 10.1021/acs.jcim.1c00045] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The identification of protein targets of small molecules is essential for drug discovery. With the increasing amount of chemogenomic data in the public domain, multiple ligand-based models for target prediction have emerged. However, these models are generally biased by the number of known ligands for different targets, which involves an under-representation of epigenetic targets, and despite the increasing importance of epigenetic targets in drug discovery, there are no open tools for epigenetic target prediction. In this work, we introduce Epigenetic Target Profiler (ETP), a freely accessible and easy-to-use web application for the prediction of epigenetic targets of small molecules. For a query compound, ETP predicts its bioactivity profile over a panel of 55 different epigenetic targets. To that aim, ETP uses a consensus model based on two binary classification models for each target, relying on support vector machines and built on molecular fingerprints of different design. A distance-to-model parameter related to the reliability of the predictions is included to facilitate their interpretability and assist in the identification of small molecules with potential epigenetic activity. Epigenetic Target Profiler is freely available at http://www.epigenetictargetprofiler.com.
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Affiliation(s)
- Norberto Sánchez-Cruz
- DIFACQUIM research group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - José L Medina-Franco
- DIFACQUIM research group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
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