1
|
Olawade DB, Teke J, Fapohunda O, Weerasinghe K, Usman SO, Ige AO, Clement David-Olawade A. Leveraging artificial intelligence in vaccine development: A narrative review. J Microbiol Methods 2024; 224:106998. [PMID: 39019262 DOI: 10.1016/j.mimet.2024.106998] [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: 06/10/2024] [Revised: 07/12/2024] [Accepted: 07/12/2024] [Indexed: 07/19/2024]
Abstract
Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity and mortality. However, traditional vaccine development methods are often time-consuming, costly, and inefficient. The advent of artificial intelligence (AI) has ushered in a new era in vaccine design, offering unprecedented opportunities to expedite the process. This narrative review explores the role of AI in vaccine development, focusing on antigen selection, epitope prediction, adjuvant identification, and optimization strategies. AI algorithms, including machine learning and deep learning, leverage genomic data, protein structures, and immune system interactions to predict antigenic epitopes, assess immunogenicity, and prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate the rational design of immunogens and the identification of novel adjuvant candidates with optimal safety and efficacy profiles. Challenges such as data heterogeneity, model interpretability, and regulatory considerations must be addressed to realize the full potential of AI in vaccine development. Integrating emerging technologies, such as single-cell omics and synthetic biology, promises to enhance vaccine design precision and scalability. This review underscores the transformative impact of AI on vaccine development and highlights the need for interdisciplinary collaborations and regulatory harmonization to accelerate the delivery of safe and effective vaccines against infectious diseases.
Collapse
Affiliation(s)
- David B Olawade
- Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, United Kingdom; Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom.
| | - Jennifer Teke
- Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom
| | | | - Kusal Weerasinghe
- Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom
| | - Sunday O Usman
- Department of Systems and Industrial Engineering, University of Arizona, USA
| | - Abimbola O Ige
- Department of Chemistry, Faculty of Science, University of Ibadan, Ibadan, Nigeria
| | | |
Collapse
|
2
|
Ekambaram S, Wang J, Dokholyan NV. CANDI: A Web Server for Predicting Molecular Targets and Pathways of Cannabis-Based Therapeutics. RESEARCH SQUARE 2024:rs.3.rs-4744915. [PMID: 39149470 PMCID: PMC11326374 DOI: 10.21203/rs.3.rs-4744915/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Background Cannabis sativa with a rich history of traditional medicinal use, has garnered significant attention in contemporary research for its potential therapeutic applications in various human diseases, including pain, inflammation, cancer, and osteoarthritis. However, the specific molecular targets and mechanisms underlying the synergistic effects of its diverse phytochemical constituents remain elusive. Understanding these mechanisms is crucial for developing targeted, effective cannabis-based therapies. Methods To investigate the molecular targets and pathways involved in the synergistic effects of cannabis compounds, we utilized DRIFT, a deep learning model that leverages attention-based neural networks to predict compound-target interactions. We considered both whole plant extracts and specific plant-based formulations. Predicted targets were then mapped to the Reactome pathway database to identify the biological processes affected. To facilitate the prediction of molecular targets and associated pathways for any user-specified cannabis formulation, we developed CANDI (Cannabis-derived compound Analysis and Network Discovery Interface), a web-based server. This platform offers a user-friendly interface for researchers and drug developers to explore the therapeutic potential of cannabis compounds. Results Our analysis using DRIFT and CANDI successfully identified numerous molecular targets of cannabis compounds, many of which are involved in pathways relevant to pain, inflammation, cancer, and other diseases. The CANDI server enables researchers to predict the molecular targets and affected pathways for any specific cannabis formulation, providing valuable insights for developing targeted therapies. Conclusions By combining computational approaches with knowledge of traditional cannabis use, we have developed the CANDI server, a tool that allows us to harness the therapeutic potential of cannabis compounds for the effective treatment of various disorders. By bridging traditional pharmaceutical development with cannabis-based medicine, we propose a novel approach for botanical-based treatment modalities.
Collapse
|
3
|
Benyamini P. Phylogenetic Tracing of Evolutionarily Conserved Zonula Occludens Toxin Reveals a "High Value" Vaccine Candidate Specific for Treating Multi-Strain Pseudomonas aeruginosa Infections. Toxins (Basel) 2024; 16:271. [PMID: 38922165 PMCID: PMC11209546 DOI: 10.3390/toxins16060271] [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: 05/03/2024] [Revised: 06/09/2024] [Accepted: 06/12/2024] [Indexed: 06/27/2024] Open
Abstract
Extensively drug-resistant Pseudomonas aeruginosa infections are emerging as a significant threat associated with adverse patient outcomes. Due to this organism's inherent properties of developing antibiotic resistance, we sought to investigate alternative strategies such as identifying "high value" antigens for immunotherapy-based purposes. Through extensive database mining, we discovered that numerous Gram-negative bacterial (GNB) genomes, many of which are known multidrug-resistant (MDR) pathogens, including P. aeruginosa, horizontally acquired the evolutionarily conserved gene encoding Zonula occludens toxin (Zot) with a substantial degree of homology. The toxin's genomic footprint among so many different GNB stresses its evolutionary importance. By employing in silico techniques such as proteomic-based phylogenetic tracing, in conjunction with comparative structural modeling, we discovered a highly conserved intermembrane associated stretch of 70 amino acids shared among all the GNB strains analyzed. The characterization of our newly identified antigen reveals it to be a "high value" vaccine candidate specific for P. aeruginosa. This newly identified antigen harbors multiple non-overlapping B- and T-cell epitopes exhibiting very high binding affinities and can adopt identical tertiary structures among the least genetically homologous P. aeruginosa strains. Taken together, using proteomic-driven reverse vaccinology techniques, we identified multiple "high value" vaccine candidates capable of eliciting a polarized immune response against all the P. aeruginosa genetic variants tested.
Collapse
Affiliation(s)
- Payam Benyamini
- Department of Health Sciences at Extension, University of California Los Angeles, 1145 Gayley Ave., Los Angeles, CA 90024, USA
| |
Collapse
|
4
|
Rosa LS, Argolo CO, Nascimento CM, Pimentel AS. Identifying Substructures That Facilitate Compounds to Penetrate the Blood-Brain Barrier via Passive Transport Using Machine Learning Explainer Models. ACS Chem Neurosci 2024; 15:2144-2159. [PMID: 38723285 PMCID: PMC11157485 DOI: 10.1021/acschemneuro.3c00840] [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: 12/26/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 06/06/2024] Open
Abstract
The local interpretable model-agnostic explanation (LIME) method was used to interpret two machine learning models of compounds penetrating the blood-brain barrier. The classification models, Random Forest, ExtraTrees, and Deep Residual Network, were trained and validated using the blood-brain barrier penetration dataset, which shows the penetrability of compounds in the blood-brain barrier. LIME was able to create explanations for such penetrability, highlighting the most important substructures of molecules that affect drug penetration in the barrier. The simple and intuitive outputs prove the applicability of this explainable model to interpreting the permeability of compounds across the blood-brain barrier in terms of molecular features. LIME explanations were filtered with a weight equal to or greater than 0.1 to obtain only the most relevant explanations. The results showed several structures that are important for blood-brain barrier penetration. In general, it was found that some compounds with nitrogenous substructures are more likely to permeate the blood-brain barrier. The application of these structural explanations may help the pharmaceutical industry and potential drug synthesis research groups to synthesize active molecules more rationally.
Collapse
Affiliation(s)
- Lucca
Caiaffa Santos Rosa
- Departamento de Química, Pontifícia Universidade Católica do
Rio de Janeiro, Rio de
Janeiro, RJ 22453-900, Brazil
| | - Caio Oliveira Argolo
- Departamento de Química, Pontifícia Universidade Católica do
Rio de Janeiro, Rio de
Janeiro, RJ 22453-900, Brazil
| | | | - Andre Silva Pimentel
- Departamento de Química, Pontifícia Universidade Católica do
Rio de Janeiro, Rio de
Janeiro, RJ 22453-900, Brazil
| |
Collapse
|
5
|
Liu JX, Zhang X, Huang YQ, Hao GF, Yang GF. Multi-level bioinformatics resources support drug target discovery of protein-protein interactions. Drug Discov Today 2024; 29:103979. [PMID: 38608830 DOI: 10.1016/j.drudis.2024.103979] [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: 01/12/2024] [Revised: 03/14/2024] [Accepted: 04/05/2024] [Indexed: 04/14/2024]
Abstract
Drug discovery often begins with a new target. Protein-protein interactions (PPIs) are crucial to multitudinous cellular processes and offer a promising avenue for drug-target discovery. PPIs are characterized by multi-level complexity: at the protein level, interaction networks can be used to identify potential targets, whereas at the residue level, the details of the interactions of individual PPIs can be used to examine a target's druggability. Much great progress has been made in target discovery through multi-level PPI-related computational approaches, but these resources have not been fully discussed. Here, we systematically survey bioinformatics tools for identifying and assessing potential drug targets, examining their characteristics, limitations and applications. This work will aid the integration of the broader protein-to-network context with the analysis of detailed binding mechanisms to support the discovery of drug targets.
Collapse
Affiliation(s)
- Jia-Xin Liu
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, PR China
| | - Xiao Zhang
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Yuan-Qin Huang
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, PR China; State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals, Guizhou University, Guiyang 550025, PR China.
| | - Guang-Fu Yang
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, PR China.
| |
Collapse
|
6
|
Li Y, Fan Z, Rao J, Chen Z, Chu Q, Zheng M, Li X. An overview of recent advances and challenges in predicting compound-protein interaction (CPI). MEDICAL REVIEW (2021) 2023; 3:465-486. [PMID: 38282802 PMCID: PMC10808869 DOI: 10.1515/mr-2023-0030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 08/30/2023] [Indexed: 01/30/2024]
Abstract
Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.
Collapse
Affiliation(s)
- Yanbei Li
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhehuan Fan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jingxin Rao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhiyi Chen
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qinyu Chu
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Mingyue Zheng
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
7
|
Ahmed AM, Ibrahim AM, Yahia R, Shady NH, Mahmoud BK, Abdelmohsen UR, Fouad MA. Evaluation of the anti-infective potential of the seed endophytic fungi of Corchorus olitorius through metabolomics and molecular docking approach. BMC Microbiol 2023; 23:355. [PMID: 37980505 PMCID: PMC10656998 DOI: 10.1186/s12866-023-03092-5] [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: 03/31/2023] [Accepted: 10/27/2023] [Indexed: 11/20/2023] Open
Abstract
BACKGROUND Endophytic fungi are very rich sources of natural antibacterial and antifungal compounds. The main aim of this study is to isolate the fungal endophytes from the medicinal plant Corchorus olitorius seeds (F. Malvaceae), followed by antimicrobial screening against various bacterial and fungal strains. RESULTS Seven endophytic fungal strains belonging to different three genera were isolated, including Penicillium, Fusarium, and Aspergillus. The seven isolated endophytic strains revealed selective noticeable activity against Escherichia coli (ATCC25922) with varied IC50s ranging from 1.19 to 10 µg /mL, in which Aspergillus sp. (Ar 6) exhibited the strongest potency against E. coli (ATCC 25,922) and candida albicans (ATCC 10,231) with IC50s 1.19 and 15 µg /mL, respectively. Therefore, the chemical profiling of Aspergillus sp. (Ar 6) crude extract was performed using LC-HR-ESI-MS and led to the dereplication of sixteen compounds of various classes (1-16). In-silico analysis of the dereplicated metabolites led to highlighting the compounds responsible for the antimicrobial activity of Aspergillus sp. extract. Moreover, molecular docking showed the potential targets of the metabolites; Astellatol (5), Aspergillipeptide A (10), and Emericellamide C (14) against E. coli and C. albicans. CONCLUSION These results will expand the knowledge of endophytes and provide us with new approaches to face the global antibiotic resistance problem and the future production of undiscovered compounds different from the antibiotics classes.
Collapse
Affiliation(s)
- Arwa Mortada Ahmed
- Department of Pharmacognosy, Faculty of Pharmacy, Daraya University, New Minia City, 61111, Egypt
| | - Ayman M Ibrahim
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Daraya University, New Minia, 61111, Egypt
| | - Ramadan Yahia
- Department of Microbiology and Immunology, Faculty of Pharmacy, Daraya University, New Minia City, Minia, Egypt
| | - Nourhan Hisham Shady
- Department of Pharmacognosy, Faculty of Pharmacy, Daraya University, New Minia City, 61111, Egypt
| | - Basma Khalaf Mahmoud
- Department of Pharmacognosy, Faculty of Pharmacy, Minia University, Minia, 61519, Egypt
| | - Usama Ramadan Abdelmohsen
- Department of Pharmacognosy, Faculty of Pharmacy, Daraya University, New Minia City, 61111, Egypt.
- Department of Pharmacognosy, Faculty of Pharmacy, Minia University, Minia, 61519, Egypt.
| | - Mostafa A Fouad
- Department of Pharmacognosy, Faculty of Pharmacy, Minia University, Minia, 61519, Egypt
| |
Collapse
|
8
|
T P, Katta B, Lulu S S, Sundararajan V. Gene expression analysis reveals GRIN1, SYT1, and SYN2 as significant therapeutic targets and drug repurposing reveals lorazepam and lorediplon as potent inhibitors to manage Alzheimer's disease. J Biomol Struct Dyn 2023:1-22. [PMID: 37691428 DOI: 10.1080/07391102.2023.2256878] [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: 04/24/2023] [Accepted: 09/02/2023] [Indexed: 09/12/2023]
Abstract
Alzheimer's disease (AD) is a slowly progressive neurodegenerative disease and a leading cause of dementia. We aim to identify key genes for the development of therapeutic targets and biomarkers for potential treatments for AD. Meta-analysis was performed on six microarray datasets and identified the differentially expressed genes between healthy and Alzheimer's disease samples. Thereafter, we filtered out the common genes which were present in at least four microarray datasets for downstream analysis. We have constructed a gene-gene network for the common genes and identified six hub genes. Furthermore, we investigated the regulatory mechanisms of these hub genes by analysing their interaction with miRNAs and transcription factors. The gene ontology analysis results highlighted the enriched terms significantly associated with hub genes. Through an extensive literature survey, we found that three of the hub genes including GRIN1, SYN2, and SYT1 were critically involved in disease development. To leverage existing drugs for potential repurposing, we predicted drug-gene interaction using the drug-gene interaction database, and performed molecular docking studies. The docking results revealed that the drug compounds had strong interactions and favorable binding with selected hub genes. Lorazepam exhibits a binding energy of -7.3 kcal/mol with GRIN1, Lorediplon exhibits binding energies of -7.7 kcal/mol and -6.3 kcal/mol with the SYT1, and SYN2 respectively. In addition, 100 ns molecular dynamics simulations were carried out for the top complexes and apo protein as well. Furthermore, the MM-PBSA free energy calculations also revealed that these complexes are stable and had favorable energies. According to our study, the identified hub gene could serve as a biomarker as well as a therapeutic target for AD, and the proposed repurposed drug molecules appear to have promising efficacy in treating the disease.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Premkumar T
- Integrative Multiomics Lab, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Bhavana Katta
- Integrative Multiomics Lab, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Sajitha Lulu S
- Integrative Multiomics Lab, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Vino Sundararajan
- Integrative Multiomics Lab, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| |
Collapse
|
9
|
Campos LA, Baltatu OC, Senar S, Ghimouz R, Alefishat E, Cipolla-Neto J. Multiplatform-Integrated Identification of Melatonin Targets for a Triad of Psychosocial-Sleep/Circadian-Cardiometabolic Disorders. Int J Mol Sci 2023; 24:ijms24010860. [PMID: 36614302 PMCID: PMC9821171 DOI: 10.3390/ijms24010860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/10/2022] [Accepted: 12/30/2022] [Indexed: 01/05/2023] Open
Abstract
Several psychosocial, sleep/circadian, and cardiometabolic disorders have intricately interconnected pathologies involving melatonin disruption. Therefore, we hypothesize that melatonin could be a therapeutic target for treating potential comorbid diseases associated with this triad of psychosocial-sleep/circadian-cardiometabolic disorders. We investigated melatonin's target prediction and tractability for this triad of disorders. The melatonin's target prediction for the proposed psychosocial-sleep/circadian-cardiometabolic disorder triad was investigated using databases from Europe PMC, ChEMBL, Open Targets Genetics, Phenodigm, and PheWAS. The association scores for melatonin receptors MT1 and MT2 with this disorder triad were explored for evidence of target-disease predictions. The potential of melatonin as a tractable target in managing the disorder triad was investigated using supervised machine learning to identify melatonin activities in cardiovascular, neuronal, and metabolic assays at the cell, tissue, and organism levels in a curated ChEMBL database. Target-disease visualization was done by graphs created using "igraph" library-based scripts and displayed using the Gephi ForceAtlas algorithm. The combined Europe PMC (data type: text mining), ChEMBL (data type: drugs), Open Targets Genetics Portal (data type: genetic associations), PhenoDigm (data type: animal models), and PheWAS (data type: genetic associations) databases yielded types and varying levels of evidence for melatonin-disease triad correlations. Of the investigated databases, 235 association scores of melatonin receptors with the targeted diseases were greater than 0.2; to classify the evidence per disease class: 37% listed psychosocial disorders, 9% sleep/circadian disorders, and 54% cardiometabolic disorders. Using supervised machine learning, 546 cardiovascular, neuronal, or metabolic experimental assays with predicted or measured melatonin activity scores were identified in the ChEMBL curated database. Of 248 registered trials, 144 phase I to IV trials for melatonin or agonists have been completed, of which 33.3% were for psychosocial disorders, 59.7% were for sleep/circadian disorders, and 6.9% were for cardiometabolic disorders. Melatonin's druggability was evidenced by evaluating target prediction and tractability for the triad of psychosocial-sleep/circadian-cardiometabolic disorders. While melatonin research and development in sleep/circadian and psychosocial disorders is more advanced, as evidenced by melatonin association scores, substantial evidence on melatonin discovery in cardiovascular and metabolic disorders supports continued R&D in cardiometabolic disorders, as evidenced by melatonin activity scores. A multiplatform analysis provided an integrative assessment of the target-disease investigations that may justify further translational research.
Collapse
Affiliation(s)
- Luciana Aparecida Campos
- Center of Innovation, Technology, and Education (CITE) at Anhembi Morumbi University—Anima Institute, Sao Jose dos Campos Technology Park, Sao Jose dos Campos 12247-016, Brazil
- Department of Public Health and Epidemiology, College of Medicine and Health Science, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
- Correspondence: (L.A.C.); (O.C.B.)
| | - Ovidiu Constantin Baltatu
- Center of Innovation, Technology, and Education (CITE) at Anhembi Morumbi University—Anima Institute, Sao Jose dos Campos Technology Park, Sao Jose dos Campos 12247-016, Brazil
- Department of Public Health and Epidemiology, College of Medicine and Health Science, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
- Correspondence: (L.A.C.); (O.C.B.)
| | | | - Rym Ghimouz
- Fatima College of Health Sciences, Abu Dhabi P.O. Box 3798, United Arab Emirates
| | - Eman Alefishat
- Department of Pharmacology, College of Medicine and Health Science, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
- Department of Biopharmaceutics and Clinical Pharmacy, Faculty of Pharmacy, The University of Jordan, Amman 11942, Jordan
- Center for Biotechnology, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | - José Cipolla-Neto
- Department of Physiology and Biophysics, Institute of Biomedical Sciences, University of São Paulo, São Paulo 05508-000, Brazil
| |
Collapse
|
10
|
Barik K, Arya PK, Singh AK, Kumar A. Potential therapeutic targets for combating Mycoplasma genitalium. 3 Biotech 2023; 13:9. [PMID: 36532859 PMCID: PMC9755450 DOI: 10.1007/s13205-022-03423-9] [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: 06/22/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
Mycoplasma genitalium (M. genitalium) has emerged as a sexually transmitted infection (STI) all over the world in the last three decades. It has been identified as a cause of male urethritis, and there is now evidence that it also causes cervicitis and pelvic inflammatory disease in women. However, the precise role of M. genitalium in diseases such as pelvic inflammatory disease, and infertility is unknown, and more research is required. It is a slow-growing organism, and with the advent of the nucleic acid amplification test (NAAT), more studies are being conducted and knowledge about the pathogenicity of this organism is being elucidated. The accumulation of data has improved our understanding of the pathogen and its role in disease transmission. Despite the widespread use of single-dose azithromycin in the sexual health field, M. genitalium is known to rapidly develop antibiotic resistance. As a result, the media frequently refer to this pathogen as the "new STI superbug." Despite their rarity, antibiotics available today have serious side effects. As the cure rates for first-line antimicrobials have decreased, it is now a challenge to determine the effective antimicrobial therapy. In this review, we summarise recent M. genitalium research and investigate potential therapeutic targets for combating this pathogen.
Collapse
Affiliation(s)
- Krishnendu Barik
- Department of Bioinformatics, Central University of South Bihar, Gaya, 824236 India
| | - Praffulla Kumar Arya
- Department of Bioinformatics, Central University of South Bihar, Gaya, 824236 India
| | - Ajay Kumar Singh
- Department of Bioinformatics, Central University of South Bihar, Gaya, 824236 India
| | - Anil Kumar
- Department of Bioinformatics, Central University of South Bihar, Gaya, 824236 India
| |
Collapse
|
11
|
Islam J, Sarkar H, Hoque H, Hasan MN, Jewel GNA. In-silico approach of identifying novel therapeutic targets against Yersinia pestis using pan and subtractive genomic analysis. Comput Biol Chem 2022; 101:107784. [DOI: 10.1016/j.compbiolchem.2022.107784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 10/30/2022] [Accepted: 11/01/2022] [Indexed: 11/06/2022]
|
12
|
Liao J, Wang Q, Wu F, Huang Z. In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets. Molecules 2022; 27:7103. [PMID: 36296697 PMCID: PMC9609013 DOI: 10.3390/molecules27207103] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/12/2022] [Accepted: 08/25/2022] [Indexed: 07/30/2023] Open
Abstract
Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.
Collapse
Affiliation(s)
- Jianbo Liao
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory of Computer-Aided Drug Design of Dongguan City, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan 523808, China
| | - Qinyu Wang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory of Computer-Aided Drug Design of Dongguan City, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, China
| | - Fengxu Wu
- Hubei Key Laboratory of Wudang Local Chinese Medicine Research, School of Pharmaceutical Sciences, Hubei University of Medicine, Shiyan 442000, China
| | - Zunnan Huang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory of Computer-Aided Drug Design of Dongguan City, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, China
- Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang 524023, China
| |
Collapse
|