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Saravanan KS, Satish KS, Saraswathy GR, Kuri U, Vastrad SJ, Giri R, Dsouza PL, Kumar AP, Nair G. Innovative target mining stratagems to navigate drug repurposing endeavours. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:303-355. [PMID: 38789185 DOI: 10.1016/bs.pmbts.2024.03.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.
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Affiliation(s)
- Kamatchi Sundara Saravanan
- Department of Pharmacognosy, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Kshreeraja S Satish
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Ganesan Rajalekshmi Saraswathy
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India.
| | - Ushnaa Kuri
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Soujanya J Vastrad
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Ritesh Giri
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Prizvan Lawrence Dsouza
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Adusumilli Pramod Kumar
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Gouri Nair
- Department of Pharmacology, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
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Feng Z, Shen Z, Li H, Li S. e-TSN: an interactive visual exploration platform for target-disease knowledge mapping from literature. Brief Bioinform 2022; 23:6809962. [PMID: 36347537 PMCID: PMC9677481 DOI: 10.1093/bib/bbac465] [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: 07/07/2022] [Revised: 09/20/2022] [Accepted: 09/27/2022] [Indexed: 11/10/2022] Open
Abstract
Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers of unstructured texts of biomedical publications provide a rich source of knowledge for drug target discovery research and demand the development of specific algorithms or tools to facilitate finding disease genes and proteins. Text mining is a method that can automatically mine helpful information related to drug target discovery from massive biomedical literature. However, there is a substantial lag between biomedical publications and the subsequent abstraction of information extracted by text mining to databases. The knowledge graph is introduced to integrate heterogeneous biomedical data. Here, we describe e-TSN (Target significance and novelty explorer, http://www.lilab-ecust.cn/etsn/), a knowledge visualization web server integrating the largest database of associations between targets and diseases from the full scientific literature by constructing significance and novelty scoring methods based on bibliometric statistics. The platform aims to visualize target-disease knowledge graphs to assist in prioritizing candidate disease-related proteins. Approved drugs and associated bioactivities for each interested target are also provided to facilitate the visualization of drug-target relationships. In summary, e-TSN is a fast and customizable visualization resource for investigating and analyzing the intricate target-disease networks, which could help researchers understand the mechanisms underlying complex disease phenotypes and improve the drug discovery and development efficiency, especially for the unexpected outbreak of infectious disease pandemics like COVID-19.
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Affiliation(s)
- Ziyan Feng
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Zihao Shen
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Honglin Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China,Innovation Center for AI and Drug Discovery, East China Normal University, Shanghai 200062, China,Lingang Laboratory, Shanghai 200031, China
| | - Shiliang Li
- Corresponding author: Shiliang Li, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China; Innovation Center for AI and Drug Discovery, East China Normal University, Shanghai 200062, China. E-mail:
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Hephzibah Cathryn R, Udhaya Kumar S, Younes S, Zayed H, George Priya Doss C. A review of bioinformatics tools and web servers in different microarray platforms used in cancer research. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 131:85-164. [PMID: 35871897 DOI: 10.1016/bs.apcsb.2022.05.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.
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Affiliation(s)
- R Hephzibah Cathryn
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - S Udhaya Kumar
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - Salma Younes
- Department of Biomedical Sciences, College of Health and Sciences, Qatar University, QU Health, Doha, Qatar
| | - Hatem Zayed
- Department of Biomedical Sciences, College of Health and Sciences, Qatar University, QU Health, Doha, Qatar
| | - C George Priya Doss
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India.
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In silico Methods for Identification of Potential Therapeutic Targets. Interdiscip Sci 2022; 14:285-310. [PMID: 34826045 PMCID: PMC8616973 DOI: 10.1007/s12539-021-00491-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 10/19/2021] [Accepted: 11/01/2021] [Indexed: 11/01/2022]
Abstract
AbstractAt the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.
Graphical abstract
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The variation in promoter sequences of the Akt3 gene between cow and buffalo revealed different responses against mastitis. J Genet Eng Biotechnol 2021; 19:164. [PMID: 34677734 PMCID: PMC8536807 DOI: 10.1186/s43141-021-00258-4] [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: 08/08/2021] [Accepted: 09/28/2021] [Indexed: 11/23/2022]
Abstract
Background Serine/threonine kinase 3 (AKT3) is a protein-coding gene that is associated with several cattle immune diseases including different tumors and cancers. The objective of this study was to investigate the differences in structures and functions of AKT3 of cow and buffalo cattle. Methods The sequence differences of gene-coding sequence (CDS) and core promoter region of AKT3 in cow and buffalo were analyzed by using bioinformatics tools and PCR sequencing. Also, the functional analysis of promoter regulating gene expression by RT-PCR was performed using 500 Holstein cows and buffalos. And, evaluation of AKT3 inflammatory response to the lipopolysaccharide (LPS)-induced mastitis was performed between both species. Results The results revealed the variation in 6 exons out of 13 exons of the two species of CDS. Also, 4 different regions in 3-kb promoters of the AKT3 gene were significantly different between cow and buffalo species, in which cow’s AKT3 promoter sequence region was started from − 371 to − 1247, while in buffalo, the sequence was started from − 371 to − 969 of the promoter crucial region. Thus, the promoter was overexpressed in cows compared to buffaloes. As a result, significant differences (P < 0.05) between the two species in the AKT3 gene expression level related to the LPS stimulation in their mammary epithelial cell line. Conclusions This study emphasized the great importance of the structural differences of AKT3 between the animal species on their different responses against immune diseases like mastitis.
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Madaj R, Geoffrey B, Sanker A, Valluri PP. Target2DeNovoDrug: a novel programmatic tool for in silico-deep learning based de novo drug design for any target of interest. J Biomol Struct Dyn 2021; 40:7511-7516. [PMID: 33703998 DOI: 10.1080/07391102.2021.1898474] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
The on-going data-science and Artificial Intelligence (AI) revolution offer researchers a fresh set of tools to approach structure-based drug design problems in the computer-aided drug design space. A novel programmatic tool that incorporates in silico and deep learning based approaches for de novo drug design for any target of interest has been reported. Once the user specifies the target of interest in the form of a representative amino acid sequence or corresponding nucleotide sequence, the programmatic workflow of the tool generates compounds from the PubChem ligand library and novel SMILES of compounds not present in any ligand library but are likely to be active against the target. Following this, the tool performs a computationally efficient In-Silico modeling of the target and the newly generated compounds and stores the results of the protein-ligand interaction in the working folder of the user. Further, for the protein-ligand complex associated with the best protein-ligand interaction, the tool performs an automated Molecular Dynamics (MD) protocol and generates plots such as RMSD (Root Mean Square Deviation) which reveal the stability of the complex. A demonstrated use of the tool has been shown with the target signatures of Tumor Necrosis Factor-Alpha, an important therapeutic target in the case of anti-inflammatory treatment. The future scope of the tool involves, running the tool on a High-Performance Cluster for all known target signatures to generate data that will be useful to drive AI and Big data driven drug discovery. The code is hosted, maintained, and supported at the GitHub repository given in the link below https://github.com/bengeof/Target2DeNovoDrugCommunicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rafal Madaj
- Centre of Molecular and Macromolecular Studies, Polish Academy of Sciences, Poland
| | | | - Akhil Sanker
- Deparment of Computer Science, SRM University, Chennai, India
| | - Pavan Preetham Valluri
- Department of Applied Mathematics and Computational Science, PSG College of Technology, Coimbatore, India
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Xiao F, Liu J, Zheng Y, Quan Z, Sun W, Fan Y, Luo C, Li H, Wu X. The targeted inhibition of prostate cancer by iron-based nanoparticles based on bioinformatics. J Biomater Appl 2020; 36:3-14. [PMID: 33283584 PMCID: PMC8217887 DOI: 10.1177/0885328220975249] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Prostate cancer is an epithelial malignant tumor of the prostate, and it is one of the malignant tumors with a high incidence of urogenital system in men. The local treatment of prostate cancer is mainly radical resection and radical radiotherapy, but they are not applicable to advanced prostate cancer. Systemic therapy mainly includes targeted therapy and immunotherapy which could cause many complications, and will affect the prognosis and quality of life of patients. It is urgent to find new treatments for prostate cancer. Bioinformatics offers hope for us to find reliable therapeutic targets. Bioinformatics can use the tumor informations in database and analyze them to screen out the best differentially expressed genes. Using the selected differentially expressed genes as targets, a gene interference plasmid was designed, and the constructed plasmid was used for targeted gene therapy. There are some problems about gene therapy that need to be solved, such as how to transfer genes to target cells is also an important challenge. Due to their large molecular weight and hydrophilic nature, they cannot enter cells through passive diffusion mechanisms. Here we synthesized a DNA carrier used surface modified iron based nanoparticles, and used it to load plasmid including ShRNA which can inhibit the expression of oncogene SLC4A4 selected by bioinformatics’ method. After that we use this iron based nanoparticles/plasmid DNA nanocomposite to treat prostate cancer cells in vitro and in vivo. The target gene SLC4A4 we had selected using bioinformatics had a strong effect on the proliferation of prostate cells; Our nanocomposite could inhibit the expression of SLC4A4 effectively, it had strong inhibitory effects on prostate cancer cells both in vivo and in vitro, and can be used as a potential method for prostate cancer treatment.
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Affiliation(s)
- Feng Xiao
- Department of Urology, Chongqing Medical University First Affiliated Hospital, Chongqing, China
| | - Jiayu Liu
- Department of Urology, Chongqing Medical University First Affiliated Hospital, Chongqing, China
| | - Yongbo Zheng
- Department of Urology, Chongqing Medical University First Affiliated Hospital, Chongqing, China
| | - Zhen Quan
- Department of Urology, Chongqing Medical University First Affiliated Hospital, Chongqing, China
| | - Wei Sun
- Fuling Center Hospital of Chongqing City, Chongqing, China
| | - Yao Fan
- Department of Urology, Chongqing Medical University First Affiliated Hospital, Chongqing, China
| | - Chunli Luo
- Chongqing Medical University, Chongqing, China
| | - Hailiang Li
- Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Xiaohou Wu
- Department of Urology, Chongqing Medical University First Affiliated Hospital, Chongqing, China
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8
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Agamah FE, Mazandu GK, Hassan R, Bope CD, Thomford NE, Ghansah A, Chimusa ER. Computational/in silico methods in drug target and lead prediction. Brief Bioinform 2020; 21:1663-1675. [PMID: 31711157 PMCID: PMC7673338 DOI: 10.1093/bib/bbz103] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 07/17/2019] [Accepted: 07/18/2019] [Indexed: 01/10/2023] Open
Abstract
Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with the fundamental stage being the identification and validation of drug targets of interest for further downstream processes. Thus, various computational methods have been developed to complement experimental approaches in drug discovery. Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during the computational drug discovery process.
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Affiliation(s)
- Francis E Agamah
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
| | - Gaston K Mazandu
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
- African Institute for Mathematical Sciences, Muizenberg, Cape Town 7945, South Africa
| | - Radia Hassan
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
| | - Christian D Bope
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
- Faculty of Sciences, University of Kinshasa, Kinshasa, Democratic Republic of Congo
| | - Nicholas E Thomford
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
- School of Medical Sciences, University of Cape Coast, PMB, Cape Coast, Ghana
| | - Anita Ghansah
- Noguchi Memorial Institute for Medical Research, College of Health Sciences, University of Ghana, PO Box LG 581, Legon, Ghana
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
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Costales MG, Childs-Disney JL, Haniff HS, Disney MD. How We Think about Targeting RNA with Small Molecules. J Med Chem 2020; 63:8880-8900. [PMID: 32212706 PMCID: PMC7486258 DOI: 10.1021/acs.jmedchem.9b01927] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
RNA offers nearly unlimited potential as a target for small molecule chemical probes and lead medicines. Many RNAs fold into structures that can be selectively targeted with small molecules. This Perspective discusses molecular recognition of RNA by small molecules and highlights key enabling technologies and properties of bioactive interactions. Sequence-based design of ligands targeting RNA has established rules for affecting RNA targets and provided a potentially general platform for the discovery of bioactive small molecules. The RNA targets that contain preferred small molecule binding sites can be identified from sequence, allowing identification of off-targets and prediction of bioactive interactions by nature of ligand recognition of functional sites. Small molecule targeted degradation of RNA targets (ribonuclease-targeted chimeras, RIBOTACs) and direct cleavage by small molecules have also been developed. These growing technologies suggest that the time is right to provide small molecule chemical probes to target functionally relevant RNAs throughout the human transcriptome.
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Affiliation(s)
- Matthew G Costales
- Department of Chemistry, The Scripps Research Institute, 130 Scripps Way, Jupiter, Florida 33458, United States
| | - Jessica L Childs-Disney
- Department of Chemistry, The Scripps Research Institute, 130 Scripps Way, Jupiter, Florida 33458, United States
| | - Hafeez S Haniff
- Department of Chemistry, The Scripps Research Institute, 130 Scripps Way, Jupiter, Florida 33458, United States
| | - Matthew D Disney
- Department of Chemistry, The Scripps Research Institute, 130 Scripps Way, Jupiter, Florida 33458, United States
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Hussain W, Rasool N, Khan YD. Insights into Machine Learning-based Approaches for Virtual Screening in Drug Discovery: Existing Strategies and Streamlining Through FP-CADD. Curr Drug Discov Technol 2020; 18:463-472. [PMID: 32767944 DOI: 10.2174/1570163817666200806165934] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 07/01/2020] [Accepted: 07/03/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Machine learning is an active area of research in computer science by the availability of big data collection of all sorts prompting interest in the development of novel tools for data mining. Machine learning methods have wide applications in computer-aided drug discovery methods. Most incredible approaches to machine learning are used in drug designing, which further aid the process of biological modelling in drug discovery. Mainly, two main categories are present which are Ligand-Based Virtual Screening (LBVS) and Structure-Based Virtual Screening (SBVS), however, the machine learning approaches fall mostly in the category of LBVS. OBJECTIVES This study exposits the major machine learning approaches being used in LBVS. Moreover, we have introduced a protocol named FP-CADD which depicts a 4-steps rule of thumb for drug discovery, the four protocols of computer-aided drug discovery (FP-CADD). Various important aspects along with SWOT analysis of FP-CADD are also discussed in this article. CONCLUSION By this thorough study, we have observed that in LBVS algorithms, Support Vector Machines (SVM) and Random Forest (RF) are those which are widely used due to high accuracy and efficiency. These virtual screening approaches have the potential to revolutionize the drug designing field. Also, we believe that the process flow presented in this study, named FP-CADD, can streamline the whole process of computer-aided drug discovery. By adopting this rule, the studies related to drug discovery can be made homogeneous and this protocol can also be considered as an evaluation criterion in the peer-review process of research articles.
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Affiliation(s)
| | | | - Yaser Daanial Khan
- Department of Computer Science, University of Management and Technology, Lahore, Pakistan
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Takakusagi Y, Takakusagi K, Sakaguchi K, Sugawara F. Phage display technology for target determination of small-molecule therapeutics: an update. Expert Opin Drug Discov 2020; 15:1199-1211. [DOI: 10.1080/17460441.2020.1790523] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Yoichi Takakusagi
- Department of Applied Biological Science, Faculty of Science and Technology, Tokyo University of Science, Chiba, Japan
- Institute of Quantum Life Science (iQLS), National Institutes of Quantum and Radiological Science and Technology (QST), Chiba, Japan
| | - Kaori Takakusagi
- Department of Applied Biological Science, Faculty of Science and Technology, Tokyo University of Science, Chiba, Japan
- Institute of Quantum Life Science (iQLS), National Institutes of Quantum and Radiological Science and Technology (QST), Chiba, Japan
| | - Kengo Sakaguchi
- Department of Applied Biological Science, Faculty of Science and Technology, Tokyo University of Science, Chiba, Japan
| | - Fumio Sugawara
- Department of Applied Biological Science, Faculty of Science and Technology, Tokyo University of Science, Chiba, Japan
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The influence of maternal and infant nutrition on cardiometabolic traits: novel findings and future research directions from four Canadian birth cohort studies. Proc Nutr Soc 2019; 78:351-361. [PMID: 31140389 DOI: 10.1017/s0029665119000612] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
A mother's nutritional choices while pregnant may have a great influence on her baby's development in the womb and during infancy. There is evidence that what a mother eats during pregnancy interacts with her genes to affect her child's susceptibility to poor health outcomes including childhood obesity, pre-diabetes, allergy and asthma. Furthermore, after what an infant eats can change his or her intestinal bacteria, which can further influence the development of these poor outcomes. In the present paper, we review the importance of birth cohorts, the formation and early findings from a multi-ethnic birth cohort alliance in Canada and summarise our future research directions for this birth cohort alliance. We summarise a method for harmonising collection and analysis of self-reported dietary data across multiple cohorts and provide examples of how this birth cohort alliance has contributed to our understanding of gestational diabetes risk; ethnic and diet-influences differences in the healthy infant microbiome; and the interplay between diet, ethnicity and birth weight. Ongoing work in this birth cohort alliance will focus on the use of metabolomic profiling to measure dietary intake, discovery of unique diet-gene and diet-epigenome interactions, and qualitative interviews with families of children at risk of metabolic syndrome. Our findings to-date and future areas of research will advance the evidence base that informs dietary guidelines in pregnancy, infancy and childhood, and will be relevant to diverse and high-risk populations of Canada and other high-income countries.
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Kaufmann J, Wentzensen N, Brinker TJ, Grabe N. Large-scale in-silico identification of a tumor-specific antigen pool for targeted immunotherapy in triple-negative breast cancer. Oncotarget 2019; 10:2515-2529. [PMID: 31069014 PMCID: PMC6493464 DOI: 10.18632/oncotarget.26808] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 02/15/2019] [Indexed: 12/16/2022] Open
Abstract
Since the advent of cetuximab, clinical cancer treatment has evolved from the standard, relatively nonspecific chemo- and radiotherapy with significant cytotoxic side effects towards immunotherapeutic approaches with selective, target-mechanism-based effects. Antibody therapies as the most successful form of cancer immunotherapy led to approved treatments for specific cancer types with increased patient survival. Thus, the identification of tumor antigens with high immunogenicity is in central focus now. In this study, we applied computational methods to comprehensively discover overexpressed molecular targets with high therapeutic relevance for clinical, immunotherapeutic cancer treatment in triple-negative breast cancer (TNBC). By actively modeling potential negative side effects utilizing expression data of 29 different, normal human tissues, we were able to develop a highly-specific coverage of TNBC patients with RNA targets. We identified here more than 400 potential tumor-specific antigens suitable for targeted therapy, including several already identified as potential targets for TNBC and other solid tumors. A specific cocktail of MAGEB4, CT83, TLX3, ACTL8, PRDM13 achieved almost 94% patient coverage in TNBC. Overall, these results show that our approach can identify and prioritize TNBC targets suitable for targeted therapy. Therefore, our method has the potential to lead to new and more effective immunotherapeutic cancer treatment.
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Affiliation(s)
- Jessica Kaufmann
- Hamamatsu Tissue Imaging and Analysis Center (TIGA), BIOQUANT, University of Heidelberg, Heidelberg, Germany.,Medical Oncology Department, Universitätsklinik Heidelberg, National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Nicolas Wentzensen
- National Cancer Institute, Division of Cancer Epidemiology & Genetics, Clinical Genetics Branch, NCI Shady Grove, Bethesda, Maryland, USA
| | - Titus J Brinker
- National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Dermatology, University Hospital Heidelberg, Heidelberg, Germany
| | - Niels Grabe
- Hamamatsu Tissue Imaging and Analysis Center (TIGA), BIOQUANT, University of Heidelberg, Heidelberg, Germany.,Medical Oncology Department, Universitätsklinik Heidelberg, National Center for Tumor Diseases (NCT), Heidelberg, Germany
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Ma X, Lv X, Zhang J. Exploiting polypharmacology for improving therapeutic outcome of kinase inhibitors (KIs): An update of recent medicinal chemistry efforts. Eur J Med Chem 2017; 143:449-463. [PMID: 29202407 DOI: 10.1016/j.ejmech.2017.11.049] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 10/12/2017] [Accepted: 11/18/2017] [Indexed: 12/23/2022]
Abstract
Polypharmacology has been increasingly advocated for the therapeutic intervention in complex pathological conditions, exemplified by cancer. Although kinase inhibitors (KIs) have revolutionized the treatment for certain types of malignancies, some major medical needs remain unmet due to the relentless advance of drug resistance and insufficient efficacy of mono-target KIs. Hence, "multiple targets, multi-dimensional activities" represents an emerging paradigm for innovative anti-cancer drug discovery. Over recent years, considerable leaps have been made in pursuit of kinase-centric polypharmacological anti-cancer therapeutics, providing avenues to tackling the limitation of mono-target KIs. In the review, we summarize the clinically important mechanisms inducing KI resistance and depict a landscape of recent medicinal chemistry efforts on exploring kinase-centric polypharmacological anti-cancer agents that targeting multiple cancer-related processes. In parallel, some inevitable challenges are emphasized for the sake of more accurate and efficient drug discovery in the field.
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Affiliation(s)
- Xiaodong Ma
- Department of Medicinal Chemistry, School of Pharmacy, Anhui University of Chinese Medicine, Hefei 230012, China; Department of Medicinal Chemistry, Anhui Academy of Chinese Medicine, Hefei 230012, China
| | - Xiaoqing Lv
- College of Medicine, Jiaxing University, Jiaxing 314001, China.
| | - Jiankang Zhang
- Department of Pharmaceutical Preparation, Hangzhou Xixi Hospital, Hangzhou 310023, China.
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15
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Tan Z, Chaudhai R, Zhang S. Polypharmacology in Drug Development: A Minireview of Current Technologies. ChemMedChem 2016; 11:1211-8. [PMID: 27154144 DOI: 10.1002/cmdc.201600067] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Revised: 03/21/2016] [Indexed: 01/09/2023]
Abstract
Polypharmacology, the process in which a single drug is able to bind to multiple targets specifically and simultaneously, is an emerging paradigm in drug development. The potency of a given drug can be increased through the engagement of multiple targets involved in a certain disease. Polypharmacology may also help identify novel applications of existing drugs through drug repositioning. However, many problems and challenges remain in this field. Rather than covering all aspects of polypharmacology, this Minireview is focused primarily on recently reported techniques, from bioinformatics technologies to cheminformatics approaches as well as text-mining-based methods, all of which have made significant contributions to the research of polypharmacology.
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Affiliation(s)
- Zhi Tan
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.,The University of Texas Graduate School of Biomedical Sciences, Houston, TX, 77030, USA
| | - Rajan Chaudhai
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Shuxing Zhang
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA. .,The University of Texas Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
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16
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Li TS, Bravo À, Furlong LI, Good BM, Su AI. A crowdsourcing workflow for extracting chemical-induced disease relations from free text. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw051. [PMID: 27087308 PMCID: PMC4834205 DOI: 10.1093/database/baw051] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2015] [Accepted: 03/17/2016] [Indexed: 01/05/2023]
Abstract
Relations between chemicals and diseases are one of the most queried biomedical interactions. Although expert manual curation is the standard method for extracting these relations from the literature, it is expensive and impractical to apply to large numbers of documents, and therefore alternative methods are required. We describe here a crowdsourcing workflow for extracting chemical-induced disease relations from free text as part of the BioCreative V Chemical Disease Relation challenge. Five non-expert workers on the CrowdFlower platform were shown each potential chemical-induced disease relation highlighted in the original source text and asked to make binary judgments about whether the text supported the relation. Worker responses were aggregated through voting, and relations receiving four or more votes were predicted as true. On the official evaluation dataset of 500 PubMed abstracts, the crowd attained a 0.505 F-score (0.475 precision, 0.540 recall), with a maximum theoretical recall of 0.751 due to errors with named entity recognition. The total crowdsourcing cost was $1290.67 ($2.58 per abstract) and took a total of 7 h. A qualitative error analysis revealed that 46.66% of sampled errors were due to task limitations and gold standard errors, indicating that performance can still be improved. All code and results are publicly available at https://github.com/SuLab/crowd_cid_relex Database URL: https://github.com/SuLab/crowd_cid_relex
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Affiliation(s)
- Tong Shu Li
- Department of Molecular and Experimental Medicine, the Scripps Research Institute, La Jolla, CA 92037, USA
| | - Àlex Bravo
- Research Programme on Biomedical Informatics (GRIB), IMIM, UPF, Barcelona, Spain
| | - Laura I Furlong
- Research Programme on Biomedical Informatics (GRIB), IMIM, UPF, Barcelona, Spain
| | - Benjamin M Good
- Department of Molecular and Experimental Medicine, the Scripps Research Institute, La Jolla, CA 92037, USA
| | - Andrew I Su
- Department of Molecular and Experimental Medicine, the Scripps Research Institute, La Jolla, CA 92037, USA
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17
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Rai S, Bhatnagar S. Hyperlipidemia, Disease Associations, and Top 10 Potential Drug Targets: A Network View. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2016; 20:152-68. [DOI: 10.1089/omi.2015.0172] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Sneha Rai
- Computational and Structural Biology Laboratory, Division of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, India
| | - Sonika Bhatnagar
- Computational and Structural Biology Laboratory, Division of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, India
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18
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Marine natural products with anti-inflammatory activity. Appl Microbiol Biotechnol 2015; 100:1645-1666. [DOI: 10.1007/s00253-015-7244-3] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2015] [Revised: 12/07/2015] [Accepted: 12/09/2015] [Indexed: 12/14/2022]
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19
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ProCon — PROteomics CONversion tool. J Proteomics 2015; 129:56-62. [DOI: 10.1016/j.jprot.2015.06.015] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Revised: 05/19/2015] [Accepted: 06/28/2015] [Indexed: 11/22/2022]
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20
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Scaling and contextualizing personalized healthcare: A case study of disease prediction algorithm integration. J Biomed Inform 2015; 57:377-85. [DOI: 10.1016/j.jbi.2015.07.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Revised: 07/21/2015] [Accepted: 07/26/2015] [Indexed: 11/18/2022]
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21
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Efficacy-oriented compatibility for component-based Chinese medicine. Acta Pharmacol Sin 2015; 36:654-8. [PMID: 25864650 DOI: 10.1038/aps.2015.8] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2014] [Accepted: 03/03/2015] [Indexed: 12/11/2022] Open
Abstract
Single-target drugs have not achieved satisfactory therapeutic effects for complex diseases involving multiple factors. Instead, innovations in recent drug research and development have revealed the emergence of compound drugs, such as cocktail therapies and "polypills", as the frontier in new drug development. A traditional Chinese medicine (TCM) prescription that is usually composed of several medicinal herbs can serve a typical representative of compound medicines. Although the traditional compatibility theory of TCM cannot be well expressed using modern scientific language nowadays, the fundamental purpose of TCM compatibility can be understood as promoting efficacy and reducing toxicity. This paper introduces the theory and methods of efficacy-oriented compatibility for developing component-based Chinese medicines.
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22
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Fu Y, Wang Y, Zhang B. Systems pharmacology for traditional Chinese medicine with application to cardio-cerebrovascular diseases. JOURNAL OF TRADITIONAL CHINESE MEDICAL SCIENCES 2014. [DOI: 10.1016/j.jtcms.2014.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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23
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Qabaja A, Jarada T, Elsheikh A, Alhajj R. Prediction of gene-based drug indications using compendia of public gene expression data and PubMed abstracts. J Bioinform Comput Biol 2014; 12:1450007. [PMID: 24969745 DOI: 10.1142/s0219720014500073] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The tremendous research effort on diseases and drug discovery has produced a huge amount of important biomedical information which is mostly hidden in the web. In addition, many databases have been created for the purpose of storing enormous amounts of information and high-throughput experiments related to drugs and diseases' effects on genes. Thus, developing an algorithm to integrate biological data from different sources forms one of the greatest challenges in the field of computational biology. Based on our belief that data integration would result in better understanding for the drug mode of action or the disease pathophysiology, we have developed a novel paradigm to integrate data from three major sources in order to predict novel therapeutic drug indications. Microarray data, biomedical text mining data, and gene interaction data have been all integrated to predict ranked lists of genes based on their relevance to a particular drug or disease molecular action. These ranked lists of genes have finally been used as a raw material for building a disease-drug connectivity map based on the enrichment between the up/down tags of a particular disease signature and the ranked lists of drugs. Using this paradigm, we have reported 13% sensitivity improvement in comparison with using microarray or text mining data independently. In addition, our paradigm is able to predict many clinically validated disease-drug associations that could not be captured using microarray or text mining data independently.
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Affiliation(s)
- Ala Qabaja
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
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24
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Lagunin AA, Goel RK, Gawande DY, Pahwa P, Gloriozova TA, Dmitriev AV, Ivanov SM, Rudik AV, Konova VI, Pogodin PV, Druzhilovsky DS, Poroikov VV. Chemo- and bioinformatics resources for in silico drug discovery from medicinal plants beyond their traditional use: a critical review. Nat Prod Rep 2014; 31:1585-611. [DOI: 10.1039/c4np00068d] [Citation(s) in RCA: 87] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
An overview of databases andin silicotools for discovery of the hidden therapeutic potential of medicinal plants.
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Affiliation(s)
- Alexey A. Lagunin
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
- Russian National Research Medical University
- Medico-Biologic Faculty
- Moscow, Russia
| | - Rajesh K. Goel
- Department of Pharmaceutical Sciences and Drug Research
- Punjabi University
- Patiala-147002, India
| | - Dinesh Y. Gawande
- Department of Pharmaceutical Sciences and Drug Research
- Punjabi University
- Patiala-147002, India
| | - Priynka Pahwa
- Department of Pharmaceutical Sciences and Drug Research
- Punjabi University
- Patiala-147002, India
| | | | | | - Sergey M. Ivanov
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
| | - Anastassia V. Rudik
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
| | - Varvara I. Konova
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
| | - Pavel V. Pogodin
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
- Russian National Research Medical University
- Medico-Biologic Faculty
- Moscow, Russia
| | | | - Vladimir V. Poroikov
- Orekhovich Institute of Biomedical Chemistry of Rus. Acad. Med. Sci
- Moscow, Russia
- Russian National Research Medical University
- Medico-Biologic Faculty
- Moscow, Russia
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25
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Mayer G, Montecchi-Palazzi L, Ovelleiro D, Jones AR, Binz PA, Deutsch EW, Chambers M, Kallhardt M, Levander F, Shofstahl J, Orchard S, Vizcaíno JA, Hermjakob H, Stephan C, Meyer HE, Eisenacher M. The HUPO proteomics standards initiative- mass spectrometry controlled vocabulary. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2013; 2013:bat009. [PMID: 23482073 PMCID: PMC3594986 DOI: 10.1093/database/bat009] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Controlled vocabularies (CVs), i.e. a collection of predefined terms describing a modeling domain, used for the semantic annotation of data, and ontologies are used in structured data formats and databases to avoid inconsistencies in annotation, to have a unique (and preferably short) accession number and to give researchers and computer algorithms the possibility for more expressive semantic annotation of data. The Human Proteome Organization (HUPO)–Proteomics Standards Initiative (PSI) makes extensive use of ontologies/CVs in their data formats. The PSI-Mass Spectrometry (MS) CV contains all the terms used in the PSI MS–related data standards. The CV contains a logical hierarchical structure to ensure ease of maintenance and the development of software that makes use of complex semantics. The CV contains terms required for a complete description of an MS analysis pipeline used in proteomics, including sample labeling, digestion enzymes, instrumentation parts and parameters, software used for identification and quantification of peptides/proteins and the parameters and scores used to determine their significance. Owing to the range of topics covered by the CV, collaborative development across several PSI working groups, including proteomics research groups, instrument manufacturers and software vendors, was necessary. In this article, we describe the overall structure of the CV, the process by which it has been developed and is maintained and the dependencies on other ontologies. Database URL: http://psidev.cvs.sourceforge.net/viewvc/psidev/psi/psi-ms/mzML/controlledVocabulary/psi-ms.obo
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Affiliation(s)
- Gerhard Mayer
- Medizinisches Proteom Center (MPC), Ruhr-Universität Bochum, D-44801 Bochum, Germany
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