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Picard M, Scott-Boyer MP, Bodein A, Leclercq M, Prunier J, Périn O, Droit A. Target repositioning using multi-layer networks and machine learning: The case of prostate cancer. Comput Struct Biotechnol J 2024; 24:464-475. [PMID: 38983753 PMCID: PMC11231507 DOI: 10.1016/j.csbj.2024.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 06/10/2024] [Accepted: 06/12/2024] [Indexed: 07/11/2024] Open
Abstract
The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.
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Affiliation(s)
- Milan Picard
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Julien Prunier
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Transformation and Innovation Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
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Kumar S, Pauline G, Vindal V. NetVA: an R package for network vulnerability and influence analysis. J Biomol Struct Dyn 2024:1-12. [PMID: 38234040 DOI: 10.1080/07391102.2024.2303607] [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: 08/28/2023] [Accepted: 01/04/2024] [Indexed: 01/19/2024]
Abstract
In biological network analysis, identifying key molecules plays a decisive role in the development of potential diagnostic and therapeutic candidates. Among various approaches of network analysis, network vulnerability analysis is quite important, as it assesses significant associations between topological properties and the functional essentiality of a network. Similarly, some node centralities are also used to screen out key molecules. Among these node centralities, escape velocity centrality (EVC), and its extended version (EVC+) outperform others, viz., Degree, Betweenness, and Clustering coefficient. Keeping this in mind, we aimed to develop a first-of-its-kind R package named NetVA, which analyzes networks to identify key molecular players (individual proteins and protein pairs/triplets) through network vulnerability and EVC+-based approaches. To demonstrate the application and relevance of our package in network analysis, previously published and publicly available protein-protein interactions (PPIs) data of human breast cancer were analyzed. This resulted in identifying some most important proteins. These included essential proteins, non-essential proteins, hubs, and bottlenecks, which play vital roles in breast cancer development. Thus, the NetVA package, available at https://github.com/kr-swapnil/NetVA with a detailed tutorial to download and use, assists in predicting potential candidates for therapeutic and diagnostic purposes by exploring various topological features of a disease-specific PPIs network.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Swapnil Kumar
- Department of Biotechnology & Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Grace Pauline
- Department of Biotechnology & Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Vaibhav Vindal
- Department of Biotechnology & Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, India
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Akash S, Bibi S, Biswas P, Mukerjee N, Khan DA, Hasan MN, Sultana NA, Hosen ME, Jardan YAB, Nafidi HA, Bourhia M. Revolutionizing anti-cancer drug discovery against breast cancer and lung cancer by modification of natural genistein: an advanced computational and drug design approach. Front Oncol 2023; 13:1228865. [PMID: 37817764 PMCID: PMC10561655 DOI: 10.3389/fonc.2023.1228865] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 08/15/2023] [Indexed: 10/12/2023] Open
Abstract
Breast and lung cancer are two of the most lethal forms of cancer, responsible for a disproportionately high number of deaths worldwide. Both doctors and cancer patients express alarm about the rising incidence of the disease globally. Although targeted treatment has achieved enormous advancements, it is not without its drawbacks. Numerous medicines and chemotherapeutic drugs have been authorized by the FDA; nevertheless, they can be quite costly and often fall short of completely curing the condition. Therefore, this investigation has been conducted to identify a potential medication against breast and lung cancer through structural modification of genistein. Genistein is the active compound in Glycyrrhiza glabra (licorice), and it exhibits solid anticancer efficiency against various cancers, including breast cancer, lung cancer, and brain cancer. Hence, the design of its analogs with the interchange of five functional groups-COOH, NH2 and OCH3, Benzene, and NH-CH2-CH2-OH-have been employed to enhance affinities compared to primary genistein. Additionally, advanced computational studies such as PASS prediction, molecular docking, ADMET, and molecular dynamics simulation were conducted. Firstly, the PASS prediction spectrum was analyzed, revealing that the designed genistein analogs exhibit improved antineoplastic activity. In the prediction data, breast and lung cancer were selected as primary targets. Subsequently, other computational investigations were gradually conducted. The mentioned compounds have shown acceptable results for in silico ADME, AMES toxicity, and hepatotoxicity estimations, which are fundamental for their oral medication. It is noteworthy that the initial binding affinity was only -8.7 kcal/mol against the breast cancer targeted protein (PDB ID: 3HB5). However, after the modification of the functional group, when calculating the binding affinities, it becomes apparent that the binding affinities increase gradually, reaching a maximum of -11.0 and -10.0 kcal/mol. Similarly, the initial binding affinity was only -8.0 kcal/mol against lung cancer (PDB ID: 2P85), but after the addition of binding affinity, it reached -9.5 kcal/mol. Finally, a molecular dynamics simulation was conducted to study the molecular models over 100 ns and examine the stability of the docked complexes. The results indicate that the selected complexes remain highly stable throughout the 100-ns molecular dynamics simulation runs, displaying strong correlations with the binding of targeted ligands within the active site of the selected protein. It is important to further investigate and proceed to clinical or wet lab experiments to determine the practical value of the proposed compounds.
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Affiliation(s)
- Shopnil Akash
- Faculty of Allied Health Science, Department of Pharmacy, Daffodil International University, Dhaka, Bangladesh
| | - Shabana Bibi
- Department of Biosciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan
| | - Partha Biswas
- Laboratory of Pharmaceutical Biotechnology and Bioinformatics, Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, Bangladesh
| | - Nobendu Mukerjee
- Department of Microbiology, West Bengal State University, Kolkata, India
| | - Dhrubo Ahmed Khan
- Laboratory of Pharmaceutical Biotechnology and Bioinformatics, Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, Bangladesh
| | - Md. Nazmul Hasan
- Laboratory of Pharmaceutical Biotechnology and Bioinformatics, Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, Bangladesh
| | - Nazneen Ahmeda Sultana
- Faculty of Allied Health Science, Department of Pharmacy, Daffodil International University, Dhaka, Bangladesh
| | - Md. Eram Hosen
- Professor Joarder DNA and Chromosome Research Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, Bangladesh
| | - Yousef A. Bin Jardan
- Department of Food Science, Faculty of Agricultural and Food Sciences, Laval University, Quebec City, QC, Canada
| | - Hiba-Allah Nafidi
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Mohammed Bourhia
- Laboratory of Chemistry and Biochemistry, Faculty of Medicine and Pharmacy, Ibn Zohr University, Laayoune, Morocco
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Rahman MR, Islam T, Zaman T, Shahjaman M, Karim MR, Huq F, Quinn JMW, Holsinger RMD, Gov E, Moni MA. Identification of molecular signatures and pathways to identify novel therapeutic targets in Alzheimer's disease: Insights from a systems biomedicine perspective. Genomics 2019; 112:1290-1299. [PMID: 31377428 DOI: 10.1016/j.ygeno.2019.07.018] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 07/01/2019] [Accepted: 07/30/2019] [Indexed: 12/20/2022]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. However, there are no peripheral biomarkers available that can detect AD onset. This study aimed to identify the molecular signatures in AD through an integrative analysis of blood gene expression data. We used two microarray datasets (GSE4226 and GSE4229) comparing peripheral blood transcriptomes of AD patients and controls to identify differentially expressed genes (DEGs). Gene set and protein overrepresentation analysis, protein-protein interaction (PPI), DEGs-Transcription Factors (TFs) interactions, DEGs-microRNAs (miRNAs) interactions, protein-drug interactions, and protein subcellular localizations analyses were performed on DEGs common to the datasets. We identified 25 common DEGs between the two datasets. Integration of genome scale transcriptome datasets with biomolecular networks revealed hub genes (NOL6, ATF3, TUBB, UQCRC1, CASP2, SND1, VCAM1, BTF3, VPS37B), common transcription factors (FOXC1, GATA2, NFIC, PPARG, USF2, YY1) and miRNAs (mir-20a-5p, mir-93-5p, mir-16-5p, let-7b-5p, mir-708-5p, mir-24-3p, mir-26b-5p, mir-17-5p, mir-193-3p, mir-186-5p). Evaluation of histone modifications revealed that hub genes possess several histone modification sites associated with AD. Protein-drug interactions revealed 10 compounds that affect the identified AD candidate biomolecules, including anti-neoplastic agents (Vinorelbine, Vincristine, Vinblastine, Epothilone D, Epothilone B, CYT997, and ZEN-012), a dermatological (Podofilox) and an immunosuppressive agent (Colchicine). The subcellular localization of molecular signatures varied, including nuclear, plasma membrane and cytosolic proteins. In the present study, it was identified blood-cell derived molecular signatures that might be useful as candidate peripheral biomarkers in AD. It was also identified potential drugs and epigenetic data associated with these molecules that may be useful in designing therapeutic approaches to ameliorate AD.
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Affiliation(s)
- Md Rezanur Rahman
- Department of Biochemistry and Biotechnology, School of Biomedical Science, Khwaja Yunus Ali University, Enayetpur, Sirajgonj, Bangladesh.
| | - Tania Islam
- Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh
| | - Toyfiquz Zaman
- Department of Biochemistry and Biotechnology, School of Biomedical Science, Khwaja Yunus Ali University, Enayetpur, Sirajgonj, Bangladesh
| | - Md Shahjaman
- Department of Statistics, Begum Rokeya University, Rangpur, Bangladesh
| | - Md Rezaul Karim
- Department of Biochemistry and Biotechnology, School of Biomedical Science, Khwaja Yunus Ali University, Enayetpur, Sirajgonj, Bangladesh
| | - Fazlul Huq
- Discipline of Pathology, School of Medical Sciences, The University of Sydney, NSW 2006, Australia
| | - Julian M W Quinn
- Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia
| | - R M Damian Holsinger
- Discipline of Pathology, School of Medical Sciences, The University of Sydney, NSW 2006, Australia; Laboratory of Molecular Neuroscience and Dementia, Brain and Mind Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Esra Gov
- Department of Bioengineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey
| | - Mohammad Ali Moni
- Discipline of Pathology, School of Medical Sciences, The University of Sydney, NSW 2006, Australia; Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.
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