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Nayar G, Altman RB. Heterogeneous network approaches to protein pathway prediction. Comput Struct Biotechnol J 2024; 23:2727-2739. [PMID: 39035835 PMCID: PMC11260399 DOI: 10.1016/j.csbj.2024.06.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 07/23/2024] Open
Abstract
Understanding protein-protein interactions (PPIs) and the pathways they comprise is essential for comprehending cellular functions and their links to specific phenotypes. Despite the prevalence of molecular data generated by high-throughput sequencing technologies, a significant gap remains in translating this data into functional information regarding the series of interactions that underlie phenotypic differences. In this review, we present an in-depth analysis of heterogeneous network methodologies for modeling protein pathways, highlighting the critical role of integrating multifaceted biological data. It outlines the process of constructing these networks, from data representation to machine learning-driven predictions and evaluations. The work underscores the potential of heterogeneous networks in capturing the complexity of proteomic interactions, thereby offering enhanced accuracy in pathway prediction. This approach not only deepens our understanding of cellular processes but also opens up new possibilities in disease treatment and drug discovery by leveraging the predictive power of comprehensive proteomic data analysis.
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Affiliation(s)
- Gowri Nayar
- Department of Biomedical Data Science, Stanford University, United States
| | - Russ B. Altman
- Department of Biomedical Data Science, Stanford University, United States
- Department of Genetics, Stanford University, United States
- Department of Medicine, Stanford University, United States
- Department of Bioengineering, Stanford University, United States
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Buzzao D, Castresana-Aguirre M, Guala D, Sonnhammer ELL. TOPAS, a network-based approach to detect disease modules in a top-down fashion. NAR Genom Bioinform 2022; 4:lqac093. [PMCID: PMC9706483 DOI: 10.1093/nargab/lqac093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/14/2022] [Accepted: 11/15/2022] [Indexed: 12/02/2022] Open
Abstract
A vast scenario of potential disease mechanisms and remedies is yet to be discovered. The field of Network Medicine has grown thanks to the massive amount of high-throughput data and the emerging evidence that disease-related proteins form ‘disease modules’. Relying on prior disease knowledge, network-based disease module detection algorithms aim at connecting the list of known disease associated genes by exploiting interaction networks. Most existing methods extend disease modules by iteratively adding connector genes in a bottom-up fashion, while top-down approaches remain largely unexplored. We have created TOPAS, an iterative approach that aims at connecting the largest number of seed nodes in a top-down fashion through connectors that guarantee the highest flow of a Random Walk with Restart in a network of functional associations. We used a corpus of 382 manually selected functional gene sets to benchmark our algorithm against SCA, DIAMOnD, MaxLink and ROBUST across four interactomes. We demonstrate that TOPAS outperforms competing methods in terms of Seed Recovery Rate, Seed to Connector Ratio and consistency during module detection. We also show that TOPAS achieves competitive performance in terms of biological relevance of detected modules and scalability.
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Affiliation(s)
- Davide Buzzao
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 171 21 Solna, Sweden
| | | | - Dimitri Guala
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 171 21 Solna, Sweden
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Pirintsos S, Panagiotopoulos A, Bariotakis M, Daskalakis V, Lionis C, Sourvinos G, Karakasiliotis I, Kampa M, Castanas E. From Traditional Ethnopharmacology to Modern Natural Drug Discovery: A Methodology Discussion and Specific Examples. Molecules 2022; 27:molecules27134060. [PMID: 35807306 PMCID: PMC9268545 DOI: 10.3390/molecules27134060] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/19/2022] [Accepted: 06/22/2022] [Indexed: 12/04/2022] Open
Abstract
Ethnopharmacology, through the description of the beneficial effects of plants, has provided an early framework for the therapeutic use of natural compounds. Natural products, either in their native form or after crude extraction of their active ingredients, have long been used by different populations and explored as invaluable sources for drug design. The transition from traditional ethnopharmacology to drug discovery has followed a straightforward path, assisted by the evolution of isolation and characterization methods, the increase in computational power, and the development of specific chemoinformatic methods. The deriving extensive exploitation of the natural product chemical space has led to the discovery of novel compounds with pharmaceutical properties, although this was not followed by an analogous increase in novel drugs. In this work, we discuss the evolution of ideas and methods, from traditional ethnopharmacology to in silico drug discovery, applied to natural products. We point out that, in the past, the starting point was the plant itself, identified by sustained ethnopharmacological research, with the active compound deriving after extensive analysis and testing. In contrast, in recent years, the active substance has been pinpointed by computational methods (in silico docking and molecular dynamics, network pharmacology), followed by the identification of the plant(s) containing the active ingredient, identified by existing or putative ethnopharmacological information. We further stress the potential pitfalls of recent in silico methods and discuss the absolute need for in vitro and in vivo validation as an absolute requirement. Finally, we present our contribution to natural products’ drug discovery by discussing specific examples, applying the whole continuum of this rapidly evolving field. In detail, we report the isolation of novel antiviral compounds, based on natural products active against influenza and SARS-CoV-2 and novel substances active on a specific GPCR, OXER1.
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Affiliation(s)
- Stergios Pirintsos
- Department of Biology, School of Sciences and Technology, University of Crete, 71409 Heraklion, Greece;
- Botanical Garden, University of Crete, 74100 Rethymnon, Greece
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Correspondence: (S.P.); (E.C.)
| | - Athanasios Panagiotopoulos
- Laboratory of Experimental Endocrinology, School of Medicine, University of Crete, 71409 Heraklion, Greece;
| | - Michalis Bariotakis
- Department of Biology, School of Sciences and Technology, University of Crete, 71409 Heraklion, Greece;
| | - Vangelis Daskalakis
- Department of Chemical Engineering, Cyprus University of Technology, Limassol 3603, Cyprus;
| | - Christos Lionis
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Clinic of Social and Family Medicine, School of Medicine, University of Crete, 71409 Heraklion, Greece
| | - George Sourvinos
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Laboratory of Clinical Virology, School of Medicine, University of Crete, 71409 Heraklion, Greece
| | - Ioannis Karakasiliotis
- Laboratory of Biology, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece;
| | - Marilena Kampa
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Laboratory of Experimental Endocrinology, School of Medicine, University of Crete, 71409 Heraklion, Greece;
| | - Elias Castanas
- Nature Crete Pharmaceuticals, 71305 Heraklion, Greece; (C.L.); (G.S.); (M.K.)
- Laboratory of Experimental Endocrinology, School of Medicine, University of Crete, 71409 Heraklion, Greece;
- Correspondence: (S.P.); (E.C.)
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