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Liu H, Guo M, Xue T, Guan J, Luo L, Zhuang Z. Screening lifespan-extending drugs in Caenorhabditis elegans via label propagation on drug-protein networks. BMC SYSTEMS BIOLOGY 2016; 10:131. [PMID: 28155715 PMCID: PMC5260106 DOI: 10.1186/s12918-016-0362-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Background One of the most challenging tasks in the exploration of anti-aging is to discover drugs that can promote longevity and delay the incidence of age-associated diseases of human. Up to date, a number of drugs, including some antioxidants, metabolites and synthetic compounds, have been found to effectively delay the aging of nematodes and insects. Results We proposed a label propagation algorithm on drug-protein network to infer drugs that can extend the lifespan of C. elegans. We collected a set of drugs of which functions on lifespan extension of C. elegans have been reliably determined, and then built a large-scale drug-protein network by collecting a set of high-confidence drugprotein interactions. A label propagation algorithm was run on the drug-protein bipartite network to predict new drugs with lifespan-extending effect on C. elegans. We calibrated the performance of the proposed method by conducting performance comparison with two classical models, kNN and SVM. We also showed that the screened drugs significantly mediate in the aging-related pathways, and have higher chemical similarities to the effective drugs than ineffective drugs in promoting longevity of C. elegans. Moreover, we carried out wet-lab experiments to verify a screened drugs, 2- Bromo-4’-nitroacetophenone, and found that it can effectively extend the lifespan of C. elegans. These results showed that our method is effective in screening lifespanextending drugs in C. elegans. Conclusions In this paper, we proposed a semi-supervised algorithm to predict drugs with lifespan-extending effects on C. elegans. In silico empirical evaluations and in vivo experiments in C. elegans have demonstrated that our method can effectively narrow down the scope of candidate drugs needed to be verified by wet lab experiments. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0362-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hui Liu
- Changzhou University, Jiangsu, 213164, China.,Changzhou NO. 7 People's Hospital, Changzhou, Jiangsu, 213011, China
| | | | - Ting Xue
- Changzhou University, Jiangsu, 213164, China
| | - Jihong Guan
- Department of Computer Science and Technology, Tongji University, Shanghai, 201804, China
| | - Libo Luo
- Changzhou NO. 7 People's Hospital, Changzhou, Jiangsu, 213011, China. @fudan.edu.cn
| | - Ziheng Zhuang
- Changzhou University, Jiangsu, 213164, China. .,Changzhou NO. 7 People's Hospital, Changzhou, Jiangsu, 213011, China.
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Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 511] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
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Murabito E, Smallbone K, Swinton J, Westerhoff HV, Steuer R. A probabilistic approach to identify putative drug targets in biochemical networks. J R Soc Interface 2011; 8:880-95. [PMID: 21123256 PMCID: PMC3104352 DOI: 10.1098/rsif.2010.0540] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2010] [Accepted: 11/09/2010] [Indexed: 01/18/2023] Open
Abstract
Network-based drug design holds great promise in clinical research as a way to overcome the limitations of traditional approaches in the development of drugs with high efficacy and low toxicity. This novel strategy aims to study how a biochemical network as a whole, rather than its individual components, responds to specific perturbations in different physiological conditions. Proteins exerting little control over normal cells and larger control over altered cells may be considered as good candidates for drug targets. The application of network-based drug design would greatly benefit from using an explicit computational model describing the dynamics of the system under investigation. However, creating a fully characterized kinetic model is not an easy task, even for relatively small networks, as it is still significantly hampered by the lack of data about kinetic mechanisms and parameters values. Here, we propose a Monte Carlo approach to identify the differences between flux control profiles of a metabolic network in different physiological states, when information about the kinetics of the system is partially or totally missing. Based on experimentally accessible information on metabolic phenotypes, we develop a novel method to determine probabilistic differences in the flux control coefficients between the two observable phenotypes. Knowledge of how differences in flux control are distributed among the different enzymatic steps is exploited to identify points of fragility in one of the phenotypes. Using a prototypical cancerous phenotype as an example, we demonstrate how our approach can assist researchers in developing compounds with high efficacy and low toxicity.
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Affiliation(s)
- Ettore Murabito
- Doctoral Training Centre Integrative Systems Biology from Molecules to Life, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
- School of Chemical Engineering and Analytical Science, The University of Manchester, Sackville Street, Manchester M60 1QD, UK
| | - Kieran Smallbone
- Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
- School of Mathematics, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
| | - Jonathan Swinton
- Computational Biology/Advanced Science and Technology Laboratory, AstraZeneca, Alderley Park, Macclesfield SK10 4TF, UK
| | - Hans V. Westerhoff
- School of Mathematics, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
- Netherlands Institute for Systems Biology, FALW, VU University Amsterdam, De Boelelaan 1085, 1081, HV Amsterdam, The Netherlands
| | - Ralf Steuer
- School of Chemical Engineering and Analytical Science, The University of Manchester, Sackville Street, Manchester M60 1QD, UK
- Institute for Theoretical Biology, Humboldt University of Berlin, Invalidenstrasse 43, 10115 Berlin, Germany
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Teusink B, Westerhoff HV, Bruggeman FJ. Comparative systems biology: from bacteria to man. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2011; 2:518-532. [PMID: 20836045 DOI: 10.1002/wsbm.74] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Comparative analyses, as carried out by comparative genomics and bioinformatics, have proven extremely powerful to obtain insight into the identity of specific genes that underlie differences and similarities across species. The central concept developed in this chapter is that important aspects of the functional differences between organisms derive not only from the differences in genetic components (which underlies comparative genomics) but also from dynamic, molecular (physical) interactions. Approaches that aim at identifying such network-based rather than component-based homologies between species we shall call Comparative Systems Biology. It will be illustrated by a number of examples from metabolic networks from prokaryotes, via yeast, to man. The potential for species comparisons, at the genome-scale using classical approaches and at the more detailed level of dynamic molecular networks will be illustrated. In our opinion, comparative systems biology, as a marriage between bioinformatics and systems biology, will offer new insights into the nature of organisms for the benefit of medicine, biotechnology, and drug design. As dynamic modeling is becoming more mainstream in cell biology, the potential of comparative systems biology will become more evident.
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Affiliation(s)
- Bas Teusink
- Systems BioInformatics, Center for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, The Netherlands.,Netherlands Institute Systems Biology (NISB), The Netherlands.,Kluyver Center for Genomics of Industrial Fermentation, The Netherlands
| | - Hans V Westerhoff
- Netherlands Institute Systems Biology (NISB), The Netherlands.,Molecular Cell Physiology, VU University Amsterdam, The Netherlands.,Manchester Centre for Integrative Systems Biology, University of Manchester, UK
| | - Frank J Bruggeman
- Systems BioInformatics, Center for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, The Netherlands.,Regulatory Networks Group, NISB, The Netherlands.,Life Sciences, Centre for Mathematics and Computer Science (CWI) Amsterdam, The Netherlands
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Schulz M, Bakker BM, Klipp E. TIde: a software for the systematic scanning of drug targets in kinetic network models. BMC Bioinformatics 2009; 10:344. [PMID: 19840374 PMCID: PMC2773792 DOI: 10.1186/1471-2105-10-344] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2009] [Accepted: 10/19/2009] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND During the stages of the development of a potent drug candidate compounds can fail for several reasons. One of them, the efficacy of a candidate, can be estimated in silico if an appropriate ordinary differential equation model of the affected pathway is available. With such a model at hand it is also possible to detect reactions having a large effect on a certain variable such as a substance concentration. RESULTS We show an algorithm that systematically tests the influence of activators and inhibitors of different type and strength acting at different positions in the network. The effect on a quantity to be selected (e.g. a steady state flux or concentration) is calculated. Moreover, combinations of two inhibitors or one inhibitor and one activator targeting different network positions are analysed. Furthermore, we present TIde (Target Identification), an open source, platform independent tool to investigate ordinary differential equation models in the common systems biology markup language format. It automatically assigns the respectively altered kinetics to the inhibited or activated reactions, performs the necessary calculations, and provides a graphical output of the analysis results. For illustration, TIde is used to detect optimal inhibitor positions in simple branched networks, a signalling pathway, and a well studied model of glycolysis in Trypanosoma brucei. CONCLUSION Using TIde, we show in the branched models under which conditions inhibitions in a certain pathway can affect a molecule concentrations in a different. In the signalling pathway we illuminate which inhibitions have an effect on the signalling characteristics of the last active kinase. Finally, we compare our set of best targets in the glycolysis model with a similar analysis showing the applicability of our tool.
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Affiliation(s)
- Marvin Schulz
- Theoretical Biophysics, Institute for Biology, Humboldt Universität zu Berlin, Invadilenstrasse, 42, 10115 Berlin, Germany.
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Abstract
The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.
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Affiliation(s)
- Andrew L Hopkins
- Division of Biological Chemistry and Drug Discovery, College of Life Science, University of Dundee, Dundee, UK.
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Espinoza-Fonseca LM. Knowledgebase for addiction-related genes: is it possible an extrapolation to rational multi-target drug design? Bioorg Med Chem 2008; 16:9346-8. [PMID: 18815048 DOI: 10.1016/j.bmc.2008.08.080] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2008] [Revised: 08/19/2008] [Accepted: 08/26/2008] [Indexed: 10/21/2022]
Abstract
In recent years the single-probe-single-target approach in drug design has started to be smoothly replaced by the single-probe-multiple-target (or multi-target) one, where a single drug is able to tackle different, but disease-related targets in a selective manner. However, the design of multi-target drugs has been hindered by a lack of a systematic network of disease-related common pathways. The recent development of the knowledgebase of addiction-related genes (KARG) has provided important hints on how to rationally design multi-target probes by connecting experimental techniques with available network models. In this perspective, the use of KARG as a template to build knowledgebases of disease-related genes for the rational multi-target drug design is suggested. Moreover, it is proposed that building knowledgebases of disease-related genes will become a necessary and ubiquitous tool in the rational design of multi-target drugs.
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Affiliation(s)
- L Michel Espinoza-Fonseca
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
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