scholarly journals Identification of disease treatment mechanisms through the multiscale interactome

2020 ◽  
Author(s):  
Camilo Ruiz ◽  
Marinka Zitnik ◽  
Jure Leskovec
Keyword(s):  
Drug Targets ◽  
Drug Effects ◽  
Interaction Network ◽  
Drug Efficacy ◽  
Therapeutic Effects ◽  
Biological Functions ◽  
Disease Treatment ◽  
Protein Protein Interaction ◽  
Associated Proteins ◽  
Human Proteins

Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins. How drugs restore these functions, however, is often unknown as a drug’s therapeutic effects are not limited only to the proteins that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach to explain disease treatment. We integrate disease-perturbed proteins, drug targets, and biological functions into a multiscale interactome network, which contains 478,728 interactions between 1,661 drugs, 840 diseases, 17,660 human proteins, and 9,798 biological functions. We find that a drug’s effectiveness can often be attributed to targeting proteins that are distinct from disease-associated proteins but that affect the same biological functions. We develop a random walk-based method that captures how drug effects propagate through a hierarchy of biological functions and are coordinated by the protein-protein interaction network in which drugs act. On three key pharmacological tasks, we find that the multiscale interactome predicts what drugs will treat a given disease more effectively than prior approaches, identifies proteins and biological functions related to treatment, and predicts genes that interfere with treatment to alter drug efficacy and cause serious adverse reactions. Our results indicate that physical interactions between proteins alone are unable to explain the therapeutic effects of drugs as many drugs treat diseases by affecting the same biological functions disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide a general framework for identifying proteins and biological functions relevant in treatment, even when drugs seem unrelated to the diseases they are recommended for.

scholarly journals Identification of disease treatment mechanisms through the multiscale interactome

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Camilo Ruiz ◽  
Marinka Zitnik ◽  
Jure Leskovec
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Drug Effects ◽  
Therapeutic Effects ◽  
Biological Functions ◽  
Protein Protein Interactions ◽  
Disease Treatment ◽  
Interactome Network ◽  
Powerful Approach ◽  
Physical Interactions

AbstractMost diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins. How drugs restore these functions, however, is often unknown as a drug’s therapeutic effects are not limited to the proteins that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach to explain disease treatment. We integrate disease-perturbed proteins, drug targets, and biological functions into a multiscale interactome network. We then develop a random walk-based method that captures how drug effects propagate through a hierarchy of biological functions and physical protein-protein interactions. On three key pharmacological tasks, the multiscale interactome predicts drug-disease treatment, identifies proteins and biological functions related to treatment, and predicts genes that alter a treatment’s efficacy and adverse reactions. Our results indicate that physical interactions between proteins alone cannot explain treatment since many drugs treat diseases by affecting the biological functions disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide a general framework for explaining treatment, even when drugs seem unrelated to the diseases they are recommended for.

scholarly journals A Multi-Pronged Computational Pipeline for Prioritizing Drug Target Strategies for Latent Tuberculosis

2020 ◽  
Vol 8 ◽  
Author(s):  
Ushashi Banerjee ◽  
Santhosh Sankar ◽  
Amit Singh ◽  
Nagasuma Chandra
Keyword(s):  
Drug Targets ◽  
Metabolic Flux ◽  
Latent Tuberculosis ◽  
Interaction Network ◽  
New Drugs ◽  
Prolonged Treatment ◽  
Flux Analysis ◽  
Protein Protein Interaction ◽  
Candidate Drug ◽  
Global Population

Tuberculosis is one of the deadliest infectious diseases worldwide and the prevalence of latent tuberculosis acts as a huge roadblock in the global effort to eradicate tuberculosis. Most of the currently available anti-tubercular drugs act against the actively replicating form of Mycobacterium tuberculosis (Mtb), and are not effective against the non-replicating dormant form present in latent tuberculosis. With about 30% of the global population harboring latent tuberculosis and the requirement for prolonged treatment duration with the available drugs in such cases, the rate of adherence and successful completion of therapy is low. This necessitates the discovery of new drugs effective against latent tuberculosis. In this work, we have employed a combination of bioinformatics and chemoinformatics approaches to identify potential targets and lead candidates against latent tuberculosis. Our pipeline adopts transcriptome-integrated metabolic flux analysis combined with an analysis of a transcriptome-integrated protein-protein interaction network to identify perturbations in dormant Mtb which leads to a shortlist of 6 potential drug targets. We perform a further selection of the candidate targets and identify potential leads for 3 targets using a range of bioinformatics methods including structural modeling, binding site association and ligand fingerprint similarities. Put together, we identify potential new strategies for targeting latent tuberculosis, new candidate drug targets as well as important lead clues for drug design.

Construction and Analysis of Protein-Protein Interaction Network

2019 ◽  
pp. 204-220
Author(s):  
Divya Dasagrandhi ◽  
Arul Salomee Kamalabai Ravindran ◽  
Anusuyadevi Muthuswamy ◽  
Jayachandran K. S.
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Data Sets ◽  
Protein Protein Interaction ◽  
Huge Data ◽  
Associated Proteins ◽  
High Throughput Experiments ◽  
Generation Sequencing ◽  
Protein Protein Interaction Network

Understanding the mechanisms of a disease is highly complicated due to the complex pathways involved in the disease progression. Despite several decades of research, the occurrence and prognosis of the diseases is not completely understood even with high throughput experiments like DNA microarray and next-generation sequencing. This is due to challenges in analysis of huge data sets. Systems biology is one of the major divisions of bioinformatics and has laid cutting edge techniques for the better understanding of these pathways. Construction of protein-protein interaction network (PPIN) guides the modern scientists to identify vital proteins through protein-protein interaction network, which facilitates the identification of new drug target and associated proteins. The chapter is focused on PPI databases, construction of PPINs, and its analysis.

scholarly journals Analysis of Protein-Protein Interaction Network of Laminopathy Based on Topological Properties

2018 ◽  
Vol 11 (2) ◽  
pp. 1091-1103
Author(s):  
Sapana Singh Yadav ◽  
Usha Chouhan
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Drug Targets ◽  
Genetic Disorders ◽  
Seed Proteins ◽  
Interaction Network ◽  
Large Degree ◽  
Closeness Centrality ◽  
Ppi Network ◽  
Protein Protein Interaction

Laminopathy is a group of rare genetic disorders, including EDMD, HGPS, Leukodystrophy and Lipodystrophy, caused by mutations in genes, encoding proteins of the nuclear lamina. Analysis of protein interaction network in the cell can be the key to understand; how complex processes, lead to diseases. Protein-protein interaction (PPI) in network analysis provides the possibility to quantify the hub proteins in large networks as well as their interacting partners. A comprehensive genes/proteins dataset related to Laminopathy is created by analysing public proteomic data and text mining of scientific literature. From this dataset the associated PPI network is acquired to understand the relationships between topology and functionality of the PPI network. The extended network of seed proteins including one giant network consisted of 381 nodes connected via 1594 edges (Fusion) and 390 nodes connected via 1645 edges (Coexpression), targeted for analysis. 20 proteins with high BC and large degree have been identified. LMNB1 and LMNA with highest BC and Closeness centrality located in the centre of the network. The backbone network derived from giant network with high BC proteins presents a clear and visual overview which shows all important proteins of Laminopathy and the crosstalk between them. Finally, the robustness of central proteins and accuracy of backbone are validated by 248 test networks. Based on the network topological parameters such as degree, closeness centrality, betweenness centrality we found out that integrated PPIN is centred on LMNB1 and LMNA. Although finding of other interacting partners strongly represented as novel drug targets for Laminopathy.

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