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 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 Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

2017 ◽  
pp. 221-236 ◽  
Author(s):  
Alexander Goncearenco ◽  
Minghui Li ◽  
Franco L. Simonetti ◽  
Benjamin A. Shoemaker ◽  
Anna R. Panchenko
Keyword(s):  
Cancer Therapy ◽  
Protein Interactions ◽  
In Silico ◽  
Drug Targets ◽  
Protein Protein Interactions ◽  
Anti Cancer

An integrated strategy for the discovery of drug targets by the analysis of protein–protein interactions

2004 ◽  
Vol 238 (2) ◽  
pp. 119-130 ◽  
Author(s):  
John M. Peltier ◽  
Srdjan Askovic ◽  
Robert R. Becklin ◽  
Cindy Lou Chepanoske ◽  
Yew-Seng J. Ho ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Protein Protein Interactions ◽  
Integrated Strategy

Engineering an acetyllysine reader with photocrosslinking amino acid for interactome profiling

2021 ◽  
Author(s):  
Babu Sudhamalla ◽  
Anirban Roy ◽  
Soumen Barman ◽  
Jyotirmayee Padhan
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Protein Interactions ◽  
Unnatural Amino Acids ◽  
Protein Protein Interactions ◽  
Site Specific ◽  
Powerful Approach

The site-specific installation of light-activable crosslinker unnatural amino acids offers a powerful approach to trap transient protein-protein interactions both in vitro and in vivo. Herein, we engineer a bromodomain to...

Mining Protein Interactome Networks to Measure Interaction Reliability and Select Hub Proteins

2013 ◽  
pp. 222-238
Author(s):  
Young-Rae Cho ◽  
Aidong Zhang
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Functional Characterization ◽  
Flow Simulation ◽  
Protein Protein Interactions ◽  
Systematic Analysis ◽  
Graph Theoretic ◽  
Interactome Network ◽  
Protein Interactome ◽  
Hub Proteins

High-throughput techniques involve large-scale detection of protein-protein interactions. This interaction data set from the genome-scale perspective is structured into an interactome network. Since the interaction evidence represents functional linkage, various graph-theoretic computational approaches have been applied to the interactome networks for functional characterization. However, this data is generally unreliable, and the typical genome-wide interactome networks have a complex connectivity. In this paper, the authors explore systematic analysis of protein interactome networks, and propose a $k$-round signal flow simulation algorithm to measure interaction reliability from connection patterns of the interactome networks. This algorithm quantitatively characterizes functional links between proteins by simulating the propagation of information signals through complex connections. In this regard, the algorithm efficiently estimates the strength of alternative paths for each interaction. The authors also present an algorithm for mining the complex interactome network structure. The algorithm restructures the network by hierarchical ordering of nodes, and this structure re-formatting process reveals hub proteins in the interactome networks. This paper demonstrates that two rounds of simulation accurately scores interaction reliability in terms of ontological correlation and functional consistency. Finally, the authors validate that the selected structural hubs represent functional core proteins.

scholarly journals Protein Dynamics in F-like Bacterial Conjugation

Biomedicines ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 362
Author(s):  
Nicholas Bragagnolo ◽  
Christina Rodriguez ◽  
Naveed Samari-Kermani ◽  
Alice Fours ◽  
Mahboubeh Korouzhdehi ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Protein Interactions ◽  
Drug Targets ◽  
Dynamic Models ◽  
Structural Information ◽  
Imaging Techniques ◽  
Antibiotic Resistance Genes ◽  
Protein Protein Interactions ◽  
Secretion Systems ◽  
Type Iv

Efficient in silico development of novel antibiotics requires high-resolution, dynamic models of drug targets. As conjugation is considered the prominent contributor to the spread of antibiotic resistance genes, targeted drug design to disrupt vital components of conjugative systems has been proposed to lessen the proliferation of bacterial antibiotic resistance. Advancements in structural imaging techniques of large macromolecular complexes has accelerated the discovery of novel protein-protein interactions in bacterial type IV secretion systems (T4SS). The known structural information regarding the F-like T4SS components and complexes has been summarized in the following review, revealing a complex network of protein-protein interactions involving domains with varying degrees of disorder. Structural predictions were performed to provide insight on the dynamicity of proteins within the F plasmid conjugative system that lack structural information.

Pseudokinase drug intervention: a potentially poisoned chalice

2013 ◽  
Vol 41 (4) ◽  
pp. 1083-1088 ◽  
Author(s):  
Jeroen Claus ◽  
Angus J.M. Cameron ◽  
Peter J. Parker
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Binding Pocket ◽  
Atp Binding ◽  
Potential Drug ◽  
Protein Protein Interactions ◽  
Drug Intervention ◽  
Potential Impact ◽  
Potential Drug Targets ◽  
Selection Of

Pseudokinases, the catalytically impaired component of the kinome, have recently been found to share more properties with active kinases than previously thought. In many pseudokinases, ATP binding and even some activity is preserved, highlighting these proteins as potential drug targets. In both active kinases and pseudokinases, binding of ATP or drugs in the nucleotide-binding pocket can stabilize specific conformations required for activity and protein–protein interactions. We discuss the implications of locking particular conformations in a selection of (pseudo)kinases and the dual potential impact on the druggability of these proteins.

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