Chemical-protein interactome and its application in off-target identification

Yang, Lun; Wang, Kejian; Wang, Lishan; Jegga, Anil G.; Qin, Shengying; He, Guang; Chen, Jian; Xiao, Yue; He, Lin

doi:10.1007/s12539-011-0051-8

Cited by 25 publications

(22 citation statements)

References 62 publications

(56 reference statements)

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“…Following this example, we plan to further combine our model with other drug-related information (e.g. chemical-protein interactome [8], [40]), thus improving the power of CMap and the efficiency of DTI prediction. In addition, although our current work is focused on drug-target binding, it is well known that the impact of DTI has to be carried out through downstream biological pathways.…”

Section: Resultsmentioning

confidence: 99%

Prediction of Drug-Target Interactions for Drug Repositioning Only Based on Genomic Expression Similarity

et al. 2013

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Small drug molecules usually bind to multiple protein targets or even unintended off-targets. Such drug promiscuity has often led to unwanted or unexplained drug reactions, resulting in side effects or drug repositioning opportunities. So it is always an important issue in pharmacology to identify potential drug-target interactions (DTI). However, DTI discovery by experiment remains a challenging task, due to high expense of time and resources. Many computational methods are therefore developed to predict DTI with high throughput biological and clinical data. Here, we initiatively demonstrate that the on-target and off-target effects could be characterized by drug-induced in vitro genomic expression changes, e.g. the data in Connectivity Map (CMap). Thus, unknown ligands of a certain target can be found from the compounds showing high gene-expression similarity to the known ligands. Then to clarify the detailed practice of CMap based DTI prediction, we objectively evaluate how well each target is characterized by CMap. The results suggest that (1) some targets are better characterized than others, so the prediction models specific to these well characterized targets would be more accurate and reliable; (2) in some cases, a family of ligands for the same target tend to interact with common off-targets, which may help increase the efficiency of DTI discovery and explain the mechanisms of complicated drug actions. In the present study, CMap expression similarity is proposed as a novel indicator of drug-target interactions. The detailed strategies of improving data quality by decreasing the batch effect and building prediction models are also effectively established. We believe the success in CMap can be further translated into other public and commercial data of genomic expression, thus increasing research productivity towards valid drug repositioning and minimal side effects.

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Section: Resultsmentioning

confidence: 99%

Prediction of Drug-Target Interactions for Drug Repositioning Only Based on Genomic Expression Similarity

et al. 2013

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“…Drug repurposing is being used to systematically identify novel indications for drugs already known or discontinued in clinical development in recent years (Achenbach et al, 2011). CPI, an approach to drug repurposing, is an interaction strength matrix of drugs across multiple human proteins aiming at exploring the unexpected drug-protein interactions, with a variety of computational strategies, including docking, chemical structure comparison and text-mining etc (Yang et al, 2011). Yang et al discovered that estrogen receptor and histone deacetylase may be two new therapeutic targets of Alzheimer's disease via CPI (Yang et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

A Potential Target of Tanshinone IIA for Acute Promyelocytic Leukemia Revealed by Inverse Docking and Drug Repurposing

Chen

2014

Asian Pacific Journal of Cancer Prevention

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Tanshinone IIA is a pharmacologically active ingredient extracted from Danshen, a Chinese traditional medicine. Its molecular mechanisms are still unclear. The present study utilized computational approaches to uncover the potential targets of this compound. In this research, PharmMapper server was used as the inverse docking tool andnd the results were verified by Autodock vina in PyRx 0.8, and by DRAR-CPI, a server for drug repositioning via the chemical-protein interactome. Results showed that the retinoic acid receptor alpha (RARα), a target protein in acute promyelocytic leukemia (APL), was in the top rank, with a pharmacophore model matching well the molecular features of Tanshinone IIA. Moreover, molecular docking and drug repurposing results showed that the complex was also matched in terms of structure and chemical-protein interactions. These results indicated that RARα may be a potential target of Tanshinone IIA for APL. The study can provide useful information for further biological and biochemical research on natural compounds.

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“…The selective optimization of side-effects (Wermuth, 2006) is a known lead development technique. Consequently both drug-target interaction networks (Section 4.1.3.; Xie et al, 2009b; Yang et al, 2010; Azuaje et al, 2011; Xie et al, 2011; Yang et al, 2011; Takarabe et al, 2012; Yu et al, 2012) and drug-disease networks (Hu & Agarwal, 2009) may be used for the prediction of side-effects. Analysis of drug-disease networks may be extended using pathway analysis (Hao et al, 2012).…”

Section: Areas Of Drug Design: An Assessment Of Network-related Admentioning

confidence: 99%

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Csermely

Korcsmáros

Kiss

et al. 2013

Pharmacology & Therapeutics

809

661

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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 gives 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 node/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. 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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Chemical-protein interactome and its application in off-target identification

Cited by 25 publications

References 62 publications

Prediction of Drug-Target Interactions for Drug Repositioning Only Based on Genomic Expression Similarity

Prediction of Drug-Target Interactions for Drug Repositioning Only Based on Genomic Expression Similarity

A Potential Target of Tanshinone IIA for Acute Promyelocytic Leukemia Revealed by Inverse Docking and Drug Repurposing

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

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