The exploration of network motifs as potential drug targets from post-translational regulatory networks

Zhang, Xiaodong; Song, Jiangning; Bork, Peer; Zhao, Xing‐Ming

doi:10.1038/srep20558

Cited by 14 publications

(7 citation statements)

References 62 publications

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“…Although large-scale proteomic studies have identified thousands of PTMs in protein domains, the functional significance of many of these PTMs are largely unknown, though analysis of PTMs in the context of conserved and variable residues in MSAs have provided new insights into speciation and functional specialization in signaling proteins 20, 21 . More recently, PTMs have emerged as a valuable source of information for identifying mutations associated with complex disease signaling 22-24 and identifying novel drug targets 25 . For example, cancer mutations can rewire signaling pathways by removing conserved phosphorylation sites or by introducing new phosphorylation sites 24, 26 .…”

Section: Introductionmentioning

confidence: 99%

“…20,21 More recently, PTMs have emerged as a valuable source of information for identifying mutations associated with complex disease signaling [22][23][24] and identifying novel drug targets. 25 For example, cancer mutations can rewire signaling pathways by removing conserved phosphorylation sites or by introducing new phosphorylation sites. 24,26 To systematically identify and characterize such mutations, however, signaling and cancer variants need to be integrated and analyzed in the context of PTMs and evolutionary patterns.…”

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confidence: 99%

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KinView: a visual comparative sequence analysis tool for integrated kinome research

et al. 2016

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Multiple sequence alignments (MSAs) are a fundamental analysis tool used throughout biology to investigate relationships between protein sequence, structure, function, evolutionary history, and patterns of disease-associated variants. However, their widespread application in systems biology research is currently hindered by the lack of user-friendly tools to simultaneously visualize, manipulate and query the information conceptualized in large sequence alignments, and the challenges in integrating MSAs with multiple orthogonal data such as cancer variants and post-translational modifications, which are often stored in heterogeneous data sources and formats. Here, we present the Multiple Sequence Alignment Ontology (MSAOnt), which represents a profile or consensus alignment in an ontological format. Subsets of the alignment are easily selected through the SPARQL Protocol and RDF Query Language for downstream statistical analysis or visualization. We have also created the Kinome Viewer (KinView), an interactive integrative visualization that places eukaryotic protein kinase cancer variants in the context of natural sequence variation and experimentally determined post-translational modifications, which play central roles in the regulation of cellular signaling pathways. Using KinView, we identified differential phosphorylation patterns between tyrosine and serine/threonine kinases in the activation segment, a major kinase regulatory region that is often mutated in proliferative diseases. We discuss cancer variants that disrupt phosphorylation sites in the activation segment, and show how KinView can be used as a comparative tool to identify differences and similarities in natural variation, cancer variants and post-translational modifications between kinase groups, families and subfamilies. Based on KinView comparisons, we identify and experimentally characterize a regulatory tyrosine (Y177PLK4) in the PLK4 C-terminal activation segment region termed the P+1 loop. To further demonstrate the application of KinView in hypothesis generation and testing, we formulate and validate a hypothesis explaining a novel predicted loss-of-function variant (D523NPKCβ) in the regulatory spine of PKCβ, a recently identified tumor suppressor kinase. KinView provides a novel, extensible interface for performing comparative analyses between subsets of kinases and for integrating multiple types of residue specific annotations in user friendly formats.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

KinView: a visual comparative sequence analysis tool for integrated kinome research

et al. 2016

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“…Xiao-Dong Zhang et al [23] hypothesized that drugs that target the same functional network motif could be combined to improve the therapeutic efficacy. They extracted drug combinations from the Drug Combination Database [25].…”

Section: Systems Biology Based Methodsmentioning

confidence: 99%

In-Silico Methodologies for Cancer Multidrug Optimization

Hasan¹,

Eldin

Khedr

et al. 2018

IJCT

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Drug combinations is considered as an effective strategy designed to control complex diseases like cancer. Combinations of drugs can effectively decrease side effects and enhance adaptive resistance. Therefore, increasing the likelihood of defeating complex diseases in a synergistic way. This is due to overcoming factors such as off-target activities, network robustness, bypass mechanisms, cross-talk across compensatory escape pathways and the mutational heterogeneity which results in alterations within multiple molecular pathways. The plurality of effective drug combinations used in clinic were found out through experience. The molecular mechanisms underlying these drug combinations are often not clear. It is not easy to suggest new drug combinations. Computational approaches are proposed to reduce the search space for defining the most promising combinations and prioritizing their experimental evaluation. In this paper, we review methods, techniques and hypotheses developed for in silico methodologies for drug combination discovery in cancer and discuss the limitations and challenges of these methods.

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“…However, drug combination prediction results could be improved by reconstructing drug targets. Although computational models have been proposed from multiple aspects for predicting drug targets [27,28], these methods all consider drug-target prediction and drug combination discovery as two separate learning tasks in different computational models. Thus, we integrated drug-target prediction [29,30] and drug combination prediction [15] into a unified framework, namely the DSML algorithm, which can accurately and effectively predict drug synergy.…”

Section: Introductionmentioning

confidence: 99%

An In Silico Method for Predicting Drug Synergy Based on Multitask Learning

Chen

Luo

Shen

et al. 2021

Interdiscip Sci Comput Life Sci

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To make better use of all kinds of knowledge to predict drug synergy, it is crucial to successfully establish a drug synergy prediction model and leverage the reconstruction of sparse known drug targets. Therefore, we present an in silico method that predicts the synergy scores of drug pairs based on multitask learning (DSML) that could fuse drug targets, protein-protein interactions, anatomical therapeutic chemical codes, a priori knowledge of drug combinations. To simultaneously reconstruct drug-target protein interactions and synergistic drug combinations, DSML benefits indirectly from the associations with relation through proteins. In cross-validation experiments, DSML improved the ability to predict drug synergy. Moreover, the reconstruction of drug-target interactions and the incorporation of multisource knowledge significantly improved drug combination predictions by a large margin. The potential drug combinations predicted by DSML demonstrate its ability to predict drug synergy.

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The exploration of network motifs as potential drug targets from post-translational regulatory networks

Cited by 14 publications

References 62 publications

KinView: a visual comparative sequence analysis tool for integrated kinome research

KinView: a visual comparative sequence analysis tool for integrated kinome research

In-Silico Methodologies for Cancer Multidrug Optimization

An In Silico Method for Predicting Drug Synergy Based on Multitask Learning

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