PTMsnp: A Web Server for the Identification of Driver Mutations That Affect Protein Post-translational Modification

Peng, Di; Li, Huiqin; Hu, Bosu; Zhang, Hongwan; Chen, Li; Lin, Shaofeng; Zuo, Zhixiang; Xue, Yu; Xie, Yubin

doi:10.3389/fcell.2020.593661

Cited by 11 publications

(7 citation statements)

References 60 publications

(62 reference statements)

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“…After clicking the ‘Search’ button, detailed annotations of the inputted biological sequences were first retrieved from the UniProt or RefSeq database (Figure 3B ). In protein mode, the functional domains, repeat regions, coiled coils, motifs, and known post-translational modifications (integrated from the PTMsnp Database ( 12 )) were automatically drawn. In nucleotide mode, the transcript organization, including exon, CDS, 5′-UTR, 3′-UTR, and RNA modification sites (integrated from RMVar ( 13 )), was visualized.…”

Section: Resultsmentioning

confidence: 99%

IBS 2.0: an upgraded illustrator for the visualization of biological sequences

Xie

Zuo

Ren

2022

Nucleic Acids Research

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The visualization of biological sequences with various functional elements is fundamental for the publication of scientific achievements in the field of molecular and cellular biology. However, due to the limitations of the currently used applications, there are still considerable challenges in the preparation of biological schematic diagrams. Here, we present a professional tool called IBS 2.0 for illustrating the organization of both protein and nucleotide sequences. With the abundant graphical elements provided in IBS 2.0, biological sequences can be easily represented in a concise and clear way. Moreover, we implemented a database visualization module in IBS 2.0, enabling batch visualization of biological sequences from the UniProt and the NCBI RefSeq databases. Furthermore, to increase the design efficiency, a resource platform that allows uploading, retrieval, and browsing of existing biological sequence diagrams has been integrated into IBS 2.0. In addition, a lightweight JS library was developed in IBS 2.0 to assist the visualization of biological sequences in customized web services. To obtain the latest version of IBS 2.0, please visit https://ibs.renlab.org.

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Section: Resultsmentioning

confidence: 99%

IBS 2.0: an upgraded illustrator for the visualization of biological sequences

Xie

Zuo

Ren

2022

Nucleic Acids Research

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“…This challenge has been addressed through a variety of creative approaches, including computational approaches that mine protein features (e.g., intrinsic disorder and sequence conservation) that are predictive of functional PTMs ( 59, 89, 90 ). Approaching this problem from the perspective of PTMs disrupted by disease-associated mutations has the potential of narrowing the focus further to quickly identify functional and perhaps targetable PTM signaling nodes ( 62, 91 ).…”

Section: Discussionmentioning

confidence: 99%

Integrating Clinical Cancer and PTM Proteomics Data Identifies a Mechanism of ACK1 Kinase Activation

Balasooriya,

Madhusanka,

López-Palacios

et al. 2023

Molecular Cancer Research

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Beyond the most common oncogenes activated by mutation (mut-drivers) there likely exists a variety of low-frequency mut-drivers, each of which is a possible frontier for targeted therapy. To identify new and understudied mut-drivers, we developed a machine learning (ML) model that integrates curated clinical cancer data and post-translational modification (PTM) proteomics databases. We applied the approach to 62,746 patient cancers spanning 84 cancer types and predicted 3,964 oncogenic mutations across 1,148 genes, many of which disrupt PTMs of known and unknown function. The list of putative mut-drivers includes established drivers and others with poorly understood roles in cancer. This ML model is available as a web application. As a case study, we focused the approach on non-receptor tyrosine kinases (NRTKs) and found a recurrent mutation in activated CDC42 kinase-1 (ACK1) that disrupts the Mig6 homology region (MHR) and ubiquitin-association (UBA) domains on the ACK1 C-terminus. By studying these domains in cultured cells, we found that disruption of the MHR domain helps activate the kinase while disruption of the UBA increases kinase stability by blocking its lysosomal degradation. This ACK1 mutation is analogous to lymphoma-associated mutations in its sister kinase, TNK1, which also disrupt a C-terminal inhibitory motif and UBA domain. This study establishes a mut-driver discovery tool for the research community and identifies a mechanism of ACK1 hyperactivation shared among ACK family kinases. Implications: This research identifies a potentially targetable activating mutation in ACK1 and other possible oncogenic mutations, including PTM-disrupting mutations, for further study.

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“…Due to a lack of experimentally confirmed VIP events, databases and prediction models concerning VIP events mainly rely on sequence-based features. One is the sequential distance of non-synonymous single nucleotide variants (nsSNVs) and phosphosites [ 10–14 ]. While characterizing the modification status alterations of phosphosites occurring at the same location of nsSNVs can be accurate, these predictors may produce false positives when facing nsSNVs occurring near phosphosites.…”

Section: Introductionmentioning

confidence: 99%

VIPpred: a novel model for predicting variant impact on phosphorylation events driving carcinogenesis

Xu,

Li,

Chen

et al. 2023

Briefings in Bioinformatics

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Disrupted protein phosphorylation due to genetic variation is a widespread phenomenon that triggers oncogenic transformation of healthy cells. However, few relevant phosphorylation disruption events have been verified due to limited biological experimental methods. Because of the lack of reliable benchmark datasets, current bioinformatics methods primarily use sequence-based traits to study variant impact on phosphorylation (VIP). Here, we increased the number of experimentally supported VIP events from less than 30 to 740 by manually curating and reanalyzing multi-omics data from 916 patients provided by the Clinical Proteomic Tumor Analysis Consortium. To predict VIP events in cancer cells, we developed VIPpred, a machine learning method characterized by multidimensional features that exhibits robust performance across different cancer types. Our method provided a pan-cancer landscape of VIP events, which are enriched in cancer-related pathways and cancer driver genes. We found that variant-induced increases in phosphorylation events tend to inhibit the protein degradation of oncogenes and promote tumor suppressor protein degradation. Our work provides new insights into phosphorylation-related cancer biology as well as novel avenues for precision therapy.

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PTMsnp: A Web Server for the Identification of Driver Mutations That Affect Protein Post-translational Modification

Cited by 11 publications

References 60 publications

IBS 2.0: an upgraded illustrator for the visualization of biological sequences

IBS 2.0: an upgraded illustrator for the visualization of biological sequences

Integrating Clinical Cancer and PTM Proteomics Data Identifies a Mechanism of ACK1 Kinase Activation

VIPpred: a novel model for predicting variant impact on phosphorylation events driving carcinogenesis

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