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Balasooriya ER, Madhusanka D, López-Palacios TP, Eastmond RJ, Jayatunge D, Owen JJ, Gashler JS, Egbert CM, Bulathsinghalage C, Liu L, Piccolo SR, Andersen JL. Integrating Clinical Cancer and PTM Proteomics Data Identifies a Mechanism of ACK1 Kinase Activation. Mol Cancer Res 2024; 22:137-151. [PMID: 37847650 PMCID: PMC10831333 DOI: 10.1158/1541-7786.mcr-23-0153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 08/17/2023] [Accepted: 10/13/2023] [Indexed: 10/19/2023]
Abstract
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 posttranslational 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 nonreceptor tyrosine kinases (NRTK) 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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Affiliation(s)
- Eranga R. Balasooriya
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
- Center for Cancer Research, Massachusetts General Hospital, Boston, Massachusetts
- Dept. of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Deshan Madhusanka
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
- Department of Oncological Sciences and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah
| | - Tania P. López-Palacios
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
- Department of Oncological Sciences and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah
| | - Riley J. Eastmond
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
| | - Dasun Jayatunge
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
- Department of Oncological Sciences and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah
| | - Jake J. Owen
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
| | - Jack S. Gashler
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
| | - Christina M. Egbert
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
| | | | - Lu Liu
- Department of Computer Science, North Dakota State University, Fargo, North Dakota
| | | | - Joshua L. Andersen
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
- Department of Oncological Sciences and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah
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Esmaili F, Pourmirzaei M, Ramazi S, Shojaeilangari S, Yavari E. A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:1266-1285. [PMID: 37863385 PMCID: PMC11082408 DOI: 10.1016/j.gpb.2023.03.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 01/16/2023] [Accepted: 03/23/2023] [Indexed: 10/22/2023]
Abstract
Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and, as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes. Disorders in the phosphorylation process lead to multiple diseases, including neurological disorders and cancers. The purpose of this review is to organize this body of knowledge associated with phosphorylation site (p-site) prediction to facilitate future research in this field. At first, we comprehensively review all related databases and introduce all steps regarding dataset creation, data preprocessing, and method evaluation in p-site prediction. Next, we investigate p-site prediction methods, which are divided into two computational groups: algorithmic and machine learning (ML). Additionally, it is shown that there are basically two main approaches for p-site prediction by ML: conventional and end-to-end deep learning methods, both of which are given an overview. Moreover, this review introduces the most important feature extraction techniques, which have mostly been used in p-site prediction. Finally, we create three test sets from new proteins related to the released version of the database of protein post-translational modifications (dbPTM) in 2022 based on general and human species. Evaluating online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release, distinct from those in the dbPTM 2019 release, reveals their limitations. In other words, the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research papers.
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Affiliation(s)
- Farzaneh Esmaili
- Department of Information Technology, Tarbiat Modares University, Tehran 14115-111, Iran
| | - Mahdi Pourmirzaei
- Department of Information Technology, Tarbiat Modares University, Tehran 14115-111, Iran
| | - Shahin Ramazi
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran 14115-111, Iran.
| | - Seyedehsamaneh Shojaeilangari
- Biomedical Engineering Group, Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST), Tehran 33535-111, Iran
| | - Elham Yavari
- Department of Information Technology, Tarbiat Modares University, Tehran 14115-111, Iran
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Xu X, Li Y, Chen T, Hou C, Yang L, Zhu P, Zhang Y, Li T. VIPpred: a novel model for predicting variant impact on phosphorylation events driving carcinogenesis. Brief Bioinform 2023; 25:bbad480. [PMID: 38156562 PMCID: PMC10782907 DOI: 10.1093/bib/bbad480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 11/14/2023] [Accepted: 12/03/2023] [Indexed: 12/30/2023] Open
Abstract
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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Affiliation(s)
- Xiaofeng Xu
- Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Ying Li
- Department of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, Shanxi 030024, China
| | - Taoyu Chen
- Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Chao Hou
- Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Liang Yang
- Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Peiyu Zhu
- Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Yi Zhang
- Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Tingting Li
- Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
- Key Laboratory for Neuroscience, Ministry of Education/National Health Commission of China, Peking University, Beijing 100191, China
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Dristy TT, Noor AR, Dey P, Saha A. Structural analysis and conformational dynamics of SOCS1 gene mutations involved in diffuse large B-cell lymphoma. Gene 2023; 864:147293. [PMID: 36813059 DOI: 10.1016/j.gene.2023.147293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 01/28/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023]
Abstract
OBJECTIVES The SOCS1 gene is frequently mutated in primary Diffuse Large B-Cell Lymphoma (DLBCL) patients and is associated with a reduced survival rate. Using various computational techniques, the current study aims to identify Single Nucleotide Polymorphisms (SNPs) in the SOCS1 gene that are associated with the mortality rate of DLBCL patients. This study also evaluates the effects of SNPs on the structural instability of the SOCS1 protein in DLBCL patient. METHODS The cBioPortal webserver was used for mutations and determining how the SNP mutations affect the SOCS1 protein with various algorithms (PolyPhen-2.0, Provean, PhD-SNPg, SNPs&GO, SIFT, FATHMM, Predict SNP and SNAP). Five webservers (I-Mutant 2.0, MUpro, mCSM, DUET and SDM) were used for protein instability and the conserved status and were also predicted through different tools (ConSurf, Expasy, SOMPA). Lastly, MD simulations were run on the two chosen mutations (S116N and V128G) using GROMACS 5.0.1 to study how the mutations change the structure of SOCS1. RESULTS Among the 93 SOCS1 mutations detected in DLBCL patients, nine mutations were found to have a detrimental effect (damaging/deleterious/pathogenic/altered) on the SOCS1 protein. All the nine selected mutations are in the conserved region and four are on the extended strand site, four on the random coil site and one on the alpha helix position of the secondary protein structure. After anticipating the structural effects of these nine mutations, two were chosen (S116N and V128G) based on mutational frequency, location within the protein, structural effect (primary, secondary and tertiary) on stability and conservation status within the SOCS1 protein. The simulation of a 50 ns time interval revealed that the Rg value of S116N (2.17 nm) is higher than that of WT (1.98 nm), indicating a loss of structural compactness. In the case of the RMSD value, this mutated type (V128G) shows more deviation (1.54 nm) in comparison to the wild-type (2.14 nm) and another mutant type (S116N) (2.12 nm). The average RMSF values of wild-type and mutant types (V128G and S116N) were 0.88 nm, 0.49 nm, and 0.93 nm, respectively. The RMSF result shows that the mutant V128G structure is more stable than the wild-type and mutant S116N structures. CONCLUSION Based on all these computational predictions, this study finds that certain mutations, particularly S116N, have a destabilising and robust effect on the SOCS1 protein. These results can be used to learn more about the importance of SOCS1 mutations in DLBCL patients and to develop new ways to treat DLBCL.
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Affiliation(s)
- Tamanna Tasnim Dristy
- Department of Genetic Engineering and Biotechnology, East West University (EWU), Bangladesh
| | - Al-Rownoka Noor
- Department of Genetic Engineering and Biotechnology, East West University (EWU), Bangladesh
| | - Puja Dey
- Faculty of Medicine, Shimane University, Japan
| | - Ayan Saha
- Department of Bioinformatics and Biotechnology, Asian University for Women, Bangladesh.
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Nussinov R, Tsai CJ, Jang H. A New View of Activating Mutations in Cancer. Cancer Res 2022; 82:4114-4123. [PMID: 36069825 PMCID: PMC9664134 DOI: 10.1158/0008-5472.can-22-2125] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/16/2022] [Accepted: 09/01/2022] [Indexed: 12/14/2022]
Abstract
A vast effort has been invested in the identification of driver mutations of cancer. However, recent studies and observations call into question whether the activating mutations or the signal strength are the major determinant of tumor development. The data argue that signal strength determines cell fate, not the mutation that initiated it. In addition to activating mutations, factors that can impact signaling strength include (i) homeostatic mechanisms that can block or enhance the signal, (ii) the types and locations of additional mutations, and (iii) the expression levels of specific isoforms of genes and regulators of proteins in the pathway. Because signal levels are largely decided by chromatin structure, they vary across cell types, states, and time windows. A strong activating mutation can be restricted by low expression, whereas a weaker mutation can be strengthened by high expression. Strong signals can be associated with cell proliferation, but too strong a signal may result in oncogene-induced senescence. Beyond cancer, moderate signal strength in embryonic neural cells may be associated with neurodevelopmental disorders, and moderate signals in aging may be associated with neurodegenerative diseases, like Alzheimer's disease. The challenge for improving patient outcomes therefore lies in determining signaling thresholds and predicting signal strength.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
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Xie Y, Li H, Luo X, Li H, Gao Q, Zhang L, Teng Y, Zhao Q, Zuo Z, Ren J. IBS 2.0: an upgraded illustrator for the visualization of biological sequences. Nucleic Acids Res 2022; 50:W420-W426. [PMID: 35580044 PMCID: PMC9252815 DOI: 10.1093/nar/gkac373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/23/2022] [Accepted: 04/29/2022] [Indexed: 11/14/2022] Open
Abstract
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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Affiliation(s)
- Yubin Xie
- School of Life Sciences, Precision Medicine Institute, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510060, China
| | - Huiqin Li
- School of Life Sciences, Precision Medicine Institute, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510060, China
| | - Xiaotong Luo
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, China
| | - Hongyu Li
- School of Life Sciences, Precision Medicine Institute, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510060, China
| | - Qiuyuan Gao
- School of Life Sciences, Precision Medicine Institute, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510060, China
| | - Luowanyue Zhang
- School of Life Sciences, Precision Medicine Institute, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510060, China
| | - Yuyan Teng
- School of Life Sciences, Precision Medicine Institute, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510060, China
| | - Qi Zhao
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, China
| | - Zhixiang Zuo
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, China
| | - Jian Ren
- School of Life Sciences, Precision Medicine Institute, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510060, China
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, China
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A global map of associations between types of protein posttranslational modifications and human genetic diseases. iScience 2021; 24:102917. [PMID: 34430807 PMCID: PMC8365368 DOI: 10.1016/j.isci.2021.102917] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 06/27/2021] [Accepted: 07/27/2021] [Indexed: 12/14/2022] Open
Abstract
There are >200 types of protein posttranslational modifications (PTMs) described in eukaryotes, each with unique proteome coverage and functions. We hypothesized that some genetic diseases may be caused by the removal of a specific type of PTMs by genomic variants and the consequent deregulation of particular functions. We collected >320,000 human PTMs representing 59 types and crossed them with >4M nonsynonymous DNA variants annotated with predicted pathogenicity and disease associations. We report >1.74M PTM-variant co-occurrences that an enrichment analysis distributed into 215 pairwise associations between 18 PTM types and 148 genetic diseases. Of them, 42% were not previously described. Removal of lysine acetylation exerts the most pronounced effect, and less studied PTM types such as S-glutathionylation or S-nitrosylation show relevance. Using pathogenicity predictions, we identified PTM sites that may produce particular diseases if prevented. Our results provide evidence of a substantial impact of PTM-specific removal on the pathogenesis of genetic diseases and phenotypes. There is an enrichment of disease-associated nsSNVs preventing certain types of PTMs We report 215 pairwise associations between 18 PTM types and 148 genetic diseases The removal of lysine acetylation exerts the most pronounced effect We report a set of PTM sites that may produce particular diseases if prevented
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