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Guo X, Ping J, Yang Y, Su X, Shu XO, Wen W, Chen Z, Zhang Y, Tao R, Jia G, He J, Cai Q, Zhang Q, Giles GG, Pearlman R, Rennert G, Vodicka P, Phipps A, Gruber SB, Casey G, Peters U, Long J, Lin W, Zheng W. Large-Scale Alternative Polyadenylation-Wide Association Studies to Identify Putative Cancer Susceptibility Genes. Cancer Res 2024; 84:2707-2719. [PMID: 38759092 PMCID: PMC11326986 DOI: 10.1158/0008-5472.can-24-0521] [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: 02/13/2024] [Revised: 03/26/2024] [Accepted: 05/15/2024] [Indexed: 05/19/2024]
Abstract
Alternative polyadenylation (APA) modulates mRNA processing in the 3'-untranslated regions (3' UTR), affecting mRNA stability and translation efficiency. Research into genetically regulated APA has the potential to provide insights into cancer risk. In this study, we conducted large APA-wide association studies to investigate associations between APA levels and cancer risk. Genetic models were built to predict APA levels in multiple tissues using genotype and RNA sequencing data from 1,337 samples from the Genotype-Tissue Expression project. Associations of genetically predicted APA levels with cancer risk were assessed by applying the prediction models to data from large genome-wide association studies of six common cancers among European ancestry populations: breast, ovarian, prostate, colorectal, lung, and pancreatic cancers. A total of 58 risk genes (corresponding to 76 APA sites) were associated with at least one type of cancer, including 25 genes previously not linked to cancer susceptibility. Of the identified risk APAs, 97.4% and 26.3% were supported by 3'-UTR APA quantitative trait loci and colocalization analyses, respectively. Luciferase reporter assays for four selected putative regulatory 3'-UTR variants demonstrated that the risk alleles of 3'-UTR variants, rs324015 (STAT6), rs2280503 (DIP2B), rs1128450 (FBXO38), and rs145220637 (LDHA), significantly increased the posttranscriptional activities of their target genes compared with reference alleles. Furthermore, knockdown of the target genes confirmed their ability to promote proliferation and migration. Overall, this study provides insights into the role of APA in the genetic susceptibility to common cancers. Significance: Systematic evaluation of associations of alternative polyadenylation with cancer risk reveals 58 putative susceptibility genes, highlighting the contribution of genetically regulated alternative polyadenylation of 3'UTRs to genetic susceptibility to cancer.
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Affiliation(s)
- Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Jie Ping
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Yaohua Yang
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
- Department of Public Health Sciences, UVA Comprehensive Cancer Center, School of Medicine, University of Virginia, Charlottesville, Virginia
| | - Xinwan Su
- International Institutes of Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, Zhejiang University, Yiwu, China
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Zhishan Chen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Yunjing Zhang
- International Institutes of Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, Zhejiang University, Yiwu, China
| | - Ran Tao
- Department of Biostatistics, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Guochong Jia
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Jingni He
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Canada
- Department of Medical Genetics, University of Calgary, Calgary, Canada
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Qingrun Zhang
- Department of Mathematics and Statistics, Alberta Children's Hospital Research Institute, Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Canada
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
| | - Rachel Pearlman
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic
| | - Amanda Phipps
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - Stephen B Gruber
- Department of Preventive Medicine and USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Weiqiang Lin
- International Institutes of Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, Zhejiang University, Yiwu, China
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
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Nussinov R, Yavuz BR, Demirel HC, Arici MK, Jang H, Tuncbag N. Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide. Front Cell Dev Biol 2024; 12:1376639. [PMID: 39015651 PMCID: PMC11249571 DOI: 10.3389/fcell.2024.1376639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/10/2024] [Indexed: 07/18/2024] Open
Abstract
The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Bengi Ruken Yavuz
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
| | | | - M. Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara, Türkiye
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
| | - Nurcan Tuncbag
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Türkiye
- School of Medicine, Koc University, Istanbul, Türkiye
- Koc University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye
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3
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Zhang G, Zhang C, Cai M, Luo C, Zhu F, Liang Z. FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics. Int J Biol Macromol 2024; 266:131180. [PMID: 38552697 DOI: 10.1016/j.ijbiomac.2024.131180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/19/2024] [Accepted: 03/26/2024] [Indexed: 04/01/2024]
Abstract
Phosphorylation modifications play important regulatory roles in most biological processes. However, the functional assignment for the vast majority of the identified phosphosites remains a major challenge. Here, we provide a deep learning framework named FuncPhos-STR as an online resource, for functional prediction and structural visualization of human proteome-level phosphosites. Based on our reported FuncPhos-SEQ framework, which was built by integrating phosphosite sequence evolution and protein-protein interaction (PPI) information, FuncPhos-STR was developed by further integrating the structural and dynamics information on AlphaFold protein structures. The characterized structural topology and dynamics features underlying functional phosphosites emphasized their molecular mechanism for regulating protein functions. By integrating the structural and dynamics, sequence evolutionary, and PPI network features from protein different dimensions, FuncPhos-STR has advantage over other reported models, with the best AUC value of 0.855. Using FuncPhos-STR, the phosphosites inside the pocket regions are accessible to higher functional scores, theoretically supporting their potential regulatory mechanism. Overall, FuncPhos-STR would accelerate the functional identification of huge unexplored phosphosites, and facilitate the elucidation of their allosteric regulation mechanisms. The web server of FuncPhos-STR is freely available at http://funcptm.jysw.suda.edu.cn/str.
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Affiliation(s)
- Guangyu Zhang
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China
| | - Cai Zhang
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China
| | - Mingyue Cai
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Cheng Luo
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Fei Zhu
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
| | - Zhongjie Liang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China; Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou 215123, China.
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4
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Chen C, Liu Y, Li Q, Zhang Z, Luo M, Liu Y, Han L. The Genetic, Pharmacogenomic, and Immune Landscapes Associated with Protein Expression across Human Cancers. Cancer Res 2023; 83:3673-3680. [PMID: 37548539 PMCID: PMC10843800 DOI: 10.1158/0008-5472.can-23-0758] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 06/15/2023] [Accepted: 08/03/2023] [Indexed: 08/08/2023]
Abstract
Proteomics is a powerful approach that can rapidly enhance our understanding of cancer development. Detailed characterization of the genetic, pharmacogenomic, and immune landscape in relation to protein expression in patients with cancer could provide new insights into the functional roles of proteins in cancer. By taking advantage of the genotype data from The Cancer Genome Atlas and protein expression data from The Cancer Proteome Atlas, we characterized the effects of genetic variants on protein expression across 31 cancer types and identified approximately 100,000 protein quantitative trait loci (pQTL). Among these, over 8000 pQTLs were associated with patient overall survival. Furthermore, characterization of the impact of protein expression on more than 350 imputed anticancer drug responses in patients revealed nearly 230,000 significant associations. In addition, approximately 21,000 significant associations were identified between protein expression and immune cell abundance. Finally, a user-friendly data portal, GPIP (https://hanlaboratory.com/GPIP), was developed featuring multiple modules that enable researchers to explore, visualize, and browse multidimensional data. This detailed analysis reveals the associations between the proteomic landscape and genetic variation, patient outcome, the immune microenvironment, and drug response across cancer types, providing a resource that may offer valuable clinical insights and encourage further functional investigations of proteins in cancer. SIGNIFICANCE Comprehensive characterization of the relationship between protein expression and the genetic, pharmacogenomic, and immune landscape of tumors across cancer types provides a foundation for investigating the role of protein expression in cancer development and treatment.
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Affiliation(s)
- Chengxuan Chen
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Yuan Liu
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Qiang Li
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Zhao Zhang
- Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX, USA
| | - Mei Luo
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Yaoming Liu
- Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX, USA
| | - Leng Han
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
- Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX, USA
- Department of Translational Medical Sciences, College of Medicine, Texas A&M University, Houston, TX, USA
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5
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Zhou X, Mitra R, Hou F, Zhou S, Wang L, Jiang W. Genomic Landscape and Potential Regulation of RNA Editing in Drug Resistance. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2207357. [PMID: 36912579 DOI: 10.1002/advs.202207357] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/31/2023] [Indexed: 05/18/2023]
Abstract
Adenosine-to-inosine RNA editing critically affects the response of cancer therapies. However, comprehensive identification of drug resistance-related RNA editing events and systematic understanding of how RNA editing mediates anticancer drug resistance remain unclear. Here, 7157 differential editing sites (DESs) are identified from 98 127 informative RNA editing sites in tumor tissues, many of which are validated in cancer cell lines. Diverse editing patterns of DESs are discovered in resistant samples, which could not be fully explained by adenosine deaminase acting on RNA enzymes. Some RNA-binding proteins are identified that potentially regulate these editing events. Notably, the DESs are significantly enriched in 3'-untranslated regions (3'-UTRs). The impact of DESs in 3'-UTR on the microRNA (miRNA) regulations is explored, and some triplets (DES, miRNA, and gene) that may contribute to drug resistance are identified. In addition, it is determined that the functions of genes enriched with DESs are associated with drug resistance, such as apoptosis, drug metabolism, and DNA synthesis involved in DNA repair. An online resource (http://www.jianglab.cn/REDR/) to support convenient retrieval of DESs is also built. The findings reveal the landscape and potential regulatory mechanism of RNA editing in drug resistance, providing new therapeutic targets for reversing drug resistance.
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Affiliation(s)
- Xu Zhou
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, P. R. China
| | - Ramkrishna Mitra
- Department of Pharmacology, Physiology, and Cancer Biology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Fei Hou
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, P. R. China
| | - Shunheng Zhou
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, P. R. China
| | - Lihong Wang
- Department of Pathophysiology, School of Medicine, Southeast University, Nanjing, 210009, P. R. China
| | - Wei Jiang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, P. R. China
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6
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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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7
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Nussinov R, Zhang M, Maloney R, Liu Y, Tsai CJ, Jang H. Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs. J Mol Biol 2022; 434:167569. [PMID: 35378118 PMCID: PMC9398924 DOI: 10.1016/j.jmb.2022.167569] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/11/2022] [Accepted: 03/25/2022] [Indexed: 01/12/2023]
Abstract
Here, we discuss the principles of allosteric activating mutations, propagation downstream of the signals that they prompt, and allosteric drugs, with examples from the Ras signaling network. We focus on Abl kinase where mutations shift the landscape toward the active, imatinib binding-incompetent conformation, likely resulting in the high affinity ATP outcompeting drug binding. Recent pharmacological innovation extends to allosteric inhibitor (GNF-5)-linked PROTAC, targeting Bcr-Abl1 myristoylation site, and broadly, allosteric heterobifunctional degraders that destroy targets, rather than inhibiting them. Designed chemical linkers in bifunctional degraders can connect the allosteric ligand that binds the target protein and the E3 ubiquitin ligase warhead anchor. The physical properties and favored conformational state of the engineered linker can precisely coordinate the distance and orientation between the target and the recruited E3. Allosteric PROTACs, noncompetitive molecular glues, and bitopic ligands, with covalent links of allosteric ligands and orthosteric warheads, increase the effective local concentration of productively oriented and placed ligands. Through covalent chemical or peptide linkers, allosteric drugs can collaborate with competitive drugs, degrader anchors, or other molecules of choice, driving innovative drug discovery.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Mingzhen Zhang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
| | - Ryan Maloney
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
| | - Yonglan Liu
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
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8
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From single-omics to interactomics: How can ligand-induced perturbations modulate single-cell phenotypes? ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 131:45-83. [PMID: 35871896 DOI: 10.1016/bs.apcsb.2022.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Cells suffer from perturbations by different stimuli, which, consequently, rise to individual alterations in their profile and function that may end up affecting the tissue as a whole. This is no different if we consider the effect of a therapeutic agent on a biological system. As cells are exposed to external ligands their profile can change at different single-omics levels. Detecting how these changes take place through different sequencing technologies is key to a better understanding of the effects of therapeutic agents. Single-cell RNA-sequencing stands out as one of the most common approaches for cell profiling and perturbation analysis. As a result, single-cell transcriptomics data can be integrated with other omics data sources, such as proteomics and epigenomics data, to clarify the perturbation effects and mechanism at the cell level. Appropriate computational tools are key to process and integrate the available information. This chapter focuses on the recent advances on ligand-induced perturbation and single-cell omics computational tools and algorithms, their current limitations, and how the deluge of data can be used to improve the current process of drug research and development.
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9
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Abstract
Three-dimensional protein structural data at the molecular level are pivotal for successful precision medicine. Such data are crucial not only for discovering drugs that act to block the active site of the target mutant protein but also for clarifying to the patient and the clinician how the mutations harbored by the patient work. The relative paucity of structural data reflects their cost, challenges in their interpretation, and lack of clinical guidelines for their utilization. Rapid technological advancements in experimental high-resolution structural determination increasingly generate structures. Computationally, modeling algorithms, including molecular dynamics simulations, are becoming more powerful, as are compute-intensive hardware, particularly graphics processing units, overlapping with the inception of the exascale era. Accessible, freely available, and detailed structural and dynamical data can be merged with big data to powerfully transform personalized pharmacology. Here we review protein and emerging genome high-resolution data, along with means, applications, and examples underscoring their usefulness in precision medicine. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, Maryland, USA; .,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, Maryland, USA;
| | - Guy Nir
- Department of Biochemistry and Molecular Biology, Department of Neuroscience, Cell Biology and Anatomy, and Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, Galveston, Texas, USA
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, Maryland, USA;
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.,Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.,Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
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10
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Martin W, Sheynkman G, Lightstone FC, Nussinov R, Cheng F. Interpretable artificial intelligence and exascale molecular dynamics simulations to reveal kinetics: Applications to Alzheimer's disease. Curr Opin Struct Biol 2022; 72:103-113. [PMID: 34628220 PMCID: PMC8860862 DOI: 10.1016/j.sbi.2021.09.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 08/30/2021] [Accepted: 09/01/2021] [Indexed: 02/03/2023]
Abstract
The rapid increase in computing power, especially with the integration of graphics processing units, has dramatically increased the capabilities of molecular dynamics simulations. To date, these capabilities extend from running very long simulations (tens to hundreds of microseconds) to thousands of short simulations. However, the expansive data generated in these simulations must be made interpretable not only by the investigator who performs them but also by others as well. Here, we demonstrate how integrating learning techniques, such as artificial intelligence, machine learning, and neural networks, into analysis pipelines can reveal the kinetics of Alzheimer's disease (AD) protein aggregation. We review select AD targets, describe current simulation methods, and introduce learning concepts and their application in AD, highlighting limitations and potential solutions.
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Affiliation(s)
- William Martin
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Gloria Sheynkman
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, 22903, USA
| | - Felice C Lightstone
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Lab, Livermore, CA, 94550, USA
| | - Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA; Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
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11
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Chen S, Liu Y, Zhang Y, Wierbowski SD, Lipkin SM, Wei X, Yu H. A full-proteome, interaction-specific characterization of mutational hotspots across human cancers. Genome Res 2022; 32:135-149. [PMID: 34963661 PMCID: PMC8744679 DOI: 10.1101/gr.275437.121] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 11/22/2021] [Indexed: 11/24/2022]
Abstract
Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically informed computational framework that characterizes the functional relevance of all 1107 published mutational hotspots identified in approximately 25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein-protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole-human interactome, we uncover genome-wide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we show that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.
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Affiliation(s)
- Siwei Chen
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA
| | - Yuan Liu
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
| | - Yingying Zhang
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA
| | - Shayne D Wierbowski
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
| | - Steven M Lipkin
- Department of Medicine, Weill Cornell Medicine, New York, New York 10021, USA
| | - Xiaomu Wei
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Department of Medicine, Weill Cornell Medicine, New York, New York 10021, USA
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
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12
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Network-based protein-protein interaction prediction method maps perturbations of cancer interactome. PLoS Genet 2021; 17:e1009869. [PMID: 34727106 PMCID: PMC8610286 DOI: 10.1371/journal.pgen.1009869] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 11/23/2021] [Accepted: 10/09/2021] [Indexed: 01/09/2023] Open
Abstract
The perturbations of protein-protein interactions (PPIs) were found to be the main cause of cancer. Previous PPI prediction methods which were trained with non-disease general PPI data were not compatible to map the PPI network in cancer. Therefore, we established a novel cancer specific PPI prediction method dubbed NECARE, which was based on relational graph convolutional network (R-GCN) with knowledge-based features. It achieved the best performance with a Matthews correlation coefficient (MCC) = 0.84±0.03 and an F1 = 91±2% compared with other methods. With NECARE, we mapped the cancer interactome atlas and revealed that the perturbations of PPIs were enriched on 1362 genes, which were named cancer hub genes. Those genes were found to over-represent with mutations occurring at protein-macromolecules binding interfaces. Furthermore, over 56% of cancer treatment-related genes belonged to hub genes and they were significantly related to the prognosis of 32 types of cancers. Finally, by coimmunoprecipitation, we confirmed that the NECARE prediction method was highly reliable with a 90% accuracy. Overall, we provided the novel network-based cancer protein-protein interaction prediction method and mapped the perturbation of cancer interactome. NECARE is available at: https://github.com/JiajunQiu/NECARE.
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13
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Nussinov R, Zhang M, Maloney R, Tsai CJ, Yavuz BR, Tuncbag N, Jang H. Mechanism of activation and the rewired network: New drug design concepts. Med Res Rev 2021; 42:770-799. [PMID: 34693559 PMCID: PMC8837674 DOI: 10.1002/med.21863] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/06/2021] [Accepted: 10/07/2021] [Indexed: 12/13/2022]
Abstract
Precision oncology benefits from effective early phase drug discovery decisions. Recently, drugging inactive protein conformations has shown impressive successes, raising the cardinal questions of which targets can profit and what are the principles of the active/inactive protein pharmacology. Cancer driver mutations have been established to mimic the protein activation mechanism. We suggest that the decision whether to target an inactive (or active) conformation should largely rest on the protein mechanism of activation. We next discuss the recent identification of double (multiple) same-allele driver mutations and their impact on cell proliferation and suggest that like single driver mutations, double drivers also mimic the mechanism of activation. We further suggest that the structural perturbations of double (multiple) in cis mutations may reveal new surfaces/pockets for drug design. Finally, we underscore the preeminent role of the cellular network which is deregulated in cancer. Our structure-based review and outlook updates the traditional Mechanism of Action, informs decisions, and calls attention to the intrinsic activation mechanism of the target protein and the rewired tumor-specific network, ushering innovative considerations in precision medicine.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, Maryland, USA.,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Mingzhen Zhang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, Maryland, USA
| | - Ryan Maloney
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, Maryland, USA
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, Maryland, USA
| | - Bengi Ruken Yavuz
- Department of Health Informatics, Graduate School of Informatics, Middle East Technical University, Ankara, Turkey
| | - Nurcan Tuncbag
- Department of Health Informatics, Graduate School of Informatics, Middle East Technical University, Ankara, Turkey.,Department of Chemical and Biological Engineering, College of Engineering, Koc University, Istanbul, Turkey.,Koc University Research Center for Translational Medicine, School of Medicine, Koc University, Istanbul, Turkey
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, Maryland, USA
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14
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Ni D, Chai Z, Wang Y, Li M, Yu Z, Liu Y, Lu S, Zhang J. Along the allostery stream: Recent advances in computational methods for allosteric drug discovery. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1585] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Duan Ni
- College of Pharmacy Ningxia Medical University Yinchuan China
- The Charles Perkins Centre University of Sydney Sydney New South Wales Australia
| | - Zongtao Chai
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital Second Military Medical University Shanghai China
| | - Ying Wang
- State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Mingyu Li
- State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education Shanghai Jiao Tong University School of Medicine Shanghai China
| | | | - Yaqin Liu
- Medicinal Chemistry and Bioinformatics Center Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Shaoyong Lu
- College of Pharmacy Ningxia Medical University Yinchuan China
- State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education Shanghai Jiao Tong University School of Medicine Shanghai China
- Medicinal Chemistry and Bioinformatics Center Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Jian Zhang
- College of Pharmacy Ningxia Medical University Yinchuan China
- State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education Shanghai Jiao Tong University School of Medicine Shanghai China
- Medicinal Chemistry and Bioinformatics Center Shanghai Jiao Tong University School of Medicine Shanghai China
- School of Pharmaceutical Sciences Zhengzhou University Zhengzhou China
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