101
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Shin WH, Kumazawa K, Imai K, Hirokawa T, Kihara D. Current Challenges and Opportunities in Designing Protein-Protein Interaction Targeted Drugs. Adv Appl Bioinform Chem 2020; 13:11-25. [PMID: 33209039 PMCID: PMC7669531 DOI: 10.2147/aabc.s235542] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 10/22/2020] [Indexed: 12/24/2022] Open
Abstract
It has been noticed that the efficiency of drug development has been decreasing in the past few decades. To overcome the situation, protein-protein interactions (PPIs) have been identified as new drug targets as early as 2000. PPIs are more abundant in human cells than single proteins and play numerous important roles in cellular processes including diseases. However, PPIs have very different physicochemical features from the conventional drug targets, which make targeting PPIs challenging. Therefore, as of now, only a small number of PPI inhibitors have been approved or progressed to a stage of clinical trial. In this article, we first overview previous works that analyzed differences between PPIs with PPI targeting ligands and conventional drugs with their binding pockets. Then, we constructed an up-to-date list of PPI targeting drugs that have been approved or are currently under clinical trial and have bound drug-target structures available. Using the dataset, we analyzed the PPIs and their ligands using several scores of druggability. Druggability scores showed that PPI sites and their drugs targeting PPIs are less druggable than conventional binding pockets and drugs, which also indicates that PPI drugs do not follow the conventional rules for drug design, such as Lipinski's rule of five. Our analyses suggest that developing a new rule would be beneficial for guiding PPI-drug discovery.
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Affiliation(s)
- Woong-Hee Shin
- Department of Chemical Science Education, Sunchon National University, Suncheon57922, Republic of Korea
| | - Keiko Kumazawa
- Pharmaceutical Discovery Research Laboratories, Teijin Pharma Limited, Tokyo191-8512, Japan
| | - Kenichiro Imai
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo135-0064, Japan
| | - Takatsugu Hirokawa
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo135-0064, Japan
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN47906, USA
- Department of Computer Science, Purdue University, West Lafayette, IN47906, USA
- Center for Cancer Research, Purdue University, West Lafayette, IN47906, USA
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Care, University of Cincinnati, Cincinnati, OH45229, USA
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102
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Wang B, Hu J, Wang Y, Zhang C, Zhou Y, Yu L, Guo X, Gao L, Chen Y. C3: connect separate connected components to form a succinct disease module. BMC Bioinformatics 2020; 21:433. [PMID: 33008305 PMCID: PMC7531168 DOI: 10.1186/s12859-020-03769-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 09/20/2020] [Indexed: 01/08/2023] Open
Abstract
Background Precise disease module is conducive to understanding the molecular mechanism of disease causation and identifying drug targets. However, due to the fragmentization of disease module in incomplete human interactome, how to determine connectivity pattern and detect a complete neighbourhood of disease based on this is still an open question. Results In this paper, we perform exploratory analysis leading to an important observation that through a few intermediate nodes, most separate connected components formed by disease-associated proteins can be effectively connected and eventually form a complete disease module. And based on the topological properties of these intermediate nodes, we propose a connect separate connected components (C3) method to detect a succinct disease module by introducing a relatively small number of intermediate nodes, which allows us to obtain more pure disease module than other methods. Then we apply C3 across a large corpus of diseases to validate this connectivity pattern of disease module. Furthermore, the connectivity of the perturbed genes in multi-omics data such as The Cancer Genome Atlas also fits this pattern. Conclusions C3 tool is not only useful in detecting a clearly-defined connected disease neighbourhood of 299 diseases and cancer with multi-omics data, but also helpful in better understanding the interconnection of phenotypically related genes in different omics data and studying complex pathological processes.
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Affiliation(s)
- Bingbo Wang
- School of Computer Science and Technology, Xidian University, Xi'an, People's Republic of China.
| | - Jie Hu
- School of Computer Science and Technology, Xidian University, Xi'an, People's Republic of China
| | - Yajun Wang
- School of Humanities and Foreign Languages, Xi'an University of Technology, Xi'an, People's Republic of China
| | - Chenxing Zhang
- School of Computer Science and Technology, Xidian University, Xi'an, People's Republic of China
| | - Yuanjun Zhou
- School of Computer Science and Technology, Xidian University, Xi'an, People's Republic of China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, People's Republic of China
| | - Xingli Guo
- School of Computer Science and Technology, Xidian University, Xi'an, People's Republic of China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an, People's Republic of China
| | - Yunru Chen
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
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103
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Yugandhar K, Wang TY, Wierbowski SD, Shayhidin EE, Yu H. Structure-based validation can drastically underestimate error rate in proteome-wide cross-linking mass spectrometry studies. Nat Methods 2020; 17:985-988. [PMID: 32994567 PMCID: PMC7534832 DOI: 10.1038/s41592-020-0959-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 08/20/2020] [Indexed: 12/18/2022]
Abstract
Thorough quality assessment of novel interactions identified by proteome-wide cross-linking mass spectrometry (XL-MS) studies is critical. Almost all current XL-MS studies have validated cross-links against known 3D structures of representative protein complexes. Here we provide theoretical and experimental evidence demonstrating this approach can drastically underestimate error rates for proteome-wide XL-MS datasets, and propose a comprehensive set of four data-quality metrics to address this issue. The current standard approach for estimating error in proteome-scale crosslinking-mass spectrometry datasets has severe limitations. A proposed set of data-quality metrics provides a more accurate assessment of error rate.
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Affiliation(s)
- Kumar Yugandhar
- Department of Computational Biology, Cornell University, Ithaca, NY, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
| | - Ting-Yi Wang
- Department of Computational Biology, Cornell University, Ithaca, NY, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
| | - Shayne D Wierbowski
- Department of Computational Biology, Cornell University, Ithaca, NY, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
| | - Elnur Elyar Shayhidin
- Department of Computational Biology, Cornell University, Ithaca, NY, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, NY, USA. .,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA.
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104
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Lu H, Zhou Q, He J, Jiang Z, Peng C, Tong R, Shi J. Recent advances in the development of protein-protein interactions modulators: mechanisms and clinical trials. Signal Transduct Target Ther 2020; 5:213. [PMID: 32968059 PMCID: PMC7511340 DOI: 10.1038/s41392-020-00315-3] [Citation(s) in RCA: 340] [Impact Index Per Article: 85.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 07/15/2020] [Accepted: 07/23/2020] [Indexed: 02/05/2023] Open
Abstract
Protein-protein interactions (PPIs) have pivotal roles in life processes. The studies showed that aberrant PPIs are associated with various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Therefore, targeting PPIs is a direction in treating diseases and an essential strategy for the development of new drugs. In the past few decades, the modulation of PPIs has been recognized as one of the most challenging drug discovery tasks. In recent years, some PPIs modulators have entered clinical studies, some of which been approved for marketing, indicating that the modulators targeting PPIs have broad prospects. Here, we summarize the recent advances in PPIs modulators, including small molecules, peptides, and antibodies, hoping to provide some guidance to the design of novel drugs targeting PPIs in the future.
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Affiliation(s)
- Haiying Lu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Academy of Medical Science & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, 610072, Chengdu, China
| | - Qiaodan Zhou
- Department of Ultrasonic, Sichuan Academy of Medical Science & Sichuan Provincial People's Hospital, 610072, Chengdu, China
| | - Jun He
- Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, 610041, Sichuan, China
| | - Zhongliang Jiang
- Miller School of Medicine, University of Miami, Miami, FL, 33136, USA
| | - Cheng Peng
- The Ministry of Education Key Laboratory of Standardization of Chinese Herbal Medicines of Ministry, State Key Laboratory Breeding Base of Systematic Research, Development and Utilization of Chinese Medicine Resources, Pharmacy College, Chengdu University of Traditional Chinese Medicine, 611137, Chengdu, China.
| | - Rongsheng Tong
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Academy of Medical Science & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, 610072, Chengdu, China.
| | - Jianyou Shi
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Academy of Medical Science & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, 610072, Chengdu, China.
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105
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Galaxy InteractoMIX: An Integrated Computational Platform for the Study of Protein-Protein Interaction Data. J Mol Biol 2020; 433:166656. [PMID: 32976910 DOI: 10.1016/j.jmb.2020.09.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 08/30/2020] [Accepted: 09/16/2020] [Indexed: 12/19/2022]
Abstract
Protein interactions play a crucial role among the different functions of a cell and are central to our understanding of cellular processes both in health and disease. Here we present Galaxy InteractoMIX (http://galaxy.interactomix.com), a platform composed of 13 different computational tools each addressing specific aspects of the study of protein-protein interactions, ranging from large-scale cross-species protein-wide interactomes to atomic resolution level of protein complexes. Galaxy InteractoMIX provides an intuitive interface where users can retrieve consolidated interactomics data distributed across several databases or uncover links between diseases and genes by analyzing the interactomes underlying these diseases. The platform makes possible large-scale prediction and curation protein interactions using the conservation of motifs, interology, or presence or absence of key sequence signatures. The range of structure-based tools includes modeling and analysis of protein complexes, delineation of interfaces and the modeling of peptides acting as inhibitors of protein-protein interactions. Galaxy InteractoMIX includes a range of ready-to-use workflows to run complex analyses requiring minimal intervention by users. The potential range of applications of the platform covers different aspects of life science, biomedicine, biotechnology and drug discovery where protein associations are studied.
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106
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Peng J, Guan J, Hui W, Shang X. A novel subnetwork representation learning method for uncovering disease-disease relationships. Methods 2020; 192:77-84. [PMID: 32946974 DOI: 10.1016/j.ymeth.2020.09.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 08/20/2020] [Accepted: 09/07/2020] [Indexed: 12/12/2022] Open
Abstract
Analyzing disease-disease relationships plays an important role for understanding disease mechanisms and finding alternative uses for a drug. A disease is usually the result of abnormal state of multiple molecular process. Since biological networks can model the interplay of multiple molecular processes, network-based methods have been proposed to uncover the disease-disease relationships recently. Given a disease and a network, the disease could be represented as a subnetwork constructed by the disease genes involved in the given network, named disease subnetwork. Because it is difficult to learn the feature representation of disease subnetworks, most existing methods are unsupervised ones without using labeled information. To fill this gap, we propose a novel method named SubNet2vec to learn the feature vectors of diseases from their corresponding subnetwork in the biological network. By utilizing the feature representation of disease subnetwork, we can analyze disease-disease relationships in a supervised fashion. The evaluation results show that the proposed framework outperforms some state-of-the-art approaches in a large margin on disease-disease/disease-drug association prediction. The source code and data are available athttps://github.com/MedicineBiology-AI/SubNet2vec.git.
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Affiliation(s)
- Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China.
| | - Jiaojiao Guan
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China.
| | - Weiwei Hui
- Vivo mobile communications (Hang Zhou) co. LTD, China.
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China.
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107
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Mizukoshi Y, Takeuchi K, Tokunaga Y, Matsuo H, Imai M, Fujisaki M, Kamoshida H, Takizawa T, Hanzawa H, Shimada I. Targeting the cryptic sites: NMR-based strategy to improve protein druggability by controlling the conformational equilibrium. SCIENCE ADVANCES 2020; 6:eabd0480. [PMID: 32998885 PMCID: PMC7527212 DOI: 10.1126/sciadv.abd0480] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 08/06/2020] [Indexed: 06/11/2023]
Abstract
Cryptic ligand binding sites, which are not evident in the unligated structures, are beneficial in tackling with difficult but attractive drug targets, such as protein-protein interactions (PPIs). However, cryptic sites have thus far not been rationally pursued in the early stages of drug development. Here, we demonstrated by nuclear magnetic resonance that the cryptic site in Bcl-xL exists in a conformational equilibrium between the open and closed conformations under the unligated condition. While the fraction of the open conformation in the unligated wild-type Bcl-xL is estimated to be low, F143W mutation that is distal from the ligand binding site can substantially elevate the population. The F143W mutant showed a higher hit rate in a phage-display peptide screening, and the hit peptide bound to the cryptic site of the wild-type Bcl-xL. Therefore, by controlling the conformational equilibrium in the cryptic site, the opportunity to identify a PPI inhibitor could be improved.
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Affiliation(s)
| | - Koh Takeuchi
- National Institute of Advanced Industrial Science and Technology (AIST), Molecular Profiling Research Center for Drug Discovery (molprof) and Cellular and Molecular Biotechnology Research Institute, Tokyo 135-0063, Japan.
| | - Yuji Tokunaga
- National Institute of Advanced Industrial Science and Technology (AIST), Molecular Profiling Research Center for Drug Discovery (molprof) and Cellular and Molecular Biotechnology Research Institute, Tokyo 135-0063, Japan
| | - Hitomi Matsuo
- Japan Biological Informatics Consortium, Tokyo 135-0063, Japan
| | - Misaki Imai
- Japan Biological Informatics Consortium, Tokyo 135-0063, Japan
| | - Miwa Fujisaki
- Japan Biological Informatics Consortium, Tokyo 135-0063, Japan
| | | | | | | | - Ichio Shimada
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan.
- RIKEN, Center for Biosystems Dynamics Research, Yokohama 230-0045, Japan
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108
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Computational methods-guided design of modulators targeting protein-protein interactions (PPIs). Eur J Med Chem 2020; 207:112764. [PMID: 32871340 DOI: 10.1016/j.ejmech.2020.112764] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 08/09/2020] [Accepted: 08/16/2020] [Indexed: 12/15/2022]
Abstract
Protein-protein interactions (PPIs) play a pivotal role in extensive biological processes and are thus crucial to human health and the development of disease states. Due to their critical implications, PPIs have been spotlighted as promising drug targets of broad-spectrum therapeutic interests. However, owing to the general properties of PPIs, such as flat surfaces, featureless conformations, difficult topologies, and shallow pockets, previous attempts were faced with serious obstacles when targeting PPIs and almost portrayed them as "intractable" for decades. To date, rapid progress in computational chemistry and structural biology methods has promoted the exploitation of PPIs in drug discovery. These techniques boost their cost-effective and high-throughput traits, and enable the study of dynamic PPI interfaces. Thus, computational methods represent an alternative strategy to target "undruggable" PPI interfaces and have attracted intense pharmaceutical interest in recent years, as exemplified by the accumulating number of successful cases. In this review, we first introduce a diverse set of computational methods used to design PPI modulators. Herein, we focus on the recent progress in computational strategies and provide a comprehensive overview covering various methodologies. Importantly, a list of recently-reported successful examples is highlighted to verify the feasibility of these computational approaches. Finally, we conclude the general role of computational methods in targeting PPIs, and also discuss future perspectives on the development of such aids.
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109
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Chai X, Zhan J, Pan J, He M, Li B, Wang J, Ma H, Wang Y, Liu S. The rational discovery of multipurpose inhibitors of the ornithine decarboxylase. FASEB J 2020; 34:10907-12921. [PMID: 32767470 DOI: 10.1096/fj.202001222r] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/30/2020] [Accepted: 07/20/2020] [Indexed: 01/02/2023]
Abstract
Metabolic reprograming is a hallmark of cancer, and the polyamine metabolic network is dysregulated in many cancers. Ornithine decarboxylase (ODC) is a rate-limiting enzyme for polyamine synthesis in the polyamine metabolic network. In many cancer cells, ODC is over-expressed, so this enzyme has been an attracting anti-cancer drug target. In the catalysis axis (pathway), ODC converts ornithine to putrescine. Meanwhile, ODC's activity is regulated by protein-protein interactions (PPIs), including the ODC-OAZ1-AZIN1 PPI axis and its monomer-dimer equilibrium. Previous studies showed that when ODC's activity is inhibited, the PPIs might counteract the inhibition efficiency. Therefore, we proposed that multipurpose inhibitors that can simultaneously inhibit ODC's activity and perturb the PPIs would be very valuable as drug candidates and molecular tools. To discover multipurpose ODC inhibitors, we established a computational pipeline by combining positive screening and negative screening. We used this pipeline for the forward screening of multipurpose ligands that might inhibit ODC's activity, block ODC-OAZ1 interaction and enhance ODC non-functional dimerization. With a combination of different experimental assays, we identified three multipurpose ODC inhibitors. At last, we showed that one of these inhibitors is a promising drug candidate. This work demonstrated that our computational pipeline is useful for discovering multipurpose ODC inhibitors, and multipurpose inhibitors would be very valuable. Similar with ODC, there are a lot of proteins in human proteome that act as both enzymes and PPI components. Therefore, this work is not only presenting new molecular tools for polyamine study, but also providing potential insights and protocols for discovering multipurpose inhibitors to target more important protein targets.
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Affiliation(s)
- Xiaoying Chai
- National "111" Center for Cellular Regulation and Molecular Pharmaceutics, Key Laboratory of Industrial Fermentation (Ministry of Education), Hubei University of Technology, Wuhan, China.,Hubei Key Laboratory of Tumor Microenvironment and Immunotherapy, The People's Hospital of China Three Gorges University, Yichang, China.,Hubei Key Laboratory of Industrial Microbiology, Institute of Biomedical and Pharmaceutical Sciences, Hubei University of Technology, Wuhan, China
| | - Jingqiong Zhan
- Hubei Key Laboratory of Tumor Microenvironment and Immunotherapy, The People's Hospital of China Three Gorges University, Yichang, China.,Department of Gynecology and Obstetrics, The First People's Hospital of Yichang, Yichang, China
| | - Jing Pan
- National "111" Center for Cellular Regulation and Molecular Pharmaceutics, Key Laboratory of Industrial Fermentation (Ministry of Education), Hubei University of Technology, Wuhan, China.,Hubei Key Laboratory of Tumor Microenvironment and Immunotherapy, The People's Hospital of China Three Gorges University, Yichang, China
| | - Mengxi He
- Hubei Key Laboratory of Tumor Microenvironment and Immunotherapy, The People's Hospital of China Three Gorges University, Yichang, China
| | - Bo Li
- Hubei Key Laboratory of Tumor Microenvironment and Immunotherapy, The People's Hospital of China Three Gorges University, Yichang, China
| | - Jing Wang
- National "111" Center for Cellular Regulation and Molecular Pharmaceutics, Key Laboratory of Industrial Fermentation (Ministry of Education), Hubei University of Technology, Wuhan, China.,Hubei Key Laboratory of Tumor Microenvironment and Immunotherapy, The People's Hospital of China Three Gorges University, Yichang, China
| | - Hongyan Ma
- National "111" Center for Cellular Regulation and Molecular Pharmaceutics, Key Laboratory of Industrial Fermentation (Ministry of Education), Hubei University of Technology, Wuhan, China.,Hubei Key Laboratory of Tumor Microenvironment and Immunotherapy, The People's Hospital of China Three Gorges University, Yichang, China
| | - Yanlin Wang
- Hubei Key Laboratory of Tumor Microenvironment and Immunotherapy, The People's Hospital of China Three Gorges University, Yichang, China
| | - Sen Liu
- National "111" Center for Cellular Regulation and Molecular Pharmaceutics, Key Laboratory of Industrial Fermentation (Ministry of Education), Hubei University of Technology, Wuhan, China.,Hubei Key Laboratory of Tumor Microenvironment and Immunotherapy, The People's Hospital of China Three Gorges University, Yichang, China.,Hubei Key Laboratory of Industrial Microbiology, Institute of Biomedical and Pharmaceutical Sciences, Hubei University of Technology, Wuhan, China
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110
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Song E, Wang R, Leopold JA, Loscalzo J. Network determinants of cardiovascular calcification and repositioned drug treatments. FASEB J 2020; 34:11087-11100. [PMID: 32638415 PMCID: PMC7497212 DOI: 10.1096/fj.202001062r] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 06/03/2020] [Accepted: 06/15/2020] [Indexed: 01/31/2023]
Abstract
Ectopic cardiovascular calcification is a highly prevalent pathology for which there are no effective novel or repurposed pharmacotherapeutics to prevent disease progression. We created a human calcification endophenotype module (ie, the "calcificasome") by mapping vascular calcification genes (proteins) to the human vascular smooth muscle-specific protein-protein interactome (218 nodes and 632 edges, P < 10-5 ). Network proximity analysis was used to demonstrate that the calcificasome overlapped significantly with endophenotype modules governing inflammation, thrombosis, and fibrosis in the human interactome (P < 0.001). A network-based drug repurposing analysis further revealed that everolimus, temsirolimus, and pomalidomide are predicted to target the calcificasome. The efficacy of these agents in limiting calcification was confirmed experimentally by treating human coronary artery smooth muscle cells in an in vitro calcification assay. Each of the drugs affected expression or activity of their predicted target in the network, and decreased calcification significantly (P < 0.009). An integrated network analytical approach identified novel mediators of ectopic cardiovascular calcification and biologically plausible candidate drugs that could be repurposed to target calcification. This methodological framework for drug repurposing has broad applicability to other diseases.
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Affiliation(s)
- Euijun Song
- Department of MedicineBrigham and Women's HospitalHarvard Medical SchoolBostonMAUSA
| | - Rui‐Sheng Wang
- Department of MedicineBrigham and Women's HospitalHarvard Medical SchoolBostonMAUSA
| | - Jane A. Leopold
- Division of Cardiovascular MedicineBrigham and Women's HospitalHarvard Medical SchoolBostonMAUSA
| | - Joseph Loscalzo
- Department of MedicineBrigham and Women's HospitalHarvard Medical SchoolBostonMAUSA
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111
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Koch E, Rosenthal B, Lundquist A, Chen CH, Kauppi K. Interactome overlap between schizophrenia and cognition. Schizophr Res 2020; 222:167-174. [PMID: 32546371 DOI: 10.1016/j.schres.2020.06.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 05/20/2020] [Accepted: 06/01/2020] [Indexed: 12/12/2022]
Abstract
Cognitive impairments constitute a core feature of schizophrenia, and a genetic overlap between schizophrenia and cognitive functioning in healthy individuals has been identified. However, due to the high polygenicity and complex genetic architecture of both traits, overlapping biological pathways have not yet been identified between schizophrenia and normal cognitive ability. Network medicine offers a framework to study underlying biological pathways through protein-protein interactions among risk genes. Here, established network-based methods were used to characterize the biological relatedness of schizophrenia and cognition by examining the genetic link between schizophrenia risk genes and genes associated with cognitive performance in healthy individuals, through the protein interactome. First, network separation showed a profound interactome overlap between schizophrenia risk genes and genes associated with cognitive performance (SAB = -0.22, z-score = -6.80, p = 5.38e-12). To characterize this overlap, network propagation was thereafter used to identify schizophrenia risk genes that are close to cognition-associated genes in the interactome network space (n = 140, of which 54 were part of the direct genetic overlap). Schizophrenia risk genes close to cognition were enriched for pathways including long-term potentiation and Alzheimer's disease, and included genes with a role in neurotransmitter systems important for cognitive functioning, such as glutamate and dopamine. These results pinpoint a subset of schizophrenia risk genes that are of particular interest for further examination in schizophrenia patient groups, of which some are druggable genes with potential as candidate targets for cognitive enhancing drugs.
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Affiliation(s)
- Elise Koch
- Umeå University, Department of Integrative Medical Biology, Sweden
| | - Brin Rosenthal
- University of California San Diego, Center for Computational Biology and Bioinformatics, United States of America
| | - Anders Lundquist
- Umeå University, Department of Statistics, School of Business, Economics and Statistics, Sweden
| | - Chi-Hua Chen
- University of California San Diego, Department of Radiology and Center for Multimodal Imaging and Genetics, United States of America
| | - Karolina Kauppi
- Umeå University, Department of Integrative Medical Biology, Sweden; Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Sweden.
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112
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Bueno AB, Sun B, Willard FS, Feng D, Ho JD, Wainscott DB, Showalter AD, Vieth M, Chen Q, Stutsman C, Chau B, Ficorilli J, Agejas FJ, Cumming GR, Jiménez A, Rojo I, Kobilka TS, Kobilka BK, Sloop KW. Structural insights into probe-dependent positive allosterism of the GLP-1 receptor. Nat Chem Biol 2020; 16:1105-1110. [PMID: 32690941 DOI: 10.1038/s41589-020-0589-7] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 06/09/2020] [Indexed: 12/19/2022]
Abstract
Drugs that promote the association of protein complexes are an emerging therapeutic strategy. We report discovery of a G protein-coupled receptor (GPCR) ligand that stabilizes an active state conformation by cooperatively binding both the receptor and orthosteric ligand, thereby acting as a 'molecular glue'. LSN3160440 is a positive allosteric modulator of the GLP-1R optimized to increase the affinity and efficacy of GLP-1(9-36), a proteolytic product of GLP-1(7-36). The compound enhances insulin secretion in a glucose-, ligand- and GLP-1R-dependent manner. Cryo-electron microscopy determined the structure of the GLP-1R bound to LSN3160440 in complex with GLP-1 and heterotrimeric Gs. The modulator binds high in the helical bundle at an interface between TM1 and TM2, allowing access to the peptide ligand. Pharmacological characterization showed strong probe dependence of LSN3160440 for GLP-1(9-36) versus oxyntomodulin that is driven by a single residue. Our findings expand protein-protein modulation drug discovery to uncompetitive, active state stabilizers for peptide hormone receptors.
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Affiliation(s)
| | | | - Francis S Willard
- Quantitative Biology, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
| | - Dan Feng
- ConfometRx, Santa Clara, CA, USA
| | - Joseph D Ho
- Lilly Biotechnology Center San Diego, San Diego, CA, USA
| | - David B Wainscott
- Quantitative Biology, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
| | - Aaron D Showalter
- Diabetes and Complications, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
| | - Michal Vieth
- Lilly Biotechnology Center San Diego, San Diego, CA, USA
| | - Qi Chen
- Discovery Chemistry Research and Technologies, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
| | - Cynthia Stutsman
- Diabetes and Complications, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
| | - Betty Chau
- Lilly Biotechnology Center San Diego, San Diego, CA, USA
| | - James Ficorilli
- Diabetes and Complications, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
| | | | | | | | | | | | | | - Kyle W Sloop
- Diabetes and Complications, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA.
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113
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Huang W, Soeung V, Boragine DM, Palzkill T. Mapping Protein-Protein Interaction Interface Peptides with Jun-Fos Assisted Phage Display and Deep Sequencing. ACS Synth Biol 2020; 9:1882-1896. [PMID: 32502338 DOI: 10.1021/acssynbio.0c00242] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Protein-protein interactions govern many cellular processes, and identifying binding interaction sites on proteins can facilitate the discovery of inhibitors to block such interactions. Here we identify peptides from a randomly fragmented plasmid encoding the β-lactamase inhibitory protein (BLIP) and the Lac repressor (LacI) that represent regions of protein-protein interactions. We utilized a Jun-Fos-assisted phage display system that has previously been used to screen cDNA and genomic libraries to identify antibody antigens. Affinity selection with polyclonal antibodies against LacI or BLIP resulted in the rapid enrichment of in-frame peptides from various regions of the proteins. Further, affinity selection with β-lactamase enriched peptides that encompass regions of BLIP previously shown to contribute strongly to the binding energy of the BLIP/β-lactamase interaction, i.e., hotspot residues. Further, one of the regions enriched by affinity selection encompassed a disulfide-constrained region of BLIP that forms part of the BLIP interaction surface in the native complex that we show also binds to β-lactamase as a disulfide-constrained macrocycle peptide with a KD of ∼1 μM. Fragmented open reading frame (ORF) libraries may efficiently identify such naturally constrained peptides at protein-protein interaction interfaces. With sufficiently deep coverage of ORFs by peptide-coding inserts, phage display and deep sequencing can provide detailed information on the domains or peptides that contribute to an interaction. Such information should enable the design of potentially therapeutic macrocycles or peptidomimetics that block the interaction.
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114
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Extensive signal integration by the phytohormone protein network. Nature 2020; 583:271-276. [PMID: 32612234 DOI: 10.1038/s41586-020-2460-0] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 04/14/2020] [Indexed: 12/31/2022]
Abstract
Plant hormones coordinate responses to environmental cues with developmental programs1, and are fundamental for stress resilience and agronomic yield2. The core signalling pathways underlying the effects of phytohormones have been elucidated by genetic screens and hypothesis-driven approaches, and extended by interactome studies of select pathways3. However, fundamental questions remain about how information from different pathways is integrated. Genetically, most phenotypes seem to be regulated by several hormones, but transcriptional profiling suggests that hormones trigger largely exclusive transcriptional programs4. We hypothesized that protein-protein interactions have an important role in phytohormone signal integration. Here, we experimentally generated a systems-level map of the Arabidopsis phytohormone signalling network, consisting of more than 2,000 binary protein-protein interactions. In the highly interconnected network, we identify pathway communities and hundreds of previously unknown pathway contacts that represent potential points of crosstalk. Functional validation of candidates in seven hormone pathways reveals new functions for 74% of tested proteins in 84% of candidate interactions, and indicates that a large majority of signalling proteins function pleiotropically in several pathways. Moreover, we identify several hundred largely small-molecule-dependent interactions of hormone receptors. Comparison with previous reports suggests that noncanonical and nontranscription-mediated receptor signalling is more common than hitherto appreciated.
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115
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Iida M, Iwata M, Yamanishi Y. Network-based characterization of disease-disease relationships in terms of drugs and therapeutic targets. Bioinformatics 2020; 36:i516-i524. [PMID: 32657408 PMCID: PMC7355285 DOI: 10.1093/bioinformatics/btaa439] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
MOTIVATION Disease states are distinguished from each other in terms of differing clinical phenotypes, but characteristic molecular features are often common to various diseases. Similarities between diseases can be explained by characteristic gene expression patterns. However, most disease-disease relationships remain uncharacterized. RESULTS In this study, we proposed a novel approach for network-based characterization of disease-disease relationships in terms of drugs and therapeutic targets. We performed large-scale analyses of omics data and molecular interaction networks for 79 diseases, including adrenoleukodystrophy, leukaemia, Alzheimer's disease, asthma, atopic dermatitis, breast cancer, cystic fibrosis and inflammatory bowel disease. We quantified disease-disease similarities based on proximities of abnormally expressed genes in various molecular networks, and showed that similarities between diseases could be explained by characteristic molecular network topologies. Furthermore, we developed a kernel matrix regression algorithm to predict the commonalities of drugs and therapeutic targets among diseases. Our comprehensive prediction strategy indicated many new associations among phenotypically diverse diseases. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Midori Iida
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Michio Iwata
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
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116
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Rosell M, Fernández-Recio J. Docking approaches for modeling multi-molecular assemblies. Curr Opin Struct Biol 2020; 64:59-65. [PMID: 32615514 PMCID: PMC7324114 DOI: 10.1016/j.sbi.2020.05.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 05/13/2020] [Accepted: 05/21/2020] [Indexed: 12/12/2022]
Abstract
Computational docking approaches aim to overcome the limited availability of experimental structural data on protein-protein interactions, which are key in biology. The field is rapidly moving from the traditional docking methodologies for modeling of binary complexes to more integrative approaches using template-based, data-driven modeling of multi-molecular assemblies. We will review here the predictive capabilities of current docking methods in blind conditions, based on the results from the most recent community-wide blind experiments. Integration of template-based and ab initio docking approaches is emerging as the optimal strategy for modeling protein complexes and multimolecular assemblies. We will also review the new methodological advances on ab initio docking and integrative modeling.
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Affiliation(s)
- Mireia Rosell
- Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain; Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de La Rioja - Gobierno de La Rioja, 26007 Logroño, Spain
| | - Juan Fernández-Recio
- Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain; Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de La Rioja - Gobierno de La Rioja, 26007 Logroño, Spain.
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117
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Perfetto L, Pastrello C, Del-Toro N, Duesbury M, Iannuccelli M, Kotlyar M, Licata L, Meldal B, Panneerselvam K, Panni S, Rahimzadeh N, Ricard-Blum S, Salwinski L, Shrivastava A, Cesareni G, Pellegrini M, Orchard S, Jurisica I, Hermjakob HH, Porras P. The IMEx Coronavirus interactome: an evolving map of Coronaviridae-Host molecular interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.06.16.153817. [PMID: 32587962 PMCID: PMC7310617 DOI: 10.1101/2020.06.16.153817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
The current Coronavirus Disease 2019 (COVID-19) pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions enables studying fine-grained resolution of the mechanisms behind the virus biology and the human organism response. Here we present a curated dataset of physical molecular interactions, manually extracted by IMEx Consortium curators focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family. Currently, the dataset comprises over 2,200 binarized interactions extracted from 86 publications. The dataset can be accessed in the standard formats recommended by the Proteomics Standards Initiative (HUPO-PSI) at the IntAct database website ( www.ebi.ac.uk/intact ), and will be continuously updated as research on COVID-19 progresses.
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Affiliation(s)
- L Perfetto
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - C Pastrello
- Krembil Research Institute, Data Science Discovery Centre for Chronic Diseases, University Health Network, 5KD-407, 60 Leonard Avenue, Toronto, ON, M5T 0S8, Canada
| | - N Del-Toro
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - M Duesbury
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK
- UCLA-DOE Institute, UCLA, Los Angeles, USA
| | - M Iannuccelli
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy
| | - M Kotlyar
- Krembil Research Institute, Data Science Discovery Centre for Chronic Diseases, University Health Network, 5KD-407, 60 Leonard Avenue, Toronto, ON, M5T 0S8, Canada
| | - L Licata
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy
| | - B Meldal
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - K Panneerselvam
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - S Panni
- Department of Biology, Ecology and Earth Sciences, Università della Calabria, Rende, Italy
| | - N Rahimzadeh
- UCLA-DOE Institute, UCLA, Los Angeles, USA
- Providence John Wayne Cancer Institute, Santa Monica, USA
| | - S Ricard-Blum
- Univ Lyon, University Claude Bernard Lyon 1, INSA Lyon, CPE, Institute of Molecular and Supramolecular Chemistry and Biochemistry (ICBMS), UMR 5246, F-69622 Villeurbanne, France
| | | | - A Shrivastava
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - G Cesareni
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy
| | - M Pellegrini
- Department of Molecular, Cell and Developmental Biology, UCLA, Los Angeles, USA
| | - S Orchard
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - I Jurisica
- Krembil Research Institute, Data Science Discovery Centre for Chronic Diseases, University Health Network, 5KD-407, 60 Leonard Avenue, Toronto, ON, M5T 0S8, Canada
- Departments of Medical Biophysics and Computer Science, University of Toronto, Toronto, ON, Canada
| | - H H Hermjakob
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - P Porras
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK
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118
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Kerr CH, Skinnider MA, Andrews DDT, Madero AM, Chan QWT, Stacey RG, Stoynov N, Jan E, Foster LJ. Dynamic rewiring of the human interactome by interferon signaling. Genome Biol 2020; 21:140. [PMID: 32539747 PMCID: PMC7294662 DOI: 10.1186/s13059-020-02050-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 05/20/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes. Comprehensive catalogs of IFN-stimulated genes have been established across species and cell types by transcriptomic and biochemical approaches, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to describe the effects of IFN signaling on the human proteome, and apply protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network. RESULTS We identify > 26,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer IFN-stimulated gene protein synthesis. CONCLUSIONS Our map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing IFN-stimulated genes in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.
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Affiliation(s)
- Craig H Kerr
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
- Current Address: Department of Genetics, Stanford University, Stanford, CA, 94305, USA
| | - Michael A Skinnider
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Daniel D T Andrews
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - Angel M Madero
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Queenie W T Chan
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - R Greg Stacey
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Nikolay Stoynov
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Eric Jan
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - Leonard J Foster
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada.
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119
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Shokri L, Inukai S, Hafner A, Weinand K, Hens K, Vedenko A, Gisselbrecht SS, Dainese R, Bischof J, Furger E, Feuz JD, Basler K, Deplancke B, Bulyk ML. A Comprehensive Drosophila melanogaster Transcription Factor Interactome. Cell Rep 2020; 27:955-970.e7. [PMID: 30995488 PMCID: PMC6485956 DOI: 10.1016/j.celrep.2019.03.071] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/04/2019] [Accepted: 03/18/2019] [Indexed: 12/14/2022] Open
Abstract
Combinatorial interactions among transcription factors (TFs) play essential roles in generating gene expression specificity and diversity in metazoans. Using yeast 2-hybrid (Y2H) assays on nearly all sequence-specific Drosophila TFs, we identified 1,983 protein-protein interactions (PPIs), more than doubling the number of currently known PPIs among Drosophila TFs. For quality assessment, we validated a subset of our interactions using MITOMI and bimolecular fluorescence complementation assays. We combined our interactome with prior PPI data to generate an integrated Drosophila TF-TF binary interaction network. Our analysis of ChIP-seq data, integrating PPI and gene expression information, uncovered different modes by which interacting TFs are recruited to DNA. We further demonstrate the utility of our Drosophila interactome in shedding light on human TF-TF interactions. This study reveals how TFs interact to bind regulatory elements in vivo and serves as a resource of Drosophila TF-TF binary PPIs for understanding tissue-specific gene regulation. Combinatorial regulation by transcription factors (TFs) is one mechanism for achieving condition and tissue-specific gene regulation. Shokri et al. mapped TF-TF interactions between most Drosophila TFs, reporting a comprehensive TF-TF network integrated with previously known interactions. They used this network to discern distinct TF-DNA binding modes.
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Affiliation(s)
- Leila Shokri
- Department of Medicine, Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Sachi Inukai
- Department of Medicine, Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Antonina Hafner
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Systems Biology Graduate Program, Harvard University, Cambridge, MA 02138, USA; Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Kathryn Weinand
- Department of Medicine, Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Bioinformatics and Integrative Genomics Ph.D. Program, Harvard University, Cambridge, MA 02138, USA
| | - Korneel Hens
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Anastasia Vedenko
- Department of Medicine, Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Stephen S Gisselbrecht
- Department of Medicine, Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Riccardo Dainese
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Johannes Bischof
- Institute of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
| | - Edy Furger
- Institute of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
| | - Jean-Daniel Feuz
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Konrad Basler
- Institute of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
| | - Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
| | - Martha L Bulyk
- Department of Medicine, Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Systems Biology Graduate Program, Harvard University, Cambridge, MA 02138, USA; Bioinformatics and Integrative Genomics Ph.D. Program, Harvard University, Cambridge, MA 02138, USA; Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
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120
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Yadav A, Vidal M, Luck K. Precision medicine - networks to the rescue. Curr Opin Biotechnol 2020; 63:177-189. [PMID: 32199228 PMCID: PMC7308189 DOI: 10.1016/j.copbio.2020.02.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 02/13/2020] [Indexed: 12/11/2022]
Abstract
Genetic variants are often not predictive of the phenotypic outcome. Individuals carrying the same pathogenic variant, associated with Mendelian or complex disease, can manifest to different extents, from severe-to-mild to no disease. Improving the accuracy of predicted clinical manifestations of genetic variants has emerged as one of the biggest challenges in precision medicine, which can only be addressed by understanding the mechanisms underlying genotype-phenotype relationships. Efforts to understand the molecular basis of these relationships have identified complex systems of interacting biomolecules that underlie cellular function. Here, we review recent advances in how modeling cellular systems as networks of interacting proteins has fueled identification of disease-associated processes, delineation of underlying molecular mechanisms, and prediction of the pathogenicity of variants. This review is intended to be inspiring for clinicians, geneticists, and network biologists alike who aim to jointly advance our understanding of human disease and accelerate progress toward precision medicine.
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Affiliation(s)
- Anupama Yadav
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
| | - Katja Luck
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA; Current address: Institute of Molecular Biology, Mainz, Germany.
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121
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Yao Z, Aboualizadeh F, Kroll J, Akula I, Snider J, Lyakisheva A, Tang P, Kotlyar M, Jurisica I, Boxem M, Stagljar I. Split Intein-Mediated Protein Ligation for detecting protein-protein interactions and their inhibition. Nat Commun 2020; 11:2440. [PMID: 32415080 PMCID: PMC7229206 DOI: 10.1038/s41467-020-16299-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 04/21/2020] [Indexed: 12/12/2022] Open
Abstract
Here, to overcome many limitations accompanying current available methods to detect protein-protein interactions (PPIs), we develop a live cell method called Split Intein-Mediated Protein Ligation (SIMPL). In this approach, bait and prey proteins are respectively fused to an intein N-terminal fragment (IN) and C-terminal fragment (IC) derived from a re-engineered split intein GP41-1. The bait/prey binding reconstitutes the intein, which splices the bait and prey peptides into a single intact protein that can be detected by regular protein detection methods such as Western blot analysis and ELISA, serving as readouts of PPIs. The method is robust and can be applied not only in mammalian cell lines but in animal models such as C. elegans. SIMPL demonstrates high sensitivity and specificity, and enables exploration of PPIs in different cellular compartments and tracking of kinetic interactions. Additionally, we establish a SIMPL ELISA platform that enables high-throughput screening of PPIs and their inhibitors. Protein-protein interactions are fundamental to the regulation of protein activity and cellular phyisology. Here the authors present Split Intein-Mediated Protein Ligation, which uses bait and prey proteins fused to intein fragments to generate single intact proteins upon interaction.
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Affiliation(s)
- Zhong Yao
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | | | - Jason Kroll
- Division of Developmental Biology, Institute of Biodynamics and Biocomplexity, Faculty of Science, Utrecht University, Utrecht, Netherlands
| | - Indira Akula
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Jamie Snider
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | | | - Priscilla Tang
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Max Kotlyar
- Krembil Research Institute, University Health Network, Toronto, ON, Canada
| | - Igor Jurisica
- Krembil Research Institute, University Health Network, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Department of Computer Science, University of Toronto, Toronto, ON, Canada.,Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovak Republic
| | - Mike Boxem
- Division of Developmental Biology, Institute of Biodynamics and Biocomplexity, Faculty of Science, Utrecht University, Utrecht, Netherlands
| | - Igor Stagljar
- Donnelly Centre, University of Toronto, Toronto, ON, Canada. .,Department of Biochemistry, University of Toronto, Toronto, ON, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada. .,Mediterranean Institute for Life Sciences, Meštrovićevo Šetalište 45, HR-21000, Split, Croatia.
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122
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Roy AA, Dhawanjewar AS, Sharma P, Singh G, Madhusudhan MS. Protein Interaction Z Score Assessment (PIZSA): an empirical scoring scheme for evaluation of protein-protein interactions. Nucleic Acids Res 2020; 47:W331-W337. [PMID: 31114890 PMCID: PMC6602501 DOI: 10.1093/nar/gkz368] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 04/24/2019] [Accepted: 05/15/2019] [Indexed: 11/24/2022] Open
Abstract
Our web server, PIZSA (http://cospi.iiserpune.ac.in/pizsa), assesses the likelihood of protein–protein interactions by assigning a Z Score computed from interface residue contacts. Our score takes into account the optimal number of atoms that mediate the interaction between pairs of residues and whether these contacts emanate from the main chain or side chain. We tested the score on 174 native interactions for which 100 decoys each were constructed using ZDOCK. The native structure scored better than any of the decoys in 146 cases and was able to rank within the 95th percentile in 162 cases. This easily outperforms a competing method, CIPS. We also benchmarked our scoring scheme on 15 targets from the CAPRI dataset and found that our method had results comparable to that of CIPS. Further, our method is able to analyse higher order protein complexes without the need to explicitly identify chains as receptors or ligands. The PIZSA server is easy to use and could be used to score any input three-dimensional structure and provide a residue pair-wise break up of the results. Attractively, our server offers a platform for users to upload their own potentials and could serve as an ideal testing ground for this class of scoring schemes.
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Affiliation(s)
- Ankit A Roy
- Indian Institute of Science Education and Research, Pune, Dr Homi Bhabha Road, Pashan, Pune 411008, India
| | - Abhilesh S Dhawanjewar
- Indian Institute of Science Education and Research, Pune, Dr Homi Bhabha Road, Pashan, Pune 411008, India.,presently at School of Biological Sciences, University of Nebraska, Lincoln, NE 68588, USA
| | - Parichit Sharma
- Indian Institute of Science Education and Research, Pune, Dr Homi Bhabha Road, Pashan, Pune 411008, India.,presently at School of Informatics, Computing & Engineering, Department of Computer Science, Indiana University, Bloomington, IN 47408, USA
| | - Gulzar Singh
- Indian Institute of Science Education and Research, Pune, Dr Homi Bhabha Road, Pashan, Pune 411008, India
| | - M S Madhusudhan
- Indian Institute of Science Education and Research, Pune, Dr Homi Bhabha Road, Pashan, Pune 411008, India
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123
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Queiroz FC, Vargas AMP, Oliveira MGA, Comarela GV, Silveira SA. ppiGReMLIN: a graph mining based detection of conserved structural arrangements in protein-protein interfaces. BMC Bioinformatics 2020; 21:143. [PMID: 32293241 PMCID: PMC7158050 DOI: 10.1186/s12859-020-3474-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 03/27/2020] [Indexed: 12/15/2022] Open
Abstract
Background Protein-protein interactions (PPIs) are fundamental in many biological processes and understanding these interactions is key for a myriad of applications including drug development, peptide design and identification of drug targets. The biological data deluge demands efficient and scalable methods to characterize and understand protein-protein interfaces. In this paper, we present ppiGReMLIN, a graph based strategy to infer interaction patterns in a set of protein-protein complexes. Our method combines an unsupervised learning strategy with frequent subgraph mining in order to detect conserved structural arrangements (patterns) based on the physicochemical properties of atoms on protein interfaces. To assess the ability of ppiGReMLIN to point out relevant conserved substructures on protein-protein interfaces, we compared our results to experimentally determined patterns that are key for protein-protein interactions in 2 datasets of complexes, Serine-protease and BCL-2. Results ppiGReMLIN was able to detect, in an automatic fashion, conserved structural arrangements that represent highly conserved interactions at the specificity binding pocket of trypsin and trypsin-like proteins from Serine-protease dataset. Also, for the BCL-2 dataset, our method pointed out conserved arrangements that include critical residue interactions within the conserved motif LXXXXD, pivotal to the binding specificity of BH3 domains of pro-apoptotic BCL-2 proteins towards apoptotic suppressors. Quantitatively, ppiGReMLIN was able to find all of the most relevant residues described in literature for our datasets, showing precision of at least 69% up to 100% and recall of 100%. Conclusions ppiGReMLIN was able to find highly conserved structures on the interfaces of protein-protein complexes, with minimum support value of 60%, in datasets of similar proteins. We showed that the patterns automatically detected on protein interfaces by our method are in agreement with interaction patterns described in the literature.
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Affiliation(s)
- Felippe C Queiroz
- Department of Computer Science, Universidade Federal de Viçosa, Av Peter Henry Rolfs, Viçosa, MG, Brazil.
| | - Adriana M P Vargas
- Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Av Peter Henry Rolfs, Viçosa, MG, Brazil
| | - Maria G A Oliveira
- Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Av Peter Henry Rolfs, Viçosa, MG, Brazil.,Instituto de Biotecnologia aplicada a Agropecuaria, BIOAGRO-UFV, Av Peter Henry Rolfs, Viçosa MG, Brazil
| | - Giovanni V Comarela
- Department of Computer Science, Universidade Federal do Espírito Santo, Av Fernando Ferrari, Vitória, ES, Brazil
| | - Sabrina A Silveira
- Department of Computer Science, Universidade Federal de Viçosa, Av Peter Henry Rolfs, Viçosa, MG, Brazil.,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
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Luck K, Kim DK, Lambourne L, Spirohn K, Begg BE, Bian W, Brignall R, Cafarelli T, Campos-Laborie FJ, Charloteaux B, Choi D, Coté AG, Daley M, Deimling S, Desbuleux A, Dricot A, Gebbia M, Hardy MF, Kishore N, Knapp JJ, Kovács IA, Lemmens I, Mee MW, Mellor JC, Pollis C, Pons C, Richardson AD, Schlabach S, Teeking B, Yadav A, Babor M, Balcha D, Basha O, Bowman-Colin C, Chin SF, Choi SG, Colabella C, Coppin G, D'Amata C, De Ridder D, De Rouck S, Duran-Frigola M, Ennajdaoui H, Goebels F, Goehring L, Gopal A, Haddad G, Hatchi E, Helmy M, Jacob Y, Kassa Y, Landini S, Li R, van Lieshout N, MacWilliams A, Markey D, Paulson JN, Rangarajan S, Rasla J, Rayhan A, Rolland T, San-Miguel A, Shen Y, Sheykhkarimli D, Sheynkman GM, Simonovsky E, Taşan M, Tejeda A, Tropepe V, Twizere JC, Wang Y, Weatheritt RJ, Weile J, Xia Y, Yang X, Yeger-Lotem E, Zhong Q, Aloy P, Bader GD, De Las Rivas J, Gaudet S, Hao T, Rak J, Tavernier J, Hill DE, Vidal M, Roth FP, Calderwood MA. A reference map of the human binary protein interactome. Nature 2020; 580:402-408. [PMID: 32296183 PMCID: PMC7169983 DOI: 10.1038/s41586-020-2188-x] [Citation(s) in RCA: 595] [Impact Index Per Article: 148.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 02/14/2020] [Indexed: 12/14/2022]
Abstract
Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships1,2. Here, we present a human “all-by-all” reference interactome map of human binary protein interactions, or “HuRI”. With ~53,000 high-quality protein-protein interactions (PPIs), HuRI has approximately four times more such interactions than high-quality curated interactions from small-scale studies. Integrating HuRI with genome3, transcriptome4, and proteome5 data enables the study of cellular function within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying specific subcellular roles of PPIs. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian diseases. HuRI represents a systematic proteome-wide reference linking genomic variation to phenotypic outcomes.
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Affiliation(s)
- Katja Luck
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dae-Kyum Kim
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Luke Lambourne
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kerstin Spirohn
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Bridget E Begg
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Wenting Bian
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ruth Brignall
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Tiziana Cafarelli
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Francisco J Campos-Laborie
- Cancer Research Center (CiC-IBMCC, CSIC/USAL), Consejo Superior de Investigaciones Científicas (CSIC) and University of Salamanca (USAL), Salamanca, Spain.,Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
| | - Benoit Charloteaux
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dongsic Choi
- The Research Institute of the McGill University Health Centre (RI-MUHC), Montreal, Quebec, Canada
| | - Atina G Coté
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Meaghan Daley
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Steven Deimling
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Alice Desbuleux
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.,Molecular Biology of Diseases, Groupe Interdisciplinaire de Génomique Appliquée (GIGA) and Laboratory of Viral Interactomes, University of Liège, Liège, Belgium
| | - Amélie Dricot
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Marinella Gebbia
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Madeleine F Hardy
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nishka Kishore
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Jennifer J Knapp
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - István A Kovács
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Network Science Institute, Northeastern University, Boston, MA, USA.,Wigner Research Centre for Physics, Institute for Solid State Physics and Optics, Budapest, Hungary
| | - Irma Lemmens
- Center for Medical Biotechnology, Vlaams Instituut voor Biotechnologie (VIB), Ghent, Belgium.,Cytokine Receptor Laboratory (CRL), Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Miles W Mee
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Joseph C Mellor
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada.,seqWell, Beverly, MA, USA
| | - Carl Pollis
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Carles Pons
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
| | - Aaron D Richardson
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sadie Schlabach
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Bridget Teeking
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Anupama Yadav
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Mariana Babor
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Dawit Balcha
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Omer Basha
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.,National Institute for Biotechnology in the Negev (NIBN), Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Christian Bowman-Colin
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Suet-Feung Chin
- Cancer Research UK (CRUK) Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Soon Gang Choi
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Claudia Colabella
- Department of Pharmaceutical Sciences, University of Perugia, Perugia, Italy.,Istituto Zooprofilattico Sperimentale dell'Umbria e delle Marche "Togo Rosati" (IZSUM), Perugia, Italy
| | - Georges Coppin
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.,Molecular Biology of Diseases, Groupe Interdisciplinaire de Génomique Appliquée (GIGA) and Laboratory of Viral Interactomes, University of Liège, Liège, Belgium
| | - Cassandra D'Amata
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - David De Ridder
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Steffi De Rouck
- Center for Medical Biotechnology, Vlaams Instituut voor Biotechnologie (VIB), Ghent, Belgium.,Cytokine Receptor Laboratory (CRL), Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Miquel Duran-Frigola
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
| | - Hanane Ennajdaoui
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Florian Goebels
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Liana Goehring
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Anjali Gopal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Ghazal Haddad
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Elodie Hatchi
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Mohamed Helmy
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Yves Jacob
- Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, UMR3569, Centre National de la Recherche Scientifique (CNRS), Paris, France.,Université Paris Diderot, Paris, France
| | - Yoseph Kassa
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Serena Landini
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Roujia Li
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Natascha van Lieshout
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Andrew MacWilliams
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dylan Markey
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Joseph N Paulson
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.,Department of Biostatistics, Product Development, Genentech Inc., South San Francisco, CA, USA
| | - Sudharshan Rangarajan
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - John Rasla
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ashyad Rayhan
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Thomas Rolland
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Adriana San-Miguel
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Yun Shen
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dayag Sheykhkarimli
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Gloria M Sheynkman
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Eyal Simonovsky
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.,National Institute for Biotechnology in the Negev (NIBN), Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Murat Taşan
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Alexander Tejeda
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Vincent Tropepe
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Jean-Claude Twizere
- Molecular Biology of Diseases, Groupe Interdisciplinaire de Génomique Appliquée (GIGA) and Laboratory of Viral Interactomes, University of Liège, Liège, Belgium
| | - Yang Wang
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Jochen Weile
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Yu Xia
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Bioengineering, McGill University, Montreal, Quebec, Canada
| | - Xinping Yang
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.,National Institute for Biotechnology in the Negev (NIBN), Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Quan Zhong
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Biological Sciences, Wright State University, Dayton, OH, USA
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
| | - Gary D Bader
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Javier De Las Rivas
- Cancer Research Center (CiC-IBMCC, CSIC/USAL), Consejo Superior de Investigaciones Científicas (CSIC) and University of Salamanca (USAL), Salamanca, Spain.,Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
| | - Suzanne Gaudet
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Janusz Rak
- The Research Institute of the McGill University Health Centre (RI-MUHC), Montreal, Quebec, Canada
| | - Jan Tavernier
- Center for Medical Biotechnology, Vlaams Instituut voor Biotechnologie (VIB), Ghent, Belgium.,Cytokine Receptor Laboratory (CRL), Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA. .,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA. .,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA. .,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.
| | - Frederick P Roth
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA. .,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. .,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada. .,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. .,Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario, Canada.
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA. .,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA. .,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
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Yugandhar K, Wang TY, Leung AKY, Lanz MC, Motorykin I, Liang J, Shayhidin EE, Smolka MB, Zhang S, Yu H. MaXLinker: Proteome-wide Cross-link Identifications with High Specificity and Sensitivity. Mol Cell Proteomics 2020; 19:554-568. [PMID: 31839598 PMCID: PMC7050104 DOI: 10.1074/mcp.tir119.001847] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Indexed: 11/06/2022] Open
Abstract
Protein-protein interactions play a vital role in nearly all cellular functions. Hence, understanding their interaction patterns and three-dimensional structural conformations can provide crucial insights about various biological processes and underlying molecular mechanisms for many disease phenotypes. Cross-linking mass spectrometry (XL-MS) has the unique capability to detect protein-protein interactions at a large scale along with spatial constraints between interaction partners. The inception of MS-cleavable cross-linkers enabled the MS2-MS3 XL-MS acquisition strategy that provides cross-link information from both MS2 and MS3 level. However, the current cross-link search algorithm available for MS2-MS3 strategy follows a "MS2-centric" approach and suffers from a high rate of mis-identified cross-links. We demonstrate the problem using two new quality assessment metrics ["fraction of mis-identifications" (FMI) and "fraction of interprotein cross-links from known interactions" (FKI)]. We then address this problem, by designing a novel "MS3-centric" approach for cross-link identification and implementing it as a search engine named MaXLinker. MaXLinker outperforms the currently popular search engine with a lower mis-identification rate, and higher sensitivity and specificity. Moreover, we performed human proteome-wide cross-linking mass spectrometry using K562 cells. Employing MaXLinker, we identified a comprehensive set of 9319 unique cross-links at 1% false discovery rate, comprising 8051 intraprotein and 1268 interprotein cross-links. Finally, we experimentally validated the quality of a large number of novel interactions identified in our study, providing a conclusive evidence for MaXLinker's robust performance.
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Affiliation(s)
- Kumar Yugandhar
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853
| | - Ting-Yi Wang
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853
| | - Alden King-Yung Leung
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853
| | - Michael Charles Lanz
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853; Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853
| | - Ievgen Motorykin
- Mass Spectrometry and Proteomics Facility, Institute of Biotechnology, Cornell University, Ithaca, New York,14853
| | - Jin Liang
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853
| | - Elnur Elyar Shayhidin
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853
| | - Marcus Bustamante Smolka
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853; Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853
| | - Sheng Zhang
- Mass Spectrometry and Proteomics Facility, Institute of Biotechnology, Cornell University, Ithaca, New York,14853
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853.
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126
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Yang T, Li X, Li XD. A bifunctional amino acid to study protein–protein interactions. RSC Adv 2020; 10:42076-42083. [PMID: 35516754 PMCID: PMC9057919 DOI: 10.1039/d0ra09110c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 11/12/2020] [Indexed: 12/14/2022] Open
Abstract
dzANA is a novel bifunctional (photo-reactive and bioorthogonal) amino acid to study protein–protein interactions.
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Affiliation(s)
- Tangpo Yang
- Department of Chemistry
- The University of Hong Kong
- China
| | - Xin Li
- Department of Chemistry
- The University of Hong Kong
- China
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127
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Perfetto L, Pastrello C, del-Toro N, Duesbury M, Iannuccelli M, Kotlyar M, Licata L, Meldal B, Panneerselvam K, Panni S, Rahimzadeh N, Ricard-Blum S, Salwinski L, Shrivastava A, Cesareni G, Pellegrini M, Orchard S, Jurisica I, Hermjakob H, Porras P. The IMEx coronavirus interactome: an evolving map of Coronaviridae-host molecular interactions. Database (Oxford) 2020; 2020:baaa096. [PMID: 33206959 PMCID: PMC7673336 DOI: 10.1093/database/baaa096] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 10/19/2020] [Accepted: 10/23/2020] [Indexed: 12/14/2022]
Abstract
The current coronavirus disease of 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus (SARS-CoV)-2, has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions can provide fine-grained resolution of the mechanisms behind the virus biology and the human organism response. We present a curated dataset of physical molecular interactions focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family that has been manually extracted by International Molecular Exchange (IMEx) Consortium curators. Currently, the dataset comprises over 4400 binarized interactions extracted from 151 publications. The dataset can be accessed in the standard formats recommended by the Proteomics Standards Initiative (HUPO-PSI) at the IntAct database website (https://www.ebi.ac.uk/intact) and will be continuously updated as research on COVID-19 progresses.
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Affiliation(s)
- L Perfetto
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - C Pastrello
- Krembil Research Institute, Data Science Discovery Centre for Chronic Diseases, University Health Network, 5KD-407, 60 Leonard Avenue, Toronto, ON, M5T 0S8, Canada
| | - N del-Toro
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - M Duesbury
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
- UCLA-DOE Institute, UCLA, Los Angeles, CA 90095, USA
| | - M Iannuccelli
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, 00133, Italy
| | - M Kotlyar
- Krembil Research Institute, Data Science Discovery Centre for Chronic Diseases, University Health Network, 5KD-407, 60 Leonard Avenue, Toronto, ON, M5T 0S8, Canada
| | - L Licata
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, 00133, Italy
| | - B Meldal
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - K Panneerselvam
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - S Panni
- Department of Biology, Ecology and Earth Sciences, Università della Calabria, Rende, 87036, Italy
| | - N Rahimzadeh
- UCLA-DOE Institute, UCLA, Los Angeles, CA 90095, USA
- Providence John Wayne Cancer Institute, Department of Translational Molecular, Santa Monica, CA 90404, USA
| | - S Ricard-Blum
- Univ Lyon, University Claude Bernard Lyon 1, INSA Lyon, CPE, Institute of Molecular and Supramolecular Chemistry and Biochemistry (ICBMS), UMR 5246, F-69622 Villeurbanne, 69622, France
| | - L Salwinski
- UCLA-DOE Institute, UCLA, Los Angeles, CA 90095, USA
| | - A Shrivastava
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - G Cesareni
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, 00133, Italy
| | - M Pellegrini
- Department of Molecular, Cell and Developmental Biology, UCLA, Los Angeles, CA 90095, USA
| | - S Orchard
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - I Jurisica
- Krembil Research Institute, Data Science Discovery Centre for Chronic Diseases, University Health Network, 5KD-407, 60 Leonard Avenue, Toronto, ON, M5T 0S8, Canada
- Departments of Medical Biophysics and Computer Science, University of Toronto, Toronto, ON, M5T 0S8, Canada
| | - H Hermjakob
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - P Porras
- European Molecular Biology Laboratory, Wellcome Genome Campus, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
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128
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Alanis-Lobato G, Schaefer MH. Generation and Interpretation of Context-Specific Human Protein-Protein Interaction Networks with HIPPIE. Methods Mol Biol 2020; 2074:135-144. [PMID: 31583636 DOI: 10.1007/978-1-4939-9873-9_11] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
High-throughput techniques for the detection of protein-protein interactions (PPIs) have enabled a systems approach for the study of the living cell. However, the increasing amount of protein interaction data, the varying quality of these measurements, and the lack of context information make it difficult to construct meaningful and reliable protein networks.The Human Integrated Protein-Protein Interaction rEference (HIPPIE) is a web tool that integrates and annotates experimentally supported human PPIs from a heterogeneous set of data sources. In HIPPIE, one can query for the interactors of one or more proteins and generate high-quality and context-specific networks. This chapter highlights HIPPIE's most important features and exemplifies its functionality through a proposed use case.
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Affiliation(s)
| | - Martin H Schaefer
- Department of Experimental Oncology, European Institute of Oncology, Milan, Italy.
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129
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Lee S, Cieply B, Yang Y, Peart N, Glaser C, Chan P, Carstens RP. Esrp1-Regulated Splicing of Arhgef11 Isoforms Is Required for Epithelial Tight Junction Integrity. Cell Rep 2019; 25:2417-2430.e5. [PMID: 30485810 PMCID: PMC6371790 DOI: 10.1016/j.celrep.2018.10.097] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 09/10/2018] [Accepted: 10/25/2018] [Indexed: 12/19/2022] Open
Abstract
The epithelial-specific splicing regulators Esrp1 and Esrp2 are required for mammalian development, including establishment of epidermal barrier functions. However, the mechanisms by which Esrp ablation causes defects in epithelial barriers remain undefined. We determined that the ablation of Esrp1 and Esrp2 impairs epithelial tight junction (TJ) integrity through loss of the epithelial isoform of Rho GTP exchange factor Arhgef11. Arhgef11 is required for the maintenance of TJs via RhoA activation and myosin light chain (MLC) phosphorylation. Ablation or depletion of Esrp1/2 or Arhgef11 inhibits MLC phosphorylation and only the epithelial Arhgef11 isoform rescues MLC phosphorylation in Arhgef11 KO epithelial cells. Mesenchymal Arhgef11 transcripts contain a C-terminal exon that binds to PAK4 and inhibits RhoA activation byArhgef11. Deletion of the mesenchymal-specific Arhgef11 exon in Esrp1/2 KO epithelial cells using CRISPR/Cas9 restored TJ function, illustrating how splicing alterations can be mechanistically linked to disease phenotypes that result from impaired functions of splicing regulators.
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Affiliation(s)
- SungKyoung Lee
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Benjamin Cieply
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yueqin Yang
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Natoya Peart
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Carl Glaser
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Patricia Chan
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russ P Carstens
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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130
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Galano-Frutos JJ, García-Cebollada H, Sancho J. Molecular dynamics simulations for genetic interpretation in protein coding regions: where we are, where to go and when. Brief Bioinform 2019; 22:3-19. [PMID: 31813950 DOI: 10.1093/bib/bbz146] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 09/22/2019] [Accepted: 10/25/2019] [Indexed: 12/18/2022] Open
Abstract
The increasing ease with which massive genetic information can be obtained from patients or healthy individuals has stimulated the development of interpretive bioinformatics tools as aids in clinical practice. Most such tools analyze evolutionary information and simple physical-chemical properties to predict whether replacement of one amino acid residue with another will be tolerated or cause disease. Those approaches achieve up to 80-85% accuracy as binary classifiers (neutral/pathogenic). As such accuracy is insufficient for medical decision to be based on, and it does not appear to be increasing, more precise methods, such as full-atom molecular dynamics (MD) simulations in explicit solvent, are also discussed. Then, to describe the goal of interpreting human genetic variations at large scale through MD simulations, we restrictively refer to all possible protein variants carrying single-amino-acid substitutions arising from single-nucleotide variations as the human variome. We calculate its size and develop a simple model that allows calculating the simulation time needed to have a 0.99 probability of observing unfolding events of any unstable variant. The knowledge of that time enables performing a binary classification of the variants (stable-potentially neutral/unstable-pathogenic). Our model indicates that the human variome cannot be simulated with present computing capabilities. However, if they continue to increase as per Moore's law, it could be simulated (at 65°C) spending only 3 years in the task if we started in 2031. The simulation of individual protein variomes is achievable in short times starting at present. International coordination seems appropriate to embark upon massive MD simulations of protein variants.
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Affiliation(s)
- Juan J Galano-Frutos
- Protein Folding and Molecular Design (ProtMol)' group at BIFI, University of Zaragoza
| | | | - Javier Sancho
- Protein Folding and Molecular Design (ProtMol)' group at BIFI, University of Zaragoza
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131
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Shen L, Yuan Y, Guo Y, Li M, Li C, Pu X. Probing the Druggablility on the Interface of the Protein-Protein Interaction and Its Allosteric Regulation Mechanism on the Drug Screening for the CXCR4 Homodimer. Front Pharmacol 2019; 10:1310. [PMID: 31787895 PMCID: PMC6855241 DOI: 10.3389/fphar.2019.01310] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 10/15/2019] [Indexed: 12/19/2022] Open
Abstract
Modulating protein–protein interactions (PPIs) with small drug-like molecules targeting it exhibits great promise in modern drug discovery. G protein-coupled receptors (GPCRs) are the largest family of targeted proteins and could form dimers in living biological cells through PPIs. However, compared to drug development of the orthosteric site, there has been lack of investigations on the druggability of the PPI interface for GPCRs and its functional implication on experiments. Thus, in order to address these issues, we constructed a novel computational strategy, which involved in molecular dynamics simulation, virtual screening and protein structure network (PSN), to study one representative GPCR homodimer (CXCR4). One druggable pocket was identified in the PPI interface and one small molecule targeting it was screened, which could strengthen PPI mainly through hydrophobic interaction between the benzene rings of the PPI molecule and TM4 of the receptor. The PSN results further reveals that the PPI molecule could increase the number of the allosteric regulation pathways between the druggable pocket of the dimer interface to the orthostatic site for the subunit A but only play minor role for the other subunit B, leading to the asymmetric change in the volume of the binding pockets for the two subunits (increase for the subunit A and minor change for the subunit B). Consequently, the screening performance of the subunit A to the antagonists is enhanced while the subunit B is unchanged nearly, implying that the PPI molecule may be beneficial to enhance the drug efficacies of the antagonists. In addition, one main regulation pathway with the highest frequency was identified for the subunit A, which consists of Trp1955.34–Tyr190ECL2–Val1965.35–Gln2005.39–Asp2626.58–Cys28N-term, revealing their importance in the allosteric regulation from the PPI molecule. The observations from the work could provide valuable information for the development of the PPI drug-like molecule for GPCRs.
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Affiliation(s)
- Liting Shen
- College of Chemistry, Sichuan University, Chengdu, China
| | - Yuan Yuan
- College of Management, Southwest University for Nationalities, Chengdu, China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu, China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, China
| | - Chuan Li
- College of Computer Science, Sichuan University, Chengdu, China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu, China
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132
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Rosell M, Rodríguez‐Lumbreras LA, Romero‐Durana M, Jiménez‐García B, Díaz L, Fernández‐Recio J. Integrative modeling of protein‐protein interactions with pyDock for the new docking challenges. Proteins 2019; 88:999-1008. [DOI: 10.1002/prot.25858] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 10/30/2019] [Accepted: 11/15/2019] [Indexed: 01/12/2023]
Affiliation(s)
- Mireia Rosell
- Barcelona Supercomputing Center (BSC) Barcelona Spain
- Instituto de Ciencias de la Vid y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja) Logroño Spain
| | - Luis A. Rodríguez‐Lumbreras
- Barcelona Supercomputing Center (BSC) Barcelona Spain
- Instituto de Ciencias de la Vid y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja) Logroño Spain
| | - Miguel Romero‐Durana
- Barcelona Supercomputing Center (BSC) Barcelona Spain
- Instituto de Ciencias de la Vid y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja) Logroño Spain
- Structural Biology Unit, Instituto de Biología Molecular de Barcelona (IBMB‐CSIC) Barcelona Spain
| | | | - Lucía Díaz
- Barcelona Supercomputing Center (BSC) Barcelona Spain
| | - Juan Fernández‐Recio
- Barcelona Supercomputing Center (BSC) Barcelona Spain
- Instituto de Ciencias de la Vid y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja) Logroño Spain
- Structural Biology Unit, Instituto de Biología Molecular de Barcelona (IBMB‐CSIC) Barcelona Spain
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133
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Affiliation(s)
- Jonathan Flint
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, United States of America
| | - Trey Ideker
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, California, United States of America
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134
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Gemovic B, Sumonja N, Davidovic R, Perovic V, Veljkovic N. Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes. Curr Med Chem 2019; 26:3890-3910. [PMID: 29446725 DOI: 10.2174/0929867325666180214113704] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 09/14/2017] [Accepted: 01/29/2018] [Indexed: 01/04/2023]
Abstract
BACKGROUND The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. OBJECTIVE Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. METHODS We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. RESULTS We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. CONCLUSION PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein-protein complexes for experimental studies.
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Affiliation(s)
- Branislava Gemovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Neven Sumonja
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Radoslav Davidovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Vladimir Perovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Nevena Veljkovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
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135
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Da Silva F, Bret G, Teixeira L, Gonzalez CF, Rognan D. Exhaustive Repertoire of Druggable Cavities at Protein-Protein Interfaces of Known Three-Dimensional Structure. J Med Chem 2019; 62:9732-9742. [PMID: 31603323 DOI: 10.1021/acs.jmedchem.9b01184] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Protein-protein interactions (PPIs) offer the unique opportunity to tailor ligands aimed at specifically stabilizing or disrupting the corresponding interfaces and providing a safer alternative to conventional ligands targeting monomeric macromolecules. Selecting biologically relevant protein-protein interfaces for either stabilization or disruption by small molecules is usually biology-driven on a case-by-case basis and does not follow a structural rationale that could be applied to an entire interactome. We herewith provide a first step to the latter goal by using a fully automated and structure-based workflow, applicable to any PPI of known three-dimensional (3D) structure, to identify and prioritize druggable cavities at and nearby PPIs of pharmacological interest. When applied to the entire Protein Data Bank, 164 514 druggable cavities were identified and classified in four groups (interfacial, rim, allosteric, orthosteric) according to their properties and spatial locations. Systematic comparison of PPI cavities with pockets deduced from druggable protein-ligand complexes shows almost no overlap in property space, suggesting that even the most druggable PPI cavities are unlikely to be addressed with conventional drug-like compound libraries. The archive is freely accessible at http://drugdesign.unistra.fr/ppiome .
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Affiliation(s)
- Franck Da Silva
- Laboratoire d'Innovation Thérapeutique , UMR 7200 CNRS-Université de Strasbourg , 67400 Illkirch , France
| | - Guillaume Bret
- Laboratoire d'Innovation Thérapeutique , UMR 7200 CNRS-Université de Strasbourg , 67400 Illkirch , France
| | - Leandro Teixeira
- Department of Microbiology and Cell Science, Genetics Institute, Institute of Food and Agricultural Sciences , University of Florida , Gainesville , Florida 32610-3610 , United States
| | - Claudio F Gonzalez
- Department of Microbiology and Cell Science, Genetics Institute, Institute of Food and Agricultural Sciences , University of Florida , Gainesville , Florida 32610-3610 , United States
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique , UMR 7200 CNRS-Université de Strasbourg , 67400 Illkirch , France
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136
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Li Q, Yang Z, Zhao Z, Luo L, Li Z, Wang L, Zhang Y, Lin H, Wang J, Zhang Y. HMNPPID-human malignant neoplasm protein-protein interaction database. Hum Genomics 2019; 13:44. [PMID: 31639057 PMCID: PMC6805303 DOI: 10.1186/s40246-019-0223-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Protein-protein interaction (PPI) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Especially, the PPIs associated with human malignant neoplasms can unveil the biology behind these neoplasms. However, such PPI database is not currently available. RESULTS In this work, a database of protein-protein interactions associated with 171 kinds of human malignant neoplasms named HMNPPID is constructed. In addition, a visualization program, named VisualPPI, is provided to facilitate the analysis of the PPI network for a specific neoplasm. CONCLUSIONS HMNPPID can hopefully become an important resource for the research on PPIs of human malignant neoplasms since it provides readily available data for healthcare professionals. Thus, they do not need to dig into a large amount of biomedical literatures any more, which may accelerate the researches on the PPIs of malignant neoplasms.
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Affiliation(s)
- Qingqing Li
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Zhihao Yang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China.
| | - Zhehuan Zhao
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Ling Luo
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Zhiheng Li
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Lei Wang
- Beijing Institute of Health Administration and Medical Information, Beijing, 100850, China.
| | - Yin Zhang
- Beijing Institute of Health Administration and Medical Information, Beijing, 100850, China
| | - Hongfei Lin
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Jian Wang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Yijia Zhang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
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137
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Fragoza R, Das J, Wierbowski SD, Liang J, Tran TN, Liang S, Beltran JF, Rivera-Erick CA, Ye K, Wang TY, Yao L, Mort M, Stenson PD, Cooper DN, Wei X, Keinan A, Schimenti JC, Clark AG, Yu H. Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations. Nat Commun 2019; 10:4141. [PMID: 31515488 PMCID: PMC6742646 DOI: 10.1038/s41467-019-11959-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 08/06/2019] [Indexed: 12/19/2022] Open
Abstract
Each human genome carries tens of thousands of coding variants. The extent to which this variation is functional and the mechanisms by which they exert their influence remains largely unexplored. To address this gap, we leverage the ExAC database of 60,706 human exomes to investigate experimentally the impact of 2009 missense single nucleotide variants (SNVs) across 2185 protein-protein interactions, generating interaction profiles for 4797 SNV-interaction pairs, of which 421 SNVs segregate at > 1% allele frequency in human populations. We find that interaction-disruptive SNVs are prevalent at both rare and common allele frequencies. Furthermore, these results suggest that 10.5% of missense variants carried per individual are disruptive, a higher proportion than previously reported; this indicates that each individual's genetic makeup may be significantly more complex than expected. Finally, we demonstrate that candidate disease-associated mutations can be identified through shared interaction perturbations between variants of interest and known disease mutations.
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Affiliation(s)
- Robert Fragoza
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Jishnu Das
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, 02139, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Shayne D Wierbowski
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Jin Liang
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Tina N Tran
- Department of Biomedical Science, Cornell University, Ithaca, NY, 14853, USA
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, 14853, USA
| | - Siqi Liang
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Juan F Beltran
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Christen A Rivera-Erick
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Kaixiong Ye
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Ting-Yi Wang
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Li Yao
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Matthew Mort
- Institute of Medical Genetics, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Peter D Stenson
- Institute of Medical Genetics, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - David N Cooper
- Institute of Medical Genetics, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Xiaomu Wei
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Alon Keinan
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA
| | - John C Schimenti
- Department of Biomedical Science, Cornell University, Ithaca, NY, 14853, USA
| | - Andrew G Clark
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, 14853, USA
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA.
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA.
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138
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Sarkar D, Saha S. Machine-learning techniques for the prediction of protein-protein interactions. J Biosci 2019; 44:104. [PMID: 31502581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Protein-protein interactions (PPIs) are important for the study of protein functions and pathways involved in different biological processes, as well as for understanding the cause and progression of diseases. Several high-throughput experimental techniques have been employed for the identification of PPIs in a few model organisms, but still, there is a huge gap in identifying all possible binary PPIs in an organism. Therefore, PPI prediction using machine-learning algorithms has been used in conjunction with experimental methods for discovery of novel protein interactions. The two most popular supervised machine-learning techniques used in the prediction of PPIs are support vector machines and random forest classifiers. Bayesian-probabilistic inference has also been used but mainly for the scoring of high-throughput PPI dataset confidence measures. Recently, deep-learning algorithms have been used for sequence-based prediction of PPIs. Several clustering methods such as hierarchical and k-means are useful as unsupervised machine-learning algorithms for the prediction of interacting protein pairs without explicit data labelling. In summary, machine-learning techniques have been widely used for the prediction of PPIs thus allowing experimental researchers to study cellular PPI networks.
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139
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Choi SG, Olivet J, Cassonnet P, Vidalain PO, Luck K, Lambourne L, Spirohn K, Lemmens I, Dos Santos M, Demeret C, Jones L, Rangarajan S, Bian W, Coutant EP, Janin YL, van der Werf S, Trepte P, Wanker EE, De Las Rivas J, Tavernier J, Twizere JC, Hao T, Hill DE, Vidal M, Calderwood MA, Jacob Y. Maximizing binary interactome mapping with a minimal number of assays. Nat Commun 2019; 10:3907. [PMID: 31467278 PMCID: PMC6715725 DOI: 10.1038/s41467-019-11809-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 08/02/2019] [Indexed: 02/06/2023] Open
Abstract
Complementary assays are required to comprehensively map complex biological entities such as genomes, proteomes and interactome networks. However, how various assays can be optimally combined to approach completeness while maintaining high precision often remains unclear. Here, we propose a framework for binary protein-protein interaction (PPI) mapping based on optimally combining assays and/or assay versions to maximize detection of true positive interactions, while avoiding detection of random protein pairs. We have engineered a novel NanoLuc two-hybrid (N2H) system that integrates 12 different versions, differing by protein expression systems and tagging configurations. The resulting union of N2H versions recovers as many PPIs as 10 distinct assays combined. Thus, to further improve PPI mapping, developing alternative versions of existing assays might be as productive as designing completely new assays. Our findings should be applicable to systematic mapping of other biological landscapes.
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Affiliation(s)
- Soon Gang Choi
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Julien Olivet
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA.,Laboratory of Viral Interactomes, Unit of Molecular Biology of Diseases, Groupe Interdisciplinaire de Génomique Appliquée (GIGA Institute), University of Liège, 7 Place du 20 Août, 4000, Liège, Belgium
| | - Patricia Cassonnet
- Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, UMR3569, Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot, Sorbonne Paris Cité, 28 rue du Docteur Roux, 75015, Paris, France
| | - Pierre-Olivier Vidalain
- Équipe Chimie, Biologie, Modélisation et Immunologie pour la Thérapie (CBMIT), Laboratoire de Chimie et Biochimie Pharmacologiques et Toxicologiques (LCBPT), Centre Interdisciplinaire Chimie Biologie-Paris (CICB-Paris), UMR8601, CNRS, Université Paris Descartes, 45 rue des Saints-Pères, 75006, Paris, France
| | - Katja Luck
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Luke Lambourne
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Kerstin Spirohn
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Irma Lemmens
- Center for Medical Biotechnology, Vlaams Instituut voor Biotechnologie (VIB), 3 Albert Baertsoenkaai, 9000, Ghent, Belgium.,Cytokine Receptor Laboratory (CRL), Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 3 Albert Baertsoenkaai, 9000, Ghent, Belgium
| | - Mélanie Dos Santos
- Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, UMR3569, Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot, Sorbonne Paris Cité, 28 rue du Docteur Roux, 75015, Paris, France
| | - Caroline Demeret
- Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, UMR3569, Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot, Sorbonne Paris Cité, 28 rue du Docteur Roux, 75015, Paris, France
| | - Louis Jones
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, 28 rue du Docteur Roux, 75015, Paris, France
| | - Sudharshan Rangarajan
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Wenting Bian
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Eloi P Coutant
- Département de Biologie Structurale et Chimie, Unité de Chimie et Biocatalyse, Institut Pasteur, UMR3523, CNRS, 28 rue du Docteur Roux, 75015, Paris, France
| | - Yves L Janin
- Département de Biologie Structurale et Chimie, Unité de Chimie et Biocatalyse, Institut Pasteur, UMR3523, CNRS, 28 rue du Docteur Roux, 75015, Paris, France
| | - Sylvie van der Werf
- Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, UMR3569, Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot, Sorbonne Paris Cité, 28 rue du Docteur Roux, 75015, Paris, France
| | - Philipp Trepte
- Neuroproteomics, Max Delbrück Center for Molecular Medicine, 10 Robert-Rössle-Str., 13125, Berlin, Germany.,Brain Development and Disease, Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), 3 Dr. Bohr-Gasse, 1030, Vienna, Austria
| | - Erich E Wanker
- Neuroproteomics, Max Delbrück Center for Molecular Medicine, 10 Robert-Rössle-Str., 13125, Berlin, Germany
| | - Javier De Las Rivas
- Cancer Research Center (CiC-IBMCC, CSIC/USAL), Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca (USAL), Campus Miguel de Unamuno, 37007, Salamanca, Spain
| | - Jan Tavernier
- Center for Medical Biotechnology, Vlaams Instituut voor Biotechnologie (VIB), 3 Albert Baertsoenkaai, 9000, Ghent, Belgium.,Cytokine Receptor Laboratory (CRL), Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 3 Albert Baertsoenkaai, 9000, Ghent, Belgium
| | - Jean-Claude Twizere
- Laboratory of Viral Interactomes, Unit of Molecular Biology of Diseases, Groupe Interdisciplinaire de Génomique Appliquée (GIGA Institute), University of Liège, 7 Place du 20 Août, 4000, Liège, Belgium
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA. .,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA. .,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA. .,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA.
| | - Yves Jacob
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA. .,Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, UMR3569, Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot, Sorbonne Paris Cité, 28 rue du Docteur Roux, 75015, Paris, France.
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140
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Sarkar D, Saha S. Machine-learning techniques for the prediction of protein–protein interactions. J Biosci 2019. [DOI: 10.1007/s12038-019-9909-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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141
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Hirose H, Hideshima T, Katoh T, Suga H. A Case Study on the Keap1 Interaction with Peptide Sequence Epitopes Selected by the Peptidomic mRNA Display. Chembiochem 2019; 20:2089-2100. [PMID: 31169361 DOI: 10.1002/cbic.201900039] [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: 01/21/2019] [Revised: 03/12/2019] [Indexed: 11/08/2022]
Abstract
Many protein-protein and peptide-protein interactions (PPIs) play key roles in the regulation of biological functions, and therefore, the modulation of PPIs has become an attractive target of new drug development. Although a number of PPIs have already been identified, over 100 000 unknown PPIs are predicted to exist. To uncover such unknown PPIs, it is important to devise a conceptually distinct method from that of currently available methods. Herein, an mRNA display by using a total RNA library derived from various human tissues, which serves as a unique method to physically isolate peptide epitopes that potentially bind to a target protein of interest, is reported. In this study, selection was performed against Kelch-like ECH-associated protein (Keap1) as a model target protein, leading to a peptide epitope originating from astrotactin-1 (ASTN1). It turned out that this ASTN1 peptide was able to interact with Keap1 more strongly than that with a known peptide derived from Nrf2; a well-known, naturally occurring Keap1 binder. This case study demonstrates the applicability of peptidomic mRNA display for the rapid exploration of consensus binding peptide motifs and the potential for the discovery of unknown PPIs with other proteins of interest.
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Affiliation(s)
- Hisaaki Hirose
- Department of Chemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Tomoki Hideshima
- Department of Chemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Takayuki Katoh
- Department of Chemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Hiroaki Suga
- Department of Chemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
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142
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Negi A, Reilly CO, Jarikote DV, Zhou J, Murphy PV. Multi-targeting protein-protein interaction inhibitors: Evolution of macrocyclic ligands with embedded carbohydrates (MECs) to improve selectivity. Eur J Med Chem 2019; 176:292-309. [PMID: 31112891 DOI: 10.1016/j.ejmech.2019.04.064] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 04/01/2019] [Accepted: 04/24/2019] [Indexed: 10/26/2022]
Abstract
Compounds targeting multiple proteins can have synergistic effects and are therefore of interest in medicinal chemistry. At the same time, inhibiting protein-protein interactions (PPI) is increasingly desired in the treatment of disorders or diseases. The development of non-peptidomimetic inhibitors is still a challenge. Herein we investigate macrocyclic scaffolds with one or two embedded carbohydrates (MECs) that present amino acid side chains, or related isosteres, as pharmacophoric groups. Firstly, retroscreening of the previously reported eannaphane-40 (E40, 40), a MEC presenting two pharmacophoric groups, against a set of 55 receptor-subtypes led to a finding of sub-micromolar inhibitory activity for E40 against three serotonergic isoforms (5HT1A/2A/2B) as well as the Na+ channel and the NK-2 receptor. We synthesised MECs with an additional pharmacophoric group compared to E40, with a view to identifying compounds where the selectivity profile was altered among the protein hits from the retroscreening. MECs were produced based on scaffolds with two monosaccharide residues, leading to the incorporation of a third pharmacophoric group. Later, homology models were prepared for four proteins (5HT1A, 5HT2A, NK2 and site-2 of the sodium channel) whose 3D structure is unknown. Inverse docking of the synthesised compounds led to the selection of a new MEC (MEC-B) for protein binding assays. MEC-B was found to have its selectivity profile modulated, in line with docking prediction, compared to E40. MEC-B is dual inhibitor of both 5-HT1A and the sodium channel with improved selectivity for these proteins compared to 5-HT2A/2B/2C, 5-HT transporter and NK2 receptor. Thus, a new multitargeting compound, with an improved selectivity profile was identified, based on a MEC peptidomimetic scaffold.
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Affiliation(s)
- Arvind Negi
- School of Chemistry, National University of Ireland Galway, University Road, Galway, H91 TK33, Ireland
| | - Ciaran O Reilly
- School of Chemistry, National University of Ireland Galway, University Road, Galway, H91 TK33, Ireland
| | - Dilip V Jarikote
- School of Chemistry, National University of Ireland Galway, University Road, Galway, H91 TK33, Ireland
| | - Jian Zhou
- School of Chemistry, National University of Ireland Galway, University Road, Galway, H91 TK33, Ireland
| | - Paul V Murphy
- School of Chemistry, National University of Ireland Galway, University Road, Galway, H91 TK33, Ireland.
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143
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Li ZL, Buck M. Modified Potential Functions Result in Enhanced Predictions of a Protein Complex by All-Atom Molecular Dynamics Simulations, Confirming a Stepwise Association Process for Native Protein-Protein Interactions. J Chem Theory Comput 2019; 15:4318-4331. [PMID: 31241940 DOI: 10.1021/acs.jctc.9b00195] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The relative prevalence of native protein-protein interactions (PPIs) are the cornerstone for understanding the structure, dynamics and mechanisms of function of protein complexes. In this study, we develop a scheme for scaling the protein-water interaction in the CHARMM36 force field, in order to better fit the solvation free energy of amino acids side-chain analogues. We find that the molecular dynamics simulation with the scaled force field, CHARMM36s, as well as a recently released version, CHARMM36m, effectively improve on the overly sticky association of proteins, such as ubiquitin. We investigate the formation of a heterodimer protein complex between the SAM domains of the EphA2 receptor and the SHIP2 enzyme by performing a combined total of 48 μs simulations with the different potential functions. While the native SAM heterodimer is only predicted at a low rate of 6.7% with the original CHARMM36 force field, the yield is increased to 16.7% with CHARMM36s, and to 18.3% with CHARMM36m. By analyzing the 25 native SAM complexes formed in the simulations, we find that their formation involves a preorientation guided by Coulomb interactions, consistent with an electrostatic steering mechanism. In 12 cases, the complex could directly transform to the native protein interaction surfaces with only small adjustments in domain orientation. In the other 13 cases, orientational and/or translational adjustments are needed to reach the native complex. Although the tendency for non-native complexes to dissociate has nearly doubled with the modified potential functions, a dissociation followed by a reassociation to the correct complex structure is still rare. Instead, the remaining non-native complexes undergo configurational changes/surface searching, which, however, rarely leads to native structures on a time scale of 250 ns. These observations provide a rich picture of the mechanisms of protein-protein complex formation and suggest that computational predictions of native complex PPIs could be improved further.
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Affiliation(s)
- Zhen-Lu Li
- Department of Physiology and Biophysics , Case Western Reserve University, School of Medicine , 10900 Euclid Avenue , Cleveland , Ohio 44106 , United States
| | - Matthias Buck
- Department of Physiology and Biophysics , Case Western Reserve University, School of Medicine , 10900 Euclid Avenue , Cleveland , Ohio 44106 , United States.,Departments of Pharmacology and Neurosciences, and Case Comprehensive Cancer Center , Case Western Reserve University, School of Medicine , 10900 Euclid Avenue , Cleveland , Ohio 44106 , United States
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144
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Zhao J, Lei Y, Hong J, Zheng C, Zhang L. AraPPINet: An Updated Interactome for the Analysis of Hormone Signaling Crosstalk in Arabidopsis thaliana. FRONTIERS IN PLANT SCIENCE 2019; 10:870. [PMID: 31333706 PMCID: PMC6625390 DOI: 10.3389/fpls.2019.00870] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 06/18/2019] [Indexed: 05/29/2023]
Abstract
Protein-protein interactions (PPIs) play fundamental roles in various cellular processes. Here, we present a new version of computational interactome that contains more than 345,000 predicted PPIs involving about 51.2% of the Arabidopsis proteins. Compared to the earlier version, the updated AraPPINet displays a higher accuracy in predicting protein interactions through performance evaluation with independent datasets. In addition to the experimental verifications of the previous version, the new version has been subjected to further validation test that demonstrates its ability to discover novel PPIs involved in hormone signaling pathways. Moreover, network analysis shows that many overlapping proteins are significantly involved in the interactions which mediated the crosstalk among plant hormones. The new version of AraPPINet provides a more reliable interactome which would facilitate the understanding of crosstalk among hormone signaling pathways in plants.
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145
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Wang E, Sun H, Wang J, Wang Z, Liu H, Zhang JZH, Hou T. End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. Chem Rev 2019; 119:9478-9508. [DOI: 10.1021/acs.chemrev.9b00055] [Citation(s) in RCA: 578] [Impact Index Per Article: 115.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Ercheng Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Huiyong Sun
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Junmei Wang
- Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Hui Liu
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - John Z. H. Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU−ECNU Center for Computational Chemistry, NYU Shanghai, Shanghai 200122, China
- Department of Chemistry, New York University, New York, New York 10003, United States
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
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146
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Luminescence- and Fluorescence-Based Complementation Assays to Screen for GPCR Oligomerization: Current State of the Art. Int J Mol Sci 2019; 20:ijms20122958. [PMID: 31213021 PMCID: PMC6627893 DOI: 10.3390/ijms20122958] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 06/05/2019] [Accepted: 06/12/2019] [Indexed: 01/22/2023] Open
Abstract
G protein-coupled receptors (GPCRs) have the propensity to form homo- and heterodimers. Dysfunction of these dimers has been associated with multiple diseases, e.g., pre-eclampsia, schizophrenia, and depression, among others. Over the past two decades, considerable efforts have been made towards the development of screening assays for studying these GPCR dimer complexes in living cells. As a first step, a robust in vitro assay in an overexpression system is essential to identify and characterize specific GPCR–GPCR interactions, followed by methodologies to demonstrate association at endogenous levels and eventually in vivo. This review focuses on protein complementation assays (PCAs) which have been utilized to study GPCR oligomerization. These approaches are typically fluorescence- and luminescence-based, making identification and localization of protein–protein interactions feasible. The GPCRs of interest are fused to complementary fluorescent or luminescent fragments that, upon GPCR di- or oligomerization, may reconstitute to a functional reporter, of which the activity can be measured. Various protein complementation assays have the disadvantage that the interaction between the reconstituted split fragments is irreversible, which can lead to false positive read-outs. Reversible systems offer several advantages, as they do not only allow to follow the kinetics of GPCR–GPCR interactions, but also allow evaluation of receptor complex modulation by ligands (either agonists or antagonists). Protein complementation assays may be used for high throughput screenings as well, which is highly relevant given the growing interest and effort to identify small molecule drugs that could potentially target disease-relevant dimers. In addition to providing an overview on how PCAs have allowed to gain better insights into GPCR–GPCR interactions, this review also aims at providing practical guidance on how to perform PCA-based assays.
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147
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Loureiro CA, Santos JD, Matos AM, Jordan P, Matos P, Farinha CM, Pinto FR. Network Biology Identifies Novel Regulators of CFTR Trafficking and Membrane Stability. Front Pharmacol 2019; 10:619. [PMID: 31231217 PMCID: PMC6559121 DOI: 10.3389/fphar.2019.00619] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 05/15/2019] [Indexed: 12/31/2022] Open
Abstract
In cystic fibrosis, the most common disease-causing mutation is F508del, which causes not only intracellular retention and degradation of CFTR, but also defective channel gating and decreased membrane stability of the small amount that reaches the plasma membrane (PM). Thus, pharmacological correction of mutant CFTR requires targeting of multiple cellular defects in order to achieve clinical benefit. Although small-molecule compounds have been identified and commercialized that can correct its folding or gating, an efficient retention of F508del CFTR at the PM has not yet been explored pharmacologically despite being recognized as a crucial factor for improving functional rescue of chloride transport. In ongoing efforts to determine the CFTR interactome at the PM, we used three complementary approaches: targeting proteins binding to tyrosine-phosphorylated CFTR, protein complexes involved in cAMP-mediated CFTR stabilization at the PM, and proteins selectively interacting at the PM with rescued F508del-CFTR but not wt-CFTR. Using co-immunoprecipitation or peptide–pull down strategies, we identified around 400 candidate proteins through sequencing of complex protein mixtures using the nano-LC Triple TOF MS technique. Key candidate proteins were validated for their robust interaction with CFTR-containing protein complexes and for their ability to modulate the amount of CFTR expressed at the cell surface of bronchial epithelial cells. Here, we describe how we explored the abovementioned experimental datasets to build a protein interaction network with the aim of identifying novel pharmacological targets to rescue CFTR function in cystic fibrosis (CF) patients. We identified and validated novel candidate proteins that were essential components of the network but not detected in previous proteomic analyses.
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Affiliation(s)
- Cláudia Almeida Loureiro
- BioISI-Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisbon, Lisbon, Portugal.,Department of Human Genetics, National Health Institute "Dr. Ricardo Jorge," Lisbon, Portugal
| | - João D Santos
- BioISI-Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisbon, Lisbon, Portugal.,Department of Chemistry and Biochemistry, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Ana Margarida Matos
- BioISI-Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisbon, Lisbon, Portugal.,Department of Human Genetics, National Health Institute "Dr. Ricardo Jorge," Lisbon, Portugal
| | - Peter Jordan
- BioISI-Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisbon, Lisbon, Portugal.,Department of Human Genetics, National Health Institute "Dr. Ricardo Jorge," Lisbon, Portugal
| | - Paulo Matos
- BioISI-Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisbon, Lisbon, Portugal.,Department of Human Genetics, National Health Institute "Dr. Ricardo Jorge," Lisbon, Portugal
| | - Carlos M Farinha
- BioISI-Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisbon, Lisbon, Portugal.,Department of Chemistry and Biochemistry, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Francisco R Pinto
- BioISI-Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisbon, Lisbon, Portugal.,Department of Chemistry and Biochemistry, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
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148
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Sonawane AR, Weiss ST, Glass K, Sharma A. Network Medicine in the Age of Biomedical Big Data. Front Genet 2019; 10:294. [PMID: 31031797 PMCID: PMC6470635 DOI: 10.3389/fgene.2019.00294] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Accepted: 03/19/2019] [Indexed: 12/13/2022] Open
Abstract
Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare.
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Affiliation(s)
- Abhijeet R. Sonawane
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Scott T. Weiss
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Amitabh Sharma
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA, United States
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149
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Ni D, Lu S, Zhang J. Emerging roles of allosteric modulators in the regulation of protein-protein interactions (PPIs): A new paradigm for PPI drug discovery. Med Res Rev 2019; 39:2314-2342. [PMID: 30957264 DOI: 10.1002/med.21585] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2018] [Revised: 03/12/2019] [Accepted: 03/24/2019] [Indexed: 12/26/2022]
Abstract
Protein-protein interactions (PPIs) are closely implicated in various types of cellular activities and are thus pivotal to health and disease states. Given their fundamental roles in a wide range of biological processes, the modulation of PPIs has enormous potential in drug discovery. However, owing to the general properties of large, flat, and featureless interfaces of PPIs, previous attempts have demonstrated that the generation of therapeutic agents targeting PPI interfaces is challenging, rendering them almost "undruggable" for decades. To date, rapid progress in chemical and structural biology techniques has promoted the exploitation of allostery as a novel approach in drug discovery. By attaching to allosteric sites that are topologically and spatially distinct from PPI interfaces, allosteric modulators can achieve improved physiochemical properties. Thus, allosteric modulators may represent an alternative strategy to target intractable PPIs and have attracted intense pharmaceutical interest. In this review, we first briefly introduce the characteristics of PPIs and then present different approaches for investigating PPIs, as well as the latest methods for modulating PPIs. Importantly, we comprehensively review the recent progress in the development of allosteric modulators to inhibit or stabilize PPIs. Finally, we conclude with future perspectives on the discovery of allosteric PPI modulators, especially the application of computational methods to aid in allosteric PPI drug discovery.
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Affiliation(s)
- Duan Ni
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Renji Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Shaoyong Lu
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Renji Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China.,Medicinal Bioinformatics Center, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Jian Zhang
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Renji Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China.,Medicinal Bioinformatics Center, Shanghai Jiao-Tong University School of Medicine, Shanghai, China.,Center for Single-Cell Omics, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
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150
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A plasma protein derived TGFβ signature is a prognostic indicator in triple negative breast cancer. NPJ Precis Oncol 2019; 3:10. [PMID: 30963111 PMCID: PMC6445093 DOI: 10.1038/s41698-019-0082-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 03/04/2019] [Indexed: 12/24/2022] Open
Abstract
We investigated the potential of in-depth quantitative plasma proteome analysis to uncover proteins predictive of progression and metastasis in triple negative breast cancer (TNBC). Analysis of samples from 24 pre-menopausal and 24 post-menopausal women with newly diagnosed TNBC who subsequently developed metastasis or remained metastasis free were utilized in the proteomic discovery set, which resulted in 43 proteins associated with tumor progression. These proteins were found to form a hierarchical network with TGFβ. The signature was further confirmed and refined by integrating plasma protein data from a murine TNBC model that encompassed mice with rapid- versus slow-growing tumors. Three genes consisting of CLIC1, MAPRE1, and SERPINA3 in the refined TGFβ signature significantly stratified overall survival (log-rank p = 0.0141) in a larger validation cohort irrespective of menopausal status, tumor stage, grade, and size.
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