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Fan S, Fleischer JR, Dokshokova L, Böhme LS, Haas G, Schmitt AM, Gätje FB, Emmalie Rosen LA, Bohnenberger H, Ghadimi M, Cui B, Xu X, Kalucka JM, Bösch F, De Oliveira T, Conradi LC. High CIB1 expression in colorectal cancer liver metastases correlates with worse survival and the replacement histopathological growth pattern. MOLECULAR THERAPY. ONCOLOGY 2024; 32:200828. [PMID: 39072289 PMCID: PMC11278321 DOI: 10.1016/j.omton.2024.200828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 05/16/2024] [Accepted: 06/07/2024] [Indexed: 07/30/2024]
Abstract
To date, nearly one-quarter of colorectal cancer (CRC) patients develop liver metastases (CRCLM), and its aggressiveness can be correlated to defined histopathological growth patterns (HGP). From the three main HGPs within CRCLM, the replacement HGP emerges as particularly aggressive, characterized by heightened tumor cell motility and vessel co-option. Here, we investigated the correlation between the expression of calcium- and integrin-binding protein 1 (CIB1), a ubiquitously expressed gene involved in various cellular processes including migration and adhesion, and disease-free (DFS) and overall survival (OS) in primary CRC patients. Additionally, we explored the correlation between CIB1 expression and different HGPs of CRCLM. Proteomic analysis was used to evaluate CIB1 expression in a cohort of 697 primary CRC patients. Additionally, single-cell and spatial RNA-sequencing datasets, along with publicly available bulk sequencing data were used to evaluate CIB1 expression in CRCLM. In silico data were further validated by formalin-fixed paraffin-embedded immunohistochemical stainings. We observed that high CIB1 expression is independently associated with worse DFS and OS, regardless of Union Internationale Contre le Cancer stage, gender, or age. Furthermore, the aggressive replacement CRCLM HGP is significantly associated with high CIB1 expression. Our findings show a correlation between CIB1 levels and the clinical aggressiveness of CRC. Moreover, CIB1 may be a novel marker to stratify HGP CRCLM.
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Affiliation(s)
- Shuang Fan
- Department of General, Visceral and Paediatric Surgery, University Medical Center Göttingen, Robert-Koch-Straβe 40, 37075 Göttingen, Germany
| | - Johannes Robert Fleischer
- Department of General, Visceral and Paediatric Surgery, University Medical Center Göttingen, Robert-Koch-Straβe 40, 37075 Göttingen, Germany
| | - Lolita Dokshokova
- Department of Biomedicine, Aarhus University, Hegh-Guldbergsgade 10, 8000 Aarhus C, Denmark
| | - Lena Sophie Böhme
- Department of General, Visceral and Paediatric Surgery, University Medical Center Göttingen, Robert-Koch-Straβe 40, 37075 Göttingen, Germany
| | - Gwendolyn Haas
- Department of General, Visceral and Paediatric Surgery, University Medical Center Göttingen, Robert-Koch-Straβe 40, 37075 Göttingen, Germany
| | - Alexandra Maria Schmitt
- Department of General, Visceral and Paediatric Surgery, University Medical Center Göttingen, Robert-Koch-Straβe 40, 37075 Göttingen, Germany
| | - Fabio Bennet Gätje
- Department of General, Visceral and Paediatric Surgery, University Medical Center Göttingen, Robert-Koch-Straβe 40, 37075 Göttingen, Germany
| | - Linde-Allegra Emmalie Rosen
- Department of General, Visceral and Paediatric Surgery, University Medical Center Göttingen, Robert-Koch-Straβe 40, 37075 Göttingen, Germany
| | | | - Michael Ghadimi
- Department of General, Visceral and Paediatric Surgery, University Medical Center Göttingen, Robert-Koch-Straβe 40, 37075 Göttingen, Germany
| | - Baolong Cui
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Göttingen, Germany
| | - Xingbo Xu
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Göttingen, Germany
| | - Joanna Maria Kalucka
- Department of Biomedicine, Aarhus University, Hegh-Guldbergsgade 10, 8000 Aarhus C, Denmark
| | - Florian Bösch
- Department of General, Visceral and Paediatric Surgery, University Medical Center Göttingen, Robert-Koch-Straβe 40, 37075 Göttingen, Germany
| | - Tiago De Oliveira
- Department of General, Visceral and Paediatric Surgery, University Medical Center Göttingen, Robert-Koch-Straβe 40, 37075 Göttingen, Germany
| | - Lena-Christin Conradi
- Department of General, Visceral and Paediatric Surgery, University Medical Center Göttingen, Robert-Koch-Straβe 40, 37075 Göttingen, Germany
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2
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Liu H, Yan P, Zhang Z, Han H, Zhou Q, Zheng J, Zhang J, Xu F, Shui W. Structural Mass Spectrometry Captures Residue-Resolved Comprehensive Conformational Rearrangements of a G Protein-Coupled Receptor. J Am Chem Soc 2024; 146:20045-20058. [PMID: 39001877 DOI: 10.1021/jacs.4c03922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/15/2024]
Abstract
G protein-coupled receptor (GPCR) structural studies with in-solution spectroscopic approaches have offered distinctive insights into GPCR activation and signaling that highly complement those yielded from structural snapshots by crystallography or cryo-EM. While most current spectroscopic approaches allow for probing structural changes at selected residues or loop regions, they are not suitable for capturing a holistic view of GPCR conformational rearrangements across multiple domains. Herein, we develop an approach based on limited proteolysis mass spectrometry (LiP-MS) to simultaneously monitor conformational alterations of a large number of residues spanning both flexible loops and structured transmembrane domains for a given GPCR. To benchmark LiP-MS for GPCR conformational profiling, we studied the adenosine 2A receptor (A2AR) in response to different ligand binding (agonist/antagonist/allosteric modulators) and G protein coupling. Systematic and residue-resolved profiling of A2AR conformational rearrangements by LiP-MS precisely captures structural mechanisms in multiple domains underlying ligand engagement, receptor activation, and allostery, and may also reflect local conformational flexibility. Furthermore, these residue-resolution structural fingerprints of the A2AR protein allow us to readily classify ligands of different pharmacology and distinguish the G protein-coupled state. Thus, our study provides a new structural MS approach that would be generalizable to characterizing conformational transition and plasticity for challenging integral membrane proteins.
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Affiliation(s)
- Hongyue Liu
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Pengfei Yan
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhaoyu Zhang
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hongbo Han
- University of Chinese Academy of Sciences, Beijing 100049, China
- Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Qingtong Zhou
- Department of Pharmacology, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China
| | - Jie Zheng
- University of Chinese Academy of Sciences, Beijing 100049, China
- Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Jian Zhang
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Fei Xu
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
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3
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Manen-Freixa L, Antolin AA. Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery. Expert Opin Drug Discov 2024:1-27. [PMID: 39004919 DOI: 10.1080/17460441.2024.2376643] [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: 03/15/2024] [Accepted: 07/02/2024] [Indexed: 07/16/2024]
Abstract
INTRODUCTION Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology. AREAS COVERED This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples. EXPERT OPINION Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.
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Affiliation(s)
- Leticia Manen-Freixa
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
| | - Albert A Antolin
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
- Center for Cancer Drug Discovery, The Division of Cancer Therapeutics, The Institute of Cancer Research, London, UK
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4
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Yang L, Guo CW, Luo QM, Guo ZF, Chen L, Ishihama Y, Li P, Yang H, Gao W. Thermostability-assisted limited proteolysis-coupled mass spectrometry for capturing drug target proteins and sites. Anal Chim Acta 2024; 1312:342755. [PMID: 38834267 DOI: 10.1016/j.aca.2024.342755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/28/2024] [Accepted: 05/20/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND Identifying drug-binding targets and their corresponding sites is crucial for drug discovery and mechanism studies. Limited proteolysis-coupled mass spectrometry (LiP-MS) is a sophisticated method used for the detection of compound and protein interactions. However, in some cases, LiP-MS cannot identify the target proteins due to the small structure changes or the lack of enrichment of low-abundant protein. To overcome this drawback, we developed a thermostability-assisted limited proteolysis-coupled mass spectrometry (TALiP-MS) approach for efficient drug target discovery. RESULTS We proved that the novel strategy, TALiP-MS, could efficiently identify target proteins of various ligands, including cyclosporin A (a calcineurin inhibitor), geldanamycin (an HSP90 inhibitor), and staurosporine (a kinase inhibitor), with accurately recognizing drug-binding domains. The TALiP protocol increased the number of target peptides detected in LiP-MS experiments by 2- to 8-fold. Meanwhile, the TALiP-MS approach can not only identify both ligand-binding stability and destabilization proteins but also shows high complementarity with the thermal proteome profiling (TPP) and machine learning-based limited proteolysis (LiP-Quant) methods. The developed TALiP-MS approach was applied to identify the target proteins of celastrol (CEL), a natural product known for its strong antioxidant and anti-cancer angiogenesis effect. Among them, four proteins, MTHFD1, UBA1, ACLY, and SND1 were further validated for their strong affinity to CEL by using cellular thermal shift assay. Additionally, the destabilized proteins induced by CEL such as TAGLN2 and CFL1 were also validated. SIGNIFICANCE Collectively, these findings underscore the efficacy of the TALiP-MS method for identifying drug targets, elucidating binding sites, and even detecting drug-induced conformational changes in target proteins in complex proteomes.
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Affiliation(s)
- Liu Yang
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 211198, PR China
| | - Chen-Wan Guo
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 211198, PR China
| | - Qi-Ming Luo
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 211198, PR China
| | - Zi-Fan Guo
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 211198, PR China
| | - Ling Chen
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 211198, PR China
| | - Yasushi Ishihama
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, 606-8501, Japan
| | - Ping Li
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 211198, PR China.
| | - Hua Yang
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 211198, PR China.
| | - Wen Gao
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 211198, PR China; Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, 606-8501, Japan.
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5
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Giannangelo C, Challis MP, Siddiqui G, Edgar R, Malcolm TR, Webb CT, Drinkwater N, Vinh N, Macraild C, Counihan N, Duffy S, Wittlin S, Devine SM, Avery VM, De Koning-Ward T, Scammells P, McGowan S, Creek DJ. Chemoproteomics validates selective targeting of Plasmodium M1 alanyl aminopeptidase as an antimalarial strategy. eLife 2024; 13:RP92990. [PMID: 38976500 PMCID: PMC11230628 DOI: 10.7554/elife.92990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024] Open
Abstract
New antimalarial drug candidates that act via novel mechanisms are urgently needed to combat malaria drug resistance. Here, we describe the multi-omic chemical validation of Plasmodium M1 alanyl metalloaminopeptidase as an attractive drug target using the selective inhibitor, MIPS2673. MIPS2673 demonstrated potent inhibition of recombinant Plasmodium falciparum (PfA-M1) and Plasmodium vivax (PvA-M1) M1 metalloaminopeptidases, with selectivity over other Plasmodium and human aminopeptidases, and displayed excellent in vitro antimalarial activity with no significant host cytotoxicity. Orthogonal label-free chemoproteomic methods based on thermal stability and limited proteolysis of whole parasite lysates revealed that MIPS2673 solely targets PfA-M1 in parasites, with limited proteolysis also enabling estimation of the binding site on PfA-M1 to within ~5 Å of that determined by X-ray crystallography. Finally, functional investigation by untargeted metabolomics demonstrated that MIPS2673 inhibits the key role of PfA-M1 in haemoglobin digestion. Combined, our unbiased multi-omic target deconvolution methods confirmed the on-target activity of MIPS2673, and validated selective inhibition of M1 alanyl metalloaminopeptidase as a promising antimalarial strategy.
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Affiliation(s)
- Carlo Giannangelo
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash UniversityParkvilleAustralia
| | - Matthew P Challis
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash UniversityParkvilleAustralia
| | - Ghizal Siddiqui
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash UniversityParkvilleAustralia
| | - Rebecca Edgar
- School of Medicine, Deakin UniversityGeelongAustralia
- The Institute for Mental and Physical Health and Clinical Translation, Deakin UniversityGeelongAustralia
| | - Tess R Malcolm
- Monash Biomedicine Discovery Institute and Department of Microbiology, Monash UniversityClaytonAustralia
- Centre to Impact AMR, Monash UniversityClaytonAustralia
| | - Chaille T Webb
- Monash Biomedicine Discovery Institute and Department of Microbiology, Monash UniversityClaytonAustralia
- Centre to Impact AMR, Monash UniversityClaytonAustralia
| | - Nyssa Drinkwater
- Monash Biomedicine Discovery Institute and Department of Microbiology, Monash UniversityClaytonAustralia
- Centre to Impact AMR, Monash UniversityClaytonAustralia
| | - Natalie Vinh
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash UniversityParkvilleAustralia
| | - Christopher Macraild
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash UniversityParkvilleAustralia
| | - Natalie Counihan
- School of Medicine, Deakin UniversityGeelongAustralia
- The Institute for Mental and Physical Health and Clinical Translation, Deakin UniversityGeelongAustralia
| | - Sandra Duffy
- Discovery Biology, Centre for Cellular Phenomics, Griffith UniversityNathanAustralia
| | - Sergio Wittlin
- Swiss Tropical and Public Health InstituteAllschwilSwitzerland
- University of BaselBaselSwitzerland
| | - Shane M Devine
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleAustralia
- Department of Medical Biology, The University of MelbourneParkvilleAustralia
| | - Vicky M Avery
- Discovery Biology, Centre for Cellular Phenomics, Griffith UniversityNathanAustralia
- School of Environment and Science, Griffith UniversityNathanAustralia
| | - Tania De Koning-Ward
- School of Medicine, Deakin UniversityGeelongAustralia
- The Institute for Mental and Physical Health and Clinical Translation, Deakin UniversityGeelongAustralia
| | - Peter Scammells
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash UniversityParkvilleAustralia
| | - Sheena McGowan
- Monash Biomedicine Discovery Institute and Department of Microbiology, Monash UniversityClaytonAustralia
- Centre to Impact AMR, Monash UniversityClaytonAustralia
| | - Darren J Creek
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash UniversityParkvilleAustralia
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6
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Wang L, Chen A, Zhang L, Zhang J, Wei S, Chen Y, Hu M, Mo Y, Li S, Zeng M, Li H, Liang C, Ren Y, Xu L, Liang W, Zhu X, Wang X, Sun D. Deciphering the molecular nexus between Omicron infection and acute kidney injury: a bioinformatics approach. Front Mol Biosci 2024; 11:1340611. [PMID: 39027131 PMCID: PMC11254815 DOI: 10.3389/fmolb.2024.1340611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 06/07/2024] [Indexed: 07/20/2024] Open
Abstract
Background The ongoing global health crisis of COVID-19, and particularly the challenges posed by recurrent infections of the Omicron variant, have significantly strained healthcare systems worldwide. There is a growing body of evidence indicating an increased susceptibility to Omicron infection in patients suffering from Acute Kidney Injury (AKI). However, the intricate molecular interplay between AKI and Omicron variant of COVID-19 remains largely enigmatic. Methods This study employed a comprehensive analysis of human RNA sequencing (RNA-seq) and microarray datasets to identify differentially expressed genes (DEGs) associated with Omicron infection in the context of AKI. We engaged in functional enrichment assessments, an examination of Protein-Protein Interaction (PPI) networks, and advanced network analysis to elucidate the cellular signaling pathways involved, identify critical hub genes, and determine the relevant controlling transcription factors and microRNAs. Additionally, we explored protein-drug interactions to highlight potential pharmacological interventions. Results Our investigation revealed significant DEGs and cellular signaling pathways implicated in both Omicron infection and AKI. We identified pivotal hub genes, including EIF2AK2, PLSCR1, GBP1, TNFSF10, C1QB, and BST2, and their associated regulatory transcription factors and microRNAs. Notably, in the murine AKI model, there was a marked reduction in EIF2AK2 expression, in contrast to significant elevations in PLSCR1, C1QB, and BST2. EIF2AK2 exhibited an inverse relationship with the primary AKI mediator, Kim-1, whereas PLSCR1 and C1QB demonstrated strong positive correlations with it. Moreover, we identified potential therapeutic agents such as Suloctidil, Apocarotenal, 3'-Azido-3'-deoxythymidine, among others. Our findings also highlighted a correlation between the identified hub genes and diseases like myocardial ischemia, schizophrenia, and liver cirrhosis. To further validate the credibility of our data, we employed an independent validation dataset to verify the hub genes. Notably, the expression patterns of PLSCR1, GBP1, BST2, and C1QB were consistent with our research findings, reaffirming the reliability of our results. Conclusion Our bioinformatics analysis has provided initial insights into the shared genetic landscape between Omicron COVID-19 infections and AKI, identifying potential therapeutic targets and drugs. This preliminary investigation lays the foundation for further research, with the hope of contributing to the development of innovative treatment strategies for these complex medical conditions.
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Affiliation(s)
- Li Wang
- Nephrology Department, Southern Medical University Affiliated Longhua People’s Hospital, Shenzhen, China
| | - Anning Chen
- Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
| | - Lantian Zhang
- Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
| | - Junwei Zhang
- Nephrology Department, Southern Medical University Affiliated Longhua People’s Hospital, Shenzhen, China
| | - Shuqi Wei
- Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
| | - Yangxiao Chen
- Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
| | - Mingliang Hu
- Nephrology Department, Southern Medical University Affiliated Longhua People’s Hospital, Shenzhen, China
| | - Yihao Mo
- Nephrology Department, Southern Medical University Affiliated Longhua People’s Hospital, Shenzhen, China
| | - Sha Li
- Nephrology Department, Southern Medical University Affiliated Longhua People’s Hospital, Shenzhen, China
| | - Min Zeng
- Nephrology Department, Southern Medical University Affiliated Longhua People’s Hospital, Shenzhen, China
| | - Huafeng Li
- Nephrology Department, Southern Medical University Affiliated Longhua People’s Hospital, Shenzhen, China
| | - Caixing Liang
- Nephrology Department, Southern Medical University Affiliated Longhua People’s Hospital, Shenzhen, China
| | - Yi Ren
- Nephrology Department, Southern Medical University Affiliated Longhua People’s Hospital, Shenzhen, China
| | - Liting Xu
- Nephrology Department, Southern Medical University Affiliated Longhua People’s Hospital, Shenzhen, China
| | - Wenhua Liang
- Nephrology Department, Southern Medical University Affiliated Longhua People’s Hospital, Shenzhen, China
| | - Xuejiao Zhu
- Department of Anesthesiology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Xiaokai Wang
- Xuzhou First People’s Hospital, Xuzhou, Jiangsu, China
| | - Donglin Sun
- Department of Urology, Shenzhen Hospital, Southern Medical University, Shenzhen, China
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7
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Holfeld A, Schuster D, Sesterhenn F, Gillingham AK, Stalder P, Haenseler W, Barrio-Hernandez I, Ghosh D, Vowles J, Cowley SA, Nagel L, Khanppnavar B, Serdiuk T, Beltrao P, Korkhov VM, Munro S, Riek R, de Souza N, Picotti P. Systematic identification of structure-specific protein-protein interactions. Mol Syst Biol 2024; 20:651-675. [PMID: 38702390 PMCID: PMC11148107 DOI: 10.1038/s44320-024-00037-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/12/2024] [Accepted: 04/15/2024] [Indexed: 05/06/2024] Open
Abstract
The physical interactome of a protein can be altered upon perturbation, modulating cell physiology and contributing to disease. Identifying interactome differences of normal and disease states of proteins could help understand disease mechanisms, but current methods do not pinpoint structure-specific PPIs and interaction interfaces proteome-wide. We used limited proteolysis-mass spectrometry (LiP-MS) to screen for structure-specific PPIs by probing for protease susceptibility changes of proteins in cellular extracts upon treatment with specific structural states of a protein. We first demonstrated that LiP-MS detects well-characterized PPIs, including antibody-target protein interactions and interactions with membrane proteins, and that it pinpoints interfaces, including epitopes. We then applied the approach to study conformation-specific interactors of the Parkinson's disease hallmark protein alpha-synuclein (aSyn). We identified known interactors of aSyn monomer and amyloid fibrils and provide a resource of novel putative conformation-specific aSyn interactors for validation in further studies. We also used our approach on GDP- and GTP-bound forms of two Rab GTPases, showing detection of differential candidate interactors of conformationally similar proteins. This approach is applicable to screen for structure-specific interactomes of any protein, including posttranslationally modified and unmodified, or metabolite-bound and unbound protein states.
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Affiliation(s)
- Aleš Holfeld
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Dina Schuster
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
- Institute of Molecular Biology and Biophysics, Department of Biology, ETH Zurich, Zurich, Switzerland
- Laboratory of Biomolecular Research, Division of Biology and Chemistry, Paul Scherrer Institute, Villigen, Switzerland
| | - Fabian Sesterhenn
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | | | - Patrick Stalder
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Walther Haenseler
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
- University Research Priority Program AdaBD (Adaptive Brain Circuits in Development and Learning), University of Zurich, Zurich, Switzerland
| | - Inigo Barrio-Hernandez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Dhiman Ghosh
- Laboratory of Physical Chemistry, ETH Zurich, Zurich, Switzerland
| | - Jane Vowles
- James and Lillian Martin Centre for Stem Cell Research, Sir William Dunn School of Pathology, University of Oxford, Oxford, UK
| | - Sally A Cowley
- James and Lillian Martin Centre for Stem Cell Research, Sir William Dunn School of Pathology, University of Oxford, Oxford, UK
| | - Luise Nagel
- Cluster of Excellence Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Basavraj Khanppnavar
- Institute of Molecular Biology and Biophysics, Department of Biology, ETH Zurich, Zurich, Switzerland
- Laboratory of Biomolecular Research, Division of Biology and Chemistry, Paul Scherrer Institute, Villigen, Switzerland
| | - Tetiana Serdiuk
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Pedro Beltrao
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Volodymyr M Korkhov
- Institute of Molecular Biology and Biophysics, Department of Biology, ETH Zurich, Zurich, Switzerland
- Laboratory of Biomolecular Research, Division of Biology and Chemistry, Paul Scherrer Institute, Villigen, Switzerland
| | - Sean Munro
- MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Roland Riek
- Laboratory of Physical Chemistry, ETH Zurich, Zurich, Switzerland
| | - Natalie de Souza
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Paola Picotti
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland.
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8
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Capuano A, D’Urso G, Gazzillo E, Lauro G, Chini MG, D’Auria MV, Ferraro MG, Iazzetti F, Irace C, Bifulco G, Casapullo A. Fatty Acid Synthase as Interacting Anticancer Target of the Terpenoid Myrianthic Acid Disclosed by MS-Based Proteomics Approaches. Int J Mol Sci 2024; 25:5918. [PMID: 38892106 PMCID: PMC11172900 DOI: 10.3390/ijms25115918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 05/23/2024] [Accepted: 05/25/2024] [Indexed: 06/21/2024] Open
Abstract
This research focuses on the target deconvolution of the natural compound myrianthic acid, a triterpenoid characterized by an ursane skeleton isolated from the roots of Myrianthus arboreus and from Oenothera maritima Nutt. (Onagraceae), using MS-based chemical proteomic techniques. Application of drug affinity responsive target stability (DARTS) and targeted-limited proteolysis coupled to mass spectrometry (t-LiP-MS) led to the identification of the enzyme fatty acid synthase (FAS) as an interesting macromolecular counterpart of myrianthic acid. This result, confirmed by comparison with the natural ursolic acid, was thoroughly investigated and validated in silico by molecular docking, which gave a precise picture of the interactions in the MA/FAS complex. Moreover, biological assays showcased the inhibitory activity of myrianthic acid against the FAS enzyme, most likely related to its antiproliferative activity towards tumor cells. Given the significance of FAS in specific pathologies, especially cancer, the myrianthic acid structural moieties could serve as a promising reference point to start the potential development of innovative approaches in therapy.
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Affiliation(s)
- Alessandra Capuano
- Department of Pharmacy, University of Salerno, 84084 Fisciano, Italy; (A.C.); (E.G.); (G.L.); (G.B.); (A.C.)
- PhD Program in Drug Discovery and Development, Department of Pharmacy, University of Salerno, 84084 Fisciano, Italy
| | - Gilda D’Urso
- Department of Pharmacy, University of Salerno, 84084 Fisciano, Italy; (A.C.); (E.G.); (G.L.); (G.B.); (A.C.)
| | - Erica Gazzillo
- Department of Pharmacy, University of Salerno, 84084 Fisciano, Italy; (A.C.); (E.G.); (G.L.); (G.B.); (A.C.)
- PhD Program in Drug Discovery and Development, Department of Pharmacy, University of Salerno, 84084 Fisciano, Italy
| | - Gianluigi Lauro
- Department of Pharmacy, University of Salerno, 84084 Fisciano, Italy; (A.C.); (E.G.); (G.L.); (G.B.); (A.C.)
| | - Maria Giovanna Chini
- Department of Biosciences and Territory, University of Molise, C.da Fonte Lappone, 86090 Pesche, Italy
| | - Maria Valeria D’Auria
- Department of Pharmacy, University of Naples “Federico II”, Via Domenico Montesano 49, 80131 Naples, Italy;
| | - Maria Grazia Ferraro
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, 80131 Naples, Italy;
| | - Federica Iazzetti
- Biochem Lab, Department of Pharmacy, School of Medicine and Surgery, University of Naples “Federico II”, Via Domenico Montesano 49, 80131 Naples, Italy; (F.I.); (C.I.)
| | - Carlo Irace
- Biochem Lab, Department of Pharmacy, School of Medicine and Surgery, University of Naples “Federico II”, Via Domenico Montesano 49, 80131 Naples, Italy; (F.I.); (C.I.)
| | - Giuseppe Bifulco
- Department of Pharmacy, University of Salerno, 84084 Fisciano, Italy; (A.C.); (E.G.); (G.L.); (G.B.); (A.C.)
| | - Agostino Casapullo
- Department of Pharmacy, University of Salerno, 84084 Fisciano, Italy; (A.C.); (E.G.); (G.L.); (G.B.); (A.C.)
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9
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Mouysset B, Le Grand M, Camoin L, Pasquier E. Poly-pharmacology of existing drugs: How to crack the code? Cancer Lett 2024; 588:216800. [PMID: 38492768 DOI: 10.1016/j.canlet.2024.216800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 02/15/2024] [Accepted: 03/05/2024] [Indexed: 03/18/2024]
Abstract
Drug development in oncology is highly challenging, with less than 5% success rate in clinical trials. This alarming figure points out the need to study in more details the multiple biological effects of drugs in specific contexts. Indeed, the comprehensive assessment of drug poly-pharmacology can provide insights into their therapeutic and adverse effects, to optimize their utilization and maximize the success rate of clinical trials. Recent technological advances have made possible in-depth investigation of drug poly-pharmacology. This review first highlights high-throughput methodologies that have been used to unveil new mechanisms of action of existing drugs. Then, we discuss how emerging chemo-proteomics strategies allow effectively dissecting the poly-pharmacology of drugs in an unsupervised manner.
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Affiliation(s)
- Baptiste Mouysset
- Centre de Recherche en Cancérologie de Marseille Inserm U1068, CNRS UMR7258, Aix-Marseille University U105, Marseille, France.
| | - Marion Le Grand
- Centre de Recherche en Cancérologie de Marseille Inserm U1068, CNRS UMR7258, Aix-Marseille University U105, Marseille, France.
| | - Luc Camoin
- Centre de Recherche en Cancérologie de Marseille Inserm U1068, CNRS UMR7258, Aix-Marseille University U105, Marseille, France.
| | - Eddy Pasquier
- Centre de Recherche en Cancérologie de Marseille Inserm U1068, CNRS UMR7258, Aix-Marseille University U105, Marseille, France.
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10
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Creek D, Giannangelo C, Challis M, Siddiqui G, Edgar R, Malcolm T, Webb C, Drinkwater N, Vinh N, MacRaild C, Counihan N, Duffy S, Wittlin S, Devine S, Avery V, de Koning-Ward T, Scammells P, McGowan S. Chemoproteomics validates selective targeting of Plasmodium M1 alanyl aminopeptidase as an antimalarial strategy. RESEARCH SQUARE 2024:rs.3.rs-3251230. [PMID: 38746424 PMCID: PMC11092810 DOI: 10.21203/rs.3.rs-3251230/v3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
New antimalarial drug candidates that act via novel mechanisms are urgently needed to combat malaria drug resistance. Here, we describe the multi-omic chemical validation of Plasmodium M1 alanyl metalloaminopeptidase as an attractive drug target using the selective inhibitor, MIPS2673. MIPS2673 demonstrated potent inhibition of recombinant Plasmodium falciparum ( Pf A-M1) and Plasmodium vivax ( Pv A-M1) M1 metalloaminopeptidases, with selectivity over other Plasmodium and human aminopeptidases, and displayed excellent in vitro antimalarial activity with no significant host cytotoxicity. Orthogonal label-free chemoproteomic methods based on thermal stability and limited proteolysis of whole parasite lysates revealed that MIPS2673 solely targets Pf A-M1 in parasites, with limited proteolysis also enabling estimation of the binding site on Pf A-M1 to within ~5 Å of that determined by X-ray crystallography. Finally, functional investigation by untargeted metabolomics demonstrated that MIPS2673 inhibits the key role of Pf A-M1 in haemoglobin digestion. Combined, our unbiased multi-omic target deconvolution methods confirmed the on-target activity of MIPS2673, and validated selective inhibition of M1 alanyl metalloaminopeptidase as a promising antimalarial strategy.
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11
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Xu C, Wang Y, Ni H, Yao M, Cheng L, Lin X. The role of orphan G protein-coupled receptors in pain. Heliyon 2024; 10:e28818. [PMID: 38590871 PMCID: PMC11000026 DOI: 10.1016/j.heliyon.2024.e28818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 03/22/2024] [Accepted: 03/25/2024] [Indexed: 04/10/2024] Open
Abstract
G protein-coupled receptors (GPCRs), which form the largest family of membrane protein receptors in humans, are highly complex signaling systems with intricate structures and dynamic conformations and locations. Among these receptors, a specific subset is referred to as orphan GPCRs (oGPCRs) and has garnered significant interest in pain research due to their role in both central and peripheral nervous system function. The diversity of GPCR functions is attributed to multiple factors, including allosteric modulators, signaling bias, oligomerization, constitutive signaling, and compartmentalized signaling. This review primarily focuses on the recent advances in oGPCR research on pain mechanisms, discussing the role of specific oGPCRs including GPR34, GPR37, GPR65, GPR83, GPR84, GPR85, GPR132, GPR151, GPR160, GPR171, GPR177, and GPR183. The orphan receptors among these receptors associated with central nervous system diseases are also briefly described. Understanding the functions of these oGPCRs can contribute not only to a deeper understanding of pain mechanisms but also offer a reference for discovering new targets for pain treatment.
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Affiliation(s)
- Chengfei Xu
- Department of Anesthesiology, The Third People's Hospital of Bengbu, Bengbu, 233000, PR China
| | - Yahui Wang
- Department of Anesthesiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, PR China
| | - Huadong Ni
- Department of Anesthesiology and Pain Research Center, Affiliated Hospital of Jiaxing University, Jiaxing, 314000, PR China
| | - Ming Yao
- Department of Anesthesiology and Pain Research Center, Affiliated Hospital of Jiaxing University, Jiaxing, 314000, PR China
| | - Liang Cheng
- Department of Anesthesiology, The Third People's Hospital of Bengbu, Bengbu, 233000, PR China
| | - Xuewu Lin
- Department of Anesthesiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, PR China
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12
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George AL, Dueñas ME, Marín-Rubio JL, Trost M. Stability-based approaches in chemoproteomics. Expert Rev Mol Med 2024; 26:e6. [PMID: 38604802 PMCID: PMC11062140 DOI: 10.1017/erm.2024.6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 01/17/2024] [Accepted: 02/22/2024] [Indexed: 04/13/2024]
Abstract
Target deconvolution can help understand how compounds exert therapeutic effects and can accelerate drug discovery by helping optimise safety and efficacy, revealing mechanisms of action, anticipate off-target effects and identifying opportunities for therapeutic expansion. Chemoproteomics, a combination of chemical biology with mass spectrometry has transformed target deconvolution. This review discusses modification-free chemoproteomic approaches that leverage the change in protein thermodynamics induced by small molecule ligand binding. Unlike modification-based methods relying on enriching specific protein targets, these approaches offer proteome-wide evaluations, driven by advancements in mass spectrometry sensitivity, increasing proteome coverage and quantitation methods. Advances in methods based on denaturation/precipitation by thermal or chemical denaturation, or by protease degradation are evaluated, emphasising the evolving landscape of chemoproteomics and its potential impact on future drug-development strategies.
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Affiliation(s)
- Amy L. George
- Laboratory for Biomedical Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne, NE2 4HH, UK
| | - Maria Emilia Dueñas
- Laboratory for Biomedical Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne, NE2 4HH, UK
| | - José Luis Marín-Rubio
- Laboratory for Biomedical Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne, NE2 4HH, UK
| | - Matthias Trost
- Laboratory for Biomedical Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne, NE2 4HH, UK
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13
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Capponi S, Wang S. AI in cellular engineering and reprogramming. Biophys J 2024:S0006-3495(24)00245-5. [PMID: 38576162 DOI: 10.1016/j.bpj.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/19/2024] [Accepted: 04/01/2024] [Indexed: 04/06/2024] Open
Abstract
During the last decade, artificial intelligence (AI) has increasingly been applied in biophysics and related fields, including cellular engineering and reprogramming, offering novel approaches to understand, manipulate, and control cellular function. The potential of AI lies in its ability to analyze complex datasets and generate predictive models. AI algorithms can process large amounts of data from single-cell genomics and multiomic technologies, allowing researchers to gain mechanistic insights into the control of cell identity and function. By integrating and interpreting these complex datasets, AI can help identify key molecular events and regulatory pathways involved in cellular reprogramming. This knowledge can inform the design of precision engineering strategies, such as the development of new transcription factor and signaling molecule cocktails, to manipulate cell identity and drive authentic cell fate across lineage boundaries. Furthermore, when used in combination with computational methods, AI can accelerate and improve the analysis and understanding of the intricate relationships between genes, proteins, and cellular processes. In this review article, we explore the current state of AI applications in biophysics with a specific focus on cellular engineering and reprogramming. Then, we showcase a couple of recent applications where we combined machine learning with experimental and computational techniques. Finally, we briefly discuss the challenges and prospects of AI in cellular engineering and reprogramming, emphasizing the potential of these technologies to revolutionize our ability to engineer cells for a variety of applications, from disease modeling and drug discovery to regenerative medicine and biomanufacturing.
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Affiliation(s)
- Sara Capponi
- IBM Almaden Research Center, San Jose, California; Center for Cellular Construction, San Francisco, California.
| | - Shangying Wang
- Bay Area Institute of Science, Altos Labs, Redwood City, California.
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14
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Hsiao Y, Zhang H, Li GX, Deng Y, Yu F, Kahrood HV, Steele JR, Schittenhelm RB, Nesvizhskii AI. Analysis and visualization of quantitative proteomics data using FragPipe-Analyst. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.05.583643. [PMID: 38496650 PMCID: PMC10942459 DOI: 10.1101/2024.03.05.583643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.
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Affiliation(s)
- Yi Hsiao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Haijian Zhang
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Ginny Xiaohe Li
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yamei Deng
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hossein Valipour Kahrood
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
- Monash Genomics & Bioinformatics Platform, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Joel R. Steele
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Ralf B. Schittenhelm
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Alexey I. Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
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15
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Zhang QQ, Chen QS, Feng F, Cao X, Chen XF, Zhang H. Benzoylaconitine: A promising ACE2-targeted agonist for enhancing cardiac function in heart failure. Free Radic Biol Med 2024; 214:206-218. [PMID: 38369076 DOI: 10.1016/j.freeradbiomed.2024.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 01/23/2024] [Accepted: 02/10/2024] [Indexed: 02/20/2024]
Abstract
Benzoylaconitine is a natural product in the treatment of cardiovascular disease. However, its pharmacological effect, direct target protein, and molecular mechanisms for the treatment of heart failure are unclear. In this study, benzoylaconitine inhibited Ang II-induced cell hypertrophy and fibrosis in rat primary cardiomyocytes and rat fibroblasts, while attenuating cardiac function and cardiac remodeling in TAC mice. Using the limited proteolysis-mass spectrometry (LiP-MS) method, the angiotensin-converting enzyme 2 (ACE2) was confirmed as a direct binding target of benzoylaconitine for the treatment of heart failure. In ACE2-knockdown cells and ACE2-/- mice, benzoylaconitine failed to ameliorate cardiomyocyte hypertrophy, fibrosis, and heart failure. Online RNA-sequence analysis indicated p38/ERK-mediated mitochondrial reactive oxygen species (ROS) and nuclear factor kappa B (NF-κB) activation are the possible downstream molecular mechanisms for the effect of BAC-ACE2 interaction. Further studies in ACE2-knockdown cells and ACE2-/- mice suggested that benzoylaconitine targeted ACE2 to suppress p38/ERK-mediated mitochondrial ROS and NF-κB pathway activation. Our findings suggest that benzoylaconitine is a promising ACE2 agonist in regulating mitochondrial ROS release and inflammation activation to improve cardiac function in the treatment of heart failure.
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Affiliation(s)
- Qi-Qiang Zhang
- Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, China
| | - Qing-Shan Chen
- Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, China
| | - Fei Feng
- School of Pharmacy, Naval Medical University (Second Military Medical University), 325 Guohe Road, Shanghai, 200433, China
| | - Xiang Cao
- Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, China
| | - Xiao-Fei Chen
- School of Pharmacy, Naval Medical University (Second Military Medical University), 325 Guohe Road, Shanghai, 200433, China.
| | - Hai Zhang
- Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
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16
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Weigert Muñoz A, Zhao W, Sieber SA. Monitoring host-pathogen interactions using chemical proteomics. RSC Chem Biol 2024; 5:73-89. [PMID: 38333198 PMCID: PMC10849124 DOI: 10.1039/d3cb00135k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 11/09/2023] [Indexed: 02/10/2024] Open
Abstract
With the rapid emergence and the dissemination of microbial resistance to conventional chemotherapy, the shortage of novel antimicrobial drugs has raised a global health threat. As molecular interactions between microbial pathogens and their mammalian hosts are crucial to establish virulence, pathogenicity, and infectivity, a detailed understanding of these interactions has the potential to reveal novel therapeutic targets and treatment strategies. Bidirectional molecular communication between microbes and eukaryotes is essential for both pathogenic and commensal organisms to colonise their host. In particular, several devastating pathogens exploit host signalling to adjust the expression of energetically costly virulent behaviours. Chemical proteomics has emerged as a powerful tool to interrogate the protein interaction partners of small molecules and has been successfully applied to advance host-pathogen communication studies. Here, we present recent significant progress made by this approach and provide a perspective for future studies.
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Affiliation(s)
- Angela Weigert Muñoz
- Center for Functional Protein Assemblies, Department of Bioscience, TUM School of Natural Sciences, Technical University of Munich Ernst-Otto-Fischer-Straße 8 D-85748 Garching Germany
| | - Weining Zhao
- College of Pharmacy, Shenzhen Technology University Shenzhen 518118 China
| | - Stephan A Sieber
- Center for Functional Protein Assemblies, Department of Bioscience, TUM School of Natural Sciences, Technical University of Munich Ernst-Otto-Fischer-Straße 8 D-85748 Garching Germany
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) Germany
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17
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Li Y, Lyu J, Wang Y, Ye M, Wang H. Ligand Modification-Free Methods for the Profiling of Protein-Environmental Chemical Interactions. Chem Res Toxicol 2024; 37:1-15. [PMID: 38146056 DOI: 10.1021/acs.chemrestox.3c00282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
Abstract
Adverse health outcomes caused by environmental chemicals are often initiated via their interactions with proteins. Essentially, one environmental chemical may interact with a number of proteins and/or a protein may interact with a multitude of environmental chemicals, forming an intricate interaction network. Omics-wide protein-environmental chemical interaction profiling (PECI) is of prominent importance for comprehensive understanding of these interaction networks, including the toxicity mechanisms of action (MoA), and for providing systematic chemical safety assessment. However, such information remains unknown for most environmental chemicals, partly due to their vast chemical diversity. In recent years, with the continuous efforts afforded, especially in mass spectrometry (MS) based omics technologies, several ligand modification-free methods have been developed, and new attention for systematic PECI profiling was gained. In this Review, we provide a comprehensive overview on these methodologies for the identification of ligand-protein interactions, including affinity interaction-based methods of affinity-driven purification, covalent modification profiling, and activity-based protein profiling (ABPP) in a competitive mode, physicochemical property changes assessment methods of ligand-directed nuclear magnetic resonance (ligand-directed NMR), MS integrated with equilibrium dialysis for the discovery of allostery systematically (MIDAS), thermal proteome profiling (TPP), limited proteolysis-coupled mass spectrometry (LiP-MS), stability of proteins from rates of oxidation (SPROX), and several intracellular downstream response characterization methods. We expect that the applications of these ligand modification-free technologies will drive a considerable increase in the number of PECI identified, facilitate unveiling the toxicological mechanisms, and ultimately contribute to systematic health risk assessment of environmental chemicals.
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Affiliation(s)
- Yanan Li
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian 116023, China
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
- The State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Jiawen Lyu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian 116023, China
| | - Yan Wang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian 116023, China
| | - Mingliang Ye
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian 116023, China
- State Key Laboratory of Medical Proteomics, Beijing, 102206, China
| | - Hailin Wang
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
- The State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
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18
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Nazli A, Qiu J, Tang Z, He Y. Recent Advances and Techniques for Identifying Novel Antibacterial Targets. Curr Med Chem 2024; 31:464-501. [PMID: 36734893 DOI: 10.2174/0929867330666230123143458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 10/30/2022] [Accepted: 11/11/2022] [Indexed: 02/04/2023]
Abstract
BACKGROUND With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly. METHODS In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification. RESULTS Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well. CONCLUSION The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.
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Affiliation(s)
- Adila Nazli
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, P. R. China
| | - Jingyi Qiu
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, 266 Fangzheng Avenue, Chongqing, 400714, P. R. China
| | - Ziyi Tang
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, 266 Fangzheng Avenue, Chongqing, 400714, P. R. China
| | - Yun He
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, P. R. China
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19
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Duke SO, Pan Z, Bajsa-Hirschel J, Tamang P, Hammerschmidt R, Lorsbach BA, Sparks TC. Molecular Targets of Herbicides and Fungicides─Are There Useful Overlaps for Fungicide Discovery? JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:20532-20548. [PMID: 38100716 PMCID: PMC10755756 DOI: 10.1021/acs.jafc.3c07166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 12/17/2023]
Abstract
New fungicide modes of action are needed for fungicide resistance management strategies. Several commercial herbicide targets found in fungi that are not utilized by commercial fungicides are discussed as possible fungicide molecular targets. These are acetyl CoA carboxylase, acetolactate synthase, 5-enolpyruvylshikimate-3-phosphate synthase, glutamine synthase, phytoene desaturase, protoporphyrinogen oxidase, long-chain fatty acid synthase, dihydropteroate synthase, hydroxyphenyl pyruvate dioxygenase, and Ser/Thr protein phosphatase. Some of the inhibitors of these herbicide targets appear to be either good fungicides or good leads for new fungicides. For example, some acetolactate synthase and dihydropteroate inhibitors are excellent fungicides. There is evidence that some herbicides have indirect benefits to certain crops due to their effects on fungal crop pathogens. Using a pesticide with both herbicide and fungicide activities based on the same molecular target could reduce the total amount of pesticide used. The limitations of such a product are discussed.
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Affiliation(s)
- Stephen O. Duke
- National
Center for Natural Products Research, School of Pharmacy, University of Mississippi, University 38667, United States
| | - Zhiqiang Pan
- Natural
Products Utilization Research Unit, United
States Department of Agriculture, University 38667, United States
| | - Joanna Bajsa-Hirschel
- Natural
Products Utilization Research Unit, United
States Department of Agriculture, University 38667, United States
| | - Prabin Tamang
- Natural
Products Utilization Research Unit, United
States Department of Agriculture, University 38667, United States
| | - Raymond Hammerschmidt
- Department
of Plant, Soil and Microbial Sciences, Michigan
State University, East Lansing, Michigan 48824, United States
| | - Beth A. Lorsbach
- Nufarm, 4020 Aerial Center Parkway, Morrisville, North Carolina 27560, United States
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20
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Ko M, Jung HY, Lee D, Jeon J, Kim J, Baek S, Lee JY, Kim JY, Kwon HJ. Inhibition of chloride intracellular channel protein 1 (CLIC1) ameliorates liver fibrosis phenotype by activating the Ca 2+-dependent Nrf2 pathway. Biomed Pharmacother 2023; 168:115776. [PMID: 37924785 DOI: 10.1016/j.biopha.2023.115776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 10/16/2023] [Accepted: 10/20/2023] [Indexed: 11/06/2023] Open
Abstract
Persistent damage to liver cells leads to liver fibrosis, which is characterized by the accumulation of scar tissue in the liver, ultimately leading to cirrhosis and serious complications. Because it is difficult to reverse cirrhosis once it has progressed, the primary focus has been on preventing the progression of liver fibrosis. However, studies on therapeutic agents for liver fibrosis are still lacking. Here, we investigated that the natural dipeptide cyclic histidine-proline (CHP, also known as diketopiperazine) shows promising potential as a therapeutic agent in models of liver injury by inhibiting the progression of fibrosis through activation of the Nrf2 pathway. To elucidate the underlying biological mechanism of CHP, we used the Cellular Thermal Shift Assay (CETSA)-LC-MS/MS, a label-free compound-based target identification platform. Chloride intracellular channel protein 1 (CLIC1) was identified as a target whose thermal stability is increased by CHP treatment. We analyzed the direct interaction of CHP with CLIC1 which revealed a potential interaction between CHP and the E228 residue of CLIC1. Biological validation experiments showed that knockdown of CLIC1 mimicked the antioxidant effect of CHP. Further investigation using a mouse model of CCl4-induced liver fibrosis in wild-type and CLIC1 KO mice revealed the critical involvement of CLIC1 in mediating the effects of CHP. Taken together, our results provide evidence that CHP exerts its anti-fibrotic effects through specific binding to CLIC1. These insights into the mechanism of action of CHP may pave the way for the development of novel therapeutic strategies for fibrosis-related diseases.
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Affiliation(s)
- Minjeong Ko
- Chemical Genomics Leader Research Lab, Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Hoe-Yune Jung
- R&D Center, NovMetaPharma Co., Ltd., Pohang 37668, Republic of Korea; School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
| | - Dohyun Lee
- R&D Center, NovMetaPharma Co., Ltd., Pohang 37668, Republic of Korea
| | - Jongsu Jeon
- R&D Center, NovMetaPharma Co., Ltd., Pohang 37668, Republic of Korea
| | - Jiho Kim
- Chemical Genomics Leader Research Lab, Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Seoyeong Baek
- R&D Center, NovMetaPharma Co., Ltd., Pohang 37668, Republic of Korea
| | - Ju Yeon Lee
- Research Center of Bioconvergence Analysis, Korea Basic Science Institute, Ochang 28119, Republic of Korea; Critical Diseases Diagnostics Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Jin Young Kim
- Research Center of Bioconvergence Analysis, Korea Basic Science Institute, Ochang 28119, Republic of Korea; Critical Diseases Diagnostics Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Ho Jeong Kwon
- Chemical Genomics Leader Research Lab, Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea.
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21
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Tian Y, Wan N, Zhang H, Shao C, Ding M, Bao Q, Hu H, Sun H, Liu C, Zhou K, Chen S, Wang G, Ye H, Hao H. Chemoproteomic mapping of the glycolytic targetome in cancer cells. Nat Chem Biol 2023; 19:1480-1491. [PMID: 37322158 DOI: 10.1038/s41589-023-01355-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 05/04/2023] [Indexed: 06/17/2023]
Abstract
Hyperactivated glycolysis is a metabolic hallmark of most cancer cells. Although sporadic information has revealed that glycolytic metabolites possess nonmetabolic functions as signaling molecules, how these metabolites interact with and functionally regulate their binding targets remains largely elusive. Here, we introduce a target-responsive accessibility profiling (TRAP) approach that measures changes in ligand binding-induced accessibility for target identification by globally labeling reactive proteinaceous lysines. With TRAP, we mapped 913 responsive target candidates and 2,487 interactions for 10 major glycolytic metabolites in a model cancer cell line. The wide targetome depicted by TRAP unveils diverse regulatory modalities of glycolytic metabolites, and these modalities involve direct perturbation of enzymes in carbohydrate metabolism, intervention of an orphan transcriptional protein's activity and modulation of targetome-level acetylation. These results further our knowledge of how glycolysis orchestrates signaling pathways in cancer cells to support their survival, and inspire exploitation of the glycolytic targetome for cancer therapy.
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Affiliation(s)
- Yang Tian
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Ning Wan
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Hanqing Zhang
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Chang Shao
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Ming Ding
- School of Life Science and Technology, China Pharmaceutical University, Nanjing, China
| | - Qiuyu Bao
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Haiyang Hu
- School of Life Science and Technology, China Pharmaceutical University, Nanjing, China
| | - Huiyong Sun
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Chenguang Liu
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Kun Zhou
- State Key Laboratory of Pharmaceutical Biotechnology, Department of Cardiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Model Animal Research Center, Nanjing University, Nanjing, China
| | - Shuai Chen
- State Key Laboratory of Pharmaceutical Biotechnology, Department of Cardiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Model Animal Research Center, Nanjing University, Nanjing, China
| | - Guangji Wang
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Hui Ye
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China.
| | - Haiping Hao
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China.
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22
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Burton NR, Polasky DA, Shikwana F, Ofori S, Yan T, Geiszler DJ, Veiga Leprevost FD, Nesvizhskii AI, Backus KM. Solid-Phase Compatible Silane-Based Cleavable Linker Enables Custom Isobaric Quantitative Chemoproteomics. J Am Chem Soc 2023; 145:21303-21318. [PMID: 37738129 DOI: 10.1021/jacs.3c05797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/24/2023]
Abstract
Mass spectrometry-based chemoproteomics has emerged as an enabling technology for functional biology and drug discovery. To address limitations of established chemoproteomics workflows, including cumbersome reagent synthesis and low throughput sample preparation, here, we established the silane-based cleavable isotopically labeled proteomics (sCIP) method. The sCIP method is enabled by a high yielding and scalable route to dialkoxydiphenylsilane fluorenylmethyloxycarbonyl (DADPS-Fmoc)-protected amino acid building blocks, which enable the facile synthesis of customizable, isotopically labeled, and chemically cleavable biotin capture reagents. sCIP is compatible with both MS1- and MS2-based quantitation, and the sCIP-MS2 method is distinguished by its click-assembled isobaric tags in which the reporter group is encoded in the sCIP capture reagent and balancer in the pan cysteine-reactive probe. The sCIP-MS2 workflow streamlines sample preparation with early stage isobaric labeling and sample pooling, allowing for high coverage and increased sample throughput via customized low cost six-plex sample multiplexing. When paired with a custom FragPipe data analysis workflow and applied to cysteine-reactive fragment screens, sCIP proteomics revealed established and unprecedented cysteine-ligand pairs, including the discovery that mitochondrial uncoupling agent FCCP acts as a covalent-reversible cysteine-reactive electrophile.
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Affiliation(s)
- Nikolas R Burton
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, United States
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Daniel A Polasky
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Flowreen Shikwana
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, United States
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Samuel Ofori
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Tianyang Yan
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, United States
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Daniel J Geiszler
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, United States
| | | | - Alexey I Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Keriann M Backus
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, United States
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095, United States
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, California 90095, United States
- DOE Institute for Genomics and Proteomics, University of California, Los Angeles, Los Angeles, California 90095, United States
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, California 90095, United States
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, California 90095, United States
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23
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Ruwolt M, Piazza I, Liu F. The potential of cross-linking mass spectrometry in the development of protein-protein interaction modulators. Curr Opin Struct Biol 2023; 82:102648. [PMID: 37423038 DOI: 10.1016/j.sbi.2023.102648] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/03/2023] [Accepted: 06/08/2023] [Indexed: 07/11/2023]
Abstract
Cross-linking mass spectrometry (XL-MS) can provide a wealth of information on endogenous protein-protein interaction (PPI) networks and protein binding interfaces. These features make XL-MS an attractive tool to support the development of PPI-targeting drugs. Though not yet widely used, applications of XL-MS to drug characterization are beginning to emerge. Here, we compare XL-MS to established structural proteomics methods in drug research, discuss the current state and remaining challenges of XL-MS technology, and provide a perspective on the future role XL-MS can play in drug development, with a particular emphasis on PPI modulators.
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Affiliation(s)
- Max Ruwolt
- Department of Structural Biology, Leibniz, Forschungsinstitut für Molekulare Pharmakologie (FMP), Robert-Roessle-Str. 10, 13125 Berlin, Germany. https://twitter.com/@MRuwolt
| | - Ilaria Piazza
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC Berlin), Berlin, Germany.
| | - Fan Liu
- Department of Structural Biology, Leibniz, Forschungsinstitut für Molekulare Pharmakologie (FMP), Robert-Roessle-Str. 10, 13125 Berlin, Germany; Charité - Universitätsmedizin Berlin, Charitépl. 1, 10117 Berlin, Germany.
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24
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Zhang X, Ruan C, Wang Y, Wang K, Liu X, Lyu J, Ye M. Integrated Protein Solubility Shift Assays for Comprehensive Drug Target Identification on a Proteome-Wide Scale. Anal Chem 2023; 95:13779-13787. [PMID: 37676971 DOI: 10.1021/acs.analchem.3c00072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Target proteins are often stabilized after binding with a ligand and thereby typically become more resistant to denaturation. Based on this phenomenon, several methods without the need to covalently modify the ligand have been developed to identify target proteins for a specific ligand. These methods usually employ complicated workflows with high cost and limited throughput. Here, we develop an iso-pH shift assay (ipHSA) method, a proteome-wide target identification method that detects ligand-induced protein solubility shifts by precipitating proteins with a single concentration of acidic agent followed by protein quantification via data-independent acquisition (DIA). Using a pan-kinase inhibitor, staurosporine, we demonstrated that ipHSA increased throughput compared to the previously developed pH-dependent protein precipitation (pHDPP) method. ipHSA was found to have high complementarity in staurosporine target identification compared with the improved isothermal shift assay (iTSA) and isosolvent shift assay (iSSA) using DIA instead of tandem mass tags (TMTs) for quantification. To further improve target identification sensitivity, we developed an integrated protein solubility shift assay (IPSSA) by pooling the supernatants yielded from ipHSA, iTSA, and iSSA methods. IPSSA exhibited increased sensitivity in screening staurosporine targets by 38, 29, and 38% compared to individual methods. Increasing the number of replicate experiments further enhanced the sensitivity of target identification. Meanwhile, IPSSA also improved the throughput and reduced the cost compared with previous methods. As a fast and efficient tool for drug target identification, IPSSA is expected to have broad applications in the study of the mechanism of action.
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Affiliation(s)
- Xiaolei Zhang
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Chengfei Ruan
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yan Wang
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Keyun Wang
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Xiaoyan Liu
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiawen Lyu
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mingliang Ye
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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25
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George AL, Sidgwick FR, Watt JE, Martin MP, Trost M, Marín-Rubio JL, Dueñas ME. Comparison of Quantitative Mass Spectrometric Methods for Drug Target Identification by Thermal Proteome Profiling. J Proteome Res 2023; 22:2629-2640. [PMID: 37439223 PMCID: PMC10407934 DOI: 10.1021/acs.jproteome.3c00111] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Indexed: 07/14/2023]
Abstract
Thermal proteome profiling (TPP) provides a powerful approach to studying proteome-wide interactions of small therapeutic molecules and their target and off-target proteins, complementing phenotypic-based drug screens. Detecting differences in thermal stability due to target engagement requires high quantitative accuracy and consistent detection. Isobaric tandem mass tags (TMTs) are used to multiplex samples and increase quantification precision in TPP analysis by data-dependent acquisition (DDA). However, advances in data-independent acquisition (DIA) can provide higher sensitivity and protein coverage with reduced costs and sample preparation steps. Herein, we explored the performance of different DIA-based label-free quantification approaches compared to TMT-DDA for thermal shift quantitation. Acute myeloid leukemia cells were treated with losmapimod, a known inhibitor of MAPK14 (p38α). Label-free DIA approaches, and particularly the library-free mode in DIA-NN, were comparable of TMT-DDA in their ability to detect target engagement of losmapimod with MAPK14 and one of its downstream targets, MAPKAPK3. Using DIA for thermal shift quantitation is a cost-effective alternative to labeled quantitation in the TPP pipeline.
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Affiliation(s)
- Amy L. George
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
| | - Frances R. Sidgwick
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
| | - Jessica E. Watt
- Newcastle
Cancer Centre, Northern Institute for Cancer Research, Medical School, Newcastle University, Paul O’Gorman Building, Framlington Place, Newcastle upon Tyne NE2 4HH, U.K.
| | - Mathew P. Martin
- Newcastle
Cancer Centre, Northern Institute for Cancer Research, Medical School, Newcastle University, Paul O’Gorman Building, Framlington Place, Newcastle upon Tyne NE2 4HH, U.K.
| | - Matthias Trost
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
| | - José Luis Marín-Rubio
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
| | - Maria Emilia Dueñas
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
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26
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Huth SW, Oakley JV, Seath CP, Geri JB, Trowbridge AD, Parker DL, Rodriguez-Rivera FP, Schwaid AG, Ramil C, Ryu KA, White CH, Fadeyi OO, Oslund RC, MacMillan DWC. μMap Photoproximity Labeling Enables Small Molecule Binding Site Mapping. J Am Chem Soc 2023; 145:16289-16296. [PMID: 37471577 PMCID: PMC10809032 DOI: 10.1021/jacs.3c03325] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
The characterization of ligand binding modes is a crucial step in the drug discovery process and is especially important in campaigns arising from phenotypic screening, where the protein target and binding mode are unknown at the outset. Elucidation of target binding regions is typically achieved by X-ray crystallography or photoaffinity labeling (PAL) approaches; yet, these methods present significant challenges. X-ray crystallography is a mainstay technique that has revolutionized drug discovery, but in many cases structural characterization is challenging or impossible. PAL has also enabled binding site mapping with peptide- and amino-acid-level resolution; however, the stoichiometric activation mode can lead to poor signal and coverage of the resident binding pocket. Additionally, each PAL probe can have its own fragmentation pattern, complicating the analysis by mass spectrometry. Here, we establish a robust and general photocatalytic approach toward the mapping of protein binding sites, which we define as identification of residues proximal to the ligand binding pocket. By utilizing a catalytic mode of activation, we obtain sets of labeled amino acids in the proximity of the target protein binding site. We use this methodology to map, in vitro, the binding sites of six protein targets, including several kinases and molecular glue targets, and furthermore to investigate the binding site of the STAT3 inhibitor MM-206, a ligand with no known crystal structure. Finally, we demonstrate the successful mapping of drug binding sites in live cells. These results establish μMap as a powerful method for the generation of amino-acid- and peptide-level target engagement data.
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Affiliation(s)
- Sean W. Huth
- Merck Center for Catalysis at Princeton University, Princeton, New Jersey 08544, United States
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - James V. Oakley
- Merck Center for Catalysis at Princeton University, Princeton, New Jersey 08544, United States
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Ciaran P. Seath
- Merck Center for Catalysis at Princeton University, Princeton, New Jersey 08544, United States
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Jacob B. Geri
- Merck Center for Catalysis at Princeton University, Princeton, New Jersey 08544, United States
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Aaron D. Trowbridge
- Merck Center for Catalysis at Princeton University, Princeton, New Jersey 08544, United States
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Dann L. Parker
- Discovery Chemistry, Merck & Co., Inc., Kenilworth, New Jersey 07033, United States
| | | | - Adam G. Schwaid
- Discovery Chemistry, Merck & Co., Inc., Cambridge, Massachusetts 02141, United States
| | - Carlo Ramil
- Discovery Chemistry, Merck & Co., Inc., Cambridge, Massachusetts 02141, United States
| | - Keun Ah Ryu
- Merck Exploratory Science Center, Merck & Co., Inc., Cambridge, Massachusetts 02141, United States
| | - Cory H. White
- Merck Exploratory Science Center, Merck & Co., Inc., Cambridge, Massachusetts 02141, United States
| | - Olugbeminiyi O. Fadeyi
- Merck Exploratory Science Center, Merck & Co., Inc., Cambridge, Massachusetts 02141, United States
| | - Rob C. Oslund
- Merck Exploratory Science Center, Merck & Co., Inc., Cambridge, Massachusetts 02141, United States
| | - David W. C. MacMillan
- Merck Center for Catalysis at Princeton University, Princeton, New Jersey 08544, United States
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
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27
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Noel D, Hallsworth JE, Gelhaye E, Darnet S, Sormani R, Morel-Rouhier M. Modes-of-action of antifungal compounds: Stressors and (target-site-specific) toxins, toxicants, or Toxin-stressors. Microb Biotechnol 2023. [PMID: 37191200 DOI: 10.1111/1751-7915.14242] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/11/2023] [Accepted: 02/16/2023] [Indexed: 05/17/2023] Open
Abstract
Fungi and antifungal compounds are relevant to the United Nation's Sustainable Development Goals. However, the modes-of-action of antifungals-whether they are naturally occurring substances or anthropogenic fungicides-are often unknown or are misallocated in terms of their mechanistic category. Here, we consider the most effective approaches to identifying whether antifungal substances are cellular stressors, toxins/toxicants (that are target-site-specific), or have a hybrid mode-of-action as Toxin-stressors (that induce cellular stress yet are target-site-specific). This newly described 'toxin-stressor' category includes some photosensitisers that target the cell membrane and, once activated by light or ultraviolet radiation, cause oxidative damage. We provide a glossary of terms and a diagrammatic representation of diverse types of stressors, toxic substances, and Toxin-stressors, a classification that is pertinent to inhibitory substances not only for fungi but for all types of cellular life. A decision-tree approach can also be used to help differentiate toxic substances from cellular stressors (Curr Opin Biotechnol 2015 33: 228-259). For compounds that target specific sites in the cell, we evaluate the relative merits of using metabolite analyses, chemical genetics, chemoproteomics, transcriptomics, and the target-based drug-discovery approach (based on that used in pharmaceutical research), focusing on both ascomycete models and the less-studied basidiomycete fungi. Chemical genetic methods to elucidate modes-of-action currently have limited application for fungi where molecular tools are not yet available; we discuss ways to circumvent this bottleneck. We also discuss ecologically commonplace scenarios in which multiple substances act to limit the functionality of the fungal cell and a number of as-yet-unresolved questions about the modes-of-action of antifungal compounds pertaining to the Sustainable Development Goals.
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Affiliation(s)
| | - John E Hallsworth
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, UK
| | - Eric Gelhaye
- Université de Lorraine, INRAE, IAM, Nancy, France
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28
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Gautam P, Sinha SK. Theoretical investigation of functional responses of bio-molecular assembly networks. SOFT MATTER 2023; 19:3803-3817. [PMID: 37191191 DOI: 10.1039/d2sm01530g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Cooperative protein-protein and protein-DNA interactions form programmable complex assemblies, often performing non-linear gene regulatory operations involved in signal transductions and cell fate determination. The apparent structure of those complex assemblies is very similar, but their functional response strongly depends on the topology of the protein-DNA interaction networks. Here, we demonstrate how the coordinated self-assembly creates gene regulatory network motifs that corroborate the existence of a precise functional response at the molecular level using thermodynamic and dynamic analyses. Our theoretical and Monte Carlo simulations show that a complex network of interactions can form a decision-making loop, such as feedback and feed-forward circuits, only by a few molecular mechanisms. We characterize each possible network of interactions by systematic variations of free energy parameters associated with the binding among biomolecules and DNA looping. We also find that the higher-order networks exhibit alternative steady states from the stochastic dynamics of each network. We capture this signature by calculating stochastic potentials and attributing their multi-stability features. We validate our findings against the Gal promoter system in yeast cells. Overall, we show that the network topology is vital in phenotype diversity in regulatory circuits.
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Affiliation(s)
- Pankaj Gautam
- Theoretical and Computational Biophysical Chemistry Group, Department of Chemistry, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India.
| | - Sudipta Kumar Sinha
- Theoretical and Computational Biophysical Chemistry Group, Department of Chemistry, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India.
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29
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Barret D, Schuster D, Rodrigues M, Leitner A, Picotti P, Schertler G, Kaupp U, Korkhov V, Marino J. Structural basis of calmodulin modulation of the rod cyclic nucleotide-gated channel. Proc Natl Acad Sci U S A 2023; 120:e2300309120. [PMID: 37011209 PMCID: PMC10104587 DOI: 10.1073/pnas.2300309120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 03/06/2023] [Indexed: 04/05/2023] Open
Abstract
Calmodulin (CaM) regulates many ion channels to control calcium entry into cells, and mutations that alter this interaction are linked to fatal diseases. The structural basis of CaM regulation remains largely unexplored. In retinal photoreceptors, CaM binds to the CNGB subunit of cyclic nucleotide-gated (CNG) channels and, thereby, adjusts the channel's Cyclic guanosine monophosphate (cGMP) sensitivity in response to changes in ambient light conditions. Here, we provide the structural characterization for CaM regulation of a CNG channel by using a combination of single-particle cryo-electron microscopy and structural proteomics. CaM connects the CNGA and CNGB subunits, resulting in structural changes both in the cytosolic and transmembrane regions of the channel. Cross-linking and limited proteolysis-coupled mass spectrometry mapped the conformational changes induced by CaM in vitro and in the native membrane. We propose that CaM is a constitutive subunit of the rod channel to ensure high sensitivity in dim light. Our mass spectrometry-based approach is generally relevant for studying the effect of CaM on ion channels in tissues of medical interest, where only minute quantities are available.
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Affiliation(s)
- Diane C. A. Barret
- Laboratory of Biomolecular Research, Paul Scherrer Institute, 5232Villigen, Switzerland
| | - Dina Schuster
- Laboratory of Biomolecular Research, Paul Scherrer Institute, 5232Villigen, Switzerland
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, 8049Zürich, Switzerland
- Institute of Molecular Biology and Biophysics, ETH Zürich, 8049Zurich, Switzerland
| | - Matthew J. Rodrigues
- Laboratory of Biomolecular Research, Paul Scherrer Institute, 5232Villigen, Switzerland
| | - Alexander Leitner
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, 8049Zürich, Switzerland
| | - Paola Picotti
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, 8049Zürich, Switzerland
| | | | - U. Benjamin Kaupp
- Life and Medical Sciences Institute, University of Bonn, 53115Bonn, Germany
- Max Planck Institute for Multidisciplinary Sciences, 37077Göttingen, Germany
| | - Volodymyr M. Korkhov
- Laboratory of Biomolecular Research, Paul Scherrer Institute, 5232Villigen, Switzerland
- Institute of Molecular Biology and Biophysics, ETH Zürich, 8049Zurich, Switzerland
| | - Jacopo Marino
- Laboratory of Biomolecular Research, Paul Scherrer Institute, 5232Villigen, Switzerland
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30
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Trudeau SJ, Hwang H, Mathur D, Begum K, Petrey D, Murray D, Honig B. PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions. Protein Sci 2023; 32:e4594. [PMID: 36776141 PMCID: PMC10019447 DOI: 10.1002/pro.4594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 02/07/2023] [Accepted: 02/09/2023] [Indexed: 02/14/2023]
Abstract
We describe the Predicting Protein-Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between 6.8 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence- and structural similarity-based metrics are established between template proteins, T, in the Protein Data Bank that bind compounds, C, and query proteins in the model database, Q. When the metrics exceed threshold values, it is assumed that C also binds to Q with a likelihood ratio (LR) derived from machine learning. If the relationship is based on structural similarity, the LR is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto coefficient identifies other small molecules that may bind to Q. An overall LR for the binding of C to Q is obtained from Naive Bayesian statistics. The PrePCI database can be queried by entering a UniProt ID or gene name for a protein to obtain a list of compounds predicted to bind to it along with associated LRs. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database to lead discovery, elucidation of drug mechanism of action, and biological function annotation are described.
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Affiliation(s)
- Stephen J. Trudeau
- Department of Systems BiologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
- Integrated Graduate Program in Cellular, Molecular and Biomedical Studies (CMBS), Columbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Howook Hwang
- Department of Systems BiologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
- Schrodinger, Inc.New YorkNew YorkUSA
| | - Deepika Mathur
- Department of Systems BiologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Kamrun Begum
- Department of Systems BiologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Donald Petrey
- Department of Systems BiologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Diana Murray
- Department of Systems BiologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Barry Honig
- Department of Systems BiologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
- Department of Biochemistry and Molecular BiophysicsColumbia University Irving Medical CenterNew YorkNew YorkUSA
- Department of MedicineColumbia UniversityNew YorkNew YorkUSA
- Zuckerman Mind Brain and Behavior InstituteColumbia UniversityNew YorkNew YorkUSA
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31
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Christofi E, Barran P. Ion Mobility Mass Spectrometry (IM-MS) for Structural Biology: Insights Gained by Measuring Mass, Charge, and Collision Cross Section. Chem Rev 2023; 123:2902-2949. [PMID: 36827511 PMCID: PMC10037255 DOI: 10.1021/acs.chemrev.2c00600] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Abstract
The investigation of macromolecular biomolecules with ion mobility mass spectrometry (IM-MS) techniques has provided substantial insights into the field of structural biology over the past two decades. An IM-MS workflow applied to a given target analyte provides mass, charge, and conformation, and all three of these can be used to discern structural information. While mass and charge are determined in mass spectrometry (MS), it is the addition of ion mobility that enables the separation of isomeric and isobaric ions and the direct elucidation of conformation, which has reaped huge benefits for structural biology. In this review, where we focus on the analysis of proteins and their complexes, we outline the typical features of an IM-MS experiment from the preparation of samples, the creation of ions, and their separation in different mobility and mass spectrometers. We describe the interpretation of ion mobility data in terms of protein conformation and how the data can be compared with data from other sources with the use of computational tools. The benefit of coupling mobility analysis to activation via collisions with gas or surfaces or photons photoactivation is detailed with reference to recent examples. And finally, we focus on insights afforded by IM-MS experiments when applied to the study of conformationally dynamic and intrinsically disordered proteins.
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Affiliation(s)
- Emilia Christofi
- Michael Barber Centre for Collaborative Mass Spectrometry, Manchester Institute of Biotechnology, University of Manchester, Princess Street, Manchester M1 7DN, United Kingdom
| | - Perdita Barran
- Michael Barber Centre for Collaborative Mass Spectrometry, Manchester Institute of Biotechnology, University of Manchester, Princess Street, Manchester M1 7DN, United Kingdom
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32
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Sun B, Liu Z, Liu J, Zhao S, Wang L, Wang F. The utility of proteases in proteomics, from sequence profiling to structure and function analysis. Proteomics 2023; 23:e2200132. [PMID: 36382392 DOI: 10.1002/pmic.202200132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 11/08/2022] [Accepted: 11/08/2022] [Indexed: 11/18/2022]
Abstract
In mass spectrometry (MS)-based bottom-up proteomics, protease digestion plays an essential role in profiling both proteome sequences and post-translational modifications (PTMs). Trypsin is the gold standard in digesting intact proteins into small-size peptides, which are more suitable for high-performance liquid chromatography (HPLC) separation and tandem MS (MS/MS) characterization. However, protein sequences lacking Lys and Arg cannot be cleaved by trypsin and may be missed in conventional proteomic analysis. Proteases with cleavage sites complementary to trypsin are widely applied in proteomic analysis to greatly improve the coverage of proteome sequences and PTM sites. In this review, we survey the common and newly emerging proteases used in proteomics analysis mainly in the last 5 years, focusing on their unique cleavage features and specific proteomics applications such as missing protein characterization, new PTM discovery, and de novo sequencing. In addition, we summarize the applications of proteases in structural proteomics and protein function analysis in recent years. Finally, we discuss the future development directions of new proteases and applications in proteomics.
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Affiliation(s)
- Binwen Sun
- Engineering Research Center for New Materials and Precision Treatment Technology of Malignant Tumors Therapy, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 463 Zhongshan Road, Dalian, 116023, China
- Engineering Technology Research Center for Translational Medicine, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
| | - Zheyi Liu
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 463 Zhongshan Road, Dalian, 116023, China
| | - Jin Liu
- Engineering Research Center for New Materials and Precision Treatment Technology of Malignant Tumors Therapy, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
- Engineering Technology Research Center for Translational Medicine, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgery, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
| | - Shan Zhao
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 463 Zhongshan Road, Dalian, 116023, China
| | - Liming Wang
- Engineering Research Center for New Materials and Precision Treatment Technology of Malignant Tumors Therapy, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
- Engineering Technology Research Center for Translational Medicine, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgery, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
| | - Fangjun Wang
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 463 Zhongshan Road, Dalian, 116023, China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing, 100049, China
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33
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Malinovska L, Cappelletti V, Kohler D, Piazza I, Tsai TH, Pepelnjak M, Stalder P, Dörig C, Sesterhenn F, Elsässer F, Kralickova L, Beaton N, Reiter L, de Souza N, Vitek O, Picotti P. Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: an advanced protocol for high-throughput applications. Nat Protoc 2023; 18:659-682. [PMID: 36526727 DOI: 10.1038/s41596-022-00771-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 07/08/2022] [Indexed: 12/23/2022]
Abstract
Proteins regulate biological processes by changing their structure or abundance to accomplish a specific function. In response to a perturbation, protein structure may be altered by various molecular events, such as post-translational modifications, protein-protein interactions, aggregation, allostery or binding to other molecules. The ability to probe these structural changes in thousands of proteins simultaneously in cells or tissues can provide valuable information about the functional state of biological processes and pathways. Here, we present an updated protocol for LiP-MS, a proteomics technique combining limited proteolysis with mass spectrometry, to detect protein structural alterations in complex backgrounds and on a proteome-wide scale. In LiP-MS, proteins undergo a brief proteolysis in native conditions followed by complete digestion in denaturing conditions, to generate structurally informative proteolytic fragments that are analyzed by mass spectrometry. We describe advances in the throughput and robustness of the LiP-MS workflow and implementation of data-independent acquisition-based mass spectrometry, which together achieve high reproducibility and sensitivity, even on large sample sizes. We introduce MSstatsLiP, an R package dedicated to the analysis of LiP-MS data for the identification of structurally altered peptides and differentially abundant proteins. The experimental procedures take 3 d, mass spectrometric measurement time and data processing depend on sample number and statistical analysis typically requires ~1 d. These improvements expand the adaptability of LiP-MS and enable wide use in functional proteomics and translational applications.
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Affiliation(s)
- Liliana Malinovska
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Valentina Cappelletti
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Devon Kohler
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Ilaria Piazza
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC Berlin), Berlin, Germany
| | - Tsung-Heng Tsai
- Department of Mathematical Sciences, Kent State University, Kent, OH, USA
| | - Monika Pepelnjak
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Patrick Stalder
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Christian Dörig
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Fabian Sesterhenn
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Franziska Elsässer
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Lucie Kralickova
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | | | | | - Natalie de Souza
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.
| | - Paola Picotti
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland.
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34
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Kurbatov I, Dolgalev G, Arzumanian V, Kiseleva O, Poverennaya E. The Knowns and Unknowns in Protein-Metabolite Interactions. Int J Mol Sci 2023; 24:ijms24044155. [PMID: 36835565 PMCID: PMC9964805 DOI: 10.3390/ijms24044155] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 02/11/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Increasing attention has been focused on the study of protein-metabolite interactions (PMI), which play a key role in regulating protein functions and directing an orchestra of cellular processes. The investigation of PMIs is complicated by the fact that many such interactions are extremely short-lived, which requires very high resolution in order to detect them. As in the case of protein-protein interactions, protein-metabolite interactions are still not clearly defined. Existing assays for detecting protein-metabolite interactions have an additional limitation in the form of a limited capacity to identify interacting metabolites. Thus, although recent advances in mass spectrometry allow the routine identification and quantification of thousands of proteins and metabolites today, they still need to be improved to provide a complete inventory of biological molecules, as well as all interactions between them. Multiomic studies aimed at deciphering the implementation of genetic information often end with the analysis of changes in metabolic pathways, as they constitute one of the most informative phenotypic layers. In this approach, the quantity and quality of knowledge about PMIs become vital to establishing the full scope of crosstalk between the proteome and the metabolome in a biological object of interest. In this review, we analyze the current state of investigation into the detection and annotation of protein-metabolite interactions, describe the recent progress in developing associated research methods, and attempt to deconstruct the very term "interaction" to advance the field of interactomics further.
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35
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Schuhmacher M, Hoogendoorn S. Out With a Bang: Celebrating Global Chemical Biology. ACS Chem Biol 2023; 18:218-222. [PMID: 36648442 DOI: 10.1021/acschembio.2c00905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
On November 8-10, 2022, 163 participants from all over the world gathered at the Campus Biotech in Geneva, Switzerland to share in the latest research in chemical biology. The fourth international symposium of the Swiss National Centres of Competence in Research (NCCR) Chemical Biology coincided with the end of this successful research consortium, and as such this event marked a celebration of the past 12 years of chemical biology research in Switzerland. The inspiring talks delivered by the 15 well-known scientists, balanced in gender, expertise, and geographic location, as well as the numerous poster presentations by junior scientists showcased the breadth of global chemical biology and the bright future ahead.
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Affiliation(s)
- Milena Schuhmacher
- Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Sascha Hoogendoorn
- Department of Organic Chemistry, Faculty of Sciences, University of Geneva, 1205 Geneva, Switzerland
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36
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Sun H, Yang K, Zhang X, Fu Y, Yarbro J, Wu Z, Chen PC, Chen T, Peng J. Evaluation of a Pooling Chemoproteomics Strategy with an FDA-Approved Drug Library. Biochemistry 2023; 62:624-632. [PMID: 35969671 PMCID: PMC9905291 DOI: 10.1021/acs.biochem.2c00256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Chemoproteomics is a key platform for characterizing the mode of action for compounds, especially for targeted protein degraders such as proteolysis targeting chimeras (PROTACs) and molecular glues. With deep proteome coverage, multiplexed tandem mass tag-mass spectrometry (TMT-MS) can tackle up to 18 samples in a single experiment. Here, we present a pooling strategy for further enhancing the throughput and apply the strategy to an FDA-approved drug library (95 best-in-class compounds). The TMT-MS-based pooling strategy was evaluated in the following steps. First, we demonstrated the capability of TMT-MS by analyzing more than 15 000 unique proteins (> 12 000 gene products) in HEK293 cells treated with five PROTACs (two BRD/BET degraders and three degraders for FAK, ALK, and BTK kinases). We then introduced a rationalized pooling strategy to separate structurally similar compounds in different pools and identified the proteomic response to 14 pools from the drug library. Finally, we validated the proteomic response from one pool by reprofiling the cells via treatment with individual drugs with sufficient replicates. Interestingly, numerous proteins were found to change upon drug treatment, including AMD1, ODC1, PRKX, PRKY, EXO1, AEN, and LRRC58 with 7-hydroxystaurosporine; C6orf64, HMGCR, and RRM2 with Sorafenib; SYS1 and ALAS1 with Venetoclax; and ATF3, CLK1, and CLK4 with Palbocilib. Thus, pooling chemoproteomics screening provides an efficient method for dissecting the molecular targets of compound libraries.
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Affiliation(s)
- Huan Sun
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA, Equal Contribution
| | - Ka Yang
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA, Equal Contribution
| | - Xue Zhang
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Yingxue Fu
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Jay Yarbro
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Zhiping Wu
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Ping-Chung Chen
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Taosheng Chen
- Chemical Biology & Therapeutics Department, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Junmin Peng
- Departments of Structural Biology and Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA, Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA,Correspondence:
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37
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Franchini L, Orlandi C. Probing the orphan receptors: Tools and directions. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2023; 195:47-76. [PMID: 36707155 DOI: 10.1016/bs.pmbts.2022.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The endogenous ligands activating a large fraction of the G Protein Coupled Receptor (GPCR) family members have yet to be identified. These receptors are commonly labeled as orphans (oGPCRs), and because of the absence of available pharmacological tools they are currently understudied. Nonetheless, genome wide association studies, together with research using animal models identified many physiological functions regulated by oGPCRs. Similarly, mutations in some oGPCRs have been associated with rare genetic disorders or with an increased risk of developing pathologies. The once underestimated pharmacological potential of targeting oGPCRs is increasingly being exploited by the development of novel tools to understand their biology and by drug discovery endeavors aimed at identifying new modulators of their activity. Here, we summarize recent advancements in the field of oGPCRs and future directions.
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Affiliation(s)
- Luca Franchini
- Department of Pharmacology and Physiology, University of Rochester Medical Center, Rochester, NY, United States
| | - Cesare Orlandi
- Department of Pharmacology and Physiology, University of Rochester Medical Center, Rochester, NY, United States.
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38
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Duran-Frigola M, Cigler M, Winter GE. Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence. J Am Chem Soc 2023; 145:2711-2732. [PMID: 36706315 PMCID: PMC9912273 DOI: 10.1021/jacs.2c11098] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.
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Affiliation(s)
- Miquel Duran-Frigola
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria,Ersilia
Open Source Initiative, 28 Belgrave Road, CB1 3DE, Cambridge, United Kingdom,
| | - Marko Cigler
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria
| | - Georg E. Winter
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria,
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39
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McNair D. Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond. Annu Rev Pharmacol Toxicol 2023; 63:77-97. [PMID: 35679624 DOI: 10.1146/annurev-pharmtox-051921-023255] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain. Effective AI/ML tools to derisk L2C and later phases of development are important to accelerate the pharmaceutical development process, ameliorate escalating development costs, and achieve greater success rates.
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Affiliation(s)
- Douglas McNair
- Global Health, Integrated Development, Bill & Melinda Gates Foundation, Seattle, Washington, USA;
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40
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Affiliation(s)
- Kenneth M Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
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41
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Feng F, Zhang W, Chai Y, Guo D, Chen X. Label-free target protein characterization for small molecule drugs: recent advances in methods and applications. J Pharm Biomed Anal 2023; 223:115107. [DOI: 10.1016/j.jpba.2022.115107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 10/08/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022]
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42
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Holfeld A, Quast JP, Bruderer R, Reiter L, de Souza N, Picotti P. Limited Proteolysis-Mass Spectrometry to Identify Metabolite-Protein Interactions. Methods Mol Biol 2023; 2554:69-89. [PMID: 36178621 DOI: 10.1007/978-1-0716-2624-5_6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Metabolite-protein interactions regulate diverse cellular processes, prompting the development of methods to investigate the metabolite-protein interactome at a global scale. One such method is our previously developed structural proteomics approach, limited proteolysis-mass spectrometry (LiP-MS), which detects proteome-wide metabolite-protein and drug-protein interactions in native bacterial, yeast, and mammalian systems, and allows identification of binding sites without chemical modification. Here we describe a detailed experimental and analytical workflow for conducting a LiP-MS experiment to detect small molecule-protein interactions, either in a single-dose (LiP-SMap) or a multiple-dose (LiP-Quant) format. LiP-Quant analysis combines the peptide-level resolution of LiP-MS with a machine learning-based framework to prioritize true protein targets of a small molecule of interest. We provide an updated R script for LiP-Quant analysis via a GitHub repository accessible at https://github.com/RolandBruderer/MiMB-LiP-Quant .
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Affiliation(s)
- Aleš Holfeld
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | - Jan-Philipp Quast
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
| | | | | | - Natalie de Souza
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Paola Picotti
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland.
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43
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Reber V, Gstaiger M. Target Deconvolution by Limited Proteolysis Coupled to Mass Spectrometry. Methods Mol Biol 2023; 2706:177-190. [PMID: 37558949 DOI: 10.1007/978-1-0716-3397-7_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
Abstract
Limited proteolysis coupled to mass spectrometry (LiP-MS) is a recent proteomics technique that allows structure-based target engagement profiling on a proteome-wide level. To achieve this, native lysates are first incubated with a compound, followed by a short incubation with a nonspecific protease. Binding of a compound can change accessibility at the binding site or induce other structural changes in the target. This leads to treatment-specific proteolytic fingerprints upon limited proteolysis, which can be analyzed by standard bottom-up MS-based proteomics. Here, we describe a basic LiP-MS protocol using the natural product rapamycin as an example compound. Along with the provided LiP-MS reference data available via ProteomeXchange with identifier PXD035183, this enables the straightforward implementation of the method by scientists with a basic biochemistry and mass spectrometry background. We describe how the procedure can easily be adapted to other protein samples and small molecules.
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Affiliation(s)
- Viviane Reber
- Institute of Molecular Systems Biology at ETH Zurich, Zurich, Switzerland
| | - Matthias Gstaiger
- Institute of Molecular Systems Biology at ETH Zurich, Zurich, Switzerland.
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44
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Patra P, B R D, Kundu P, Das M, Ghosh A. Recent advances in machine learning applications in metabolic engineering. Biotechnol Adv 2023; 62:108069. [PMID: 36442697 DOI: 10.1016/j.biotechadv.2022.108069] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 10/18/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022]
Abstract
Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.
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Affiliation(s)
- Pradipta Patra
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Disha B R
- B.M.S College of Engineering, Basavanagudi, Bengaluru, Karnataka 560019, India
| | - Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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Zhang L, Wang CC, Chen X. Predicting drug-target binding affinity through molecule representation block based on multi-head attention and skip connection. Brief Bioinform 2022; 23:6782838. [PMID: 36411674 DOI: 10.1093/bib/bbac468] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 09/13/2022] [Accepted: 09/29/2022] [Indexed: 11/22/2022] Open
Abstract
Exiting computational models for drug-target binding affinity prediction have much room for improvement in prediction accuracy, robustness and generalization ability. Most deep learning models lack interpretability analysis and few studies provide application examples. Based on these observations, we presented a novel model named Molecule Representation Block-based Drug-Target binding Affinity prediction (MRBDTA). MRBDTA is composed of embedding and positional encoding, molecule representation block and interaction learning module. The advantages of MRBDTA are reflected in three aspects: (i) developing Trans block to extract molecule features through improving the encoder of transformer, (ii) introducing skip connection at encoder level in Trans block and (iii) enhancing the ability to capture interaction sites between proteins and drugs. The test results on two benchmark datasets manifest that MRBDTA achieves the best performance compared with 11 state-of-the-art models. Besides, through replacing Trans block with single Trans encoder and removing skip connection in Trans block, we verified that Trans block and skip connection could effectively improve the prediction accuracy and reliability of MRBDTA. Then, relying on multi-head attention mechanism, we performed interpretability analysis to illustrate that MRBDTA can correctly capture part of interaction sites between proteins and drugs. In case studies, we firstly employed MRBDTA to predict binding affinities between Food and Drug Administration-approved drugs and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication-related proteins. Secondly, we compared true binding affinities between 3C-like proteinase and 185 drugs with those predicted by MRBDTA. The final results of case studies reveal reliable performance of MRBDTA in drug design for SARS-CoV-2.
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Affiliation(s)
- Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.,Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
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Challis MP, Devine SM, Creek DJ. Current and emerging target identification methods for novel antimalarials. Int J Parasitol Drugs Drug Resist 2022; 20:135-144. [PMID: 36410177 PMCID: PMC9771836 DOI: 10.1016/j.ijpddr.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/04/2022] [Accepted: 11/07/2022] [Indexed: 11/13/2022]
Abstract
New antimalarial compounds with novel mechanisms of action are urgently needed to combat the recent rise in antimalarial drug resistance. Phenotypic high-throughput screens have proven to be a successful method for identifying new compounds, however, do not provide mechanistic information about the molecular target(s) responsible for antimalarial action. Current and emerging target identification methods such as in vitro resistance generation, metabolomics screening, chemoproteomic approaches and biophysical assays measuring protein stability across the whole proteome have successfully identified novel drug targets. This review provides an overview of these techniques, comparing their strengths and weaknesses and how they can be utilised for antimalarial target identification.
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Affiliation(s)
- Matthew P. Challis
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, Victoria, 3052, Australia
| | - Shane M. Devine
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, Victoria, 3052, Australia
| | - Darren J. Creek
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, Victoria, 3052, Australia,Corresponding author. Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia.
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Zhang X, Wang K, Wu S, Ruan C, Li K, Wang Y, Zhu H, Liu X, Liu Z, Li G, Hu L, Ye M. Highly effective identification of drug targets at the proteome level by pH-dependent protein precipitation. Chem Sci 2022; 13:12403-12418. [PMID: 36382280 PMCID: PMC9629037 DOI: 10.1039/d2sc03326g] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 09/20/2022] [Indexed: 09/09/2023] Open
Abstract
Fully understanding the target spaces of drugs is essential for investigating the mechanism of drug action and side effects, as well as for drug discovery and repurposing. In this study, we present an energetics-based approach, termed pH-dependent protein precipitation (pHDPP), to probe the ligand-induced protein stability shift for proteome-wide drug target identification. We demonstrate that pHDPP works for a diverse array of ligands, including a folate derivative, an ATP analog, a CDK inhibitor and an immunosuppressant, enabling highly specific identification of target proteins from total cell lysates. This approach is compared to thermal and solvent-induced denaturation approaches with a pan-kinase inhibitor as the model drug, demonstrating its high sensitivity and high complementarity to other approaches. Dihydroartemisinin (DHA), a dominant derivative of artemisinin to treat malaria, is known to have an extraordinary effect on the treatment of various cancers. However, the anti-tumor mechanisms remain unknown. pHDPP was applied to reveal the target space of DHA and 45 potential target proteins were identified. Pathway analysis indicated that these target proteins were mainly involved in metabolism and apoptosis pathways. Two cancer-related target proteins, ALDH7A1 and HMGB1, were validated by structural simulation and AI-based target prediction methods. And they were further validated to have strong affinity to DHA by using cellular thermal shift assay (CETSA). In summary, pHDPP is a powerful tool to construct the target protein space to reveal the mechanism of drug action and would have broad application in drug discovery studies.
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Affiliation(s)
- Xiaolei Zhang
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
- Center for Supramolecular Chemical Biology, State Key Laboratory of Supramolecular Structure and Materials, School of Life Sciences, Jilin University Changchun 130012 China
| | - Keyun Wang
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
| | - Sijin Wu
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
| | - Chengfei Ruan
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
| | - Kejia Li
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
| | - Yan Wang
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
| | - He Zhu
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
| | - Xiaoyan Liu
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
| | - Zhen Liu
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
| | - Guohui Li
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
| | - Lianghai Hu
- Center for Supramolecular Chemical Biology, State Key Laboratory of Supramolecular Structure and Materials, School of Life Sciences, Jilin University Changchun 130012 China
| | - Mingliang Ye
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R & A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
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Yan W, Wang D, Wan N, Wang S, Shao C, Zhang H, Zhao Z, Lu W, Tian Y, Ye H, Hao H. Living Cell-Target Responsive Accessibility Profiling Reveals Silibinin Targeting ACSL4 for Combating Ferroptosis. Anal Chem 2022; 94:14820-14826. [PMID: 36260072 DOI: 10.1021/acs.analchem.2c03515] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We report a living cell-target responsive accessibility profiling (LC-TRAP) approach to identify the targetome of silibinin (SIL), a well-established hepatoprotective natural product (NP), in HepG2 cells. Proteins showing accessibility changes, probed by covalent lysine labeling reagents and leveraged by multiplexed quantitative proteomics, following the administration of SIL to the living cells were assigned as potential targets. Among the assigned targetome, ACSL4, an enzyme essential for ferroptosis induction, might be involved in the hepatoprotective effects of SIL and hence was intensively validated. We first demonstrated that SIL protected HepG2 cells from ferroptosis dependent on ACSL4. Then, we used biophysical assays and a SIL-derivatized chemical probe to corroborate that SIL can bind to ACSL4. The ensuing enzymatic assays showed that SIL inhibited ACSL4 enzymatic activity, thereby mitigating the ACSL4-mediated ferroptosis. As such, we revealed that ACSL4 inhibition, using SIL as a model compound, represents a promising hepatoprotective strategy. Further, since TRAP probes the accessibility changes of reactive proteinaceous lysines, it can pinpoint the proximal regions where the ligand engagement may occur. Thus, the LC-TRAP analysis of SIL, the newly discovered ligand of ACSL4, and arachidonic acid (AA), the substrate, intriguingly showed that SIL and AA both affected the conformation of the K536-proximal region of ACSL4, albeit through distinct binding patterns. Collectively, we describe a straightforward LC-TRAP workflow that does not involve ligand-derived probe synthesis and is widely applicable to target discovery of NPs.
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Affiliation(s)
- Wenchao Yan
- School of Pharmacy, China Pharmaceutical University, Tongjiaxiang No. 24, Nanjing 210009, Jiangsu, China
| | - Dexiang Wang
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Tongjiaxiang No. 24, Nanjing 210009, Jiangsu, China
| | - Ning Wan
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Tongjiaxiang No. 24, Nanjing 210009, Jiangsu, China
| | - Shun Wang
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Tongjiaxiang No. 24, Nanjing 210009, Jiangsu, China
| | - Chang Shao
- School of Pharmacy, China Pharmaceutical University, Tongjiaxiang No. 24, Nanjing 210009, Jiangsu, China
| | - Hanqing Zhang
- School of Pharmacy, China Pharmaceutical University, Tongjiaxiang No. 24, Nanjing 210009, Jiangsu, China
| | - Zhou Zhao
- School of Pharmacy, China Pharmaceutical University, Tongjiaxiang No. 24, Nanjing 210009, Jiangsu, China
| | - Wenjie Lu
- School of Pharmacy, China Pharmaceutical University, Tongjiaxiang No. 24, Nanjing 210009, Jiangsu, China
| | - Yang Tian
- School of Pharmacy, China Pharmaceutical University, Tongjiaxiang No. 24, Nanjing 210009, Jiangsu, China
| | - Hui Ye
- Jiangsu Provincial Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Tongjiaxiang No. 24, Nanjing 210009, Jiangsu, China
| | - Haiping Hao
- School of Pharmacy, China Pharmaceutical University, Tongjiaxiang No. 24, Nanjing 210009, Jiangsu, China
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Melder FTI, Lindemann P, Welle A, Trouillet V, Heißler S, Nazaré M, Selbach M. Compound Interaction Screen on a Photoactivatable Cellulose Membrane (CISCM) Identifies Drug Targets. ChemMedChem 2022; 17:e202200346. [PMID: 35867055 PMCID: PMC9826412 DOI: 10.1002/cmdc.202200346] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Indexed: 01/11/2023]
Abstract
Identifying the protein targets of drugs is an important but tedious process. Existing proteomic approaches enable unbiased target identification but lack the throughput needed to screen larger compound libraries. Here, we present a compound interaction screen on a photoactivatable cellulose membrane (CISCM) that enables target identification of several drugs in parallel. To this end, we use diazirine-based undirected photoaffinity labeling (PAL) to immobilize compounds on cellulose membranes. Functionalized membranes are then incubated with protein extract and specific targets are identified via quantitative affinity purification and mass spectrometry. CISCM reliably identifies known targets of natural products in less than three hours of analysis time per compound. In summary, we show that combining undirected photoimmobilization of compounds on cellulose with quantitative interaction proteomics provides an efficient means to identify the targets of natural products.
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Affiliation(s)
- F. Teresa I. Melder
- Proteome Dynamics LabMax Delbruck Center for Molecular Medicine in the Helmholtz AssociationRobert-Roessle-Str. 1013125BerlinGermany
| | - Peter Lindemann
- Medicinal ChemistryLeibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP)13125BerlinGermany
| | - Alexander Welle
- Institute of Functional Interfaces and Karlsruhe Nano Micro Facility (KNMFi)Karlsruhe Institute of Technology (KIT)Hermann-von-Helmholtz-Platz 176344Eggenstein-LeopoldshafenGermany
| | - Vanessa Trouillet
- Institute for Applied Materials (IAM-ESS) and Karlsruhe Nano Micro Facility (KNMFi)Karlsruhe Institute of Technology (KIT)Hermann-von-Helmholtz-Platz 176344Eggenstein-LeopoldshafenGermany
| | - Stefan Heißler
- Institute of Functional Interfaces and Karlsruhe Nano Micro Facility (KNMFi)Karlsruhe Institute of Technology (KIT)Hermann-von-Helmholtz-Platz 176344Eggenstein-LeopoldshafenGermany
| | - Marc Nazaré
- Medicinal ChemistryLeibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP)13125BerlinGermany
| | - Matthias Selbach
- Proteome Dynamics LabMax Delbruck Center for Molecular Medicine in the Helmholtz AssociationRobert-Roessle-Str. 1013125BerlinGermany
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Fang H, Sun Z, Chen Z, Chen A, Sun D, Kong Y, Fang H, Qian G. Bioinformatics and systems-biology analysis to determine the effects of Coronavirus disease 2019 on patients with allergic asthma. Front Immunol 2022; 13:988479. [PMID: 36211429 PMCID: PMC9537444 DOI: 10.3389/fimmu.2022.988479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 08/30/2022] [Indexed: 12/05/2022] Open
Abstract
Background The coronavirus disease (COVID-19) pandemic has posed a significant challenge for global health systems. Increasing evidence shows that asthma phenotypes and comorbidities are major risk factors for COVID-19 symptom severity. However, the molecular mechanisms underlying the association between COVID-19 and asthma are poorly understood. Therefore, we conducted bioinformatics and systems biology analysis to identify common pathways and molecular biomarkers in patients with COVID-19 and asthma, as well as potential molecular mechanisms and candidate drugs for treating patients with both COVID-19 and asthma. Methods Two sets of differentially expressed genes (DEGs) from the GSE171110 and GSE143192 datasets were intersected to identify common hub genes, shared pathways, and candidate drugs. In addition, murine models were utilized to explore the expression levels and associations of the hub genes in asthma and lung inflammation/injury. Results We discovered 157 common DEGs between the asthma and COVID-19 datasets. A protein–protein-interaction network was built using various combinatorial statistical approaches and bioinformatics tools, which revealed several hub genes and critical modules. Six of the hub genes were markedly elevated in murine asthmatic lungs and were positively associated with IL-5, IL-13 and MUC5AC, which are the key mediators of allergic asthma. Gene Ontology and pathway analysis revealed common associations between asthma and COVID-19 progression. Finally, we identified transcription factor–gene interactions, DEG–microRNA coregulatory networks, and potential drug and chemical-compound interactions using the hub genes. Conclusion We identified the top 15 hub genes that can be used as novel biomarkers of COVID-19 and asthma and discovered several promising candidate drugs that might be helpful for treating patients with COVID-19 and asthma.
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Affiliation(s)
- Hongwei Fang
- Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhun Sun
- Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
| | - Zhouyi Chen
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Anning Chen
- Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
| | - Donglin Sun
- Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
| | - Yan Kong
- Department of Anesthesiology (High-Tech Branch), The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Hao Fang
- Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Anesthesiology, Minhang Hospital, Fudan University, Shanghai, China
- *Correspondence: Guojun Qian, ; Hao Fang,
| | - Guojun Qian
- Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
- *Correspondence: Guojun Qian, ; Hao Fang,
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