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Lin H, Riching K, Lai MP, Lu D, Cheng R, Qi X, Wang J. Lysineless HiBiT and NanoLuc Tagging Systems as Alternative Tools Monitoring Targeted Protein Degradation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594249. [PMID: 38798562 PMCID: PMC11118299 DOI: 10.1101/2024.05.14.594249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Target protein degradation (TPD) has emerged as a revolutionary approach in drug discovery, leveraging the cell's intrinsic machinery to selectively degrade disease-associated proteins. Proteolysis-Targeting Chimeras (PROTACs) exemplify this strategy, exploiting heterobifunctional molecules to induce ubiquitination and subsequent degradation of target proteins. The clinical advancement of PROTACs underscores their potential in therapeutic intervention, with numerous projects progressing through clinical stages. However, monitoring subtle changes in protein abundance induced by TPD molecules demands highly sensitive assays. Nano-luciferase (nLuc) fusion proteins, or the NanoBiT technology derived from it, offer a robust screening platform due to their high sensitivity and stability. Despite these advantages, concerns have arisen regarding potential degradation artifacts introduced by tagging systems due to the presence of lysine residues on them, prompting the development of alternative tools. In this study, we introduce HiBiT-RR and nLuc K0 , variants devoid of lysine residues, to mitigate such artifacts. Our findings demonstrate that HiBiT-RR maintains similar sensitivity and binding affinity with the original HiBiT. Moreover, the comparison between nLuc WT and nLuc K0 constructs reveals variations in degradation patterns induced by certain PROTAC molecules, emphasizing the importance of choosing appropriate tagging systems to ensure the reliability of experimental outcomes in studying protein degradation processes.
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Danishuddin, Jamal MS, Song KS, Lee KW, Kim JJ, Park YM. Revolutionizing Drug Targeting Strategies: Integrating Artificial Intelligence and Structure-Based Methods in PROTAC Development. Pharmaceuticals (Basel) 2023; 16:1649. [PMID: 38139776 PMCID: PMC10747325 DOI: 10.3390/ph16121649] [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: 10/24/2023] [Revised: 11/20/2023] [Accepted: 11/22/2023] [Indexed: 12/24/2023] Open
Abstract
PROteolysis TArgeting Chimera (PROTAC) is an emerging technology in chemical biology and drug discovery. This technique facilitates the complete removal of the target proteins that are "undruggable" or challenging to target through chemical molecules via the Ubiquitin-Proteasome System (UPS). PROTACs have been widely explored and outperformed not only in cancer but also in other diseases. During the past few decades, several academic institutes and pharma companies have poured more efforts into PROTAC-related technologies, setting the stage for several major degrader trial readouts in clinical phases. Despite their promising results, the formation of robust ternary orientation, off-target activity, poor permeability, and binding affinity are some of the limitations that hinder their development. Recent advancements in computational technologies have facilitated progress in the development of PROTACs. Researchers have been able to utilize these technologies to explore a wider range of E3 ligases and optimize linkers, thereby gaining a better understanding of the effectiveness and safety of PROTACs in clinical settings. In this review, we briefly explore the computational strategies reported to date for the formation of PROTAC components and discuss the key challenges and opportunities for further research in this area.
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Affiliation(s)
- Danishuddin
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea;
| | | | - Kyoung-Seob Song
- Department of Medical Science, Kosin University College of Medicine, 194 Wachi-ro, Yeongdo-gu, Busan 49104, Republic of Korea;
| | - Keun-Woo Lee
- Division of Life Science, Department of Bio & Medical Big-Data (BK4 Program), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Republic of Korea
- Angel i-Drug Design (AiDD), 33-3 Jinyangho-ro 44, Jinju 52650, Republic of Korea
| | - Jong-Joo Kim
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea;
| | - Yeong-Min Park
- Department of Integrative Biological Sciences and Industry, Sejong University, 209, Neugdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea
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Liu Y, Yang J, Wang T, Luo M, Chen Y, Chen C, Ronai Z, Zhou Y, Ruppin E, Han L. Expanding PROTACtable genome universe of E3 ligases. Nat Commun 2023; 14:6509. [PMID: 37845222 PMCID: PMC10579327 DOI: 10.1038/s41467-023-42233-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 09/28/2023] [Indexed: 10/18/2023] Open
Abstract
Proteolysis-targeting chimera (PROTAC) and other targeted protein degradation (TPD) molecules that induce degradation by the ubiquitin-proteasome system (UPS) offer new opportunities to engage targets that remain challenging to be inhibited by conventional small molecules. One fundamental element in the degradation process is the E3 ligase. However, less than 2% amongst hundreds of E3 ligases in the human genome have been engaged in current studies in the TPD field, calling for the recruiting of additional ones to further enhance the therapeutic potential of TPD. To accelerate the development of PROTACs utilizing under-explored E3 ligases, we systematically characterize E3 ligases from seven different aspects, including chemical ligandability, expression patterns, protein-protein interactions (PPI), structure availability, functional essentiality, cellular location, and PPI interface by analyzing 30 large-scale data sets. Our analysis uncovers several E3 ligases as promising extant PROTACs. In total, combining confidence score, ligandability, expression pattern, and PPI, we identified 76 E3 ligases as PROTAC-interacting candidates. We develop a user-friendly and flexible web portal ( https://hanlaboratory.com/E3Atlas/ ) aimed at assisting researchers to rapidly identify E3 ligases with promising TPD activities against specifically desired targets, facilitating the development of these therapies in cancer and beyond.
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Affiliation(s)
- Yuan Liu
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN, USA
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Jingwen Yang
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN, USA
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Tianlu Wang
- Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Mei Luo
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Yamei Chen
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN, USA
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Chengxuan Chen
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN, USA
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Ze'ev Ronai
- Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, 92037, USA
| | - Yubin Zhou
- Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
- Department of Translational Medical Sciences, College of Medicine, Texas A&M University, Houston, TX, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, 20892, MD, USA.
| | - Leng Han
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA.
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN, USA.
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA.
- Department of Translational Medical Sciences, College of Medicine, Texas A&M University, Houston, TX, USA.
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Rui H, Ashton KS, Min J, Wang C, Potts PR. Protein-protein interfaces in molecular glue-induced ternary complexes: classification, characterization, and prediction. RSC Chem Biol 2023; 4:192-215. [PMID: 36908699 PMCID: PMC9994104 DOI: 10.1039/d2cb00207h] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 01/02/2023] [Indexed: 01/04/2023] Open
Abstract
Molecular glues are a class of small molecules that stabilize the interactions between proteins. Naturally occurring molecular glues are present in many areas of biology where they serve as central regulators of signaling pathways. Importantly, several clinical compounds act as molecular glue degraders that stabilize interactions between E3 ubiquitin ligases and target proteins, leading to their degradation. Molecular glues hold promise as a new generation of therapeutic agents, including those molecular glue degraders that can redirect the protein degradation machinery in a precise way. However, rational discovery of molecular glues is difficult in part due to the lack of understanding of the protein-protein interactions they stabilize. In this review, we summarize the structures of known molecular glue-induced ternary complexes and the interface properties. Detailed analysis shows different mechanisms of ternary structure formation. Additionally, we also review computational approaches for predicting protein-protein interfaces and highlight the promises and challenges. This information will ultimately help inform future approaches for rational molecular glue discovery.
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Affiliation(s)
- Huan Rui
- Center for Research Acceleration by Digital Innovation, Amgen Research Thousand Oaks CA 91320 USA
| | - Kate S Ashton
- Medicinal Chemistry, Amgen Research Thousand Oaks CA 91320 USA
| | - Jaeki Min
- Induced Proximity Platform, Amgen Research Thousand Oaks CA 91320 USA
| | - Connie Wang
- Digital, Technology & Innovation, Amgen Thousand Oaks CA 91320 USA
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Gao F, Huang K, Xing Y. Artificial Intelligence in Omics. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:811-813. [PMID: 36640826 PMCID: PMC10025753 DOI: 10.1016/j.gpb.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 12/20/2022] [Accepted: 01/08/2023] [Indexed: 01/13/2023]
Affiliation(s)
- Feng Gao
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China.
| | - Kun Huang
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA; IUPUI Fairbanks School of Public Health, Indianapolis, IN 46202, USA; Regenstrief Institute, Indianapolis, IN 46202, USA.
| | - Yi Xing
- Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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