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Wang J, Watson JL, Lisanza SL. Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models. Cold Spring Harb Perspect Biol 2024; 16:a041472. [PMID: 38438190 PMCID: PMC11216169 DOI: 10.1101/cshperspect.a041472] [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: 03/06/2024]
Abstract
Designing proteins with tailored structures and functions is a long-standing goal in bioengineering. Recently, deep learning advances have enabled protein structure prediction at near-experimental accuracy, which has catalyzed progress in protein design as well. We review recent studies that use structure-prediction neural networks to design proteins, via approaches such as activation maximization, inpainting, or denoising diffusion. These methods have led to major improvements over previous methods in wet-lab success rates for designing protein binders, metalloproteins, enzymes, and oligomeric assemblies. These results show that structure-prediction models are a powerful foundation for developing protein-design tools and suggest that continued improvement of their accuracy and generality will be key to unlocking the full potential of protein design.
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Affiliation(s)
- Jue Wang
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, Washington 98195, USA
- DeepMind, London EC4A 3BF, United Kingdom
| | - Joseph L Watson
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
| | - Sidney L Lisanza
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, Washington 98195, USA
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2
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Ma B, Liu D, Wang Z, Zhang D, Jian Y, Zhang K, Zhou T, Gao Y, Fan Y, Ma J, Gao Y, Chen Y, Chen S, Liu J, Li X, Li L. A Top-Down Design Approach for Generating a Peptide PROTAC Drug Targeting Androgen Receptor for Androgenetic Alopecia Therapy. J Med Chem 2024; 67:10336-10349. [PMID: 38836467 DOI: 10.1021/acs.jmedchem.4c00828] [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: 06/06/2024]
Abstract
While large-scale artificial intelligence (AI) models for protein structure prediction and design are advancing rapidly, the translation of deep learning models for practical macromolecular drug development remains limited. This investigation aims to bridge this gap by combining cutting-edge methodologies to create a novel peptide-based PROTAC drug development paradigm. Using ProteinMPNN and RFdiffusion, we identified binding peptides for androgen receptor (AR) and Von Hippel-Lindau (VHL), followed by computational modeling with Alphafold2-multimer and ZDOCK to predict spatial interrelationships. Experimental validation confirmed the designed peptide's binding ability to AR and VHL. Transdermal microneedle patching technology was seamlessly integrated for the peptide PROTAC drug delivery in androgenic alopecia treatment. In summary, our approach provides a generic method for generating peptide PROTACs and offers a practical application for designing potential therapeutic drugs for androgenetic alopecia. This showcases the potential of interdisciplinary approaches in advancing drug development and personalized medicine.
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Affiliation(s)
- Bohan Ma
- Department of Urology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an 710049, China
| | - Donghua Liu
- Department of Urology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an 710049, China
| | - Zhe Wang
- Institute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang 310000, China
| | - Dize Zhang
- Department of Urology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an 710049, China
| | - Yanlin Jian
- Department of Urology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an 710049, China
| | - Kun Zhang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an 710049, China
| | - Tianyang Zhou
- Department of Urology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an 710049, China
| | - Yibo Gao
- Department of Urology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an 710049, China
| | - Yizeng Fan
- Department of Urology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an 710049, China
| | - Jian Ma
- Department of Urology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an 710049, China
| | - Yang Gao
- Department of Urology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an 710049, China
| | - Yule Chen
- Department of Urology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an 710049, China
| | - Si Chen
- School of Medicine, Shanghai University, 99 Shangda Road, Shanghai 200444, China
| | - Jing Liu
- Department of Urology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an 710049, China
| | - Xiang Li
- School of Pharmacy, Second Military Medical University, 325 Guohe Road, Shanghai 200433, China
| | - Lei Li
- Department of Urology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an 710049, China
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3
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Armbruster A, Mohamed AM, Phan HT, Weber W. Lighting the way: recent developments and applications in molecular optogenetics. Curr Opin Biotechnol 2024; 87:103126. [PMID: 38554641 DOI: 10.1016/j.copbio.2024.103126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/27/2024] [Accepted: 03/08/2024] [Indexed: 04/02/2024]
Abstract
Molecular optogenetics utilizes genetically encoded, light-responsive protein switches to control the function of molecular processes. Over the last two years, there have been notable advances in the development of novel optogenetic switches, their utilization in elucidating intricate signaling pathways, and their progress toward practical applications in biotechnological processes, material sciences, and therapeutic applications. In this review, we discuss these areas, offer insights into recent developments, and contemplate future directions.
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Affiliation(s)
- Anja Armbruster
- INM - Leibniz Institute for New Materials, Campus D2 2, 66123 Saarbrücken, Germany; CIBSS - Centre for Integrative Biological Signalling Studies, University of Freiburg, Schänzlestr. 18, 79104 Freiburg, Germany; Faculty of Biology, University of Freiburg, Schänzlestr. 1, 79104 Freiburg, Germany
| | - Asim Me Mohamed
- INM - Leibniz Institute for New Materials, Campus D2 2, 66123 Saarbrücken, Germany
| | - Hoang T Phan
- INM - Leibniz Institute for New Materials, Campus D2 2, 66123 Saarbrücken, Germany
| | - Wilfried Weber
- INM - Leibniz Institute for New Materials, Campus D2 2, 66123 Saarbrücken, Germany; CIBSS - Centre for Integrative Biological Signalling Studies, University of Freiburg, Schänzlestr. 18, 79104 Freiburg, Germany; Saarland University, Department of Materials Science and Engineering, Campus D2 2, 66123 Saarbrücken, Germany.
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4
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Flynn CD, Chang D. Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities. Diagnostics (Basel) 2024; 14:1100. [PMID: 38893627 PMCID: PMC11172335 DOI: 10.3390/diagnostics14111100] [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: 05/05/2024] [Revised: 05/22/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.
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Affiliation(s)
- Connor D. Flynn
- Department of Chemistry, Weinberg College of Arts & Sciences, Northwestern University, Evanston, IL 60208, USA
| | - Dingran Chang
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL 60208, USA
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5
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Torres SV, Valle MB, Mackessy SP, Menzies SK, Casewell NR, Ahmadi S, Burlet NJ, Muratspahić E, Sappington I, Overath MD, Rivera-de-Torre E, Ledergerber J, Laustsen AH, Boddum K, Bera AK, Kang A, Brackenbrough E, Cardoso IA, Crittenden EP, Edge RJ, Decarreau J, Ragotte RJ, Pillai AS, Abedi M, Han HL, Gerben SR, Murray A, Skotheim R, Stuart L, Stewart L, Fryer TJA, Jenkins TP, Baker D. De novo designed proteins neutralize lethal snake venom toxins. RESEARCH SQUARE 2024:rs.3.rs-4402792. [PMID: 38798548 PMCID: PMC11118692 DOI: 10.21203/rs.3.rs-4402792/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Snakebite envenoming remains a devastating and neglected tropical disease, claiming over 100,000 lives annually and causing severe complications and long-lasting disabilities for many more1,2. Three-finger toxins (3FTx) are highly toxic components of elapid snake venoms that can cause diverse pathologies, including severe tissue damage3 and inhibition of nicotinic acetylcholine receptors (nAChRs) resulting in life-threatening neurotoxicity4. Currently, the only available treatments for snakebite consist of polyclonal antibodies derived from the plasma of immunized animals, which have high cost and limited efficacy against 3FTxs5,6,7. Here, we use deep learning methods to de novo design proteins to bind short- and long-chain α-neurotoxins and cytotoxins from the 3FTx family. With limited experimental screening, we obtain protein designs with remarkable thermal stability, high binding affinity, and near-atomic level agreement with the computational models. The designed proteins effectively neutralize all three 3FTx sub-families in vitro and protect mice from a lethal neurotoxin challenge. Such potent, stable, and readily manufacturable toxin-neutralizing proteins could provide the basis for safer, cost-effective, and widely accessible next-generation antivenom therapeutics. Beyond snakebite, our computational design methodology should help democratize therapeutic discovery, particularly in resource-limited settings, by substantially reducing costs and resource requirements for development of therapies to neglected tropical diseases.
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Affiliation(s)
- Susana Vázquez Torres
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA
| | - Melisa Benard Valle
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Stephen P. Mackessy
- Department of Biological Sciences, University of Northern Colorado, Greeley, CO, 80639, USA
| | - Stefanie K. Menzies
- Centre for Snakebite Research & Interventions, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
- Centre for Drugs & Diagnostics, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
- Biomedical & Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster, United Kingdom LA1 4YG8
| | - Nicholas R. Casewell
- Centre for Snakebite Research & Interventions, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
- Centre for Drugs & Diagnostics, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
| | - Shirin Ahmadi
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Nick J. Burlet
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Edin Muratspahić
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Isaac Sappington
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA
| | - Max D. Overath
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Esperanza Rivera-de-Torre
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Jann Ledergerber
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Andreas H. Laustsen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Kim Boddum
- Sophion Bioscience, DK-2750 Ballerup, Denmark
| | - Asim K. Bera
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Alex Kang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Evans Brackenbrough
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Iara A. Cardoso
- Centre for Snakebite Research & Interventions, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
| | - Edouard P. Crittenden
- Centre for Snakebite Research & Interventions, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
| | - Rebecca J. Edge
- Department of Infection Biology and Microbiomes, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, L3 5RF, United Kingdom
| | - Justin Decarreau
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Robert J. Ragotte
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Arvind S. Pillai
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Mohamad Abedi
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Hannah L. Han
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Stacey R. Gerben
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Analisa Murray
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Rebecca Skotheim
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lynda Stuart
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lance Stewart
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Thomas J. A. Fryer
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
- Media Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA
| | - Timothy P. Jenkins
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105,USA
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6
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Wang H, Chen M, Wei X, Xia R, Pei D, Huang X, Han B. Computational tools for plant genomics and breeding. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-024-2578-6. [PMID: 38676814 DOI: 10.1007/s11427-024-2578-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 03/25/2024] [Indexed: 04/29/2024]
Abstract
Plant genomics and crop breeding are at the intersection of biotechnology and information technology. Driven by a combination of high-throughput sequencing, molecular biology and data science, great advances have been made in omics technologies at every step along the central dogma, especially in genome assembling, genome annotation, epigenomic profiling, and transcriptome profiling. These advances further revolutionized three directions of development. One is genetic dissection of complex traits in crops, along with genomic prediction and selection. The second is comparative genomics and evolution, which open up new opportunities to depict the evolutionary constraints of biological sequences for deleterious variant discovery. The third direction is the development of deep learning approaches for the rational design of biological sequences, especially proteins, for synthetic biology. All three directions of development serve as the foundation for a new era of crop breeding where agronomic traits are enhanced by genome design.
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Affiliation(s)
- Hai Wang
- State Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Joint International Research Laboratory of Crop Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China.
- Sanya Institute of China Agricultural University, Sanya, 572025, China.
- Hainan Yazhou Bay Seed Laboratory, Sanya, 572025, China.
| | - Mengjiao Chen
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of the State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China
| | - Xin Wei
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, 200234, China
| | - Rui Xia
- College of Horticulture, South China Agricultural University, Guangzhou, 510640, China
| | - Dong Pei
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of the State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China
| | - Xuehui Huang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, 200234, China
| | - Bin Han
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, 200233, China
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7
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van Aalen EA, Lurvink JJJ, Vermeulen L, van Gerven B, Ni Y, Arts R, Merkx M. Turning Antibodies into Ratiometric Bioluminescent Sensors for Competition-Based Homogeneous Immunoassays. ACS Sens 2024; 9:1401-1409. [PMID: 38380622 PMCID: PMC10964239 DOI: 10.1021/acssensors.3c02478] [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: 11/20/2023] [Revised: 02/02/2024] [Accepted: 02/08/2024] [Indexed: 02/22/2024]
Abstract
Here we present LUCOS (Luminescent Competition Sensor), a modular and broadly applicable bioluminescent diagnostic platform enabling the detection of both small molecules and protein biomarkers. The construction of LUCOS sensors entails the covalent and site-specific coupling of a bioluminescent sensor component to an analyte-specific antibody via protein G-mediated photoconjugation. Target detection is accomplished through intramolecular competition with a tethered analyte competitor for antibody binding. We established two variants of LUCOS: an inherent ratiometric LUCOSR variant and an intensiometric LUCOSI version, which can be used for ratiometric detection upon the addition of a split calibrator luciferase. To demonstrate the versatility of the LUCOS platform, sensors were developed for the detection of the small molecule cortisol and the protein biomarker NT-proBNP. Sensors for both targets displayed analyte-dependent changes in the emission ratio and enabled detection in the micromolar concentration range (KD,app = 16-92 μM). Furthermore, we showed that the response range of the LUCOS sensor can be adjusted by attenuating the affinity of the tethered NT-proBNP competitor, which enabled detection in the nanomolar concentration range (KD,app = 317 ± 26 nM). Overall, the LUCOS platform offers a highly versatile and easy method to convert commercially available monoclonal antibodies into bioluminescent biosensors that provide a homogeneous alternative for the competitive immunoassay.
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Affiliation(s)
- Eva A. van Aalen
- Laboratory
of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, Eindhoven 5600 MB, The
Netherlands
- Institute
for Complex Molecular Systems, Eindhoven
University of Technology, P.O. Box 513, Eindhoven 5600 MB, The Netherlands
| | - Joep J. J. Lurvink
- Laboratory
of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, Eindhoven 5600 MB, The
Netherlands
- Institute
for Complex Molecular Systems, Eindhoven
University of Technology, P.O. Box 513, Eindhoven 5600 MB, The Netherlands
| | - Leandra Vermeulen
- Laboratory
of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, Eindhoven 5600 MB, The
Netherlands
- Institute
for Complex Molecular Systems, Eindhoven
University of Technology, P.O. Box 513, Eindhoven 5600 MB, The Netherlands
| | - Benice van Gerven
- Laboratory
of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, Eindhoven 5600 MB, The
Netherlands
- Institute
for Complex Molecular Systems, Eindhoven
University of Technology, P.O. Box 513, Eindhoven 5600 MB, The Netherlands
| | - Yan Ni
- Laboratory
of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, Eindhoven 5600 MB, The
Netherlands
- Institute
for Complex Molecular Systems, Eindhoven
University of Technology, P.O. Box 513, Eindhoven 5600 MB, The Netherlands
| | - Remco Arts
- Laboratory
of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, Eindhoven 5600 MB, The
Netherlands
- Institute
for Complex Molecular Systems, Eindhoven
University of Technology, P.O. Box 513, Eindhoven 5600 MB, The Netherlands
| | - Maarten Merkx
- Laboratory
of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, Eindhoven 5600 MB, The
Netherlands
- Institute
for Complex Molecular Systems, Eindhoven
University of Technology, P.O. Box 513, Eindhoven 5600 MB, The Netherlands
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8
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Bennett NR, Watson JL, Ragotte RJ, Borst AJ, See DL, Weidle C, Biswas R, Shrock EL, Leung PJY, Huang B, Goreshnik I, Ault R, Carr KD, Singer B, Criswell C, Vafeados D, Sanchez MG, Kim HM, Torres SV, Chan S, Baker D. Atomically accurate de novo design of single-domain antibodies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.14.585103. [PMID: 38562682 PMCID: PMC10983868 DOI: 10.1101/2024.03.14.585103] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Despite the central role that antibodies play in modern medicine, there is currently no way to rationally design novel antibodies to bind a specific epitope on a target. Instead, antibody discovery currently involves time-consuming immunization of an animal or library screening approaches. Here we demonstrate that a fine-tuned RFdiffusion network is capable of designing de novo antibody variable heavy chains (VHH's) that bind user-specified epitopes. We experimentally confirm binders to four disease-relevant epitopes, and the cryo-EM structure of a designed VHH bound to influenza hemagglutinin is nearly identical to the design model both in the configuration of the CDR loops and the overall binding pose.
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Affiliation(s)
- Nathaniel R. Bennett
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Graduate Program in Molecular Engineering, University of Washington, Seattle, WA 98105, USA
| | - Joseph L. Watson
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Robert J. Ragotte
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Andrew J. Borst
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Déjenaé L. See
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Connor Weidle
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Riti Biswas
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Graduate Program in Molecular Engineering, University of Washington, Seattle, WA 98105, USA
| | - Ellen L. Shrock
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Philip J. Y. Leung
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Graduate Program in Molecular Engineering, University of Washington, Seattle, WA 98105, USA
| | - Buwei Huang
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Inna Goreshnik
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Russell Ault
- Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kenneth D. Carr
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Benedikt Singer
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Cameron Criswell
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Dionne Vafeados
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | | | - Ho Min Kim
- Center for Biomolecular and Cellular Structure, Institute for Basic Science (IBS), Daejeon, 34126, Republic of Korea
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Susana Vázquez Torres
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Sidney Chan
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
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Chu AE, Lu T, Huang PS. Sparks of function by de novo protein design. Nat Biotechnol 2024; 42:203-215. [PMID: 38361073 DOI: 10.1038/s41587-024-02133-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 01/09/2024] [Indexed: 02/17/2024]
Abstract
Information in proteins flows from sequence to structure to function, with each step causally driven by the preceding one. Protein design is founded on inverting this process: specify a desired function, design a structure executing this function, and find a sequence that folds into this structure. This 'central dogma' underlies nearly all de novo protein-design efforts. Our ability to accomplish these tasks depends on our understanding of protein folding and function and our ability to capture this understanding in computational methods. In recent years, deep learning-derived approaches for efficient and accurate structure modeling and enrichment of successful designs have enabled progression beyond the design of protein structures and towards the design of functional proteins. We examine these advances in the broader context of classical de novo protein design and consider implications for future challenges to come, including fundamental capabilities such as sequence and structure co-design and conformational control considering flexibility, and functional objectives such as antibody and enzyme design.
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Affiliation(s)
- Alexander E Chu
- Biophysics Program, Stanford University, Palo Alto, CA, USA
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA
- Google DeepMind, London, UK
| | - Tianyu Lu
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA
| | - Po-Ssu Huang
- Biophysics Program, Stanford University, Palo Alto, CA, USA.
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA.
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Weinberg ZY, Soliman SS, Kim MS, Chen IP, Ott M, El-Samad H. De novo-designed minibinders expand the synthetic biology sensing repertoire. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.12.575267. [PMID: 38293112 PMCID: PMC10827046 DOI: 10.1101/2024.01.12.575267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Synthetic and chimeric receptors capable of recognizing and responding to user-defined antigens have enabled "smart" therapeutics based on engineered cells. These cell engineering tools depend on antigen sensors which are most often derived from antibodies. Advances in the de novo design of proteins have enabled the design of protein binders with the potential to target epitopes with unique properties and faster production timelines compared to antibodies. Building upon our previous work combining a de novo-designed minibinder of the Spike protein of SARS-CoV-2 with the synthetic receptor synNotch (SARSNotch), we investigated whether minibinders can be readily adapted to a diversity of cell engineering tools. We show that the Spike minibinder LCB1 easily generalizes to a next-generation proteolytic receptor SNIPR that performs similarly to our previously reported SARSNotch. LCB1-SNIPR successfully enables the detection of live SARS-CoV-2, an improvement over SARSNotch which can only detect cell-expressed Spike. To test the generalizability of minibinders to diverse applications, we tested LCB1 as an antigen sensor for a chimeric antigen receptor (CAR). LCB1-CAR enabled CD8+ T cells to cytotoxically target Spike-expressing cells. Our findings suggest that minibinders represent a novel class of antigen sensors that have the potential to dramatically expand the sensing repertoire of cell engineering tools.
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Affiliation(s)
| | | | - Matthew S. Kim
- Tetrad Gradudate Program, UCSF, San Francisco CA
- Cell Design Institute, San Francisco CA
| | - Irene P. Chen
- Gladstone Institutes, San Francisco CA
- Department of Medicine, UCSF, San Francisco CA
| | - Melanie Ott
- Gladstone Institutes, San Francisco CA
- Department of Medicine, UCSF, San Francisco CA
- Chan Zuckerberg Biohub–San Francisco, San Francisco CA
| | - Hana El-Samad
- Department of Biochemistry & Biophysics, UCSF, San Francisco CA
- Cell Design Institute, San Francisco CA
- Chan Zuckerberg Biohub–San Francisco, San Francisco CA
- Altos Labs, San Francisco CA
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