1
|
Notin P, Rollins N, Gal Y, Sander C, Marks D. Machine learning for functional protein design. Nat Biotechnol 2024; 42:216-228. [PMID: 38361074 DOI: 10.1038/s41587-024-02127-0] [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: 07/01/2023] [Accepted: 01/05/2024] [Indexed: 02/17/2024]
Abstract
Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.
Collapse
Affiliation(s)
- Pascal Notin
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Computer Science, University of Oxford, Oxford, UK.
| | | | - Yarin Gal
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Chris Sander
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Debora Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
| |
Collapse
|
2
|
Hutchinson M, Ruffolo JA, Haskins N, Iannotti M, Vozza G, Pham T, Mehzabeen N, Shandilya H, Rickert K, Croasdale-Wood R, Damschroder M, Fu Y, Dippel A, Gray JJ, Kaplan G. Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen. MAbs 2024; 16:2362775. [PMID: 38899735 PMCID: PMC11195458 DOI: 10.1080/19420862.2024.2362775] [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: 12/22/2023] [Accepted: 05/29/2024] [Indexed: 06/21/2024] Open
Abstract
Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and in silico antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (Tonset, Tm, Tagg), affinity (KD) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in Tm1), with most clones retaining the favorable developability profile of the parental antibody. Additional in silico tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of in silico antibody optimization without the need to predict the antibody-antigen interface, which is notoriously difficult in the absence of crystal structures.
Collapse
Affiliation(s)
- Mark Hutchinson
- Biologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USA
| | - Jeffrey A. Ruffolo
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, USA
- Profluent Bio, Machine Learning, Berkeley, CA, USA
| | - Nantaporn Haskins
- Biologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USA
- Currently at Protein Engineering, R&D, Amgen Inc, Rockville, MD, USA
| | - Michael Iannotti
- Biologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USA
- Honigman LLP, Intellectual Property, Washington, DC, United States
| | - Giuliana Vozza
- Biopharmaceuticals Development, R&D, AstraZeneca, Cambridge, UK
| | - Tony Pham
- Biologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USA
| | | | | | - Keith Rickert
- Biologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USA
| | | | | | - Ying Fu
- Biologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USA
| | - Andrew Dippel
- Biologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USA
| | - Jeffrey J. Gray
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, USA
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Gilad Kaplan
- Biologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USA
| |
Collapse
|
3
|
Zheng L, Gong H, Zhang J, Guo L, Zhai Z, Xia S, Hu Z, Chang J, Jiang Y, Huang X, Ge J, Zhang B, Yan M. Strategies to improve the therapeutic efficacy of mesenchymal stem cell-derived extracellular vesicle (MSC-EV): a promising cell-free therapy for liver disease. Front Bioeng Biotechnol 2023; 11:1322514. [PMID: 38155924 PMCID: PMC10753838 DOI: 10.3389/fbioe.2023.1322514] [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: 10/17/2023] [Accepted: 11/29/2023] [Indexed: 12/30/2023] Open
Abstract
Liver disease has emerged as a significant worldwide health challenge due to its diverse causative factors and therapeutic complexities. The majority of liver diseases ultimately progress to end-stage liver disease and liver transplantation remains the only effective therapy with the limitations of donor organ shortage, lifelong immunosuppressants and expensive treatment costs. Numerous pre-clinical studies have revealed that extracellular vesicles released by mesenchymal stem cells (MSC-EV) exhibited considerable potential in treating liver diseases. Although natural MSC-EV has many potential advantages, some characteristics of MSC-EV, such as heterogeneity, uneven therapeutic effect, and rapid clearance in vivo constrain its clinical translation. In recent years, researchers have explored plenty of ways to improve the therapeutic efficacy and rotation rate of MSC-EV in the treatment of liver disease. In this review, we summarized current strategies to enhance the therapeutic potency of MSC-EV, mainly including optimization culture conditions in MSC or modifications of MSC-EV, aiming to facilitate the development and clinical application of MSC-EV in treating liver disease.
Collapse
Affiliation(s)
- Lijuan Zheng
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China
- Institute of Clinical Pharmacy, Central South University, Changsha, China
| | - Hui Gong
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China
- Institute of Clinical Pharmacy, Central South University, Changsha, China
- International Research Center for Precision Medicine, Transformative Technology and Software Services, Changsha, China
| | - Jing Zhang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
| | - Linna Guo
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China
- Institute of Clinical Pharmacy, Central South University, Changsha, China
- International Research Center for Precision Medicine, Transformative Technology and Software Services, Changsha, China
| | - Zhuofan Zhai
- Xiangya School of Medicine, Central South University, Changsha, China
| | - Shuang Xia
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China
- Institute of Clinical Pharmacy, Central South University, Changsha, China
- International Research Center for Precision Medicine, Transformative Technology and Software Services, Changsha, China
| | - Zhiyu Hu
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China
- Institute of Clinical Pharmacy, Central South University, Changsha, China
- International Research Center for Precision Medicine, Transformative Technology and Software Services, Changsha, China
| | - Jing Chang
- Xiangya School of Medicine, Central South University, Changsha, China
| | - Yizhu Jiang
- Xiangya School of Medicine, Central South University, Changsha, China
| | - Xinran Huang
- Xiangya School of Medicine, Central South University, Changsha, China
| | - Jingyi Ge
- Xiangya School of Medicine, Central South University, Changsha, China
| | - Bikui Zhang
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China
- Institute of Clinical Pharmacy, Central South University, Changsha, China
- International Research Center for Precision Medicine, Transformative Technology and Software Services, Changsha, China
| | - Miao Yan
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China
- Institute of Clinical Pharmacy, Central South University, Changsha, China
- International Research Center for Precision Medicine, Transformative Technology and Software Services, Changsha, China
| |
Collapse
|
4
|
Chungyoun M, Gray JJ. AI Models for Protein Design are Driving Antibody Engineering. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2023; 28:100473. [PMID: 37484815 PMCID: PMC10361400 DOI: 10.1016/j.cobme.2023.100473] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Therapeutic antibody engineering seeks to identify antibody sequences with specific binding to a target and optimized drug-like properties. When guided by deep learning, antibody generation methods can draw on prior knowledge and experimental efforts to improve this process. By leveraging the increasing quantity and quality of predicted structures of antibodies and target antigens, powerful structure-based generative models are emerging. In this review, we tie the advancements in deep learning-based protein structure prediction and design to the study of antibody therapeutics.
Collapse
Affiliation(s)
- Michael Chungyoun
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21287, USA
- Program in Molecular Biophysics, institute for Nanobiotechnology, and Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21287, USA
| |
Collapse
|
5
|
Wang J, Liu T, Gu S, Yang HH, Xie W, Gao C, Gu D. Cytoplasm Hydrogelation-Mediated Cardiomyocyte Sponge Alleviated Coxsackievirus B3 Infection. NANO LETTERS 2023; 23:8881-8890. [PMID: 37751402 PMCID: PMC10573321 DOI: 10.1021/acs.nanolett.3c01983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/27/2023] [Indexed: 09/28/2023]
Abstract
Viral myocarditis (VMC), commonly caused by coxsackievirus B3 (CVB3) infection, lacks specific treatments and leads to serious heart conditions. Current treatments, such as IFNα and ribavirin, show limited effectiveness. Herein, rather than inhibiting virus replication, this study introduces a novel cardiomyocyte sponge, intracellular gelated cardiomyocytes (GCs), to trap and neutralize CVB3 via a receptor-ligand interaction, such as CAR and CD55. By maintaining cellular morphology, GCs serve as sponges for CVB3, inhibiting infection. In vitro results revealed that GCs could inhibit CVB3 infection on HeLa cells. In vivo, GCs exhibited a strong immune escape ability and effectively inhibited CVB3-induced viral myocarditis with a high safety profile. The most significant implication of this study is to develop a universal antivirus infection strategy via intracellular gelation of the host cell, which can be employed not only for treating defined pathogenic viruses but also for a rapid response to infection outbreaks caused by mutable and unknown viruses.
Collapse
Affiliation(s)
- Jingzhe Wang
- Department
of Laboratory Medicine, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University,
Shenzhen Second People’s Hospital, Shenzhen Key Laboratory
of Medical Laboratory and Molecular Diagnostics, Shenzhen 518035, China
- Shenzhen
Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Tonggong Liu
- Department
of Laboratory Medicine, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University,
Shenzhen Second People’s Hospital, Shenzhen Key Laboratory
of Medical Laboratory and Molecular Diagnostics, Shenzhen 518035, China
| | - Siyao Gu
- Shenzhen
Key Laboratory of Health Science and Technology, Institute of Biopharmaceutical
and Health Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Hui-hui Yang
- Department
of Laboratory Medicine, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University,
Shenzhen Second People’s Hospital, Shenzhen Key Laboratory
of Medical Laboratory and Molecular Diagnostics, Shenzhen 518035, China
| | - Weidong Xie
- Shenzhen
Key Laboratory of Health Science and Technology, Institute of Biopharmaceutical
and Health Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Cheng Gao
- Department
of Laboratory Medicine, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University,
Shenzhen Second People’s Hospital, Shenzhen Key Laboratory
of Medical Laboratory and Molecular Diagnostics, Shenzhen 518035, China
| | - Dayong Gu
- Department
of Laboratory Medicine, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University,
Shenzhen Second People’s Hospital, Shenzhen Key Laboratory
of Medical Laboratory and Molecular Diagnostics, Shenzhen 518035, China
| |
Collapse
|