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Cheng J, Liang T, Xie XQ, Feng Z, Meng L. A new era of antibody discovery: an in-depth review of AI-driven approaches. Drug Discov Today 2024; 29:103984. [PMID: 38642702 DOI: 10.1016/j.drudis.2024.103984] [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: 10/12/2023] [Revised: 04/02/2024] [Accepted: 04/15/2024] [Indexed: 04/22/2024]
Abstract
Given their high affinity and specificity for a range of macromolecules, antibodies are widely used in the treatment of autoimmune diseases, cancers, inflammatory diseases, and Alzheimer's disease (AD). Traditional experimental methods are time-consuming, expensive, and labor-intensive. Recent advances in artificial intelligence (AI) technologies provide complementary methods that can reduce the time and costs required for antibody design by minimizing failures and increasing the success rate of experimental tests. In this review, we scrutinize the plethora of AI-driven methodologies that have been deployed over the past 4 years for modeling antibody structures, predicting antibody-antigen interactions, optimizing antibody affinity, and generating novel antibody candidates. We also briefly address the challenges faced in integrating AI-based models with traditional antibody discovery pipelines and highlight the potential future directions in this burgeoning field.
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Affiliation(s)
- Jin Cheng
- School of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng, 224005, China
| | - Tianjian Liang
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA; Department of Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA.
| | - Zhiwei Feng
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA.
| | - Li Meng
- School of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng, 224005, China.
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Bravi B. Development and use of machine learning algorithms in vaccine target selection. NPJ Vaccines 2024; 9:15. [PMID: 38242890 PMCID: PMC10798987 DOI: 10.1038/s41541-023-00795-8] [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: 08/04/2023] [Accepted: 12/07/2023] [Indexed: 01/21/2024] Open
Abstract
Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.
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Affiliation(s)
- Barbara Bravi
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK.
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Zhuang L, Ye Z, Li L, Yang L, Gong W. Next-Generation TB Vaccines: Progress, Challenges, and Prospects. Vaccines (Basel) 2023; 11:1304. [PMID: 37631874 PMCID: PMC10457792 DOI: 10.3390/vaccines11081304] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 07/28/2023] [Accepted: 07/28/2023] [Indexed: 08/27/2023] Open
Abstract
Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is a prevalent global infectious disease and a leading cause of mortality worldwide. Currently, the only available vaccine for TB prevention is Bacillus Calmette-Guérin (BCG). However, BCG demonstrates limited efficacy, particularly in adults. Efforts to develop effective TB vaccines have been ongoing for nearly a century. In this review, we have examined the current obstacles in TB vaccine research and emphasized the significance of understanding the interaction mechanism between MTB and hosts in order to provide new avenues for research and establish a solid foundation for the development of novel vaccines. We have also assessed various TB vaccine candidates, including inactivated vaccines, attenuated live vaccines, subunit vaccines, viral vector vaccines, DNA vaccines, and the emerging mRNA vaccines as well as virus-like particle (VLP)-based vaccines, which are currently in preclinical stages or clinical trials. Furthermore, we have discussed the challenges and opportunities associated with developing different types of TB vaccines and outlined future directions for TB vaccine research, aiming to expedite the development of effective vaccines. This comprehensive review offers a summary of the progress made in the field of novel TB vaccines.
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Affiliation(s)
- Li Zhuang
- Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, Eighth Medical Center of Chinese PLA General Hospital, Beijing 100091, China
- Hebei North University, Zhangjiakou 075000, China
| | - Zhaoyang Ye
- Hebei North University, Zhangjiakou 075000, China
| | - Linsheng Li
- Hebei North University, Zhangjiakou 075000, China
| | - Ling Yang
- Hebei North University, Zhangjiakou 075000, China
| | - Wenping Gong
- Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, Eighth Medical Center of Chinese PLA General Hospital, Beijing 100091, China
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Penhaskashi J, Sekimoto O, Chiappelli F. Permafrost viremia and immune tweening. Bioinformation 2023; 19:685-691. [PMID: 37885785 PMCID: PMC10598357 DOI: 10.6026/97320630019685] [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: 06/01/2023] [Revised: 06/30/2023] [Accepted: 06/30/2023] [Indexed: 10/28/2023] Open
Abstract
The immune system, an exquisitely regulated physiological system, utilizes a wide spectrum of soluble factors and multiple cell populations and subpopulations at diverse states of maturation to monitor and protect the organism against foreign organisms. Immune surveillance is ensured by distinguishing self-antigens from self-associated with non-self (e.g., viral) peptides presented by major histocompatibility complexes (MHC). Pathology is often identified as unregulated inflammatory responses (e.g., cytokine storm), or recognizing self as a non-self entity (i.e., auto-immunity). Artificial intelligence (AI), and in particular specific machine learning (ML) paradigms (e.g., Deep Learning [DL]) proffer powerful algorithms to better understand and more accurately predict immune responses, immune regulation and homeostasis, and immune reactivity to challenges (i.e., immune allostasis) by their intrinsic ability to interpret immune parameters, pathways and events by analyzing large amounts of complex data and drawing predictive inferences (i.e., immune tweening). We propose here that DL models play an increasingly significant role in better defining and characterizing immunological surveillance to ancient and novel virus species released by thawing permafrost.
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Affiliation(s)
- Jaden Penhaskashi
- />Division of West Valley Dental Implant Center, Encino, CA 91316, USA
| | | | - Francesco Chiappelli
- />Dental Group of Sherman Oaks, CA 91403 , USA
- />Center for the Health Sciences, UCLA, Los Angeles, CA, USA
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Chen T, Ding Z, Lan J, Wong G. Advances and perspectives in the development of vaccines against highly pathogenic bunyaviruses. Front Cell Infect Microbiol 2023; 13:1174030. [PMID: 37274315 PMCID: PMC10234439 DOI: 10.3389/fcimb.2023.1174030] [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: 02/25/2023] [Accepted: 05/03/2023] [Indexed: 06/06/2023] Open
Abstract
Increased human activities around the globe and the rapid development of once rural regions have increased the probability of contact between humans and wild animals. A majority of bunyaviruses are of zoonotic origin, and outbreaks may result in the substantial loss of lives, economy contraction, and social instability. Many bunyaviruses require manipulation in the highest levels of biocontainment, such as Biosafety Level 4 (BSL-4) laboratories, and the scarcity of this resource has limited the development speed of vaccines for these pathogens. Meanwhile, new technologies have been created, and used to innovate vaccines, like the mRNA vaccine platform and bioinformatics-based antigen design. Here, we summarize current vaccine developments for three different bunyaviruses requiring work in the highest levels of biocontainment: Crimean-Congo Hemorrhagic Fever Virus (CCHFV), Rift Valley Fever Virus (RVFV), and Hantaan virus (HTNV), and provide perspectives and potential future directions that can be further explored to advance specific vaccines for humans and livestock.
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Affiliation(s)
- Tong Chen
- Viral Hemorrhagic Fevers Research Unit, Chinese Academy of Sciences (CAS) Key Laboratory of Molecular Virology & Immunology, Institut Pasteur of Shanghai, Chinese Academy of Sciences (CAS), Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhe Ding
- Viral Hemorrhagic Fevers Research Unit, Chinese Academy of Sciences (CAS) Key Laboratory of Molecular Virology & Immunology, Institut Pasteur of Shanghai, Chinese Academy of Sciences (CAS), Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jiaming Lan
- Viral Hemorrhagic Fevers Research Unit, Chinese Academy of Sciences (CAS) Key Laboratory of Molecular Virology & Immunology, Institut Pasteur of Shanghai, Chinese Academy of Sciences (CAS), Shanghai, China
| | - Gary Wong
- Viral Hemorrhagic Fevers Research Unit, Chinese Academy of Sciences (CAS) Key Laboratory of Molecular Virology & Immunology, Institut Pasteur of Shanghai, Chinese Academy of Sciences (CAS), Shanghai, China
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