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Gordon GL, Raybould MIJ, Wong A, Deane CM. Prospects for the computational humanization of antibodies and nanobodies. Front Immunol 2024; 15:1399438. [PMID: 38812514 PMCID: PMC11133524 DOI: 10.3389/fimmu.2024.1399438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 05/02/2024] [Indexed: 05/31/2024] Open
Abstract
To be viable therapeutics, antibodies must be tolerated by the human immune system. Rational approaches to reduce the risk of unwanted immunogenicity involve maximizing the 'humanness' of the candidate drug. However, despite the emergence of new discovery technologies, many of which start from entirely human gene fragments, most antibody therapeutics continue to be derived from non-human sources with concomitant humanization to increase their human compatibility. Early experimental humanization strategies that focus on CDR loop grafting onto human frameworks have been critical to the dominance of this discovery route but do not consider the context of each antibody sequence, impacting their success rate. Other challenges include the simultaneous optimization of other drug-like properties alongside humanness and the humanization of fundamentally non-human modalities such as nanobodies. Significant efforts have been made to develop in silico methodologies able to address these issues, most recently incorporating machine learning techniques. Here, we outline these recent advancements in antibody and nanobody humanization, focusing on computational strategies that make use of the increasing volume of sequence and structural data available and the validation of these tools. We highlight that structural distinctions between antibodies and nanobodies make the application of antibody-focused in silico tools to nanobody humanization non-trivial. Furthermore, we discuss the effects of humanizing mutations on other essential drug-like properties such as binding affinity and developability, and methods that aim to tackle this multi-parameter optimization problem.
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Affiliation(s)
| | | | | | - Charlotte M. Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom
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Abd Wahab NH, Hasikin K, Wee Lai K, Xia K, Bei L, Huang K, Wu X. Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices. PeerJ Comput Sci 2024; 10:e1943. [PMID: 38686003 PMCID: PMC11057655 DOI: 10.7717/peerj-cs.1943] [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: 09/28/2023] [Accepted: 02/27/2024] [Indexed: 05/02/2024]
Abstract
Background Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas. Methodology Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles. Results The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies' flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring. Conclusions Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors.
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Affiliation(s)
- Nur Haninie Abd Wahab
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Engineering Services Division, Ministry of Health Malaysia, Putrajaya, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Center of Intelligent Systems for Emerging Technology, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Kaijian Xia
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Affiliated Changshu Hospital, Soochow University Changshu, Jiangsu, China
| | - Lulu Bei
- School of Information Engineering, Xuzhou University of Technology, Xuzhou, China
| | - Kai Huang
- JiangSu XCMG HanYun Technologies Co., LTD., Xuzhou, China
| | - Xiang Wu
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- School of Medical Information & Engineering, Xuzhou Medical University, Xuzhou, China
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Zhang Y, Tong Y, Xia X, Wu Q, Su Y. A domain-label-guided translation model for molecular optimization. Methods 2024; 224:71-78. [PMID: 38395182 DOI: 10.1016/j.ymeth.2024.02.005] [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: 12/21/2023] [Revised: 02/11/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024] Open
Abstract
Molecular optimization, which aims to improve molecular properties by modifying complex molecular structures, is a crucial and challenging task in drug discovery. In recent years, translation models provide a promising way to transform low-property molecules to high-property molecules, which enables molecular optimization to achieve remarkable progress. However, most existing models require matched molecular pairs, which are prone to be limited by the datasets. Although some models do not require matched molecular pairs, their performance is usually sacrificed due to the lack of useful supervising information. To address this issue, a domain-label-guided translation model is proposed in this paper, namely DLTM. In the model, the domain label information of molecules is exploited as a control condition to obtain different embedding representations, enabling the model to generate diverse molecules. Besides, the model adopts a classifier network to identify the property categories of transformed molecules, guiding the model to generate molecules with desired properties. The performance of DLTM is verified on two optimization tasks, namely the quantitative estimation of drug-likeness and penalized logP. Experimental results show that the proposed DLTM is superior to the compared baseline models.
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Affiliation(s)
- Yajie Zhang
- School of Computer Science and Technology, Anhui University, Hefei, 230601, China.
| | - Yongqi Tong
- School of Computer Science and Technology, Anhui University, Hefei, 230601, China.
| | - Xin Xia
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China; School of Artificial Intelligence, Anhui University, Hefei, 230601, China.
| | - Qingwen Wu
- Affiliated Hospital of Jining Medical University, Jining, 272007, China.
| | - Yansen Su
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China; School of Artificial Intelligence, Anhui University, Hefei, 230601, China.
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Li W, Lin H, Huang Z, Xie S, Zhou Y, Gong R, Jiang Q, Xiang C, Huang J. DOTAD: A Database of Therapeutic Antibody Developability. Interdiscip Sci 2024:10.1007/s12539-024-00613-2. [PMID: 38530613 DOI: 10.1007/s12539-024-00613-2] [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: 08/13/2023] [Revised: 01/25/2024] [Accepted: 01/27/2024] [Indexed: 03/28/2024]
Abstract
The development of therapeutic antibodies is an important aspect of new drug discovery pipelines. The assessment of an antibody's developability-its suitability for large-scale production and therapeutic use-is a particularly important step in this process. Given that experimental assays to assess antibody developability in large scale are expensive and time-consuming, computational methods have been a more efficient alternative. However, the antibody research community faces significant challenges due to the scarcity of readily accessible data on antibody developability, which is essential for training and validating computational models. To address this gap, DOTAD (Database Of Therapeutic Antibody Developability) has been built as the first database dedicated exclusively to the curation of therapeutic antibody developability information. DOTAD aggregates all available therapeutic antibody sequence data along with various developability metrics from the scientific literature, offering researchers a robust platform for data storage, retrieval, exploration, and downloading. In addition to serving as a comprehensive repository, DOTAD enhances its utility by integrating a web-based interface that features state-of-the-art tools for the assessment of antibody developability. This ensures that users not only have access to critical data but also have the convenience of analyzing and interpreting this information. The DOTAD database represents a valuable resource for the scientific community, facilitating the advancement of therapeutic antibody research. It is freely accessible at http://i.uestc.edu.cn/DOTAD/ , providing an open data platform that supports the continuous growth and evolution of computational methods in the field of antibody development.
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Affiliation(s)
- Wenzhen Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Hongyan Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Ziru Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Shiyang Xie
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yuwei Zhou
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Rong Gong
- School of Computer Science and Technology, Aba Teachers University, Aba, 623002, China
| | - Qianhu Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - ChangCheng Xiang
- School of Computer Science and Technology, Aba Teachers University, Aba, 623002, China.
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, 611844, China.
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Liu M, Wu T, Li X, Zhu Y, Chen S, Huang J, Zhou F, Liu H. ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization. Front Genet 2024; 15:1352504. [PMID: 38487252 PMCID: PMC10937565 DOI: 10.3389/fgene.2024.1352504] [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: 12/13/2023] [Accepted: 02/19/2024] [Indexed: 03/17/2024] Open
Abstract
Background: Cancer is a significant global health problem that continues to cause a high number of deaths worldwide. Traditional cancer treatments often come with risks that can compromise the functionality of vital organs. As a potential alternative to these conventional therapies, Anticancer peptides (ACPs) have garnered attention for their small size, high specificity, and reduced toxicity, making them as a promising option for cancer treatments. Methods: However, the process of identifying effective ACPs through wet-lab screening experiments is time-consuming and requires a lot of labor. To overcome this challenge, a deep ensemble learning method is constructed to predict anticancer peptides (ACPs) in this study. To evaluate the reliability of the framework, four different datasets are used in this study for training and testing. During the training process of the model, integration of feature selection methods, feature dimensionality reduction measures, and optimization of the deep ensemble model are carried out. Finally, we explored the interpretability of features that affected the final prediction results and built a web server platform to facilitate anticancer peptides prediction, which can be used by all researchers for further studies. This web server can be accessed at http://lmylab.online:5001/. Results: The result of this study achieves an accuracy rate of 98.53% and an AUC (Area under Curve) value of 0.9972 on the ACPfel dataset, it has improvements on other datasets as well.
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Affiliation(s)
- Mingyou Liu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Tao Wu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
| | - Xue Li
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Yingxue Zhu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Sen Chen
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Fengfeng Zhou
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Hongmei Liu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
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Singh R, Chandley P, Rohatgi S. Recent Advances in the Development of Monoclonal Antibodies and Next-Generation Antibodies. Immunohorizons 2023; 7:886-897. [PMID: 38149884 PMCID: PMC10759153 DOI: 10.4049/immunohorizons.2300102] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/07/2023] [Indexed: 12/28/2023] Open
Abstract
mAbs are highly indispensable tools for diagnostic, prophylactic, and therapeutic applications. The first technique, hybridoma technology, was based on fusion of B lymphocytes with myeloma cells, which resulted in generation of single mAbs against a specific Ag. Along with hybridoma technology, several novel and alternative methods have been developed to improve mAb generation, ranging from electrofusion to the discovery of completely novel technologies such as B cell immortalization; phage, yeast, bacterial, ribosome, and mammalian display systems; DNA/RNA encoded Abs; single B cell technology; transgenic animals; and artificial intelligence/machine learning. This commentary outlines the evolution, methodology, advantages, and limitations of various mAb production techniques. Furthermore, with the advent of next-generation Ab technologies such as single-chain variable fragments, nanobodies, bispecific Abs, Fc-engineered Abs, Ab biosimilars, Ab mimetics, and Ab-drug conjugates, the healthcare and pharmaceutical sectors have become resourceful to develop highly specific mAb treatments against various diseases such as cancer and autoimmune and infectious diseases.
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Affiliation(s)
- Rohit Singh
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Uttarakhand, India
| | - Pankaj Chandley
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Uttarakhand, India
| | - Soma Rohatgi
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Uttarakhand, India
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