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Zheng X, Zhu H, Zhao X, Wang J, Li Q, Zhao X. Emerging affinity methods for protein-drug interaction analysis. J Pharm Biomed Anal 2024; 249:116371. [PMID: 39047466 DOI: 10.1016/j.jpba.2024.116371] [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: 05/30/2024] [Revised: 07/15/2024] [Accepted: 07/15/2024] [Indexed: 07/27/2024]
Abstract
The study of protein-drug interaction plays a crucial role in understanding drug mechanisms, identifying new drug targets and biomarkers, and facilitating drug development and disease treatment. In recent years, significant progress has been made in various protein-drug interaction research methods due to the rapid development and in-depth application of mass spectrometry, nuclear magnetic resonance, Raman spectroscopy, and other technologies. The progress has enhanced the sensitivity, precision, accuracy, and applicability of analytical methods, enabling the establishment of drug-protein interaction networks. This review discusses various emerging research methods, such as native mass spectrometry, infrared spectroscopy, nuclear magnetic resonance and spectrum, biosensor technologies employing surface enhanced Raman, electrochemistry, and magneto resistive signals, as well as affinity magnetic levitation and affinity chromatography. The article also delves into the principles, applications, advantages, and limitations of these technologies.
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Affiliation(s)
- Xinxin Zheng
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, College of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China
| | - Huiting Zhu
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, College of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China
| | - Xue Zhao
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, College of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China
| | - Jing Wang
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, College of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China
| | - Qian Li
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, College of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China
| | - Xinfeng Zhao
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, College of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China.
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Chen M, Wang H, Zhang Y, Jiang H, Li T, Liu L, Zhao Y. Label-Free Multiplex Profiling of Exosomal Proteins with a Deep Learning-Driven 3D Surround-Enhancing SERS Platform for Early Cancer Diagnosis. Anal Chem 2024; 96:6794-6801. [PMID: 38624007 DOI: 10.1021/acs.analchem.4c00669] [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: 04/17/2024]
Abstract
Identification of protein profiling on plasma exosomes by SERS can be a promising strategy for early cancer diagnosis. However, it is still challenging to detect multiple exosomal proteins simultaneously by SERS since the Raman signals of exosomes detected by conventional colloidal nanocrystals or two-dimensional SERS substrates are incomplete and complex. Herein, we develop a novel three-dimensional (3D) surround-enhancing SERS platform, named 3D se-SERS, for the multiplex detection of exosomal proteins. In this 3D se-SERS, proteins and exosomes are covered with "hotspots" generated by the gold nanoparticles, which surround the analytes densely and three-dimensionally, providing sensitive and comprehensive SERS signals. Combining this 3D se-SERS with a deep learning model, we successfully quantitatively profiled seven proteins including CD63, CD81, CD9, CD151, CD171, TSPAN8, and PD-L1 on the surface of plasma exosomes from patients, which can predict the occurrence and advancement of lung cancer. This 3D se-SERS integrating deep learning technique benefits from high sensitivity and significant multiplexing ability for comprehensive analysis of proteins and exosomes, demonstrating the potential of deep learning-driven 3D se-SERS technology for plasma exosome-based early cancer diagnosis.
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Affiliation(s)
- Miao Chen
- School of Life Sciences, Central South University, Changsha 410013, China
| | - Haoyang Wang
- School of Life Sciences, Central South University, Changsha 410013, China
| | - Yibin Zhang
- Department of Pathology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Hanyu Jiang
- School of Life Sciences, Central South University, Changsha 410013, China
| | - Tan Li
- School of Life Sciences, Central South University, Changsha 410013, China
| | - Lixin Liu
- School of Life Sciences, Central South University, Changsha 410013, China
| | - Yuetao Zhao
- School of Life Sciences, Central South University, Changsha 410013, China
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Nandipati M, Fatoki O, Desai S. Bridging Nanomanufacturing and Artificial Intelligence-A Comprehensive Review. MATERIALS (BASEL, SWITZERLAND) 2024; 17:1621. [PMID: 38612135 PMCID: PMC11012965 DOI: 10.3390/ma17071621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 02/05/2024] [Accepted: 03/29/2024] [Indexed: 04/14/2024]
Abstract
Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution-Industry 4.0-as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed.
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Affiliation(s)
- Mutha Nandipati
- Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA; (M.N.); (O.F.)
| | - Olukayode Fatoki
- Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA; (M.N.); (O.F.)
| | - Salil Desai
- Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA; (M.N.); (O.F.)
- Center of Excellence in Product Design and Advanced Manufacturing, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
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Zhang YP, Chen HJ, Hu Y, Lin L, Wen HY, Pang DW, Zhang S, Wang ZG, Liu SL. Accurate Cancer Screening and Prediction of PD-L1-Guided Immunotherapy Efficacy Using Quantum Dot Nanosphere Self-Assembly and Machine Learning. NANO LETTERS 2024; 24:1816-1824. [PMID: 38270101 DOI: 10.1021/acs.nanolett.3c05060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
Accurate quantification of exosomal PD-L1 protein in tumors is closely linked to the response to immunotherapy, but robust methods to achieve high-precision quantitative detection of PD-L1 expression on the surface of circulating exosomes are still lacking. In this work, we developed a signal amplification approach based on aptamer recognition and DNA scaffold hybridization-triggered assembly of quantum dot nanospheres, which enables bicolor phenotyping of exosomes to accurately screen for cancers and predict PD-L1-guided immunotherapeutic effects through machine learning. Through DNA-mediated assembly, we utilized two aptamers for simultaneous ultrasensitive detection of exosomal antigens, which have synergistic roles in tumor diagnosis and treatment prediction, and thus, we achieved better sample classification and prediction through machine-learning algorithms. With a drop of blood, we can distinguish between different cancer patients and healthy individuals and predict the outcome of immunotherapy. This approach provides valuable insights into the development of personalized diagnostics and precision medicine.
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Affiliation(s)
- Yu-Peng Zhang
- Technology Center, Shanghai Tobacco Group Co., Ltd., Shanghai 201315, P. R. China
| | - Hua-Jie Chen
- Engineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, P. R. China
| | - Yusi Hu
- Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, College of Chemistry, and School of Medicine, Nankai University, Tianjin 300071, P. R. China
| | - Leping Lin
- Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, College of Chemistry, and School of Medicine, Nankai University, Tianjin 300071, P. R. China
| | - Hai-Yan Wen
- Engineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, P. R. China
| | - Dai-Wen Pang
- Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, College of Chemistry, and School of Medicine, Nankai University, Tianjin 300071, P. R. China
| | - Shiwu Zhang
- Tianjin Union Medical Center, Tianjin 300121, P. R. China
| | - Zhi-Gang Wang
- Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, College of Chemistry, and School of Medicine, Nankai University, Tianjin 300071, P. R. China
| | - Shu-Lin Liu
- Engineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, P. R. China
- Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, College of Chemistry, and School of Medicine, Nankai University, Tianjin 300071, P. R. China
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