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Zheng JJ, Li QZ, Wang Z, Wang X, Zhao Y, Gao X. Computer-aided nanodrug discovery: recent progress and future prospects. Chem Soc Rev 2024; 53:9059-9132. [PMID: 39148378 DOI: 10.1039/d3cs00575e] [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: 08/17/2024]
Abstract
Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through in silico methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio-nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs.
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Affiliation(s)
- Jia-Jia Zheng
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China.
| | - Qiao-Zhi Li
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China.
| | - Zhenzhen Wang
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China.
| | - Xiaoli Wang
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China.
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Yuliang Zhao
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China.
| | - Xingfa Gao
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China.
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Dong C, Li D, Liu J. Glass Transition Temperature Prediction of Polymers via Graph Reinforcement Learning. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2024; 40:18568-18580. [PMID: 39166275 DOI: 10.1021/acs.langmuir.4c01906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2024]
Abstract
An expansive array of graph-based models has been utilized for accurate prediction of the structure-property relation of polymers. However, these approaches notably underutilize unsupervised structural information. Concentrating on the domain of heterocyclic polymers, particularly polyimides, this study delves into the glass transition temperature (Tg) prediction, aiming to fully exploit the potential within both the global and local structures of molecules. To achieve this, a graph reinforcement learning framework termed Molecular Structural Regularized Graph Convolutional Network with Reinforcement Learning (MSRGCN-RL) is proposed. Experimental results highlight the crucial role of both global and local structural regularization in precise Tg prediction. Concurrently, optimization of MSRGCN training through RL proves essential. This research leads the way in integrating Graph Neural Networks (GNNs) with reinforcement learning methodologies for the property prediction of polymers.
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Affiliation(s)
- Caibo Dong
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Dazi Li
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Jun Liu
- Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials, Beijing University of Chemical Technology, Beijing 100029, China
- State Key Laboratory of Organic-Inorganic Composites, College of Materials Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
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Liu N, Wang HW. Graphene in cryo-EM specimen optimization. Curr Opin Struct Biol 2024; 86:102823. [PMID: 38688075 DOI: 10.1016/j.sbi.2024.102823] [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: 02/03/2024] [Revised: 03/16/2024] [Accepted: 04/06/2024] [Indexed: 05/02/2024]
Abstract
Specimen preparation is a critical but challenging step in high-resolution cryogenic electron microscopy (cryo-EM) structural analysis of macromolecules. In the past decade, graphene has gained much recognition as the supporting substrate to optimize cryo-EM specimen preparation. It improves macromolecule embedding in ice, reduces beam-induced motion, while imposing negligible background noise. Various types of graphene-coated cryo-EM grids were implemented to improve the robustness and efficiency of specimen preparation. Graphene functionalization by different means has been proved specifically useful in addressing challenges related to the air-water interface (AWI), such as preferential orientation and sample denaturation. Graphene sandwich specimen preparation sets a new direction to explore in cryo-EM analysis of biological specimens. In this review, we discuss the current challenges and future prospects of graphene application in cryo-EM analysis of macromolecules.
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Affiliation(s)
- Nan Liu
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Hong-Wei Wang
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing 100084, China.
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Wei Y, Chen AX, Lin Y, Wei T, Qiao B. Allosteric regulation in SARS-CoV-2 spike protein. Phys Chem Chem Phys 2024; 26:6582-6589. [PMID: 38329233 DOI: 10.1039/d4cp00106k] [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: 02/09/2024]
Abstract
Allosteric regulation is common in protein-protein interactions and is thus promising in drug design. Previous experimental and simulation work supported the presence of allosteric regulation in the SARS-CoV-2 spike protein. Here the route of allosteric regulation in SARS-CoV-2 spike protein is examined by all-atom explicit solvent molecular dynamics simulations, contrastive machine learning, and the Ohm approach. It was found that peptide binding to the polybasic cleavage sites, especially the one at the first subunit of the trimeric spike protein, activates the fluctuation of the spike protein's backbone, which eventually propagates to the receptor-binding domain on the third subunit that binds to ACE2. Remarkably, the allosteric regulation routes starting from the polybasic cleavage sites share a high fraction (39-67%) of the critical amino acids with the routes starting from the nitrogen-terminal domains, suggesting the presence of an allosteric regulation network in the spike protein. Our study paves the way for the rational design of allosteric antibody inhibitors.
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Affiliation(s)
- Yong Wei
- Department of Computer Science, High Point University, High Point, NC 27268, USA
| | - Amy X Chen
- Thomas Jefferson High School for Science and Technology, Alexandria, VA 22312, USA
| | - Yuewei Lin
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Tao Wei
- Department of Chemical Engineering and Department of Biomedical Engineering, University of South Carolina, Columbia, SC 29208, USA.
| | - Baofu Qiao
- Department of Natural Sciences, Baruch College, City University of New York, New York, NY 10010, USA.
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Kim MJ, Martin CA, Kim J, Jablonski MM. Computational methods in glaucoma research: Current status and future outlook. Mol Aspects Med 2023; 94:101222. [PMID: 37925783 PMCID: PMC10842846 DOI: 10.1016/j.mam.2023.101222] [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: 08/01/2023] [Revised: 10/06/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023]
Abstract
Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.
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Affiliation(s)
- Minjae J Kim
- Department of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
| | - Cole A Martin
- Department of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
| | - Jinhwa Kim
- Graduate School of Artificial Intelligence, Graduate School of Metaverse, Department of Management Information Systems, Sogang University, 1 Shinsoo-Dong, Mapo-Gu, Seoul, South Korea.
| | - Monica M Jablonski
- Department of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
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Han BG, Avila-Sakar A, Remis J, Glaeser RM. Challenges in making ideal cryo-EM samples. Curr Opin Struct Biol 2023; 81:102646. [PMID: 37392555 DOI: 10.1016/j.sbi.2023.102646] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 06/02/2023] [Accepted: 06/02/2023] [Indexed: 07/03/2023]
Abstract
Recognizing that interaction with the air-water interface (AWI) is a major challenge for cryo-EM, we first review current approaches designed to avoid it. Of these, immobilizing particles on affinity grids is arguably the most promising. In addition, we review efforts to gain more reliable control of the sample thicknesses, not the least important reason being to prevent immobilized particles from coming in contact with the AWI of the remaining buffer. It is emphasized that avoiding such a contact is as important for cryo-ET as for single-particle cryo-EM. Finally, looking to the future, it is proposed that immobilized samples might be used to perform time-resolved biochemical experiments directly on EM grids rather than just in test tubes or cuvettes.
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Affiliation(s)
- Bong-Gyoon Han
- Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720, USA
| | - Agustin Avila-Sakar
- Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720, USA
| | - Jonathan Remis
- Department of Physics, University of California, Berkeley, CA 94720, USA
| | - Robert M Glaeser
- Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720, USA.
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Gago D, Corvo MC, Chagas R, Ferreira LM, Coelhoso I. Protein Adsorption Performance of a Novel Functionalized Cellulose-Based Polymer. Polymers (Basel) 2022; 14:polym14235122. [PMID: 36501515 PMCID: PMC9736165 DOI: 10.3390/polym14235122] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/10/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022] Open
Abstract
Dicarboxymethyl cellulose (DCMC) was synthesized and tested for protein adsorption. The prepared polymer was characterized by inductively coupled plasma atomic emission spectrometry (ICP-AES), attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) and solid state nuclear magnetic resonance (ssNMR) to confirm the functionalization of cellulose. This work shows that protein adsorption onto DCMC is charge dependent. The polymer adsorbs positively charged proteins, cytochrome C and lysozyme, with adsorption capacities of 851 and 571 mg g-1, respectively. In both experiments, the adsorption process follows the Langmuir adsorption isotherm. The adsorption kinetics by DCMC is well described by the pseudo second-order model, and adsorption equilibrium was reached within 90 min. Moreover, DCMC was successfully reused for five consecutive adsorption-desorption cycles, without compromising the removal efficiency (98-99%).
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Affiliation(s)
- Diana Gago
- LAQV-REQUIMTE, Chemistry Department, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
| | - Marta C. Corvo
- i3N/Cenimat, Materials Science Department, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
| | - Ricardo Chagas
- LAQV-REQUIMTE, Chemistry Department, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
- Food4Sustainability—Associação para a Inovação no Alimento Sustentável, Centro Empresarial de Idanha-a-Nova, Zona Industrial, 6060-182 Idanha-a-Nova, Portugal
| | - Luísa M. Ferreira
- LAQV-REQUIMTE, Chemistry Department, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
| | - Isabel Coelhoso
- LAQV-REQUIMTE, Chemistry Department, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
- Correspondence:
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