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Chen G, Yu L, Shi F, Shen J, Zhang Y, Liu G, Mei X, Li X, Xu X, Xue C, Chang Y. A comprehensive review of sulfated fucan from sea cucumber: Antecedent and prospect. Carbohydr Polym 2024; 341:122345. [PMID: 38876715 DOI: 10.1016/j.carbpol.2024.122345] [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: 03/13/2024] [Revised: 05/26/2024] [Accepted: 05/27/2024] [Indexed: 06/16/2024]
Abstract
Sulfated fucan from sea cucumber is mainly consists of L-fucose and sulfate groups. Recent studies have confirmed that the structure of sulfated fucan mainly consists of repeating units, typically tetrasaccharides. However, there is growing evidence indicating the presence of irregular domains with heterogeneous units that have not been extensively explored. Moreover, as a key contributor to the nutritional benefits of sea cucumbers, sulfated fucan demonstrates a range of biological activities, such as anti-inflammatory, anticancer, hypolipidemic, anti-hyperglycemic, antioxidant, and anticoagulant properties. These biological activities are profoundly influenced by the structural features of sulfated fucan including molecular weight and distribution patterns of sulfate groups. The latest research indicates that sulfated fucan is dispersed in the extracellular matrix of the body wall of sea cucumbers. This article aimed to review the research progress on the in-situ distribution, structures, structural elucidation strategies, functions, and structure-activity relationships of sulfated fucan, especially in the last decade. It also provided insights into the major challenges and potential solutions in the research and development of sulfated fucan. Moreover, the fucanase and carbohydrate binding modules are anticipated to play pivotal roles in advancing this field.
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Affiliation(s)
- Guangning Chen
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
| | - Long Yu
- College of Medicine and Public Health, Flinders University, Bedford Park, Adelaide 5042, Australia
| | - Feifei Shi
- College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Jingjing Shen
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
| | - Yuying Zhang
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
| | - Guanchen Liu
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
| | - Xuanwei Mei
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
| | - Xinyu Li
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
| | - Xiaoqi Xu
- College of Food and Light Industry, Nanjing Tech University, Nanjing 211800, China
| | - Changhu Xue
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
| | - Yaoguang Chang
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China.
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2
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Zhang H, Zhou Y, Zhang Z, Sun H, Pan Z, Mou M, Zhang W, Ye Q, Hou T, Li H, Hsieh CY, Zhu F. Large Language Model-Based Natural Language Encoding Could Be All You Need for Drug Biomedical Association Prediction. Anal Chem 2024. [PMID: 39011990 DOI: 10.1021/acs.analchem.4c01793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
Analyzing drug-related interactions in the field of biomedicine has been a critical aspect of drug discovery and development. While various artificial intelligence (AI)-based tools have been proposed to analyze drug biomedical associations (DBAs), their feature encoding did not adequately account for crucial biomedical functions and semantic concepts, thereby still hindering their progress. Since the advent of ChatGPT by OpenAI in 2022, large language models (LLMs) have demonstrated rapid growth and significant success across various applications. Herein, LEDAP was introduced, which uniquely leveraged LLM-based biotext feature encoding for predicting drug-disease associations, drug-drug interactions, and drug-side effect associations. Benefiting from the large-scale knowledgebase pre-training, LLMs had great potential in drug development analysis owing to their holistic understanding of natural language and human topics. LEDAP illustrated its notable competitiveness in comparison with other popular DBA analysis tools. Specifically, even in simple conjunction with classical machine learning methods, LLM-based feature representations consistently enabled satisfactory performance across diverse DBA tasks like binary classification, multiclass classification, and regression. Our findings underpinned the considerable potential of LLMs in drug development research, indicating a catalyst for further progress in related fields.
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Affiliation(s)
- Hanyu Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Yuan Zhou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Zhichao Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Huaicheng Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Qing Ye
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Honglin Li
- Innovation Center for AI and Drug Discovery, East China Normal University, Shanghai 200062, China
| | - Chang-Yu Hsieh
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
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3
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Harvey DJ. Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2021-2022. MASS SPECTROMETRY REVIEWS 2024. [PMID: 38925550 DOI: 10.1002/mas.21873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/05/2024] [Accepted: 02/12/2024] [Indexed: 06/28/2024]
Abstract
The use of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry for the analysis of carbohydrates and glycoconjugates is a well-established technique and this review is the 12th update of the original article published in 1999 and brings coverage of the literature to the end of 2022. As with previous review, this review also includes a few papers that describe methods appropriate to analysis by MALDI, such as sample preparation, even though the ionization method is not MALDI. The review follows the same format as previous reviews. It is divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of computer software for structural identification. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other general areas such as medicine, industrial processes, natural products and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. MALDI is still an ideal technique for carbohydrate analysis, particularly in its ability to produce single ions from each analyte and advancements in the technique and range of applications show little sign of diminishing.
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Wang Y, Liu Y, Liu S, Cheng L, Liu X. Recent advances in N-glycan biomarker discovery among human diseases. Acta Biochim Biophys Sin (Shanghai) 2024. [PMID: 38910518 DOI: 10.3724/abbs.2024101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024] Open
Abstract
N-glycans play important roles in a variety of biological processes. In recent years, analytical technologies with high resolution and sensitivity have advanced exponentially, enabling analysts to investigate N-glycomic changes in different states. Specific glycan and glycosylation signatures have been identified in multiple diseases, including cancer, autoimmune diseases, nervous system disorders, and metabolic and cardiovascular diseases. These glycans demonstrate comparable or superior indicating capability in disease diagnosis and prognosis over routine biomarkers. Moreover, synchronous glycan alterations concurrent with disease initiation and progression provide novel insights into pathogenetic mechanisms and potential treatment targets. This review elucidates the biological significance of N-glycans, compares the existing glycomic technologies, and delineates the clinical performance of N-glycans across a range of diseases.
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Affiliation(s)
- Yi Wang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yuanyuan Liu
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Si Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Liming Cheng
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xin Liu
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
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Agarwal V, McShan AC. The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins. Nat Chem Biol 2024:10.1038/s41589-024-01638-w. [PMID: 38907110 DOI: 10.1038/s41589-024-01638-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 04/29/2024] [Indexed: 06/23/2024]
Abstract
Artificial intelligence-driven advances in protein structure prediction in recent years have raised the question: has the protein structure-prediction problem been solved? Here, with a focus on nonglobular proteins, we highlight the many strengths and potential weaknesses of DeepMind's AlphaFold2 in the context of its biological and therapeutic applications. We summarize the subtleties associated with evaluation of AlphaFold2 model quality and reliability using the predicted local distance difference test (pLDDT) and predicted aligned error (PAE) values. We highlight various classes of proteins that AlphaFold2 can be applied to and the caveats involved. Concrete examples of how AlphaFold2 models can be integrated with experimental data in the form of small-angle X-ray scattering (SAXS), solution NMR, cryo-electron microscopy (cryo-EM) and X-ray diffraction are discussed. Finally, we highlight the need to move beyond structure prediction of rigid, static structural snapshots toward conformational ensembles and alternate biologically relevant states. The overarching theme is that careful consideration is due when using AlphaFold2-generated models to generate testable hypotheses and structural models, rather than treating predicted models as de facto ground truth structures.
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Affiliation(s)
- Vinayak Agarwal
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, USA.
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.
| | - Andrew C McShan
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, USA.
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6
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Yom A, Chiang A, Lewis NE. Boltzmann Model Predicts Glycan Structures from Lectin Binding. Anal Chem 2024; 96:8332-8341. [PMID: 38720429 PMCID: PMC11162346 DOI: 10.1021/acs.analchem.3c04992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2024]
Abstract
Glycans are complex oligosaccharides that are involved in many diseases and biological processes. Unfortunately, current methods for determining glycan composition and structure (glycan sequencing) are laborious and require a high level of expertise. Here, we assess the feasibility of sequencing glycans based on their lectin binding fingerprints. By training a Boltzmann model on lectin binding data, we predict the approximate structures of 88 ± 7% of N-glycans and 87 ± 13% of O-glycans in our test set. We show that our model generalizes well to the pharmaceutically relevant case of Chinese hamster ovary (CHO) cell glycans. We also analyze the motif specificity of a wide array of lectins and identify the most and least predictive lectins and glycan features. These results could help streamline glycoprotein research and be of use to anyone using lectins for glycobiology.
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Affiliation(s)
- Aria Yom
- Department of Physics, University of California, San Diego, California 92093, United States
| | - Austin Chiang
- Department of Pediatrics, University of California, San Diego, California 92093, United States
- Immunology Center of Georgia, Augusta University, Augusta, Georgia 30912, United States
- Department of Medicine, Augusta University, Augusta, Georgia 30912, United States
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, California 92093, United States
- Department of Bioengineering, University of California, San Diego, California 92093, United States
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7
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Yao G, Ke W, Xia B, Gao Z. Nanopore-based glycan sequencing: state of the art and future prospects. Chem Sci 2024; 15:6229-6243. [PMID: 38699252 PMCID: PMC11062086 DOI: 10.1039/d4sc01466a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 04/02/2024] [Indexed: 05/05/2024] Open
Abstract
Sequencing of biomacromolecules is a crucial cornerstone in life sciences. Glycans, one of the fundamental biomolecules, derive their physiological and pathological functions from their structures. Glycan sequencing faces challenges due to its structural complexity and current detection technology limitations. As a highly sensitive sensor, nanopores can directly convert nucleic acid sequence information into electrical signals, spearheading the revolution of third-generation nucleic acid sequencing technologies. However, their potential for deciphering complex glycans remains untapped. Initial attempts demonstrated the significant sensitivity of nanopores in glycan sensing, which provided the theoretical basis and insights for the realization of nanopore-based glycan sequencing. Here, we present three potential technical routes to employ nanopore technology in glycan sequencing for the first time. The three novel technical routes include: strand sequencing, capturing glycan chains as they translocate through nanopores; sequential hydrolysis sequencing, capturing released monosaccharides one by one; splicing sequencing, mapping signals from hydrolyzed glycan fragments to an oligosaccharide database/library. Designing suitable nanopores, enzymes, and motors, and extracting characteristic signals pose major challenges, potentially aided by artificial intelligence. It would be highly desirable to design an all-in-one high-throughput glycan sequencer instrument by integrating a sample processing unit, nanopore array, and signal acquisition system into a microfluidic device. The nanopore sequencer invention calls for intensive multidisciplinary cooperation including electrochemistry, glycochemistry, engineering, materials, enzymology, etc. Advancing glycan sequencing will promote the development of basic research and facilitate the discovery of glycan-based drugs and disease markers, fostering progress in glycoscience and even life sciences.
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Affiliation(s)
- Guangda Yao
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences 201203 Shanghai China
- School of Life Science and Technology, Shanghai Tech University 201210 Shanghai China
- Lingang Laboratory 200031 Shanghai China
| | - Wenjun Ke
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences 201203 Shanghai China
- University of Chinese Academy of Sciences 100049 Beijing China
| | - Bingqing Xia
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences 201203 Shanghai China
- University of Chinese Academy of Sciences 100049 Beijing China
| | - Zhaobing Gao
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences 201203 Shanghai China
- University of Chinese Academy of Sciences 100049 Beijing China
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences 528400 Zhongshan China
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8
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Dixon D, Sattar H, Moros N, Kesireddy SR, Ahsan H, Lakkimsetti M, Fatima M, Doshi D, Sadhu K, Junaid Hassan M. Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review. Cureus 2024; 16:e59954. [PMID: 38854327 PMCID: PMC11161909 DOI: 10.7759/cureus.59954] [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] [Accepted: 05/08/2024] [Indexed: 06/11/2024] Open
Abstract
This comprehensive literature review explores the transformative impact of artificial intelligence (AI) predictive analytics on healthcare, particularly in improving patient outcomes regarding disease progression, treatment response, and recovery rates. AI, encompassing capabilities such as learning, problem-solving, and decision-making, is leveraged to predict disease progression, optimize treatment plans, and enhance recovery rates through the analysis of vast datasets, including electronic health records (EHRs), imaging, and genetic data. The utilization of machine learning (ML) and deep learning (DL) techniques in predictive analytics enables personalized medicine by facilitating the early detection of conditions, precision in drug discovery, and the tailoring of treatment to individual patient profiles. Ethical considerations, including data privacy, bias, and accountability, emerge as vital in the responsible implementation of AI in healthcare. The findings underscore the potential of AI predictive analytics in revolutionizing clinical decision-making and healthcare delivery, emphasizing the necessity of ethical guidelines and continuous model validation to ensure its safe and effective use in augmenting human judgment in medical practice.
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Affiliation(s)
- Diny Dixon
- Medicine, Jubilee Mission Medical College and Research Institute, Thrissur, IND
| | - Hina Sattar
- Medicine, Dow University of Health Sciences, Karachi, PAK
| | - Natalia Moros
- Medicine, Pontifical Javeriana University Medical School, Bogotá, COL
| | | | - Huma Ahsan
- Medicine, Jinnah Postgraduate Medical Centre, Karachi, PAK
| | | | - Madiha Fatima
- Medicine, Fatima Jinnah Medical University, Lahore, PAK
| | - Dhruvi Doshi
- Medicine, Gujarat Cancer Society Medical College, Hospital & Research Centre, Ahmedabad, IND
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9
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Li H, Peralta AG, Schoffelen S, Hansen AH, Arnsdorf J, Schinn SM, Skidmore J, Choudhury B, Paulchakrabarti M, Voldborg BG, Chiang AW, Lewis NE. LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.27.587044. [PMID: 38585977 PMCID: PMC10996628 DOI: 10.1101/2024.03.27.587044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Glycosylation affects many vital functions of organisms. Therefore, its surveillance is critical from basic science to biotechnology, including biopharmaceutical development and clinical diagnostics. However, conventional glycan structure analysis faces challenges with throughput and cost. Lectins offer an alternative approach for analyzing glycans, but they only provide glycan epitopes and not full glycan structure information. To overcome these limitations, we developed LeGenD, a lectin and AI-based approach to predict N-glycan structures and determine their relative abundance in purified proteins based on lectin-binding patterns. We trained the LeGenD model using 309 glycoprofiles from 10 recombinant proteins, produced in 30 glycoengineered CHO cell lines. Our approach accurately reconstructed experimentally-measured N-glycoprofiles of bovine Fetuin B and IgG from human sera. Explanatory AI analysis with SHapley Additive exPlanations (SHAP) helped identify the critical lectins for glycoprofile predictions. Our LeGenD approach thus presents an alternative approach for N-glycan analysis.
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Affiliation(s)
- Haining Li
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Angelo G. Peralta
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Sanne Schoffelen
- National Biologics Facility Department of Biotechnology and Biomedicine, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby Denmark
| | - Anders Holmgaard Hansen
- National Biologics Facility Department of Biotechnology and Biomedicine, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby Denmark
| | - Johnny Arnsdorf
- National Biologics Facility Department of Biotechnology and Biomedicine, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby Denmark
| | - Song-Min Schinn
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Jonathan Skidmore
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA
| | - Biswa Choudhury
- Glycobiology Research and Training Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Mousumi Paulchakrabarti
- Glycobiology Research and Training Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Bjorn G. Voldborg
- National Biologics Facility Department of Biotechnology and Biomedicine, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby Denmark
| | - Austin W.T. Chiang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Nathan E. Lewis
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
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10
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Lundstrøm J, Bojar D. The evolving world of milk oligosaccharides: Biochemical diversity understood by computational advances. Carbohydr Res 2024; 537:109069. [PMID: 38402731 DOI: 10.1016/j.carres.2024.109069] [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: 01/24/2024] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 02/27/2024]
Abstract
Milk oligosaccharides, complex carbohydrates unique to mammalian milk, play crucial roles in infant nutrition and immune development. This review explores their biochemical diversity, tracing the evolutionary paths that have led to their variation across different species. We highlight the intersection of nutrition, biology, and chemistry in understanding these compounds. Additionally, we discuss the latest computational methods and analytical techniques that have revolutionized the study of milk oligosaccharides, offering insights into their structural complexity and functional roles. This brief but essential review not only aims to provide a deeper understanding of milk oligosaccharides but also discuss the road toward their potential applications.
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Affiliation(s)
- Jon Lundstrøm
- Department of Chemistry and Molecular Biology, University of Gothenburg, 41390, Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 41390, Gothenburg, Sweden
| | - Daniel Bojar
- Department of Chemistry and Molecular Biology, University of Gothenburg, 41390, Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 41390, Gothenburg, Sweden.
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11
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Zhao L, Chen J, Bai B, Song G, Zhang J, Yu H, Huang S, Wang Z, Lu G. Topical drug delivery strategies for enhancing drug effectiveness by skin barriers, drug delivery systems and individualized dosing. Front Pharmacol 2024; 14:1333986. [PMID: 38293666 PMCID: PMC10825035 DOI: 10.3389/fphar.2023.1333986] [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: 11/06/2023] [Accepted: 12/27/2023] [Indexed: 02/01/2024] Open
Abstract
Topical drug delivery is widely used in various diseases because of the advantages of not passing through the gastrointestinal tract, avoiding gastrointestinal irritation and hepatic first-pass effect, and reaching the lesion directly to reduce unnecessary adverse reactions. The skin helps the organism to defend itself against a huge majority of external aggressions and is one of the most important lines of defense of the body. However, the skin's strong barrier ability is also a huge obstacle to the effectiveness of topical medications. Allowing the bioactive, composition in a drug to pass through the stratum corneum barrier as needed to reach the target site is the most essential need for the bioactive, composition to exert its therapeutic effect. The state of the skin barrier, the choice of delivery system for the bioactive, composition, and individualized disease detection and dosing planning influence the effectiveness of topical medications. Nowadays, enhancing transdermal absorption of topically applied drugs is the hottest research area. However, enhancing transdermal absorption of drugs is not the first choice to improve the effectiveness of all drugs. Excessive transdermal absorption enhances topical drug accumulation at non-target sites and the occurrence of adverse reactions. This paper introduces topical drug delivery strategies to improve drug effectiveness from three perspectives: skin barrier, drug delivery system and individualized drug delivery, describes the current status and shortcomings of topical drug research, and provides new directions and ideas for topical drug research.
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Affiliation(s)
- Lin Zhao
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jiamei Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Bai Bai
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Guili Song
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jingwen Zhang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Han Yu
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Shiwei Huang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zhang Wang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Guanghua Lu
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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12
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Springer SA, Siddiqui SS. Editorial: Glycans: molecules at the interface of immunity and disease. Front Immunol 2024; 14:1354723. [PMID: 38250063 PMCID: PMC10796668 DOI: 10.3389/fimmu.2023.1354723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 01/23/2024] Open
Affiliation(s)
| | - Shoib Sawar Siddiqui
- Life and Medical Sciences, University of Hertfordshire, Hatfield, United Kingdom
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13
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Pérez S. Computational modeling of protein-carbohydrate interactions: Current trends and future challenges. Adv Carbohydr Chem Biochem 2023; 83:133-149. [PMID: 37968037 DOI: 10.1016/bs.accb.2023.10.003] [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] [Indexed: 11/17/2023]
Abstract
The article leads the reader through an up-to-date presentation of the concepts, developments, and main applications of computational modeling to study protein-carbohydrate interactions. It follows with the presentation of some current issues and perspectives arising from the expected evolution of generic methodological developments in deep learning, immersive analytics, and virtual reality for molecular visualization and data management. Such methodological developments for macromolecular interactions would greatly benefit a wide range of scientific endeavors in the field of carbohydrate chemistry and biochemistry, including the following interrelated efforts dealing with highly crowded media, with examples concerning glycoside transferases, the extracellular matrix, and the exploration of interactions between complex carbohydrates and intrinsically disordered proteins.
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Affiliation(s)
- Serge Pérez
- Centre de Recherches sur les Macromolécules Végétales, CNRS, Université Grenoble Alpes, Grenoble, France.
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Pokharel S, Pratyush P, Ismail HD, Ma J, KC DB. Integrating Embeddings from Multiple Protein Language Models to Improve Protein O-GlcNAc Site Prediction. Int J Mol Sci 2023; 24:16000. [PMID: 37958983 PMCID: PMC10650050 DOI: 10.3390/ijms242116000] [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: 08/17/2023] [Revised: 11/02/2023] [Accepted: 11/04/2023] [Indexed: 11/15/2023] Open
Abstract
O-linked β-N-acetylglucosamine (O-GlcNAc) is a distinct monosaccharide modification of serine (S) or threonine (T) residues of nucleocytoplasmic and mitochondrial proteins. O-GlcNAc modification (i.e., O-GlcNAcylation) is involved in the regulation of diverse cellular processes, including transcription, epigenetic modifications, and cell signaling. Despite the great progress in experimentally mapping O-GlcNAc sites, there is an unmet need to develop robust prediction tools that can effectively locate the presence of O-GlcNAc sites in protein sequences of interest. In this work, we performed a comprehensive evaluation of a framework for prediction of protein O-GlcNAc sites using embeddings from pre-trained protein language models. In particular, we compared the performance of three protein sequence-based large protein language models (pLMs), Ankh, ESM-2, and ProtT5, for prediction of O-GlcNAc sites and also evaluated various ensemble strategies to integrate embeddings from these protein language models. Upon investigation, the decision-level fusion approach that integrates the decisions of the three embedding models, which we call LM-OGlcNAc-Site, outperformed the models trained on these individual language models as well as other fusion approaches and other existing predictors in almost all of the parameters evaluated. The precise prediction of O-GlcNAc sites will facilitate the probing of O-GlcNAc site-specific functions of proteins in physiology and diseases. Moreover, these findings also indicate the effectiveness of combined uses of multiple protein language models in post-translational modification prediction and open exciting avenues for further research and exploration in other protein downstream tasks. LM-OGlcNAc-Site's web server and source code are publicly available to the community.
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Affiliation(s)
- Suresh Pokharel
- Department of Computer Science, Michigan Technological University, Houghton, MI 49931, USA; (S.P.); (P.P.); (H.D.I.)
| | - Pawel Pratyush
- Department of Computer Science, Michigan Technological University, Houghton, MI 49931, USA; (S.P.); (P.P.); (H.D.I.)
| | - Hamid D. Ismail
- Department of Computer Science, Michigan Technological University, Houghton, MI 49931, USA; (S.P.); (P.P.); (H.D.I.)
| | - Junfeng Ma
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Georgetown University, Washington, DC 20057, USA;
| | - Dukka B. KC
- Department of Computer Science, Michigan Technological University, Houghton, MI 49931, USA; (S.P.); (P.P.); (H.D.I.)
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15
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Hagg A, Kirschner KN. Open-Source Machine Learning in Computational Chemistry. J Chem Inf Model 2023; 63:4505-4532. [PMID: 37466636 PMCID: PMC10430767 DOI: 10.1021/acs.jcim.3c00643] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Indexed: 07/20/2023]
Abstract
The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.
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Affiliation(s)
- Alexander Hagg
- Institute
of Technology, Resource and Energy-Efficient Engineering (TREE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
- Department
of Electrical Engineering, Mechanical Engineering and Technical Journalism, University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
| | - Karl N. Kirschner
- Institute
of Technology, Resource and Energy-Efficient Engineering (TREE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
- Department
of Computer Science, University of Applied
Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
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16
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Costa J, Hayes C, Lisacek F. Protein glycosylation and glycoinformatics for novel biomarker discovery in neurodegenerative diseases. Ageing Res Rev 2023; 89:101991. [PMID: 37348818 DOI: 10.1016/j.arr.2023.101991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/25/2023] [Accepted: 06/18/2023] [Indexed: 06/24/2023]
Abstract
Glycosylation is a common post-translational modification of brain proteins including cell surface adhesion molecules, synaptic proteins, receptors and channels, as well as intracellular proteins, with implications in brain development and functions. Using advanced state-of-the-art glycomics and glycoproteomics technologies in conjunction with glycoinformatics resources, characteristic glycosylation profiles in brain tissues are increasingly reported in the literature and growing evidence shows deregulation of glycosylation in central nervous system disorders, including aging associated neurodegenerative diseases. Glycan signatures characteristic of brain tissue are also frequently described in cerebrospinal fluid due to its enrichment in brain-derived molecules. A detailed structural analysis of brain and cerebrospinal fluid glycans collected in publications in healthy and neurodegenerative conditions was undertaken and data was compiled to create a browsable dedicated set in the GlyConnect database of glycoproteins (https://glyconnect.expasy.org/brain). The shared molecular composition of cerebrospinal fluid with brain enhances the likelihood of novel glycobiomarker discovery for neurodegeneration, which may aid in unveiling disease mechanisms, therefore, providing with novel therapeutic targets as well as diagnostic and progression monitoring tools.
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Affiliation(s)
- Júlia Costa
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, 2780-157 Oeiras, Portugal.
| | - Catherine Hayes
- Proteome Informatics Group, Swiss Institute of Bioinformatics, CH-1227 Geneva, Switzerland
| | - Frédérique Lisacek
- Proteome Informatics Group, Swiss Institute of Bioinformatics, CH-1227 Geneva, Switzerland; Computer Science Department, University of Geneva, CH-1227 Geneva, Switzerland; Section of Biology, University of Geneva, CH-1211 Geneva, Switzerland
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17
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Chau TH, Chernykh A, Kawahara R, Thaysen-Andersen M. Critical considerations in N-glycoproteomics. Curr Opin Chem Biol 2023; 73:102272. [PMID: 36758418 DOI: 10.1016/j.cbpa.2023.102272] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/30/2022] [Accepted: 01/05/2023] [Indexed: 02/10/2023]
Abstract
N-Glycoproteomics, the system-wide study of glycans asparagine-linked to protein carriers, holds a unique and still largely untapped potential to provide deep insights into the complexity and dynamics of the heterogeneous N-glycoproteome. Despite the advent of innovative analytical and informatics tools aiding the analysis, N-glycoproteomics remains challenging and consequently largely restricted to specialised laboratories. Aiming to stimulate discussions of method harmonisation, data standardisation and reporting guidelines to make N-glycoproteomics more reproducible and accessible to the community, we here discuss critical considerations related to the design and execution of N-glycoproteomics experiments and highlight good practices in N-glycopeptide data collection, analysis, interpretation and sharing. Giving the rapid maturation and, expectedly, a wide-spread implementation of N-glycoproteomics capabilities across the community in future years, this piece aims to point out common pitfalls, to encourage good data sharing and documentation practices, and to highlight practical solutions and strategies to enhance the insight into the N-glycoproteome.
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Affiliation(s)
- The Huong Chau
- School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, Australia; Biomolecular Discovery Research Centre, Macquarie University, Sydney, Australia
| | - Anastasia Chernykh
- School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, Australia; Biomolecular Discovery Research Centre, Macquarie University, Sydney, Australia
| | - Rebeca Kawahara
- School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, Australia; Biomolecular Discovery Research Centre, Macquarie University, Sydney, Australia
| | - Morten Thaysen-Andersen
- School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, Australia; Biomolecular Discovery Research Centre, Macquarie University, Sydney, Australia; Institute for Glyco-core Research (iGCORE), Nagoya University, Nagoya, Japan.
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18
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HS, an Ancient Molecular Recognition and Information Storage Glycosaminoglycan, Equips HS-Proteoglycans with Diverse Matrix and Cell-Interactive Properties Operative in Tissue Development and Tissue Function in Health and Disease. Int J Mol Sci 2023; 24:ijms24021148. [PMID: 36674659 PMCID: PMC9867265 DOI: 10.3390/ijms24021148] [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: 11/15/2022] [Revised: 12/23/2022] [Accepted: 12/27/2022] [Indexed: 01/11/2023] Open
Abstract
Heparan sulfate is a ubiquitous, variably sulfated interactive glycosaminoglycan that consists of repeating disaccharides of glucuronic acid and glucosamine that are subject to a number of modifications (acetylation, de-acetylation, epimerization, sulfation). Variable heparan sulfate chain lengths and sequences within the heparan sulfate chains provide structural diversity generating interactive oligosaccharide binding motifs with a diverse range of extracellular ligands and cellular receptors providing instructional cues over cellular behaviour and tissue homeostasis through the regulation of essential physiological processes in development, health, and disease. heparan sulfate and heparan sulfate-PGs are integral components of the specialized glycocalyx surrounding cells. Heparan sulfate is the most heterogeneous glycosaminoglycan, in terms of its sequence and biosynthetic modifications making it a difficult molecule to fully characterize, multiple ligands also make an elucidation of heparan sulfate functional properties complicated. Spatio-temporal presentation of heparan sulfate sulfate groups is an important functional determinant in tissue development and in cellular control of wound healing and extracellular remodelling in pathological tissues. The regulatory properties of heparan sulfate are mediated via interactions with chemokines, chemokine receptors, growth factors and morphogens in cell proliferation, differentiation, development, tissue remodelling, wound healing, immune regulation, inflammation, and tumour development. A greater understanding of these HS interactive processes will improve therapeutic procedures and prognoses. Advances in glycosaminoglycan synthesis and sequencing, computational analytical carbohydrate algorithms and advanced software for the evaluation of molecular docking of heparan sulfate with its molecular partners are now available. These advanced analytic techniques and artificial intelligence offer predictive capability in the elucidation of heparan sulfate conformational effects on heparan sulfate-ligand interactions significantly aiding heparan sulfate therapeutics development.
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Xie J, Luo X, Deng X, Tang Y, Tian W, Cheng H, Zhang J, Zou Y, Guo Z, Xie X. Advances in artificial intelligence to predict cancer immunotherapy efficacy. Front Immunol 2023; 13:1076883. [PMID: 36685496 PMCID: PMC9845588 DOI: 10.3389/fimmu.2022.1076883] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 12/09/2022] [Indexed: 01/05/2023] Open
Abstract
Tumor immunotherapy, particularly the use of immune checkpoint inhibitors, has yielded impressive clinical benefits. Therefore, it is critical to accurately screen individuals for immunotherapy sensitivity and forecast its efficacy. With the application of artificial intelligence (AI) in the medical field in recent years, an increasing number of studies have indicated that the efficacy of immunotherapy can be better anticipated with the help of AI technology to reach precision medicine. This article focuses on the current prediction models based on information from histopathological slides, imaging-omics, genomics, and proteomics, and reviews their research progress and applications. Furthermore, we also discuss the existing challenges encountered by AI in the field of immunotherapy, as well as the future directions that need to be improved, to provide a point of reference for the early implementation of AI-assisted diagnosis and treatment systems in the future.
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Affiliation(s)
- Jindong Xie
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xiyuan Luo
- School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xinpei Deng
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yuhui Tang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Wenwen Tian
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Hui Cheng
- School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Junsheng Zhang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yutian Zou
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,*Correspondence: Xiaoming Xie, ; Zhixing Guo, ; Yutian Zou,
| | - Zhixing Guo
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,*Correspondence: Xiaoming Xie, ; Zhixing Guo, ; Yutian Zou,
| | - Xiaoming Xie
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,*Correspondence: Xiaoming Xie, ; Zhixing Guo, ; Yutian Zou,
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