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Deng B, Vanagas L, Alonso AM, Angel SO. Proteomics Applications in Toxoplasma gondii: Unveiling the Host-Parasite Interactions and Therapeutic Target Discovery. Pathogens 2023; 13:33. [PMID: 38251340 PMCID: PMC10821451 DOI: 10.3390/pathogens13010033] [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: 11/13/2023] [Revised: 12/25/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024] Open
Abstract
Toxoplasma gondii, a protozoan parasite with the ability to infect various warm-blooded vertebrates, including humans, is the causative agent of toxoplasmosis. This infection poses significant risks, leading to severe complications in immunocompromised individuals and potentially affecting the fetus through congenital transmission. A comprehensive understanding of the intricate molecular interactions between T. gondii and its host is pivotal for the development of effective therapeutic strategies. This review emphasizes the crucial role of proteomics in T. gondii research, with a specific focus on host-parasite interactions, post-translational modifications (PTMs), PTM crosstalk, and ongoing efforts in drug discovery. Additionally, we provide an overview of recent advancements in proteomics techniques, encompassing interactome sample preparation methods such as BioID (BirA*-mediated proximity-dependent biotin identification), APEX (ascorbate peroxidase-mediated proximity labeling), and Y2H (yeast two hybrid), as well as various proteomics approaches, including single-cell analysis, DIA (data-independent acquisition), targeted, top-down, and plasma proteomics. Furthermore, we discuss bioinformatics and the integration of proteomics with other omics technologies, highlighting its potential in unraveling the intricate mechanisms of T. gondii pathogenesis and identifying novel therapeutic targets.
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Affiliation(s)
- Bin Deng
- Department of Biology and VBRN Proteomics Facility, University of Vermont, Burlington, VT 05405, USA
| | - Laura Vanagas
- Laboratorio de Parasitología Molecular, Instituto Tecnológico de Chascomús (CONICET-UNSAM), Chascomús 7130, Provincia de Buenos Aires, Argentina; (L.V.); (S.O.A.); (A.M.A.)
- Escuela de Bio y Nanotecnologías (UNSAM), 25 de Mayo y Francia. C.P., San Martín 1650, Provincia de Buenos Aires, Argentina
| | - Andres M. Alonso
- Laboratorio de Parasitología Molecular, Instituto Tecnológico de Chascomús (CONICET-UNSAM), Chascomús 7130, Provincia de Buenos Aires, Argentina; (L.V.); (S.O.A.); (A.M.A.)
- Escuela de Bio y Nanotecnologías (UNSAM), 25 de Mayo y Francia. C.P., San Martín 1650, Provincia de Buenos Aires, Argentina
| | - Sergio O. Angel
- Laboratorio de Parasitología Molecular, Instituto Tecnológico de Chascomús (CONICET-UNSAM), Chascomús 7130, Provincia de Buenos Aires, Argentina; (L.V.); (S.O.A.); (A.M.A.)
- Escuela de Bio y Nanotecnologías (UNSAM), 25 de Mayo y Francia. C.P., San Martín 1650, Provincia de Buenos Aires, Argentina
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Wang B, Zhang X, Xu C, Han X, Wang Y, Situ C, Li Y, Guo X. DeepSP: A Deep Learning Framework for Spatial Proteomics. J Proteome Res 2023. [PMID: 37314414 DOI: 10.1021/acs.jproteome.2c00394] [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: 06/15/2023]
Abstract
The study of protein subcellular localization (PSL) is a fundamental step toward understanding the mechanism of protein function. The recent development of mass spectrometry (MS)-based spatial proteomics to quantify the distribution of proteins across subcellular fractions provides us a high-throughput approach to predict unknown PSLs based on known PSLs. However, the accuracy of PSL annotations in spatial proteomics is limited by the performance of existing PSL predictors based on traditional machine learning algorithms. In this study, we present a novel deep learning framework named DeepSP for PSL prediction of an MS-based spatial proteomics data set. DeepSP constructs the new feature map of a difference matrix by capturing detailed changes between different subcellular fractions of protein occupancy profiles and uses the convolutional block attention module to improve the prediction performance of PSL. DeepSP achieved significant improvement in accuracy and robustness for PSL prediction in independent test sets and unknown PSL prediction compared to current state-of-the-art machine learning predictors. As an efficient and robust framework for PSL prediction, DeepSP is expected to facilitate spatial proteomics studies and contributes to the elucidation of protein functions and the regulation of biological processes.
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Affiliation(s)
- Bing Wang
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Xiangzheng Zhang
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
| | - Chen Xu
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
| | - Xudong Han
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Yue Wang
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
| | - Chenghao Situ
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
| | - Yan Li
- Department of Clinical Laboratory, Sir Run Run Hospital, Nanjing Medical University, Nanjing 211100, China
| | - Xuejiang Guo
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
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Shami A, Alharbi NK, Al-Saeed FA, Alsaegh AA, Al Syaad KM, Abd El-Rahim IHA, Mostafa YS, Ahmed AE. In Silico Subtractive Proteomics and Molecular Docking Approaches for the Identification of Novel Inhibitors against Streptococcus pneumoniae Strain D39. Life (Basel) 2023; 13:life13051128. [PMID: 37240772 DOI: 10.3390/life13051128] [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: 01/30/2023] [Revised: 04/10/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
Streptococcus pneumoniae is a notorious Gram-positive pathogen present asymptomatically in the nasophayrnx of humans. According to the World Health Organization (W.H.O), pneumococcus causes approximately one million deaths yearly. Antibiotic resistance in S. pneumoniae is raising considerable concern around the world. There is an immediate need to address the major issues that have arisen as a result of persistent infections caused by S. pneumoniae. In the present study, subtractive proteomics was used in which the entire proteome of the pathogen consisting of 1947 proteins is effectively decreased to a finite number of possible targets. Various kinds of bioinformatics tools and software were applied for the discovery of novel inhibitors. The CD-HIT analysis revealed 1887 non-redundant sequences from the entire proteome. These non-redundant proteins were submitted to the BLASTp against the human proteome and 1423 proteins were screened as non-homologous. Further, databases of essential genes (DEGG) and J browser identified almost 171 essential proteins. Moreover, non-homologous, essential proteins were subjected in KEGG Pathway Database which shortlisted six unique proteins. In addition, the subcellular localization of these unique proteins was checked and cytoplasmic proteins were chosen for the druggability analysis, which resulted in three proteins, namely DNA binding response regulator (SPD_1085), UDP-N-acetylmuramate-L-alanine Ligase (SPD_1349) and RNA polymerase sigma factor (SPD_0958), which can act as a promising potent drug candidate to limit the toxicity caused by S. pneumoniae. The 3D structures of these proteins were predicted by Swiss Model, utilizing the homology modeling approach. Later, molecular docking by PyRx software 0.8 version was used to screen a library of phytochemicals retrieved from PubChem and ZINC databases and already approved drugs from DrugBank database against novel druggable targets to check their binding affinity with receptor proteins. The top two molecules from each receptor protein were selected based on the binding affinity, RMSD value, and the highest conformation. Finally, the absorption, distribution, metabolism, excretion, and toxicity (ADMET) analyses were carried out by utilizing the SWISS ADME and Protox tools. This research supported the discovery of cost-effective drugs against S. pneumoniae. However, more in vivo/in vitro research should be conducted on these targets to investigate their pharmacological efficacy and their function as efficient inhibitors.
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Affiliation(s)
- Ashwag Shami
- Department of Biology, College of Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11617, Saudi Arabia
| | - Nada K Alharbi
- Department of Biology, College of Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11617, Saudi Arabia
| | - Fatimah A Al-Saeed
- Research Centre, Department of Biology, College of Science, King Khalid University, Abha 61413, Saudi Arabia
- Advanced Material Science (RCAMS), King Khalid University, Abha 61413, Saudi Arabia
| | - Aiman A Alsaegh
- Department of Laboratory Medicine, Faculty of Applied Medical Sciences, Umm Al-Qura University, Makkah Al-Mukarramah 24382, Saudi Arabia
| | - Khalid M Al Syaad
- Biology Department, Faculty of Science, King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia
- The Research Center, Faculty of Science, King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia
| | - Ibrahim H A Abd El-Rahim
- Department of Environmental and Health Research, Umm Al-Qura University, P.O. Box 6287, Makkah Al-Mukarramah 21955, Saudi Arabia
| | - Yasser Sabry Mostafa
- Biology Department, Faculty of Science, King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia
| | - Ahmed Ezzat Ahmed
- Biology Department, Faculty of Science, King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia
- Department of Theriogenology, Faculty of Veterinary Medicine, South Valley University, Qena 83523, Egypt
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Xiao W, Pan D, Niu Z, Fan Y, Wu S, Wu W. Opportunities and challenges of high-pressure ion exchange chromatography for nuclide separation and enrichment. CHINESE CHEM LETT 2022. [DOI: 10.1016/j.cclet.2022.03.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Nakai K, Wei L. Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics. FRONTIERS IN BIOINFORMATICS 2022; 2:910531. [PMID: 36304291 PMCID: PMC9580943 DOI: 10.3389/fbinf.2022.910531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
Prediction of subcellular localization of proteins from their amino acid sequences has a long history in bioinformatics and is still actively developing, incorporating the latest advances in machine learning and proteomics. Notably, deep learning-based methods for natural language processing have made great contributions. Here, we review recent advances in the field as well as its related fields, such as subcellular proteomics and the prediction/recognition of subcellular localization from image data.
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Affiliation(s)
- Kenta Nakai
- Institute of Medical Science, The University of Tokyo, Minato-Ku, Japan
- *Correspondence: Kenta Nakai,
| | - Leyi Wei
- School of Software, Shandong University, Jinan, China
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