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学者姓名：李春华
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摘要 ：
Prevention of Clostridium difficile infection is challenging worldwide owing to its high morbidity and mortality rates. C. difficile is currently being classified as an urgent threat by the CDC. Devising a new therapeutic strategy become indispensable against C. difficile infection due to its high rates of reinfection and increasing antimicrobial resistance. The current study is based on core proteome data of C. difficile to identify promising vaccine and drug candidates. Immunoinformatics and vaccinomics approaches were employed to construct multi-epitope-based chimeric vaccine constructs from top-ranked T- and B-cell epitopes. The efficacy of the designed vaccine was assessed by immunological analysis, immune receptor binding potential and immune simulation analyses. Additionally, subtractive proteomics and druggability analyses prioritized several promising and alternative drug targets against C. difficile. These include FMN-dependent nitroreductase which was prioritized for pharmacophore-based virtual screening of druggable molecule databases to predict potent inhibitors. A MolPort-001-785-965 druggable molecule was found to exhibit significant binding affinity with the conserved residues of FMN-dependent nitroreductase. The experimental validation of the therapeutic targets prioritized in the current study may worthy to identify new strategies to combat the drug-resistant C. difficile infection.
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| GB/T 7714 | Aiman, Sara , Farooq, Qurrat ul Ain , Han, Zhongjie et al. Core-genome-mediated promising alternative drug and multi-epitope vaccine targets prioritization against infectious Clostridium difficile [J]. | PLOS ONE , 2024 , 19 (1) . |
| MLA | Aiman, Sara et al. "Core-genome-mediated promising alternative drug and multi-epitope vaccine targets prioritization against infectious Clostridium difficile" . | PLOS ONE 19 . 1 (2024) . |
| APA | Aiman, Sara , Farooq, Qurrat ul Ain , Han, Zhongjie , Aslam, Muneeba , Zhang, Jilong , Khan, Asifullah et al. Core-genome-mediated promising alternative drug and multi-epitope vaccine targets prioritization against infectious Clostridium difficile . | PLOS ONE , 2024 , 19 (1) . |
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摘要 ：
Graded activation contributes to the precise regulation of GPCR activity, presenting new opportunities for drug design. In this work, a total of 10 mu s enhanced-sampling simulations are performed to provide molecular insights into the binding dynamics differences of the neurotensin receptor 1 (NTSR1) to the full agonist SRI-9829, partial agonist RTI-3a and inverse agonist SR48692. The possible graded activation mechanism of NTSR1 is revealed by an integrated analysis utilizing the reweighted potential of mean force (PMF), deep learning (DL) and transfer entropy (TE). Specifically, the orthosteric pocket is observed to undergo expansion and contraction, with the Gprotein-binding site experiencing interconversions among the inactive, intermediate and active-like states. Detailed structural comparisons capture subtle conformational differences arising from ligand binding in allosteric signaling, which can well explain the graded activation. Critical microswitches that contribute to graded activation are efficiently identified with the DL model. TE calculations enable the visualization of allosteric communication networks within the receptor, elucidating the driver-responder relationships associated with signal transduction. Fortunately, the dissociation of the full agonist from the orthosteric pocket is observed. The current findings systematically reveal the mechanism of NTSR1 graded activation, and also provide implications for structure-based drug design.
关键词 ：
MD simulation MD simulation GPCR GPCR Graded activation Graded activation
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| GB/T 7714 | Wu, Zhixiang , Sun, Xiaohan , Su, Jingjie et al. Revealing the graded activation mechanism of neurotensin receptor 1 [J]. | INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES , 2024 , 278 . |
| MLA | Wu, Zhixiang et al. "Revealing the graded activation mechanism of neurotensin receptor 1" . | INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES 278 (2024) . |
| APA | Wu, Zhixiang , Sun, Xiaohan , Su, Jingjie , Zhang, Xinyu , Hu, Jianping , Li, Chunhua . Revealing the graded activation mechanism of neurotensin receptor 1 . | INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES , 2024 , 278 . |
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摘要 ：
Peptides are pivotal in numerous biological activities by engaging in up to 40 % of protein-protein interactions in many cellular processes. Due to their exceptional specificity and effectiveness, peptides have emerged as promising candidates for drug design. However, accurately predicting protein-peptide binding affinity remains a challenging. Aiming at the problem, we develop a prediction model PepPAP based on convolutional neural network and multi-head attention, which relies solely on sequence features. These features include physicochemical properties, intrinsic disorder, sequence encoding, and especially interface propensity which is extracted from 16,689 non-redundant protein-peptide complexes. Notably, the adopted regression stratification crossvalidation scheme proposed in our previous work is beneficial to improve the prediction for the cases with extreme binding affinity values. On three benchmark test datasets: T100, a series of peptides targeting to PDZ domain and CXCR4, PepPAP shows excellent performance, outperforming the existing methods and demonstrating its good generalization ability. Furthermore, PepPAP has good results in binary interaction prediction, and the analysis of the feature space distribution visualization highlights PepPAP's effectiveness. To the best of our knowledge, PepPAP is the first sequence-based deep attention model for wide-genome protein-peptide binding affinity prediction, and holds the potential to offer valuable insights for the peptide-based drug design.
关键词 ：
Binding affinity prediction Binding affinity prediction Multi-head attention Multi-head attention Protein-peptide interactions Protein-peptide interactions Interface propensity Interface propensity Convolutional neural network Convolutional neural network
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| GB/T 7714 | Sun, Xiaohan , Wu, Zhixiang , Su, Jingjie et al. A deep attention model for wide-genome protein-peptide binding affinity prediction at a sequence level [J]. | INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES , 2024 , 276 . |
| MLA | Sun, Xiaohan et al. "A deep attention model for wide-genome protein-peptide binding affinity prediction at a sequence level" . | INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES 276 (2024) . |
| APA | Sun, Xiaohan , Wu, Zhixiang , Su, Jingjie , Li, Chunhua . A deep attention model for wide-genome protein-peptide binding affinity prediction at a sequence level . | INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES , 2024 , 276 . |
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摘要 ：
Protein missense mutations and resulting protein stability changes are important causes for many human genetic diseases. However, the accurate prediction of stability changes due to mutations remains a challenging problem. To address this problem, we have developed an unbiased effective model: PMSPcnn that is based on a convolutional neural network. We have included an anti -symmetry property to build a balanced training dataset, which improves the prediction, in particular for stabilizing mutations. Persistent homology, which is an effective approach for characterizing protein structures, is used to obtain topological features. Additionally, a regression stratification cross -validation scheme has been proposed to improve the prediction for mutations with extreme DD G. For three test datasets: S sym , p53, and myoglobin, PMSPcnn achieves a better performance than currently existing predictors. PMSPcnn also outperforms currently available methods for membrane proteins. Overall, PMSPcnn is a promising method for the prediction of protein stability caused mutations.
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| GB/T 7714 | Sun, Xiaohan , Yang, Shuang , Wu, Zhixiang et al. PMSPcnn: Predicting protein stability changes upon single point mutations with convolutional neural network [J]. | STRUCTURE , 2024 , 32 (6) . |
| MLA | Sun, Xiaohan et al. "PMSPcnn: Predicting protein stability changes upon single point mutations with convolutional neural network" . | STRUCTURE 32 . 6 (2024) . |
| APA | Sun, Xiaohan , Yang, Shuang , Wu, Zhixiang , Su, Jingjie , Hu, Fangrui , Chang, Fubin et al. PMSPcnn: Predicting protein stability changes upon single point mutations with convolutional neural network . | STRUCTURE , 2024 , 32 (6) . |
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摘要 ：
The inwardly rectifying potassium channel Kir3.2, a member of the inward rectifier potassium (Kir) channel family, exerts important biological functions through transporting potassium ions outside of the cell, during which a large-scale synergistic movement occurs among its different domains. Currently, it is not fully understood how the binding of the ligand to the Kir3.2 channel leads to the structural changes and which key residues are responsible for the channel gating and allosteric dynamics. Here, we construct the Gaussian network model (GNM) of the Kir3.2 channel with the secondary structure and covalent interaction information considered (sscGNM), which shows a better performance in reproducing the channel's flexibility compared with the traditional GNM. In addition, the sscANM-based perturbation method is used to simulate the channel's conformational transition caused by the activator PIP2's binding. By applying certain forces to the PIP2 binding pocket, the coarse-grained calculations generate the similar conformational changes to the experimental observation, suggesting that the topology structure as well as PIP2 binding are crucial to the allosteric activation of the Kir3.2 channel. We also utilize the sscGNM-based thermodynamic cycle method developed by us to identify the key residues whose mutations significantly alter the channel's binding free energy with PIP2. We identify not only the residues important for the specific binding but also the ones critical for the allosteric transition coupled with PIP2 binding. This study is helpful for understanding the working mechanism of Kir3.2 channels and can provide important information for related drug design.
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| GB/T 7714 | Zhao, Yingchun , Zhang, Xinyu , Liu, Lamei et al. Insights into Activation Dynamics and Functional Sites of Inwardly Rectifying Potassium Channel Kir3.2 by an Elastic Network Model Combined with Perturbation Methods [J]. | JOURNAL OF PHYSICAL CHEMISTRY B , 2024 , 128 (6) : 1360-1370 . |
| MLA | Zhao, Yingchun et al. "Insights into Activation Dynamics and Functional Sites of Inwardly Rectifying Potassium Channel Kir3.2 by an Elastic Network Model Combined with Perturbation Methods" . | JOURNAL OF PHYSICAL CHEMISTRY B 128 . 6 (2024) : 1360-1370 . |
| APA | Zhao, Yingchun , Zhang, Xinyu , Liu, Lamei , Hu, Fangrui , Chang, Fubin , Han, Zhongjie et al. Insights into Activation Dynamics and Functional Sites of Inwardly Rectifying Potassium Channel Kir3.2 by an Elastic Network Model Combined with Perturbation Methods . | JOURNAL OF PHYSICAL CHEMISTRY B , 2024 , 128 (6) , 1360-1370 . |
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摘要 ：
考虑转移熵和空间近邻进化信息的基于集成模型的蛋白质变构位点预测方法，属于蛋白质功能位点预测技术领域。包括四个步骤：一是查找蛋白质表面潜在的变构口袋，二是提取口袋特征，三是产生多份训练子集并筛选获得最优特征组合，四是构建基于多个子模型的集成模型以预测蛋白质变构位点。本发明首次将动力学转移熵和空间协同进化信息用于蛋白质变构位点预测，其中空间协同进化信息是我们之前开发的，可以很好地考虑氨基酸残基空间近邻的协同进化性。
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| GB/T 7714 | 李春华 , 胡芳睿 , 孙晓晗 et al. 考虑转移熵和空间近邻进化信息的基于集成模型的蛋白质变构位点预测方法 : CN202310445893.X[P]. | 2023-04-23 . |
| MLA | 李春华 et al. "考虑转移熵和空间近邻进化信息的基于集成模型的蛋白质变构位点预测方法" : CN202310445893.X. | 2023-04-23 . |
| APA | 李春华 , 胡芳睿 , 孙晓晗 , 孔晓天 . 考虑转移熵和空间近邻进化信息的基于集成模型的蛋白质变构位点预测方法 : CN202310445893.X. | 2023-04-23 . |
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摘要 ：
Shigella sonnei is a gram-negative bacterium and is the primary cause of shigellosis in advanced countries. An exceptional rise in the prevalence of the disease has been reported in Asia, the Middle East, and Latin America. To date, no preventive vaccine is available against S. sonnei infections. This pathogen has shown resistances towards both first- and second-line antibiotics. Therefore, an effective broad spectrum vaccine development against shigellosis is indispensable. In the present study, vaccinomics-aided immunoinformatics strategies were pursued to identify potential vaccine candidates from the S. sonnei whole proteome data. Pathogen essential proteins that are non-homologous to human and human gut microbiome proteome set, are feasible candidates for this purpose. Three antigenic outer membrane proteins were prioritized to predict lead epitopes based on reverse vaccinology approach. Multi-epitope-based chimeric vaccines was designed using lead B- and T-cell epitopes combined with suitable linker and adjuvant peptide sequences to enhance immune responses against the designed vaccine. The SS-MEVC construct was prioritized based on multiple physicochemical, immunological properties, and immune-receptors docking scores. Immune simulation analysis predicted strong immunogenic response capability of the designed vaccine construct. The Molecular dynamic simulations analysis ensured stable molecular interactions of lead vaccine construct with the host receptors. In silico restriction and cloning analysis predicted feasible cloning capability of the SS-MEVC construct within the E. coli expression system. The proposed vaccine construct is predicted to be more safe, effective and capable of inducing robust immune responses against S. sonnei infections and may be worthy of examination via in vitro/in vivo assays.
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| GB/T 7714 | Aiman, Sara , Ahmad, Abbas , Khan, Asifullah et al. Vaccinomics-aided next-generation novel multi-epitope-based vaccine engineering against multidrug resistant Shigella Sonnei: Immunoinformatics and chemoinformatics approaches [J]. | PLOS ONE , 2023 , 18 (11) . |
| MLA | Aiman, Sara et al. "Vaccinomics-aided next-generation novel multi-epitope-based vaccine engineering against multidrug resistant Shigella Sonnei: Immunoinformatics and chemoinformatics approaches" . | PLOS ONE 18 . 11 (2023) . |
| APA | Aiman, Sara , Ahmad, Abbas , Khan, Asifullah , Malik, Abdul , Alkholief, Musaed , Akhtar, Suhail et al. Vaccinomics-aided next-generation novel multi-epitope-based vaccine engineering against multidrug resistant Shigella Sonnei: Immunoinformatics and chemoinformatics approaches . | PLOS ONE , 2023 , 18 (11) . |
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摘要 ：
Dengue Virus (DENV) is a serious threat to human life worldwide and is one of the most dangerous vector-borne diseases, causing thousands of deaths annually. We constructed a comprehensive PPI map of DENV with its host Homo sapiens and performed various bioinformatics analyses. We found 1195 interactions between 858 human and 10 DENV proteins. Pathway enrichment analysis was performed on the two sets of gene products, and the top 5 human proteins with the maximum number of interactions with dengue viral proteins revealed noticeable results. The non-structural protein NS1 in DENV had the maximum number of interactions with the host protein, followed by NS5 and NS3. Among the human proteins, HBA1 and UBE2I were associated with 7 viral proteins, and 3 human proteins (CSNK2A1, RRP12, and HSP90AB1) were found to interact with 6 viral proteins. Pharmacophore-based virtual screening of millions of compounds in the public databases was performed to identify potential DENV-NS1 inhibitors. The lead compounds were selected based on RMSD values, docking scores, and strong binding affinities. The top ten hit compounds were subjected to ADME profiling which identified compounds C2 (MolPort-044-180-163) and C6 (MolPort-001-742-737) as lead inhibitors against DENV-NS1. Molecular dynamics trajectory analysis and intermolecular interactions between NS1 and the ligands displayed the molecular stability of the complexes in the cellular environment. The in-silico approaches used in this study could pave the way for the development of potential specie-specific drugs and help in eliminating deadly viral infections. Therefore, experimental and clinical assays are required to validate the results of this study.
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| GB/T 7714 | Farooq, Qurrat ul Ain , Aiman, Sara , Shaukat, Zeeshan et al. A comprehensive protein interaction map and druggability investigation prioritized dengue virus NS1 protein as promising therapeutic candidate [J]. | PLOS ONE , 2023 , 18 (7) . |
| MLA | Farooq, Qurrat ul Ain et al. "A comprehensive protein interaction map and druggability investigation prioritized dengue virus NS1 protein as promising therapeutic candidate" . | PLOS ONE 18 . 7 (2023) . |
| APA | Farooq, Qurrat ul Ain , Aiman, Sara , Shaukat, Zeeshan , Ali, Yasir , Khan, Asifullah , Samad, Abdus et al. A comprehensive protein interaction map and druggability investigation prioritized dengue virus NS1 protein as promising therapeutic candidate . | PLOS ONE , 2023 , 18 (7) . |
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摘要 ：
The YTH domain of YTHDF3 belongs to a class of protein "readers" recognizing the N6-methyladenosine (m(6)A) modification in mRNA. Although static crystal structure reveals m(6)A recognition by a conserved aromatic cage, the dynamic process in recognition and importance of aromatic cage residues are not completely clear. Here, molecular dynamics (MD) simulations are performed to explore the issues and negative selectivity of YTHDF3 toward unmethylated substrate. Our results reveal that there exist conformation selectivity and induced-fit in YTHDF3 binding with m(6)A-modified RNA, where recognition loop and loop6 play important roles in the specific recognition. m(6)A modification enhances the stability of YTHDF3 in complex with RNA. The methyl group of m(6)A, like a warhead, enters into the aromatic cage of YTHDF3, where Trp492 anchors the methyl group and constraints m(6)A, making m(6)A further stabilized by pi-pi stacking interactions from Trp438 and Trp497. In addition, the methylation enhances the hydrophobicity of adenosine, facilitating water molecules excluded out of the aromatic cage, which is another reason for the specific recognition and stronger intermolecular interaction. Finally, the comparative analyses of hydrogen bonds and binding free energy between the methylated and unmethylated complexes reveal the physical basis for the preferred recognition of m(6)A-modified RNA by YTHDF3. This study sheds light on the mechanism by which YTHDF3 specifically recognizes m(6)A-modified RNA and can provide important information for structure-based drug design.
关键词 ：
dynamic process dynamic process YTHDF3 YTHDF3 N6-methyladenosine N6-methyladenosine specific recognition and interaction specific recognition and interaction
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| GB/T 7714 | Zhou, Wenxue , Han, Zhongjie , Wu, Zhixiang et al. Specific recognition between YTHDF3 and m(6)A-modified RNA: An all-atom molecular dynamics simulation study [J]. | PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS , 2022 , 90 (11) : 1965-1972 . |
| MLA | Zhou, Wenxue et al. "Specific recognition between YTHDF3 and m(6)A-modified RNA: An all-atom molecular dynamics simulation study" . | PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS 90 . 11 (2022) : 1965-1972 . |
| APA | Zhou, Wenxue , Han, Zhongjie , Wu, Zhixiang , Gong, Weikang , Yang, Shuang , Chen, Lei et al. Specific recognition between YTHDF3 and m(6)A-modified RNA: An all-atom molecular dynamics simulation study . | PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS , 2022 , 90 (11) , 1965-1972 . |
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摘要 ：
The three-dimensional (3D) chromosomal structure plays an essential role in all DNA-templated processes, including gene transcription, DNA replication and other cellular processes. Although developing chromosome conformation capture (3C) methods, such as Hi-C, which can generate chromosomal contact data characterized genome-wide chromosomal structural properties, understanding 3D genomic nature-based on Hi-C data remains lacking. Here, we propose a persistent spectral simplicial complex (PerSpectSC) model to describe Hi-C data for the first time. Specifically, a filtration process is introduced to generate a series of nested simplicial complexes at different scales. For each of these simplicial complexes, its spectral information can be calculated from the corresponding Hodge Laplacian matrix. PerSpectSC model describes the persistence and variation of the spectral information of the nested simplicial complexes during the filtration process. Different from all previous models, our PerSpectSC-based features provide a quantitative global-scale characterization of chromosome structures and topology. Our descriptors can successfully classify cell types and also cellular differentiation stages for all the 24 types of chromosomes simultaneously. In particular, persistent minimum best characterizes cell types and Dim (1) persistent multiplicity best characterizes cellular differentiation. These results demonstrate the great potential of our PerSpectSC-based models in polymeric data analysis.
关键词 ：
machine learning machine learning Hi-C data Hi-C data Hodge Laplacian Hodge Laplacian persistent spectral simplicial complex persistent spectral simplicial complex chromosomal featurization chromosomal featurization
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| GB/T 7714 | Gong, Weikang , Wee, JunJie , Wu, Min-Chun et al. Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation [J]. | BRIEFINGS IN BIOINFORMATICS , 2022 , 23 (4) . |
| MLA | Gong, Weikang et al. "Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation" . | BRIEFINGS IN BIOINFORMATICS 23 . 4 (2022) . |
| APA | Gong, Weikang , Wee, JunJie , Wu, Min-Chun , Sun, Xiaohan , Li, Chunhua , Xia, Kelin . Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation . | BRIEFINGS IN BIOINFORMATICS , 2022 , 23 (4) . |
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