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学者姓名：李春华
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Abstract ：
Protein-DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein-DNA interactions holds great significance for revealing the intricate mechanisms in protein-DNA recognition and for providing essential guidance for protein engineering. Aiming at protein-DNA interaction hotspots, this work introduces an effective prediction method, ESPDHot based on a stacked ensemble machine learning framework. Here, the interface residue whose mutation leads to a binding free energy change (Delta Delta G) exceeding 2 kcal/mol is defined as a hotspot. To tackle the imbalanced data set issue, the adaptive synthetic sampling (ADASYN), an oversampling technique, is adopted to synthetically generate new minority samples, thereby rectifying data imbalance. As for molecular characteristics, besides traditional features, we introduce three new characteristic types including residue interface preference proposed by us, residue fluctuation dynamics characteristics, and coevolutionary features. Combining the Boruta method with our previously developed Random Grouping strategy, we obtained an optimal set of features. Finally, a stacking classifier is constructed to output prediction results, which integrates three classical predictors, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN) as the first layer, and Logistic Regression (LR) algorithm as the second one. Notably, ESPDHot outperforms the current state-of-the-art predictors, achieving superior performance on the independent test data set, with F1, MCC, and AUC reaching 0.571, 0.516, and 0.870, respectively.
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| GB/T 7714 | Tao, Lianci , Zhou, Tong , Wu, Zhixiang et al. ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots [J]. | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2024 , 64 (8) : 3548-3557 . |
| MLA | Tao, Lianci et al. "ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots" . | JOURNAL OF CHEMICAL INFORMATION AND MODELING 64 . 8 (2024) : 3548-3557 . |
| APA | Tao, Lianci , Zhou, Tong , Wu, Zhixiang , Hu, Fangrui , Yang, Shuang , Kong, Xiaotian et al. ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots . | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2024 , 64 (8) , 3548-3557 . |
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Abstract ：
Allostery is one of the most direct and efficient ways to regulate protein functions. The diverse allosteric sites make it possible to design allosteric modulators of differential selectivity and improved safety compared with those of orthosteric drugs targeting conserved orthosteric sites. Here, we develop an ensemble machine learning method AllosES to predict protein allosteric sites in which the new and effective features are utilized, including the entropy transfer-based dynamic property, secondary structure features, and our previously proposed spatial neighbor-based evolutionary information besides the traditional physicochemical properties. To overcome the class imbalance problem, the multiple grouping strategy is proposed, which is applied to feature selection and model construction. The ensemble model is constructed where multiple submodels are trained on multiple training subsets, respectively, and their results are then integrated to be the final output. AllosES achieves a prediction performance of 0.556 MCC on the independent test set D24, and additionally, AllosES can rank the real allosteric sites in the top three for 83.3/89.3% of allosteric proteins from the test set D24/D28, outperforming the state-of-the-art peer methods. The comprehensive results demonstrate that AllosES is a promising method for protein allosteric site prediction.
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| GB/T 7714 | Hu, Fangrui , Chang, Fubin , Tao, Lianci et al. Prediction of Protein Allosteric Sites with Transfer Entropy and Spatial Neighbor-Based Evolutionary Information Learned by an Ensemble Model [J]. | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2024 , 64 (15) : 6197-6204 . |
| MLA | Hu, Fangrui et al. "Prediction of Protein Allosteric Sites with Transfer Entropy and Spatial Neighbor-Based Evolutionary Information Learned by an Ensemble Model" . | JOURNAL OF CHEMICAL INFORMATION AND MODELING 64 . 15 (2024) : 6197-6204 . |
| APA | Hu, Fangrui , Chang, Fubin , Tao, Lianci , Sun, Xiaohan , Liu, Lamei , Zhao, Yingchun et al. Prediction of Protein Allosteric Sites with Transfer Entropy and Spatial Neighbor-Based Evolutionary Information Learned by an Ensemble Model . | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2024 , 64 (15) , 6197-6204 . |
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Abstract ：
The dynamics of RNAs are related intimately to their functions. Molecular flexibility, as a starting point for understanding their dynamics, has been utilized to predict many characteristics associated with their functions. Since the experimental measurement methods are time-consuming and labor-intensive, it is urgently needed to develop reliable theoretical methods to predict RNA flexibility. In this work, we develop an effective machine learning method, RNAfcg, to predict RNA flexibility, where the Random Forest (RF) is trained by features including the topological centralities, flexibility-rigidity index, and global characteristics first introduced by us, as well as some traditional sequence and structural features. The analyses show that the three types of features introduced first have significant contributions to RNA flexibility prediction, among which the topological type contributes the most, which indicates the importance of structural topology in determining RNA flexibility. The performance comparison indicates that RNAfcg outperforms the state-of-the-art machine learning methods and the commonly used Gaussian Network Model (GNM) models, achieving a much higher Pearson correlation coefficient (PCC) of 0.6619 on the test data set. This work is helpful for understanding RNA dynamics and can be used to predict RNA function information. The source code is available at https://github.com/ChunhuaLab/RNAfcg/.
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| GB/T 7714 | Chang, Fubin , Liu, Lamei , Hu, Fangrui et al. RNAfcg: RNA Flexibility Prediction Based on Topological Centrality and Global Features [J]. | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2024 , 64 (19) : 7786-7792 . |
| MLA | Chang, Fubin et al. "RNAfcg: RNA Flexibility Prediction Based on Topological Centrality and Global Features" . | JOURNAL OF CHEMICAL INFORMATION AND MODELING 64 . 19 (2024) : 7786-7792 . |
| APA | Chang, Fubin , Liu, Lamei , Hu, Fangrui , Sun, Xiaohan , Zhao, Yingchun , Zhang, Na et al. RNAfcg: RNA Flexibility Prediction Based on Topological Centrality and Global Features . | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2024 , 64 (19) , 7786-7792 . |
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Abstract ：
Metal ion is an indispensable factor for the proper folding, structural stability and functioning of RNA molecules. However, it is very difficult for experimental methods to detect them in RNAs. With the increase of experimentally resolved RNA structures, it becomes possible to identify the metal ion-binding sites in RNA structures through in-silico methods. Here, we propose an approach called Metal3DRNA to identify the binding sites of the most common metal ions (Mg2+, Na+ and K+) in RNA structures by using a three-dimensional convolutional neural network model. The negative samples, screened out based on the analysis for binding surroundings of metal ions, are more like positive ones than the randomly selected ones, which are beneficial to a powerful predictor construction. The microenvironments of the spatial distributions of C, O, N and P atoms around a sample are extracted as features. Metal3DRNA shows a promising prediction power, generally surpassing the state-of-the-art methods FEATURE and MetalionRNA. Finally, utilizing the visualization method, we inspect the contributions of nucleotide atoms to the classification in several cases, which provides a visualization that helps to comprehend the model. The method will be helpful for RNA structure prediction and dynamics simulation study.
Keyword ：
deep learning method deep learning method RNA structure RNA structure visualization visualization metal ion-binding site metal ion-binding site microenvironment microenvironment
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| GB/T 7714 | Zhao, Yanpeng , Wang, Jingjing , Chang, Fubin et al. Identification of metal ion-binding sites in RNA structures using deep learning method [J]. | BRIEFINGS IN BIOINFORMATICS , 2023 , 24 (2) . |
| MLA | Zhao, Yanpeng et al. "Identification of metal ion-binding sites in RNA structures using deep learning method" . | BRIEFINGS IN BIOINFORMATICS 24 . 2 (2023) . |
| APA | Zhao, Yanpeng , Wang, Jingjing , Chang, Fubin , Gong, Weikang , Liu, Yang , Li, Chunhua . Identification of metal ion-binding sites in RNA structures using deep learning method . | BRIEFINGS IN BIOINFORMATICS , 2023 , 24 (2) . |
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Abstract ：
目的 构建CK2天然产物类抑制剂的定量构效关系(quantitative structure-activity relationship,QSAR)模型,揭示影响该类抑制剂活性的结构因素,为新型CK2抑制剂的开发提供理论基础和实验依据.方法 基于文献报道的115个多骨架CK2天然产物类抑制剂,采用遗传算法(genetic algorithm,GA)联合多元线性回归(multiple linear regression,MLR)方法,建立了基于优选的Dragon描述符的QSAR模型,以留一法交叉验证系数Q2LOO以及相关系数R2作为模型内部验证的评价指标;通过Q2ext和R2ext评估模型的外部预测能力.结果 最优2D-QSAR模型由8个描述符组成,基于训练集内部验证的统计学参数为Q2Loo=0.7914、R2=0.8220;基于测试集外部验证的统计学参数为Q2ext=0.7921、R2ext=0.7998,表明该模型具有较高的可靠性和预测能力.结论 影响CK2天然产物类抑制剂活性的分子描述符包括IVDE、CATS2D_08_DA、nArX、IC1、Chi_D/Dt、SdssC、F08[C-O]以及C-006.本研究可为新型CK2抗癌抑制剂的发现提供实验指导.
Keyword ：
定量结构-活性关系 定量结构-活性关系 天然产物类抑制剂 天然产物类抑制剂 遗传算法 遗传算法 多元线性回归 多元线性回归 蛋白激酶CK2 蛋白激酶CK2
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| GB/T 7714 | 张雪文 , 张娜 , 李春华 et al. 蛋白激酶CK2天然产物类抑制剂的定量构效关系研究 [J]. | 北京生物医学工程 , 2023 , 42 (1) : 81-87 . |
| MLA | 张雪文 et al. "蛋白激酶CK2天然产物类抑制剂的定量构效关系研究" . | 北京生物医学工程 42 . 1 (2023) : 81-87 . |
| APA | 张雪文 , 张娜 , 李春华 , 孙国辉 , 赵丽娇 , 钟儒刚 . 蛋白激酶CK2天然产物类抑制剂的定量构效关系研究 . | 北京生物医学工程 , 2023 , 42 (1) , 81-87 . |
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Abstract ：
Protein-deoxyribonucleic acid (DNA) interactions are important in a variety of biological processes. Accurately predicting protein DNA binding affinity has been one of the most attractive and challenging issues in computational biology. However, the existing approaches still have much room for improvement. In this work, we propose an ensemble model for Protein-DNA Binding Affinity prediction (emPDBA), which combines six base models with one meta-model. The complexes are classified into four types based on the DNA structure (double-stranded or other forms) and the percentage of interface residues. For each type, emPDBA is trained with the sequence-based, structure-based and energy features from binding partners and complex structures. Through feature selection by the sequential forward selection method, it is found that there do exist considerable differences in the key factors contributing to intermolecular binding affinity. The complex classification is beneficial for the important feature extraction for binding affinity prediction. The performance comparison of our method with other peer ones on the independent testing dataset shows that emPDBA outperforms the state-of-the-art methods with the Pearson correlation coefficient of 0.53 and the mean absolute error of 1.11 kcal/mol. The comprehensive results demonstrate that our method has a good performance for protein-DNA binding affinity prediction.Availability and implementation: The source code is available at https://github.com/ChunhuaLiLab/emPDBA/.
Keyword ：
protein-DNA binding affinity protein-DNA binding affinity pairwise potential pairwise potential complex classification complex classification ensemble model ensemble model
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| GB/T 7714 | Yang, Shuang , Gong, Weikang , Zhou, Tong et al. emPDBA: protein-DNA binding affinity prediction by combining features from binding partners and interface learned with ensemble regression model [J]. | BRIEFINGS IN BIOINFORMATICS , 2023 , 24 (4) . |
| MLA | Yang, Shuang et al. "emPDBA: protein-DNA binding affinity prediction by combining features from binding partners and interface learned with ensemble regression model" . | BRIEFINGS IN BIOINFORMATICS 24 . 4 (2023) . |
| APA | Yang, Shuang , Gong, Weikang , Zhou, Tong , Sun, Xiaohan , Chen, Lei , Zhou, Wenxue et al. emPDBA: protein-DNA binding affinity prediction by combining features from binding partners and interface learned with ensemble regression model . | BRIEFINGS IN BIOINFORMATICS , 2023 , 24 (4) . |
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Abstract ：
Within over 800 members of G-protein-coupled receptors, there are numerous orphan receptors whose endogenous ligands are largely unknown, providing many opportunities for novel drug discovery. However, the lack of an in-depth understanding of the intrinsic working mechanism for orphan receptors severely limits the related rational drug design. The G-protein-coupled receptor 52 (GPR52) is a unique orphan receptor that constitutively increases cellular 5'-cyclic adenosine monophosphate (cAMP) levels without binding any exogenous agonists and has been identified as a promising therapeutic target for central nervous system disorders. Although recent structural biology studies have provided snapshots of both active and inactive states of GPR52, the mechanism of the conformational transition between these states remains unclear. Here, an acceptable self-activation pathway for GPR52 was proposed through 6 mu s Gaussian accelerated molecular dynamics (GaMD) simulations, in which the receptor spontaneously transitions from the active state to that matching the inactive crystal structure. According to the three intermediate states of the receptor obtained by constructing a reweighted potential of mean force, how the allosteric regulation occurs between the extracellular orthosteric binding pocket and the intracellular G-protein-binding site is revealed. Combined with the independent gradient model, several important microswitch residues and the allosteric communication pathway that directly links the two regions are both identified. Transfer entropy calculations not only reveal the complex allosteric signaling within GPR52 but also confirm the unique role of ECL2 in allosteric regulation, which is mutually validated with the results of GaMD simulations. Overall, this work elucidates the allosteric mechanism of GPR52 at the atomic level, providing the most detailed information to date on the self-activation of the orphan receptor.
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| GB/T 7714 | Wu, Zhixiang , Han, Zhongjie , Tao, Lianci et al. Dynamic Insights into the Self-Activation Pathway and Allosteric Regulation of the Orphan G-Protein-Coupled Receptor GPR52 [J]. | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2023 , 63 (18) : 5847-5862 . |
| MLA | Wu, Zhixiang et al. "Dynamic Insights into the Self-Activation Pathway and Allosteric Regulation of the Orphan G-Protein-Coupled Receptor GPR52" . | JOURNAL OF CHEMICAL INFORMATION AND MODELING 63 . 18 (2023) : 5847-5862 . |
| APA | Wu, Zhixiang , Han, Zhongjie , Tao, Lianci , Sun, Xiaohan , Su, Jingjie , Hu, Jianping et al. Dynamic Insights into the Self-Activation Pathway and Allosteric Regulation of the Orphan G-Protein-Coupled Receptor GPR52 . | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2023 , 63 (18) , 5847-5862 . |
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Abstract ：
We propose an improved transfer entropy approach called the dynamic version of the force constant fitted Gaussian network model based on molecular dynamics ensemble (dfcfGNMMD) to explore the allosteric mechanism of human mitochondrial phenylalanyl-tRNA synthetase (hmPheRS), one of the aminoacyl-tRNA synthetases that play a crucial role in translation of the genetic code. The dfcfGNMMD method can provide reliable estimates of the transfer entropy and give new insights into the role of the anticodon binding domain in driving the catalytic domain in aminoacylation activity and into the effects of tRNA binding and residue mutation on the enzyme activity, revealing the causal mechanism of the allosteric communication in hmPheRS. In addition, we incorporate the residue dynamic and co-evolutionary information to further investigate the key residues in hmPheRS allostery. This study sheds light on the mechanisms of hmPheRS allostery and can provide important information for related drug design.
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| GB/T 7714 | Han, Zhongjie , Wang, Xiaoli , Wu, Zhixiang et al. Study of the Allosteric Mechanism of Human Mitochondrial Phenylalanyl-tRNA Synthetase by Transfer Entropy via an Improved Gaussian Network Model and Co-evolution Analyses [J]. | JOURNAL OF PHYSICAL CHEMISTRY LETTERS , 2023 , 14 (14) : 3452-3460 . |
| MLA | Han, Zhongjie et al. "Study of the Allosteric Mechanism of Human Mitochondrial Phenylalanyl-tRNA Synthetase by Transfer Entropy via an Improved Gaussian Network Model and Co-evolution Analyses" . | JOURNAL OF PHYSICAL CHEMISTRY LETTERS 14 . 14 (2023) : 3452-3460 . |
| APA | Han, Zhongjie , Wang, Xiaoli , Wu, Zhixiang , Li, Chunhua . Study of the Allosteric Mechanism of Human Mitochondrial Phenylalanyl-tRNA Synthetase by Transfer Entropy via an Improved Gaussian Network Model and Co-evolution Analyses . | JOURNAL OF PHYSICAL CHEMISTRY LETTERS , 2023 , 14 (14) , 3452-3460 . |
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Abstract ：
Monkeypox virus (MPXV) is an orthopoxvirus, causing zoonotic infections in humans with smallpox-like symptoms. The WHO reported MPXV cases in May 2022 and the outbreak caused significant morbidity threats to immunocompromised individuals and children. Currently, no clinically validated therapies are available against MPXV infections. The present study is based on immunoinformatics approaches to design mRNA-based novel vaccine models against MPXV. Three proteins were prioritized based on high antigenicity, low allergenicity, and toxicity values to predict T- and B-cell epitopes. Lead T- and B-cell epitopes were used to design vaccine constructs, linked with epitope-specific linkers and adjuvant to enhance immune responses. Additional sequences, including Kozak sequence, MITD sequence, tPA sequence, Goblin 5', 3' UTRs, and a poly(A) tail were added to design stable and highly immunogenic mRNA vaccine construct. High-quality structures were predicted by molecular modeling and 3D-structural validation of the vaccine construct. Population coverage and epitope-conservancy speculated broader protection of designed vaccine model against multiple MPXV infectious strains. MPXV-V4 was eventually prioritized based on its physicochemical and immunological parameters and docking scores. Molecular dynamics and immune simulations analyses predicted significant structural stability and binding affinity of the top-ranked vaccine model with immune receptors to elicit cellular and humoral immunogenic responses against the MPXV. The pursuance of experimental and clinical follow-up of these prioritized constructs may lay the groundwork to develop safe and effective vaccine against MPXV.Communicated by Ramaswamy H. Sarma
Keyword ：
mRNA vaccine mRNA vaccine monkeypox monkeypox multi-epitope vaccine construct multi-epitope vaccine construct > > Reverse vaccinology Reverse vaccinology immunoinformatics immunoinformatics
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| GB/T 7714 | Aiman, Sara , Ali, Yasir , Malik, Abdul et al. Immunoinformatic-guided novel mRNA vaccine designing to elicit immunogenic responses against the endemic Monkeypox virus [J]. | JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS , 2023 , 42 (12) : 6292-6306 . |
| MLA | Aiman, Sara et al. "Immunoinformatic-guided novel mRNA vaccine designing to elicit immunogenic responses against the endemic Monkeypox virus" . | JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS 42 . 12 (2023) : 6292-6306 . |
| APA | Aiman, Sara , Ali, Yasir , Malik, Abdul , Alkholief, Musaed , Ahmad, Abbas , Akhtar, Suhail et al. Immunoinformatic-guided novel mRNA vaccine designing to elicit immunogenic responses against the endemic Monkeypox virus . | JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS , 2023 , 42 (12) , 6292-6306 . |
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Abstract ：
Opioid receptors, a kind of G protein-coupled receptors (GPCRs), mainly mediate an analgesic response via allosterically transducing the signal of endogenous ligand binding in the extracellular domain to couple to effector proteins in the intracellular domain. The delta opioid receptor (DOP) is associated with emotional control besides pain control, which makes it an attractive therapeutic target. However, its allosteric mechanism and key residues responsible for the structural stability and signal communication are not completely clear. Here we utilize the Gaussian network model (GNM) and amino acid network (AAN) combined with perturbation methods to explore the issues. The constructed fcfGNMMD, where the force constants are optimized with the inverse covariance estimation based on the correlated fluctuations from the available DOP molecular dynamics (MD) ensemble, shows a better performance than traditional GNM in reproducing residue fluctuations and cross-correlations and in capturing function-ally low-frequency modes. Additionally, fcfGNM(MD) can consider implicitly the environmental effects to some extent. The lowest mode can well divide DOP segments and identify the two sodium ion (important allosteric regulator) binding coordination shells, and from the fastest modes, the key residues important for structure stabilization are identified. Using fcfGNMMD combined with a dynamic perturbation-response method, we explore the key residues related to the sodium ion binding. Interestingly, we identify not only the key residues in sodium ion binding shells but also the ones far away from the perturbation sites, which are involved in binding with DOP ligands, suggesting the possible long-range allosteric modulation of sodium binding for the ligand binding to DOP. Furthermore, utilizing the weighted AAN combined with attack perturbations, we identify the key residues for allosteric communication. This work helps strengthen the understanding of the allosteric communication mechanism in delta opioid receptor and can provide valuable information for drug design.
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| GB/T 7714 | Chen, Lei , Gong, Weikang , Han, Zhongjie et al. Key Residues in delta Opioid Receptor Allostery Explored by the Elastic Network Model and the Complex Network Model Combined with the Perturbation Method [J]. | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2022 , 62 (24) : 6727-6738 . |
| MLA | Chen, Lei et al. "Key Residues in delta Opioid Receptor Allostery Explored by the Elastic Network Model and the Complex Network Model Combined with the Perturbation Method" . | JOURNAL OF CHEMICAL INFORMATION AND MODELING 62 . 24 (2022) : 6727-6738 . |
| APA | Chen, Lei , Gong, Weikang , Han, Zhongjie , Zhou, Wenxue , Yang, Shuang , Li, Chunhua . Key Residues in delta Opioid Receptor Allostery Explored by the Elastic Network Model and the Complex Network Model Combined with the Perturbation Method . | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2022 , 62 (24) , 6727-6738 . |
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