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Bai H, Li QZ, Qi YC, Zhai YY, Jin W. The prediction of tumor and normal tissues based on the DNA methylation values of ten key sites. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2022; 1865:194841. [PMID: 35798200 DOI: 10.1016/j.bbagrm.2022.194841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 05/28/2022] [Accepted: 06/28/2022] [Indexed: 06/15/2023]
Abstract
Abnormal DNA methylation can alter the gene expression to promote or inhibit tumorigenesis in colon adenocarcinoma (COAD). However, the finding important genes and key sites of abnormal DNA methylation which result in the occurrence of COAD is still an eventful task. Here, we studied the effects of DNA methylation in the 12 types of genomic features on the changes of gene expression in COAD, the 10 important COAD-related genes and the key abnormal DNA methylation sites were identified. The effects of important genes on the prognosis were verified by survival analysis. Moreover, it was shown that the important genes were participated in cancer pathways and were hub genes in a co-expression network. Based on the DNA methylation levels in the ten sites, the least diversity increment algorithm for predicting tumor tissues and normal tissues in seventeen cancer types are proposed. The better results are obtained in jackknife test. For example, the predictive accuracies are 94.17 %, 91.28 %, 89.04 % and 88.89 %, respectively, for COAD, rectum adenocarcinoma, pancreatic adenocarcinoma and cholangiocarcinoma. Finally, by computing enrichment score of infiltrating immunocytes and the activity of immune pathways, we found that the genes are highly correlated with immune microenvironment.
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Affiliation(s)
- Hui Bai
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Qian-Zhong Li
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China; The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, Hohhot 010070, China.
| | - Ye-Chen Qi
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Yuan-Yuan Zhai
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Wen Jin
- Inner Mongolia key laboratory of gene regulation of the metabolic disease, Department of Clinical Medical Research Center, Inner Mongolia People's Hospital, Hohhot 010010, China
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2
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Xu S, Hu X, Feng Z, Pang J, Sun K, You X, Wang Z. Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors. Front Genet 2022; 12:793800. [PMID: 35058970 PMCID: PMC8764267 DOI: 10.3389/fgene.2021.793800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022] Open
Abstract
The realization of many protein functions is inseparable from the interaction with ligands; in particular, the combination of protein and metal ion ligands performs an important biological function. Currently, it is a challenging work to identify the metal ion ligand-binding residues accurately by computational approaches. In this study, we proposed an improved method to predict the binding residues of 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Mn2+, Ca2+, Mg2+, Na+, and K+). Based on the basic feature parameters of amino acids, and physicochemical and predicted structural information, we added another two features of amino acid correlation information and binding residue propensity factors. With the optimized parameters, we used the GBM algorithm to predict metal ion ligand-binding residues. In the obtained results, the Sn and MCC values were over 10.17% and 0.297, respectively. Besides, the Sn and MCC values of transition metals were higher than 34.46% and 0.564, respectively. In order to test the validity of our model, another method (Random Forest) was also used in comparison. The better results of this work indicated that the proposed method would be a valuable tool to predict metal ion ligand-binding residues.
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Affiliation(s)
- Shuang Xu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
| | - Xiuzhen Hu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
| | - Zhenxing Feng
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
| | - Jing Pang
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
| | - Kai Sun
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
| | - Xiaoxiao You
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
| | - Ziyang Wang
- College of Sciences, Inner Mongolia University of Technology, Hohhot, China.,Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China
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Zhang C, Huang J. Interactions Between Nucleosomes: From Atomistic Simulation to Polymer Model. Front Mol Biosci 2021; 8:624679. [PMID: 33912585 PMCID: PMC8072053 DOI: 10.3389/fmolb.2021.624679] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 02/09/2021] [Indexed: 11/23/2022] Open
Abstract
The organization of genomes in space and time dimension plays an important role in gene expression and regulation. Chromatin folding occurs in a dynamic, structured way that is subject to biophysical rules and biological processes. Nucleosomes are the basic unit of chromatin in living cells, and here we report on the effective interactions between two nucleosomes in physiological conditions using explicit-solvent all-atom simulations. Free energy landscapes derived from umbrella sampling simulations agree well with recent experimental and simulation results. Our simulations reveal the atomistic details of the interactions between nucleosomes in solution and can be used for constructing the coarse-grained model for chromatin in a bottom-up manner.
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Affiliation(s)
- Chengwei Zhang
- College of Life Sciences, Zhejiang University, Hangzhou, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Jing Huang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, China
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Hu X, Feng Z, Zhang X, Liu L, Wang S. The Identification of Metal Ion Ligand-Binding Residues by Adding the Reclassified Relative Solvent Accessibility. Front Genet 2020; 11:214. [PMID: 32265982 PMCID: PMC7096583 DOI: 10.3389/fgene.2020.00214] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 02/24/2020] [Indexed: 11/13/2022] Open
Abstract
Many proteins realize their special functions by binding with specific metal ion ligands during a cell's life cycle. The ability to correctly identify metal ion ligand-binding residues is valuable for the human health and the design of molecular drug. Precisely identifying these residues, however, remains challenging work. We have presented an improved computational approach for predicting the binding residues of 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Ca2+, Mg2+, Mn2+, Na+, and K+) by adding reclassified relative solvent accessibility (RSA). The best accuracy of fivefold cross-validation was higher than 77.9%, which was about 16% higher than the previous result on the same dataset. It was found that different reclassification of the RSA information can make different contributions to the identification of specific ligand binding residues. Our study has provided an additional understanding of the effect of the RSA on the identification of metal ion ligand binding residues.
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Affiliation(s)
| | - Zhenxing Feng
- College of Sciences, Inner Mongolla University of Technology, Hohhot, China
| | - Xiaojin Zhang
- College of Sciences, Inner Mongolla University of Technology, Hohhot, China
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Wang S, Hu X, Feng Z, Zhang X, Liu L, Sun K, Xu S. Recognizing ion ligand binding sites by SMO algorithm. BMC Mol Cell Biol 2019; 20:53. [PMID: 31823742 PMCID: PMC6905020 DOI: 10.1186/s12860-019-0237-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Background In many important life activities, the execution of protein function depends on the interaction between proteins and ligands. As an important protein binding ligand, the identification of the binding site of the ion ligands plays an important role in the study of the protein function. Results In this study, four acid radical ion ligands (NO2−,CO32−,SO42−,PO43−) and ten metal ion ligands (Zn2+,Cu2+,Fe2+,Fe3+,Ca2+,Mg2+,Mn2+,Na+,K+,Co2+) are selected as the research object, and the Sequential minimal optimization (SMO) algorithm based on sequence information was proposed, better prediction results were obtained by 5-fold cross validation. Conclusions An efficient method for predicting ion ligand binding sites was presented.
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Affiliation(s)
- Shan Wang
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Xiuzhen Hu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China.
| | - Zhenxing Feng
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Xiaojin Zhang
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Liu Liu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Kai Sun
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Shuang Xu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
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Liu L, Hu X, Feng Z, Zhang X, Wang S, Xu S, Sun K. Prediction of acid radical ion binding residues by K-nearest neighbors classifier. BMC Mol Cell Biol 2019; 20:52. [PMID: 31823720 PMCID: PMC6904995 DOI: 10.1186/s12860-019-0238-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Background Proteins perform their functions by interacting with acid radical ions. Recently, it was a challenging work to precisely predict the binding residues of acid radical ion ligands in the research field of molecular drug design. Results In this study, we proposed an improved method to predict the acid radical ion binding residues by using K-nearest Neighbors classifier. Meanwhile, we constructed datasets of four acid radical ion ligand (NO2−, CO32−, SO42−, PO43−) binding residues from BioLip database. Then, based on the optimal window length for each acid radical ion ligand, we refined composition information and position conservative information and extracted them as feature parameters for K-nearest Neighbors classifier. In the results of 5-fold cross-validation, the Matthew’s correlation coefficient was higher than 0.45, the values of accuracy, sensitivity and specificity were all higher than 69.2%, and the false positive rate was lower than 30.8%. Further, we also performed an independent test to test the practicability of the proposed method. In the obtained results, the sensitivity was higher than 40.9%, the values of accuracy and specificity were higher than 84.2%, the Matthew’s correlation coefficient was higher than 0.116, and the false positive rate was lower than 15.4%. Finally, we identified binding residues of the six metal ion ligands. In the predicted results, the values of accuracy, sensitivity and specificity were all higher than 77.6%, the Matthew’s correlation coefficient was higher than 0.6, and the false positive rate was lower than 19.6%. Conclusions Taken together, the good results of our prediction method added new insights in the prediction of the binding residues of acid radical ion ligands.
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Affiliation(s)
| | - Xiuzhen Hu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China.
| | - Zhenxing Feng
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Xiaojin Zhang
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Shan Wang
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Shuang Xu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Kai Sun
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China
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Feng ZX, Li QZ, Meng JJ. Modeling the relationship of diverse genomic signatures to gene expression levels with the regulation of long-range enhancer-promoter interactions. BIOPHYSICS REPORTS 2019. [DOI: 10.1007/s41048-019-0089-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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Jin W, Li QZ, Liu Y, Zuo YC. Effect of the key histone modifications on the expression of genes related to breast cancer. Genomics 2019; 112:853-858. [PMID: 31170440 DOI: 10.1016/j.ygeno.2019.05.026] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 05/16/2019] [Accepted: 05/30/2019] [Indexed: 02/07/2023]
Abstract
Abnormal histone modifications (HMs) and transcription factors (TFs) can alter the expression of cancer-related genes to promote tumorigenesis. We studied the variations of 11 HMs and 2 TFs in human breast cancer cells (MCF-7) compared to human normal mammary epithelial cells (HMEC), and the effects of HMs/TFs in various regions of the genome on the expression changes of breast cancer-related genes. Based on HMs and TFs signals' differences between MCF-7 and HMEC flanking TSSs, the up- and down-regulated genes in MCF-7 were predicted by Random Forest, and important HMs and regions were found. Results indicate that H3K79me2, H3K27ac, and H3K4me1 are particularly important for the changes of gene expression in MCF-7. Especially, H3K79me2 around the 60-th bin flanking TSSs may be the key for regulating gene expression. Our studies reveal H3K79me2 may be a core HM for breast cancer.
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Affiliation(s)
- Wen Jin
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Qian-Zhong Li
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China; The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, Hohhot 010070, China.
| | - Yuan Liu
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Yong-Chun Zuo
- The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, Hohhot 010070, China
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Feng ZX, Li QZ, Meng JJ. Recognition of the long range enhancer-promoter interactions by further adding DNA structure properties and transcription factor binding motifs in human cell lines. J Theor Biol 2018; 445:136-150. [PMID: 29476833 DOI: 10.1016/j.jtbi.2018.02.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 02/07/2018] [Accepted: 02/20/2018] [Indexed: 11/17/2022]
Abstract
The enhancer-promoter interactions (EPIs) with strong tissue-specificity play an important role in cis-regulatory mechanism of human cell lines. However, it still remains a challenging work to predict these interactions so far. Due to that these interactions are regulated by the cooperativeness of diverse functional genomic signatures, DNA spatial structure and DNA sequence elements. In this paper, by adding DNA structure properties and transcription factor binding motifs, we presented an improved computational method to predict EPIs in human cell lines. In comparison with the results of other group on the same datasets, our best accuracies by cross-validation test were about 15%-24% higher in the same cell lines, and the accuracies by independent test were about 11%-15% higher in new cell lines. Meanwhile, we found that transcription factor binding motifs and DNA structure properties have important information that would largely determine long range EPIs prediction. From the distribution comparisons, we also found their distinct differences between interacting and non-interacting sets in each cell line. Then, the correlation analysis and network models for relationships among top-ranked functional genomic signatures indicated that diverse genomic signatures would cooperatively establish a complex regulatory network to facilitate long range EPIs. The experimental results provided additional insights about the roles of DNA intrinsic properties and functional genomic signatures in EPIs prediction.
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Affiliation(s)
- Zhen-Xing Feng
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Qian-Zhong Li
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China; The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, Hohhot, 010070, China.
| | - Jian-Jun Meng
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
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