1
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Montgomery T, Uh K, Lee K. TET enzyme driven epigenetic reprogramming in early embryos and its implication on long-term health. Front Cell Dev Biol 2024; 12:1358649. [PMID: 39149518 PMCID: PMC11324557 DOI: 10.3389/fcell.2024.1358649] [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: 12/20/2023] [Accepted: 07/23/2024] [Indexed: 08/17/2024] Open
Abstract
Mammalian embryo development is initiated by the union of paternal and maternal gametes. Upon fertilization, their epigenome landscape is transformed through a series of finely orchestrated mechanisms that are crucial for survival and successful embryogenesis. Specifically, maternal or oocyte-specific reprogramming factors modulate germ cell specific epigenetic marks into their embryonic states. Rapid and dynamic changes in epigenetic marks such as DNA methylation and histone modifications are observed during early embryo development. These changes govern the structure of embryonic genome prior to zygotic genome activation. Differential changes in epigenetic marks are observed between paternal and maternal genomes because the structure of the parental genomes allows interaction with specific oocyte reprogramming factors. For instance, the paternal genome is targeted by the TET family of enzymes which oxidize the 5-methylcytosine (5mC) epigenetic mark into 5-hydroxymethylcytosine (5hmC) to lower the level of DNA methylation. The maternal genome is mainly protected from TET3-mediated oxidation by the maternal factor, STELLA. The TET3-mediated DNA demethylation occurs at the global level and is clearly observed in many mammalian species. Other epigenetic modulating enzymes, such as DNA methyltransferases, provide fine tuning of the DNA methylation level by initiating de novo methylation. The mechanisms which initiate the epigenetic reprogramming of gametes are critical for proper activation of embryonic genome and subsequent establishment of pluripotency and normal development. Clinical cases or diseases linked to mutations in reprogramming modulators exist, emphasizing the need to understand mechanistic actions of these modulators. In addition, embryos generated via in vitro embryo production system often present epigenetic abnormalities. Understanding mechanistic actions of the epigenetic modulators will potentially improve the well-being of individuals suffering from these epigenetic disorders and correct epigenetic abnormalities in embryos produced in vitro. This review will summarize the current understanding of epigenetic reprogramming by TET enzymes during early embryogenesis and highlight its clinical relevance and potential implication for assisted reproductive technologies.
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Affiliation(s)
- Ty Montgomery
- Division of Animal Sciences, University of Missouri, Columbia, MO, United States
| | - Kyungjun Uh
- Futuristic Animal Resource and Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju-si, Republic of Korea
| | - Kiho Lee
- Division of Animal Sciences, University of Missouri, Columbia, MO, United States
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2
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Gao W, Nie J, Yao J, Wang J, Wang S, Zhang X, Liu Y, Liu Y. Genomic survey and expression analysis of cellulose synthase superfamily and COBRA-like gene family in Zanthoxylum bungeanum stipule thorns. PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS : AN INTERNATIONAL JOURNAL OF FUNCTIONAL PLANT BIOLOGY 2024; 30:369-382. [PMID: 38633272 PMCID: PMC11018584 DOI: 10.1007/s12298-024-01432-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 12/24/2023] [Accepted: 03/01/2024] [Indexed: 04/19/2024]
Abstract
The Cellulose Synthase gene (CS) superfamily and COBRA-like (COBL) gene family are essential for synthesizing cellulose and hemicellulose, which play a crucial role in cell wall biosynthesis and the hardening of plant tissues. Our study identified 126 ZbCS and 31 ZbCOBL genes from the Zanthoxylum bungeanum (Zb) genome. Phylogenetic analysis and conservative domain analysis unfolded that ZbCS and ZbCOBL genes were divided into seven and two subfamilies, respectively. Gene duplication data suggested that more than 75% of these genes had tandem and fragment duplications. Codon usage patterns analysis indicated that the ZbCS and ZbCOBL genes prefer ending with A/T base, with weak codon preference. Furthermore, seven key ZbCS and five key ZbCOBL genes were identified based on the content of cellulose and hemicellulose and the expression characteristics of ZbCS and ZbCOBL genes in various stages of stipule thorns. Altogether, these results improve the understanding of CS and COBL genes and provide valuable reference data for cultivating Zb with soft thorns. Supplementary Information The online version contains supplementary material available at 10.1007/s12298-024-01432-x.
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Affiliation(s)
- Weilong Gao
- College of Forestry, Northwest A&F University, Yangling, 712100 China
| | - Jiangbo Nie
- College of Forestry, Northwest A&F University, Yangling, 712100 China
| | - Jia Yao
- College of Forestry, Northwest A&F University, Yangling, 712100 China
| | - Jianxin Wang
- College of Forestry, Northwest A&F University, Yangling, 712100 China
| | - Shengshu Wang
- College of Forestry, Northwest A&F University, Yangling, 712100 China
| | - Xueli Zhang
- College of Forestry, Northwest A&F University, Yangling, 712100 China
| | - Yonghong Liu
- College of Forestry, Northwest A&F University, Yangling, 712100 China
| | - Yulin Liu
- College of Forestry, Northwest A&F University, Yangling, 712100 China
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3
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Zhang HQ, Liu SH, Li R, Yu JW, Ye DX, Yuan SS, Lin H, Huang CB, Tang H. MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier. ACS OMEGA 2024; 9:8439-8447. [PMID: 38405489 PMCID: PMC10882704 DOI: 10.1021/acsomega.3c09587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/16/2024] [Accepted: 01/22/2024] [Indexed: 02/27/2024]
Abstract
In biological organisms, metal ion-binding proteins participate in numerous metabolic activities and are closely associated with various diseases. To accurately predict whether a protein binds to metal ions and the type of metal ion-binding protein, this study proposed a classifier named MIBPred. The classifier incorporated advanced Word2Vec technology from the field of natural language processing to extract semantic features of the protein sequence language and combined them with position-specific score matrix (PSSM) features. Furthermore, an ensemble learning model was employed for the metal ion-binding protein classification task. In the model, we independently trained XGBoost, LightGBM, and CatBoost algorithms and integrated the output results through an SVM voting mechanism. This innovative combination has led to a significant breakthrough in the predictive performance of our model. As a result, we achieved accuracies of 95.13% and 85.19%, respectively, in predicting metal ion-binding proteins and their types. Our research not only confirms the effectiveness of Word2Vec technology in extracting semantic information from protein sequences but also highlights the outstanding performance of the MIBPred classifier in the problem of metal ion-binding protein types. This study provides a reliable tool and method for the in-depth exploration of the structure and function of metal ion-binding proteins.
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Affiliation(s)
- Hong-Qi Zhang
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Shang-Hua Liu
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Rui Li
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Jun-Wen Yu
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Dong-Xin Ye
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Shi-Shi Yuan
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Hao Lin
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Cheng-Bing Huang
- School
of Computer Science and Technology, Aba Teachers University, Aba 623002, China
| | - Hua Tang
- School
of Basic Medical Sciences, Southwest Medical
University, Luzhou 646000, China
- Central
Nervous System Drug Key Laboratory of Sichuan Province, Luzhou 646000, China
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4
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Choudhury S, Bajiya N, Patiyal S, Raghava GPS. MRSLpred-a hybrid approach for predicting multi-label subcellular localization of mRNA at the genome scale. FRONTIERS IN BIOINFORMATICS 2024; 4:1341479. [PMID: 38379813 PMCID: PMC10877048 DOI: 10.3389/fbinf.2024.1341479] [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: 11/20/2023] [Accepted: 01/15/2024] [Indexed: 02/22/2024] Open
Abstract
In the past, several methods have been developed for predicting the single-label subcellular localization of messenger RNA (mRNA). However, only limited methods are designed to predict the multi-label subcellular localization of mRNA. Furthermore, the existing methods are slow and cannot be implemented at a transcriptome scale. In this study, a fast and reliable method has been developed for predicting the multi-label subcellular localization of mRNA that can be implemented at a genome scale. Machine learning-based methods have been developed using mRNA sequence composition, where the XGBoost-based classifier achieved an average area under the receiver operator characteristic (AUROC) of 0.709 (0.668-0.732). In addition to alignment-free methods, we developed alignment-based methods using motif search techniques. Finally, a hybrid technique that combines the XGBoost model and the motif-based approach has been developed, achieving an average AUROC of 0.742 (0.708-0.816). Our method-MRSLpred-outperforms the existing state-of-the-art classifier in terms of performance and computation efficiency. A publicly accessible webserver and a standalone tool have been developed to facilitate researchers (webserver: https://webs.iiitd.edu.in/raghava/mrslpred/).
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Affiliation(s)
| | | | | | - Gajendra P. S. Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
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5
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Šimelis K, Saraç H, Salah E, Nishio K, McAllister TE, Corner TP, Tumber A, Belle R, Schofield CJ, Suga H, Kawamura A. Selective targeting of human TET1 by cyclic peptide inhibitors: Insights from biochemical profiling. Bioorg Med Chem 2024; 99:117597. [PMID: 38262305 DOI: 10.1016/j.bmc.2024.117597] [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] [Received: 11/27/2023] [Revised: 01/08/2024] [Accepted: 01/10/2024] [Indexed: 01/25/2024]
Abstract
Ten-Eleven Translocation (TET) enzymes are Fe(II)/2OG-dependent oxygenases that play important roles in epigenetic regulation, but selective inhibition of the TETs is an unmet challenge. We describe the profiling of previously identified TET1-binding macrocyclic peptides. TiP1 is established as a potent TET1 inhibitor (IC50 = 0.26 µM) with excellent selectivity over other TETs and 2OG oxygenases. TiP1 alanine scanning reveals the critical roles of Trp10 and Glu11 residues for inhibition of TET isoenzymes. The results highlight the utility of the RaPID method to identify potent enzyme inhibitors with selectivity over closely related paralogues. The structure-activity relationship data generated herein may find utility in the development of chemical probes for the TETs.
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Affiliation(s)
- Klemensas Šimelis
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research, University of Oxford, 12 Mansfield Road, OX1 3TA Oxford, United Kingdom
| | - Hilal Saraç
- Chemistry - School of Natural and Environmental Sciences, Newcastle University, Bedson Building, NE1 7RU Newcastle upon Tyne, United Kingdom
| | - Eidarus Salah
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research, University of Oxford, 12 Mansfield Road, OX1 3TA Oxford, United Kingdom
| | - Kosuke Nishio
- Department of Chemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Tom E McAllister
- Chemistry - School of Natural and Environmental Sciences, Newcastle University, Bedson Building, NE1 7RU Newcastle upon Tyne, United Kingdom
| | - Thomas P Corner
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research, University of Oxford, 12 Mansfield Road, OX1 3TA Oxford, United Kingdom
| | - Anthony Tumber
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research, University of Oxford, 12 Mansfield Road, OX1 3TA Oxford, United Kingdom
| | - Roman Belle
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research, University of Oxford, 12 Mansfield Road, OX1 3TA Oxford, United Kingdom; Chemistry - School of Natural and Environmental Sciences, Newcastle University, Bedson Building, NE1 7RU Newcastle upon Tyne, United Kingdom
| | - Christopher J Schofield
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research, University of Oxford, 12 Mansfield Road, OX1 3TA Oxford, United Kingdom
| | - Hiroaki Suga
- Department of Chemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Akane Kawamura
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research, University of Oxford, 12 Mansfield Road, OX1 3TA Oxford, United Kingdom; Chemistry - School of Natural and Environmental Sciences, Newcastle University, Bedson Building, NE1 7RU Newcastle upon Tyne, United Kingdom.
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6
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Wang H, Liu B, Chen H, Xu P, Xue H, Yuan J. Dynamic changes of DNA methylation induced by benzo(a)pyrene in cancer. Genes Environ 2023; 45:21. [PMID: 37391844 DOI: 10.1186/s41021-023-00278-1] [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: 02/28/2023] [Accepted: 06/07/2023] [Indexed: 07/02/2023] Open
Abstract
Benzo(a)pyrene (BaP), the earliest and most significant carcinogen among polycyclic aromatic hydrocarbons (PAHs), has been found in foods, tobacco smoke, and automobiles exhaust, etc. Exposure to BaP induced DNA damage directly, or oxidative stress-related damage, resulting in cell apoptosis and carcinogenesis in human respiratory system, digestive system, reproductive system, etc. Moreover, BaP triggered genome-wide epigenetic alterations by methylation, which might cause disturbances in regulation of gene expression, and thereby induced cancer. It has been proved that BaP reduced genome-wide DNA methylation, and activated proto-oncogene by hypomethylation in the promoter region, but silenced tumor suppressor genes by promoter hypermethylation, resulting in cancer initiation and progression. Here we summarized the changes in DNA methylation in BaP exposure, and revealed the methylation of DNA plays a role in cancer development.
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Affiliation(s)
- Huizeng Wang
- Department of Laboratory Medicine, the Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010050, China
| | - Bingchun Liu
- Stem Cell Research Center, the Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010050, China
| | - Hong Chen
- Department of Laboratory Medicine, the Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010050, China
| | - Peixin Xu
- Department of Laboratory Medicine, the Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010050, China
| | - Huiting Xue
- College of Basic Medicine, Inner Mongolia Medical University, Hohhot, 010010, China.
| | - Jianlong Yuan
- Department of Laboratory Medicine, the Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010050, China.
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7
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Liu S, Liang Y, Li J, Yang S, Liu M, Liu C, Yang D, Zuo Y. Integrating reduced amino acid composition into PSSM for improving copper ion-binding protein prediction. Int J Biol Macromol 2023:124993. [PMID: 37307968 DOI: 10.1016/j.ijbiomac.2023.124993] [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: 03/28/2023] [Revised: 05/12/2023] [Accepted: 05/19/2023] [Indexed: 06/14/2023]
Abstract
Copper ion-binding proteins play an essential role in metabolic processes and are critical factors in many diseases, such as breast cancer, lung cancer, and Menkes disease. Many algorithms have been developed for predicting metal ion classification and binding sites, but none have been applied to copper ion-binding proteins. In this study, we developed a copper ion-bound protein classifier, RPCIBP, which integrating the reduced amino acid composition into position-specific score matrix (PSSM). The reduced amino acid composition filters out a large number of useless evolutionary features, improving the operational efficiency and predictive ability of the model (feature dimension from 2900 to 200, ACC from 83 % to 85.1 %). Compared with the basic model using only three sequence feature extraction methods (ACC in training set between 73.8 %-86.2 %, ACC in test set between 69.3 %-87.5 %), the model integrating the evolutionary features of the reduced amino acid composition showed higher accuracy and robustness (ACC in training set between 83.1 %-90.8 %, ACC in test set between 79.1 %-91.9 %). Best copper ion-binding protein classifiers filtered by feature selection progress were deployed in a user-friendly web server (http://bioinfor.imu.edu.cn/RPCIBP). RPCIBP can accurately predict copper ion-binding proteins, which is convenient for further structural and functional studies, and conducive to mechanism exploration and target drug development.
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Affiliation(s)
- Shanghua Liu
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010021, China; Inner Mongolia International Mongolian Hospital, Hohhot 010065, China
| | - Yuchao Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010021, China; Digital College, Inner Mongolia Intelligent Union Big Data Academy, Hohhot 010010, China
| | - Jinzhao Li
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010021, China
| | - Siqi Yang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010021, China
| | - Ming Liu
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010021, China
| | - Chengfang Liu
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010021, China
| | - Dezhi Yang
- Inner Mongolia International Mongolian Hospital, Hohhot 010065, China.
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010021, China; Inner Mongolia International Mongolian Hospital, Hohhot 010065, China; Digital College, Inner Mongolia Intelligent Union Big Data Academy, Hohhot 010010, China.
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8
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Su D, Xiong Y, Wei H, Wang S, Ke J, Liang P, Zhang H, Yu Y, Zuo Y, Yang L. Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance. Heliyon 2023; 9:e16147. [PMID: 37215759 PMCID: PMC10199194 DOI: 10.1016/j.heliyon.2023.e16147] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/04/2023] [Accepted: 05/07/2023] [Indexed: 05/24/2023] Open
Abstract
Transcription factors are protein molecules that act as regulators of gene expression. Aberrant protein activity of transcription factors can have a significant impact on tumor progression and metastasis in tumor patients. In this study, 868 immune-related transcription factors were identified from the transcription factor activity profile of 1823 ovarian cancer patients. The prognosis-related transcription factors were identified through univariate Cox analysis and random survival tree analysis, and two distinct clustering subtypes were subsequently derived based on these transcription factors. We assessed the clinical significance and genomics landscape of the two clustering subtypes and found statistically significant differences in prognosis, response to immunotherapy, and chemotherapy among ovarian cancer patients with different subtypes. Multi-scale Embedded Gene Co-expression Network Analysis was used to identify differential gene modules between the two clustering subtypes, which allowed us to conduct further analysis of biological pathways that exhibited significant differences between them. Finally, a ceRNA network was constructed to analyze lncRNA-miRNA-mRNA regulatory pairs with differential expression levels between two clustering subtypes. We expected that our study may provide some useful references for stratifying and treating patients with ovarian cancer.
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Affiliation(s)
- Dongqing Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yuqiang Xiong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Haodong Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Shiyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Jiawei Ke
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Pengfei Liang
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Haoxin Zhang
- Department of Gastrointestinal Oncology, Harbin Medical University Cancer Hospital, Harbin 150081, China
| | - Yao Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yongchun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
- Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd., Hohhot, 010010, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
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9
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Li J, He X, Gao S, Liang Y, Qi Z, Xi Q, Zuo Y, Xing Y. The Metal-binding Protein Atlas (MbPA): an integrated database for curating metalloproteins in all aspects. J Mol Biol 2023:168117. [PMID: 37086947 DOI: 10.1016/j.jmb.2023.168117] [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: 10/29/2022] [Revised: 04/14/2023] [Accepted: 04/17/2023] [Indexed: 04/24/2023]
Abstract
Metal-binding proteins are essential for the vital activities and engage in their roles by acting in concert with metal cations. MbPA (The Metal-binding Protein Atlas) is the most comprehensive resource up to now dedicated to curating metal-binding proteins. Currently, it contains 106373 entries and 440187 sites related to 54 metals and 8169 species. Users can view all metal-binding proteins and species-specific proteins in MbPA. There are also metal-proteomics data that quantitatively describes protein expression in different tissues and organs. By analyzing the data of the amino acid residues at the metal-binding site, it is found that about 80% of the metal ions tend to bind to cysteine, aspartic acid, glutamic acid, and histidine. Moreover, we use Diversity Measure to confirm that the diversity of metal-binding is specific in different area of periodic table, and further elucidate the binding modes of 19 transition metals on 20 amino acids. In addition, MbPA also embraces 6855 potential pathogenic mutations related to metalloprotein. The resource is freely available at http://bioinfor.imu.edu.cn/mbpa.
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Affiliation(s)
- Jinzhao Li
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Xiang He
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Shuang Gao
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Yuchao Liang
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Zhi Qi
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China; Key Laboratory of Forage and Endemic Crop Biotechnology, Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Qilemuge Xi
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Yongchun Zuo
- The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, College of life sciences, Inner Mongolia University, Hohhot, 010021, China.
| | - Yongqiang Xing
- The Inner Mongolia Key Laboratory of Functional Genome Bioinformatics, School of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou 014010, China.
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10
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Bi Y, Li F, Guo X, Wang Z, Pan T, Guo Y, Webb GI, Yao J, Jia C, Song J. Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations. Brief Bioinform 2022; 23:bbac467. [PMID: 36341591 PMCID: PMC10148739 DOI: 10.1093/bib/bbac467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/09/2022] [Accepted: 09/29/2022] [Indexed: 11/09/2022] Open
Abstract
Subcellular localization of messenger RNAs (mRNAs) plays a key role in the spatial regulation of gene activity. The functions of mRNAs have been shown to be closely linked with their localizations. As such, understanding of the subcellular localizations of mRNAs can help elucidate gene regulatory networks. Despite several computational methods that have been developed to predict mRNA localizations within cells, there is still much room for improvement in predictive performance, especially for the multiple-location prediction. In this study, we proposed a novel multi-label multi-class predictor, termed Clarion, for mRNA subcellular localization prediction. Clarion was developed based on a manually curated benchmark dataset and leveraged the weighted series method for multi-label transformation. Extensive benchmarking tests demonstrated Clarion achieved competitive predictive performance and the weighted series method plays a crucial role in securing superior performance of Clarion. In addition, the independent test results indicate that Clarion outperformed the state-of-the-art methods and can secure accuracy of 81.47, 91.29, 79.77, 92.10, 89.15, 83.74, 80.74, 79.23 and 84.74% for chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus and ribosome, respectively. The webserver and local stand-alone tool of Clarion is freely available at http://monash.bioweb.cloud.edu.au/Clarion/.
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Affiliation(s)
- Yue Bi
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, Victoria 3800, Australia
| | - Fuyi Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, Victoria 3000, Australia
| | - Xudong Guo
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Zhikang Wang
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Tong Pan
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3004, Australia
| | - Geoffrey I Webb
- Monash Data Futures Institute, Monash University, Melbourne, Victoria 3800, Australia
| | | | - Cangzhi Jia
- School of Science, Dalian Maritime University, Dalian 116026, China
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, Victoria 3800, Australia
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11
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Chao L, Yang S, Li H, Long C, Xi Q, Zuo Y. Competitive binding of TET1 and DNMT3A/B cooperates the DNA methylation pattern in human embryonic stem cells. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2022; 1865:194861. [PMID: 35998875 DOI: 10.1016/j.bbagrm.2022.194861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 08/06/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
DNMT3A/B and TET1 play indispensable roles in regulating DNA methylation that undergoes extensive reprogramming during mammalian embryogenesis. Yet the competitive and cooperative relationships between TET1 and DNMT3A/B remain largely unknown in the human embryonic stem cells. Here, we revealed that the main DNA-binding domain of TET1 contains more positive charges by using charge reduction of amino acid alphabet, followed by DNMT3A and DNMT3B. The genome-wide binding profiles showed that TET1 prefers binding to the proximal promoters and CpG islands compared with DNMT3A/B. Moreover, the binding regions of these three transcription factors can be divided into specific and co-binding regions. And a stronger inhibitory effect of DNMT3A on TET1 demethylation was observed in co-binding regions. Furthermore, we integrated TET1 knockout data to further discuss the competitive binding patterns of TET1 and DNMT3A/B. The lack of TET1 increased the occupation of DNMT3A/B at the specific binding regions of TET1 causing focal hypermethylation. The knockout of TET1 was also accompanied by a reduction of DNMT3A/B binding in the co-binding regions, further confirming the cooperative binding function between TET1 and DNMT3A/B. In conclusion, our studies found that the competitive binding of TET1 and DNMT3A/B cooperatively shapes the global DNA methylation pattern in human embryonic stem cells.
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Affiliation(s)
- Lemuge Chao
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Siqi Yang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Hanshuang Li
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Chunshen Long
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Qilemuge Xi
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China..
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12
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A Statistical Analysis of the Sequence and Structure of Thermophilic and Non-Thermophilic Proteins. Int J Mol Sci 2022; 23:ijms231710116. [PMID: 36077513 PMCID: PMC9456548 DOI: 10.3390/ijms231710116] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/29/2022] [Accepted: 08/31/2022] [Indexed: 11/17/2022] Open
Abstract
Thermophilic proteins have various practical applications in theoretical research and in industry. In recent years, the demand for thermophilic proteins on an industrial scale has been increasing; therefore, the engineering of thermophilic proteins has become a hot direction in the field of protein engineering. However, the exact mechanism of thermostability of proteins is not yet known, for engineering thermophilic proteins knowing the basis of thermostability is necessary. In order to understand the basis of the thermostability in proteins, we have made a statistical analysis of the sequences, secondary structures, hydrogen bonds, salt bridges, DHA (Donor-Hydrogen-Accepter) angles, and bond lengths of ten pairs of thermophilic proteins and their non-thermophilic orthologous. Our findings suggest that polar amino acids contribute to thermostability in proteins by forming hydrogen bonds and salt bridges which provide resistance against protein denaturation. Short bond length and a wider DHA angle provide greater bond stability in thermophilic proteins. Moreover, the increased frequency of aromatic amino acids in thermophilic proteins contributes to thermal stability by forming more aromatic interactions. Additionally, the coil, helix, and loop in the secondary structure also contribute to thermostability.
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13
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Muylaert C, Van Hemelrijck LA, Maes A, De Veirman K, Menu E, Vanderkerken K, De Bruyne E. Aberrant DNA methylation in multiple myeloma: A major obstacle or an opportunity? Front Oncol 2022; 12:979569. [PMID: 36059621 PMCID: PMC9434119 DOI: 10.3389/fonc.2022.979569] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 07/22/2022] [Indexed: 11/30/2022] Open
Abstract
Drug resistance (DR) of cancer cells leading to relapse is a huge problem nowadays to achieve long-lasting cures for cancer patients. This also holds true for the incurable hematological malignancy multiple myeloma (MM), which is characterized by the accumulation of malignant plasma cells in the bone marrow (BM). Although new treatment approaches combining immunomodulatory drugs, corticosteroids, proteasome inhibitors, alkylating agents, and monoclonal antibodies have significantly improved median life expectancy, MM remains incurable due to the development of DR, with the underlying mechanisms remaining largely ill-defined. It is well-known that MM is a heterogeneous disease, encompassing both genetic and epigenetic aberrations. In normal circumstances, epigenetic modifications, including DNA methylation and posttranslational histone modifications, play an important role in proper chromatin structure and transcriptional regulation. However, in MM, numerous epigenetic defects or so-called ‘epimutations’ have been observed and this especially at the level of DNA methylation. These include genome-wide DNA hypomethylation, locus specific hypermethylation and somatic mutations, copy number variations and/or deregulated expression patterns in DNA methylation modifiers and regulators. The aberrant DNA methylation patterns lead to reduced gene expression of tumor suppressor genes, genomic instability, DR, disease progression, and high-risk disease. In addition, the frequency of somatic mutations in the DNA methylation modifiers seems increased in relapsed patients, again suggesting a role in DR and relapse. In this review, we discuss the recent advances in understanding the involvement of aberrant DNA methylation patterns and/or DNA methylation modifiers in MM development, progression, and relapse. In addition, we discuss their involvement in MM cell plasticity, driving myeloma cells to a cancer stem cell state characterized by a more immature and drug-resistant phenotype. Finally, we briefly touch upon the potential of DNA methyltransferase inhibitors to prevent relapse after treatment with the current standard of care agents and/or new, promising (immuno) therapies.
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14
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Xiong Y, Wang S, Wei H, Li H, Lv Y, Chi M, Su D, Lu Q, Yu Y, Zuo Y, Yang L. Deep learning-based transcription factor activity for stratification of breast cancer patients. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2022; 1865:194838. [PMID: 35690313 DOI: 10.1016/j.bbagrm.2022.194838] [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: 04/04/2022] [Revised: 05/19/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
Transcription factors directly bind to DNA and regulate the expression of the gene, causing epigenetic modification of the DNA. They often mediate epigenetic parameters of transcriptional and posttranscriptional mechanisms, and their expression activities can be used to characterize genomic aberrations in cancer cell. In this study, the activity profile of transcription factors inferred by VIPER algorithm. The autoencoder model was applied for compressing the transcription factor activity profile for obtaining more useful transformed features for stratifying patients into two different breast cancer subtypes. The deep learning-based subtypes exhibited superior prognostic value and yielded better risk-stratification than the transcription factor activity-based method. Importantly, according to transformed features, a deep neural network was constructed to predict the subtypes, and achieved the accuracy of 94.98% and area under the ROC curve of 0.9663, respectively. The proposed subtypes were found to be significantly associated with immune infiltration, tumor immunogenicity and so on. Furthermore, the ceRNA network was constructed for the breast cancer subtypes. Besides, 11 master regulators were found to be associated with patients in cluster 1. Given the robustness performance of our deep learning model over multiple breast cancer cohorts, we expected this model may be useful in the area of prognosis prediction and lead some possibility for personalized medicine in breast cancer patients.
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Affiliation(s)
- Yuqiang Xiong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shiyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Haodong Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Hanshuang Li
- The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yingli Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Meng Chi
- Department of Anesthesiology, Harbin Medical University Cancer Hospital, Harbin 150081, China
| | - Dongqing Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Qianzi Lu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yao Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yongchun Zuo
- The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China; Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mingolia Wesure Date Technology Co., Ltd., Hohhot 010010, China.
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
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15
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Zheng L, Liu D, Li YA, Yang S, Liang Y, Xing Y, Zuo Y. RaacFold: a webserver for 3D visualization and analysis of protein structure by using reduced amino acid alphabets. Nucleic Acids Res 2022; 50:W633-W638. [PMID: 35639512 PMCID: PMC9252778 DOI: 10.1093/nar/gkac415] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 04/23/2022] [Accepted: 05/09/2022] [Indexed: 12/11/2022] Open
Abstract
Protein structure exhibits greater complexity and diversity than DNA structure, and usually affects the interpretation of the function, interactions and biological annotations. Reduced amino acid alphabets (Raaa) exhibit a powerful ability to decrease protein complexity and identify functional conserved regions, which motivated us to create RaacFold. The RaacFold provides 687 reduced amino acid clusters (Raac) based on 58 reduction methods and offers three analysis tools: Protein Analysis, Align Analysis, and Multi Analysis. The Protein Analysis and Align Analysis provide reduced representations of sequence-structure according to physicochemical similarities and computational biology strategies. With the simplified representations, the protein structure can be viewed more concise and clearer to capture biological insight than the unreduced structure. Thus, the design of artificial protein will be more convenient, and redundant interference is avoided. In addition, Multi Analysis allows users to explore biophysical variation and conservation in the evolution of protein structure and function. This supplies important information for the identification and exploration of the nonhomologous functions of paralogs. Simultaneously, RaacFold provides powerful 2D and 3D rendering performance with advanced parameters for sequences, structures, and related annotations. RaacFold is freely available at http://bioinfor.imu.edu.cn/raacfold.
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Affiliation(s)
- Lei Zheng
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Dongyang Liu
- Photosynthesis Research Center, Key Laboratory of Photobiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Siqi Yang
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yuchao Liang
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yongqiang Xing
- The Inner Mongolia Key Laboratory of Functional Genome Bioinformatics, School of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou 014010, China.,Department of Biological Sciences, Center for Systems Biology, the University of Texas at Dallas, Richardson, TX 75080-3021, USA
| | - Yongchun Zuo
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
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16
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Song YP, Lv JW, Zhao Y, Chen X, Zhang ZC, Fan YJ, Zhang C, Gao L, Huang Y, Wang H, Xu DX. DNA hydroxymethylation reprogramming of β-oxidation genes mediates early-life arsenic-evoked hepatic lipid accumulation in adult mice. JOURNAL OF HAZARDOUS MATERIALS 2022; 430:128511. [PMID: 35739688 DOI: 10.1016/j.jhazmat.2022.128511] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 02/10/2022] [Accepted: 02/15/2022] [Indexed: 06/15/2023]
Abstract
The metabolic disorders are becoming an epidemic disease endangering public health in countries. Environmental factors are mainly reason for the growth of metabolic disorders. Previous research suggests that DNA methylation is a potential mechanism. Recently, it has been reported that DNA hydroxymethylation is also a stable marker of epigenetic reprogramming. Hence, the study aims to investigate whether DNA hydroxymehylation mediates early-life environmental stress-evoked metabolic disorder in adulthood. Mice were orally administered with arsenic (As), an environmental stressor, throughout pregnancy. We show that early-life As exposure induces glucose intolerance and hepatic lipid accumulation in adulthood. Early-life As exposure alters epigenetic reprogramming and expression of lipid metabolism-related genes including β-oxidation-specific genes in adulthood. Of interest, early-life As exposure alters epigenetic reprogramming of hepatic lipid metabolism partially through reducing DNA hydroxymethylation modification of β-oxidation-related genes in developing liver. Mechanistically, early-life As exposure suppresses ten-eleven translocation (TET) activity through downregulating isocitrate dehydrogenases (Idh) and reducing alpha-ketoglutarate (α-KG) content in the developing liver. In addition, early-life As exposure inhibits TET1 binding to CpG-rich fragments of β-oxidation-related genes in developing liver. This study provide novel evidence that early-life environmental stress leads to later life metabolic disorders by altering hepatic DNA hydroxymethylation reprogramming.
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Affiliation(s)
- Ya-Ping Song
- Department of Toxicology, Anhui Medical University, Hefei 230032, China
| | - Jin-Wei Lv
- Department of Toxicology, Anhui Medical University, Hefei 230032, China
| | - Ying Zhao
- Department of Toxicology, Anhui Medical University, Hefei 230032, China
| | - Xu Chen
- Department of Toxicology, Anhui Medical University, Hefei 230032, China
| | - Zhi-Cheng Zhang
- Department of Toxicology, Anhui Medical University, Hefei 230032, China
| | - Yi-Jun Fan
- Department of Toxicology, Anhui Medical University, Hefei 230032, China
| | - Cheng Zhang
- Department of Toxicology, Anhui Medical University, Hefei 230032, China
| | - Lan Gao
- Department of Toxicology, Anhui Medical University, Hefei 230032, China
| | - Yichao Huang
- Department of Toxicology, Anhui Medical University, Hefei 230032, China
| | - Hua Wang
- Department of Toxicology, Anhui Medical University, Hefei 230032, China
| | - De-Xiang Xu
- Department of Toxicology, Anhui Medical University, Hefei 230032, China.
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17
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Xiong X, Yang M, Yu H, Hu Y, Yang L, Zhu Y, Fei X, Pan B, Xiong Y, Fu W, Li J. MicroRNA‐342‐3p regulates yak oocyte meiotic maturation by targeting DNA methyltransferase 1. Reprod Domest Anim 2022; 57:761-770. [DOI: 10.1111/rda.14119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 03/24/2022] [Accepted: 03/27/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Xianrong Xiong
- Key Laboratory of Qinghai‐Tibetan Plateau Animal Genetic Resource Reservation and Exploitation of Ministry of Education Southwest Minzu University Chengdu Sichuan 610041 P.R. China
| | - Manzhen Yang
- Key Laboratory for Animal Science of National Ethnic Affairs Commission Southwest Minzu University Chengdu Sichuan 610041 P.R. China
| | - Hailing Yu
- Key Laboratory for Animal Science of National Ethnic Affairs Commission Southwest Minzu University Chengdu Sichuan 610041 P.R. China
| | - Yulei Hu
- Key Laboratory of Qinghai‐Tibetan Plateau Animal Genetic Resource Reservation and Exploitation of Ministry of Education Southwest Minzu University Chengdu Sichuan 610041 P.R. China
| | - Luyu Yang
- Key Laboratory for Animal Science of National Ethnic Affairs Commission Southwest Minzu University Chengdu Sichuan 610041 P.R. China
| | - Yanjin Zhu
- Key Laboratory of Qinghai‐Tibetan Plateau Animal Genetic Resource Reservation and Exploitation of Ministry of Education Southwest Minzu University Chengdu Sichuan 610041 P.R. China
| | - Xixi Fei
- Key Laboratory of Qinghai‐Tibetan Plateau Animal Genetic Resource Reservation and Exploitation of Ministry of Education Southwest Minzu University Chengdu Sichuan 610041 P.R. China
| | - Bangting Pan
- Key Laboratory of Qinghai‐Tibetan Plateau Animal Genetic Resource Reservation and Exploitation of Ministry of Education Southwest Minzu University Chengdu Sichuan 610041 P.R. China
| | - Yan Xiong
- Key Laboratory for Animal Science of National Ethnic Affairs Commission Southwest Minzu University Chengdu Sichuan 610041 P.R. China
| | - Wei Fu
- Key Laboratory of Qinghai‐Tibetan Plateau Animal Genetic Resource Reservation and Exploitation of Ministry of Education Southwest Minzu University Chengdu Sichuan 610041 P.R. China
| | - Jian Li
- Key Laboratory of Qinghai‐Tibetan Plateau Animal Genetic Resource Reservation and Exploitation of Ministry of Education Southwest Minzu University Chengdu Sichuan 610041 P.R. China
- Key Laboratory for Animal Science of National Ethnic Affairs Commission Southwest Minzu University Chengdu Sichuan 610041 P.R. China
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18
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Zhao Z, Yang W, Zhai Y, Liang Y, Zhao Y. Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm. Front Genet 2022; 12:821996. [PMID: 35154264 PMCID: PMC8837382 DOI: 10.3389/fgene.2021.821996] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 12/07/2021] [Indexed: 12/13/2022] Open
Abstract
The exploration of DNA-binding proteins (DBPs) is an important aspect of studying biological life activities. Research on life activities requires the support of scientific research results on DBPs. The decline in many life activities is closely related to DBPs. Generally, the detection method for identifying DBPs is achieved through biochemical experiments. This method is inefficient and requires considerable manpower, material resources and time. At present, several computational approaches have been developed to detect DBPs, among which machine learning (ML) algorithm-based computational techniques have shown excellent performance. In our experiments, our method uses fewer features and simpler recognition methods than other methods and simultaneously obtains satisfactory results. First, we use six feature extraction methods to extract sequence features from the same group of DBPs. Then, this feature information is spliced together, and the data are standardized. Finally, the extreme gradient boosting (XGBoost) model is used to construct an effective predictive model. Compared with other excellent methods, our proposed method has achieved better results. The accuracy achieved by our method is 78.26% for PDB2272 and 85.48% for PDB186. The accuracy of the experimental results achieved by our strategy is similar to that of previous detection methods.
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Affiliation(s)
- Ziye Zhao
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Wen Yang
- International Medical Center, Shenzhen University General Hospital, Shenzhen, China
| | - Yixiao Zhai
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Yingjian Liang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
- *Correspondence: Yingjian Liang, ; Yuming Zhao,
| | - Yuming Zhao
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
- *Correspondence: Yingjian Liang, ; Yuming Zhao,
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19
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Liu M, Chen H, Gao D, Ma CY, Zhang ZY. Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:7493834. [PMID: 35069791 PMCID: PMC8769816 DOI: 10.1155/2022/7493834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 12/16/2021] [Indexed: 11/28/2022]
Abstract
Helicobacter pylori (H. pylori) is the most common risk factor for gastric cancer worldwide. The membrane proteins of the H. pylori are involved in bacterial adherence and play a vital role in the field of drug discovery. Thus, an accurate and cost-effective computational model is needed to predict the uncharacterized membrane proteins of H. pylori. In this study, a reliable benchmark dataset consisted of 114 membrane and 219 nonmembrane proteins was constructed based on UniProt. A support vector machine- (SVM-) based model was developed for discriminating H. pylori membrane proteins from nonmembrane proteins by using sequence information. Cross-validation showed that our method achieved good performance with an accuracy of 91.29%. It is anticipated that the proposed model will be useful for the annotation of H. pylori membrane proteins and the development of new anti-H. pylori agents.
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Affiliation(s)
- Mujiexin Liu
- Ineye Hospital of Chengdu University of TCM, Chengdu University of TCM, Chengdu 610084, China
| | - Hui Chen
- School of Healthcare Technology, Chengdu Neusoft University, 611844 Chengdu, China
| | - Dong Gao
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Cai-Yi Ma
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhao-Yue Zhang
- School of Healthcare Technology, Chengdu Neusoft University, 611844 Chengdu, China
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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20
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Zhang Z, Gong Y, Gao B, Li H, Gao W, Zhao Y, Dong B. SNAREs-SAP: SNARE Proteins Identification With PSSM Profiles. Front Genet 2022; 12:809001. [PMID: 34987554 PMCID: PMC8721734 DOI: 10.3389/fgene.2021.809001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 11/15/2021] [Indexed: 12/20/2022] Open
Abstract
Soluble N-ethylmaleimide sensitive factor activating protein receptor (SNARE) proteins are a large family of transmembrane proteins located in organelles and vesicles. The important roles of SNARE proteins include initiating the vesicle fusion process and activating and fusing proteins as they undergo exocytosis activity, and SNARE proteins are also vital for the transport regulation of membrane proteins and non-regulatory vesicles. Therefore, there is great significance in establishing a method to efficiently identify SNARE proteins. However, the identification accuracy of the existing methods such as SNARE CNN is not satisfied. In our study, we developed a method based on a support vector machine (SVM) that can effectively recognize SNARE proteins. We used the position-specific scoring matrix (PSSM) method to extract features of SNARE protein sequences, used the support vector machine recursive elimination correlation bias reduction (SVM-RFE-CBR) algorithm to rank the importance of features, and then screened out the optimal subset of feature data based on the sorted results. We input the feature data into the model when building the model, used 10-fold crossing validation for training, and tested model performance by using an independent dataset. In independent tests, the ability of our method to identify SNARE proteins achieved a sensitivity of 68%, specificity of 94%, accuracy of 92%, area under the curve (AUC) of 84%, and Matthew’s correlation coefficient (MCC) of 0.48. The results of the experiment show that the common evaluation indicators of our method are excellent, indicating that our method performs better than other existing classification methods in identifying SNARE proteins.
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Affiliation(s)
- Zixiao Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Yue Gong
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Bo Gao
- Department of Radiology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Hongfei Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Wentao Gao
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Yuming Zhao
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Benzhi Dong
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
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21
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WGBS combined with RNA-seq analysis revealed that Dnmt1 affects the methylation modification and gene expression changes during mouse oocyte vitrification. Theriogenology 2022; 177:11-21. [PMID: 34653792 DOI: 10.1016/j.theriogenology.2021.09.032] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 01/01/2023]
Abstract
Understanding the molecular level changes of oocyte cryopreservation and the subsequent warming process is essential for improving the oocyte cryopreservation technologies. Here, we collected the mature metaphase II (MII) oocytes from mice and vitrified. After thawing, single-cell whole-genome bisulphite sequencing (scWGBS) and single-cell RNA sequencing (scRNA-seq) were used to investigate the molecular attributes of this process. Compared to the fresh oocytes, the vitrified oocytes had lower global methylation and gene expression levels, and 1426 genes up-regulated and 3321 genes down-regulated. The 1426 up-regulated differentially expressed genes (DEGs) in the vitrified oocytes were mainly associated with the histone ubiquitination, while the 3321 down-regulated genes were mainly enriched in the mitochondrion organisation and ATP metabolism processes. The differentially methylated regions (DMRs) were mainly located in promoter, intron and exon region of genes, and the length of DMRs in the vitrified oocytes were also significantly lower than that of the fresh oocytes. Notably, there were no significant difference in the expression levels of DNA demethylases (Tet1, Tet2 and Tet3) and methyltransferases (Dnmt3a and Dnmt3b) between two treatments of oocytes. However, Dnmt1 and kcnq1ot1, which are responsible for maintaining DNA methylation, were significantly down regulated in the vitrified oocytes. Gene regulatory network (GRN) analysis showed the Dnmt1 and kcnq1ot1 play a core role in regulating methylation and expression levels of downstream genes. Moreover, some genes associated with oocyte quality were significantly down-regulated in the vitrified oocytes. The present data provides a new perspective for understanding the impact of vitrification on oocytes.
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22
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Gong Y, Liao B, Wang P, Zou Q. DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins. Front Pharmacol 2021; 12:771808. [PMID: 34916947 PMCID: PMC8669608 DOI: 10.3389/fphar.2021.771808] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 11/15/2021] [Indexed: 01/09/2023] Open
Abstract
Drug targets are biological macromolecules or biomolecule structures capable of specifically binding a therapeutic effect with a particular drug or regulating physiological functions. Due to the important value and role of drug targets in recent years, the prediction of potential drug targets has become a research hotspot. The key to the research and development of modern new drugs is first to identify potential drug targets. In this paper, a new predictor, DrugHybrid_BS, is developed based on hybrid features and Bagging-SVM to identify potentially druggable proteins. This method combines the three features of monoDiKGap (k = 2), cross-covariance, and grouped amino acid composition. It removes redundant features and analyses key features through MRMD and MRMD2.0. The cross-validation results show that 96.9944% of the potentially druggable proteins can be accurately identified, and the accuracy of the independent test set has reached 96.5665%. This all means that DrugHybrid_BS has the potential to become a useful predictive tool for druggable proteins. In addition, the hybrid key features can identify 80.0343% of the potentially druggable proteins combined with Bagging-SVM, which indicates the significance of this part of the features for research.
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Affiliation(s)
- Yuxin Gong
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China.,Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, China
| | - Bo Liao
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China.,Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, China
| | - Peng Wang
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China.,Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
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23
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Jia Y, Huang S, Zhang T. KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest. Front Genet 2021; 12:811158. [PMID: 34912382 PMCID: PMC8667860 DOI: 10.3389/fgene.2021.811158] [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: 11/08/2021] [Accepted: 11/15/2021] [Indexed: 02/04/2023] Open
Abstract
DNA-binding protein (DBP) is a protein with a special DNA binding domain that is associated with many important molecular biological mechanisms. Rapid development of computational methods has made it possible to predict DBP on a large scale; however, existing methods do not fully integrate DBP-related features, resulting in rough prediction results. In this article, we develop a DNA-binding protein identification method called KK-DBP. To improve prediction accuracy, we propose a feature extraction method that fuses multiple PSSM features. The experimental results show a prediction accuracy on the independent test dataset PDB186 of 81.22%, which is the highest of all existing methods.
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Affiliation(s)
- Yuran Jia
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianjiao Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
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24
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Yang YH, Wang JS, Yuan SS, Liu ML, Su W, Lin H, Zhang ZY. A Survey for Predicting ATP Binding Residues of Proteins Using Machine Learning Methods. Curr Med Chem 2021; 29:789-806. [PMID: 34514982 DOI: 10.2174/0929867328666210910125802] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 06/29/2021] [Accepted: 07/04/2021] [Indexed: 11/22/2022]
Abstract
Protein-ligand interactions are necessary for majority protein functions. Adenosine-5'-triphosphate (ATP) is one such ligand that plays vital role as a coenzyme in providing energy for cellular activities, catalyzing biological reaction and signaling. Knowing ATP binding residues of proteins is helpful for annotation of protein function and drug design. However, due to the huge amounts of protein sequences influx into databases in the post-genome era, experimentally identifying ATP binding residues is cost-ineffective and time-consuming. To address this problem, computational methods have been developed to predict ATP binding residues. In this review, we briefly summarized the application of machine learning methods in detecting ATP binding residues of proteins. We expect this review will be helpful for further research.
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Affiliation(s)
- Yu-He Yang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Jia-Shu Wang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Shi-Shi Yuan
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Meng-Lu Liu
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Wei Su
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Zhao-Yue Zhang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
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25
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Zulfiqar H, Sun ZJ, Huang QL, Yuan SS, Lv H, Dao FY, Lin H, Li YW. Deep-4mCW2V: A sequence-based predictor to identify N4-methylcytosine sites in Escherichia coli. Methods 2021; 203:558-563. [PMID: 34352373 DOI: 10.1016/j.ymeth.2021.07.011] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 07/22/2021] [Accepted: 07/29/2021] [Indexed: 10/20/2022] Open
Abstract
N4-methylcytosine (4mC) is a type of DNA modification which could regulate several biological progressions such as transcription regulation, replication and gene expressions. Precisely recognizing 4mC sites in genomic sequences can provide specific knowledge about their genetic roles. This study aimed to develop a deep learning-based model to predict 4mC sites in the Escherichia coli. In the model, DNA sequences were encoded by word embedding technique 'word2vec'. The obtained features were inputted into 1-D convolutional neural network (CNN) to discriminate 4mC sites from non-4mC sites in Escherichia coli genome. The examination on independent dataset showed that our model could yield the overall accuracy of 0.861, which was about 4.3% higher than the existing model. To provide convenience to scholars, we provided the data and source code of the model which can be freely download from https://github.com/linDing-groups/Deep-4mCW2V.
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Affiliation(s)
- Hasan Zulfiqar
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zi-Jie Sun
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qin-Lai Huang
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shi-Shi Yuan
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lv
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Ying Dao
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lin
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Yan-Wen Li
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China; Key Laboratory of Intelligent Information Processing of Jilin Province, Northeast Normal University, Changchun 130117, China; Institute of Computational Biology, Northeast Normal University, Changchun 130117, China.
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26
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Long C, Li H, Li X, Yang W, Zuo Y. Nuclear Transfer Arrest Embryos Show Massive Dysregulation of Genes Involved in Transcription Pathways. Int J Mol Sci 2021; 22:ijms22158187. [PMID: 34360962 PMCID: PMC8347363 DOI: 10.3390/ijms22158187] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 07/27/2021] [Accepted: 07/28/2021] [Indexed: 11/28/2022] Open
Abstract
Somatic cell nuclear transfer (SCNT) technology can reprogram terminally differentiated cell nuclei into a totipotent state. However, the underlying molecular barriers of SCNT embryo development remain incompletely elucidated. Here, we observed that transcription-related pathways were incompletely activated in nuclear transfer arrest (NTA) embryos compared to normal SCNT embryos and in vivo fertilized (WT) embryos, which hinders the development of SCNT embryos. We further revealed the transcription pathway associated gene regulatory networks (GRNs) and found the aberrant transcription pathways can lead to the massive dysregulation of genes in NTA embryos. The predicted target genes of transcription pathways contain a series of crucial factors in WT embryos, which play an important role in catabolic process, pluripotency regulation, epigenetic modification and signal transduction. In NTA embryos, however, these genes were varying degrees of inhibition and show a defect in synergy. Overall, our research found that the incomplete activation of transcription pathways is another potential molecular barrier for SCNT embryos besides the incomplete reprogramming of epigenetic modifications, broadening the understanding of molecular mechanism of SCNT embryonic development.
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27
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Zhou J, Bo S, Wang H, Zheng L, Liang P, Zuo Y. Identification of Disease-Related 2-Oxoglutarate/Fe (II)-Dependent Oxygenase Based on Reduced Amino Acid Cluster Strategy. Front Cell Dev Biol 2021; 9:707938. [PMID: 34336861 PMCID: PMC8323781 DOI: 10.3389/fcell.2021.707938] [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: 05/11/2021] [Accepted: 06/10/2021] [Indexed: 11/17/2022] Open
Abstract
The 2-oxoglutarate/Fe (II)-dependent (2OG) oxygenase superfamily is mainly responsible for protein modification, nucleic acid repair and/or modification, and fatty acid metabolism and plays important roles in cancer, cardiovascular disease, and other diseases. They are likely to become new targets for the treatment of cancer and other diseases, so the accurate identification of 2OG oxygenases is of great significance. Many computational methods have been proposed to predict functional proteins to compensate for the time-consuming and expensive experimental identification. However, machine learning has not been applied to the study of 2OG oxygenases. In this study, we developed OGFE_RAAC, a prediction model to identify whether a protein is a 2OG oxygenase. To improve the performance of OGFE_RAAC, 673 amino acid reduction alphabets were used to determine the optimal feature representation scheme by recoding the protein sequence. The 10-fold cross-validation test showed that the accuracy of the model in identifying 2OG oxygenases is 91.04%. Besides, the independent dataset results also proved that the model has excellent generalization and robustness. It is expected to become an effective tool for the identification of 2OG oxygenases. With further research, we have also found that the function of 2OG oxygenases may be related to their polarity and hydrophobicity, which will help the follow-up study on the catalytic mechanism of 2OG oxygenases and the way they interact with the substrate. Based on the model we built, a user-friendly web server was established and can be friendly accessed at http://bioinfor.imu.edu.cn/ogferaac.
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Affiliation(s)
- Jian Zhou
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Suling Bo
- College of Computer and Information, Inner Mongolia Medical University, Hohhot, China
| | - Hao Wang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Pengfei Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
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28
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Min X, Lu F, Li C. Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction. Curr Pharm Des 2021; 27:1847-1855. [PMID: 33234095 DOI: 10.2174/1381612826666201124112710] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 07/29/2020] [Accepted: 08/06/2020] [Indexed: 11/22/2022]
Abstract
Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation, which tightly controls gene expression. Identification of EPIs can help us better decipher gene regulation and understand disease mechanisms. However, experimental methods to identify EPIs are constrained by funds, time, and manpower, while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literature. First, we briefly introduce existing sequence- based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means, and evaluation strategies. Finally, we concluded with the challenges these methods are confronted with and suggest several future opportunities. We hope this review will provide a useful reference for further studies on enhancer-promoter interactions.
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Affiliation(s)
- Xiaoping Min
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Fengqing Lu
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Chunyan Li
- Graduate School, Yunnan Minzu University, Kunming 650504, China
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29
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Zulfiqar H, Khan RS, Hassan F, Hippe K, Hunt C, Ding H, Song XM, Cao R. Computational identification of N4-methylcytosine sites in the mouse genome with machine-learning method. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:3348-3363. [PMID: 34198389 DOI: 10.3934/mbe.2021167] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/24/2023]
Abstract
N4-methylcytosine (4mC) is a kind of DNA modification which could regulate multiple biological processes. Correctly identifying 4mC sites in genomic sequences can provide precise knowledge about their genetic roles. This study aimed to develop an ensemble model to predict 4mC sites in the mouse genome. In the proposed model, DNA sequences were encoded by k-mer, enhanced nucleic acid composition and composition of k-spaced nucleic acid pairs. Subsequently, these features were optimized by using minimum redundancy maximum relevance (mRMR) with incremental feature selection (IFS) and five-fold cross-validation. The obtained optimal features were inputted into random forest classifier for discriminating 4mC from non-4mC sites in mouse. On the independent dataset, our model could yield the overall accuracy of 85.41%, which was approximately 3.8% -6.3% higher than the two existing models, i4mC-Mouse and 4mCpred-EL respectively. The data and source code of the model can be freely download from https://github.com/linDing-groups/model_4mc.
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Affiliation(s)
- Hasan Zulfiqar
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Rida Sarwar Khan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Farwa Hassan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Kyle Hippe
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
| | - Cassandra Hunt
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
| | - Hui Ding
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xiao-Ming Song
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
- School of Life Sciences, North China University of Science and Technology, Tangshan, Hebei 063210, China
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
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30
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Zhang ZM, Guan ZX, Wang F, Zhang D, Ding H. Application of Machine Learning Methods in Predicting Nuclear Receptors and their Families. Med Chem 2021; 16:594-604. [PMID: 31584374 DOI: 10.2174/1573406415666191004125551] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 06/18/2019] [Accepted: 08/23/2019] [Indexed: 11/22/2022]
Abstract
Nuclear receptors (NRs) are a superfamily of ligand-dependent transcription factors that are closely related to cell development, differentiation, reproduction, homeostasis, and metabolism. According to the alignments of the conserved domains, NRs are classified and assigned the following seven subfamilies or eight subfamilies: (1) NR1: thyroid hormone like (thyroid hormone, retinoic acid, RAR-related orphan receptor, peroxisome proliferator activated, vitamin D3- like), (2) NR2: HNF4-like (hepatocyte nuclear factor 4, retinoic acid X, tailless-like, COUP-TFlike, USP), (3) NR3: estrogen-like (estrogen, estrogen-related, glucocorticoid-like), (4) NR4: nerve growth factor IB-like (NGFI-B-like), (5) NR5: fushi tarazu-F1 like (fushi tarazu-F1 like), (6) NR6: germ cell nuclear factor like (germ cell nuclear factor), and (7) NR0: knirps like (knirps, knirpsrelated, embryonic gonad protein, ODR7, trithorax) and DAX like (DAX, SHP), or dividing NR0 into (7) NR7: knirps like and (8) NR8: DAX like. Different NRs families have different structural features and functions. Since the function of a NR is closely correlated with which subfamily it belongs to, it is highly desirable to identify NRs and their subfamilies rapidly and effectively. The knowledge acquired is essential for a proper understanding of normal and abnormal cellular mechanisms. With the advent of the post-genomics era, huge amounts of sequence-known proteins have increased explosively. Conventional methods for accurately classifying the family of NRs are experimental means with high cost and low efficiency. Therefore, it has created a greater need for bioinformatics tools to effectively recognize NRs and their subfamilies for the purpose of understanding their biological function. In this review, we summarized the application of machine learning methods in the prediction of NRs from different aspects. We hope that this review will provide a reference for further research on the classification of NRs and their families.
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Affiliation(s)
- Zi-Mei Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zheng-Xing Guan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fang Wang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Dan Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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31
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Dao FY, Lv H, Su W, Sun ZJ, Huang QL, Lin H. iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network. Brief Bioinform 2021; 22:6158360. [PMID: 33751027 DOI: 10.1093/bib/bbab047] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Revised: 01/28/2021] [Accepted: 01/29/2021] [Indexed: 01/09/2023] Open
Abstract
DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription factor-binding site, etc. Moreover, the related locus of disease (or trait) are usually enriched in the DHS regions. Therefore, the detection of DHS region is of great significance. In this study, we develop a deep learning-based algorithm to identify whether an unknown sequence region would be potential DHS. The proposed method showed high prediction performance on both training datasets and independent datasets in different cell types and developmental stages, demonstrating that the method has excellent superiority in the identification of DHSs. Furthermore, for the convenience of related wet-experimental researchers, the user-friendly web-server iDHS-Deep was established at http://lin-group.cn/server/iDHS-Deep/, by which users can easily distinguish DHS and non-DHS and obtain the corresponding developmental stage ofDHS.
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Affiliation(s)
- Fu-Ying Dao
- Informational Biology at University of Electronic Science and Technology of China, China
| | - Hao Lv
- Informational Biology at University of Electronic Science and Technology of China, China
| | - Wei Su
- Informational Biology at University of Electronic Science and Technology of China, China
| | - Zi-Jie Sun
- Informational Biology at University of Electronic Science and Technology of China, China
| | - Qin-Lai Huang
- Informational Biology at University of Electronic Science and Technology of China, China
| | - Hao Lin
- Informational Biology at University of Electronic Science and Technology of China, China
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32
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Huang Q, Zhou W, Guo F, Xu L, Zhang L. 6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning. PeerJ 2021; 9:e10813. [PMID: 33604189 PMCID: PMC7866889 DOI: 10.7717/peerj.10813] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 12/30/2020] [Indexed: 01/03/2023] Open
Abstract
With the accumulation of data on 6mA modification sites, an increasing number of scholars have begun to focus on the identification of 6mA sites. Despite the recognized importance of 6mA sites, methods for their identification remain lacking, with most existing methods being aimed at their identification in individual species. In the present study, we aimed to develop an identification method suitable for multiple species. Based on previous research, we propose a method for 6mA site recognition. Our experiments prove that the proposed 6mA-Pred method is effective for identifying 6mA sites in genes from taxa such as rice, Mus musculus, and human. A series of experimental results show that 6mA-Pred is an excellent method. We provide the source code used in the study, which can be obtained from http://39.100.246.211:5004/6mA_Pred/.
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Affiliation(s)
- Qianfei Huang
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Wenyang Zhou
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Fei Guo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Lichao Zhang
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
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33
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Wang H, Xi Q, Liang P, Zheng L, Hong Y, Zuo Y. IHEC_RAAC: a online platform for identifying human enzyme classes via reduced amino acid cluster strategy. Amino Acids 2021; 53:239-251. [PMID: 33486591 DOI: 10.1007/s00726-021-02941-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 01/11/2021] [Indexed: 12/18/2022]
Abstract
Enzymes have been proven to play considerable roles in disease diagnosis and biological functions. The feature extraction that truly reflects the intrinsic properties of protein is the most critical step for the automatic identification of enzymes. Although lots of feature extraction methods have been proposed, some challenges remain. In this study, we developed a predictor called IHEC_RAAC, which has the capability to identify whether a protein is a human enzyme and distinguish the function of the human enzyme. To improve the feature representation ability, protein sequences were encoded by a new feature-vector called 'reduced amino acid cluster'. We calculated 673 amino acid reduction alphabets to determine the optimal feature representative scheme. The tenfold cross-validation test showed that the accuracy of IHEC_RAAC to identify human enzymes was 74.66% and further discriminate the human enzyme classes with an accuracy of 54.78%, which was 2.06% and 8.68% higher than the state-of-the-art predictors, respectively. Additionally, the results from the independent dataset indicated that IHEC_RAAC can effectively predict human enzymes and human enzyme classes to further provide guidance for protein research. A user-friendly web server, IHEC_RAAC, is freely accessible at http://bioinfor.imu.edu.cn/ihecraac .
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Affiliation(s)
- Hao Wang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Qilemuge Xi
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Pengfei Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Yan Hong
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
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34
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Screening of Prospective Plant Compounds as H1R and CL1R Inhibitors and Its Antiallergic Efficacy through Molecular Docking Approach. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021. [DOI: 10.1155/2021/6683407] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Allergens have the ability to enter the body and cause illness. Leukotriene is the widespread allergen which could stimulate mast cells to discharge histamine which causes allergy symptoms. An effective strategy for treating leukotriene-induced allergy is to find the inhibitors of leukotriene or histamine activity from phytochemicals. For this purpose, a library of 8,500 phytochemicals was generated using MOE software. The structures of histamine-1 receptor and cysteinyl leukotriene receptor-1 were predicted by the homology modeling method through the SWISS model. The phytochemicals were docked with predicted structures of histamine-1 and cysteinyl leukotriene receptor-1 in MOE software to determine the binding affinity of the phytochemicals against the targets. Moreover, chemoinformatics properties and ADMET of phytochemicals were assessed to find the drug likeness behavior of compounds. Compound ID 10054216 has the lowest
-score value for H-1 receptor that is -18.9186 kcal/mol which is lower than the value of standard -15.167 kcal/mol. The other compounds 393471, 71448939, 10722577, and 442614 also showed good
-score values than the standard. Moreover, compound ID 11843082 has the lowest
-score value for CL1R that is -15.481 kcal/mol which is lower than the value of standard -12.453 kcal/mol. The other compounds 72284, 5282102, 66559251, and 102506430 also showed good
-score values than the standard. In this research article, we performed molecular docking to find the best inhibitors against H1R and CL1R and their antiallergic efficacy. This in silico knowledge will be helpful in near future for the design of novel, safe, and less costing H-1 receptor and CL1R inhibitors with the aim to improve human life quality.
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Matuleviciute R, Cunha PP, Johnson RS, Foskolou IP. Oxygen regulation of TET enzymes. FEBS J 2021; 288:7143-7161. [PMID: 33410283 DOI: 10.1111/febs.15695] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 12/23/2020] [Accepted: 01/04/2021] [Indexed: 12/15/2022]
Abstract
Hypoxia has a significant impact on many physiological and pathological processes. Over the recent years, its role in modulation of epigenetic remodelling has also become clearer. In cancer, low oxygen environments and aberrant epigenomes often go hand in hand, and changes in DNA methylation are now commonly recognised as potential outcome indicators. TET (ten-eleven translocation) family enzymes are alpha-ketoglutarate-, iron- and oxygen-dependent DNA demethylases and are key players in these processes. Although TETs have historically been considered tumour suppressors, recent studies suggest that their functions in cancer might not be straightforward. Recently, inhibition of TETs has been reported to have positive impact in cancer immunotherapy and vaccination studies. This underlines the current interest in developing targeted pharmaceutical inhibitors of these enzymes. Here, we will survey the complexity of TET roles in cancer, and its hypoxic modulation, as well as highlight the potential of these enzymes as therapeutic targets.
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Affiliation(s)
- Rugile Matuleviciute
- Department of Physiology, Development and Neuroscience, University of Cambridge, UK
| | - Pedro P Cunha
- Department of Physiology, Development and Neuroscience, University of Cambridge, UK
| | - Randall S Johnson
- Department of Physiology, Development and Neuroscience, University of Cambridge, UK.,Department of Cell and Molecular Biology (CMB), Karolinska Institutet, Solna, Sweden
| | - Iosifina P Foskolou
- Department of Physiology, Development and Neuroscience, University of Cambridge, UK.,Department of Cell and Molecular Biology (CMB), Karolinska Institutet, Solna, Sweden
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36
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Xu B, Liu D, Wang Z, Tian R, Zuo Y. Multi-substrate selectivity based on key loops and non-homologous domains: new insight into ALKBH family. Cell Mol Life Sci 2021; 78:129-141. [PMID: 32642789 PMCID: PMC11072825 DOI: 10.1007/s00018-020-03594-9] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 06/24/2020] [Accepted: 07/03/2020] [Indexed: 12/16/2022]
Abstract
AlkB homologs (ALKBH) are a family of specific demethylases that depend on Fe2+ and α-ketoglutarate to catalyze demethylation on different substrates, including ssDNA, dsDNA, mRNA, tRNA, and proteins. Previous studies have made great progress in determining the sequence, structure, and molecular mechanism of the ALKBH family. Here, we first review the multi-substrate selectivity of the ALKBH demethylase family from the perspective of sequence and structural evolution. The construction of the phylogenetic tree and the comparison of key loops and non-homologous domains indicate that the paralogs with close evolutionary relationship have similar domain compositions. The structures show that the lack and variations of four key loops change the shape of clefts to cause the differences in substrate affinity, and non-homologous domains may be related to the compatibility of multiple substrates. We anticipate that the new insights into selectivity determinants of the ALKBH family are useful for understanding the demethylation mechanisms.
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Affiliation(s)
- Baofang Xu
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Dongyang Liu
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
- Key Laboratory of Photobiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zerong Wang
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Ruixia Tian
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Yongchun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
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Abstract
Background:
The basic building block of a body is protein which is a complex system
whose structure plays a key role in activation, catalysis, messaging and disease states. Therefore,
careful investigation of protein structure is necessary for the diagnosis of diseases and for the drug
designing. Protein structures are described at their different levels of complexity: primary (chain),
secondary (helical), tertiary (3D), and quaternary structure. Analyzing complex 3D structure of
protein is a difficult task but it can be analyzed as a network of interconnection between its
component, where amino acids are considered as nodes and interconnection between them are
edges.
Objective:
Many literature works have proven that the small world network concept provides
many new opportunities to investigate network of biological systems. The objective of this paper is
analyzing the protein structure using small world concept.
Methods:
Protein is analyzed using small world network concept, specifically where extreme
condition is having a degree distribution which follows power law. For the correct verification of
the proposed approach, dataset of the Oncogene protein structure is analyzed using Python
programming.
Results:
Protein structure is plotted as network of amino acids (Residue Interaction Graph (RIG))
using distance matrix of nodes with given threshold, then various centrality measures (i.e., degree
distribution, Degree-Betweenness correlation, and Betweenness-Closeness correlation) are
calculated for 1323 nodes and graphs are plotted.
Conclusion:
Ultimately, it is concluded that there exist hubs with higher centrality degree but less
in number, and they are expected to be robust toward harmful effects of mutations with new
functions.
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Affiliation(s)
- Neetu Kumari
- Department of Computer Science, Banaras Hindu University, Varanasi, India
| | - Anshul Verma
- Department of Computer Science, Banaras Hindu University, Varanasi, India
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38
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Zuo Y, Song M, Li H, Chen X, Cao P, Zheng L, Cao G. Analysis of the Epigenetic Signature of Cell Reprogramming by Computational DNA Methylation Profiles. Curr Bioinform 2020. [DOI: 10.2174/1574893614666190919103752] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
DNA methylation plays an important role in the reprogramming process.
Understanding the underlying molecular mechanism of reprogramming is crucial for answering
fundamental questions regarding the transition of cell identity.
Methods:
In this study, based on the genome-wide DNA methylation data from different cell lines,
comparative methylation profiles were proposed to identify the epigenetic signature of cell
reprogramming.
Results:
The density profile of CpG methylation showed that pluripotent cells are more polarized
than Human Dermal Fibroblasts (HDF) cells. The heterogeneity of iPS has a greater deviation in
the DNA hypermethylation pattern. The result of regional distribution showed that the differential
CpG sites between pluripotent cells and HDFs tend to accumulate in the gene body and CpG shelf
regions, whereas the internal differential methylation CpG sites (DMCs) of three types of
pluripotent cells tend to accumulate in the TSS1500 region. Furthermore, a series of endogenous
markers of cell reprogramming were identified based on the integrative analysis, including focal
adhesion, pluripotency maintenance and transcription regulation. The calcium signaling pathway
was detected as one of the signatures between NT cells and iPS cells. Finally, the regional bias of
DNA methylation for key pluripotency factors was discussed. Our studies provide new insight into
the barrier identification of cell reprogramming.
Conclusion:
Our studies analyzed some epigenetic markers and barriers of nuclear reprogramming,
hoping to provide new insight into understanding the underlying molecular mechanism
of reprogramming.
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Affiliation(s)
- Yongchun Zuo
- The College of Veterinary Medicine, Inner Mongolia Agricultural University, Hohhot 010018, China
| | - Mingmin Song
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Hanshuang Li
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Xing Chen
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Pengbo Cao
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Lei Zheng
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Guifang Cao
- The College of Veterinary Medicine, Inner Mongolia Agricultural University, Hohhot 010018, China
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Liu K, Chen W. iMRM: a platform for simultaneously identifying multiple kinds of RNA modifications. Bioinformatics 2020; 36:3336-3342. [PMID: 32134472 DOI: 10.1093/bioinformatics/btaa155] [Citation(s) in RCA: 107] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 02/26/2020] [Accepted: 02/28/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION RNA modifications play critical roles in a series of cellular and developmental processes. Knowledge about the distributions of RNA modifications in the transcriptomes will provide clues to revealing their functions. Since experimental methods are time consuming and laborious for detecting RNA modifications, computational methods have been proposed for this aim in the past five years. However, there are some drawbacks for both experimental and computational methods in simultaneously identifying modifications occurred on different nucleotides. RESULTS To address such a challenge, in this article, we developed a new predictor called iMRM, which is able to simultaneously identify m6A, m5C, m1A, ψ and A-to-I modifications in Homo sapiens, Mus musculus and Saccharomyces cerevisiae. In iMRM, the feature selection technique was used to pick out the optimal features. The results from both 10-fold cross-validation and jackknife test demonstrated that the performance of iMRM is superior to existing methods for identifying RNA modifications. AVAILABILITY AND IMPLEMENTATION A user-friendly web server for iMRM was established at http://www.bioml.cn/XG_iRNA/home. The off-line command-line version is available at https://github.com/liukeweiaway/iMRM. CONTACT greatchen@ncst.edu.cn. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kewei Liu
- School of Life Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063009, China
| | - Wei Chen
- School of Life Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063009, China.,Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
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40
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Abstract
Background:
Thermophilic proteins can maintain good activity under high temperature,
therefore, it is important to study thermophilic proteins for the thermal stability of proteins.
Objective:
In order to solve the problem of low precision and low efficiency in predicting
thermophilic proteins, a prediction method based on feature fusion and machine learning was
proposed in this paper.
Methods:
For the selected thermophilic data sets, firstly, the thermophilic protein sequence was
characterized based on feature fusion by the combination of g-gap dipeptide, entropy density and
autocorrelation coefficient. Then, Kernel Principal Component Analysis (KPCA) was used to reduce
the dimension of the expressed protein sequence features in order to reduce the training time and
improve efficiency. Finally, the classification model was designed by using the classification
algorithm.
Results:
A variety of classification algorithms was used to train and test on the selected thermophilic
dataset. By comparison, the accuracy of the Support Vector Machine (SVM) under the jackknife
method was over 92%. The combination of other evaluation indicators also proved that the SVM
performance was the best.
Conclusion:
Because of choosing an effectively feature representation method and a robust
classifier, the proposed method is suitable for predicting thermophilic proteins and is superior to
most reported methods.
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Affiliation(s)
- Xian-Fang Wang
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Peng Gao
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Yi-Feng Liu
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Hong-Fei Li
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Fan Lu
- School of Computer and Information Engineering, Henan Normal University, Henan, China
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41
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Wang Z, Liu D, Xu B, Tian R, Zuo Y. Modular arrangements of sequence motifs determine the functional diversity of KDM proteins. Brief Bioinform 2020; 22:5912575. [PMID: 32987405 DOI: 10.1093/bib/bbaa215] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Histone lysine demethylases (KDMs) play a vital role in regulating chromatin dynamics and transcription. KDM proteins are given modular activities by its sequence motifs with obvious roles division, which endow the complex and diverse functions. In our review, according to functional features, we classify sequence motifs into four classes: catalytic motifs, targeting motifs, regulatory motifs and potential motifs. JmjC, as the main catalytic motif, combines to Fe2+ and α-ketoglutarate by residues H-D/E-H and S-N-N/Y-K-N/Y-T/S. Targeting motifs make catalytic motifs recognize specific methylated lysines, such as PHD that helps KDM5 to demethylate H3K4me3. Regulatory motifs consist of a functional network. For example, NLS, Ser-rich, TPR and JmjN motifs regulate the nuclear localization. And interactions through the CW-type-C4H2C2-SWIRM are necessary to the demethylase activity of KDM1B. Additionally, many conservative domains that have potential functions but no deep exploration are reviewed for the first time. These conservative domains are usually amino acid-rich regions, which have great research value. The arrangements of four types of sequence motifs generate that KDM proteins diversify toward modular activities and biological functions. Finally, we draw a blueprint of functional mechanisms to discuss the modular activity of KDMs.
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Affiliation(s)
- Zerong Wang
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University
| | - Dongyang Liu
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University. He is now studying for a master's degree at the institute of botany of the Chinese Academy of Sciences. His research interests include bioinformatics and computational genomics
| | - Baofang Xu
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University
| | - Ruixia Tian
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University
| | - Yongchun Zuo
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University. His research interests include bioinformatics and integration analysis of multiomics in cell reprogramming
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42
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Li R, Li H, Yang S, Feng X. The Influences of Palindromes in mRNA on Protein Folding Rates. Protein Pept Lett 2020; 27:303-312. [PMID: 31612810 DOI: 10.2174/0929866526666191014144015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 06/14/2019] [Accepted: 06/29/2019] [Indexed: 01/21/2023]
Abstract
BACKGROUND It is currently believed that protein folding rates are influenced by protein structure, environment and temperature, amino acid sequence and so on. We have been working for long to determine whether and in what ways mRNA affects the protein folding rate. A large number of palindromes aroused our attention in our previous research. Whether these palindromes do have important influences on protein folding rates and what's the mechanism? Very few related studies are focused on these problems. OBJECTIVE In this article, our motivation is to find out if palindromes have important influences on protein folding rates and what's the mechanism. METHODS In this article, the parameters of the palindromes were defined and calculated, the linear regression analysis between the values of each parameter and the experimental protein folding rates were done. Furthermore, to compare the results of different kinds of proteins, proteins were classified into the two-state proteins and the multi-state proteins. For the two kinds of proteins, the above linear regression analysis were performed respectively. RESULTS Protein folding rates were negatively correlated to the palindrome frequencies for all proteins. An extremely significant negative linear correlation appeared in the relationship between palindrome densities and protein folding rates. And the repeatedly used bases by different palindromes simultaneously have an important effect on the relationship between palindrome density and protein folding rate. CONCLUSION The palindromes have important influences on protein folding rates, and the repeatedly used bases in different palindromes simultaneously play a key role in influencing the protein folding rates.
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Affiliation(s)
- Ruifang Li
- College of Physics and Electronic Information, Inner Mongolia Normal University, Hohhot 010022, China
| | - Hong Li
- School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Sarula Yang
- College of Physics and Electronic Information, Inner Mongolia Normal University, Hohhot 010022, China
| | - Xue Feng
- College of Physics and Electronic Information, Inner Mongolia Normal University, Hohhot 010022, China
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43
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Sun Z, Huang S, Zheng L, Liang P, Yang W, Zuo Y. ICTC-RAAC: An improved web predictor for identifying the types of ion channel-targeted conotoxins by using reduced amino acid cluster descriptors. Comput Biol Chem 2020; 89:107371. [PMID: 32950852 DOI: 10.1016/j.compbiolchem.2020.107371] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 09/01/2020] [Accepted: 09/02/2020] [Indexed: 12/27/2022]
Abstract
Conotoxins are small peptide toxins which are rich in disulfide and have the unique diversity of sequences. It is significant to correctly identify the types of ion channel-targeted conotoxins because that they are considered as the optimal pharmacological candidate medicine in drug design owing to their ability specifically binding to ion channels and interfering with neural transmission. Comparing with other feature extracting methods, the reduced amino acid cluster (RAAC) better resolved in simplifying protein complexity and identifying functional conserved regions. Thus, in our study, 673 RAACs generated from 74 types of reduced amino acid alphabet were comprehensively assessed to establish a state-of-the-art predictor for predicting ion channel-targeted conotoxins. The results showed Type 20, Cluster 9 (T = 20, C = 9) in the tripeptide composition (N = 3) achieved the best accuracy, 89.3%, which was based on the algorithm of amino acids reduction of variance maximization. Further, the ANOVA with incremental feature selection (IFS) was used for feature selection to improve prediction performance. Finally, the cross-validation results showed that the best overall accuracy we calculated was 96.4% and 1.8% higher than the best accuracy of previous studies. Based on the predictor we proposed, a user-friendly webserver was established and can be friendly accessed at http://bioinfor.imu.edu.cn/ictcraac.
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Affiliation(s)
- Zijie Sun
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China; School of Mathematical Sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Shenghui Huang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Pengfei Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Wuritu Yang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
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Cao P, Li H, Zuo Y, Nashun B. Characterization of DNA Methylation Patterns and Mining of Epigenetic Markers During Genomic Reprogramming in SCNT Embryos. Front Cell Dev Biol 2020; 8:570107. [PMID: 32984351 PMCID: PMC7492385 DOI: 10.3389/fcell.2020.570107] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 08/13/2020] [Indexed: 12/15/2022] Open
Abstract
Somatic cell nuclear transfer (SCNT), also known as somatic cell cloning, is a commonly used technique to study epigenetic reprogramming. Although SCNT has the advantages of being safe and able to obtain pluripotent cells, early developmental arrest happens in most SCNT embryos. Overcoming epigenetic barriers is currently the primary strategy for improving reprogramming efficiency and improving developmental rate in SCNT embryos. In this study, we analyzed DNA methylation profiles of in vivo fertilized embryos and SCNT embryos with different developmental fates. Overall DNA methylation level was higher in SCNT embryos during global de-methylation process compared to in vivo fertilized embryos. In addition, promoter region, first intron and 3′UTR were found to be the major genomic regions that were hyper-methylated in SCNT embryos. Surprisingly, we found the length of re-methylated region was directly related to the change of methylation level. Furthermore, a number of genes including Dppa2 and Dppa4 which are important for early zygotic genome activation (ZGA) were not properly activated in SCNT embryos. This study comprehensively analyzed genome-wide DNA methylation patterns in SCNT embryos and provided candidate target genes for improving efficiency of genomic reprogramming in SCNT embryos. Since SCNT technology has been widely used in agricultural and pastoral production, protection of endangered animals, and therapeutic cloning, the findings of this study have significant importance for all these fields.
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Affiliation(s)
- Pengbo Cao
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Hanshuang Li
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Buhe Nashun
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
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45
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Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 22:1043-1050. [PMID: 33294291 PMCID: PMC7691157 DOI: 10.1016/j.omtn.2020.07.035] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 07/28/2020] [Indexed: 12/12/2022]
Abstract
Transcription factors play key roles in cell-fate decisions by regulating 3D genome conformation and gene expression. The traditional view is that methylation of DNA hinders transcription factors binding to them, but recent research has shown that many transcription factors prefer to bind to methylated DNA. Therefore, identifying such transcription factors and understanding their functions is a stepping-stone for studying methylation-mediated biological processes. In this paper, a two-step discriminated method was proposed to recognize transcription factors and their preference for methylated DNA based only on sequences information. In the first step, the proposed model was used to discriminate transcription factors from non-transcription factors. The areas under the curve (AUCs) are 0.9183 and 0.9116, respectively, for the 5-fold cross-validation test and independent dataset test. Subsequently, for the classification of transcription factors that prefer methylated DNA and transcription factors that prefer non-methylated DNA, our model could produce the AUCs of 0.7744 and 0.7356, respectively, for the 5-fold cross-validation test and independent dataset test. Based on the proposed model, a user-friendly web server called TFPred was built, which can be freely accessed at http://lin-group.cn/server/TFPred/.
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46
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Li X, Tang Q, Tang H, Chen W. Identifying Antioxidant Proteins by Combining Multiple Methods. Front Bioeng Biotechnol 2020; 8:858. [PMID: 32793581 PMCID: PMC7391787 DOI: 10.3389/fbioe.2020.00858] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 07/03/2020] [Indexed: 11/13/2022] Open
Abstract
Antioxidant proteins play important roles in preventing free radical oxidation from damaging cells and DNA. They have become ideal candidates of disease prevention and treatment. Therefore, it is urgent to identify antioxidants from natural compounds. Since experimental methods are still cost ineffective, a series of computational methods have been proposed to identify antioxidant proteins. However, the performance of the current methods are still not satisfactory. In this study, a support vector machine based method, called Vote9, was proposed to identify antioxidants, in which the sequences were encoded by using the features generated from 9 optimal individual models. Results from jackknife test demonstrated that Vote9 is comparable with the best one of the existing predictors for this task. We hope that Vote9 will become a useful tool or at least can play a complementary role to the existing methods for identifying antioxidants.
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Affiliation(s)
- Xianhai Li
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China.,Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Qiang Tang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Hua Tang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Wei Chen
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China.,Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China.,School of Life Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, China
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47
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Guan ZX, Li SH, Zhang ZM, Zhang D, Yang H, Ding H. A Brief Survey for MicroRNA Precursor Identification Using Machine Learning Methods. Curr Genomics 2020; 21:11-25. [PMID: 32655294 PMCID: PMC7324890 DOI: 10.2174/1389202921666200214125102] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 01/24/2020] [Accepted: 01/30/2020] [Indexed: 11/22/2022] Open
Abstract
MicroRNAs, a group of short non-coding RNA molecules, could regulate gene expression. Many diseases are associated with abnormal expression of miRNAs. Therefore, accurate identification of miRNA precursors is necessary. In the past 10 years, experimental methods, comparative genomics methods, and artificial intelligence methods have been used to identify pre-miRNAs. However, experimental methods and comparative genomics methods have their disadvantages, such as time-consuming. In contrast, machine learning-based method is a better choice. Therefore, the review summarizes the current advances in pre-miRNA recognition based on computational methods, including the construction of benchmark datasets, feature extraction methods, prediction algorithms, and the results of the models. And we also provide valid information about the predictors currently available. Finally, we give the future perspectives on the identification of pre-miRNAs. The review provides scholars with a whole background of pre-miRNA identification by using machine learning methods, which can help researchers have a clear understanding of progress of the research in this field.
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Affiliation(s)
- Zheng-Xing Guan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu610054, China
| | - Shi-Hao Li
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu610054, China
| | - Zi-Mei Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu610054, China
| | - Dan Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu610054, China
| | - Hui Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu610054, China
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu610054, China
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48
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Zheng L, Liu D, Yang W, Yang L, Zuo Y. RaacLogo: a new sequence logo generator by using reduced amino acid clusters. Brief Bioinform 2020; 22:5855392. [PMID: 32524143 DOI: 10.1093/bib/bbaa096] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 04/12/2020] [Accepted: 04/29/2020] [Indexed: 12/15/2022] Open
Abstract
Sequence logos give a fast and concise display in visualizing consensus sequence. Protein exhibits greater complexity and diversity than DNA, which usually affects the graphical representation of the logo. Reduced amino acids perform powerful ability for simplifying complexity of sequence alignment, which motivated us to establish RaacLogo. As a new sequence logo generator by using reduced amino acid alphabets, RaacLogo can easily generate many different simplified logos tailored to users by selecting various reduced amino acid alphabets that consisted of more than 40 clustering algorithms. This current web server provides 74 types of reduced amino acid alphabet, which were manually extracted to generate 673 reduced amino acid clusters (RAACs) for dealing with protein alignment. A two-dimensional selector was proposed for easily selecting desired RAACs with underlying biology knowledge. It is anticipated that the RaacLogo web server will play more high-potential roles for protein sequence alignment, topological estimation and protein design experiments. RaacLogo is freely available at http://bioinfor.imu.edu.cn/raaclogo.
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Affiliation(s)
- Lei Zheng
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University
| | - Dongyang Liu
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University
| | - Wuritu Yang
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University
| | - Yongchun Zuo
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University
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49
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Liang P, Yang W, Chen X, Long C, Zheng L, Li H, Zuo Y. Machine Learning of Single-Cell Transcriptome Highly Identifies mRNA Signature by Comparing F-Score Selection with DGE Analysis. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 20:155-163. [PMID: 32169803 PMCID: PMC7066034 DOI: 10.1016/j.omtn.2020.02.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 12/27/2019] [Accepted: 02/05/2020] [Indexed: 12/21/2022]
Abstract
Human preimplantation development is a complex process involving dramatic changes in transcriptional architecture. For a better understanding of their time-spatial development, it is indispensable to identify key genes. Although the single-cell RNA sequencing (RNA-seq) techniques could provide detailed clustering signatures, the identification of decisive factors remains difficult. Additionally, it requires high experimental cost and a long experimental period. Thus, it is highly desired to develop computational methods for identifying effective genes of development signature. In this study, we first developed a predictor called EmPredictor to identify developmental stages of human preimplantation embryogenesis. First, we compared the F-score of feature selection algorithms with differential gene expression (DGE) analysis to find specific signatures of the development stage. In addition, by training the support vector machine (SVM), four types of signature subsets were comprehensively discussed. The prediction results showed that a feature subset with 1,881 genes from the F-score algorithm obtained the best predictive performance, which achieved the highest accuracy of 93.3% on the cross-validation set. Further function enrichment demonstrated that the gene set selected by the feature selection method was involved in more development-related pathways and cell fate determination biomarkers. This indicates that the F-score algorithm should be preferentially proposed for detecting key genes of multi-period data in mammalian early development.
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Affiliation(s)
- Pengfei Liang
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Wuritu Yang
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Xing Chen
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Chunshen Long
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Lei Zheng
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Hanshuang Li
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yongchun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China.
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50
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Yu Y, Wang S, Wang Y, Cao Y, Yu C, Pan Y, Su D, Lu Q, Zuo Y, Yang L. Using Reduced Amino Acid Alphabet and Biological Properties to Analyze and Predict Animal Neurotoxin Protein. Curr Drug Metab 2020; 21:810-817. [PMID: 32433000 DOI: 10.2174/1389200221666200520090555] [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: 10/25/2019] [Revised: 01/07/2020] [Accepted: 01/15/2020] [Indexed: 11/22/2022]
Abstract
AIMS Because of the high affinity of these animal neurotoxin proteins for some special target site, they were usually used as pharmacological tools and therapeutic agents in medicine to gain deep insights into the function of the nervous system. BACKGROUND AND OBJECTIVE The animal neurotoxin proteins are one of the most common functional groups among the animal toxin proteins. Thus, it was very important to characterize and predict the animal neurotoxin proteins. METHODS In this study, the differences between the animal neurotoxin proteins and non-toxin proteins were analyzed. RESULT Significant differences were found between them. In addition, the support vector machine was proposed to predict the animal neurotoxin proteins. The predictive results of our classifier achieved the overall accuracy of 96.46%. Furthermore, the random forest and k-nearest neighbors were applied to predict the animal neurotoxin proteins. CONCLUSION The compared results indicated that the predictive performances of our classifier were better than other two algorithms.
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Affiliation(s)
- Yao Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shiyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yakun Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yiyin Cao
- Public Health College, Harbin Medical University, Harbin 150081, China
| | - Chunlu Yu
- Public Health College, Harbin Medical University, Harbin 150081, China
| | - Yi Pan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Dongqing Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Qianzi Lu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yongchun Zuo
- The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
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