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Liang Y, Guo Y, Zhai Y, Zhou J, Yang W, Zuo Y. Disease trend analysis platform accurately predicts the occurrence of cervical cancer under mixed diseases. Methods 2024; 230:108-115. [PMID: 39111721 DOI: 10.1016/j.ymeth.2024.07.011] [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: 06/29/2024] [Revised: 07/26/2024] [Accepted: 07/29/2024] [Indexed: 08/17/2024] Open
Abstract
Cervical cancer (CC) is one of the most common gynecological malignancies. Cytological screening, while being the most common and accurate method for detecting cervical cancer, is both time-consuming and costly. Predicting CC based on bioinformatics can assist in the rapid early screening of CC in clinical practice. Most recent CC prediction methods require a large amount of detection data or sequencing data and are not ideal for CC detection in complex disease samples. We developed the Disease trend analysis platform (Dtap), which can quickly predict the occurrence of diseases using only blood routine data. Blood routine data was collected from 1,292 cervical cancer patients, 4,860 patients with complex diseases, and 4,980 healthy individuals from various sources. The results show that the Dtap-based trend model maintained good and stable performance in the prediction task of multiple datasets as well as complex disease samples. Finally, we built DTAPCC (http://bioinfor.imu.edu.cn/dtapcc), a Dtap-based CC disease prediction platform, to help users quickly predict CC and visualize trend features.
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Affiliation(s)
- Yuchao Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot 010021, PR China; Inner Mongolia International Mongolian Hospital, Hohhot 010065, PR China
| | - Yuting Guo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot 010021, PR China
| | - Yifei Zhai
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot 010021, PR China
| | - Jian Zhou
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot 010021, PR China
| | - Wuritu Yang
- Computer Department, Hohhot Vocational College, Hohhot 010020, PR China.
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot 010021, PR China; Inner Mongolia International Mongolian Hospital, Hohhot 010065, PR China; Computer Department, Hohhot Vocational College, Hohhot 010020, PR China.
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2
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Feng C, Wei H, Li X, Feng B, Xu C, Zhu X, Liu R. A stacking-based algorithm for antifreeze protein identification using combined physicochemical, pseudo amino acid composition, and reduction property features. Comput Biol Med 2024; 176:108534. [PMID: 38754217 DOI: 10.1016/j.compbiomed.2024.108534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 04/03/2024] [Accepted: 04/28/2024] [Indexed: 05/18/2024]
Abstract
Antifreeze proteins have wide applications in the medical and food industries. In this study, we propose a stacking-based classifier that can effectively identify antifreeze proteins. Initially, feature extraction was performed in three aspects: reduction properties, scalable pseudo amino acid composition, and physicochemical properties. A hybrid feature set comprised of the combined information from these three categories was obtained. Subsequently, we trained the training set based on LightGBM, XGBoost, and RandomForest algorithms, and the training outcomes were passed to the Logistic algorithm for matching, thereby establishing a stacking algorithm. The proposed algorithm was tested on the test set and an independent validation set. Experimental data indicates that the algorithm achieved a recognition accuracy of 98.3 %, and an accuracy of 98.5 % on the validation set. Lastly, we analyzed the reasons why numerical features achieved high recognition capabilities from multiple aspects. Data dimensionality reduction and the analysis from two-dimensional and three-dimensional views revealed separability between positive and negative samples, and the protein three-dimensional structure further demonstrated significant differences in related features between the two samples. Analysis of the classifier revealed that Hr*Hr, HrHr, and Sc-PseAAC_1, 188D(152,116,57,183) were among the seven most important numerical features affecting algorithm recognition. For Hr*Hr and HrHr, supportive sequence level evidence for the reduction dictionary was found in terms of conservation area analysis, multiple sequence alignment, and amino acid conservative substitution. Moreover, the importance of the reduction dictionary was recognized through a comparative analysis of importance before and after the reduction, realizing the effectiveness of the dictionary in improving feature importance. A decision tree model has been utilized to discern the distinctions between dipeptides associated with the physical and chemical properties of His(H), Iso(I), Leu(L), and Lys(K) and other dipeptides. We finally analyzed the other seven features of importance, and data analysis confirmed that hydrophobicity, secondary structure, charge properties, van der Waals forces, and solvent accessibility are also factors affecting the antifreeze capability of proteins.
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Affiliation(s)
- Changli Feng
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Haiyan Wei
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Xin Li
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Bin Feng
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Chugui Xu
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Xiaorong Zhu
- Department of Information Science and Technology, Taishan University, Taian, 271000, China.
| | - Ruijun Liu
- School of Software, Beihang University, Beijing, 100191, China.
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3
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Wan H, Zhang Y, Huang S. Prediction of thermophilic protein using 2-D general series correlation pseudo amino acid features. Methods 2023; 218:141-148. [PMID: 37604248 DOI: 10.1016/j.ymeth.2023.08.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/08/2023] [Accepted: 08/18/2023] [Indexed: 08/23/2023] Open
Abstract
The demand for thermophilic protein has been increasing in protein engineering recently. Many machine-learning methods for identifying thermophilic proteins have emerged during this period. However, most machine learning-based thermophilic protein identification studies have only focused on accuracy. The relationship between the features' meaning and the proteins' physicochemical properties has yet to be studied in depth. In this article, we focused on the relationship between the features and the thermal stability of thermophilic proteins. This method used 2-D general series correlation pseudo amino acid (SC-PseAAC-General) features and realized accuracy of 82.76% using the J48 classifier. In addition, this research found the presence of higher frequencies of glutamic acid in thermophilic proteins, which help thermophilic proteins maintain their thermal stability by forming hydrogen bonds and salt bridges that prevent denaturation at high temperatures.
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Affiliation(s)
- Hao Wan
- College of Life Science, Qingdao University, Qingdao 266071, China.
| | - Yanan Zhang
- College of Life Science, Qingdao University, Qingdao 266071, China
| | - Shibo Huang
- Beidahuang Industry Group General Hospital, Harbin 150001, China
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4
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Zhu W, Yuan SS, Li J, Huang CB, Lin H, Liao B. A First Computational Frame for Recognizing Heparin-Binding Protein. Diagnostics (Basel) 2023; 13:2465. [PMID: 37510209 PMCID: PMC10377868 DOI: 10.3390/diagnostics13142465] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 07/13/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023] Open
Abstract
Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields.
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Affiliation(s)
- Wen Zhu
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou 571158, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou 571158, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
| | - Shi-Shi Yuan
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jian Li
- School of Basic Medical Sciences, Chengdu University, Chengdu 610106, China
| | - Cheng-Bing Huang
- School of Computer Science and Technology, ABa Teachers University, Chengdu 623002, China
| | - Hao Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bo Liao
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou 571158, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou 571158, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
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5
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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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6
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Chen R, Li F, Guo X, Bi Y, Li C, Pan S, Coin LJM, Song J. ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species. Brief Bioinform 2023; 24:bbad170. [PMID: 37150785 PMCID: PMC10565902 DOI: 10.1093/bib/bbad170] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 04/12/2023] [Accepted: 04/14/2023] [Indexed: 05/09/2023] Open
Abstract
A-to-I editing is the most prevalent RNA editing event, which refers to the change of adenosine (A) bases to inosine (I) bases in double-stranded RNAs. Several studies have revealed that A-to-I editing can regulate cellular processes and is associated with various human diseases. Therefore, accurate identification of A-to-I editing sites is crucial for understanding RNA-level (i.e. transcriptional) modifications and their potential roles in molecular functions. To date, various computational approaches for A-to-I editing site identification have been developed; however, their performance is still unsatisfactory and needs further improvement. In this study, we developed a novel stacked-ensemble learning model, ATTIC (A-To-I ediTing predICtor), to accurately identify A-to-I editing sites across three species, including Homo sapiens, Mus musculus and Drosophila melanogaster. We first comprehensively evaluated 37 RNA sequence-derived features combined with 14 popular machine learning algorithms. Then, we selected the optimal base models to build a series of stacked ensemble models. The final ATTIC framework was developed based on the optimal models improved by the feature selection strategy for specific species. Extensive cross-validation and independent tests illustrate that ATTIC outperforms state-of-the-art tools for predicting A-to-I editing sites. We also developed a web server for ATTIC, which is publicly available at http://web.unimelb-bioinfortools.cloud.edu.au/ATTIC/. We anticipate that ATTIC can be utilized as a useful tool to accelerate the identification of A-to-I RNA editing events and help characterize their roles in post-transcriptional regulation.
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Affiliation(s)
- Ruyi Chen
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
- The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, VIC 3000, Australia
| | - Fuyi Li
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
- The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, VIC 3000, Australia
| | - Xudong Guo
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
| | - Yue Bi
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, VIC 3800, Australia
| | - Chen Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, VIC 3800, Australia
| | - Shirui Pan
- School of Information and Communication Technology, Griffith University, QLD 4222, Australia
| | - Lachlan J M Coin
- The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, VIC 3000, Australia
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, VIC 3800, Australia
- Monash Data Futures Institute, Monash University, VIC 3800, Australia
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7
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Zulfiqar H, Guo Z, Grace-Mercure BK, Zhang ZY, Gao H, Lin H, Wu Y. Empirical Comparison and Recent Advances of Computational Prediction of Hormone Binding Proteins Using Machine Learning Methods. Comput Struct Biotechnol J 2023; 21:2253-2261. [PMID: 37035551 PMCID: PMC10073991 DOI: 10.1016/j.csbj.2023.03.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 03/15/2023] [Accepted: 03/16/2023] [Indexed: 03/19/2023] Open
Abstract
Hormone binding proteins (HBPs) belong to the group of soluble carrier proteins. These proteins selectively and non-covalently interact with hormones and promote growth hormone signaling in human and other animals. The HBPs are useful in many medical and commercial fields. Thus, the identification of HBPs is very important because it can help to discover more details about hormone binding proteins. Meanwhile, the experimental methods are time-consuming and expensive for hormone binding proteins recognition. Computational prediction methods have played significant roles in the correct recognition of hormone binding proteins with the use of sequence information and ML algorithms. In this review, we compared and assessed the implementation of ML-based tools in recognition of HBPs in a unique way. We hope that this study will give enough awareness and knowledge for research on HBPs.
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8
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Nguyen Q, Tran HV, Nguyen BP, Do TTT. Identifying Transcription Factors That Prefer Binding to Methylated DNA Using Reduced G-Gap Dipeptide Composition. ACS OMEGA 2022; 7:32322-32330. [PMID: 36119976 PMCID: PMC9475634 DOI: 10.1021/acsomega.2c03696] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/23/2022] [Indexed: 06/15/2023]
Abstract
Transcription factors (TFs) play an important role in gene expression and regulation of 3D genome conformation. TFs have ability to bind to specific DNA fragments called enhancers and promoters. Some TFs bind to promoter DNA fragments which are near the transcription initiation site and form complexes that allow polymerase enzymes to bind to initiate transcription. Previous studies showed that methylated DNAs had ability to inhibit and prevent TFs from binding to DNA fragments. However, recent studies have found that there were TFs that could bind to methylated DNA fragments. The identification of these TFs is an important steppingstone to a better understanding of cellular gene expression mechanisms. However, as experimental methods are often time-consuming and labor-intensive, developing computational methods is essential. In this study, we propose two machine learning methods for two problems: (1) identifying TFs and (2) identifying TFs that prefer binding to methylated DNA targets (TFPMs). For the TF identification problem, the proposed method uses the position-specific scoring matrix for data representation and a deep convolutional neural network for modeling. This method achieved 90.56% sensitivity, 83.96% specificity, and an area under the receiver operating characteristic curve (AUC) of 0.9596 on an independent test set. For the TFPM identification problem, we propose to use the reduced g-gap dipeptide composition for data representation and the support vector machine algorithm for modeling. This method achieved 82.61% sensitivity, 64.86% specificity, and an AUC of 0.8486 on another independent test set. These results are higher than those of other studies on the same problems.
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Affiliation(s)
- Quang
H. Nguyen
- School
of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet, Hanoi 100000, Vietnam
| | - Hoang V. Tran
- School
of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet, Hanoi 100000, Vietnam
| | - Binh P. Nguyen
- School
of Mathematics and Statistics, Victoria
University of Wellington, Kelburn Parade, Wellington 6140, New Zealand
| | - Trang T. T. Do
- School
of Innovation, Design and Technology, Wellington
Institute of Technology, 21 Kensington Avenue, Lower Hutt 5012, New Zealand
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9
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Yuan SS, Gao D, Xie XQ, Ma CY, Su W, Zhang ZY, Zheng Y, Ding H. IBPred: a sequence-based predictor for identifying ion binding protein in phage. Comput Struct Biotechnol J 2022; 20:4942-4951. [PMID: 36147670 PMCID: PMC9474292 DOI: 10.1016/j.csbj.2022.08.053] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 08/23/2022] [Accepted: 08/24/2022] [Indexed: 11/16/2022] Open
Abstract
Ion binding proteins (IBPs) can selectively and non-covalently interact with ions. IBPs in phages also play an important role in biological processes. Therefore, accurate identification of IBPs is necessary for understanding their biological functions and molecular mechanisms that involve binding to ions. Since molecular biology experimental methods are still labor-intensive and cost-ineffective in identifying IBPs, it is helpful to develop computational methods to identify IBPs quickly and efficiently. In this work, a random forest (RF)-based model was constructed to quickly identify IBPs. Based on the protein sequence information and residues’ physicochemical properties, the dipeptide composition combined with the physicochemical correlation between two residues were proposed for the extraction of features. A feature selection technique called analysis of variance (ANOVA) was used to exclude redundant information. By comparing with other classified methods, we demonstrated that our method could identify IBPs accurately. Based on the model, a Python package named IBPred was built with the source code which can be accessed at https://github.com/ShishiYuan/IBPred.
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10
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Liang Y, Yang S, Zheng L, Wang H, Zhou J, Huang S, Yang L, Zuo Y. Research progress of reduced amino acid alphabets in protein analysis and prediction. Comput Struct Biotechnol J 2022; 20:3503-3510. [PMID: 35860409 PMCID: PMC9284397 DOI: 10.1016/j.csbj.2022.07.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/30/2022] [Accepted: 07/01/2022] [Indexed: 11/29/2022] Open
Abstract
A comprehensive summary of the literature on the reduced amino acid alphabets. A systematic review of the development history of reduced amino acid alphabets. Rich application cases of amino acid reduction alphabets are described in the article. A detailed analysis of the properties and uses of the reduced amino acid alphabets.
Proteins are the executors of cellular physiological activities, and accurate structural and function elucidation are crucial for the refined mapping of proteins. As a feature engineering method, the reduction of amino acid composition is not only an important method for protein structure and function analysis, but also opens a broad horizon for the complex field of machine learning. Representing sequences with fewer amino acid types greatly reduces the complexity and noise of traditional feature engineering in dimension, and provides more interpretable predictive models for machine learning to capture key features. In this paper, we systematically reviewed the strategy and method studies of the reduced amino acid (RAA) alphabets, and summarized its main research in protein sequence alignment, functional classification, and prediction of structural properties, respectively. In the end, we gave a comprehensive analysis of 672 RAA alphabets from 74 reduction methods.
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Affiliation(s)
- Yuchao Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Siqi Yang
- 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
| | - Hao Wang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Jian Zhou
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Shenghui Huang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
- Corresponding authors.
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
- Corresponding authors.
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11
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Feng C, Wu J, Wei H, Xu L, Zou Q. CRCF: A Method of Identifying Secretory Proteins of Malaria Parasites. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2149-2157. [PMID: 34061749 DOI: 10.1109/tcbb.2021.3085589] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Malaria is a mosquito-borne disease that results in millions of cases and deaths annually. The development of a fast computational method that identifies secretory proteins of the malaria parasite is important for research on antimalarial drugs and vaccines. Thus, a method was developed to identify the secretory proteins of malaria parasites. In this method, a reduced alphabet was selected to recode the original protein sequence. A feature synthesis method was used to synthesise three different types of feature information. Finally, the random forest method was used as a classifier to identify the secretory proteins. In addition, a web server was developed to share the proposed algorithm. Experiments using the benchmark dataset demonstrated that the overall accuracy achieved by the proposed method was greater than 97.8 percent using the 10-fold cross-validation method. Furthermore, the reduced schemes and characteristic performance analyses are discussed.
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12
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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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13
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Pikalyova K, Orlov A, Lin A, Tarasova O, Marcou M, Horvath D, Poroikov V, Varnek A. HIV-1 drug resistance profiling using amino acid sequence space cartography. Bioinformatics 2022; 38:2307-2314. [PMID: 35157024 DOI: 10.1093/bioinformatics/btac090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 01/03/2022] [Accepted: 02/08/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Human immunodeficiency virus (HIV) drug resistance is a global healthcare issue. The emergence of drug resistance influenced the efficacy of treatment regimens, thus stressing the importance of treatment adaptation. Computational methods predicting the drug resistance profile from genomic data of HIV isolates are advantageous for monitoring drug resistance in patients. However, existing computational methods for drug resistance prediction are either not suitable for emerging HIV strains with complex mutational patterns or lack interpretability, which is of paramount importance in clinical practice. The approach reported here overcomes these limitations and combines high accuracy of predictions and interpretability of the models. RESULTS In this work, a new methodology based on generative topographic mapping (GTM) for biological sequence space representation and quantitative genotype-phenotype relationships prediction purposes was introduced. The GTM-based resistance landscapes allowed us to predict the resistance of HIV strains based on sequencing and drug resistance data for three viral proteins [integrase (IN), protease (PR) and reverse transcriptase (RT)] from Stanford HIV drug resistance database. The average balanced accuracy for PR inhibitors was 0.89 ± 0.01, for IN inhibitors 0.85 ± 0.01, for non-nucleoside RT inhibitors 0.73 ± 0.01 and for nucleoside RT inhibitors 0.84 ± 0.01. We have demonstrated in several case studies that GTM-based resistance landscapes are useful for visualization and analysis of sequence space as well as for treatment optimization purposes. Here, GTMs were applied for the in-depth analysis of the relationships between mutation pattern and drug resistance using mutation landscapes. This allowed us to predict retrospectively the importance of the presence of particular mutations (e.g. V32I, L10F and L33F in HIV PR) for the resistance development. This study highlights some perspectives of GTM applications in clinical informatics and particularly in the field of sequence space exploration. AVAILABILITY AND IMPLEMENTATION https://github.com/karinapikalyova/ISIDASeq. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Karina Pikalyova
- Laboratoire de Chémoinformatique, UMR 7140, Université de Strasbourg, Strasbourg 67000, France
| | - Alexey Orlov
- Laboratoire de Chémoinformatique, UMR 7140, Université de Strasbourg, Strasbourg 67000, France
| | - Arkadii Lin
- Laboratoire de Chémoinformatique, UMR 7140, Université de Strasbourg, Strasbourg 67000, France
| | - Olga Tarasova
- Institute of Biomedical Chemistry, Moscow 119121, Russia
| | - MarcouGilles Marcou
- Laboratoire de Chémoinformatique, UMR 7140, Université de Strasbourg, Strasbourg 67000, France
| | - Dragos Horvath
- Laboratoire de Chémoinformatique, UMR 7140, Université de Strasbourg, Strasbourg 67000, France
| | | | - Alexandre Varnek
- Laboratoire de Chémoinformatique, UMR 7140, Université de Strasbourg, Strasbourg 67000, France
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14
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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: 6] [Impact Index Per Article: 3.0] [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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15
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Wan H, Zhang J, Ding Y, Wang H, Tian G. Immunoglobulin Classification Based on FC* and GC* Features. Front Genet 2022; 12:827161. [PMID: 35140745 PMCID: PMC8819591 DOI: 10.3389/fgene.2021.827161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 12/22/2021] [Indexed: 11/13/2022] Open
Abstract
Immunoglobulins have a pivotal role in disease regulation. Therefore, it is vital to accurately identify immunoglobulins to develop new drugs and research related diseases. Compared with utilizing high-dimension features to identify immunoglobulins, this research aimed to examine a method to classify immunoglobulins and non-immunoglobulins using two features, FC* and GC*. Classification of 228 samples (109 immunoglobulin samples and 119 non-immunoglobulin samples) revealed that the overall accuracy was 80.7% in 10-fold cross-validation using the J48 classifier implemented in Weka software. The FC* feature identified in this study was found in the immunoglobulin subtype domain, which demonstrated that this extracted feature could represent functional and structural properties of immunoglobulins for forecasting.
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Affiliation(s)
- Hao Wan
- Institute of Advanced Cross-field Science, College of Life Science, Qingdao University, Qingdao, China
| | - Jina Zhang
- Geneis (Beijing) Co., Ltd., Beijing, China
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Hetian Wang
- Beidahuang Industry Group General Hospital, Harbin, China
- *Correspondence: Hetian Wang, ; Geng Tian,
| | - Geng Tian
- Geneis (Beijing) Co., Ltd., Beijing, China
- *Correspondence: Hetian Wang, ; Geng Tian,
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16
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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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17
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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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18
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Gu X, Guo L, Liao B, Jiang Q. Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D. Front Genet 2021; 12:796327. [PMID: 34925468 PMCID: PMC8672092 DOI: 10.3389/fgene.2021.796327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 11/15/2021] [Indexed: 11/13/2022] Open
Abstract
Phages have seriously affected the biochemical systems of the world, and not only are phages related to our health, but medical treatments for many cancers and skin infections are related to phages; therefore, this paper sought to identify phage proteins. In this paper, a Pseudo-188D model was established. The digital features of the phage were extracted by PseudoKNC, an appropriate vector was selected by the AdaBoost tool, and features were extracted by 188D. Then, the extracted digital features were combined together, and finally, the viral proteins of the phage were predicted by a stochastic gradient descent algorithm. Our model effect reached 93.4853%. To verify the stability of our model, we randomly selected 80% of the downloaded data to train the model and used the remaining 20% of the data to verify the robustness of our model.
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Affiliation(s)
- Xiaomei Gu
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Institute of Yangtze River Delta, University of Electronic Science and Technology of China, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.,School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Lina Guo
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Bo Liao
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.,School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Qinghua Jiang
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.,School of Mathematics and Statistics, Hainan Normal University, Haikou, China
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19
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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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20
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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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21
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Zhao YW, Zhang S, Ding H. Recent development of machine learning methods in sumoylation sites prediction. Curr Med Chem 2021; 29:894-907. [PMID: 34525906 DOI: 10.2174/0929867328666210915112030] [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: 03/10/2021] [Revised: 07/24/2021] [Accepted: 08/07/2021] [Indexed: 11/22/2022]
Abstract
Sumoylation of proteins is an important reversible post-translational modification of proteins and mediates a variety of cellular processes. Sumo-modified proteins can change their subcellular localization, activity and stability. In addition, it also plays an important role in various cellular processes such as transcriptional regulation and signal transduction. The abnormal sumoylation is involved in many diseases, including neurodegeneration and immune-related diseases, as well as the development of cancer. Therefore, identification of the sumoylation site (SUMO site) is fundamental to understanding their molecular mechanisms and regulatory roles. In contrast to labor-intensive and costly experimental approaches, computational prediction of sumoylation sites in silico also attracted much attention for its accuracy, convenience and speed. At present, many computational prediction models have been used to identify SUMO sites, but these contents have not been comprehensively summarized and reviewed. Therefore, the research progress of relevant models is summarized and discussed in this paper. We will briefly summarize the development of bioinformatics methods on sumoylation site prediction. We will mainly focus on the benchmark dataset construction, feature extraction, machine learning method, published results and online tools. We hope the review will provide more help for wet-experimental scholars.
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Affiliation(s)
- Yi-Wei Zhao
- School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Shihua Zhang
- College of Life Science and Health, Wuhan University of Science and Technology, Wuhan 430065. China
| | - Hui Ding
- 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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22
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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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23
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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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24
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Zulfiqar H, Yuan SS, Huang QL, Sun ZJ, Dao FY, Yu XL, Lin H. Identification of cyclin protein using gradient boost decision tree algorithm. Comput Struct Biotechnol J 2021; 19:4123-4131. [PMID: 34527186 PMCID: PMC8346528 DOI: 10.1016/j.csbj.2021.07.013] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 07/15/2021] [Accepted: 07/15/2021] [Indexed: 12/12/2022] Open
Abstract
Cyclin proteins are capable to regulate the cell cycle by forming a complex with cyclin-dependent kinases to activate cell cycle. Correct recognition of cyclin proteins could provide key clues for studying their functions. However, their sequences share low similarity, which results in poor prediction for sequence similarity-based methods. Thus, it is urgent to construct a machine learning model to identify cyclin proteins. This study aimed to develop a computational model to discriminate cyclin proteins from non-cyclin proteins. In our model, protein sequences were encoded by seven kinds of features that are amino acid composition, composition of k-spaced amino acid pairs, tri peptide composition, pseudo amino acid composition, geary correlation, normalized moreau-broto autocorrelation and composition/transition/distribution. Afterward, these features were optimized by using analysis of variance (ANOVA) and minimum redundancy maximum relevance (mRMR) with incremental feature selection (IFS) technique. A gradient boost decision tree (GBDT) classifier was trained on the optimal features. Five-fold cross-validated results showed that our model would identify cyclins with an accuracy of 93.06% and AUC value of 0.971, which are higher than the two recent studies on the same data.
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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
| | - 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
| | - Qin-Lai Huang
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zi-Jie Sun
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Ying Dao
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xiao-Long Yu
- School of Materials Science and Engineering, Hainan University, Haikou 570228, 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
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25
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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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26
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Xu L, Ru X, Song R. Application of Machine Learning for Drug-Target Interaction Prediction. Front Genet 2021; 12:680117. [PMID: 34234813 PMCID: PMC8255962 DOI: 10.3389/fgene.2021.680117] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 05/28/2021] [Indexed: 11/13/2022] Open
Abstract
Exploring drug–target interactions by biomedical experiments requires a lot of human, financial, and material resources. To save time and cost to meet the needs of the present generation, machine learning methods have been introduced into the prediction of drug–target interactions. The large amount of available drug and target data in existing databases, the evolving and innovative computer technologies, and the inherent characteristics of various types of machine learning have made machine learning techniques the mainstream method for drug–target interaction prediction research. In this review, details of the specific applications of machine learning in drug–target interaction prediction are summarized, the characteristics of each algorithm are analyzed, and the issues that need to be further addressed and explored for future research are discussed. The aim of this review is to provide a sound basis for the construction of high-performance models.
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Affiliation(s)
- Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Xiaoqing Ru
- Department of Computer Science, University of Tsukuba, Tsukuba, Japan
| | - Rong Song
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
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27
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Ao C, Zou Q, Yu L. RFhy-m2G: Identification of RNA N2-methylguanosine modification sites based on random forest and hybrid features. Methods 2021; 203:32-39. [PMID: 34033879 DOI: 10.1016/j.ymeth.2021.05.016] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 05/04/2021] [Accepted: 05/20/2021] [Indexed: 12/31/2022] Open
Abstract
N2-methylguanosine is a post-transcriptional modification of RNA that is found in eukaryotes and archaea. The biological function of m2G modification discovered so far is to control and stabilize the three-dimensional structure of tRNA and the dynamic barrier of reverse transcription. To discover additional biological functions of m2G, it is necessary to develop time-saving and labor-saving calculation tools to identify m2G. In this paper, based on hybrid features and a random forest, a novel predictor, RFhy-m2G, was developed to identify the m2G modification sites for three species. The hybrid feature used by the predictor is used to fuse the three features of ENAC, PseDNC, and NPPS. These three features include primary sequence derivation properties, physicochemical properties, and position-specific properties. Since there are redundant features in hybrid features, MRMD2.0 is used for optimal feature selection. Through feature analysis, it is found that the optimal hybrid features obtained still contain three kinds of properties, and the hybrid features can more accurately identify m2G modification sites and improve prediction performance. Based on five-fold cross-validation and independent testing to evaluate the prediction model, the accuracies obtained were 0.9982 and 0.9417, respectively. The robustness of the predictor is demonstrated by comparisons with other predictors.
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Affiliation(s)
- Chunyan Ao
- School of Computer Science and Technology, Xidian University, Xi'an, China; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, China.
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28
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Cao Y, Yu C, Huang S, Wang S, Zuo Y, Yang L. Characterization and Prediction of Presynaptic and Postsynaptic Neurotoxins Based on Reduced Amino Acids and Biological Properties. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200707150512] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Presynaptic and postsynaptic neurotoxins are two important neurotoxins. Due to the important
role of presynaptic and postsynaptic neurotoxins in pharmacology and neuroscience, identification of them becomes very
important in biology.
Method:
In this study, the statistical test and F-score were used to calculate the difference between amino acids and
biological properties. The support vector machine was used to predict the presynaptic and postsynaptic neurotoxins by
using the reduced amino acid alphabet types.
Results:
By using the reduced amino acid alphabet as the input parameters of support vector machine, the overall accuracy
of our classifier had increased to 91.07%, which was the highest overall accuracy in this study. When compared with the
other published methods, better predictive results were obtained by our classifier.
Conclusion:
In summary, we analyzed the differences between two neurotoxins in amino acids and biological properties,
and constructed a classifier that could predict these two neurotoxins by using the reduced amino acid alphabet.
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Affiliation(s)
- Yiyin Cao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Chunlu Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shenghui Huang
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Shiyuan Wang
- 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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29
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Dong GF, Zheng L, Huang SH, Gao J, Zuo YC. Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities. Front Genet 2021; 12:669328. [PMID: 33959153 PMCID: PMC8093877 DOI: 10.3389/fgene.2021.669328] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 03/23/2021] [Indexed: 02/03/2023] Open
Abstract
Antimicrobial peptides (AMPs) are considered as potential substitutes of antibiotics in the field of new anti-infective drug design. There have been several machine learning algorithms and web servers in identifying AMPs and their functional activities. However, there is still room for improvement in prediction algorithms and feature extraction methods. The reduced amino acid (RAA) alphabet effectively solved the problems of simplifying protein complexity and recognizing the structure conservative region. This article goes into details about evaluating the performances of more than 5,000 amino acid reduced descriptors generated from 74 types of amino acid reduced alphabet in the first stage and the second stage to construct an excellent two-stage classifier, Identification of Antimicrobial Peptides by Reduced Amino Acid Cluster (iAMP-RAAC), for identifying AMPs and their functional activities, respectively. The results show that the first stage AMP classifier is able to achieve the accuracy of 97.21 and 97.11% for the training data set and independent test dataset. In the second stage, our classifier still shows good performance. At least three of the four metrics, sensitivity (SN), specificity (SP), accuracy (ACC), and Matthews correlation coefficient (MCC), exceed the calculation results in the literature. Further, the ANOVA with incremental feature selection (IFS) is used for feature selection to further improve prediction performance. The prediction performance is further improved after the feature selection of each stage. At last, a user-friendly web server, iAMP-RAAC, is established at http://bioinfor.imu.edu. cn/iampraac.
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Affiliation(s)
- Gai-Fang Dong
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Lei Zheng
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Sheng-Hui Huang
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Jing Gao
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Yong-Chun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
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30
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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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31
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ANPrAod: Identify Antioxidant Proteins by Fusing Amino Acid Clustering Strategy and N-Peptide Combination. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5518209. [PMID: 33927782 PMCID: PMC8049822 DOI: 10.1155/2021/5518209] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/02/2021] [Accepted: 03/10/2021] [Indexed: 11/18/2022]
Abstract
Antioxidant proteins perform significant functions in disease control and delaying aging which can prevent free radicals from damaging organisms. Accurate identification of antioxidant proteins has important implications for the development of new drugs and the treatment of related diseases, as they play a critical role in the control or prevention of cancer and aging-related conditions. Since experimental identification techniques are time-consuming and expensive, many computational methods have been proposed to identify antioxidant proteins. Although the accuracy of these methods is acceptable, there are still some challenges. In this study, we developed a computational model called ANPrAod to identify antioxidant proteins based on a support vector machine. In order to eliminate potential redundant features and improve prediction accuracy, 673 amino acid reduction alphabets were calculated by us to find the optimal feature representation scheme. The final model could produce an overall accuracy of 87.53% with the ROC of 0.7266 in five-fold cross-validation, which was better than the existing methods. The results of the independent dataset also demonstrated the excellent robustness and reliability of ANPrAod, which could be a promising tool for antioxidant protein identification and contribute to hypothesis-driven experimental design.
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32
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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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33
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Ou J, Liu H, Nirala NK, Stukalov A, Acharya U, Green MR, Zhu LJ. dagLogo: An R/Bioconductor package for identifying and visualizing differential amino acid group usage in proteomics data. PLoS One 2020; 15:e0242030. [PMID: 33156866 PMCID: PMC7647101 DOI: 10.1371/journal.pone.0242030] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 10/23/2020] [Indexed: 11/18/2022] Open
Abstract
Sequence logos have been widely used as graphical representations of conserved nucleic acid and protein motifs. Due to the complexity of the amino acid (AA) alphabet, rich post-translational modification, and diverse subcellular localization of proteins, few versatile tools are available for effective identification and visualization of protein motifs. In addition, various reduced AA alphabets based on physicochemical, structural, or functional properties have been valuable in the study of protein alignment, folding, structure prediction, and evolution. However, there is lack of tools for applying reduced AA alphabets to the identification and visualization of statistically significant motifs. To fill this gap, we developed an R/Bioconductor package dagLogo, which has several advantages over existing tools. First, dagLogo allows various formats for input sets and provides comprehensive options to build optimal background models. It implements different reduced AA alphabets to group AAs of similar properties. Furthermore, dagLogo provides statistical and visual solutions for differential AA (or AA group) usage analysis of both large and small data sets. Case studies showed that dagLogo can better identify and visualize conserved protein sequence patterns from different types of inputs and can potentially reveal the biological patterns that could be missed by other logo generators.
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Affiliation(s)
- Jianhong Ou
- Department of Molecular, Cell, and Cancer Biology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
- Regeneration NEXT, Duke University School of Medicine, Duke University, Durham, North Carolina, United States of America
| | - Haibo Liu
- Department of Molecular, Cell, and Cancer Biology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
| | - Niraj K. Nirala
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
| | - Alexey Stukalov
- Institute of Virology, Technical University of Munich, Munich, Germany
| | - Usha Acharya
- Department of Molecular, Cell, and Cancer Biology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
| | - Michael R. Green
- Department of Molecular, Cell, and Cancer Biology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
| | - Lihua Julie Zhu
- Department of Molecular, Cell, and Cancer Biology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
- * E-mail:
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34
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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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35
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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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36
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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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37
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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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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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Feng C, Ma Z, Yang D, Li X, Zhang J, Li Y. A Method for Prediction of Thermophilic Protein Based on Reduced Amino Acids and Mixed Features. Front Bioeng Biotechnol 2020; 8:285. [PMID: 32432088 PMCID: PMC7214540 DOI: 10.3389/fbioe.2020.00285] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 03/18/2020] [Indexed: 11/13/2022] Open
Abstract
The thermostability of proteins is a key factor considered during enzyme engineering, and finding a method that can identify thermophilic and non-thermophilic proteins will be helpful for enzyme design. In this study, we established a novel method combining mixed features and machine learning to achieve this recognition task. In this method, an amino acid reduction scheme was adopted to recode the amino acid sequence. Then, the physicochemical characteristics, auto-cross covariance (ACC), and reduced dipeptides were calculated and integrated to form a mixed feature set, which was processed using correlation analysis, feature selection, and principal component analysis (PCA) to remove redundant information. Finally, four machine learning methods and a dataset containing 500 random observations out of 915 thermophilic proteins and 500 random samples out of 793 non-thermophilic proteins were used to train and predict the data. The experimental results showed that 98.2% of thermophilic and non-thermophilic proteins were correctly identified using 10-fold cross-validation. Moreover, our analysis of the final reserved features and removed features yielded information about the crucial, unimportant and insensitive elements, it also provided essential information for enzyme design.
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Affiliation(s)
- Changli Feng
- College of Information Science and Technology, Taishan University, Tai’an, China
| | - Zhaogui Ma
- College of Information Science and Technology, Taishan University, Tai’an, China
| | - Deyun Yang
- College of Information Science and Technology, Taishan University, Tai’an, China
| | - Xin Li
- College of Information Science and Technology, Taishan University, Tai’an, China
| | - Jun Zhang
- Department of Rehabilitation, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
| | - Yanjuan Li
- Information and Computer Engineering College, Northeast Forestry University, Harbin, China
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40
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Dao FY, Lv H, Yang YH, Zulfiqar H, Gao H, Lin H. Computational identification of N6-methyladenosine sites in multiple tissues of mammals. Comput Struct Biotechnol J 2020; 18:1084-1091. [PMID: 32435427 PMCID: PMC7229270 DOI: 10.1016/j.csbj.2020.04.015] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Revised: 04/20/2020] [Accepted: 04/21/2020] [Indexed: 12/12/2022] Open
Abstract
N6-methyladenosine (m6A) is the methylation of the adenosine at the nitrogen-6 position, which is the most abundant RNA methylation modification and involves a series of important biological processes. Accurate identification of m6A sites in genome-wide is invaluable for better understanding their biological functions. In this work, an ensemble predictor named iRNA-m6A was established to identify m6A sites in multiple tissues of human, mouse and rat based on the data from high-throughput sequencing techniques. In the proposed predictor, RNA sequences were encoded by physical-chemical property matrix, mono-nucleotide binary encoding and nucleotide chemical property. Subsequently, these features were optimized by using minimum Redundancy Maximum Relevance (mRMR) feature selection method. Based on the optimal feature subset, the best m6A classification models were trained by Support Vector Machine (SVM) with 5-fold cross-validation test. Prediction results on independent dataset showed that our proposed method could produce the excellent generalization ability. We also established a user-friendly webserver called iRNA-m6A which can be freely accessible at http://lin-group.cn/server/iRNA-m6A. This tool will provide more convenience to users for studying m6A modification in different tissues.
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Affiliation(s)
| | | | - Yu-He Yang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hasan Zulfiqar
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Gao
- 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
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Li HF, Wang XF, Tang H. Predicting Bacteriophage Enzymes and Hydrolases by Using Combined Features. Front Bioeng Biotechnol 2020; 8:183. [PMID: 32266225 PMCID: PMC7105632 DOI: 10.3389/fbioe.2020.00183] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 02/24/2020] [Indexed: 12/19/2022] Open
Abstract
Bacteriophage is a type of virus that could infect the host bacteria. They have been applied in the treatment of pathogenic bacterial infection. Phage enzymes and hydrolases play the most important role in the destruction of bacterial cells. Correctly identifying the hydrolases coded by phage is not only beneficial to their function study, but also conducive to antibacteria drug discovery. Thus, this work aims to recognize the enzymes and hydrolases in phage. A combination of different features was used to represent samples of phage and hydrolase. A feature selection technique called analysis of variance was developed to optimize features. The classification was performed by using support vector machine (SVM). The prediction process includes two steps. The first step is to identify phage enzymes. The second step is to determine whether a phage enzyme is hydrolase or not. The jackknife cross-validated results showed that our method could produce overall accuracies of 85.1 and 94.3%, respectively, for the two predictions, demonstrating that the proposed method is promising.
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Affiliation(s)
- Hong-Fei Li
- Department of Pathophysiology, Key Laboratory of Medical Electrophysiology, Ministry of Education, Southwest Medical University, Luzhou, China.,School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Xian-Fang Wang
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Hua Tang
- Department of Pathophysiology, Key Laboratory of Medical Electrophysiology, Ministry of Education, Southwest Medical University, Luzhou, China
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Wang S, Zhang Q, Yu C, Cao Y, Zuo Y, Yang L. Immune cell infiltration-based signature for prognosis and immunogenomic analysis in breast cancer. Brief Bioinform 2020; 22:2020-2031. [PMID: 32141494 DOI: 10.1093/bib/bbaa026] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 01/30/2020] [Accepted: 02/17/2020] [Indexed: 12/18/2022] Open
Abstract
Breast cancer is one of the most human malignant diseases and the leading cause of cancer-related death in the world. However, the prognostic and therapeutic benefits of breast cancer patients cannot be predicted accurately by the current stratifying system. In this study, an immune-related prognostic score was established in 22 breast cancer cohorts with a total of 6415 samples. An extensive immunogenomic analysis was conducted to explore the relationships between immune score, prognostic significance, infiltrating immune cells, cancer genotypes and potential immune escape mechanisms. Our analysis revealed that this immune score was a promising biomarker for estimating overall survival in breast cancer. This immune score was associated with important immunophenotypic factors, such as immune escape and mutation load. Further analysis revealed that patients with high immune scores exhibited therapeutic benefits from chemotherapy and immunotherapy. Based on these results, we can conclude that this immune score may be a useful tool for overall survival prediction and treatment guidance for patients with breast cancer.
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