201
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Sankari ES, Manimegalai D. Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC. J Theor Biol 2018; 455:319-328. [DOI: 10.1016/j.jtbi.2018.07.032] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 06/27/2018] [Accepted: 07/23/2018] [Indexed: 10/28/2022]
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202
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Butt AH, Rasool N, Khan YD. Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC. Mol Biol Rep 2018; 45:2295-2306. [PMID: 30238411 DOI: 10.1007/s11033-018-4391-5] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 09/14/2018] [Indexed: 11/30/2022]
Abstract
For many biological functions membrane proteins (MPs) are considered crucial. Due to this nature of MPs, many pharmaceutical agents have reflected them as attractive targets. It bears indispensable importance that MPs are predicted with accurate measures using effective and efficient computational models (CMs). Annotation of MPs using in vitro analytical techniques is time-consuming and expensive; and in some cases, it can prove to be intractable. Due to this scenario, automated prediction and annotation of MPs through CM based techniques have appeared to be useful. Based on the use of computational intelligence and statistical moments based feature set, an MP prediction framework is proposed. Furthermore, the previously used dataset has been enhanced by incorporating new MPs from the latest release of UniProtKB. Rigorous experimentation proves that the use of statistical moments with a multilayer neural network, trained using back-propagation based prediction techniques allows more thorough results.
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Affiliation(s)
- Ahmad Hassan Butt
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, C-II, Johar Town, P.O. Box 10033, Lahore, 54770, Pakistan.
| | - Nouman Rasool
- Department of Life Sciences, School of Science, University of Management and Technology, C-II, Johar Town, P.O. Box 10033, Lahore, 54770, Pakistan
| | - Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, C-II, Johar Town, P.O. Box 10033, Lahore, 54770, Pakistan
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203
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Wang MY, Liang JW, Olounfeh KM, Sun Q, Zhao N, Meng FH. A Comprehensive In Silico Method to Study the QSTR of the Aconitine Alkaloids for Designing Novel Drugs. Molecules 2018; 23:E2385. [PMID: 30231506 PMCID: PMC6225272 DOI: 10.3390/molecules23092385] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Revised: 09/11/2018] [Accepted: 09/12/2018] [Indexed: 12/22/2022] Open
Abstract
A combined in silico method was developed to predict potential protein targets that are involved in cardiotoxicity induced by aconitine alkaloids and to study the quantitative structure⁻toxicity relationship (QSTR) of these compounds. For the prediction research, a Protein-Protein Interaction (PPI) network was built from the extraction of useful information about protein interactions connected with aconitine cardiotoxicity, based on nearly a decade of literature and the STRING database. The software Cytoscape and the PharmMapper server were utilized to screen for essential proteins in the constructed network. The Calcium-Calmodulin-Dependent Protein Kinase II alpha (CAMK2A) and gamma (CAMK2G) were identified as potential targets. To obtain a deeper insight on the relationship between the toxicity and the structure of aconitine alkaloids, the present study utilized QSAR models built in Sybyl software that possess internal robustness and external high predictions. The molecular dynamics simulation carried out here have demonstrated that aconitine alkaloids possess binding stability for the receptor CAMK2G. In conclusion, this comprehensive method will serve as a tool for following a structural modification of the aconitine alkaloids and lead to a better insight into the cardiotoxicity induced by the compounds that have similar structures to its derivatives.
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Affiliation(s)
- Ming-Yang Wang
- School of Pharmacy, China Medical University, Shenyang 110122, Liaoning, China.
| | - Jing-Wei Liang
- School of Pharmacy, China Medical University, Shenyang 110122, Liaoning, China.
| | | | - Qi Sun
- School of Pharmacy, China Medical University, Shenyang 110122, Liaoning, China.
| | - Nan Zhao
- School of Pharmacy, China Medical University, Shenyang 110122, Liaoning, China.
| | - Fan-Hao Meng
- School of Pharmacy, China Medical University, Shenyang 110122, Liaoning, China.
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204
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He W, Ju Y, Zeng X, Liu X, Zou Q. Sc-ncDNAPred: A Sequence-Based Predictor for Identifying Non-coding DNA in Saccharomyces cerevisiae. Front Microbiol 2018; 9:2174. [PMID: 30258427 PMCID: PMC6144933 DOI: 10.3389/fmicb.2018.02174] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 08/24/2018] [Indexed: 12/22/2022] Open
Abstract
With the rapid development of high-speed sequencing technologies and the implementation of many whole genome sequencing project, research in the genomics is advancing from genome sequencing to genome synthesis. Synthetic biology technologies such as DNA-based molecular assemblies, genome editing technology, directional evolution technology and DNA storage technology, and other cutting-edge technologies emerge in succession. Especially the rapid growth and development of DNA assembly technology may greatly push forward the success of artificial life. Meanwhile, DNA assembly technology needs a large number of target sequences of known information as data support. Non-coding DNA (ncDNA) sequences occupy most of the organism genomes, thus accurate recognizing of them is necessary. Although experimental methods have been proposed to detect ncDNA sequences, they are expensive for performing genome wide detections. Thus, it is necessary to develop machine-learning methods for predicting non-coding DNA sequences. In this study, we collected the ncDNA benchmark dataset of Saccharomyces cerevisiae and reported a support vector machine-based predictor, called Sc-ncDNAPred, for predicting ncDNA sequences. The optimal feature extraction strategy was selected from a group included mononucleotide, dimer, trimer, tetramer, pentamer, and hexamer, using support vector machine learning method. Sc-ncDNAPred achieved an overall accuracy of 0.98. For the convenience of users, an online web-server has been built at: http://server.malab.cn/Sc_ncDNAPred/index.jsp.
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Affiliation(s)
- Wenying He
- School of Computer Science and Technology, Tianjin University, Tianjin, China
| | - Ying Ju
- School of Information Science and Technology, Xiamen University, Xiamen, China
| | - Xiangxiang Zeng
- School of Information Science and Technology, Xiamen University, Xiamen, China
| | - Xiangrong Liu
- School of Information Science and Technology, Xiamen University, Xiamen, China
| | - Quan Zou
- School of Computer Science and Technology, Tianjin University, Tianjin, China.,Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, China
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205
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Chen W, Ding H, Zhou X, Lin H, Chou KC. iRNA(m6A)-PseDNC: Identifying N 6-methyladenosine sites using pseudo dinucleotide composition. Anal Biochem 2018; 561-562:59-65. [PMID: 30201554 DOI: 10.1016/j.ab.2018.09.002] [Citation(s) in RCA: 126] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 08/31/2018] [Accepted: 09/03/2018] [Indexed: 01/28/2023]
Abstract
As a prevalent post-transcriptional modification, N6-methyladenosine (m6A) plays key roles in a series of biological processes. Although experimental technologies have been developed and applied to identify m6A sites, they are still cost-ineffective for transcriptome-wide detections of m6A. As good complements to the experimental techniques, some computational methods have been proposed to identify m6A sites. However, their performance remains unsatisfactory. In this study, we firstly proposed an Euclidean distance based method to construct a high quality benchmark dataset. By encoding the RNA sequences using pseudo nucleotide composition, a new predictor called iRNA(m6A)-PseDNC was developed to identify m6A sites in the Saccharomyces cerevisiae genome. It has been demonstrated by the 10-fold cross validation test that the performance of iRNA(m6A)-PseDNC is superior to the existing methods. Meanwhile, for the convenience of most experimental scientists, established at the site http://lin-group.cn/server/iRNA(m6A)-PseDNC.php is its web-server, by which users can easily get their desired results without need to go through the detailed mathematics. It is anticipated that iRNA(m6A)-PseDNC will become a useful high throughput tool for identifying m6A sites in the S. cerevisiae genome.
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Affiliation(s)
- Wei Chen
- School of Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, 063000, China; Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611730, China; Gordon Life Science Institute, Boston, MA, 02478, USA.
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Xu Zhou
- School of Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, 063000, China.
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China; Gordon Life Science Institute, Boston, MA, 02478, USA.
| | - Kuo-Chen Chou
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China; Gordon Life Science Institute, Boston, MA, 02478, USA.
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206
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Qiu WR, Sun BQ, Xiao X, Xu ZC, Jia JH, Chou KC. iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier. Genomics 2018; 110:239-246. [DOI: 10.1016/j.ygeno.2017.10.008] [Citation(s) in RCA: 99] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 10/23/2017] [Accepted: 10/25/2017] [Indexed: 01/23/2023]
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207
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Kumar R, Kumari B, Kumar M. Proteome-wide prediction and annotation of mitochondrial and sub-mitochondrial proteins by incorporating domain information. Mitochondrion 2018; 42:11-22. [DOI: 10.1016/j.mito.2017.10.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 07/21/2017] [Accepted: 10/06/2017] [Indexed: 12/22/2022]
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208
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He J, Fang T, Zhang Z, Huang B, Zhu X, Xiong Y. PseUI: Pseudouridine sites identification based on RNA sequence information. BMC Bioinformatics 2018; 19:306. [PMID: 30157750 PMCID: PMC6114832 DOI: 10.1186/s12859-018-2321-0] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Accepted: 08/21/2018] [Indexed: 01/28/2023] Open
Abstract
Background Pseudouridylation is the most prevalent type of posttranscriptional modification in various stable RNAs of all organisms, which significantly affects many cellular processes that are regulated by RNA. Thus, accurate identification of pseudouridine (Ψ) sites in RNA will be of great benefit for understanding these cellular processes. Due to the low efficiency and high cost of current available experimental methods, it is highly desirable to develop computational methods for accurately and efficiently detecting Ψ sites in RNA sequences. However, the predictive accuracy of existing computational methods is not satisfactory and still needs improvement. Results In this study, we developed a new model, PseUI, for Ψ sites identification in three species, which are H. sapiens, S. cerevisiae, and M. musculus. Firstly, five different kinds of features including nucleotide composition (NC), dinucleotide composition (DC), pseudo dinucleotide composition (pseDNC), position-specific nucleotide propensity (PSNP), and position-specific dinucleotide propensity (PSDP) were generated based on RNA segments. Then, a sequential forward feature selection strategy was used to gain an effective feature subset with a compact representation but discriminative prediction power. Based on the selected feature subsets, we built our model by using a support vector machine (SVM). Finally, the generalization of our model was validated by both the jackknife test and independent validation tests on the benchmark datasets. The experimental results showed that our model is more accurate and stable than the previously published models. We have also provided a user-friendly web server for our model at http://zhulab.ahu.edu.cn/PseUI, and a brief instruction for the web server is provided in this paper. By using this instruction, the academic users can conveniently get their desired results without complicated calculations. Conclusion In this study, we proposed a new predictor, PseUI, to detect Ψ sites in RNA sequences. It is shown that our model outperformed the existing state-of-art models. It is expected that our model, PseUI, will become a useful tool for accurate identification of RNA Ψ sites. Electronic supplementary material The online version of this article (10.1186/s12859-018-2321-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jingjing He
- School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China
| | - Ting Fang
- School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China
| | - Zizheng Zhang
- School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China
| | - Bei Huang
- School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China
| | - Xiaolei Zhu
- School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China.
| | - Yi Xiong
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
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209
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Chen W, Yang H, Feng P, Ding H, Lin H. iDNA4mC: identifying DNA N4-methylcytosine sites based on nucleotide chemical properties. Bioinformatics 2018; 33:3518-3523. [PMID: 28961687 DOI: 10.1093/bioinformatics/btx479] [Citation(s) in RCA: 205] [Impact Index Per Article: 34.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Accepted: 07/25/2017] [Indexed: 12/24/2022] Open
Abstract
Motivation DNA N4-methylcytosine (4mC) is an epigenetic modification. The knowledge about the distribution of 4mC is helpful for understanding its biological functions. Although experimental methods have been proposed to detect 4mC sites, they are expensive for performing genome-wide detections. Thus, it is necessary to develop computational methods for predicting 4mC sites. Results In this work, we developed iDNA4mC, the first webserver to identify 4mC sites, in which DNA sequences are encoded with both nucleotide chemical properties and nucleotide frequency. The predictive results of the rigorous jackknife test and cross species test demonstrated that the performance of iDNA4mC is quite promising and holds high potential to become a useful tool for identifying 4mC sites. Availability and implementation The user-friendly web-server, iDNA4mC, is freely accessible at http://lin.uestc.edu.cn/server/iDNA4mC. Contact chenweiimu@gmail.com or hlin@uestc.edu.cn.
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Affiliation(s)
- Wei Chen
- Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China
| | - Hui Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Pengmian Feng
- Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, School of Public Health, North China University of Science and Technology, Tangshan 063000, China
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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210
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Kalmykova SD, Arapidi GP, Urban AS, Osetrova MS, Gordeeva VD, Ivanov VT, Govorun VM. In Silico Analysis of Peptide Potential Biological Functions. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2018. [DOI: 10.1134/s106816201804009x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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211
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He W, Jia C, Zou Q. 4mCPred: machine learning methods for DNA N4-methylcytosine sites prediction. Bioinformatics 2018; 35:593-601. [DOI: 10.1093/bioinformatics/bty668] [Citation(s) in RCA: 103] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Revised: 07/07/2018] [Accepted: 07/24/2018] [Indexed: 11/14/2022] Open
Affiliation(s)
- Wenying He
- School of Computer Science and Technology, Tianjin University, Tianjin, China
| | - Cangzhi Jia
- Department of Mathematics, Dalian Maritime University, Dalian, China
| | - Quan Zou
- School of Computer Science and Technology, Tianjin University, Tianjin, China
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212
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Shoombuatong W, Schaduangrat N, Nantasenamat C. Unraveling the bioactivity of anticancer peptides as deduced from machine learning. EXCLI JOURNAL 2018; 17:734-752. [PMID: 30190664 PMCID: PMC6123611 DOI: 10.17179/excli2018-1447] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Accepted: 07/10/2018] [Indexed: 12/13/2022]
Abstract
Cancer imposes a global health burden as it represents one of the leading causes of morbidity and mortality while also giving rise to significant economic burden owing to the associated expenditures for its monitoring and treatment. In spite of advancements in cancer therapy, the low success rate and recurrence of tumor has necessitated the ongoing search for new therapeutic agents. Aside from drugs based on small molecules and protein-based biopharmaceuticals, there has been an intense effort geared towards the development of peptide-based therapeutics owing to its favorable and intrinsic properties of being relatively small, highly selective, potent, safe and low in production costs. In spite of these advantages, there are several inherent weaknesses that are in need of attention in the design and development of therapeutic peptides. An abundance of data on bioactive and therapeutic peptides have been accumulated over the years and the burgeoning area of artificial intelligence has set the stage for the lucrative utilization of machine learning to make sense of these large and high-dimensional data. This review summarizes the current state-of-the-art on the application of machine learning for studying the bioactivity of anticancer peptides along with future outlook of the field. Data and R codes used in the analysis herein are available on GitHub at https://github.com/Shoombuatong2527/anticancer-peptides-review.
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Affiliation(s)
- Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
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213
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Novel 3D Structure Based Model for Activity Prediction and Design of Antimicrobial Peptides. Sci Rep 2018; 8:11189. [PMID: 30046138 PMCID: PMC6060096 DOI: 10.1038/s41598-018-29566-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Accepted: 07/13/2018] [Indexed: 01/10/2023] Open
Abstract
The emergence and worldwide spread of multi-drug resistant bacteria makes an urgent challenge for the development of novel antibacterial agents. A perspective weapon to fight against severe infections caused by drug-resistant microorganisms is antimicrobial peptides (AMPs). AMPs are a diverse class of naturally occurring molecules that are produced as a first line of defense by all multi-cellular organisms. Limited by the number of experimental determinate 3D structure, most of the prediction or classification methods of AMPs were based on 2D descriptors, including sequence, amino acid composition, peptide net charge, hydrophobicity, amphiphilic, etc. Due to the rapid development of structural simulation methods, predicted models of proteins (or peptides) have been successfully applied in structure based drug design, for example as targets of virtual ligand screening. Here, we establish the activity prediction model based on the predicted 3D structure of AMPs molecule. To our knowledge, it is the first report of prediction method based on 3D descriptors of AMPs. Novel AMPs were designed by using the model, and their antibacterial effect was measured by in vitro experiments.
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214
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Zhao Q, Zhang Y, Hu H, Ren G, Zhang W, Liu H. IRWNRLPI: Integrating Random Walk and Neighborhood Regularized Logistic Matrix Factorization for lncRNA-Protein Interaction Prediction. Front Genet 2018; 9:239. [PMID: 30023002 PMCID: PMC6040094 DOI: 10.3389/fgene.2018.00239] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 06/15/2018] [Indexed: 11/13/2022] Open
Abstract
Long non-coding RNA (lncRNA) plays an important role in many important biological processes and has attracted widespread attention. Although the precise functions and mechanisms for most lncRNAs are still unknown, we are certain that lncRNAs usually perform their functions by interacting with the corresponding RNA- binding proteins. For example, lncRNA-protein interactions play an important role in post transcriptional gene regulation, such as splicing, translation, signaling, and advances in complex diseases. However, experimental verification of lncRNA-protein interactions prediction is time-consuming and laborious. In this work, we propose a computational method, named IRWNRLPI, to find the potential associations between lncRNAs and proteins. IRWNRLPI integrates two algorithms, random walk and neighborhood regularized logistic matrix factorization, which can optimize a lot more than using an algorithm alone. Moreover, the method is semi-supervised and does not require negative samples. Based on the leave-one-out cross validation, we obtain the AUC of 0.9150 and the AUPR of 0.7138, demonstrating its reliable performance. In addition, by means of case study in the “Mus musculus,” many lncRNA-protein interactions which are predicted by our method can be successfully confirmed by experiments. This suggests that IRWNRLPI will be a useful bioinformatics resource in biomedical research.
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Affiliation(s)
- Qi Zhao
- School of Mathematics, Liaoning University, Shenyang, China.,Research Center for Computer Simulating and Information Processing of Bio-Macromolecules of Liaoning Province, Shenyang, China
| | - Yue Zhang
- School of Mathematics, Liaoning University, Shenyang, China
| | - Huan Hu
- School of Life Science, Liaoning University, Shenyang, China
| | - Guofei Ren
- School of Information, Liaoning University, Shenyang, China
| | - Wen Zhang
- School of Computer, Wuhan University, Wuhan, China
| | - Hongsheng Liu
- Research Center for Computer Simulating and Information Processing of Bio-Macromolecules of Liaoning Province, Shenyang, China.,School of Life Science, Liaoning University, Shenyang, China.,Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, China
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215
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Manavalan B, Subramaniyam S, Shin TH, Kim MO, Lee G. Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy. J Proteome Res 2018; 17:2715-2726. [PMID: 29893128 DOI: 10.1021/acs.jproteome.8b00148] [Citation(s) in RCA: 141] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Cell-penetrating peptides (CPPs) can enter cells as a variety of biologically active conjugates and have various biomedical applications. To offset the cost and effort of designing novel CPPs in laboratories, computational methods are necessitated to identify candidate CPPs before in vitro experimental studies. We developed a two-layer prediction framework called machine-learning-based prediction of cell-penetrating peptides (MLCPPs). The first-layer predicts whether a given peptide is a CPP or non-CPP, whereas the second-layer predicts the uptake efficiency of the predicted CPPs. To construct a two-layer prediction framework, we employed four different machine-learning methods and five different compositions including amino acid composition (AAC), dipeptide composition, amino acid index, composition-transition-distribution, and physicochemical properties (PCPs). In the first layer, hybrid features (combination of AAC and PCP) and extremely randomized tree outperformed state-of-the-art predictors in CPP prediction with an accuracy of 0.896 when tested on independent data sets, whereas in the second layer, hybrid features obtained through feature selection protocol and random forest produced an accuracy of 0.725 that is better than state-of-the-art predictors. We anticipate that our method MLCPP will become a valuable tool for predicting CPPs and their uptake efficiency and might facilitate hypothesis-driven experimental design. The MLCPP server interface along with the benchmarking and independent data sets are freely accessible at www.thegleelab.org/MLCPP .
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Affiliation(s)
- Balachandran Manavalan
- Department of Physiology , Ajou University School of Medicine , Suwon 443380 , Republic of Korea
| | | | - Tae Hwan Shin
- Department of Physiology , Ajou University School of Medicine , Suwon 443380 , Republic of Korea.,Institute of Molecular Science and Technology , Ajou University , Suwon 443721 , Republic of Korea
| | - Myeong Ok Kim
- Division of Life Science and Applied Life Science (BK21 Plus), College of Natural Sciences , Gyeongsang National University , Jinju 52828 , Republic of Korea
| | - Gwang Lee
- Department of Physiology , Ajou University School of Medicine , Suwon 443380 , Republic of Korea.,Institute of Molecular Science and Technology , Ajou University , Suwon 443721 , Republic of Korea
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216
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Ning Q, Zhao X, Bao L, Ma Z, Zhao X. Detecting Succinylation sites from protein sequences using ensemble support vector machine. BMC Bioinformatics 2018; 19:237. [PMID: 29940836 PMCID: PMC6016146 DOI: 10.1186/s12859-018-2249-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 06/14/2018] [Indexed: 12/14/2022] Open
Abstract
Background Lysine succinylation is a new kind of post-translational modification which plays a key role in protein conformation regulation and cellular function control. To understand the mechanism of succinylation profoundly, it is necessary to identify succinylation sites in proteins accurately. However, traditional methods, experimental approaches, are labor-intensive and time-consuming. Computational prediction methods have been proposed recent years, and they are popular because of their convenience and high speed. In this study, we developed a new method to predict succinylation sites in protein combining multiple features, including amino acid composition, binary encoding, physicochemical property and grey pseudo amino acid composition, with a feature selection scheme (information gain). And then, it was trained using SVM (Support Vector Machine) and an ensemble learning algorithm. Results The performance of this method was measured with an accuracy of 89.14% and a MCC (Matthew Correlation Coefficient) of 0.79 using 10-fold cross validation on training dataset and an accuracy of 84.5% and a MCC of 0.2 on independent dataset. Conclusions The conclusions made from this study can help to understand more of the succinylation mechanism. These results suggest that our method was very promising for predicting succinylation sites. The source code and data of this paper are freely available athttps://github.com/ningq669/PSuccE. Electronic supplementary material The online version of this article (10.1186/s12859-018-2249-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Qiao Ning
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - Xiaosa Zhao
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - Lingling Bao
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - Zhiqiang Ma
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China.
| | - Xiaowei Zhao
- Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun, 130117, China.
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217
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Xu L, Liang G, Shi S, Liao C. SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins. Int J Mol Sci 2018; 19:ijms19061773. [PMID: 29914044 PMCID: PMC6032279 DOI: 10.3390/ijms19061773] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Revised: 06/10/2018] [Accepted: 06/11/2018] [Indexed: 12/20/2022] Open
Abstract
Antioxidant proteins can be beneficial in disease prevention. More attention has been paid to the functionality of antioxidant proteins. Therefore, identifying antioxidant proteins is important for the study. In our work, we propose a computational method, called SeqSVM, for predicting antioxidant proteins based on their primary sequence features. The features are removed to reduce the redundancy by max relevance max distance method. Finally, the antioxidant proteins are identified by support vector machine (SVM). The experimental results demonstrated that our method performs better than existing methods, with the overall accuracy of 89.46%. Although a proposed computational method can attain an encouraging classification result, the experimental results are verified based on the biochemical approaches, such as wet biochemistry and molecular biology techniques.
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Affiliation(s)
- Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518060, China.
| | - Guangmin Liang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518060, China.
| | - Shuhua Shi
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518060, China.
| | - Changrui Liao
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
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218
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Khan YD, Rasool N, Hussain W, Khan SA, Chou KC. iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC. Anal Biochem 2018; 550:109-116. [DOI: 10.1016/j.ab.2018.04.021] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 04/19/2018] [Accepted: 04/21/2018] [Indexed: 01/29/2023]
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219
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Meher PK, Sahu TK, Mohanty J, Gahoi S, Purru S, Grover M, Rao AR. nifPred: Proteome-Wide Identification and Categorization of Nitrogen-Fixation Proteins of Diaztrophs Based on Composition-Transition-Distribution Features Using Support Vector Machine. Front Microbiol 2018; 9:1100. [PMID: 29896173 PMCID: PMC5986947 DOI: 10.3389/fmicb.2018.01100] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 05/08/2018] [Indexed: 11/13/2022] Open
Abstract
As inorganic nitrogen compounds are essential for basic building blocks of life (e.g., nucleotides and amino acids), the role of biological nitrogen-fixation (BNF) is indispensible. All nitrogen fixing microbes rely on the same nitrogenase enzyme for nitrogen reduction, which is in fact an enzyme complex consists of as many as 20 genes. However, the occurrence of six genes viz., nifB, nifD, nifE, nifH, nifK, and nifN has been proposed to be essential for a functional nitrogenase enzyme. Therefore, identification of these genes is important to understand the mechanism of BNF as well as to explore the possibilities for improving BNF from agricultural sustainability point of view. Further, though the computational tools are available for the annotation and phylogenetic analysis of nifH gene sequences alone, to the best of our knowledge no tool is available for the computational prediction of the above mentioned six categories of nitrogen-fixation (nif) genes or proteins. Thus, we proposed an approach, which is first of its kind for the computational identification of nif proteins encoded by the six categories of nif genes. Sequence-derived features were employed to map the input sequences into vectors of numeric observations that were subsequently fed to the support vector machine as input. Two types of classifier were constructed: (i) a binary classifier for classification of nif and non-nitrogen-fixation (non-nif) proteins, and (ii) a multi-class classifier for classification of six categories of nif proteins. Higher accuracies were observed for the combination of composition-transition-distribution (CTD) feature set and radial kernel, as compared to the other feature-kernel combinations. The overall accuracies were observed >90% in both binary and multi-class classifications. The developed approach further achieved >92% accuracy, while evaluated with blind (independent) test datasets. The developed approach also produced higher accuracy in identifying nif proteins, while evaluated using proteome-wide datasets of several species. Furthermore, we established a prediction server nifPred (http://webapp.cabgrid.res.in/nifPred) to assist the scientific community for proteome-wide identification of six categories of nif proteins. Besides, the source code of nifPred is also available at https://github.com/PrabinaMeher/nifPred. The developed web server is expected to supplement the transcriptional profiling and comparative genomics studies for the identification and functional annotation of genes related to BNF.
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Affiliation(s)
- Prabina K Meher
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Tanmaya K Sahu
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Jyotilipsa Mohanty
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India.,Department of Bioinformatics, Orissa University of Agriculture and Technology, Bhubaneswar, India
| | - Shachi Gahoi
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Supriya Purru
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Monendra Grover
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Atmakuri R Rao
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
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220
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Song J, Tang J, Guo F. Identification of Inhibitors of MMPS Enzymes via a Novel Computational Approach. Int J Biol Sci 2018; 14:863-871. [PMID: 29989088 PMCID: PMC6036742 DOI: 10.7150/ijbs.24588] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Accepted: 02/28/2018] [Indexed: 12/12/2022] Open
Abstract
Matrix metalloproteases (MMPs) are a family of zinc-dependent proteinases that play complex and diverse roles in metabolism, which are vital for physiological development. In this paper, we present a novel method to identify peptide binding to seven matrix metalloproteases. First, we propose a novel sampling criteria for constructing a training set for each new peptide motif. Then, we select nine physicochemical properties of amino acids and compute their auto-cross covariance to effectively extract features for both natural and non-natural amino acids. Finally, we adopt random forest to predict binding values of each peptide motif respectively with seven MMPs. Our method verifies on 1300 known peptide motifs binding to seven MMPs and achieved preeminent Pearson-product-moment correlation coefficient (PCC) and root mean squared error (RMSE) on all seven MMPs, especially of 0.9181 and 9.3827 on MMP-7. We predict binding values of 4000 peptide motifs and identify peptides preferentially bind to MMP-2 and MMP-7. We herein report 4 novel inhibitor candidates of Asp-Ile-Phe, Asp-Ile-Tyr, Asp-Ile-Lys and Hser-Gly-Phe with high potency and selectivity binding to MMP-2, as well as 6 novel inhibitor candidates of Chg-Ile-Ile, Chg-Ile-Leu, Chg-Ile-Glu, Chg-Ile-Met, Chg-Val-Ile and Chg-Val-Leu selectively binding to MMP-7. Our findings facilitate the identification of inhibitors with good potency as well as desirable selectivity, providing significant insights of candidate inhibitor drugs.
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Affiliation(s)
- Jian Song
- School of Computer Science and Technology, Tianjin University, Tianjin 300350, China
- Tianjin University Institute of Computational Biology, Tianjin University, Tianjin 300350, China
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Jijun Tang
- School of Computer Science and Technology, Tianjin University, Tianjin 300350, China
- Tianjin University Institute of Computational Biology, Tianjin University, Tianjin 300350, China
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
| | - Fei Guo
- School of Computer Science and Technology, Tianjin University, Tianjin 300350, China
- Tianjin University Institute of Computational Biology, Tianjin University, Tianjin 300350, China
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221
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DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC. J Theor Biol 2018; 452:22-34. [PMID: 29753757 DOI: 10.1016/j.jtbi.2018.05.006] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Revised: 04/21/2018] [Accepted: 05/04/2018] [Indexed: 11/21/2022]
Abstract
A DNA-binding protein (DNA-BP) is a protein that can bind and interact with a DNA. Identification of DNA-BPs using experimental methods is expensive as well as time consuming. As such, fast and accurate computational methods are sought for predicting whether a protein can bind with a DNA or not. In this paper, we focus on building a new computational model to identify DNA-BPs in an efficient and accurate way. Our model extracts meaningful information directly from the protein sequences, without any dependence on functional domain or structural information. After feature extraction, we have employed Random Forest (RF) model to rank the features. Afterwards, we have used Recursive Feature Elimination (RFE) method to extract an optimal set of features and trained a prediction model using Support Vector Machine (SVM) with linear kernel. Our proposed method, named as DNA-binding Protein Prediction model using Chou's general PseAAC (DPP-PseAAC), demonstrates superior performance compared to the state-of-the-art predictors on standard benchmark dataset. DPP-PseAAC achieves accuracy values of 93.21%, 95.91% and 77.42% for 10-fold cross-validation test, jackknife test and independent test respectively. The source code of DPP-PseAAC, along with relevant dataset and detailed experimental results, can be found at https://github.com/srautonu/DNABinding. A publicly accessible web interface has also been established at: http://77.68.43.135:8080/DPP-PseAAC/.
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222
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Zhang LQ, Li QZ. Estimating the effects of transcription factors binding and histone modifications on gene expression levels in human cells. Oncotarget 2018; 8:40090-40103. [PMID: 28454114 PMCID: PMC5522221 DOI: 10.18632/oncotarget.16988] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Accepted: 03/11/2017] [Indexed: 12/22/2022] Open
Abstract
Transcription factors and histone modifications are vital for the regulation of gene expression. Hence, to estimate the effects of transcription factors binding and histone modifications on gene expression, we construct a statistical model for the genome-wide 15 transcription factors binding data, 10 histone modifications profiles and DNase-I hypersensitivity data in three mammalian. Remarkably, our results show POLR2A and H3K36me3 can highly and consistently predict gene expression in three cell lines. And H3K4me3, H3K27me3 and H3K9ac are more reliable predictors than other histone modifications in human embryonic stem cells. Moreover, genome-wide statistical redundancies exist within and between transcription factors and histone modifications, and these phenomena may be caused by the regulation mechanism. In further study, we find that even though transcription factors and histone modifications offer similar effects on expression levels of genome-wide genes, the effects of transcription factors and histone modifications on predictive abilities are different for genes in independent biological processes.
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Affiliation(s)
- Lu-Qiang Zhang
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, China
| | - Qian-Zhong Li
- Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, China
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223
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Zhang S, Zhuang W, Xu Z. Prediction of DNase I hypersensitive sites in plant genome using multiple modes of pseudo components. Anal Biochem 2018; 549:149-156. [DOI: 10.1016/j.ab.2018.03.025] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2018] [Revised: 03/23/2018] [Accepted: 03/27/2018] [Indexed: 12/25/2022]
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224
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Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression. Oncotarget 2018; 8:49359-49369. [PMID: 28467816 PMCID: PMC5564774 DOI: 10.18632/oncotarget.17210] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Accepted: 03/30/2017] [Indexed: 01/24/2023] Open
Abstract
Toxicity evaluation is an extremely important process during drug development. It is usually initiated by experiments on animals, which is time-consuming and costly. To speed up such a process, a quantitative structure-activity relationship (QSAR) study was performed to develop a computational model for correlating the structures of 581 aromatic compounds with their aquatic toxicity to tetrahymena pyriformis. A set of 68 molecular descriptors derived solely from the structures of the aromatic compounds were calculated based on Gaussian 03, HyperChem 7.5, and TSAR V3.3. A comprehensive feature selection method, minimum Redundancy Maximum Relevance (mRMR)-genetic algorithm (GA)-support vector regression (SVR) method, was applied to select the best descriptor subset in QSAR analysis. The SVR method was employed to model the toxicity potency from a training set of 500 compounds. Five-fold cross-validation method was used to optimize the parameters of SVR model. The new SVR model was tested on an independent dataset of 81 compounds. Both high internal consistent and external predictive rates were obtained, indicating the SVR model is very promising to become an effective tool for fast detecting the toxicity.
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225
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Song J, Wang Y, Li F, Akutsu T, Rawlings ND, Webb GI, Chou KC. iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Brief Bioinform 2018. [DOI: 10.1093/bib/bby028 epub ahead of print].] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Jiangning Song
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia and ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia
| | - Yanan Wang
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
| | - Fuyi Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, 611-0011, Japan
| | - Neil D Rawlings
- EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Geoffrey I Webb
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, USA and Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
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226
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iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition. Oncotarget 2018; 8:41178-41188. [PMID: 28476023 PMCID: PMC5522291 DOI: 10.18632/oncotarget.17104] [Citation(s) in RCA: 146] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Accepted: 03/15/2017] [Indexed: 01/24/2023] Open
Abstract
Occurring at cytosine (C) of RNA, 5-methylcytosine (m5C) is an important post-transcriptional modification (PTCM). The modification plays significant roles in biological processes by regulating RNA metabolism in both eukaryotes and prokaryotes. It may also, however, cause cancers and other major diseases. Given an uncharacterized RNA sequence that contains many C residues, can we identify which one of them can be of m5C modification, and which one cannot? It is no doubt a crucial problem, particularly with the explosive growth of RNA sequences in the postgenomic age. Unfortunately, so far no user-friendly web-server whatsoever has been developed to address such a problem. To meet the increasingly high demand from most experimental scientists working in the area of drug development, we have developed a new predictor called iRNAm5C-PseDNC by incorporating ten types of physical-chemical properties into pseudo dinucleotide composition via the auto/cross-covariance approach. Rigorous jackknife tests show that its anticipated accuracy is quite high. For most experimental scientists’ convenience, a user-friendly web-server for the predictor has been provided at http://www.jci-bioinfo.cn/iRNAm5C-PseDNC along with a step-by-step user guide, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. It has not escaped our notice that the approach presented here can also be used to deal with many other problems in genome analysis.
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227
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iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition. J Theor Biol 2018; 442:11-21. [DOI: 10.1016/j.jtbi.2018.01.008] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 12/23/2017] [Accepted: 01/10/2018] [Indexed: 02/08/2023]
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228
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Manavalan B, Shin TH, Kim MO, Lee G. AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest. Front Pharmacol 2018; 9:276. [PMID: 29636690 PMCID: PMC5881105 DOI: 10.3389/fphar.2018.00276] [Citation(s) in RCA: 117] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 03/12/2018] [Indexed: 12/31/2022] Open
Abstract
The use of therapeutic peptides in various inflammatory diseases and autoimmune disorders has received considerable attention; however, the identification of anti-inflammatory peptides (AIPs) through wet-lab experimentation is expensive and often time consuming. Therefore, the development of novel computational methods is needed to identify potential AIP candidates prior to in vitro experimentation. In this study, we proposed a random forest (RF)-based method for predicting AIPs, called AIPpred (AIP predictor in primary amino acid sequences), which was trained with 354 optimal features. First, we systematically studied the contribution of individual composition [amino acid-, dipeptide composition (DPC), amino acid index, chain-transition-distribution, and physicochemical properties] in AIP prediction. Since the performance of the DPC-based model is significantly better than that of other composition-based models, we applied a feature selection protocol on this model and identified the optimal features. AIPpred achieved an area under the curve (AUC) value of 0.801 in a 5-fold cross-validation test, which was ∼2% higher than that of the control RF predictor trained with all DPC composition features, indicating the efficiency of the feature selection protocol. Furthermore, we evaluated the performance of AIPpred on an independent dataset, with results showing that our method outperformed an existing method, as well as 3 different machine learning methods developed in this study, with an AUC value of 0.814. These results indicated that AIPpred will be a useful tool for predicting AIPs and might efficiently assist the development of AIP therapeutics and biomedical research. AIPpred is freely accessible at www.thegleelab.org/AIPpred.
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Affiliation(s)
| | - Tae H Shin
- Department of Physiology, Ajou University School of Medicine, Suwon, South Korea.,Institute of Molecular Science and Technology, Ajou University, Suwon, South Korea
| | - Myeong O Kim
- Division of Life Science and Applied Life Science (BK21 Plus), College of Natural Sciences, Gyeongsang National University, Jinju, South Korea
| | - Gwang Lee
- Department of Physiology, Ajou University School of Medicine, Suwon, South Korea.,Institute of Molecular Science and Technology, Ajou University, Suwon, South Korea
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229
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Morshedian A, Razmara J, Lotfi S. A novel approach for protein structure prediction based on an estimation of distribution algorithm. Soft comput 2018. [DOI: 10.1007/s00500-018-3130-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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230
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Carrot-Zhang J, Majewski J. LoLoPicker: detecting low allelic-fraction variants from low-quality cancer samples. Oncotarget 2018; 8:37032-37040. [PMID: 28416765 PMCID: PMC5514890 DOI: 10.18632/oncotarget.16144] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Accepted: 12/27/2016] [Indexed: 11/25/2022] Open
Abstract
Introduction Although several programs are designed to identify variants with low allelic-fraction, further improvement is needed, especially to push the detection limit of low allelic-faction variants in low-quality, ”noisy” tumor samples. Results We developed LoLoPicker, an efficient tool dedicated to calling somatic variants from next-generation sequencing (NGS) data of tumor sample against the matched normal sample plus a user-defined control panel of additional normal samples. The control panel allows accurately estimating background error rate and therefore ensures high-accuracy mutation detection. Conclusions Compared to other methods, we showed a superior performance of LoLoPicker with significantly improved specificity. The algorithm of LoLoPicker is particularly useful for calling low allelic-fraction variants from low-quality cancer samples such as formalin-fixed and paraffin-embedded (FFPE) samples. Implementation and Availability: The main scripts are implemented in Python-2.7 and the package is released athttps://github.com/jcarrotzhang/LoLoPicker.
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Affiliation(s)
- Jian Carrot-Zhang
- Cancer Program, The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Jacek Majewski
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.,Genome Quebec Innovation Centre, Montreal, Quebec, Canada
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231
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Abstract
Cancer is a serious health issue worldwide. Traditional treatment methods focus on killing cancer cells by using anticancer drugs or radiation therapy, but the cost of these methods is quite high, and in addition there are side effects. With the discovery of anticancer peptides, great progress has been made in cancer treatment. For the purpose of prompting the application of anticancer peptides in cancer treatment, it is necessary to use computational methods to identify anticancer peptides (ACPs). In this paper, we propose a sequence-based model for identifying ACPs (SAP). In our proposed SAP, the peptide is represented by 400D features or 400D features with g-gap dipeptide features, and then the unrelated features are pruned using the maximum relevance-maximum distance method. The experimental results demonstrate that our model performs better than some existing methods. Furthermore, our model has also been extended to other classifiers, and the performance is stable compared with some state-of-the-art works.
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Affiliation(s)
- Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518060, China.
| | - Guangmin Liang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518060, China.
| | - Longjie Wang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518060, China.
| | - Changrui Liao
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
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232
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Usmani SS, Kumar R, Bhalla S, Kumar V, Raghava GPS. In Silico Tools and Databases for Designing Peptide-Based Vaccine and Drugs. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2018; 112:221-263. [PMID: 29680238 DOI: 10.1016/bs.apcsb.2018.01.006] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The prolonged conventional approaches of drug screening and vaccine designing prerequisite patience, vigorous effort, outrageous cost as well as additional manpower. Screening and experimentally validating thousands of molecules for a specific therapeutic property never proved to be an easy task. Similarly, traditional way of vaccination includes administration of either whole or attenuated pathogen, which raises toxicity and safety issues. Emergence of sequencing and recombinant DNA technology led to the epitope-based advanced vaccination concept, i.e., small peptides (epitope) can stimulate specific immune response. Advent of bioinformatics proved to be an adjunct in vaccine and drug designing. Genomic study of pathogens aid to identify and analyze the protective epitope. A number of in silico tools have been developed to design immunotherapy as well as peptide-based drugs in the last two decades. These tools proved to be a catalyst in drug and vaccine designing. This review solicits therapeutic peptide databases as well as in silico tools developed for designing peptide-based vaccine and drugs.
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Affiliation(s)
- Salman Sadullah Usmani
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India; Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Rajesh Kumar
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India; Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Sherry Bhalla
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Vinod Kumar
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India; Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Gajendra P S Raghava
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India; Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India.
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233
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Dullius A, Goettert MI, de Souza CFV. Whey protein hydrolysates as a source of bioactive peptides for functional foods – Biotechnological facilitation of industrial scale-up. J Funct Foods 2018. [DOI: 10.1016/j.jff.2017.12.063] [Citation(s) in RCA: 98] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
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234
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Qiu WR, Sun BQ, Xiao X, Xu ZC, Chou KC. iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC. Oncotarget 2018; 7:44310-44321. [PMID: 27322424 PMCID: PMC5190098 DOI: 10.18632/oncotarget.10027] [Citation(s) in RCA: 141] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 05/29/2016] [Indexed: 12/30/2022] Open
Abstract
Protein hydroxylation is a posttranslational modification (PTM), in which a CH group in Pro (P) or Lys (K) residue has been converted into a COH group, or a hydroxyl group (−OH) is converted into an organic compound. Closely associated with cellular signaling activities, this type of PTM is also involved in some major diseases, such as stomach cancer and lung cancer. Therefore, from the angles of both basic research and drug development, we are facing a challenging problem: for an uncharacterized protein sequence containing many residues of P or K, which ones can be hydroxylated, and which ones cannot? With the explosive growth of protein sequences in the post-genomic age, the problem has become even more urgent. To address such a problem, we have developed a predictor called iHyd-PseCp by incorporating the sequence-coupled information into the general pseudo amino acid composition (PseAAC) and introducing the “Random Forest” algorithm to operate the calculation. Rigorous jackknife tests indicated that the new predictor remarkably outperformed the existing state-of-the-art prediction method for the same purpose. For the convenience of most experimental scientists, a user-friendly web-server for iHyd-PseCp has been established at http://www.jci-bioinfo.cn/iHyd-PseCp, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.
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Affiliation(s)
- Wang-Ren Qiu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.,Department of Computer Science and Bond Life Science Center, University of Missouri, Columbia, MO, USA
| | - Bi-Qian Sun
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Xuan Xiao
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.,Gordon Life Science Institute, Boston, MA, USA
| | - Zhao-Chun Xu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA, USA.,Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia.,Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
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235
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Chen W, Feng P, Yang H, Ding H, Lin H, Chou KC. iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences. Oncotarget 2018; 8:4208-4217. [PMID: 27926534 PMCID: PMC5354824 DOI: 10.18632/oncotarget.13758] [Citation(s) in RCA: 199] [Impact Index Per Article: 33.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Accepted: 11/23/2016] [Indexed: 01/14/2023] Open
Abstract
Catalyzed by adenosine deaminase (ADAR), the adenosine to inosine (A-to-I) editing in RNA is not only involved in various important biological processes, but also closely associated with a series of major diseases. Therefore, knowledge about the A-to-I editing sites in RNA is crucially important for both basic research and drug development. Given an uncharacterized RNA sequence that contains many adenosine (A) residues, can we identify which one of them can be of A-to-I editing, and which one cannot? Unfortunately, so far no computational method whatsoever has been developed to address such an important problem based on the RNA sequence information alone. To fill this empty area, we have proposed a predictor called iRNA-AI by incorporating the chemical properties of nucleotides and their sliding occurrence density distribution along a RNA sequence into the general form of pseudo nucleotide composition (PseKNC). It has been shown by the rigorous jackknife test and independent dataset test that the performance of the proposed predictor is quite promising. For the convenience of most experimental scientists, a user-friendly web-server for iRNA-AI has been established at http://lin.uestc.edu.cn/server/iRNA-AI/, by which users can easily get their desired results without the need to go through the mathematical details.
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Affiliation(s)
- Wei Chen
- Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, Tangshan, China.,Gordon Life Science Institute, Belmont, Massachusetts, United States of America
| | - Pengmian Feng
- Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Hui Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Gordon Life Science Institute, Belmont, Massachusetts, United States of America
| | - Kuo-Chen Chou
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Gordon Life Science Institute, Belmont, Massachusetts, United States of America
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236
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iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition. Oncotarget 2018; 7:69783-69793. [PMID: 27626500 PMCID: PMC5342515 DOI: 10.18632/oncotarget.11975] [Citation(s) in RCA: 157] [Impact Index Per Article: 26.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Accepted: 09/06/2016] [Indexed: 02/07/2023] Open
Abstract
The initiation of replication is an extremely important process in DNA life cycle. Given an uncharacterized DNA sequence, can we identify where its origin of replication (ORI) is located? It is no doubt a fundamental problem in genome analysis. Particularly, with the rapid development of genome sequencing technology that results in a huge amount of sequence data, it is highly desired to develop computational methods for rapidly and effectively identifying the ORIs in these genomes. Unfortunately, by means of the existing computational methods, such as sequence alignment or kmer strategies, it could hardly achieve decent success rates. To address this problem, we developed a predictor called “iOri-Human”. Rigorous jackknife tests have shown that its overall accuracy and stability in identifying human ORIs are over 75% and 50%, respectively. In the predictor, it is through the pseudo nucleotide composition (an extension of pseudo amino acid composition) that 96 physicochemical properties for the 16 possible constituent dinucleotides have been incorporated to reflect the global sequence patterns in DNA as well as its local sequence patterns. Moreover, a user-friendly web-server for iOri-Human has been established at http://lin.uestc.edu.cn/server/iOri-Human.html, by which users can easily get their desired results without the need to through the complicated mathematics involved.
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237
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A Two-Step Feature Selection Method to Predict Cancerlectins by Multiview Features and Synthetic Minority Oversampling Technique. BIOMED RESEARCH INTERNATIONAL 2018; 2018:9364182. [PMID: 29568772 PMCID: PMC5820548 DOI: 10.1155/2018/9364182] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 12/25/2017] [Accepted: 12/26/2017] [Indexed: 11/18/2022]
Abstract
Cancerlectins have an inhibitory effect on the growth of cancer cells and are currently being employed as therapeutic agents. The accurate identification of the cancerlectins should provide insight into the molecular mechanisms of cancers. In this study, a new computational method based on the RF (Random Forest) algorithm is proposed for further improving the performance of identifying cancerlectins. Hybrid feature space before feature selection is developed by combining different individual feature spaces, CTD (Composition, Transition, and Distribution), PseAAC (Pseudo Amino Acid Composition), PSSM (Position-Specific Scoring Matrix), and disorder. The SMOTE (Synthetic Minority Oversampling Technique) is applied to solve the imbalanced data problem. To reduce feature redundancy and computation complexity, we propose a two-step feature selection process to select informative features. A 5-fold cross-validation technique is used for the evaluation of various prediction strategies. The proposed method achieves a sensitivity of 0.779, a specificity of 0.717, an accuracy of 0.748, and an MCC (Matthew's Correlation Coefficient) of 0.497. The prediction results are also compared with other existing methods on the same dataset using 5-fold cross-validation. The comparison results demonstrate the high effectiveness of our method for predicting cancerlectins.
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238
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Qiu WR, Xiao X, Xu ZC, Chou KC. iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier. Oncotarget 2018; 7:51270-51283. [PMID: 27323404 PMCID: PMC5239474 DOI: 10.18632/oncotarget.9987] [Citation(s) in RCA: 132] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2016] [Accepted: 05/23/2016] [Indexed: 11/26/2022] Open
Abstract
Protein phosphorylation is a posttranslational modification (PTM or PTLM), where a phosphoryl group is added to the residue(s) of a protein molecule. The most commonly phosphorylated amino acids occur at serine (S), threonine (T), and tyrosine (Y). Protein phosphorylation plays a significant role in a wide range of cellular processes; meanwhile its dysregulation is also involved with many diseases. Therefore, from the angles of both basic research and drug development, we are facing a challenging problem: for an uncharacterized protein sequence containing many residues of S, T, or Y, which ones can be phosphorylated, and which ones cannot? To address this problem, we have developed a predictor called iPhos-PseEn by fusing four different pseudo component approaches (amino acids’ disorder scores, nearest neighbor scores, occurrence frequencies, and position weights) into an ensemble classifier via a voting system. Rigorous cross-validations indicated that the proposed predictor remarkably outperformed its existing counterparts. For the convenience of most experimental scientists, a user-friendly web-server for iPhos-PseEn has been established at http://www.jci-bioinfo.cn/iPhos-PseEn, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.
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Affiliation(s)
- Wang-Ren Qiu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.,Department of Computer Science and Bond Life Science Center, University of Missouri, Columbia, MO, USA
| | - Xuan Xiao
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.,Gordon Life Science Institute, Boston, MA, USA
| | - Zhao-Chun Xu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA, USA.,Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia.,Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
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239
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Feng P, Yang H, Ding H, Lin H, Chen W, Chou KC. iDNA6mA-PseKNC: Identifying DNA N 6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics 2018; 111:96-102. [PMID: 29360500 DOI: 10.1016/j.ygeno.2018.01.005] [Citation(s) in RCA: 188] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 12/24/2017] [Accepted: 01/07/2018] [Indexed: 11/29/2022]
Abstract
N6-methyladenine (6mA) is one kind of post-replication modification (PTM or PTRM) occurring in a wide range of DNA sequences. Accurate identification of its sites will be very helpful for revealing the biological functions of 6mA, but it is time-consuming and expensive to determine them by experiments alone. Unfortunately, so far, no bioinformatics tool is available to do so. To fill in such an empty area, we have proposed a novel predictor called iDNA6mA-PseKNC that is established by incorporating nucleotide physicochemical properties into Pseudo K-tuple Nucleotide Composition (PseKNC). It has been observed via rigorous cross-validations that the predictor's sensitivity (Sn), specificity (Sp), accuracy (Acc), and stability (MCC) are 93%, 100%, 96%, and 0.93, respectively. For the convenience of most experimental scientists, a user-friendly web server for iDNA6mA-PseKNC has been established at http://lin-group.cn/server/iDNA6mA-PseKNC, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.
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Affiliation(s)
- Pengmian Feng
- Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, School of Public Health, North China University of Science and Technology, Tangshan 063000, China
| | - Hui Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; Gordon Life Science Institute, Boston, MA 02478, USA.
| | - Wei Chen
- Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, Tangshan 063000, China; Gordon Life Science Institute, Boston, MA 02478, USA.
| | - Kuo-Chen Chou
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; Gordon Life Science Institute, Boston, MA 02478, USA.
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240
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Xiao X, Ye HX, Liu Z, Jia JH, Chou KC. iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition. Oncotarget 2018; 7:34180-9. [PMID: 27147572 PMCID: PMC5085147 DOI: 10.18632/oncotarget.9057] [Citation(s) in RCA: 109] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Accepted: 04/09/2016] [Indexed: 11/25/2022] Open
Abstract
DNA replication, occurring in all living organisms and being the basis for biological inheritance, is the process of producing two identical replicas from one original DNA molecule. To in-depth understand such an important biological process and use it for developing new strategy against genetics diseases, the knowledge of duplication origin sites in DNA is indispensible. With the explosive growth of DNA sequences emerging in the postgenomic age, it is highly desired to develop high throughput tools to identify these regions purely based on the sequence information alone. In this paper, by incorporating the dinucleotide position-specific propensity information into the general pseudo nucleotide composition and using the random forest classifier, a new predictor called iROS-gPseKNC was proposed. Rigorously cross-validations have indicated that the proposed predictor is significantly better than the best existing method in sensitivity, specificity, overall accuracy, and stability. Furthermore, a user-friendly web-server for iROS-gPseKNC has been established at http://www.jci-bioinfo.cn/iROS-gPseKNC, by which users can easily get their desired results without the need to bother the complicated mathematics, which were presented just for the integrity of the methodology itself.
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Affiliation(s)
- Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, 333403, China.,Information School, ZheJiang Textile and Fashion College, NingBo, 315211, China.,Gordon Life Science Institute, Boston, Massachusetts, 02478, USA
| | - Han-Xiao Ye
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, 333403, China
| | - Zi Liu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Jian-Hua Jia
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, 333403, China
| | - Kuo-Chen Chou
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, 21589, Saudi Arabia.,Gordon Life Science Institute, Boston, Massachusetts, 02478, USA
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241
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Meher PK, Sahu TK, Gahoi S, Rao AR. ir-HSP: Improved Recognition of Heat Shock Proteins, Their Families and Sub-types Based On g-Spaced Di-peptide Features and Support Vector Machine. Front Genet 2018; 8:235. [PMID: 29379521 PMCID: PMC5770798 DOI: 10.3389/fgene.2017.00235] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 12/27/2017] [Indexed: 12/24/2022] Open
Abstract
Heat shock proteins (HSPs) play a pivotal role in cell growth and variability. Since conventional approaches are expensive and voluminous protein sequence information is available in the post-genomic era, development of an automated and accurate computational tool is highly desirable for prediction of HSPs, their families and sub-types. Thus, we propose a computational approach for reliable prediction of all these components in a single framework and with higher accuracy as well. The proposed approach achieved an overall accuracy of ~84% in predicting HSPs, ~97% in predicting six different families of HSPs, and ~94% in predicting four types of DnaJ proteins, with bench mark datasets. The developed approach also achieved higher accuracy as compared to most of the existing approaches. For easy prediction of HSPs by experimental scientists, a user friendly web server ir-HSP is made freely accessible at http://cabgrid.res.in:8080/ir-hsp. The ir-HSP was further evaluated for proteome-wide identification of HSPs by using proteome datasets of eight different species, and ~50% of the predicted HSPs in each species were found to be annotated with InterPro HSP families/domains. Thus, the developed computational method is expected to supplement the currently available approaches for prediction of HSPs, to the extent of their families and sub-types.
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Affiliation(s)
- Prabina K Meher
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Tanmaya K Sahu
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Shachi Gahoi
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Atmakuri R Rao
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
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242
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Jia J, Liu Z, Xiao X, Liu B, Chou KC. iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC. Oncotarget 2018; 7:34558-70. [PMID: 27153555 PMCID: PMC5085176 DOI: 10.18632/oncotarget.9148] [Citation(s) in RCA: 149] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2016] [Accepted: 04/09/2016] [Indexed: 01/22/2023] Open
Abstract
Carbonylation is a posttranslational modification (PTM or PTLM), where a carbonyl group is added to lysine (K), proline (P), arginine (R), and threonine (T) residue of a protein molecule. Carbonylation plays an important role in orchestrating various biological processes but it is also associated with many diseases such as diabetes, chronic lung disease, Parkinson's disease, Alzheimer's disease, chronic renal failure, and sepsis. Therefore, from the angles of both basic research and drug development, we are facing a challenging problem: for an uncharacterized protein sequence containing many residues of K, P, R, or T, which ones can be carbonylated, and which ones cannot? To address this problem, we have developed a predictor called iCar-PseCp by incorporating the sequence-coupled information into the general pseudo amino acid composition, and balancing out skewed training dataset by Monte Carlo sampling to expand positive subset. Rigorous target cross-validations on a same set of carbonylation-known proteins indicated that the new predictor remarkably outperformed its existing counterparts. For the convenience of most experimental scientists, a user-friendly web-server for iCar-PseCp has been established at http://www.jci-bioinfo.cn/iCar-PseCp, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. It has not escaped our notice that the formulation and approach presented here can also be used to analyze many other problems in computational proteomics.
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Affiliation(s)
- Jianhua Jia
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403 China.,Gordon Life Science Institute, Boston, MA 02478, USA
| | - Zi Liu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403 China.,Gordon Life Science Institute, Boston, MA 02478, USA
| | - Bingxiang Liu
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403 China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, USA.,Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia
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243
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pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Genomics 2018; 110:50-58. [DOI: 10.1016/j.ygeno.2017.08.005] [Citation(s) in RCA: 180] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 08/10/2017] [Accepted: 08/11/2017] [Indexed: 11/22/2022]
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244
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Bi-PSSM: Position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins. J Theor Biol 2017; 435:116-124. [DOI: 10.1016/j.jtbi.2017.09.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2017] [Revised: 09/12/2017] [Accepted: 09/15/2017] [Indexed: 02/08/2023]
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245
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Carvalho TFM, Silva JCF, Calil IP, Fontes EPB, Cerqueira FR. Rama: a machine learning approach for ribosomal protein prediction in plants. Sci Rep 2017; 7:16273. [PMID: 29176736 PMCID: PMC5701237 DOI: 10.1038/s41598-017-16322-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 10/30/2017] [Indexed: 12/04/2022] Open
Abstract
Ribosomal proteins (RPs) play a fundamental role within all type of cells, as they are major components of ribosomes, which are essential for translation of mRNAs. Furthermore, these proteins are involved in various physiological and pathological processes. The intrinsic biological relevance of RPs motivated advanced studies for the identification of unrevealed RPs. In this work, we propose a new computational method, termed Rama, for the prediction of RPs, based on machine learning techniques, with a particular interest in plants. To perform an effective classification, Rama uses a set of fundamental attributes of the amino acid side chains and applies a two-step procedure to classify proteins with unknown function as RPs. The evaluation of the resultant predictive models showed that Rama could achieve mean sensitivity, precision, and specificity of 0.91, 0.91, and 0.82, respectively. Furthermore, a list of proteins that have no annotation in Phytozome v.10, and are annotated as RPs in Phytozome v.12, were correctly classified by our models. Additional computational experiments have also shown that Rama presents high accuracy to differentiate ribosomal proteins from RNA-binding proteins. Finally, two novel proteins of Arabidopsis thaliana were validated in biological experiments. Rama is freely available at http://inctipp.bioagro.ufv.br:8080/Rama .
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Affiliation(s)
| | - José Cleydson F Silva
- Computer Science Department, Universidade Federal de Viçosa, 36570-900, Minas Gerais, Brazil
- National Institute of Science and Technology in Plant-Pest Interactions/BIOAGRO, Universidade Federal de Viçosa, 36570-900, Minas Gerais, Brazil
| | - Iara Pinheiro Calil
- National Institute of Science and Technology in Plant-Pest Interactions/BIOAGRO, Universidade Federal de Viçosa, 36570-900, Minas Gerais, Brazil
| | - Elizabeth Pacheco Batista Fontes
- National Institute of Science and Technology in Plant-Pest Interactions/BIOAGRO, Universidade Federal de Viçosa, 36570-900, Minas Gerais, Brazil.
| | - Fabio Ribeiro Cerqueira
- Computer Science Department, Universidade Federal de Viçosa, 36570-900, Minas Gerais, Brazil.
- Department of Production Engineering, Universidade Federal Fluminense, Petrópolis, 25650-050, Rio de Janeiro, Brazil.
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246
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Cheng X, Xiao X, Chou KC. pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics 2017; 34:1448-1456. [DOI: 10.1093/bioinformatics/btx711] [Citation(s) in RCA: 127] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 10/31/2017] [Indexed: 01/19/2023] Open
Affiliation(s)
- Xiang Cheng
- Computer Science, Jingdezhen Ceramic Institute, Jingdezhen, China
- Computational Biology, Gordon Life Science Institute, Boston, MA, USA
| | - Xuan Xiao
- Computer Science, Jingdezhen Ceramic Institute, Jingdezhen, China
- Computational Biology, Gordon Life Science Institute, Boston, MA, USA
| | - Kuo-Chen Chou
- Computer Science, Jingdezhen Ceramic Institute, Jingdezhen, China
- Computational Biology, Gordon Life Science Institute, Boston, MA, USA
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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247
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Qiu WR, Sun BQ, Tang H, Huang J, Lin H. Identify and analysis crotonylation sites in histone by using support vector machines. Artif Intell Med 2017; 83:75-81. [DOI: 10.1016/j.artmed.2017.02.007] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2016] [Revised: 01/25/2017] [Indexed: 10/20/2022]
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248
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A novel hierarchical selective ensemble classifier with bioinformatics application. Artif Intell Med 2017; 83:82-90. [DOI: 10.1016/j.artmed.2017.02.005] [Citation(s) in RCA: 124] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Revised: 02/09/2017] [Accepted: 02/10/2017] [Indexed: 10/20/2022]
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249
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Xu C, Ge L, Zhang Y, Dehmer M, Gutman I. Computational prediction of therapeutic peptides based on graph index. J Biomed Inform 2017; 75:63-69. [DOI: 10.1016/j.jbi.2017.09.011] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 09/14/2017] [Accepted: 09/25/2017] [Indexed: 11/25/2022]
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250
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Liu H, Ren G, Hu H, Zhang L, Ai H, Zhang W, Zhao Q. LPI-NRLMF: lncRNA-protein interaction prediction by neighborhood regularized logistic matrix factorization. Oncotarget 2017; 8:103975-103984. [PMID: 29262614 PMCID: PMC5732780 DOI: 10.18632/oncotarget.21934] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Accepted: 08/28/2017] [Indexed: 01/08/2023] Open
Abstract
LncRNA-protein interactions play important roles in many important cellular processes including signaling, transcriptional regulation, and even the generation and progression of complex diseases. However, experimental methods for determining proteins bound by a specific lncRNA remain expensive, difficult and time-consuming, and only a few theoretical approaches are available for predicting potential lncRNA-protein associations. In this study, we developed a novel matrix factorization computational approach to uncover lncRNA-protein relationships, namely lncRNA-protein interactions prediction by neighborhood regularized logistic matrix factorization (LPI-NRLMF). Moreover, it is a semi-supervised and does not need negative samples. As a result, new model obtained reliable performance in the leave-one-out cross validation (the AUC of 0.9025 and AUPR of 0.6924), which significantly improved the prediction performance of previous models. Furthermore, the case study demonstrated that many lncRNA-protein interactions predicted by our method can be successfully confirmed by experiments. It is anticipated that LPI-NRLMF could serve as a useful resource for potential lncRNA-protein association identification.
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Affiliation(s)
- Hongsheng Liu
- School of Life Science, Liaoning University, Shenyang, 110036, China.,Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang, 110036, China.,Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, 110036, China
| | - Guofei Ren
- School of Information, Liaoning University, Shenyang, 110036, China
| | - Huan Hu
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Li Zhang
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Haixin Ai
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Wen Zhang
- School of Computer, Wuhan University, Wuhan, 430072, China
| | - Qi Zhao
- Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang, 110036, China.,School of Mathematics, Liaoning University, Shenyang, 110036, China
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