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Purohit K, Reddy N, Sunna A. Exploring the Potential of Bioactive Peptides: From Natural Sources to Therapeutics. Int J Mol Sci 2024; 25:1391. [PMID: 38338676 PMCID: PMC10855437 DOI: 10.3390/ijms25031391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/18/2024] [Accepted: 01/21/2024] [Indexed: 02/12/2024] Open
Abstract
Bioactive peptides, specific protein fragments with positive health effects, are gaining traction in drug development for advantages like enhanced penetration, low toxicity, and rapid clearance. This comprehensive review navigates the intricate landscape of peptide science, covering discovery to functional characterization. Beginning with a peptidomic exploration of natural sources, the review emphasizes the search for novel peptides. Extraction approaches, including enzymatic hydrolysis, microbial fermentation, and specialized methods for disulfide-linked peptides, are extensively covered. Mass spectrometric analysis techniques for data acquisition and identification, such as liquid chromatography, capillary electrophoresis, untargeted peptide analysis, and bioinformatics, are thoroughly outlined. The exploration of peptide bioactivity incorporates various methodologies, from in vitro assays to in silico techniques, including advanced approaches like phage display and cell-based assays. The review also discusses the structure-activity relationship in the context of antimicrobial peptides (AMPs), ACE-inhibitory peptides (ACEs), and antioxidative peptides (AOPs). Concluding with key findings and future research directions, this interdisciplinary review serves as a comprehensive reference, offering a holistic understanding of peptides and their potential therapeutic applications.
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Affiliation(s)
- Kruttika Purohit
- School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia;
- Australian Research Council Industrial Transformation Training Centre for Facilitated Advancement of Australia’s Bioactives (FAAB), Sydney, NSW 2109, Australia;
| | - Narsimha Reddy
- Australian Research Council Industrial Transformation Training Centre for Facilitated Advancement of Australia’s Bioactives (FAAB), Sydney, NSW 2109, Australia;
- School of Science, Parramatta Campus, Western Sydney University, Penrith, NSW 2751, Australia
| | - Anwar Sunna
- School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia;
- Australian Research Council Industrial Transformation Training Centre for Facilitated Advancement of Australia’s Bioactives (FAAB), Sydney, NSW 2109, Australia;
- Biomolecular Discovery Research Centre, Macquarie University, Sydney, NSW 2109, Australia
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2
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Asim MN, Ibrahim MA, Imran Malik M, Dengel A, Ahmed S. Circ-LocNet: A Computational Framework for Circular RNA Sub-Cellular Localization Prediction. Int J Mol Sci 2022; 23:ijms23158221. [PMID: 35897818 PMCID: PMC9329987 DOI: 10.3390/ijms23158221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/15/2022] [Accepted: 07/20/2022] [Indexed: 02/04/2023] Open
Abstract
Circular ribonucleic acids (circRNAs) are novel non-coding RNAs that emanate from alternative splicing of precursor mRNA in reversed order across exons. Despite the abundant presence of circRNAs in human genes and their involvement in diverse physiological processes, the functionality of most circRNAs remains a mystery. Like other non-coding RNAs, sub-cellular localization knowledge of circRNAs has the aptitude to demystify the influence of circRNAs on protein synthesis, degradation, destination, their association with different diseases, and potential for drug development. To date, wet experimental approaches are being used to detect sub-cellular locations of circular RNAs. These approaches help to elucidate the role of circRNAs as protein scaffolds, RNA-binding protein (RBP) sponges, micro-RNA (miRNA) sponges, parental gene expression modifiers, alternative splicing regulators, and transcription regulators. To complement wet-lab experiments, considering the progress made by machine learning approaches for the determination of sub-cellular localization of other non-coding RNAs, the paper in hand develops a computational framework, Circ-LocNet, to precisely detect circRNA sub-cellular localization. Circ-LocNet performs comprehensive extrinsic evaluation of 7 residue frequency-based, residue order and frequency-based, and physio-chemical property-based sequence descriptors using the five most widely used machine learning classifiers. Further, it explores the performance impact of K-order sequence descriptor fusion where it ensembles similar as well dissimilar genres of statistical representation learning approaches to reap the combined benefits. Considering the diversity of statistical representation learning schemes, it assesses the performance of second-order, third-order, and going all the way up to seventh-order sequence descriptor fusion. A comprehensive empirical evaluation of Circ-LocNet over a newly developed benchmark dataset using different settings reveals that standalone residue frequency-based sequence descriptors and tree-based classifiers are more suitable to predict sub-cellular localization of circular RNAs. Further, K-order heterogeneous sequence descriptors fusion in combination with tree-based classifiers most accurately predict sub-cellular localization of circular RNAs. We anticipate this study will act as a rich baseline and push the development of robust computational methodologies for the accurate sub-cellular localization determination of novel circRNAs.
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Affiliation(s)
- Muhammad Nabeel Asim
- German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany; (M.A.I.); (A.D.); (S.A.)
- Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany
- Correspondence:
| | - Muhammad Ali Ibrahim
- German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany; (M.A.I.); (A.D.); (S.A.)
- Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Muhammad Imran Malik
- School of Computer Science & Electrical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan;
| | - Andreas Dengel
- German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany; (M.A.I.); (A.D.); (S.A.)
- Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Sheraz Ahmed
- German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany; (M.A.I.); (A.D.); (S.A.)
- DeepReader GmbH, Trippstadter Str. 122, 67663 Kaiserslautern, Germany
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Yan K, Wen J, Liu JX, Xu Y, Liu B. Protein Fold Recognition by Combining Support Vector Machines and Pairwise Sequence Similarity Scores. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2008-2016. [PMID: 31940548 DOI: 10.1109/tcbb.2020.2966450] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Protein fold recognition is one of the most essential steps for protein structure prediction, aiming to classify proteins into known protein folds. There are two main computational approaches: one is the template-based method based on the alignment scores between query-template protein pairs and the other is the machine learning method based on the feature representation and classifier. These two approaches have their own advantages and disadvantages. Can we combine these methods to establish more accurate predictors for protein fold recognition? In this study, we made an initial attempt and proposed two novel algorithms: TSVM-fold and ESVM-fold. TSVM-fold was based on the Support Vector Machines (SVMs), which utilizes a set of pairwise sequence similarity scores generated by three complementary template-based methods, including HHblits, SPARKS-X, and DeepFR. These scores measured the global relationships between query sequences and templates. The comprehensive features of the attributes of the sequences were fed into the SVMs for the prediction. Then the TSVM-fold was further combined with the HHblits algorithm so as to improve its generalization ability. The combined method is called ESVM-fold. Experimental results in two rigorous benchmark datasets (LE and YK datasets) showed that the proposed methods outperform some state-of-the-art methods, indicating that the TSVM-fold and ESVM-fold are efficient predictors for protein fold recognition.
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4
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Perpetuo L, Klein J, Ferreira R, Guedes S, Amado F, Leite-Moreira A, Silva AMS, Thongboonkerd V, Vitorino R. How can artificial intelligence be used for peptidomics? Expert Rev Proteomics 2021; 18:527-556. [PMID: 34343059 DOI: 10.1080/14789450.2021.1962303] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
INTRODUCTION Peptidomics is an emerging field of omics sciences using advanced isolation, analysis, and computational techniques that enable qualitative and quantitative analyses of various peptides in biological samples. Peptides can act as useful biomarkers and as therapeutic molecules for diseases. AREAS COVERED The use of therapeutic peptides can be predicted quickly and efficiently using data-driven computational methods, particularly artificial intelligence (AI) approach. Various AI approaches are useful for peptide-based drug discovery, such as support vector machine, random forest, extremely randomized trees, and other more recently developed deep learning methods. AI methods are relatively new to the development of peptide-based therapies, but these techniques already become essential tools in protein science by dissecting novel therapeutic peptides and their functions (Figure 1).[Figure: see text]. EXPERT OPINION Researchers have shown that AI models can facilitate the development of peptidomics and selective peptide therapies in the field of peptide science. Biopeptide prediction is important for the discovery and development of successful peptide-based drugs. Due to their ability to predict therapeutic roles based on sequence details, many AI-dependent prediction tools have been developed (Figure 1).
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Affiliation(s)
- Luís Perpetuo
- iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro
| | - Julie Klein
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1297, Institute of Cardiovascular and Metabolic Disease, Université Toulouse III, Toulouse, France
| | - Rita Ferreira
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Sofia Guedes
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Francisco Amado
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Adelino Leite-Moreira
- UnIC, Departamento de Cirurgia e Fisiologia, Faculdade de Medicina da Universidade do Porto, Porto
| | - Artur M S Silva
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Visith Thongboonkerd
- Medical Proteomics Unit, Office for Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Rui Vitorino
- iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro.,LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro.,UnIC, Departamento de Cirurgia e Fisiologia, Faculdade de Medicina da Universidade do Porto, Porto
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5
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Zhu W, Guo Y, Zou Q. Prediction of presynaptic and postsynaptic neurotoxins based on feature extraction. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:5943-5958. [PMID: 34517517 DOI: 10.3934/mbe.2021297] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
A neurotoxin is essentially a protein that mainly acts on the nervous system; it has a selective toxic effect on the central nervous system and neuromuscular nodes, can cause muscle paralysis and respiratory paralysis, and has strong lethality. According to their principle of action, neurotoxins are divided into presynaptic neurotoxins and postsynaptic neurotoxins. Correctly identifying presynaptic and postsynaptic nerve toxins provides important clues for future drug development and the discovery of drug targets. Therefore, a predictive model, Neu_LR, was constructed in this paper. The monoMonokGap method was used to extract the frequency characteristics of presynaptic and postsynaptic neurotoxin sequences and carry out feature selection, then, based on the important features obtained after dimensionality reduction, the prediction model Neu_LR was constructed using a logistic regression algorithm, and ten-fold cross-validation and independent test set validation were used. The final accuracy rates were 99.6078 and 94.1176%, respectively, which proved that the Neu_LR model had good predictive performance and robustness, and could meet the prediction requirements of presynaptic and postsynaptic neurotoxins. The data and source code of the model can be freely download from https://github.com/gyx123681/.
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Affiliation(s)
- Wen Zhu
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Yuxin Guo
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
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6
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Xu L, Liang G, Chen B, Tan X, Xiang H, Liao C. A Computational Method for the Identification of Endolysins and Autolysins. Protein Pept Lett 2020; 27:329-336. [PMID: 31577192 DOI: 10.2174/0929866526666191002104735] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 06/27/2019] [Accepted: 09/03/2019] [Indexed: 12/21/2022]
Abstract
BACKGROUND Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. OBJECTIVE In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. METHODS We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. RESULTS Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. CONCLUSION The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.
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Affiliation(s)
- Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Guangmin Liang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Baowen Chen
- School of Software, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Xu Tan
- School of Software, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Huaikun Xiang
- School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Changrui Liao
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen, China
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7
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Zhan XK, You ZH, Li LP, Li Y, Wang Z, Pan J. Using Random Forest Model Combined With Gabor Feature to Predict Protein-Protein Interaction From Protein Sequence. Evol Bioinform Online 2020; 16:1176934320934498. [PMID: 32655275 PMCID: PMC7328357 DOI: 10.1177/1176934320934498] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 05/20/2020] [Indexed: 12/12/2022] Open
Abstract
Protein-protein interactions (PPIs) play a crucial role in the life cycles of
living cells. Thus, it is important to understand the underlying mechanisms of
PPIs. Although many high-throughput technologies have generated large amounts of
PPI data in different organisms, the experiments for detecting PPIs are still
costly and time-consuming. Therefore, novel computational methods are urgently
needed for predicting PPIs. For this reason, developing a new computational
method for predicting PPIs is drawing more and more attention. In this study, we
proposed a novel computational method based on texture feature of protein
sequence for predicting PPIs. Especially, the Gabor feature is used to extract
texture feature and protein evolutionary information from Position-Specific
Scoring Matrix, which is generated by Position-Specific Iterated Basic Local
Alignment Search Tool. Then, random forest–based classifiers are used to infer
the protein interactions. When performed on PPI data sets of yeast,
human, and Helicobacter pylori, we obtained good
results with average accuracies of 92.10%, 97.03%, and 86.45%, respectively. To
better evaluate the proposed method, we compared Gabor feature, Discrete Cosine
Transform, and Local Phase Quantization. Our results show that the proposed
method is both feasible and stable and the Gabor feature descriptor is reliable
in extracting protein sequence information. Furthermore, additional experiments
have been conducted to predict PPIs of other 4 species data sets. The promising
results indicate that our proposed method is both powerful and robust.
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Affiliation(s)
- Xin-Ke Zhan
- School of Information Engineering, Xijing University, Xi'an, China
| | - Zhu-Hong You
- School of Information Engineering, Xijing University, Xi'an, China
| | - Li-Ping Li
- School of Information Engineering, Xijing University, Xi'an, China
| | - Yang Li
- School of Information Engineering, Xijing University, Xi'an, China
| | - Zheng Wang
- School of Information Engineering, Xijing University, Xi'an, China
| | - Jie Pan
- School of Information Engineering, Xijing University, Xi'an, China
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8
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Abstract
During the last three decades or so, many efforts have been made to study the protein cleavage
sites by some disease-causing enzyme, such as HIV (Human Immunodeficiency Virus) protease
and SARS (Severe Acute Respiratory Syndrome) coronavirus main proteinase. It has become increasingly
clear <i>via</i> this mini-review that the motivation driving the aforementioned studies is quite wise,
and that the results acquired through these studies are very rewarding, particularly for developing peptide
drugs.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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9
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10
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Liu B, Gao X, Zhang H. BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches. Nucleic Acids Res 2020; 47:e127. [PMID: 31504851 PMCID: PMC6847461 DOI: 10.1093/nar/gkz740] [Citation(s) in RCA: 222] [Impact Index Per Article: 55.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 08/07/2019] [Accepted: 08/17/2019] [Indexed: 12/14/2022] Open
Abstract
As the first web server to analyze various biological sequences at sequence level based on machine learning approaches, many powerful predictors in the field of computational biology have been developed with the assistance of the BioSeq-Analysis. However, the BioSeq-Analysis can be only applied to the sequence-level analysis tasks, preventing its applications to the residue-level analysis tasks, and an intelligent tool that is able to automatically generate various predictors for biological sequence analysis at both residue level and sequence level is highly desired. In this regard, we decided to publish an important updated server covering a total of 26 features at the residue level and 90 features at the sequence level called BioSeq-Analysis2.0 (http://bliulab.net/BioSeq-Analysis2.0/), by which the users only need to upload the benchmark dataset, and the BioSeq-Analysis2.0 can generate the predictors for both residue-level analysis and sequence-level analysis tasks. Furthermore, the corresponding stand-alone tool was also provided, which can be downloaded from http://bliulab.net/BioSeq-Analysis2.0/download/. To the best of our knowledge, the BioSeq-Analysis2.0 is the first tool for generating predictors for biological sequence analysis tasks at residue level. Specifically, the experimental results indicated that the predictors developed by BioSeq-Analysis2.0 can achieve comparable or even better performance than the existing state-of-the-art predictors.
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Affiliation(s)
- Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.,Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China
| | - Xin Gao
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China
| | - Hanyu Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China
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11
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Liu B. BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches. Brief Bioinform 2020; 20:1280-1294. [PMID: 29272359 DOI: 10.1093/bib/bbx165] [Citation(s) in RCA: 188] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 11/08/2017] [Indexed: 01/07/2023] Open
Abstract
With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems is how to computationally analyze their structures and functions. Machine learning techniques are playing key roles in this field. Typically, predictors based on machine learning techniques contain three main steps: feature extraction, predictor construction and performance evaluation. Although several Web servers and stand-alone tools have been developed to facilitate the biological sequence analysis, they only focus on individual step. In this regard, in this study a powerful Web server called BioSeq-Analysis (http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/) has been proposed to automatically complete the three main steps for constructing a predictor. The user only needs to upload the benchmark data set. BioSeq-Analysis can generate the optimized predictor based on the benchmark data set, and the performance measures can be reported as well. Furthermore, to maximize user's convenience, its stand-alone program was also released, which can be downloaded from http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/download/, and can be directly run on Windows, Linux and UNIX. Applied to three sequence analysis tasks, experimental results showed that the predictors generated by BioSeq-Analysis even outperformed some state-of-the-art methods. It is anticipated that BioSeq-Analysis will become a useful tool for biological sequence analysis.
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12
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Zheng H, Yang H, Gong D, Mai L, Qiu X, Chen L, Su X, Wei R, Zeng Z. Progress in the Mechanism and Clinical Application of Cilostazol. Curr Top Med Chem 2020; 19:2919-2936. [PMID: 31763974 DOI: 10.2174/1568026619666191122123855] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 07/27/2019] [Accepted: 08/02/2019] [Indexed: 12/20/2022]
Abstract
Cilostazol is a unique platelet inhibitor that has been used clinically for more than 20 years. As a phosphodiesterase type III inhibitor, cilostazol is capable of reversible inhibition of platelet aggregation and vasodilation, has antiproliferative effects, and is widely used in the treatment of peripheral arterial disease, cerebrovascular disease, percutaneous coronary intervention, etc. This article briefly reviews the pharmacological mechanisms and clinical application of cilostazol.
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Affiliation(s)
- Huilei Zheng
- Department of Medical Examination & Health Management, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.,Guangxi Key Laboratory of Precision Medicine in Cardio-cerebrovascular Diseases Control and Prevention,Nanning, Guangxi, China.,Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning, Guangxi, China
| | - Hua Yang
- Guangxi Key Laboratory of Precision Medicine in Cardio-cerebrovascular Diseases Control and Prevention,Nanning, Guangxi, China.,Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning, Guangxi, China.,Department of Critical Care Medicine, Second People's Hospital of Nanning, Nanning, Guangxi, China
| | - Danping Gong
- Guangxi Key Laboratory of Precision Medicine in Cardio-cerebrovascular Diseases Control and Prevention,Nanning, Guangxi, China.,Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning, Guangxi, China.,Elderly Cardiology Ward, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Lanxian Mai
- Guangxi Key Laboratory of Precision Medicine in Cardio-cerebrovascular Diseases Control and Prevention,Nanning, Guangxi, China.,Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning, Guangxi, China.,Disciplinary Construction Office, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Xiaoling Qiu
- Guangxi Key Laboratory of Precision Medicine in Cardio-cerebrovascular Diseases Control and Prevention,Nanning, Guangxi, China.,Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning, Guangxi, China
| | - Lidai Chen
- Guangxi Key Laboratory of Precision Medicine in Cardio-cerebrovascular Diseases Control and Prevention,Nanning, Guangxi, China.,Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning, Guangxi, China
| | - Xiaozhou Su
- Guangxi Key Laboratory of Precision Medicine in Cardio-cerebrovascular Diseases Control and Prevention,Nanning, Guangxi, China.,Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning, Guangxi, China
| | - Ruoqi Wei
- Department of Computer Science and Engineering, University of Bridgeport,126 Park Ave, BRIDGEPORT, CT 06604, United States
| | - Zhiyu Zeng
- Guangxi Key Laboratory of Precision Medicine in Cardio-cerebrovascular Diseases Control and Prevention,Nanning, Guangxi, China.,Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning, Guangxi, China.,Elderly Cardiology Ward, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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13
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Some illuminating remarks on molecular genetics and genomics as well as drug development. Mol Genet Genomics 2020; 295:261-274. [PMID: 31894399 DOI: 10.1007/s00438-019-01634-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 12/05/2019] [Indexed: 02/07/2023]
Abstract
Facing the explosive growth of biological sequences unearthed in the post-genomic age, one of the most important but also most difficult problems in computational biology is how to express a biological sequence with a discrete model or a vector, but still keep it with considerable sequence-order information or its special pattern. To deal with such a challenging problem, the ideas of "pseudo amino acid components" and "pseudo K-tuple nucleotide composition" have been proposed. The ideas and their approaches have further stimulated the birth for "distorted key theory", "wenxing diagram", and substantially strengthening the power in treating the multi-label systems, as well as the establishment of the famous "5-steps rule". All these logic developments are quite natural that are very useful not only for theoretical scientists but also for experimental scientists in conducting genetics/genomics analysis and drug development. Presented in this review paper are also their future perspectives; i.e., their impacts will become even more significant and propounding.
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14
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Shao YT, Liu XX, Lu Z, Chou KC. pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning. ACTA ACUST UNITED AC 2020. [DOI: 10.4236/ns.2020.127042] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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15
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Shao Y, Chou KC. pLoc_Deep-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by Deep Learning. ACTA ACUST UNITED AC 2020. [DOI: 10.4236/ns.2020.126034] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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16
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Agüero-Chapin G, Galpert D, Molina-Ruiz R, Ancede-Gallardo E, Pérez-Machado G, De la Riva GA, Antunes A. Graph Theory-Based Sequence Descriptors as Remote Homology Predictors. Biomolecules 2019; 10:E26. [PMID: 31878100 PMCID: PMC7022958 DOI: 10.3390/biom10010026] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/16/2019] [Accepted: 12/18/2019] [Indexed: 12/23/2022] Open
Abstract
Alignment-free (AF) methodologies have increased in popularity in the last decades as alternative tools to alignment-based (AB) algorithms for performing comparative sequence analyses. They have been especially useful to detect remote homologs within the twilight zone of highly diverse gene/protein families and superfamilies. The most popular alignment-free methodologies, as well as their applications to classification problems, have been described in previous reviews. Despite a new set of graph theory-derived sequence/structural descriptors that have been gaining relevance in the detection of remote homology, they have been omitted as AF predictors when the topic is addressed. Here, we first go over the most popular AF approaches used for detecting homology signals within the twilight zone and then bring out the state-of-the-art tools encoding graph theory-derived sequence/structure descriptors and their success for identifying remote homologs. We also highlight the tendency of integrating AF features/measures with the AB ones, either into the same prediction model or by assembling the predictions from different algorithms using voting/weighting strategies, for improving the detection of remote signals. Lastly, we briefly discuss the efforts made to scale up AB and AF features/measures for the comparison of multiple genomes and proteomes. Alongside the achieved experiences in remote homology detection by both the most popular AF tools and other less known ones, we provide our own using the graphical-numerical methodologies, MARCH-INSIDE, TI2BioP, and ProtDCal. We also present a new Python-based tool (SeqDivA) with a friendly graphical user interface (GUI) for delimiting the twilight zone by using several similar criteria.
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Affiliation(s)
- Guillermin Agüero-Chapin
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos s/n 4450-208 Porto, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
| | - Deborah Galpert
- Departamento de Ciencia de la Computación. Universidad Central ¨Marta Abreu¨ de Las Villas (UCLV), Santa Clara 54830, Cuba;
| | - Reinaldo Molina-Ruiz
- Centro de Bioactivos Químicos (CBQ), Universidad Central ¨Marta Abreu¨ de Las Villas (UCLV), Santa Clara 54830, Cuba;
| | - Evys Ancede-Gallardo
- Programa de Doctorado en Fisicoquímica Molecular, Facultad de Ciencias Exactas, Universidad Andrés Bello, Av. República 239, Santiago 8370146, Chile;
| | - Gisselle Pérez-Machado
- EpiDisease S.L. Spin-Off of Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 46980 Valencia, Spain;
| | - Gustavo A. De la Riva
- Laboratorio de Biotecnología Aplicada S. de R.L. de C.V., GRECA Inc., Carretera La Piedad-Carapán, km 3.5, La Piedad, Michoacán 59300, Mexico;
- Tecnológico Nacional de México, Instituto Tecnológico de la Piedad, Av. Ricardo Guzmán Romero, Santa Fe, La Piedad de Cavadas, Michoacán 59370, Mexico
| | - Agostinho Antunes
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos s/n 4450-208 Porto, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
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17
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Chen Z, Wang X, Gao P, Liu H, Song B. Predicting Disease Related microRNA Based on Similarity and Topology. Cells 2019; 8:cells8111405. [PMID: 31703479 PMCID: PMC6912199 DOI: 10.3390/cells8111405] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 10/31/2019] [Accepted: 11/05/2019] [Indexed: 12/19/2022] Open
Abstract
It is known that many diseases are caused by mutations or abnormalities in microRNA (miRNA). The usual method to predict miRNA disease relationships is to build a high-quality similarity network of diseases and miRNAs. All unobserved associations are ranked by their similarity scores, such that a higher score indicates a greater probability of a potential connection. However, this approach does not utilize information within the network. Therefore, in this study, we propose a machine learning method, called STIM, which uses network topology information to predict disease-miRNA associations. In contrast to the conventional approach, STIM constructs features according to information on similarity and topology in networks and then uses a machine learning model to predict potential associations. To verify the reliability and accuracy of our method, we compared STIM to other classical algorithms. The results of fivefold cross validation demonstrated that STIM outperforms many existing methods, particularly in terms of the area under the curve. In addition, the top 30 candidate miRNAs recommended by STIM in a case study of lung neoplasm have been confirmed in previous experiments, which proved the validity of the method.
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Affiliation(s)
- Zhihua Chen
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China
| | - Xinke Wang
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Peng Gao
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hongju Liu
- College of Information Technology and Computer Science, University of the Cordilleras, Baguio 2600, Philippines
| | - Bosheng Song
- School of Information Science and Engineering, Hunan University, Changsha 410082, China
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18
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Lan J, Liu Z, Liao C, Merkler DJ, Han Q, Li J. A Study for Therapeutic Treatment against Parkinson's Disease via Chou's 5-steps Rule. Curr Top Med Chem 2019; 19:2318-2333. [PMID: 31629395 DOI: 10.2174/1568026619666191019111528] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 08/05/2019] [Accepted: 08/22/2019] [Indexed: 11/22/2022]
Abstract
The enzyme L-DOPA decarboxylase (DDC), also called aromatic-L-amino-acid decarboxylase, catalyzes the biosynthesis of dopamine, serotonin, and trace amines. Its deficiency or perturbations in expression result in severe motor dysfunction or a range of neurodegenerative and psychiatric disorders. A DDC substrate, L-DOPA, combined with an inhibitor of the enzyme is still the most effective treatment for symptoms of Parkinson's disease. In this review, we provide an update regarding the structures, functions, and inhibitors of DDC, particularly with regards to the treatment of Parkinson's disease. This information will provide insight into the pharmacological treatment of Parkinson's disease.
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Affiliation(s)
- Jianqiang Lan
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Life and Pharmaceutical Sciences, Hainan University, Haikou, Hainan 570228, China
| | - Zhongqiang Liu
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Life and Pharmaceutical Sciences, Hainan University, Haikou, Hainan 570228, China
| | - Chenghong Liao
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Life and Pharmaceutical Sciences, Hainan University, Haikou, Hainan 570228, China
| | - David J Merkler
- Department of Chemistry, University of South Florida, Tampa, FL, 33620, United States
| | - Qian Han
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Life and Pharmaceutical Sciences, Hainan University, Haikou, Hainan 570228, China
| | - Jianyong Li
- Department of Biochemistry, Virginia Tech, Blacksburg, VA 24061, United States
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19
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Chou KC. Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses. Int J Pept Res Ther 2019. [DOI: 10.1007/s10989-019-09910-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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20
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21
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Abstract
Background:DNA-binding proteins, binding to DNA, widely exist in living cells, participating in many cell activities. They can participate some DNA-related cell activities, for instance DNA replication, transcription, recombination, and DNA repair.Objective:Given the importance of DNA-binding proteins, studies for predicting the DNA-binding proteins have been a popular issue over the past decades. In this article, we review current machine-learning methods which research on the prediction of DNA-binding proteins through feature representation methods, classifiers, measurements, dataset and existing web server.Method:The prediction methods of DNA-binding protein can be divided into two types, based on amino acid composition and based on protein structure. In this article, we accord to the two types methods to introduce the application of machine learning in DNA-binding proteins prediction.Results:Machine learning plays an important role in the classification of DNA-binding proteins, and the result is better. The best ACC is above 80%.Conclusion:Machine learning can be widely used in many aspects of biological information, especially in protein classification. Some issues should be considered in future work. First, the relationship between the number of features and performance must be explored. Second, many features are used to predict DNA-binding proteins and propose solutions for high-dimensional spaces.
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Affiliation(s)
- Kaiyang Qu
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Leyi Wei
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- College of Intelligence and Computing, Tianjin University, Tianjin, China
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22
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Qu K, Wei L, Yu J, Wang C. Identifying Plant Pentatricopeptide Repeat Coding Gene/Protein Using Mixed Feature Extraction Methods. FRONTIERS IN PLANT SCIENCE 2019; 9:1961. [PMID: 30687359 PMCID: PMC6335366 DOI: 10.3389/fpls.2018.01961] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 12/17/2018] [Indexed: 05/04/2023]
Abstract
Motivation: Pentatricopeptide repeat (PPR) is a triangular pentapeptide repeat domain that plays a vital role in plant growth. In this study, we seek to identify PPR coding genes and proteins using a mixture of feature extraction methods. We use four single feature extraction methods focusing on the sequence, physical, and chemical properties as well as the amino acid composition, and mix the features. The Max-Relevant-Max-Distance (MRMD) technique is applied to reduce the feature dimension. Classification uses the random forest, J48, and naïve Bayes with 10-fold cross-validation. Results: Combining two of the feature extraction methods with the random forest classifier produces the highest area under the curve of 0.9848. Using MRMD to reduce the dimension improves this metric for J48 and naïve Bayes, but has little effect on the random forest results. Availability and Implementation: The webserver is available at: http://server.malab.cn/MixedPPR/index.jsp.
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Affiliation(s)
- Kaiyang Qu
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Leyi Wei
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jiantao Yu
- College of Information Engineering, North-West A&F University, Yangling, China
| | - Chunyu Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States
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23
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Liu B, Chen J, Guo M, Wang X. Protein Remote Homology Detection and Fold Recognition Based on Sequence-Order Frequency Matrix. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:292-300. [PMID: 29990004 DOI: 10.1109/tcbb.2017.2765331] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Protein remote homology detection and fold recognition are two critical tasks for the studies of protein structures and functions. Currently, the profile-based methods achieve the state-of-the-art performance in these fields. However, the widely used sequence profiles, like position-specific frequency matrix (PSFM) and position-specific scoring matrix (PSSM), ignore the sequence-order effects along protein sequence. In this study, we have proposed a novel profile, called sequence-order frequency matrix (SOFM), to extract the sequence-order information of neighboring residues from multiple sequence alignment (MSA). Combined with two profile feature extraction approaches, top-n-grams and the Smith-Waterman algorithm, the SOFMs are applied to protein remote homology detection and fold recognition, and two predictors called SOFM-Top and SOFM-SW are proposed. Experimental results show that SOFM contains more information content than other profiles, and these two predictors outperform other state-of-the-art methods. It is anticipated that SOFM will become a very useful profile in the studies of protein structures and functions.
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24
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Liu B, Jiang S, Zou Q. HITS-PR-HHblits: protein remote homology detection by combining PageRank and Hyperlink-Induced Topic Search. Brief Bioinform 2018; 21:298-308. [PMID: 30403770 DOI: 10.1093/bib/bby104] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 10/03/2018] [Accepted: 10/04/2018] [Indexed: 11/12/2022] Open
Abstract
As one of the most important fundamental problems in protein sequence analysis, protein remote homology detection is critical for both theoretical research (protein structure and function studies) and real world applications (drug design). Although several computational predictors have been proposed, their detection performance is still limited. In this study, we treat protein remote homology detection as a document retrieval task, where the proteins are considered as documents and its aim is to find the highly related documents with the query documents in a database. A protein similarity network was constructed based on the true labels of proteins in the database, and the query proteins were then connected into the network based on the similarity scores calculated by three ranking methods, including PSI-BLAST, Hmmer and HHblits. The PageRank algorithm and Hyperlink-Induced Topic Search (HITS) algorithm were respectively performed on this network to move the homologous proteins of query proteins to the neighbors of the query proteins in the network. Finally, PageRank and HITS algorithms were combined, and a predictor called HITS-PR-HHblits was proposed to further improve the predictive performance. Tested on the SCOP and SCOPe benchmark datasets, the experimental results showed that the proposed protocols outperformed other state-of-the-art methods. For the convenience of the most experimental scientists, a web server for HITS-PR-HHblits was established at http://bioinformatics.hitsz.edu.cn/HITS-PR-HHblits, by which the users can easily get the results without the need to go through the mathematical details. The HITS-PR-HHblits predictor is a protocol for protein remote homology detection using different sets of programs, which will become a very useful computational tool for proteome analysis.
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Affiliation(s)
- Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China
| | - Shuangyan Jiang
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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25
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Contreras-Torres E. Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC. J Theor Biol 2018; 454:139-145. [DOI: 10.1016/j.jtbi.2018.05.033] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 05/23/2018] [Accepted: 05/28/2018] [Indexed: 11/24/2022]
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26
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Li LP, Wang YB, You ZH, Li Y, An JY. PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation. Int J Mol Sci 2018; 19:ijms19041029. [PMID: 29596363 PMCID: PMC5979371 DOI: 10.3390/ijms19041029] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Revised: 03/20/2018] [Accepted: 03/21/2018] [Indexed: 11/30/2022] Open
Abstract
Protein–protein interactions (PPI) are key to protein functions and regulations within the cell cycle, DNA replication, and cellular signaling. Therefore, detecting whether a pair of proteins interact is of great importance for the study of molecular biology. As researchers have become aware of the importance of computational methods in predicting PPIs, many techniques have been developed for performing this task computationally. However, there are few technologies that really meet the needs of their users. In this paper, we develop a novel and efficient sequence-based method for predicting PPIs. The evolutionary features are extracted from the position-specific scoring matrix (PSSM) of protein. The features are then fed into a robust relevance vector machine (RVM) classifier to distinguish between the interacting and non-interacting protein pairs. In order to verify the performance of our method, five-fold cross-validation tests are performed on the Saccharomyces cerevisiae dataset. A high accuracy of 94.56%, with 94.79% sensitivity at 94.36% precision, was obtained. The experimental results illustrated that the proposed approach can extract the most significant features from each protein sequence and can be a bright and meaningful tool for the research of proteomics.
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Affiliation(s)
- Li-Ping Li
- Department of Information Engineering, Xijing University, Xi'an 710123, China.
| | - Yan-Bin Wang
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China.
| | - Zhu-Hong You
- Department of Information Engineering, Xijing University, Xi'an 710123, China.
| | - Yang Li
- Department of Information Engineering, Xijing University, Xi'an 710123, China.
| | - Ji-Yong An
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, China.
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27
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PSBinder: A Web Service for Predicting Polystyrene Surface-Binding Peptides. BIOMED RESEARCH INTERNATIONAL 2017; 2017:5761517. [PMID: 29445741 PMCID: PMC5763211 DOI: 10.1155/2017/5761517] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 11/02/2017] [Indexed: 11/18/2022]
Abstract
Polystyrene surface-binding peptides (PSBPs) are useful as affinity tags to build a highly effective ELISA system. However, they are also a quite common type of target-unrelated peptides (TUPs) in the panning of phage-displayed random peptide library. As TUP, PSBP will mislead the analysis of panning results if not identified. Therefore, it is necessary to find a way to quickly and easily foretell if a peptide is likely to be a PSBP or not. In this paper, we describe PSBinder, a predictor based on SVM. To our knowledge, it is the first web server for predicting PSBP. The SVM model was built with the feature of optimized dipeptide composition and 87.02% (MCC = 0.74; AUC = 0.91) of peptides were correctly classified by fivefold cross-validation. PSBinder can be used to exclude highly possible PSBP from biopanning results or to find novel candidates for polystyrene affinity tags. Either way, it is valuable for biotechnology community.
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28
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Liu B, Yang F, Huang DS, Chou KC. iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics 2017; 34:33-40. [DOI: 10.1093/bioinformatics/btx579] [Citation(s) in RCA: 235] [Impact Index Per Article: 33.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 09/13/2017] [Indexed: 12/30/2022] Open
Affiliation(s)
- Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- The Gordon Life Science Institute, Boston, MA, USA
| | - Fan Yang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - De-Shuang Huang
- Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China
| | - Kuo-Chen Chou
- The Gordon Life Science Institute, Boston, MA, USA
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
- Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia
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29
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Wei L, Tang J, Zou Q. Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2016.06.026] [Citation(s) in RCA: 196] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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30
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Das JK, Pal Choudhury P. Chemical property based sequence characterization of PpcA and its homolog proteins PpcB-E: A mathematical approach. PLoS One 2017; 12:e0175031. [PMID: 28362850 PMCID: PMC5376323 DOI: 10.1371/journal.pone.0175031] [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: 12/16/2016] [Accepted: 03/20/2017] [Indexed: 11/19/2022] Open
Abstract
Periplasmic c7 type cytochrome A (PpcA) protein is determined in Geobacter sulfurreducens along with its other four homologs (PpcB-E). From the crystal structure viewpoint the observation emerges that PpcA protein can bind with Deoxycholate (DXCA), while its other homologs do not. But it is yet to be established with certainty the reason behind this from primary protein sequence information. This study is primarily based on primary protein sequence analysis through the chemical basis of embedded amino acids. Firstly, we look for the chemical group specific score of amino acids. Along with this, we have developed a new methodology for the phylogenetic analysis based on chemical group dissimilarities of amino acids. This new methodology is applied to the cytochrome c7 family members and pinpoint how a particular sequence is differing with others. Secondly, we build a graph theoretic model on using amino acid sequences which is also applied to the cytochrome c7 family members and some unique characteristics and their domains are highlighted. Thirdly, we search for unique patterns as subsequences which are common among the group or specific individual member. In all the cases, we are able to show some distinct features of PpcA that emerges PpcA as an outstanding protein compared to its other homologs, resulting towards its binding with deoxycholate. Similarly, some notable features for the structurally dissimilar protein PpcD compared to the other homologs are also brought out. Further, the five members of cytochrome family being homolog proteins, they must have some common significant features which are also enumerated in this study.
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Affiliation(s)
- Jayanta Kumar Das
- Applied Statistics Unit, Indian Statistical Institute, 203 B.T Road, Kolkata-700108, West Bengal, India
| | - Pabitra Pal Choudhury
- Applied Statistics Unit, Indian Statistical Institute, 203 B.T Road, Kolkata-700108, West Bengal, India
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31
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OOgenesis_Pred: A sequence-based method for predicting oogenesis proteins by six different modes of Chou's pseudo amino acid composition. J Theor Biol 2017; 414:128-136. [DOI: 10.1016/j.jtbi.2016.11.028] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Revised: 11/25/2016] [Accepted: 11/29/2016] [Indexed: 12/22/2022]
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32
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Liu B, Wu H, Chou KC. Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences. ACTA ACUST UNITED AC 2017. [DOI: 10.4236/ns.2017.94007] [Citation(s) in RCA: 91] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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33
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Wei L, Bowen Z, Zhiyong C, Gao X, Liao M. Exploring local discriminative information from evolutionary profiles for cytokine–receptor interaction prediction. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.02.078] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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34
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Chen J, Guo M, Wang X, Liu B. A comprehensive review and comparison of different computational methods for protein remote homology detection. Brief Bioinform 2016; 19:231-244. [DOI: 10.1093/bib/bbw108] [Citation(s) in RCA: 81] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Indexed: 01/02/2023] Open
Affiliation(s)
- Junjie Chen
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Mingyue Guo
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Xiaolong Wang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
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35
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Tong J, Li L, Bai M, Li K. A New Descriptor of Amino Acids-SVGER and its Applications in Peptide QSAR. Mol Inform 2016; 36. [PMID: 27739658 DOI: 10.1002/minf.201501023] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Accepted: 09/27/2016] [Indexed: 11/10/2022]
Abstract
In the study of peptide quantitative structure activity relationship (QSAR), a new descriptor of amino acids (SVGER) was calculated. It was applied in two peptides which are angiotensin converting enzyme inhibitors and bitter tasting threshold of di-peptide. QSAR models were built by stepwise multiple regression-multiple linear regression (SMR-MLR) and stepwise multiple regression-partial least square regression (SMR-PLS). In the SMR-MLR models for angiotensin converting enzyme inhibitors, the squared cross-validation correlation coefficient (QLOO2 ) was 0.907, squared correlation coefficient between predicted and observed activities (Rcum2 ) was 0.977 and external multiple correlation coefficient (Qext2 ) was 0.867. The corresponding data for the bitter tasting threshold of di-peptide were 0.802, 0.966, 0.719. While in the SMR-PLS model, QLOO2 , Rcum2 and Qext2 were 0.804, 0.915, 0.858 for angiotensin converting enzyme inhibitors and 0.782, 0.881, 0.747 for bitter tasting threshold of di-peptide. Our results showed that descriptor SVGER can afford good account of relationships between activity and structure of peptide drugs.
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Affiliation(s)
- Jianbo Tong
- Shaanxi University of Science & Technology, Xi'an, PR China
| | - Lingxiao Li
- Shaanxi University of Science & Technology, Xi'an, PR China
| | - Min Bai
- Shaanxi University of Science & Technology, Xi'an, PR China
| | - Kangnan Li
- Shaanxi University of Science & Technology, Xi'an, PR China
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36
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Qiu WR, Zheng QS, Sun BQ, Xiao X. Multi-iPPseEvo: A Multi-label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou′s General PseAAC via Grey System Theory. Mol Inform 2016; 36. [DOI: 10.1002/minf.201600085] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Accepted: 09/07/2016] [Indexed: 01/19/2023]
Affiliation(s)
- Wang-Ren Qiu
- Computer Department; Jingdezhen Ceramic Institute; Jingdezhen 333403 China
- Department of Computer Science; University of Missouri; Columbia, MO USA
- Bond Life Science Center; University of Missouri; Columbia, MO USA
| | - Quan-Shu Zheng
- Computer Department; Jingdezhen Ceramic Institute; Jingdezhen 333403 China
| | - Bi-Qian Sun
- Computer Department; Jingdezhen Ceramic Institute; Jingdezhen 333403 China
| | - Xuan Xiao
- Computer Department; Jingdezhen Ceramic Institute; Jingdezhen 333403 China
- Gordon Life Science Institute; Boston, Massachusetts 02478 United States of America
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Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology. Int J Genomics 2016; 2016:7604641. [PMID: 27478823 PMCID: PMC4961832 DOI: 10.1155/2016/7604641] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Revised: 05/24/2016] [Accepted: 06/14/2016] [Indexed: 01/03/2023] Open
Abstract
Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.
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Huang HH. An ensemble distance measure of k-mer and Natural Vector for the phylogenetic analysis of multiple-segmented viruses. J Theor Biol 2016; 398:136-44. [DOI: 10.1016/j.jtbi.2016.03.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Revised: 02/25/2016] [Accepted: 03/02/2016] [Indexed: 11/29/2022]
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Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences. BIOMED RESEARCH INTERNATIONAL 2016; 2016:4783801. [PMID: 27314023 PMCID: PMC4893571 DOI: 10.1155/2016/4783801] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2016] [Accepted: 04/12/2016] [Indexed: 01/08/2023]
Abstract
We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments on Yeast and Human datasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the Yeast dataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.
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40
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Chen J, Liu B, Huang D. Protein Remote Homology Detection Based on an Ensemble Learning Approach. BIOMED RESEARCH INTERNATIONAL 2016; 2016:5813645. [PMID: 27294123 PMCID: PMC4875977 DOI: 10.1155/2016/5813645] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Accepted: 02/21/2016] [Indexed: 12/15/2022]
Abstract
Protein remote homology detection is one of the central problems in bioinformatics. Although some computational methods have been proposed, the problem is still far from being solved. In this paper, an ensemble classifier for protein remote homology detection, called SVM-Ensemble, was proposed with a weighted voting strategy. SVM-Ensemble combined three basic classifiers based on different feature spaces, including Kmer, ACC, and SC-PseAAC. These features consider the characteristics of proteins from various perspectives, incorporating both the sequence composition and the sequence-order information along the protein sequences. Experimental results on a widely used benchmark dataset showed that the proposed SVM-Ensemble can obviously improve the predictive performance for the protein remote homology detection. Moreover, it achieved the best performance and outperformed other state-of-the-art methods.
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Affiliation(s)
- Junjie Chen
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
| | - Bingquan Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Dong Huang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
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41
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Zhao X, Ning Q, Ai M, Chai H, Yang G. Identification of S-glutathionylation sites in species-specific proteins by incorporating five sequence-derived features into the general pseudo-amino acid composition. J Theor Biol 2016; 398:96-102. [PMID: 27025952 DOI: 10.1016/j.jtbi.2016.03.030] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2015] [Revised: 02/29/2016] [Accepted: 03/17/2016] [Indexed: 11/25/2022]
Abstract
As a selective and reversible protein post-translational modification, S-glutathionylation generates mixed disulfides between glutathione (GSH) and cysteine residues, and plays an important role in regulating protein activity, stability, and redox regulation. To fully understand S-glutathionylation mechanisms, identification of substrates and specific S-Glutathionylated sites is crucial. Experimental identification of S-glutathionylated sites is labor-intensive and time consuming, so establishing an effective computational method is much desirable due to their convenient and fast speed. Therefore, in this study, a new bioinformatics tool named SSGlu (Species-Specific identification of Protein S-glutathionylation Sites) was developed to identify species-specific protein S-glutathionylated sites, utilizing support vector machines that combine multiple sequence-derived features with a two-step feature selection. By 5-fold cross validation, the performance of SSGlu was measured with an AUC of 0.8105 and 0.8041 for Homo sapiens and Mus musculus, respectively. Additionally, SSGlu was compared with the existing methods, and the higher MCC and AUC of SSGlu demonstrated that SSGlu was very promising to predict S-glutathionylated sites. Furthermore, a site-specific analysis showed that S-glutathionylation intimately correlated with the features derived from its surrounding sites. The conclusions derived from this study might help to understand more of the S-glutathionylation mechanism and guide the related experimental validation. For public access, SSGlu is freely accessible at http://59.73.198.144:8080/SSGlu/.
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Affiliation(s)
- Xiaowei Zhao
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China.
| | - Qiao Ning
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
| | - Meiyue Ai
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
| | - Haiting Chai
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
| | - Guifu Yang
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
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42
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Ding H, Liang ZY, Guo FB, Huang J, Chen W, Lin H. Predicting bacteriophage proteins located in host cell with feature selection technique. Comput Biol Med 2016; 71:156-61. [PMID: 26945463 DOI: 10.1016/j.compbiomed.2016.02.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Revised: 02/18/2016] [Accepted: 02/18/2016] [Indexed: 10/22/2022]
Abstract
A bacteriophage is a virus that can infect a bacterium. The fate of an infected bacterium is determined by the bacteriophage proteins located in the host cell. Thus, reliably identifying bacteriophage proteins located in the host cell is extremely important to understand their functions and discover potential anti-bacterial drugs. Thus, in this paper, a computational method was developed to recognize bacteriophage proteins located in host cells based only on their amino acid sequences. The analysis of variance (ANOVA) combined with incremental feature selection (IFS) was proposed to optimize the feature set. Using a jackknife cross-validation, our method can discriminate between bacteriophage proteins located in a host cell and the bacteriophage proteins not located in a host cell with a maximum overall accuracy of 84.2%, and can further classify bacteriophage proteins located in host cell cytoplasm and in host cell membranes with a maximum overall accuracy of 92.4%. To enhance the value of the practical applications of the method, we built a web server called PHPred (〈http://lin.uestc.edu.cn/server/PHPred〉). We believe that the PHPred will become a powerful tool to study bacteriophage proteins located in host cells and to guide related drug discovery.
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Affiliation(s)
- Hui Ding
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Zhi-Yong Liang
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Feng-Biao Guo
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jian Huang
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Chen
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China; Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China.
| | - Hao Lin
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China.
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43
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Liu B, Fang L. WITHDRAWN: Identification of microRNA precursor based on gapped n-tuple structure status composition kernel. Comput Biol Chem 2016:S1476-9271(16)30036-6. [PMID: 26935400 DOI: 10.1016/j.compbiolchem.2016.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2016] [Accepted: 02/01/2016] [Indexed: 10/22/2022]
Abstract
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.
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Affiliation(s)
- Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China; Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
| | - Longyun Fang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
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44
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Ramirez-Malule H, Restrepo A, Cardona W, Junne S, Neubauer P, Rios-Estepa R. Inversion of the stereochemical configuration (3S, 5S)-clavaminic acid into (3R, 5R)-clavulanic acid: A computationally-assisted approach based on experimental evidence. J Theor Biol 2016; 395:40-50. [PMID: 26835563 DOI: 10.1016/j.jtbi.2016.01.028] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Revised: 01/19/2016] [Accepted: 01/21/2016] [Indexed: 11/17/2022]
Abstract
Clavulanic acid (CA), a potent inhibitor of β-lactamase enzymes, is produced by Streptomyces clavuligerus (Sc) cultivation processes, for which low yields are commonly obtained. Improved knowledge of the clavam biosynthetic pathway, especially the steps involved in the inversion of 3S-5S into 3R-5R stereochemical configuration, would help to eventually identify bottlenecks in the pathway. In this work, we studied the role of acetate in CA biosynthesis by a combined continuous culture and computational simulation approach. From this we derived a new model for the synthesis of N-acetyl-glycyl-clavaminic acid (NAG-clavam) by Sc. Acetylated compounds, such as NAG-clavam and N-acetyl-clavaminic acid, have been reported in the clavam pathway. Although the acetyl group is present in the β-lactam intermediate NAG-clavam, it is unknown how this group is incorporated. Hence, under the consideration of the experimentally proven accumulation of acetate during CA biosynthesis, and the fact that an acetyl group is present in the NAG-clavam structure, a computational evaluation of the tentative formation of NAG-clavam was performed for the purpose of providing further understanding. The proposed reaction mechanism consists of two steps: first, acetate reacts with ATP to produce a reactive acylphosphate intermediate; second, a direct nucleophilic attack of the terminal amino group of N-glycyl-clavaminic on the carbonyl carbon of the acylphosphate intermediate leads to a tetrahydral intermediate, which collapses and produces ADP and N-acetyl-glycyl-clavaminic acid. The calculations suggest that for the proposed reaction mechanism, the reaction proceeds until completion of the first step, without the direct action of an enzyme, where acetate and ATP are involved. For this step, the computed activation energy was ≅2.82kcal/mol while the reaction energy was ≅2.38kcal/mol. As this is an endothermic chemical process with a relatively small activation energy, the reaction rate should be considerably high. The calculations offered in this work should not be considered as a definite characterization of the potential energy surface for the reaction between acetate and ATP, but rather as a first approximation that provides valuable insight about the reaction mechanism. Finally, a complete route for the inversion of the stereochemical configuration from (3S, 5S)-clavaminic acid into (3R, 5R)-clavulanic acid is proposed, including a novel alternative for the double epimerization using proline racemase and NAG-clavam formation.
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Affiliation(s)
- Howard Ramirez-Malule
- Grupo de Bioprocesos, Departamento de Ingeniería Química, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
| | - Albeiro Restrepo
- Grupo de Química Física Teórica, Instituto de Química, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
| | - Wilson Cardona
- Grupo de Química de Plantas Colombianas, Instituto de Química, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
| | - Stefan Junne
- Chair of Bioprocess Engineering, Department of Biotechnology, Technische Universität Berlin, Ackerstr. 76, ACK 24, 13355 Berlin, Germany
| | - Peter Neubauer
- Chair of Bioprocess Engineering, Department of Biotechnology, Technische Universität Berlin, Ackerstr. 76, ACK 24, 13355 Berlin, Germany
| | - Rigoberto Rios-Estepa
- Grupo de Bioprocesos, Departamento de Ingeniería Química, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia.
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Zou Q, Zeng J, Cao L, Ji R. A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2014.12.123] [Citation(s) in RCA: 124] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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46
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Survey of Natural Language Processing Techniques in Bioinformatics. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:674296. [PMID: 26525745 PMCID: PMC4615216 DOI: 10.1155/2015/674296] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2015] [Revised: 06/12/2015] [Accepted: 06/21/2015] [Indexed: 01/02/2023]
Abstract
Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives. First, we aim to search for knowledge on biology, retrieve references using text mining methods, and reconstruct databases. For example, protein-protein interactions and gene-disease relationship can be mined from PubMed. Then, we analyze the applications of text mining and natural language processing techniques in bioinformatics, including predicting protein structure and function, detecting noncoding RNA. Finally, numerous methods and applications, as well as their contributions to bioinformatics, are discussed for future use by text mining and natural language processing researchers.
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47
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Using weighted features to predict recombination hotspots in Saccharomyces cerevisiae. J Theor Biol 2015; 382:15-22. [DOI: 10.1016/j.jtbi.2015.06.030] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2015] [Revised: 06/04/2015] [Accepted: 06/20/2015] [Indexed: 01/06/2023]
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48
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Rare k-mer DNA: Identification of sequence motifs and prediction of CpG island and promoter. J Theor Biol 2015; 387:88-100. [PMID: 26427337 DOI: 10.1016/j.jtbi.2015.09.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2015] [Revised: 09/10/2015] [Accepted: 09/15/2015] [Indexed: 12/20/2022]
Abstract
Empirical analysis on k-mer DNA has been proven as an effective tool in finding unique patterns in DNA sequences which can lead to the discovery of potential sequence motifs. In an extensive study of empirical k-mer DNA on hundreds of organisms, the researchers found unique multi-modal k-mer spectra occur in the genomes of organisms from the tetrapod clade only which includes all mammals. The multi-modality is caused by the formation of the two lowest modes where k-mers under them are referred as the rare k-mers. The suppression of the two lowest modes (or the rare k-mers) can be attributed to the CG dinucleotide inclusions in them. Apart from that, the rare k-mers are selectively distributed in certain genomic features of CpG Island (CGI), promoter, 5' UTR, and exon. We correlated the rare k-mers with hundreds of annotated features using several bioinformatic tools, performed further intrinsic rare k-mer analyses within the correlated features, and modeled the elucidated rare k-mer clustering feature into a classifier to predict the correlated CGI and promoter features. Our correlation results show that rare k-mers are highly associated with several annotated features of CGI, promoter, 5' UTR, and open chromatin regions. Our intrinsic results show that rare k-mers have several unique topological, compositional, and clustering properties in CGI and promoter features. Finally, the performances of our RWC (rare-word clustering) method in predicting the CGI and promoter features are ranked among the top three, in eight of the CGI and promoter evaluations, among eight of the benchmarked datasets.
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49
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Survey of Programs Used to Detect Alternative Splicing Isoforms from Deep Sequencing Data In Silico. BIOMED RESEARCH INTERNATIONAL 2015; 2015:831352. [PMID: 26421304 PMCID: PMC4573434 DOI: 10.1155/2015/831352] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2014] [Revised: 02/17/2015] [Accepted: 03/02/2015] [Indexed: 11/29/2022]
Abstract
Next-generation sequencing techniques have been rapidly emerging. However, the massive sequencing reads hide a great deal of unknown important information. Advances have enabled researchers to discover alternative splicing (AS) sites and isoforms using computational approaches instead of molecular experiments. Given the importance of AS for gene expression and protein diversity in eukaryotes, detecting alternative splicing and isoforms represents a hot topic in systems biology and epigenetics research. The computational methods applied to AS prediction have improved since the emergence of next-generation sequencing. In this study, we introduce state-of-the-art research on AS and then compare the research methods and software tools available for AS based on next-generation sequencing reads. Finally, we discuss the prospects of computational methods related to AS.
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50
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Kou G, Feng Y. Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts. J Theor Biol 2015; 380:392-8. [DOI: 10.1016/j.jtbi.2015.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Revised: 06/02/2015] [Accepted: 06/04/2015] [Indexed: 10/23/2022]
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