1
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Nielsen H. Protein Sorting Prediction. Methods Mol Biol 2024; 2715:27-63. [PMID: 37930519 DOI: 10.1007/978-1-0716-3445-5_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global property-based, and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches are described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.
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Affiliation(s)
- Henrik Nielsen
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark.
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2
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Do TTT, Nguyen-Vo TH, Pham HT, Trinh QH, Nguyen BP. iNSP-GCAAP: Identifying nonclassical secreted proteins using global composition of amino acid properties. Proteomics 2023; 23:e2100134. [PMID: 36401584 DOI: 10.1002/pmic.202100134] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 08/02/2022] [Accepted: 11/10/2022] [Indexed: 11/21/2022]
Abstract
Nonclassical secreted proteins (NSPs) refer to a group of proteins released into the extracellular environment under the facilitation of different biological transporting pathways apart from the Sec/Tat system. As experimental determination of NSPs is often costly and requires skilled handling techniques, computational approaches are necessary. In this study, we introduce iNSP-GCAAP, a computational prediction framework, to identify NSPs. We propose using global composition of a customized set of amino acid properties to encode sequence data and use the random forest (RF) algorithm for classification. We used the training dataset introduced by Zhang et al. (Bioinformatics, 36(3), 704-712, 2020) to develop our model and test it with the independent test set in the same study. The area under the receiver operating characteristic curve on that test set was 0.9256, which outperformed other state-of-the-art methods using the same datasets. Our framework is also deployed as a user-friendly web-based application to support the research community to predict NSPs.
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Affiliation(s)
- Trang T T Do
- School of Innovation, Design and Technology, Wellington Institute of Technology, Lower Hutt, New Zealand
| | - Thanh-Hoang Nguyen-Vo
- School of Mathematics and Statistics, Victoria University of Wellington, Wellington, New Zealand
| | - Hung T Pham
- Faculty of Information Technology, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam
| | - Quang H Trinh
- School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam
| | - Binh P Nguyen
- School of Mathematics and Statistics, Victoria University of Wellington, Wellington, New Zealand
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3
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Dai W, Li J, Li Q, Cai J, Su J, Stubenrauch C, Wang J. PncsHub: a platform for annotating and analyzing non-classically secreted proteins in Gram-positive bacteria. Nucleic Acids Res 2022; 50:D848-D857. [PMID: 34551435 PMCID: PMC8728121 DOI: 10.1093/nar/gkab814] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 08/30/2021] [Accepted: 09/07/2021] [Indexed: 12/28/2022] Open
Abstract
From industry to food to health, bacteria play an important role in all facets of life. Some of the most important bacteria have been purposely engineered to produce commercial quantities of antibiotics and therapeutics, and non-classical secretion systems are at the forefront of these technologies. Unlike the classical Sec or Tat pathways, non-classically secreted proteins share few common characteristics and use much more diverse secretion pathways for protein transport. Systematically categorizing and investigating the non-classically secreted proteins will enable a deeper understanding of their associated secretion mechanisms and provide a landscape of the Gram-positive secretion pathway distribution. We therefore developed PncsHub (https://pncshub.erc.monash.edu/), the first universal platform for comprehensively annotating and analyzing Gram-positive bacterial non-classically secreted proteins. PncsHub catalogs 4,914 non-classically secreted proteins, which are delicately categorized into 8 subtypes (including the 'unknown' subtype) and annotated with data compiled from up to 26 resources and visualisation tools. It incorporates state-of-the-art predictors to identify new and homologous non-classically secreted proteins and includes three analytical modules to visualise the relationships between known and putative non-classically secreted proteins. As such, PncsHub aims to provide integrated services for investigating, predicting and identifying non-classically secreted proteins to promote hypothesis-driven laboratory-based experiments.
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Affiliation(s)
- Wei Dai
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325011, China
| | - Jiahui Li
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Qi Li
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Jiasheng Cai
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Jianzhong Su
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325011, China
- School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Christopher Stubenrauch
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia
- Centre to Impact AMR, Monash University, VIC 3800, Australia
| | - Jiawei Wang
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia
- Centre to Impact AMR, Monash University, VIC 3800, Australia
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4
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Ras-Carmona A, Gomez-Perosanz M, Reche PA. Prediction of unconventional protein secretion by exosomes. BMC Bioinformatics 2021; 22:333. [PMID: 34134630 PMCID: PMC8210391 DOI: 10.1186/s12859-021-04219-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/21/2021] [Indexed: 01/08/2023] Open
Abstract
MOTIVATION In eukaryotes, proteins targeted for secretion contain a signal peptide, which allows them to proceed through the conventional ER/Golgi-dependent pathway. However, an important number of proteins lacking a signal peptide can be secreted through unconventional routes, including that mediated by exosomes. Currently, no method is available to predict protein secretion via exosomes. RESULTS Here, we first assembled a dataset including the sequences of 2992 proteins secreted by exosomes and 2961 proteins that are not secreted by exosomes. Subsequently, we trained different random forests models on feature vectors derived from the sequences in this dataset. In tenfold cross-validation, the best model was trained on dipeptide composition, reaching an accuracy of 69.88% ± 2.08 and an area under the curve (AUC) of 0.76 ± 0.03. In an independent dataset, this model reached an accuracy of 75.73% and an AUC of 0.840. After these results, we developed ExoPred, a web-based tool that uses random forests to predict protein secretion by exosomes. CONCLUSION ExoPred is available for free public use at http://imath.med.ucm.es/exopred/ . Datasets are available at http://imath.med.ucm.es/exopred/datasets/ .
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Affiliation(s)
- Alvaro Ras-Carmona
- Laboratory of Immunomedicine, Department of Immunology, Faculty of Medicine, Complutense University of Madrid, Pza Ramón y Cajal, s/n, 28040 Madrid, Spain
| | - Marta Gomez-Perosanz
- Laboratory of Immunomedicine, Department of Immunology, Faculty of Medicine, Complutense University of Madrid, Pza Ramón y Cajal, s/n, 28040 Madrid, Spain
| | - Pedro A. Reche
- Laboratory of Immunomedicine, Department of Immunology, Faculty of Medicine, Complutense University of Madrid, Pza Ramón y Cajal, s/n, 28040 Madrid, Spain
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5
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Zheng D, Pang G, Liu B, Chen L, Yang J. Learning transferable deep convolutional neural networks for the classification of bacterial virulence factors. Bioinformatics 2020; 36:3693-3702. [PMID: 32251507 DOI: 10.1093/bioinformatics/btaa230] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 03/25/2020] [Accepted: 04/01/2020] [Indexed: 12/23/2022] Open
Abstract
MOTIVATION Identification of virulence factors (VFs) is critical to the elucidation of bacterial pathogenesis and prevention of related infectious diseases. Current computational methods for VF prediction focus on binary classification or involve only several class(es) of VFs with sufficient samples. However, thousands of VF classes are present in real-world scenarios, and many of them only have a very limited number of samples available. RESULTS We first construct a large VF dataset, covering 3446 VF classes with 160 495 sequences, and then propose deep convolutional neural network models for VF classification. We show that (i) for common VF classes with sufficient samples, our models can achieve state-of-the-art performance with an overall accuracy of 0.9831 and an F1-score of 0.9803; (ii) for uncommon VF classes with limited samples, our models can learn transferable features from auxiliary data and achieve good performance with accuracy ranging from 0.9277 to 0.9512 and F1-score ranging from 0.9168 to 0.9446 when combined with different predefined features, outperforming traditional classifiers by 1-13% in accuracy and by 1-16% in F1-score. AVAILABILITY AND IMPLEMENTATION All of our datasets are made publicly available at http://www.mgc.ac.cn/VFNet/, and the source code of our models is publicly available at https://github.com/zhengdd0422/VFNet. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dandan Zheng
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100176, China
| | - Guansong Pang
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Bo Liu
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100176, China
| | - Lihong Chen
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100176, China
| | - Jian Yang
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100176, China
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Wang C, Wu J, Xu L, Zou Q. NonClasGP-Pred: robust and efficient prediction of non-classically secreted proteins by integrating subset-specific optimal models of imbalanced data. Microb Genom 2020; 6:mgen000483. [PMID: 33245691 PMCID: PMC8116686 DOI: 10.1099/mgen.0.000483] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 11/06/2020] [Indexed: 01/01/2023] Open
Abstract
Non-classically secreted proteins (NCSPs) are proteins that are located in the extracellular environment, although there is a lack of known signal peptides or secretion motifs. They usually perform different biological functions in intracellular and extracellular environments, and several of their biological functions are linked to bacterial virulence and cell defence. Accurate protein localization is essential for all living organisms, however, the performance of existing methods developed for NCSP identification has been unsatisfactory and in particular suffer from data deficiency and possible overfitting problems. Further improvement is desirable, especially to address the lack of informative features and mining subset-specific features in imbalanced datasets. In the present study, a new computational predictor was developed for NCSP prediction of gram-positive bacteria. First, to address the possible prediction bias caused by the data imbalance problem, ten balanced subdatasets were generated for ensemble model construction. Then, the F-score algorithm combined with sequential forward search was used to strengthen the feature representation ability for each of the training subdatasets. Third, the subset-specific optimal feature combination process was adopted to characterize the original data from different aspects, and all subdataset-based models were integrated into a unified model, NonClasGP-Pred, which achieved an excellent performance with an accuracy of 93.23 %, a sensitivity of 100 %, a specificity of 89.01 %, a Matthew's correlation coefficient of 87.68 % and an area under the curve value of 0.9975 for ten-fold cross-validation. Based on assessment on the independent test dataset, the proposed model outperformed state-of-the-art available toolkits. For availability and implementation, see: http://lab.malab.cn/~wangchao/softwares/NonClasGP/.
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Affiliation(s)
- Chao Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, PR China
| | - Jin Wu
- School of Management, Shenzhen Polytechnic, Shenzhen, PR China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, PR China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, PR China
- Hainan Key Laboratory for Computational Science and Application, Hainan Normal University, Haikou, PR China
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7
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Zhang J, Lv L, Lu D, Kong D, Al-Alashaari MAA, Zhao X. Variable selection from a feature representing protein sequences: a case of classification on bacterial type IV secreted effectors. BMC Bioinformatics 2020; 21:480. [PMID: 33109082 PMCID: PMC7590791 DOI: 10.1186/s12859-020-03826-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 10/19/2020] [Indexed: 12/13/2022] Open
Abstract
Background Classification of certain proteins with specific functions is momentous for biological research. Encoding approaches of protein sequences for feature extraction play an important role in protein classification. Many computational methods (namely classifiers) are used for classification on protein sequences according to various encoding approaches. Commonly, protein sequences keep certain labels corresponding to different categories of biological functions (e.g., bacterial type IV secreted effectors or not), which makes protein prediction a fantasy. As to protein prediction, a kernel set of protein sequences keeping certain labels certified by biological experiments should be existent in advance. However, it has been hardly ever seen in prevailing researches. Therefore, unsupervised learning rather than supervised learning (e.g. classification) should be considered. As to protein classification, various classifiers may help to evaluate the effectiveness of different encoding approaches. Besides, variable selection from an encoded feature representing protein sequences is an important issue that also needs to be considered. Results Focusing on the latter problem, we propose a new method for variable selection from an encoded feature representing protein sequences. Taking a benchmark dataset containing 1947 protein sequences as a case, experiments are made to identify bacterial type IV secreted effectors (T4SE) from protein sequences, which are composed of 399 T4SE and 1548 non-T4SE. Comparable and quantified results are obtained only using certain components of the encoded feature, i.e., position-specific scoring matix, and that indicates the effectiveness of our method. Conclusions Certain variables other than an encoded feature they belong to do work for discrimination between different types of proteins. In addition, ensemble classifiers with an automatic assignment of different base classifiers do achieve a better classification result.
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Affiliation(s)
- Jian Zhang
- College of Artificial Intelligence, Wuxi Vocational College of Science and Technology, No. 8 Xinxi Road, Wuxi, 214028, China
| | - Lixin Lv
- College of Artificial Intelligence, Wuxi Vocational College of Science and Technology, No. 8 Xinxi Road, Wuxi, 214028, China
| | - Donglei Lu
- College of Artificial Intelligence, Wuxi Vocational College of Science and Technology, No. 8 Xinxi Road, Wuxi, 214028, China
| | - Denan Kong
- College of Information and Computer Engineering, Northeast Forestry University, No. 26 Hexing Road, Harbin, 150040, China
| | | | - Xudong Zhao
- College of Information and Computer Engineering, Northeast Forestry University, No. 26 Hexing Road, Harbin, 150040, China.
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8
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Zhang Y, Yu S, Xie R, Li J, Leier A, Marquez-Lago TT, Akutsu T, Smith AI, Ge Z, Wang J, Lithgow T, Song J. PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins. Bioinformatics 2020; 36:704-712. [PMID: 31393553 DOI: 10.1093/bioinformatics/btz629] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 07/17/2019] [Accepted: 08/07/2019] [Indexed: 12/17/2022] Open
Abstract
MOTIVATION Gram-positive bacteria have developed secretion systems to transport proteins across their cell wall, a process that plays an important role during host infection. These secretion mechanisms have also been harnessed for therapeutic purposes in many biotechnology applications. Accordingly, the identification of features that select a protein for efficient secretion from these microorganisms has become an important task. Among all the secreted proteins, 'non-classical' secreted proteins are difficult to identify as they lack discernable signal peptide sequences and can make use of diverse secretion pathways. Currently, several computational methods have been developed to facilitate the discovery of such non-classical secreted proteins; however, the existing methods are based on either simulated or limited experimental datasets. In addition, they often employ basic features to train the models in a simple and coarse-grained manner. The availability of more experimentally validated datasets, advanced feature engineering techniques and novel machine learning approaches creates new opportunities for the development of improved predictors of 'non-classical' secreted proteins from sequence data. RESULTS In this work, we first constructed a high-quality dataset of experimentally verified 'non-classical' secreted proteins, which we then used to create benchmark datasets. Using these benchmark datasets, we comprehensively analyzed a wide range of features and assessed their individual performance. Subsequently, we developed a two-layer Light Gradient Boosting Machine (LightGBM) ensemble model that integrates several single feature-based models into an overall prediction framework. At this stage, LightGBM, a gradient boosting machine, was used as a machine learning approach and the necessary parameter optimization was performed by a particle swarm optimization strategy. All single feature-based LightGBM models were then integrated into a unified ensemble model to further improve the predictive performance. Consequently, the final ensemble model achieved a superior performance with an accuracy of 0.900, an F-value of 0.903, Matthew's correlation coefficient of 0.803 and an area under the curve value of 0.963, and outperforming previous state-of-the-art predictors on the independent test. Based on our proposed optimal ensemble model, we further developed an accessible online predictor, PeNGaRoo, to serve users' demands. We believe this online web server, together with our proposed methodology, will expedite the discovery of non-classically secreted effector proteins in Gram-positive bacteria and further inspire the development of next-generation predictors. AVAILABILITY AND IMPLEMENTATION http://pengaroo.erc.monash.edu/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yanju Zhang
- Bioinformatics Group, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Sha Yu
- Bioinformatics Group, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.,Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, VIC 3800, Australia
| | - Ruopeng Xie
- Bioinformatics Group, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.,Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, VIC 3800, Australia
| | - Jiahui Li
- Bioinformatics Group, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.,Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC 3800, Australia
| | - André Leier
- Department of Genetics, AL, USA.,Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - Tatiana T Marquez-Lago
- Department of Genetics, AL, USA.,Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan
| | - A Ian Smith
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, VIC 3800, Australia.,ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, VIC 3800, Australia
| | - Zongyuan Ge
- Monash e-Research Centre and Faculty of Engineering, Monash University, Melbourne, VIC 3800, Australia
| | - Jiawei Wang
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC 3800, Australia
| | - Trevor Lithgow
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC 3800, Australia
| | - Jiangning Song
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, VIC 3800, Australia.,ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, VIC 3800, Australia
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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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10
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Chou KC. An Insightful 10-year Recollection Since the Emergence of the 5-steps Rule. Curr Pharm Des 2020; 25:4223-4234. [PMID: 31782354 DOI: 10.2174/1381612825666191129164042] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 11/25/2019] [Indexed: 11/22/2022]
Abstract
OBJECTIVE One of the most challenging and also the most difficult problems is how to formulate a biological sequence with a vector but considerably keep its sequence order information. METHODS To address such a problem, the approach of Pseudo Amino Acid Components or PseAAC has been developed. RESULTS AND CONCLUSION It has become increasingly clear via the 10-year recollection that the aforementioned proposal has been indeed very powerful.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, Massachusetts 02478, United States.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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11
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12
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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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13
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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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14
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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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15
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Nielsen H, Petsalaki EI, Zhao L, Stühler K. Predicting eukaryotic protein secretion without signals. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2019; 1867:140174. [DOI: 10.1016/j.bbapap.2018.11.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 10/30/2018] [Accepted: 11/29/2018] [Indexed: 10/27/2022]
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16
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Chou KC. Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs. Curr Med Chem 2019; 26:4918-4943. [PMID: 31060481 DOI: 10.2174/0929867326666190507082559] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 01/29/2019] [Accepted: 01/31/2019] [Indexed: 12/16/2022]
Abstract
The smallest unit of life is a cell, which contains numerous protein molecules. Most
of the functions critical to the cell’s survival are performed by these proteins located in its different
organelles, usually called ‘‘subcellular locations”. Information of subcellular localization
for a protein can provide useful clues about its function. To reveal the intricate pathways at the
cellular level, knowledge of the subcellular localization of proteins in a cell is prerequisite.
Therefore, one of the fundamental goals in molecular cell biology and proteomics is to determine
the subcellular locations of proteins in an entire cell. It is also indispensable for prioritizing
and selecting the right targets for drug development. Unfortunately, it is both timeconsuming
and costly to determine the subcellular locations of proteins purely based on experiments.
With the avalanche of protein sequences generated in the post-genomic age, it is highly
desired to develop computational methods for rapidly and effectively identifying the subcellular
locations of uncharacterized proteins based on their sequences information alone. Actually,
considerable progresses have been achieved in this regard. This review is focused on those
methods, which have the capacity to deal with multi-label proteins that may simultaneously
exist in two or more subcellular location sites. Protein molecules with this kind of characteristic
are vitally important for finding multi-target drugs, a current hot trend in drug development.
Focused in this review are also those methods that have use-friendly web-servers established so
that the majority of experimental scientists can use them to get the desired results without the
need to go through the detailed mathematics involved.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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17
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Abstract
The smallest unit of life is a cell, which contains numerous protein molecules. Most
of the functions critical to the cell’s survival are performed by these proteins located in its different
organelles, usually called ‘‘subcellular locations”. Information of subcellular localization
for a protein can provide useful clues about its function. To reveal the intricate pathways at the
cellular level, knowledge of the subcellular localization of proteins in a cell is prerequisite.
Therefore, one of the fundamental goals in molecular cell biology and proteomics is to determine
the subcellular locations of proteins in an entire cell. It is also indispensable for prioritizing
and selecting the right targets for drug development. Unfortunately, it is both timeconsuming
and costly to determine the subcellular locations of proteins purely based on experiments.
With the avalanche of protein sequences generated in the post-genomic age, it is highly
desired to develop computational methods for rapidly and effectively identifying the subcellular
locations of uncharacterized proteins based on their sequences information alone. Actually,
considerable progresses have been achieved in this regard. This review is focused on those
methods, which have the capacity to deal with multi-label proteins that may simultaneously
exist in two or more subcellular location sites. Protein molecules with this kind of characteristic
are vitally important for finding multi-target drugs, a current hot trend in drug development.
Focused in this review are also those methods that have use-friendly web-servers established so
that the majority of experimental scientists can use them to get the desired results without the
need to go through the detailed mathematics involved.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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18
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Identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection via Chou's 5-steps rule. Biophys Chem 2019; 253:106227. [DOI: 10.1016/j.bpc.2019.106227] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 07/04/2019] [Accepted: 07/10/2019] [Indexed: 01/12/2023]
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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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Xiao X, Cheng X, Chen G, Mao Q, Chou KC. pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset. Med Chem 2019; 15:496-509. [DOI: 10.2174/1573406415666181217114710] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 10/23/2018] [Accepted: 12/12/2018] [Indexed: 12/17/2022]
Abstract
Background/Objective:Knowledge of protein subcellular localization is vitally important for both basic research and drug development. Facing the avalanche of protein sequences emerging in the post-genomic age, it is urgent to develop computational tools for timely and effectively identifying their subcellular localization based on the sequence information alone. Recently, a predictor called “pLoc-mVirus” was developed for identifying the subcellular localization of virus proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, known as “multiplex proteins”, may simultaneously occur in, or move between two or more subcellular location sites. Despite the fact that it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mVirus was trained by an extremely skewed dataset in which some subset was over 10 times the size of the other subsets. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset.Methods:Using the Chou's general PseAAC (Pseudo Amino Acid Composition) approach and the IHTS (Inserting Hypothetical Training Samples) treatment to balance out the training dataset, we have developed a new predictor called “pLoc_bal-mVirus” for predicting the subcellular localization of multi-label virus proteins.Results:Cross-validation tests on exactly the same experiment-confirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mVirus, the existing state-of-theart predictor for the same purpose.Conclusion:Its user-friendly web-server is available at http://www.jci-bioinfo.cn/pLoc_balmVirus/, by which the majority of experimental scientists can easily get their desired results without the need to go through the detailed complicated mathematics. Accordingly, pLoc_bal-mVirus will become a very useful tool for designing multi-target drugs and in-depth understanding of the biological process in a cell.
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Affiliation(s)
- Xuan Xiao
- Gordon Life Science Institute, Boston, MA 02478, United States
| | - Xiang Cheng
- Gordon Life Science Institute, Boston, MA 02478, United States
| | - Genqiang Chen
- College of Chemistry, Chemical Engineering and Biotechnology, Donghua University, Shanghai 201620, China
| | - Qi Mao
- College of Information Science and Technology, Donghua University, Shanghai, China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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Esna Ashari Z, Brayton KA, Broschat SL. Prediction of T4SS Effector Proteins for Anaplasma phagocytophilum Using OPT4e, A New Software Tool. Front Microbiol 2019; 10:1391. [PMID: 31293540 PMCID: PMC6598457 DOI: 10.3389/fmicb.2019.01391] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 06/03/2019] [Indexed: 01/01/2023] Open
Abstract
Type IV secretion systems (T4SS) are used by a number of bacterial pathogens to attack the host cell. The complex protein structure of the T4SS is used to directly translocate effector proteins into host cells, often causing fatal diseases in humans and animals. Identification of effector proteins is the first step in understanding how they function to cause virulence and pathogenicity. Accurate prediction of effector proteins via a machine learning approach can assist in the process of their identification. The main goal of this study is to predict a set of candidate effectors for the tick-borne pathogen Anaplasma phagocytophilum, the causative agent of anaplasmosis in humans. To our knowledge, we present the first computational study for effector prediction with a focus on A. phagocytophilum. In a previous study, we systematically selected a set of optimal features from more than 1,000 possible protein characteristics for predicting T4SS effector candidates. This was followed by a study of the features using the proteome of Legionella pneumophila strain Philadelphia deduced from its complete genome. In this manuscript we introduce the OPT4e software package for Optimal-features Predictor for T4SS Effector proteins. An earlier version of OPT4e was verified using cross-validation tests, accuracy tests, and comparison with previous results for L. pneumophila. We use OPT4e to predict candidate effectors from the proteomes of A. phagocytophilum strains HZ and HGE-1 and predict 48 and 46 candidates, respectively, with 16 and 18 deemed most probable as effectors. These latter include the three known validated effectors for A. phagocytophilum.
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Affiliation(s)
- Zhila Esna Ashari
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States
| | - Kelly A Brayton
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States.,Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, WA, United States.,Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA, United States
| | - Shira L Broschat
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States.,Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, WA, United States.,Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA, United States
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Zhang J, Zhang Y, Ma Z. In silico Prediction of Human Secretory Proteins in Plasma Based on Discrete Firefly Optimization and Application to Cancer Biomarkers Identification. Front Genet 2019; 10:542. [PMID: 31244885 PMCID: PMC6563772 DOI: 10.3389/fgene.2019.00542] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 05/21/2019] [Indexed: 12/20/2022] Open
Abstract
The early control and prevention of cancer contributes effectively interventions and cancer therapies. Secretory protein, one of the richest biomarkers, is proved important as molecular signposts of the physiological state of a cell. In this work, we aim to propose a proteomic high-throughput technology platform to facilitate detection of early cancer by means of biomarkers that secreted into the bloodstream. We compile a new benchmark dataset of human secretory proteins in plasma. A series of sequence-derived features, which have been proved involved in the structure and function of the secretory proteins, are collected to mathematically encode these proteins. Considering the influence of potential irrelevant or redundant features, we introduce discrete firefly optimization algorithm to perform feature selection. We evaluate and compare the proposed method SCRIP (Secretory proteins in plasma) with state-of-the-art approaches on benchmark datasets and independent testing datasets. SCRIP achieves the average AUC values of 0.876 and 0.844 in five-fold the cross-validation and independent test, respectively. Besides that, we also test SCRIP on proteins in four types of cancer tissues and successfully detect 66∼77% potential cancer biomarkers.
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Affiliation(s)
- Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University, Xinyang, China
- Henan Key Laboratory of Education Big Data Analysis and Application, Xinyang, China
| | - Yu Zhang
- Information Engineering College, Huanghuai University, Zhumadian, China
- Henan Key Laboratory of Smart Lighting, Zhumadian, China
| | - Zhiqiang Ma
- Department of Computer Science, College of Humanities & Sciences of Northeast Normal University, Changchun, China
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Using an optimal set of features with a machine learning-based approach to predict effector proteins for Legionella pneumophila. PLoS One 2019; 14:e0202312. [PMID: 30682021 PMCID: PMC6347213 DOI: 10.1371/journal.pone.0202312] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Accepted: 01/12/2019] [Indexed: 12/26/2022] Open
Abstract
Type IV secretion systems exist in a number of bacterial pathogens and are used to secrete effector proteins directly into host cells in order to change their environment making the environment hospitable for the bacteria. In recent years, several machine learning algorithms have been developed to predict effector proteins, potentially facilitating experimental verification. However, inconsistencies exist between their results. Previously we analysed the disparate sets of predictive features used in these algorithms to determine an optimal set of 370 features for effector prediction. This study focuses on the best way to use these optimal features by designing three machine learning classifiers, comparing our results with those of others, and obtaining de novo results. We chose the pathogen Legionella pneumophila strain Philadelphia-1, a cause of Legionnaires’ disease, because it has many validated effector proteins and others have developed machine learning prediction tools for it. While all of our models give good results indicating that our optimal features are quite robust, Model 1, which uses all 370 features with a support vector machine, has slightly better accuracy. Moreover, Model 1 predicted 472 effector proteins that are deemed highly probable to be effectors and include 94% of known effectors. Although the results of our three models agree well with those of other researchers, their models only predicted 126 and 311 candidate effectors.
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Xiao X, Xu ZC, Qiu WR, Wang P, Ge HT, Chou KC. iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition. Genomics 2018; 111:1785-1793. [PMID: 30529532 DOI: 10.1016/j.ygeno.2018.12.001] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 11/20/2018] [Accepted: 12/04/2018] [Indexed: 12/20/2022]
Abstract
The promoter is a regulatory DNA region about 81-1000 base pairs long, usually located near the transcription start site (TSS) along upstream of a given gene. By combining a certain protein called transcription factor, the promoter provides the starting point for regulated gene transcription, and hence plays a vitally important role in gene transcriptional regulation. With explosive growth of DNA sequences in the post-genomic age, it has become an urgent challenge to develop computational method for effectively identifying promoters because the information thus obtained is very useful for both basic research and drug development. Although some prediction methods were developed in this regard, most of them were limited at merely identifying whether a query DNA sequence being of a promoter or not. However, based on their strength-distinct levels for transcriptional activation and expression, promoter should be divided into two categories: strong and weak types. Here a new two-layer predictor, called "iPSW(2L)-PseKNC", was developed by fusing the physicochemical properties of nucleotides and their nucleotide density into PseKNC (pseudo K-tuple nucleotide composition). Its 1st-layer serves to predict whether a query DNA sequence sample is of promoter or not, while its 2nd-layer is able to predict the strength of promoters. It has been observed through rigorous cross-validations that the 1st-layer sub-predictor is remarkably superior to the existing state-of-the-art predictors in identifying the promoters and non-promoters, and that the 2nd-layer sub-predictor can do what is beyond the reach of the existing predictors. Moreover, the web-server for iPSW(2L)-PseKNC has been established at http://www.jci-bioinfo.cn/iPSW(2L)-PseKNC, by which the majority of experimental scientists can easily get the results they need.
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Affiliation(s)
- Xuan Xiao
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China; The Gordon Life Science Institute, Boston, MA 02478, USA.
| | - Zhao-Chun Xu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.
| | - Wang-Ren Qiu
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China; The Gordon Life Science Institute, Boston, MA 02478, USA
| | - Peng Wang
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Hui-Ting Ge
- Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Kuo-Chen Chou
- The Gordon Life Science Institute, Boston, MA 02478, USA; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
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26
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Zhang J, Chai H, Guo S, Guo H, Li Y. High-Throughput Identification of Mammalian Secreted Proteins Using Species-Specific Scheme and Application to Human Proteome. Molecules 2018; 23:molecules23061448. [PMID: 29903999 PMCID: PMC6099666 DOI: 10.3390/molecules23061448] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 05/29/2018] [Accepted: 05/30/2018] [Indexed: 02/02/2023] Open
Abstract
Secreted proteins are widely spread in living organisms and cells. Since secreted proteins are easy to be detected in body fluids, urine, and saliva in clinical diagnosis, they play important roles in biomarkers for disease diagnosis and vaccine production. In this study, we propose a novel predictor for accurate high-throughput identification of mammalian secreted proteins that is based on sequence-derived features. We combine the features of amino acid composition, sequence motifs, and physicochemical properties to encode collected proteins. Detailed feature analyses prove the effectiveness of the considered features. Based on the differences across various species of secreted proteins, we introduce the species-specific scheme, which is expected to further explore the intrinsic attributes of specific secreted proteins. Experiments on benchmark datasets prove the effectiveness of our proposed method. The test on independent testing dataset also promises a good generalization capability. When compared with the traditional universal model, we experimentally demonstrate that the species-specific scheme is capable of significantly improving the prediction performance. We use our method to make predictions on unreviewed human proteome, and find 272 potential secreted proteins with probabilities that are higher than 99%. A user-friendly web server, named iMSPs (identification of Mammalian Secreted Proteins), which implements our proposed method, is designed and is available for free for academic use at: http://www.inforstation.com/webservers/iMSP/.
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Affiliation(s)
- Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.
| | - Haiting Chai
- College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK.
| | - Song Guo
- School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.
| | - Huaping Guo
- School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.
| | - Yanling Li
- School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.
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27
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Liang Y, Zhang S, Ding S. Accurate prediction of Gram-negative bacterial secreted protein types by fusing multiple statistical features from PSI-BLAST profile. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2018; 29:469-481. [PMID: 29688029 DOI: 10.1080/1062936x.2018.1459835] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 03/27/2018] [Indexed: 06/08/2023]
Abstract
Gram-negative bacterial secreted proteins play different roles in invaded eukaryotic cells and cause various diseases. Prediction of Gram-negative bacterial secreted protein types is a meaningful and challenging task. In this paper, we develop a multiple statistical features extraction model based on the dipeptide composition (DPC) descriptor and the detrended moving-average auto-cross-correlation analysis (DMACA) descriptor by PSI-BLAST profile. A 610-dimensional feature vector was constructed on the training set, and the feature extraction model was denoted DPC-DMACA-PSSM. A support vector machine was then selected as a classifier, and the bias-free jackknife test method was used for evaluating the accuracy. Our predictor achieves favourable performance for overall accuracy on the test set and also outperforms the other published approaches. The results show that our approach offers a reliable tool for the identification of Gram-negative bacterial secreted protein types.
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Affiliation(s)
- Y Liang
- a School of Science , Xi'an Polytechnic University , Xi'an 710048 , PR China
| | - S Zhang
- b School of Mathematics and Statistics , Xidian University , Xi'an 710071 , PR China
| | - S Ding
- c Department of Sciences , Dalian Nationalities University , Dalian 116600 , PR China
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28
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Esna Ashari Z, Dasgupta N, Brayton KA, Broschat SL. An optimal set of features for predicting type IV secretion system effector proteins for a subset of species based on a multi-level feature selection approach. PLoS One 2018; 13:e0197041. [PMID: 29742157 PMCID: PMC5942808 DOI: 10.1371/journal.pone.0197041] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2017] [Accepted: 04/25/2018] [Indexed: 01/16/2023] Open
Abstract
Type IV secretion systems (T4SS) are multi-protein complexes in a number of bacterial pathogens that can translocate proteins and DNA to the host. Most T4SSs function in conjugation and translocate DNA; however, approximately 13% function to secrete proteins, delivering effector proteins into the cytosol of eukaryotic host cells. Upon entry, these effectors manipulate the host cell’s machinery for their own benefit, which can result in serious illness or death of the host. For this reason recognition of T4SS effectors has become an important subject. Much previous work has focused on verifying effectors experimentally, a costly endeavor in terms of money, time, and effort. Having good predictions for effectors will help to focus experimental validations and decrease testing costs. In recent years, several scoring and machine learning-based methods have been suggested for the purpose of predicting T4SS effector proteins. These methods have used different sets of features for prediction, and their predictions have been inconsistent. In this paper, an optimal set of features is presented for predicting T4SS effector proteins using a statistical approach. A thorough literature search was performed to find features that have been proposed. Feature values were calculated for datasets of known effectors and non-effectors for T4SS-containing pathogens for four genera with a sufficient number of known effectors, Legionella pneumophila, Coxiella burnetii, Brucella spp, and Bartonella spp. The features were ranked, and less important features were filtered out. Correlations between remaining features were removed, and dimensional reduction was accomplished using principal component analysis and factor analysis. Finally, the optimal features for each pathogen were chosen by building logistic regression models and evaluating each model. The results based on evaluation of our logistic regression models confirm the effectiveness of our four optimal sets of features, and based on these an optimal set of features is proposed for all T4SS effector proteins.
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Affiliation(s)
- Zhila Esna Ashari
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, United States of America
- * E-mail:
| | - Nairanjana Dasgupta
- Department of Mathematics and Statistics, Washington State University, Pullman, Washington, United States of America
| | - Kelly A. Brayton
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, United States of America
- Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, Washington, United States of America
- Paul G. Allen School for Global Animal Health, Washington State University, Pullman, Washington, United States of America
| | - Shira L. Broschat
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, United States of America
- Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, Washington, United States of America
- Paul G. Allen School for Global Animal Health, Washington State University, Pullman, Washington, United States of America
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29
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Monteiro R, Chafsey I, Leroy S, Chambon C, Hébraud M, Livrelli V, Pizza M, Pezzicoli A, Desvaux M. Differential biotin labelling of the cell envelope proteins in lipopolysaccharidic diderm bacteria: Exploring the proteosurfaceome of Escherichia coli using sulfo-NHS-SS-biotin and sulfo-NHS-PEG4-bismannose-SS-biotin. J Proteomics 2018; 181:16-23. [PMID: 29609094 DOI: 10.1016/j.jprot.2018.03.026] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Revised: 02/15/2018] [Accepted: 03/23/2018] [Indexed: 12/28/2022]
Abstract
Surface proteins are the major factor for the interaction between bacteria and its environment, playing an important role in infection, colonisation, virulence and adaptation. However, the study of surface proteins has proven difficult mainly due to their hydrophobicity and/or relatively low abundance compared with cytoplasmic proteins. To overcome these issues new proteomic strategies have been developed, such as cell-surface protein labelling using biotinylation reagents. Sulfo-NHS-SS-biotin is the most commonly used reagent to investigate the proteins expressed at the cell surface of various organisms but its use in lipopolysaccharidic diderm bacteria (archetypical Gram-negative bacteria) remains limited to a handful of species. While generally pass over in silence, some periplasmic proteins, but also some inner membrane lipoproteins, integral membrane proteins and cytoplasmic proteins (cytoproteins) are systematically identified following this approach. To limit cell lysis and diffusion of the sulfo-NHS-SS-biotin through the outer membrane, biotin labelling was tested over short incubation times and proved to be as efficient for 1 min at room temperature. To further limit labelling of protein located below the outer membrane, the use of high-molecular weight sulfo-NHS-PEG4-bismannose-SS-biotin appeared to recover differentially cell-envelope proteins compared to low-molecular weight sulfo-NHS-SS-biotin. Actually, the sulfo-NHS-SS-biotin recovers at a higher extent the proteins completely or partly exposed in the periplasm than sulfo-NHS-PEG4-bismannose-SS-biotin, namely periplasmic and integral membrane proteins as well as inner membrane and outer membrane lipoproteins. These results highlight that protein labelling using biotinylation reagents of different sizes provides a sophisticated and accurate way to differentially explore the cell envelope proteome of lipopolysaccharidic diderm bacteria. SIGNIFICANCE While generally pass over in silence, some periplasmic proteins, inner membrane lipoproteins (IMLs), integral membrane proteins (IMPs) and cytoplasmic proteins (cytoproteins) are systematically identified following cell-surface biotin labelling in lipopolysaccharidic diderm bacteria (archetypal Gram-negative bacteria). The use of biotinylation molecules of different sizes, namely sulfo-NHS-SS-biotin and sulfo-NHS-PEG4-bismannose-SS-biotin, was demonstrated to provide a sophisticated and accurate way to differentially explore the cell envelope proteome of lipopolysaccharidic diderm bacteria.
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Affiliation(s)
- Ricardo Monteiro
- Université Clermont Auvergne, INRA, UMR454 MEDiS, F-63000 Clermont-Ferrand, France; GSK, Via Fiorentina 1, 53100 Siena, Italy
| | - Ingrid Chafsey
- Université Clermont Auvergne, INRA, UMR454 MEDiS, F-63000 Clermont-Ferrand, France
| | - Sabine Leroy
- Université Clermont Auvergne, INRA, UMR454 MEDiS, F-63000 Clermont-Ferrand, France
| | - Christophe Chambon
- INRA, Plate-Forme d'Exploration du Métabolisme, F-63122 Saint-Genès Champanelle, France
| | - Michel Hébraud
- Université Clermont Auvergne, INRA, UMR454 MEDiS, F-63000 Clermont-Ferrand, France; INRA, Plate-Forme d'Exploration du Métabolisme, F-63122 Saint-Genès Champanelle, France
| | - Valérie Livrelli
- Centre de Recherche en Nutrition Humaine Auvergne, UMR UCA INSERM U1071, USC-INRA 2018, Clermont Université - Université d'Auvergne, Faculté de Pharmacie, CHU Clermont-Ferrand, Service Bactériologie Mycologie Parasitologie, Clermont-Ferrand, France
| | | | | | - Mickaël Desvaux
- Université Clermont Auvergne, INRA, UMR454 MEDiS, F-63000 Clermont-Ferrand, France.
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Abstract
Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global-property-based and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches is described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.
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Affiliation(s)
- Henrik Nielsen
- Technical University of Denmark, Kemitorvet, Building 208, DK-2800, Kgs. Lyngby, Denmark.
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Nielsen H. Predicting Subcellular Localization of Proteins by Bioinformatic Algorithms. Curr Top Microbiol Immunol 2017; 404:129-158. [PMID: 26728066 DOI: 10.1007/82_2015_5006] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
When predicting the subcellular localization of proteins from their amino acid sequences, there are basically three approaches: signal-based, global property-based, and homology-based. Each of these has its advantages and drawbacks, and it is important when comparing methods to know which approach was used. Various statistical and machine learning algorithms are used with all three approaches, and various measures and standards are employed when reporting the performances of the developed methods. This chapter presents a number of available methods for prediction of sorting signals and subcellular localization, but rather than providing a checklist of which predictors to use, it aims to function as a guide for critical assessment of prediction methods.
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Affiliation(s)
- Henrik Nielsen
- Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Kemitorvet building 208, 2800, Lyngby, Denmark.
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32
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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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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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Sharma A, Kumar D, Kumar S, Rampuria S, Reddy AR, Kirti PB. Ectopic Expression of an Atypical Hydrophobic Group 5 LEA Protein from Wild Peanut, Arachis diogoi Confers Abiotic Stress Tolerance in Tobacco. PLoS One 2016; 11:e0150609. [PMID: 26938884 PMCID: PMC4777422 DOI: 10.1371/journal.pone.0150609] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Accepted: 02/16/2016] [Indexed: 11/23/2022] Open
Abstract
Late embryogenesis abundant (LEA) proteins are a group of hydrophilic proteins, which accumulate in plants under varied stress conditions like drought, salinity, extreme temperatures and oxidative stress suggesting their role in the protection of plants against these stresses. A transcript derived fragment (TDF) corresponding to LEA gene, which got differentially expressed in wild peanut, Arachis diogoi against the late leaf spot pathogen, Phaeoisariopsis personata was used in this study. We have cloned its full length cDNA by RACE-PCR, which was designated as AdLEA. AdLEA belongs to the atypical Group 5C of LEA protein family as confirmed by sequence analysis. Group 5C LEA protein subfamily contains Pfam LEA_2 domain and is highly hydrophobic. In native conditions, expression of AdLEA was upregulated considerably upon hormonal and abiotic stress treatments emphasizing its role in abiotic stress tolerance. Subcellular localization studies showed that AdLEA protein is distributed in both nucleus and cytosol. Ectopic expression of AdLEA in tobacco resulted in enhanced tolerance of plants to dehydration, salinity and oxidative stress with the transgenic plants showing higher chlorophyll content and reduced lipid peroxidation as compared to wild type plants. Overexpressed AdLEA tobacco plants maintained better photosynthetic efficiency under drought conditions as demonstrated by chlorophyll fluorescence measurements. These plants showed enhanced transcript accumulation of some stress-responsive genes. Our study also elucidates that ROS levels were significantly reduced in leaves and stomatal guard cells of transgenic plants upon stress treatments. These results suggest that AdLEA confers multiple stress tolerance to plants, which make it a potential gene for genetic modification in plants.
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Affiliation(s)
- Akanksha Sharma
- Department of Plant Sciences, University of Hyderabad, Hyderabad, 500046, India
| | - Dilip Kumar
- Department of Plant Sciences, University of Hyderabad, Hyderabad, 500046, India
- Department of Postharvest Science of Fresh Produce, ARO, The Volcani Center, Bet Dagan, 50250, Israel
| | - Sumit Kumar
- Department of Plant Sciences, University of Hyderabad, Hyderabad, 500046, India
| | - Sakshi Rampuria
- Department of Plant Sciences, University of Hyderabad, Hyderabad, 500046, India
| | - Attipalli R. Reddy
- Department of Plant Sciences, University of Hyderabad, Hyderabad, 500046, India
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Peng Z, Liang W, Liu W, Wu B, Tang B, Tan C, Zhou R, Chen H. Genomic characterization of Pasteurella multocida HB01, a serotype A bovine isolate from China. Gene 2016; 581:85-93. [PMID: 26827796 DOI: 10.1016/j.gene.2016.01.041] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Revised: 01/10/2016] [Accepted: 01/18/2016] [Indexed: 10/22/2022]
Abstract
Pasteurella multocida infects various domestic and feral animals, generally causing clinical disease. To investigate P. multocida disease in cattle, we sequenced the complete genome of P. multocida HB01 (GenBank accession CP006976), a serotype A organism isolated from a cow in China. The genome is composed of a single circular chromosome of 2,416,068 base pairs containing 2212 protein-coding sequences, 6 ribosomal rRNA operons, and 56 tRNA genes. The present study confirms that P. multocida HB01 possesses a more complete metabolic pathway with an intact trichloroacetic acid cycle for anabolism compared with A. pleuropneumoniae and Haemophilus parasuis. This is the first time that this metabolic mechanism of P. multocida has been described. We also identified a full spectrum of genes related to known virulence factors of P. multocida. The differences in virulence factors between strains of different serotypes and origins were also compared. This comprehensive comparative genome analysis will help in further studies of the metabolic pathways, genetic basis of serotype, and virulence of P. multocida.
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Affiliation(s)
- Zhong Peng
- State Key Laboratory of Agricultural Microbiology, The Cooperative Innovation Center for Sustainable Pig Production, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China.
| | - Wan Liang
- State Key Laboratory of Agricultural Microbiology, The Cooperative Innovation Center for Sustainable Pig Production, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China; Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, The Cooperative Innovation Center for Sustainable Pig Production, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
| | - Wenjing Liu
- State Key Laboratory of Agricultural Microbiology, The Cooperative Innovation Center for Sustainable Pig Production, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China.
| | - Bin Wu
- State Key Laboratory of Agricultural Microbiology, The Cooperative Innovation Center for Sustainable Pig Production, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China.
| | - Biao Tang
- State Key Laboratory of Genetic Engineering, Department of Microbiology, School of Life Sciences, Fudan University, Shanghai 200000, China.
| | - Chen Tan
- State Key Laboratory of Agricultural Microbiology, The Cooperative Innovation Center for Sustainable Pig Production, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China.
| | - Rui Zhou
- State Key Laboratory of Agricultural Microbiology, The Cooperative Innovation Center for Sustainable Pig Production, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China.
| | - Huanchun Chen
- State Key Laboratory of Agricultural Microbiology, The Cooperative Innovation Center for Sustainable Pig Production, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China.
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Chen J, Xu H, He PA, Dai Q, Yao Y. A multiple information fusion method for predicting subcellular locations of two different types of bacterial protein simultaneously. Biosystems 2016; 139:37-45. [DOI: 10.1016/j.biosystems.2015.12.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2015] [Revised: 10/08/2015] [Accepted: 12/10/2015] [Indexed: 12/14/2022]
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Lonsdale A, Davis MJ, Doblin MS, Bacic A. Better Than Nothing? Limitations of the Prediction Tool SecretomeP in the Search for Leaderless Secretory Proteins (LSPs) in Plants. FRONTIERS IN PLANT SCIENCE 2016; 7:1451. [PMID: 27729919 PMCID: PMC5037178 DOI: 10.3389/fpls.2016.01451] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 09/12/2016] [Indexed: 05/14/2023]
Abstract
In proteomic analyses of the plant secretome, the presence of putative leaderless secretory proteins (LSPs) is difficult to confirm due to the possibility of contamination from other sub-cellular compartments. In the absence of a plant-specific tool for predicting LSPs, the mammalian-trained SecretomeP has been applied to plant proteins in multiple studies to identify the most likely LSPs. This study investigates the effectiveness of using SecretomeP on plant proteins, identifies its limitations and provides a benchmark for its use. In the absence of experimentally verified LSPs we exploit the common-feature hypothesis behind SecretomeP and use known classically secreted proteins (CSPs) of plants as a proxy to evaluate its accuracy. We show that, contrary to the common-feature hypothesis, plant CSPs are a poor proxy for evaluating LSP detection due to variation in the SecretomeP prediction scores when the signal peptide (SP) is modified. Removing the SP region from CSPs and comparing the predictive performance against non-secretory proteins indicates that commonly used threshold scores of 0.5 and 0.6 result in false-positive rates in excess of 0.3 when applied to plants proteins. Setting the false-positive rate to 0.05, consistent with the original mammalian performance of SecretomeP, yields only a marginally higher true positive rate compared to false positives. Therefore the use of SecretomeP on plant proteins is not recommended. This study investigates the trade-offs of using SecretomeP on plant proteins and provides insights into predictive features for future development of plant-specific common-feature tools.
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Affiliation(s)
- Andrew Lonsdale
- ARC Centre of Excellence in Plant Cell Walls, School of BioSciences, The University of MelbourneParkville, VIC, Australia
| | - Melissa J. Davis
- The Walter and Eliza Hall Institute of Medical ResearchParkville, VIC, Australia
- Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of MelbourneParkville, VIC, Australia
| | - Monika S. Doblin
- ARC Centre of Excellence in Plant Cell Walls, School of BioSciences, The University of MelbourneParkville, VIC, Australia
| | - Antony Bacic
- ARC Centre of Excellence in Plant Cell Walls, School of BioSciences, The University of MelbourneParkville, VIC, Australia
- *Correspondence: Antony Bacic,
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Liu B, Chen J, Wang X. Protein remote homology detection by combining Chou’s distance-pair pseudo amino acid composition and principal component analysis. Mol Genet Genomics 2015; 290:1919-31. [DOI: 10.1007/s00438-015-1044-4] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Accepted: 04/06/2015] [Indexed: 02/07/2023]
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Pacharawongsakda E, Theeramunkong T. Predict subcellular locations of singleplex and multiplex proteins by semi-supervised learning and dimension-reducing general mode of Chou's PseAAC. IEEE Trans Nanobioscience 2014; 12:311-20. [PMID: 23864226 DOI: 10.1109/tnb.2013.2272014] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Predicting protein subcellular location is one of major challenges in Bioinformatics area since such knowledge helps us understand protein functions and enables us to select the targeted proteins during drug discovery process. While many computational techniques have been proposed to improve predictive performance for protein subcellular location, they have several shortcomings. In this work, we propose a method to solve three main issues in such techniques; i) manipulation of multiplex proteins which may exist or move between multiple cellular compartments, ii) handling of high dimensionality in input and output spaces and iii) requirement of sufficient labeled data for model training. Towards these issues, this work presents a new computational method for predicting proteins which have either single or multiple locations. The proposed technique, namely iFLAST-CORE, incorporates the dimensionality reduction in the feature and label spaces with co-training paradigm for semi-supervised multi-label classification. For this purpose, the Singular Value Decomposition (SVD) is applied to transform the high-dimensional feature space and label space into the lower-dimensional spaces. After that, due to limitation of labeled data, the co-training regression makes use of unlabeled data by predicting the target values in the lower-dimensional spaces of unlabeled data. In the last step, the component of SVD is used to project labels in the lower-dimensional space back to those in the original space and an adaptive threshold is used to map a numeric value to a binary value for label determination. A set of experiments on viral proteins and gram-negative bacterial proteins evidence that our proposed method improve the classification performance in terms of various evaluation metrics such as Aiming (or Precision), Coverage (or Recall) and macro F-measure, compared to the traditional method that uses only labeled data.
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Liu B, Xu J, Fan S, Xu R, Zhou J, Wang X. PseDNA-Pro: DNA-Binding Protein Identification by Combining Chou’s PseAAC and Physicochemical Distance Transformation. Mol Inform 2014; 34:8-17. [DOI: 10.1002/minf.201400025] [Citation(s) in RCA: 135] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2014] [Accepted: 05/27/2014] [Indexed: 11/06/2022]
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41
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Li L, Yu S, Xiao W, Li Y, Li M, Huang L, Zheng X, Zhou S, Yang H. Prediction of bacterial protein subcellular localization by incorporating various features into Chou's PseAAC and a backward feature selection approach. Biochimie 2014; 104:100-7. [PMID: 24929100 DOI: 10.1016/j.biochi.2014.06.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2014] [Accepted: 06/01/2014] [Indexed: 02/08/2023]
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Hahn A, Stevanovic M, Brouwer E, Bublak D, Tripp J, Schorge T, Karas M, Schleiff E. Secretome analysis of Anabaena sp. PCC 7120 and the involvement of the TolC-homologue HgdD in protein secretion. Environ Microbiol 2014; 17:767-80. [PMID: 24890022 DOI: 10.1111/1462-2920.12516] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2013] [Accepted: 05/18/2014] [Indexed: 12/01/2022]
Abstract
Secretion of proteins is a central strategy of bacteria to influence and respond to their environment. Until now, there has been very few discoveries regarding the cyanobacterial secrotome or the secretion machineries involved. For a mutant of the outer membrane channel TolC-homologue HgdD of Anabaena sp. PCC 7120, a filamentous and heterocyst-forming cyanobacterium, an altered secretome profile was reported. To define the role of HgdD in protein secretion, we have developed a method to isolate extracellular proteins of Anabaena sp. PCC 7120 wild type and an hgdD loss-of-function mutant. We identified 51 proteins of which the majority is predicted to have an extracellular secretion signal, while few seem to be localized in the periplasmic space. Eight proteins were exclusively identified in the secretome of wild-type cells, which coincides with the distribution of type I secretion signal. We selected three candidates and generated hemagglutinin-tagged fusion proteins which could be exclusively detected in the extracellular protein fraction. However, these proteins are not secreted in the hgdD-mutant background, where they are rapidly degraded. This confirms a direct function of HgdD in protein secretion and points to the existence of a quality control mechanism at least for proteins secreted in an HgdD-dependent pathway.
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Affiliation(s)
- Alexander Hahn
- Institute of Molecular Biosciences, Cell Biology of Plants, Goethe University, Max-von-Laue Str. 9, Frankfurt/am Main, 60438, Germany
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iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. BIOMED RESEARCH INTERNATIONAL 2014; 2014:623149. [PMID: 24967386 PMCID: PMC4055483 DOI: 10.1155/2014/623149] [Citation(s) in RCA: 97] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2014] [Revised: 04/22/2014] [Accepted: 04/23/2014] [Indexed: 11/17/2022]
Abstract
In eukaryotic genes, exons are generally interrupted by introns. Accurately removing introns and joining exons together are essential processes in eukaryotic gene expression. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapid and effective detection of splice sites that play important roles in gene structure annotation and even in RNA splicing. Although a series of computational methods were proposed for splice site identification, most of them neglected the intrinsic local structural properties. In the present study, a predictor called “iSS-PseDNC” was developed for identifying splice sites. In the new predictor, the sequences were formulated by a novel feature-vector called “pseudo dinucleotide composition” (PseDNC) into which six DNA local structural properties were incorporated. It was observed by the rigorous cross-validation tests on two benchmark datasets that the overall success rates achieved by iSS-PseDNC in identifying splice donor site and splice acceptor site were 85.45% and 87.73%, respectively. It is anticipated that iSS-PseDNC may become a useful tool for identifying splice sites and that the six DNA local structural properties described in this paper may provide novel insights for in-depth investigations into the mechanism of RNA splicing.
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Fan YN, Xiao X, Min JL, Chou KC. iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking. Int J Mol Sci 2014; 15:4915-37. [PMID: 24651462 PMCID: PMC3975431 DOI: 10.3390/ijms15034915] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2014] [Revised: 02/12/2014] [Accepted: 02/16/2014] [Indexed: 12/20/2022] Open
Abstract
Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.
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Affiliation(s)
- Yue-Nong Fan
- Computer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen 333046, Jiangxi, China.
| | - Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen 333046, Jiangxi, China.
| | - Jian-Liang Min
- Computer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen 333046, Jiangxi, China.
| | - Kuo-Chen Chou
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.
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Du P, Gu S, Jiao Y. PseAAC-General: fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets. Int J Mol Sci 2014; 15:3495-506. [PMID: 24577312 PMCID: PMC3975349 DOI: 10.3390/ijms15033495] [Citation(s) in RCA: 242] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2014] [Revised: 02/13/2014] [Accepted: 02/14/2014] [Indexed: 11/16/2022] Open
Abstract
The general form pseudo-amino acid composition (PseAAC) has been widely used to represent protein sequences in predicting protein structural and functional attributes. We developed the program PseAAC-General to generate various different modes of Chou’s general PseAAC, such as the gene ontology mode, the functional domain mode, and the sequential evolution mode. This program allows the users to define their own desired modes. In every mode, 544 physicochemical properties of the amino acids are available for choosing. The computing efficiency is at least 100 times that of existing programs, which makes it able to facilitate the extensive studies on proteins and peptides. The PseAAC-General is freely available via SourceForge. It runs on both Linux and Windows.
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Affiliation(s)
- Pufeng Du
- School of Computer Science and Technology, Tianjin University, Tianjin 300072, China.
| | - Shuwang Gu
- School of Computer Science and Technology, Tianjin University, Tianjin 300072, China.
| | - Yasen Jiao
- School of Computer Science and Technology, Tianjin University, Tianjin 300072, China.
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iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components. Int J Mol Sci 2014; 15:1746-66. [PMID: 24469313 PMCID: PMC3958819 DOI: 10.3390/ijms15021746] [Citation(s) in RCA: 211] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2014] [Revised: 01/14/2014] [Accepted: 01/16/2014] [Indexed: 01/22/2023] Open
Abstract
Meiosis and recombination are the two opposite aspects that coexist in a DNA system. As a driving force for evolution by generating natural genetic variations, meiotic recombination plays a very important role in the formation of eggs and sperm. Interestingly, the recombination does not occur randomly across a genome, but with higher probability in some genomic regions called “hotspots”, while with lower probability in so-called “coldspots”. With the ever-increasing amount of genome sequence data in the postgenomic era, computational methods for effectively identifying the hotspots and coldspots have become urgent as they can timely provide us with useful insights into the mechanism of meiotic recombination and the process of genome evolution as well. To meet the need, we developed a new predictor called “iRSpot-TNCPseAAC”, in which a DNA sample was formulated by combining its trinucleotide composition (TNC) and the pseudo amino acid components (PseAAC) of the protein translated from the DNA sample according to its genetic codes. The former was used to incorporate its local or short-rage sequence order information; while the latter, its global and long-range one. Compared with the best existing predictor in this area, iRSpot-TNCPseAAC achieved higher rates in accuracy, Mathew’s correlation coefficient, and sensitivity, indicating that the new predictor may become a useful tool for identifying the recombination hotspots and coldspots, or, at least, become a complementary tool to the existing methods. It has not escaped our notice that the aforementioned novel approach to incorporate the DNA sequence order information into a discrete model may also be used for many other genome analysis problems. The web-server for iRSpot-TNCPseAAC is available at http://www.jci-bioinfo.cn/iRSpot-TNCPseAAC. Furthermore, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the current web server to obtain their desired result without the need to follow the complicated mathematical equations.
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Emamjomeh A, Goliaei B, Zahiri J, Ebrahimpour R. Predicting protein–protein interactions between human and hepatitis C virus via an ensemble learning method. ACTA ACUST UNITED AC 2014; 10:3147-54. [DOI: 10.1039/c4mb00410h] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
We developed a novel method to predict human–HCV protein–protein interactions, the most comprehensive study of this type.
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Affiliation(s)
- Abbasali Emamjomeh
- Institute of Biochemistry and Biophysics (IBB)
- University of Tehran
- Tehran, Iran
| | - Bahram Goliaei
- Institute of Biochemistry and Biophysics (IBB)
- University of Tehran
- Tehran, Iran
| | - Javad Zahiri
- Institute of Biochemistry and Biophysics (IBB)
- University of Tehran
- Tehran, Iran
- Department of Mathematics
- K.N. Toosi University of Technology
| | - Reza Ebrahimpour
- Brain and Intelligent Systems Research Lab
- Department of Electrical and Computer Engineering
- Shahid Rajaee Teacher Training University
- Tehran, Iran
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Yang X, Guo Y, Luo J, Pu X, Li M. Effective identification of Gram-negative bacterial type III secreted effectors using position-specific residue conservation profiles. PLoS One 2013; 8:e84439. [PMID: 24391954 PMCID: PMC3877298 DOI: 10.1371/journal.pone.0084439] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2013] [Accepted: 11/07/2013] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Type III secretion systems (T3SSs) are central to the pathogenesis and specifically deliver their secreted substrates (type III secreted proteins, T3SPs) into host cells. Since T3SPs play a crucial role in pathogen-host interactions, identifying them is crucial to our understanding of the pathogenic mechanisms of T3SSs. This study reports a novel and effective method for identifying the distinctive residues which are conserved different from other SPs for T3SPs prediction. Moreover, the importance of several sequence features was evaluated and further, a promising prediction model was constructed. RESULTS Based on the conservation profiles constructed by a position-specific scoring matrix (PSSM), 52 distinctive residues were identified. To our knowledge, this is the first attempt to identify the distinct residues of T3SPs. Of the 52 distinct residues, the first 30 amino acid residues are all included, which is consistent with previous studies reporting that the secretion signal generally occurs within the first 30 residue positions. However, the remaining 22 positions span residues 30-100 were also proven by our method to contain important signal information for T3SP secretion because the translocation of many effectors also depends on the chaperone-binding residues that follow the secretion signal. For further feature optimisation and compression, permutation importance analysis was conducted to select 62 optimal sequence features. A prediction model across 16 species was developed using random forest to classify T3SPs and non-T3 SPs, with high receiver operating curve of 0.93 in the 10-fold cross validation and an accuracy of 94.29% for the test set. Moreover, when performing on a common independent dataset, the results demonstrate that our method outperforms all the others published to date. Finally, the novel, experimentally confirmed T3 effectors were used to further demonstrate the model's correct application. The model and all data used in this paper are freely available at http://cic.scu.edu.cn/bioinformatics/T3SPs.zip.
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Affiliation(s)
- Xiaojiao Yang
- College of Chemistry, Sichuan University, Chengdu, P.R.China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu, P.R.China
| | - Jiesi Luo
- College of Chemistry, Sichuan University, Chengdu, P.R.China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu, P.R.China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, P.R.China
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Xu Y, Shao XJ, Wu LY, Deng NY, Chou KC. iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ 2013; 1:e171. [PMID: 24109555 PMCID: PMC3792191 DOI: 10.7717/peerj.171] [Citation(s) in RCA: 228] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2013] [Accepted: 09/06/2013] [Indexed: 11/20/2022] Open
Abstract
As one of the most important and universal posttranslational modifications (PTMs) of proteins, S-nitrosylation (SNO) plays crucial roles in a variety of biological processes, including the regulation of cellular dynamics and many signaling events. Knowledge of SNO sites in proteins is very useful for drug development and basic research as well. Unfortunately, it is both time-consuming and costly to determine the SNO sites purely based on biological experiments. Facing the explosive protein sequence data generated in the post-genomic era, we are challenged to develop automated vehicles for timely and effectively determining the SNO sites for uncharacterized proteins. To address the challenge, a new predictor called iSNO-AAPair was developed by taking into account the coupling effects for all the pairs formed by the nearest residues and the pairs by the next nearest residues along protein chains. The cross-validation results on a state-of-the-art benchmark have shown that the new predictor outperformed the existing predictors. The same was true when tested by the independent proteins whose experimental SNO sites were known. A user-friendly web-server for iSNO-AAPair was established at http://app.aporc.org/iSNO-AAPair/, by which users can easily obtain their desired results without the need to follow the mathematical equations involved during its development.
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Affiliation(s)
- Yan Xu
- Department of Information and Computer Science, University of Science and Technology Beijing , Beijing , China ; Gordon Life Science Institute , Belmont, MA , USA
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50
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Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou's pseudo amino acid compositions. J Theor Biol 2013; 335:205-12. [DOI: 10.1016/j.jtbi.2013.06.034] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Revised: 05/26/2013] [Accepted: 06/29/2013] [Indexed: 12/19/2022]
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