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Charoenkwan P, Waramit S, Chumnanpuen P, Schaduangrat N, Shoombuatong W. TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus. PLoS One 2023; 18:e0290538. [PMID: 37624802 PMCID: PMC10456195 DOI: 10.1371/journal.pone.0290538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
Hepatitis C virus (HCV) infection is a concerning health issue that causes chronic liver diseases. Despite many successful therapeutic outcomes, no effective HCV vaccines are currently available. Focusing on T cell activity, the primary effector for HCV clearance, T cell epitopes of HCV (TCE-HCV) are considered promising elements to accelerate HCV vaccine efficacy. Thus, accurate and rapid identification of TCE-HCVs is recommended to obtain more efficient therapy for chronic HCV infection. In this study, a novel sequence-based stacked approach, termed TROLLOPE, is proposed to accurately identify TCE-HCVs from sequence information. Specifically, we employed 12 different sequence-based feature descriptors from heterogeneous perspectives, such as physicochemical properties, composition-transition-distribution information and composition information. These descriptors were used in cooperation with 12 popular machine learning (ML) algorithms to create 144 base-classifiers. To maximize the utility of these base-classifiers, we used a feature selection strategy to determine a collection of potential base-classifiers and integrated them to develop the meta-classifier. Comprehensive experiments based on both cross-validation and independent tests demonstrated the superior predictive performance of TROLLOPE compared with conventional ML classifiers, with cross-validation and independent test accuracies of 0.745 and 0.747, respectively. Finally, a user-friendly online web server of TROLLOPE (http://pmlabqsar.pythonanywhere.com/TROLLOPE) has been developed to serve research efforts in the large-scale identification of potential TCE-HCVs for follow-up experimental verification.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, Thailand
| | - Sajee Waramit
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, Thailand
| | - Pramote Chumnanpuen
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, Thailand
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok, Thailand
| | - Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
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Charoenkwan P, Anuwongcharoen N, Nantasenamat C, Hasan MM, Shoombuatong W. In Silico Approaches for the Prediction and Analysis of Antiviral Peptides: A Review. Curr Pharm Des 2021; 27:2180-2188. [PMID: 33138759 DOI: 10.2174/1381612826666201102105827] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 08/20/2020] [Indexed: 11/22/2022]
Abstract
In light of the growing resistance toward current antiviral drugs, efforts to discover novel and effective antiviral therapeutic agents remain a pressing scientific effort. Antiviral peptides (AVPs) represent promising therapeutic agents due to their extraordinary advantages in terms of potency, efficacy and pharmacokinetic properties. The growing volume of newly discovered peptide sequences in the post-genomic era requires computational approaches for timely and accurate identification of AVPs. Machine learning (ML) methods such as random forest and support vector machine represent robust learning algorithms that are instrumental in successful peptide-based drug discovery. Therefore, this review summarizes the current state-of-the-art application of ML methods for identifying AVPs directly from the sequence information. We compare the efficiency of these methods in terms of the underlying characteristics of the dataset used along with feature encoding methods, ML algorithms, cross-validation methods and prediction performance. Finally, guidelines for the development of robust AVP models are also discussed. It is anticipated that this review will serve as a useful guide for the design and development of robust AVP and related therapeutic peptide predictors in the future.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Nuttapat Anuwongcharoen
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
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Abstract
Background:
Bioluminescence is a unique and significant phenomenon in nature.
Bioluminescence is important for the lifecycle of some organisms and is valuable in biomedical
research, including for gene expression analysis and bioluminescence imaging technology. In recent
years, researchers have identified a number of methods for predicting bioluminescent proteins
(BLPs), which have increased in accuracy, but could be further improved.
Method:
In this study, a new bioluminescent proteins prediction method, based on a voting
algorithm, is proposed. Four methods of feature extraction based on the amino acid sequence were
used. 314 dimensional features in total were extracted from amino acid composition,
physicochemical properties and k-spacer amino acid pair composition. In order to obtain the highest
MCC value to establish the optimal prediction model, a voting algorithm was then used to build the
model. To create the best performing model, the selection of base classifiers and vote counting rules
are discussed.
Results:
The proposed model achieved 93.4% accuracy, 93.4% sensitivity and
91.7% specificity in the test set, which was better than any other method. A previous prediction of
bioluminescent proteins in three lineages was also improved using the model building method,
resulting in greatly improved accuracy.
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Affiliation(s)
- Shulin Zhao
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying Ju
- School of Informatics, Xiamen University, Xiamen, China
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba Science City, Japan
| | - Jun Zhang
- Rehabilitation Department, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Shuguang Han
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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Matos-Filipe P, Preto AJ, Koukos PI, Mourão J, Bonvin AMJJ, Moreira IS. MENSAdb: a thorough structural analysis of membrane protein dimers. Database (Oxford) 2021; 2021:baab013. [PMID: 33822911 PMCID: PMC8023553 DOI: 10.1093/database/baab013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 01/19/2021] [Accepted: 03/01/2021] [Indexed: 11/14/2022]
Abstract
Membrane proteins (MPs) are key players in a variety of different cellular processes and constitute the target of around 60% of all Food and Drug Administration-approved drugs. Despite their importance, there is still a massive lack of relevant structural, biochemical and mechanistic information mainly due to their localization within the lipid bilayer. To help fulfil this gap, we developed the MEmbrane protein dimer Novel Structure Analyser database (MENSAdb). This interactive web application summarizes the evolutionary and physicochemical properties of dimeric MPs to expand the available knowledge on the fundamental principles underlying their formation. Currently, MENSAdb contains features of 167 unique MPs (63% homo- and 37% heterodimers) and brings insights into the conservation of residues, accessible solvent area descriptors, average B-factors, intermolecular contacts at 2.5 Å and 4.0 Å distance cut-offs, hydrophobic contacts, hydrogen bonds, salt bridges, π-π stacking, T-stacking and cation-π interactions. The regular update and organization of all these data into a unique platform will allow a broad community of researchers to collect and analyse a large number of features efficiently, thus facilitating their use in the development of prediction models associated with MPs. Database URL: http://www.moreiralab.com/resources/mensadb.
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Affiliation(s)
- Pedro Matos-Filipe
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra 3005-504, Portugal
| | - António J Preto
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra 3005-504, Portugal
- PhD Programme in Experimental Biology and Biomedicine, Institute for Interdisciplinary Research, University of Coimbra, Coimbra, 3030-789, Portugal
| | - Panagiotis I Koukos
- Bijvoet Centre for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht, 3584, CH, Netherlands
| | - Joana Mourão
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra 3005-504, Portugal
| | - Alexandre M J J Bonvin
- Bijvoet Centre for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht, 3584, CH, Netherlands
| | - Irina S Moreira
- Department of Life Sciences, University of Coimbra, Coimbra, 3000-456, Portugal
- Center for Neuroscience and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
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iBLP: An XGBoost-Based Predictor for Identifying Bioluminescent Proteins. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6664362. [PMID: 33505515 PMCID: PMC7808816 DOI: 10.1155/2021/6664362] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 12/13/2020] [Accepted: 12/28/2020] [Indexed: 02/07/2023]
Abstract
Bioluminescent proteins (BLPs) are a class of proteins that widely distributed in many living organisms with various mechanisms of light emission including bioluminescence and chemiluminescence from luminous organisms. Bioluminescence has been commonly used in various analytical research methods of cellular processes, such as gene expression analysis, drug discovery, cellular imaging, and toxicity determination. However, the identification of bioluminescent proteins is challenging as they share poor sequence similarities among them. In this paper, we briefly reviewed the development of the computational identification of BLPs and subsequently proposed a novel predicting framework for identifying BLPs based on eXtreme gradient boosting algorithm (XGBoost) and using sequence-derived features. To train the models, we collected BLP data from bacteria, eukaryote, and archaea. Then, for getting more effective prediction models, we examined the performances of different feature extraction methods and their combinations as well as classification algorithms. Finally, based on the optimal model, a novel predictor named iBLP was constructed to identify BLPs. The robustness of iBLP has been proved by experiments on training and independent datasets. Comparison with other published method further demonstrated that the proposed method is powerful and could provide good performance for BLP identification. The webserver and software package for BLP identification are freely available at http://lin-group.cn/server/iBLP.
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Charoenkwan P, Kanthawong S, Nantasenamat C, Hasan MM, Shoombuatong W. iDPPIV-SCM: A Sequence-Based Predictor for Identifying and Analyzing Dipeptidyl Peptidase IV (DPP-IV) Inhibitory Peptides Using a Scoring Card Method. J Proteome Res 2020; 19:4125-4136. [PMID: 32897718 DOI: 10.1021/acs.jproteome.0c00590] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The inhibition of dipeptidyl peptidase IV (DPP-IV, E.C.3.4.14.5) is well recognized as a new avenue for the treatment of Type 2 diabetes (T2D). Until now, peptide-like DDP-IV inhibitors have been shown to normalize the blood glucose concentration in T2D subjects. To the best of our knowledge, there is yet no computational model for predicting and analyzing DPP-IV inhibitory peptides using sequence information. In this study, we present for the first time a simple and easily interpretable sequence-based predictor using the scoring card method (SCM) for modeling the bioactivity of DPP-IV inhibitory peptides (iDPPIV-SCM). Particularly, the iDPPIV-SCM was developed by employing the SCM method together with the propensity scores of amino acids. Rigorous independent test results demonstrated that the proposed iDPPIV-SCM was found to be superior to those of well-known machine learning (ML) classifiers (e.g., k-nearest neighbor, logistic regression, and decision tree) with demonstrated improvements of 2-11, 4-22, and 7-10% for accuracy, MCC, and AUC, respectively, while also achieving comparable results to that of the support vector machine. Furthermore, the analysis of estimated propensity scores of amino acids as derived from the iDPPIV-SCM was performed so as to provide a more in-depth understanding on the molecular basis for enhancing the DPP-IV inhibitory potency. Taken together, these results revealed that iDPPIV-SCM was superior to those of other well-known ML classifiers owing to its simplicity, interpretability, and validity. For the convenience of biologists, the predictive model is deployed as a publicly accessible web server at http://camt.pythonanywhere.com/iDPPIV-SCM. It is anticipated that iDPPIV-SCM can serve as an important tool for the rapid screening of promising DPP-IV inhibitory peptides prior to their synthesis.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Sakawrat Kanthawong
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
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iBitter-SCM: Identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides. Genomics 2020; 112:2813-2822. [DOI: 10.1016/j.ygeno.2020.03.019] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 03/19/2020] [Accepted: 03/22/2020] [Indexed: 12/21/2022]
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Zhang D, Guan ZX, Zhang ZM, Li SH, Dao FY, Tang H, Lin H. Recent Development of Computational Predicting Bioluminescent Proteins. Curr Pharm Des 2020; 25:4264-4273. [PMID: 31696804 DOI: 10.2174/1381612825666191107100758] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 11/04/2019] [Indexed: 12/22/2022]
Abstract
Bioluminescent Proteins (BLPs) are widely distributed in many living organisms that act as a key role of light emission in bioluminescence. Bioluminescence serves various functions in finding food and protecting the organisms from predators. With the routine biotechnological application of bioluminescence, it is recognized to be essential for many medical, commercial and other general technological advances. Therefore, the prediction and characterization of BLPs are significant and can help to explore more secrets about bioluminescence and promote the development of application of bioluminescence. Since the experimental methods are money and time-consuming for BLPs identification, bioinformatics tools have played important role in fast and accurate prediction of BLPs by combining their sequences information with machine learning methods. In this review, we summarized and compared the application of machine learning methods in the prediction of BLPs from different aspects. We wish that this review will provide insights and inspirations for researches on BLPs.
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Affiliation(s)
- Dan Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zheng-Xing Guan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zi-Mei Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shi-Hao Li
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Ying Dao
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hua Tang
- Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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9
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Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation. J Comput Aided Mol Des 2020; 34:1105-1116. [DOI: 10.1007/s10822-020-00323-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 06/10/2020] [Indexed: 12/11/2022]
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10
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Charoenkwan P, Kanthawong S, Schaduangrat N, Yana J, Shoombuatong W. PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method. Cells 2020; 9:E353. [PMID: 32028709 PMCID: PMC7072630 DOI: 10.3390/cells9020353] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Revised: 01/20/2020] [Accepted: 01/27/2020] [Indexed: 12/16/2022] Open
Abstract
Although, existing methods have been successful in predicting phage (or bacteriophage) virion proteins (PVPs) using various types of protein features and complex classifiers, such as support vector machine and naïve Bayes, these two methods do not allow interpretability. However, the characterization and analysis of PVPs might be of great significance to understanding the molecular mechanisms of bacteriophage genetics and the development of antibacterial drugs. Hence, we herein proposed a novel method (PVPred-SCM) based on the scoring card method (SCM) in conjunction with dipeptide composition to identify and characterize PVPs. In PVPred-SCM, the propensity scores of 400 dipeptides were calculated using the statistical discrimination approach. Rigorous independent validation test showed that PVPred-SCM utilizing only dipeptide composition yielded an accuracy of 77.56%, indicating that PVPred-SCM performed well relative to the state-of-the-art method utilizing a number of protein features. Furthermore, the propensity scores of dipeptides were used to provide insights into the biochemical and biophysical properties of PVPs. Upon comparison, it was found that PVPred-SCM was superior to the existing methods considering its simplicity, interpretability, and implementation. Finally, in an effort to facilitate high-throughput prediction of PVPs, we provided a user-friendly web-server for identifying the likelihood of whether or not these sequences are PVPs. It is anticipated that PVPred-SCM will become a useful tool or at least a complementary existing method for predicting and analyzing PVPs.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand;
| | - Sakawrat Kanthawong
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand;
| | - Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand;
| | - Janchai Yana
- Department of Chemistry, Faculty of Science and Technology, Chiang Mai Rajabhat University, Chiang Mai 50300, Thailand;
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand;
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Schaduangrat N, Nantasenamat C, Prachayasittikul V, Shoombuatong W. Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation. Int J Mol Sci 2019; 20:ijms20225743. [PMID: 31731751 PMCID: PMC6888698 DOI: 10.3390/ijms20225743] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 11/07/2019] [Accepted: 11/13/2019] [Indexed: 12/31/2022] Open
Abstract
In spite of the large-scale production and widespread distribution of vaccines and antiviral drugs, viruses remain a prominent human disease. Recently, the discovery of antiviral peptides (AVPs) has become an influential antiviral agent due to their extraordinary advantages. With the avalanche of newly-found peptide sequences in the post-genomic era, there is a great demand to develop a sequence-based predictor for timely identifying AVPs as this information is very useful for both basic research and drug development. In this study, we propose a novel sequence-based meta-predictor with an effective feature representation, called Meta-iAVP, for the accurate prediction of AVPs from given peptide sequences. Herein, the effective feature representation was extracted from a set of prediction scores derived from various machine learning algorithms and types of features. To the best of our knowledge, the model proposed herein represents the first meta-based approach for the prediction of AVPs. An overall accuracy and Matthews correlation coefficient of 95.20% and 0.90, respectively, was achieved from the independent test set on an objective benchmark dataset. Comparative analysis suggested that Meta-iAVP was superior to that of existing methods and therefore represents a useful tool for AVP prediction. Finally, in an effort to facilitate high-throughput prediction of AVPs, the model was deployed as the Meta-iAVP web server and is made freely available online at http://codes.bio/meta-iavp/ where users can submit query peptide sequences for determining the likelihood of whether or not these peptides are AVPs.
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Affiliation(s)
- Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand; (N.S.); (C.N.)
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand; (N.S.); (C.N.)
| | - Virapong Prachayasittikul
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand;
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand; (N.S.); (C.N.)
- Correspondence: ; Tel.: +66-2441-4371 (ext. 2715)
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Laengsri V, Nantasenamat C, Schaduangrat N, Nuchnoi P, Prachayasittikul V, Shoombuatong W. TargetAntiAngio: A Sequence-Based Tool for the Prediction and Analysis of Anti-Angiogenic Peptides. Int J Mol Sci 2019; 20:E2950. [PMID: 31212918 PMCID: PMC6628072 DOI: 10.3390/ijms20122950] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 06/13/2019] [Accepted: 06/14/2019] [Indexed: 11/21/2022] Open
Abstract
Cancer remains one of the major causes of death worldwide. Angiogenesis is crucial for the pathogenesis of various human diseases, especially solid tumors. The discovery of anti-angiogenic peptides is a promising therapeutic route for cancer treatment. Thus, reliably identifying anti-angiogenic peptides is extremely important for understanding their biophysical and biochemical properties that serve as the basis for the discovery of new anti-cancer drugs. This study aims to develop an efficient and interpretable computational model called TargetAntiAngio for predicting and characterizing anti-angiogenic peptides. TargetAntiAngio was developed using the random forest classifier in conjunction with various classes of peptide features. It was observed via an independent validation test that TargetAntiAngio can identify anti-angiogenic peptides with an average accuracy of 77.50% on an objective benchmark dataset. Comparisons demonstrated that TargetAntiAngio is superior to other existing methods. In addition, results revealed the following important characteristics of anti-angiogenic peptides: (i) disulfide bond forming Cys residues play an important role for inhibiting blood vessel proliferation; (ii) Cys located at the C-terminal domain can decrease endothelial formatting activity and suppress tumor growth; and (iii) Cyclic disulfide-rich peptides contribute to the inhibition of angiogenesis and cell migration, selectivity and stability. Finally, for the convenience of experimental scientists, the TargetAntiAngio web server was established and made freely available online.
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Affiliation(s)
- Vishuda Laengsri
- Department of Clinical Microscopy, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
- Center for Research and Innovation, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
| | - Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
| | - Pornlada Nuchnoi
- Department of Clinical Microscopy, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
- Center for Research and Innovation, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
| | - Virapong Prachayasittikul
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
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Schaduangrat N, Nantasenamat C, Prachayasittikul V, Shoombuatong W. ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides. Molecules 2019; 24:E1973. [PMID: 31121946 PMCID: PMC6571645 DOI: 10.3390/molecules24101973] [Citation(s) in RCA: 120] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 05/07/2019] [Accepted: 05/17/2019] [Indexed: 01/01/2023] Open
Abstract
Anticancer peptides (ACPs) have emerged as a new class of therapeutic agent for cancer treatment due to their lower toxicity as well as greater efficacy, selectivity and specificity when compared to conventional small molecule drugs. However, the experimental identification of ACPs still remains a time-consuming and expensive endeavor. Therefore, it is desirable to develop and improve upon existing computational models for predicting and characterizing ACPs. In this study, we present a bioinformatics tool called the ACPred, which is an interpretable tool for the prediction and characterization of the anticancer activities of peptides. ACPred was developed by utilizing powerful machine learning models (support vector machine and random forest) and various classes of peptide features. It was observed by a jackknife cross-validation test that ACPred can achieve an overall accuracy of 95.61% in identifying ACPs. In addition, analysis revealed the following distinguishing characteristics that ACPs possess: (i) hydrophobic residue enhances the cationic properties of α-helical ACPs resulting in better cell penetration; (ii) the amphipathic nature of the α-helical structure plays a crucial role in its mechanism of cytotoxicity; and (iii) the formation of disulfide bridges on β-sheets is vital for structural maintenance which correlates with its ability to kill cancer cells. Finally, for the convenience of experimental scientists, the ACPred web server was established and made freely available online.
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Affiliation(s)
- Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
| | - Virapong Prachayasittikul
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
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Zhang J, Chai H, Yang G, Ma Z. Prediction of bioluminescent proteins by using sequence-derived features and lineage-specific scheme. BMC Bioinformatics 2017; 18:294. [PMID: 28583090 PMCID: PMC5460367 DOI: 10.1186/s12859-017-1709-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Accepted: 05/25/2017] [Indexed: 11/10/2022] Open
Abstract
Background Bioluminescent proteins (BLPs) widely exist in many living organisms. As BLPs are featured by the capability of emitting lights, they can be served as biomarkers and easily detected in biomedical research, such as gene expression analysis and signal transduction pathways. Therefore, accurate identification of BLPs is important for disease diagnosis and biomedical engineering. In this paper, we propose a novel accurate sequence-based method named PredBLP (Prediction of BioLuminescent Proteins) to predict BLPs. Results We collect a series of sequence-derived features, which have been proved to be involved in the structure and function of BLPs. These features include amino acid composition, dipeptide composition, sequence motifs and physicochemical properties. We further prove that the combination of four types of features outperforms any other combinations or individual features. To remove potential irrelevant or redundant features, we also introduce Fisher Markov Selector together with Sequential Backward Selection strategy to select the optimal feature subsets. Additionally, we design a lineage-specific scheme, which is proved to be more effective than traditional universal approaches. Conclusion Experiment on benchmark datasets proves the robustness of PredBLP. We demonstrate that lineage-specific models significantly outperform universal ones. We also test the generalization capability of PredBLP based on independent testing datasets as well as newly deposited BLPs in UniProt. PredBLP is proved to be able to exceed many state-of-art methods. A web server named PredBLP, which implements the proposed method, is free available for academic use. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1709-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jian Zhang
- School of Computer Science and Information Technology, Northeast Normal University, Changchun, Jilin Province, 130117, People's Republic of China.,School of Computer and Information Technology, Xinyang Normal University, Xinyang, Henan Province, 464000, People's Republic of China
| | - Haiting Chai
- School of Computer Science and Information Technology, Northeast Normal University, Changchun, Jilin Province, 130117, People's Republic of China
| | - Guifu Yang
- School of Computer Science and Information Technology, Northeast Normal University, Changchun, Jilin Province, 130117, People's Republic of China
| | - Zhiqiang Ma
- School of Computer Science and Information Technology, Northeast Normal University, Changchun, Jilin Province, 130117, People's Republic of China.
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Vasylenko T, Liou YF, Chiou PC, Chu HW, Lai YS, Chou YL, Huang HL, Ho SY. SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides. BMC Bioinformatics 2016; 17:514. [PMID: 28155663 PMCID: PMC5260027 DOI: 10.1186/s12859-016-1371-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Background Bacterial tyrosine-kinases (BY-kinases), which play an important role in numerous cellular processes, are characterized as a separate class of enzymes and share no structural similarity with their eukaryotic counterparts. However, in silico methods for predicting BY-kinases have not been developed yet. Since these enzymes are involved in key regulatory processes, and are promising targets for anti-bacterial drug design, it is desirable to develop a simple and easily interpretable predictor to gain new insights into bacterial tyrosine phosphorylation. This study proposes a novel SCMBYK method for predicting and characterizing BY-kinases. Results A dataset consisting of 797 BY-kinases and 783 non-BY-kinases was established to design the SCMBYK predictor, which achieved training and test accuracies of 97.55 and 96.73%, respectively. Furthermore, the leave-one-phylum-out method was used to predict specific bacterial phyla hosts of target sequences, gaining 97.39% average test accuracy. After analyzing SCMBYK-derived propensity scores, four characteristics of BY-kinases were determined: 1) BY-kinases tend to be composed of α-helices; 2) the amino-acid content of extracellular regions of BY-kinases is expected to be dominated by residues such as Val, Ile, Phe and Tyr; 3) BY-kinases structurally resemble nuclear proteins; 4) different domains play different roles in triggering BY-kinase activity. Conclusions The SCMBYK predictor is an effective method for identification of possible BY-kinases. Furthermore, it can be used as a part of a novel drug repurposing method, which recognizes putative BY-kinases and matches them to approved drugs. Among other results, our analysis revealed that azathioprine could suppress the virulence of M. tuberculosis, and thus be considered as a potential antibiotic for tuberculosis treatment. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1371-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tamara Vasylenko
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Yi-Fan Liou
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Po-Chin Chiou
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Hsiao-Wei Chu
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Yung-Sung Lai
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Yu-Ling Chou
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Hui-Ling Huang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan. .,College of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan. .,Center for Bioinformatics Research, National Chiao Tung University, Hsinchu, Taiwan.
| | - Shinn-Ying Ho
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan. .,College of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan. .,Center for Bioinformatics Research, National Chiao Tung University, Hsinchu, Taiwan.
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Nath A, Subbiah K. Unsupervised learning assisted robust prediction of bioluminescent proteins. Comput Biol Med 2016; 68:27-36. [DOI: 10.1016/j.compbiomed.2015.10.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Revised: 09/27/2015] [Accepted: 10/28/2015] [Indexed: 10/22/2022]
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17
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Liou YF, Vasylenko T, Yeh CL, Lin WC, Chiu SH, Charoenkwan P, Shu LS, Ho SY, Huang HL. SCMMTP: identifying and characterizing membrane transport proteins using propensity scores of dipeptides. BMC Genomics 2015; 16 Suppl 12:S6. [PMID: 26677931 PMCID: PMC4682407 DOI: 10.1186/1471-2164-16-s12-s6] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Identifying putative membrane transport proteins (MTPs) and understanding the transport mechanisms involved remain important challenges for the advancement of structural and functional genomics. However, the transporter characters are mainly acquired from MTP crystal structures which are hard to crystalize. Therefore, it is desirable to develop bioinformatics tools for the effective large-scale analysis of available sequences to identify novel transporters and characterize such transporters. RESULTS This work proposes a novel method (SCMMTP) based on the scoring card method (SCM) using dipeptide composition to identify and characterize MTPs from an existing dataset containing 900 MTPs and 660 non-MTPs which are separated into a training dataset consisting 1,380 proteins and an independent dataset consisting 180 proteins. The SCMMTP produced estimating propensity scores for amino acids and dipeptides as MTPs. The SCMMTP training and test accuracy levels respectively reached 83.81% and 76.11%. The test accuracy of support vector machine (SVM) using a complicated classification method with a low possibility for biological interpretation and position-specific substitution matrix (PSSM) as a protein feature is 80.56%, thus SCMMTP is comparable to SVM-PSSM. To identify MTPs, SCMMTP is applied to three datasets including: 1) human transmembrane proteins, 2) a photosynthetic protein dataset, and 3) a human protein database. MTPs showing α-helix rich structure is agreed with previous studies. The MTPs used residues with low hydration energy. It is hypothesized that, after filtering substrates, the hydrated water molecules need to be released from the pore regions. CONCLUSIONS SCMMTP yields estimating propensity scores for amino acids and dipeptides as MTPs, which can be used to identify novel MTPs and characterize transport mechanisms for use in further experiments. AVAILABILITY http://iclab.life.nctu.edu.tw/iclab_webtools/SCMMTP/.
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Affiliation(s)
- Yi-Fan Liou
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
| | - Tamara Vasylenko
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
| | - Chia-Lun Yeh
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
| | - Wei-Chun Lin
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
| | - Shih-Hsiang Chiu
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
| | - Phasit Charoenkwan
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
| | - Li-Sun Shu
- Department of Information Management, Overseas Chinese University, Taichung, Taiwan
| | - Shinn-Ying Ho
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
- Center for Bioinformatics Research, National Chiao Tung University, Hsinchu City, Taiwan
| | - Hui-Ling Huang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
- Center for Bioinformatics Research, National Chiao Tung University, Hsinchu City, Taiwan
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SCMPSP: Prediction and characterization of photosynthetic proteins based on a scoring card method. BMC Bioinformatics 2015; 16 Suppl 1:S8. [PMID: 25708243 PMCID: PMC4331707 DOI: 10.1186/1471-2105-16-s1-s8] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Photosynthetic proteins (PSPs) greatly differ in their structure and function as they are involved in numerous subprocesses that take place inside an organelle called a chloroplast. Few studies predict PSPs from sequences due to their high variety of sequences and structues. This work aims to predict and characterize PSPs by establishing the datasets of PSP and non-PSP sequences and developing prediction methods. RESULTS A novel bioinformatics method of predicting and characterizing PSPs based on scoring card method (SCMPSP) was used. First, a dataset consisting of 649 PSPs was established by using a Gene Ontology term GO:0015979 and 649 non-PSPs from the SwissProt database with sequence identity <= 25%.- Several prediction methods are presented based on support vector machine (SVM), decision tree J48, Bayes, BLAST, and SCM. The SVM method using dipeptide features-performed well and yielded - a test accuracy of 72.31%. The SCMPSP method uses the estimated propensity scores of 400 dipeptides - as PSPs and has a test accuracy of 71.54%, which is comparable to that of the SVM method. The derived propensity scores of 20 amino acids were further used to identify informative physicochemical properties for characterizing PSPs. The analytical results reveal the following four characteristics of PSPs: 1) PSPs favour hydrophobic side chain amino acids; 2) PSPs are composed of the amino acids prone to form helices in membrane environments; 3) PSPs have low interaction with water; and 4) PSPs prefer to be composed of the amino acids of electron-reactive side chains. CONCLUSIONS The SCMPSP method not only estimates the propensity of a sequence to be PSPs, it also discovers characteristics that further improve understanding of PSPs. The SCMPSP source code and the datasets used in this study are available at http://iclab.life.nctu.edu.tw/SCMPSP/.
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Liou YF, Charoenkwan P, Srinivasulu Y, Vasylenko T, Lai SC, Lee HC, Chen YH, Huang HL, Ho SY. SCMHBP: prediction and analysis of heme binding proteins using propensity scores of dipeptides. BMC Bioinformatics 2014; 15 Suppl 16:S4. [PMID: 25522279 PMCID: PMC4290654 DOI: 10.1186/1471-2105-15-s16-s4] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background Heme binding proteins (HBPs) are metalloproteins that contain a heme ligand (an iron-porphyrin complex) as the prosthetic group. Several computational methods have been proposed to predict heme binding residues and thereby to understand the interactions between heme and its host proteins. However, few in silico methods for identifying HBPs have been proposed. Results This work proposes a scoring card method (SCM) based method (named SCMHBP) for predicting and analyzing HBPs from sequences. A balanced dataset of 747 HBPs (selected using a Gene Ontology term GO:0020037) and 747 non-HBPs (selected from 91,414 putative non-HBPs) with an identity of 25% was firstly established. Consequently, a set of scores that quantified the propensity of amino acids and dipeptides to be HBPs is estimated using SCM to maximize the predictive accuracy of SCMHBP. Finally, the informative physicochemical properties of 20 amino acids are identified by utilizing the estimated propensity scores to be used to categorize HBPs. The training and mean test accuracies of SCMHBP applied to three independent test datasets are 85.90% and 71.57%, respectively. SCMHBP performs well relative to comparison with such methods as support vector machine (SVM), decision tree J48, and Bayes classifiers. The putative non-HBPs with high sequence propensity scores are potential HBPs, which can be further validated by experimental confirmation. The propensity scores of individual amino acids and dipeptides are examined to elucidate the interactions between heme and its host proteins. The following characteristics of HBPs are derived from the propensity scores: 1) aromatic side chains are important to the effectiveness of specific HBP functions; 2) a hydrophobic environment is important in the interaction between heme and binding sites; and 3) the whole HBP has low flexibility whereas the heme binding residues are relatively flexible. Conclusions SCMHBP yields knowledge that improves our understanding of HBPs rather than merely improves the prediction accuracy in predicting HBPs.
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