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Yadav AK, Gupta PK, Singh TR. PMTPred: machine-learning-based prediction of protein methyltransferases using the composition of k-spaced amino acid pairs. Mol Divers 2024:10.1007/s11030-024-10937-2. [PMID: 39033257 DOI: 10.1007/s11030-024-10937-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Accepted: 07/10/2024] [Indexed: 07/23/2024]
Abstract
Protein methyltransferases (PMTs) are a group of enzymes that help catalyze the transfer of a methyl group to its substrates. These enzymes play an important role in epigenetic regulation and can methylate various substrates with DNA, RNA, protein, and small-molecule secondary metabolites. Dysregulation of methyltransferases is implicated in various human cancers. However, in light of the well-recognized significance of PMTs, reliable and efficient identification methods are essential. In the present work, we propose a machine-learning-based method for the identification of PMTs. Various sequence-based features were calculated, and prediction models were trained using various machine-learning algorithms using a tenfold cross-validation technique. After evaluating each model on the dataset, the SVM-based CKSAAP model achieved the highest prediction accuracy with balanced sensitivity and specificity. Also, this SVM model outperformed deep-learning algorithms for the prediction of PMTs. In addition, cross-database validation was performed to ensure the robustness of the model. Feature importance was assessed using shapley additive explanations (SHAP) values, providing insights into the contributions of different features to the model's predictions. Finally, the SVM-based CKSAAP model was implemented in a standalone tool, PMTPred, due to its consistent performance during independent testing and cross-database evaluation. We believe that PMTPred will be a useful and efficient tool for the identification of PMTs. The PMTPred is freely available for download at https://github.com/ArvindYadav7/PMTPred and http://www.bioinfoindia.org/PMTPred/home.html for research and academic use.
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Affiliation(s)
- Arvind Kumar Yadav
- Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Solan- 173234, Himachal Pradesh, India
| | - Pradeep Kumar Gupta
- Department of Computer Science and Engineering, Jaypee University of Information Technology, Solan- 173234, Himachal Pradesh, India
- School of Computing, Department of Data Science and Engineering, Mohan Babu University, Tirupati- 517102, Andhra Pradesh, India
| | - Tiratha Raj Singh
- Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Solan- 173234, Himachal Pradesh, India.
- Centre of Excellence in Healthcare Technologies and Informatics (CHETI), Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Solan- 173234, Himachal Pradesh, India.
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2
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Madugula SS, Pujar P, Nammi B, Wang S, Jayasinghe-Arachchige VM, Pham T, Mashburn D, Artiles M, Liu J. Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum. J Chem Inf Model 2024; 64:4897-4911. [PMID: 38838358 DOI: 10.1021/acs.jcim.4c00625] [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] [Indexed: 06/07/2024]
Abstract
The recent development of CRISPR-Cas technology holds promise to correct gene-level defects for genetic diseases. The key element of the CRISPR-Cas system is the Cas protein, a nuclease that can edit the gene of interest assisted by guide RNA. However, these Cas proteins suffer from inherent limitations such as large size, low cleavage efficiency, and off-target effects, hindering their widespread application as a gene editing tool. Therefore, there is a need to identify novel Cas proteins with improved editing properties, for which it is necessary to understand the underlying features governing the Cas families. In this study, we aim to elucidate the unique protein features associated with Cas9 and Cas12 families and identify the features distinguishing each family from non-Cas proteins. Here, we built Random Forest (RF) binary classifiers to distinguish Cas12 and Cas9 proteins from non-Cas proteins, respectively, using the complete protein feature spectrum (13,494 features) encoding various physiochemical, topological, constitutional, and coevolutionary information on Cas proteins. Furthermore, we built multiclass RF classifiers differentiating Cas9, Cas12, and non-Cas proteins. All the models were evaluated rigorously on the test and independent data sets. The Cas12 and Cas9 binary models achieved a high overall accuracy of 92% and 95% on their respective independent data sets, while the multiclass classifier achieved an F1 score of close to 0.98. We observed that Quasi-Sequence-Order (QSO) descriptors like Schneider.lag and Composition descriptors like charge, volume, and polarizability are predominant in the Cas12 family. Conversely Amino Acid Composition descriptors, especially Tripeptide Composition (TPC), predominate the Cas9 family. Four of the top 10 descriptors identified in Cas9 classification are tripeptides PWN, PYY, HHA, and DHI, which are seen to be conserved across all Cas9 proteins and located within different catalytically important domains of the Streptococcus pyogenes Cas9 (SpCas9) structure. Among these, DHI and HHA are well-known to be involved in the DNA cleavage activity of the SpCas9 protein. Mutation studies have highlighted the significance of the PWN tripeptide in PAM recognition and DNA cleavage activity of SpCas9, while Y450 from the PYY tripeptide plays a crucial role in reducing off-target effects and improving the specificity in SpCas9. Leveraging our machine learning (ML) pipeline, we identified numerous Cas9 and Cas12 family-specific features. These features offer valuable insights for future experimental and computational studies aiming at designing Cas systems with enhanced gene-editing properties. These features suggest plausible structural modifications that can effectively guide the development of Cas proteins with improved editing capabilities.
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Affiliation(s)
- Sita Sirisha Madugula
- Department of Pharmaceutical Sciences, University of North Texas System College of Pharmacy, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, Texas 76107, United States
| | - Pranav Pujar
- Department of Industrial, Manufacturing and Systems Engineering, University of Texas at Arlington, 701 South Nedderman Drive, Arlington, Texas 76019, United States
| | - Bharani Nammi
- Department of Industrial, Manufacturing and Systems Engineering, University of Texas at Arlington, 701 South Nedderman Drive, Arlington, Texas 76019, United States
| | - Shouyi Wang
- Department of Industrial, Manufacturing and Systems Engineering, University of Texas at Arlington, 701 South Nedderman Drive, Arlington, Texas 76019, United States
| | - Vindi M Jayasinghe-Arachchige
- Department of Pharmaceutical Sciences, University of North Texas System College of Pharmacy, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, Texas 76107, United States
| | - Tyler Pham
- School of Biomedical Sciences, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, Texas 76107, United States
| | - Dominic Mashburn
- Department of Pharmaceutical Sciences, University of North Texas System College of Pharmacy, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, Texas 76107, United States
| | - Maria Artiles
- School of Biomedical Sciences, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, Texas 76107, United States
| | - Jin Liu
- Department of Pharmaceutical Sciences, University of North Texas System College of Pharmacy, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, Texas 76107, United States
- School of Biomedical Sciences, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, Texas 76107, United States
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3
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Ghafoor H, Asim MN, Ibrahim MA, Ahmed S, Dengel A. CAPTURE: Comprehensive anti-cancer peptide predictor with a unique amino acid sequence encoder. Comput Biol Med 2024; 176:108538. [PMID: 38759585 DOI: 10.1016/j.compbiomed.2024.108538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/26/2024] [Accepted: 04/28/2024] [Indexed: 05/19/2024]
Abstract
Anticancer peptides (ACPs) key properties including bioactivity, high efficacy, low toxicity, and lack of drug resistance make them ideal candidates for cancer therapies. To deeply explore the potential of ACPs and accelerate development of cancer therapies, although 53 Artificial Intelligence supported computational predictors have been developed for ACPs and non ACPs classification but only one predictor has been developed for ACPs functional types annotations. Moreover, these predictors extract amino acids distribution patterns to transform peptides sequences into statistical vectors that are further fed to classifiers for discriminating peptides sequences and annotating peptides functional classes. Overall, these predictors remain fail in extracting diverse types of amino acids distribution patterns from peptide sequences. The paper in hand presents a unique CARE encoder that transforms peptides sequences into statistical vectors by extracting 4 different types of distribution patterns including correlation, distribution, composition, and transition. Across public benchmark dataset, proposed encoder potential is explored under two different evaluation settings namely; intrinsic and extrinsic. Extrinsic evaluation indicates that 12 different machine learning classifiers achieve superior performance with the proposed encoder as compared to 55 existing encoders. Furthermore, an intrinsic evaluation reveals that, unlike existing encoders, the proposed encoder generates more discriminative clusters for ACPs and non-ACPs classes. Across 8 public benchmark ACPs and non-ACPs classification datasets, proposed encoder and Adaboost classifier based CAPTURE predictor outperforms existing predictors with an average accuracy, recall and MCC score of 1%, 4%, and 2% respectively. In generalizeability evaluation case study, across 7 benchmark anti-microbial peptides classification datasets, CAPTURE surpasses existing predictors by an average AU-ROC of 2%. CAPTURE predictive pipeline along with label powerset method outperforms state-of-the-art ACPs functional types predictor by 5%, 5%, 5%, 6%, and 3% in terms of average accuracy, subset accuracy, precision, recall, and F1 respectively. CAPTURE web application is available at https://sds_genetic_analysis.opendfki.de/CAPTURE.
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Affiliation(s)
- Hina Ghafoor
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, 67663, Germany; German Research Center for Artificial Intelligence GmbH, Kaiserslautern, 67663, Germany
| | - Muhammad Nabeel Asim
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, 67663, Germany.
| | - Muhammad Ali Ibrahim
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, 67663, Germany; German Research Center for Artificial Intelligence GmbH, Kaiserslautern, 67663, Germany
| | - Sheraz Ahmed
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, 67663, Germany
| | - Andreas Dengel
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, 67663, Germany; German Research Center for Artificial Intelligence GmbH, Kaiserslautern, 67663, Germany
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4
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Liao YH, Chen SZ, Bin YN, Zhao JP, Feng XL, Zheng CH. UsIL-6: An unbalanced learning strategy for identifying IL-6 inducing peptides by undersampling technique. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108176. [PMID: 38677081 DOI: 10.1016/j.cmpb.2024.108176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 03/26/2024] [Accepted: 04/11/2024] [Indexed: 04/29/2024]
Abstract
BACKGROUND AND OBJECTIVE Interleukin-6 (IL-6) is the critical factor of early warning, monitoring, and prognosis in the inflammatory storm of COVID-19 cases. IL-6 inducing peptides, which can induce cytokine IL-6 production, are very important for the development of diagnosis and immunotherapy. Although the existing methods have some success in predicting IL-6 inducing peptides, there is still room for improvement in the performance of these models in practical application. METHODS In this study, we proposed UsIL-6, a high-performance bioinformatics tool for identifying IL-6 inducing peptides. First, we extracted five groups of physicochemical properties and sequence structural information from IL-6 inducing peptide sequences, and obtained a 636-dimensional feature vector, we also employed NearMiss3 undersampling method and normalization method StandardScaler to process the data. Then, a 40-dimensional optimal feature vector was obtained by Boruta feature selection method. Finally, we combined this feature vector with extreme randomization tree classifier to build the final model UsIL-6. RESULTS The AUC value of UsIL-6 on the independent test dataset was 0.87, and the BACC value was 0.808, which indicated that UsIL-6 had better performance than the existing methods in IL-6 inducing peptide recognition. CONCLUSIONS The performance comparison on independent test dataset confirmed that UsIL-6 could achieve the highest performance, best robustness, and most excellent generalization ability. We hope that UsIL-6 will become a valuable method to identify, annotate and characterize new IL-6 inducing peptides.
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Affiliation(s)
- Yan-Hong Liao
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China
| | - Shou-Zhi Chen
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China
| | - Yan-Nan Bin
- School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China
| | - Jian-Ping Zhao
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China.
| | - Xin-Long Feng
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China.
| | - Chun-Hou Zheng
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China; School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China
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5
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Madugula SS, Pujar P, Bharani N, Wang S, Jayasinghe-Arachchige VM, Pham T, Mashburn D, Artilis M, Liu J. Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.22.576286. [PMID: 38328240 PMCID: PMC10849529 DOI: 10.1101/2024.01.22.576286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
The recent development of CRISPR-Cas technology holds promise to correct gene-level defects for genetic diseases. The key element of the CRISPR-Cas system is the Cas protein, a nuclease that can edit the gene of interest assisted by guide RNA. However, these Cas proteins suffer from inherent limitations like large size, low cleavage efficiency, and off-target effects, hindering their widespread application as a gene editing tool. Therefore, there is a need to identify novel Cas proteins with improved editing properties, for which it is necessary to understand the underlying features governing the Cas families. In the current study, we aim to elucidate the unique protein attributes associated with Cas9 and Cas12 families and identify the features that distinguish each family from the other. Here, we built Random Forest (RF) binary classifiers to distinguish Cas12 and Cas9 proteins from non-Cas proteins, respectively, using the complete protein feature spectrum (13,495 features) encoding various physiochemical, topological, constitutional, and coevolutionary information of Cas proteins. Furthermore, we built multiclass RF classifiers differentiating Cas9, Cas12, and Non-Cas proteins. All the models were evaluated rigorously on the test and independent datasets. The Cas12 and Cas9 binary models achieved a high overall accuracy of 95% and 97% on their respective independent datasets, while the multiclass classifier achieved a high F1 score of 0.97. We observed that Quasi-sequence-order descriptors like Schneider-lag descriptors and Composition descriptors like charge, volume, and polarizability are essential for the Cas12 family. More interestingly, we discovered that Amino Acid Composition descriptors, especially the Tripeptide Composition (TPC) descriptors, are important for the Cas9 family. Four of the identified important descriptors of Cas9 classification are tripeptides PWN, PYY, HHA, and DHI, which are seen to be conserved across all the Cas9 proteins and were located within different catalytically important domains of the Cas9 protein structure. Among these four tripeptides, tripeptides DHI and HHA are well-known to be involved in the DNA cleavage activity of the Cas9 protein. We therefore propose the the other two tripeptides, PWN and PYY, may also be essential for the Cas9 family. Our identified important descriptors enhanced the understanding of the catalytic mechanisms of Cas9 and Cas12 proteins and provide valuable insights into design of novel Cas systems to achieve enhanced gene-editing properties.
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Affiliation(s)
- Sita Sirisha Madugula
- Department of Pharmaceutical Sciences, University of North Texas System College of Pharmacy, University of North Texas Health Science Center, Fort Worth, Texas, United States
| | - Pranav Pujar
- Department of Industrial, Manufacturing and Systems Engineering, University of Texas at Arlington, Arlington, Texas, United States
| | - Nammi Bharani
- Department of Industrial, Manufacturing and Systems Engineering, University of Texas at Arlington, Arlington, Texas, United States
| | - Shouyi Wang
- Department of Industrial, Manufacturing and Systems Engineering, University of Texas at Arlington, Arlington, Texas, United States
| | - Vindi M. Jayasinghe-Arachchige
- Department of Pharmaceutical Sciences, University of North Texas System College of Pharmacy, University of North Texas Health Science Center, Fort Worth, Texas, United States
| | - Tyler Pham
- Graduate School of Biomedical Sciences, University of North Texas Health Science Center, Fort Worth, Texas
| | - Dominic Mashburn
- Department of Pharmaceutical Sciences, University of North Texas System College of Pharmacy, University of North Texas Health Science Center, Fort Worth, Texas, United States
| | - Maria Artilis
- Department of Pharmaceutical Sciences, University of North Texas System College of Pharmacy, University of North Texas Health Science Center, Fort Worth, Texas, United States
| | - Jin Liu
- Department of Pharmaceutical Sciences, University of North Texas System College of Pharmacy, University of North Texas Health Science Center, Fort Worth, Texas, United States
- Graduate School of Biomedical Sciences, University of North Texas Health Science Center, Fort Worth, Texas
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Chung CR, Liou JT, Wu LC, Horng JT, Lee TY. Multi-label classification and features investigation of antimicrobial peptides with various functional classes. iScience 2023; 26:108250. [PMID: 38025779 PMCID: PMC10679894 DOI: 10.1016/j.isci.2023.108250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 07/15/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
The challenge of drug-resistant bacteria to global public health has led to increased attention on antimicrobial peptides (AMPs) as a targeted therapeutic alternative with a lower risk of resistance. However, high production costs and limitations in functional class prediction have hindered progress in this field. In this study, we used multi-label classifiers with binary relevance and algorithm adaptation techniques to predict different functions of AMPs across a wide range of pathogen categories, including bacteria, mammalian cells, fungi, viruses, and cancer cells. Our classifiers attained promising AUC scores varying from 0.8492 to 0.9126 on independent testing data. Forward feature selection identified sequence order and charge as critical, with specific amino acids (C and E) as discriminative. These findings provide valuable insights for the design of antimicrobial peptides (AMPs) with multiple functionalities, thus contributing to the broader effort to combat drug-resistant pathogens.
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Affiliation(s)
- Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Jhen-Ting Liou
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Li-Ching Wu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taoyuan City, Taiwan
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu City, Taiwan
- Center for Intelligent Drug Systems and Smart Biodevices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu City, Taiwan
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7
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Chen J, Gu Z, Lai L, Pei J. In silico protein function prediction: the rise of machine learning-based approaches. MEDICAL REVIEW (2021) 2023; 3:487-510. [PMID: 38282798 PMCID: PMC10808870 DOI: 10.1515/mr-2023-0038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 10/11/2023] [Indexed: 01/30/2024]
Abstract
Proteins function as integral actors in essential life processes, rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation. Within the context of protein research, an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings. Due to the exorbitant costs and limited throughput inherent in experimental investigations, computational models offer a promising alternative to accelerate protein function annotation. In recent years, protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks. This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction. In this review, we elucidate the historical evolution and research paradigms of computational methods for predicting protein function. Subsequently, we summarize the progress in protein and molecule representation as well as feature extraction techniques. Furthermore, we assess the performance of machine learning-based algorithms across various objectives in protein function prediction, thereby offering a comprehensive perspective on the progress within this field.
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Affiliation(s)
- Jiaxiao Chen
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Zhonghui Gu
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Luhua Lai
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
- Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing, China
| | - Jianfeng Pei
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing, China
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8
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Deng H, Ding M, Wang Y, Li W, Liu G, Tang Y. ACP-MLC: A two-level prediction engine for identification of anticancer peptides and multi-label classification of their functional types. Comput Biol Med 2023; 158:106844. [PMID: 37058760 DOI: 10.1016/j.compbiomed.2023.106844] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 03/09/2023] [Accepted: 03/30/2023] [Indexed: 04/07/2023]
Abstract
Anticancer peptides (ACPs), a series of short bioactive peptides, are promising candidates in fighting against cancer due to their high activity, low toxicity, and not likely cause drug resistance. The accurate identification of ACPs and classification of their functional types is of great importance for investigating their mechanisms of action and developing peptide-based anticancer therapies. Here, we provided a computational tool, called ACP-MLC, to address binary classification and multi-label classification of ACPs for a given peptide sequence. Briefly, ACP-MLC is a two-level prediction engine, in which the 1st-level model predicts whether a query sequence is an ACP or not by random forest algorithm, and the 2nd-level model predicts which tissue types the sequence might target by the binary relevance algorithm. Development and evaluation by high-quality datasets, our ACP-MLC yielded an area under the receiver operating characteristic curve (AUC) of 0.888 on the independent test set for the 1st-level prediction, and obtained 0.157 hamming loss, 0.577 subset accuracy, 0.802 F1-scoremacro, and 0.826 F1-scoremicro on the independent test set for the 2nd-level prediction. A systematic comparison demonstrated that ACP-MLC outperformed existing binary classifiers and other multi-label learning classifiers for ACP prediction. Finally, we interpreted the important features of ACP-MLC by the SHAP method. User-friendly software and the datasets are available at https://github.com/Nicole-DH/ACP-MLC. We believe that the ACP-MLC would be a powerful tool in ACP discovery.
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9
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Peng X, Wang X, Guo Y, Ge Z, Li F, Gao X, Song J. RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins. Brief Bioinform 2022; 23:6596984. [PMID: 35649392 PMCID: PMC9294422 DOI: 10.1093/bib/bbac215] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/25/2022] [Accepted: 05/06/2022] [Indexed: 11/27/2022] Open
Abstract
RNA binding proteins (RBPs) are critical for the post-transcriptional control of RNAs and play vital roles in a myriad of biological processes, such as RNA localization and gene regulation. Therefore, computational methods that are capable of accurately identifying RBPs are highly desirable and have important implications for biomedical and biotechnological applications. Here, we propose a two-stage deep transfer learning-based framework, termed RBP-TSTL, for accurate prediction of RBPs. In the first stage, the knowledge from the self-supervised pre-trained model was extracted as feature embeddings and used to represent the protein sequences, while in the second stage, a customized deep learning model was initialized based on an annotated pre-training RBPs dataset before being fine-tuned on each corresponding target species dataset. This two-stage transfer learning framework can enable the RBP-TSTL model to be effectively trained to learn and improve the prediction performance. Extensive performance benchmarking of the RBP-TSTL models trained using the features generated by the self-supervised pre-trained model and other models trained using hand-crafting encoding features demonstrated the effectiveness of the proposed two-stage knowledge transfer strategy based on the self-supervised pre-trained models. Using the best-performing RBP-TSTL models, we further conducted genome-scale RBP predictions for Homo sapiens, Arabidopsis thaliana, Escherichia coli, and Salmonella and established a computational compendium containing all the predicted putative RBPs candidates. We anticipate that the proposed RBP-TSTL approach will be explored as a useful tool for the characterization of RNA-binding proteins and exploration of their sequence–structure–function relationships.
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Affiliation(s)
- Xinxin Peng
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia.,Monash Data Futures Institute, Monash University, Melbourne, Victoria 3800, Australia
| | - Xiaoyu Wang
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia.,Monash Data Futures Institute, Monash University, Melbourne, Victoria 3800, Australia
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3004, Australia
| | - Zongyuan Ge
- Monash e-Research Centre and Faculty of Engineering, Monash University, Melbourne, VIC 3800, Australia
| | - Fuyi Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia.,Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, Victoria 3000, Australia.,College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.,KAUST Computational Bioscience Research Center, King Abdullah University of Science and Technology
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia.,Monash Data Futures Institute, Monash University, Melbourne, Victoria 3800, Australia
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10
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Singh O, Hsu WL, Su ECY. ILeukin10Pred: A Computational Approach for Predicting IL-10-Inducing Immunosuppressive Peptides Using Combinations of Amino Acid Global Features. BIOLOGY 2021; 11:biology11010005. [PMID: 35053004 PMCID: PMC8773200 DOI: 10.3390/biology11010005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 11/25/2021] [Accepted: 12/15/2021] [Indexed: 01/03/2023]
Abstract
Simple Summary Interleukin-10 is a cytokine that exhibits potent anti-inflammatory characteristics that play an essential role in limiting the host’s immune response to pathogens and regulating the growth or differentiation of various immune cells. Moreover, interleukin-10 prediction via conventional approaches is time-consuming and labor-intensive. Hence, researchers are inclined towards an alternative approach to predict interleukin-10-inducing peptides. Additionally, numerous in silico tools are available to predict T cell epitopes. These methods generally follow a direct or indirect approach where they directly predict cytotoxic T-lymphocyte epitopes rather than major histocompatibility complex binders or indirectly predict single components of the T cell recognition pathway. However, very few studies are available that address cytokine-specific predictions. Our research utilized a computer-aided approach to develop a model to predict IL-10-inducing peptides. This study outperformed the existing state-of-the-art method and achieved an accuracy of 87.5% and Matthew’s correlation coefficient (MCC) of 0.755 on the hybrid feature types and outperformed an existing state-of-the-art method based on dipeptide compositions that achieved an accuracy of 81.24% and an MCC value of 0.59. Therefore, our model is promising to assist in predicting immunosuppressive peptides that induce interleukin-10 cytokines. Abstract Interleukin (IL)-10 is a homodimer cytokine that plays a crucial role in suppressing inflammatory responses and regulating the growth or differentiation of various immune cells. However, the molecular mechanism of IL-10 regulation is only partially understood because its regulation is environment or cell type-specific. In this study, we developed a computational approach, ILeukin10Pred (interleukin-10 prediction), by employing amino acid sequence-based features to predict and identify potential immunosuppressive IL-10-inducing peptides. The dataset comprises 394 experimentally validated IL-10-inducing and 848 non-inducing peptides. Furthermore, we split the dataset into a training set (80%) and a test set (20%). To train and validate the model, we applied a stratified five-fold cross-validation method. The final model was later evaluated using the holdout set. An extra tree classifier (ETC)-based model achieved an accuracy of 87.5% and Matthew’s correlation coefficient (MCC) of 0.755 on the hybrid feature types. It outperformed an existing state-of-the-art method based on dipeptide compositions that achieved an accuracy of 81.24% and an MCC value of 0.59. Our experimental results showed that the combination of various features achieved better predictive performance..
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Affiliation(s)
- Onkar Singh
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei 115, Taiwan; (O.S.); (W.-L.H.)
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
| | - Wen-Lian Hsu
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei 115, Taiwan; (O.S.); (W.-L.H.)
- Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan
| | - Emily Chia-Yu Su
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
- Correspondence: ; Tel.: +886-2-66382736 (ext. 1515); Fax: +886-2-66380233
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11
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Agrawal S, Sisodia DS, Nagwani NK. Augmented sequence features and subcellular localization for functional characterization of unknown protein sequences. Med Biol Eng Comput 2021; 59:2297-2310. [PMID: 34545514 DOI: 10.1007/s11517-021-02436-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 08/29/2021] [Indexed: 11/24/2022]
Abstract
Advances in high-throughput techniques lead to evolving a large number of unknown protein sequences (UPS). Functional characterization of UPS is significant for the investigation of disease symptoms and drug repositioning. Protein subcellular localization is imperative for the functional characterization of protein sequences. Diverse techniques are used on protein sequences for feature extraction. However, many times a single feature extraction technique leads to poor prediction performance. In this paper, two feature augmentations are described through sequence induced, physicochemical, and evolutionary information of the amino acid residues. While augmented features preserve the sequence-order-information and protein-residue-properties. Two bacterial protein datasets Gram-Positive (G +) and Gram-Negative (G-) are utilized for the experimental work. After performing essential preprocessing on protein datasets, two sets of feature vectors are obtained. These feature vectors are used separately to train the different individual and ensembles such as decision tree (C 4.5), k-nearest neighbor (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), support vector machine (SVM), AdaBoost, gradient boosting machine (GBM), and random forest (RF) with fivefold cross-validation. Prediction results of the model demonstrate that overall accuracy reported by C4.5 is highest 99.57% on G + and 97.47% on G- datasets with known protein sequences. Similarly, for the UPS overall accuracy of G + is 85.17% with SVM and 82.45% with G- dataset using MLP.
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Affiliation(s)
- Saurabh Agrawal
- Department of Computer Science & Engineering, National Institute of Technology Raipur, GE Road, Raipur, Chhattisgarh, 492010, India.
| | - Dilip Singh Sisodia
- Department of Computer Science & Engineering, National Institute of Technology Raipur, GE Road, Raipur, Chhattisgarh, 492010, India
| | - Naresh Kumar Nagwani
- Department of Computer Science & Engineering, National Institute of Technology Raipur, GE Road, Raipur, Chhattisgarh, 492010, India
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12
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Chen Z, Zhao P, Li C, Li F, Xiang D, Chen YZ, Akutsu T, Daly RJ, Webb GI, Zhao Q, Kurgan L, Song J. iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization. Nucleic Acids Res 2021; 49:e60. [PMID: 33660783 PMCID: PMC8191785 DOI: 10.1093/nar/gkab122] [Citation(s) in RCA: 107] [Impact Index Per Article: 35.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 02/05/2021] [Accepted: 02/25/2021] [Indexed: 12/14/2022] Open
Abstract
Sequence-based analysis and prediction are fundamental bioinformatic tasks that facilitate understanding of the sequence(-structure)-function paradigm for DNAs, RNAs and proteins. Rapid accumulation of sequences requires equally pervasive development of new predictive models, which depends on the availability of effective tools that support these efforts. We introduce iLearnPlus, the first machine-learning platform with graphical- and web-based interfaces for the construction of machine-learning pipelines for analysis and predictions using nucleic acid and protein sequences. iLearnPlus provides a comprehensive set of algorithms and automates sequence-based feature extraction and analysis, construction and deployment of models, assessment of predictive performance, statistical analysis, and data visualization; all without programming. iLearnPlus includes a wide range of feature sets which encode information from the input sequences and over twenty machine-learning algorithms that cover several deep-learning approaches, outnumbering the current solutions by a wide margin. Our solution caters to experienced bioinformaticians, given the broad range of options, and biologists with no programming background, given the point-and-click interface and easy-to-follow design process. We showcase iLearnPlus with two case studies concerning prediction of long noncoding RNAs (lncRNAs) from RNA transcripts and prediction of crotonylation sites in protein chains. iLearnPlus is an open-source platform available at https://github.com/Superzchen/iLearnPlus/ with the webserver at http://ilearnplus.erc.monash.edu/.
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Affiliation(s)
- Zhen Chen
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450046, China
| | - Pei Zhao
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang 455000, China
| | - Chen Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Fuyi Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia.,Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria 3000, Australia
| | - Dongxu Xiang
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Yong-Zi Chen
- Laboratory of Tumor Cell Biology, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan
| | - Roger J Daly
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Geoffrey I Webb
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Quanzhi Zhao
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450046, China.,Key Laboratory of Rice Biology in Henan Province, Henan Agricultural University, Zhengzhou 450046, China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
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Mishra A, Khanal R, Kabir WU, Hoque T. AIRBP: Accurate identification of RNA-binding proteins using machine learning techniques. Artif Intell Med 2021; 113:102034. [PMID: 33685590 DOI: 10.1016/j.artmed.2021.102034] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 01/19/2021] [Accepted: 02/09/2021] [Indexed: 12/25/2022]
Abstract
Identification of RNA-binding proteins (RBPs) that bind to ribonucleic acid molecules is an important problem in Computational Biology and Bioinformatics. It becomes indispensable to identify RBPs as they play crucial roles in post-transcriptional control of RNAs and RNA metabolism as well as have diverse roles in various biological processes such as splicing, mRNA stabilization, mRNA localization, and translation, RNA synthesis, folding-unfolding, modification, processing, and degradation. The existing experimental techniques for identifying RBPs are time-consuming and expensive. Therefore, identifying RBPs directly from the sequence using computational methods can be useful to annotate RBPs and assist the experimental design efficiently. In this work, we present a method called AIRBP, which is designed using an advanced machine learning technique, called stacking, to effectively predict RBPs by utilizing features extracted from evolutionary information, physiochemical properties, and disordered properties. Moreover, our method, AIRBP, use the majority vote from RBPPred, DeepRBPPred, and the stacking model for the prediction for RBPs. The results show that AIRBP attains Accuracy (ACC), Balanced Accuracy (BACC), F1-score, and Mathews Correlation Coefficient (MCC) of 95.84 %, 94.71 %, 0.928, and 0.899, respectively, based on the training dataset, using 10-fold cross-validation (CV). Further evaluation of AIRBP on independent test set reveals that it achieves ACC, BACC, F1-score, and MCC of 94.36 %, 94.28 %, 0.897, and 0.860, for Human test set; 91.25 %, 93.00 %, 0.896, and 0.835 for S. cerevisiae test set; and 90.60 %, 90.41 %, 0.934, and 0.775 for A. thaliana test set, respectively. These results indicate that the AIRBP outperforms the existing Deep- and TriPepSVM methods. Therefore, the proposed better-performing AIRBP can be useful for accurate identification and annotation of RBPs directly from the sequence and help gain valuable insight to treat critical diseases. Availability: Code-data is available here: http://cs.uno.edu/∼tamjid/Software/AIRBP/code_data.zip.
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Affiliation(s)
- Avdesh Mishra
- Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX, USA
| | - Reecha Khanal
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA
| | - Wasi Ul Kabir
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA
| | - Tamjidul Hoque
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA.
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14
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The search for RNA-binding proteins: a technical and interdisciplinary challenge. Biochem Soc Trans 2021; 49:393-403. [PMID: 33492363 PMCID: PMC7925008 DOI: 10.1042/bst20200688] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 12/21/2020] [Accepted: 12/21/2020] [Indexed: 12/13/2022]
Abstract
RNA-binding proteins are customarily regarded as important facilitators of gene expression. In recent years, RNA–protein interactions have also emerged as a pervasive force in the regulation of homeostasis. The compendium of proteins with provable RNA-binding function has swelled from the hundreds to the thousands astride the partnership of mass spectrometry-based proteomics and RNA sequencing. At the foundation of these advances is the adaptation of RNA-centric capture methods that can extract bound protein that has been cross-linked in its native environment. These methods reveal snapshots in time displaying an extensive network of regulation and a wealth of data that can be used for both the discovery of RNA-binding function and the molecular interfaces at which these interactions occur. This review will focus on the impact of these developments on our broader perception of post-transcriptional regulation, and how the technical features of current capture methods, as applied in mammalian systems, create a challenging medium for interpretation by systems biologists and target validation by experimental researchers.
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15
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The evolutionary relationship of S15/NS1RNA binding domains with a similar protein domain pattern - A computational approach. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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16
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Enhanced prediction of anti-tubercular peptides from sequence information using divergence measure-based intuitionistic fuzzy-rough feature selection. Soft comput 2020. [DOI: 10.1007/s00500-020-05363-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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17
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Yang H, Deng Z, Pan X, Shen HB, Choi KS, Wang L, Wang S, Wu J. RNA-binding protein recognition based on multi-view deep feature and multi-label learning. Brief Bioinform 2020; 22:5893431. [PMID: 32808039 DOI: 10.1093/bib/bbaa174] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 06/17/2020] [Accepted: 07/09/2020] [Indexed: 12/28/2022] Open
Abstract
RNA-binding protein (RBP) is a class of proteins that bind to and accompany RNAs in regulating biological processes. An RBP may have multiple target RNAs, and its aberrant expression can cause multiple diseases. Methods have been designed to predict whether a specific RBP can bind to an RNA and the position of the binding site using binary classification model. However, most of the existing methods do not take into account the binding similarity and correlation between different RBPs. While methods employing multiple labels and Long Short Term Memory Network (LSTM) are proposed to consider binding similarity between different RBPs, the accuracy remains low due to insufficient feature learning and multi-label learning on RNA sequences. In response to this challenge, the concept of RNA-RBP Binding Network (RRBN) is proposed in this paper to provide theoretical support for multi-label learning to identify RBPs that can bind to RNAs. It is experimentally shown that the RRBN information can significantly improve the prediction of unknown RNA-RBP interactions. To further improve the prediction accuracy, we present the novel computational method iDeepMV which integrates multi-view deep learning technology under the multi-label learning framework. iDeepMV first extracts data from the views of amino acid sequence and dipeptide component based on the RNA sequences as the original view. Deep neural network models are then designed for the respective views to perform deep feature learning. The extracted deep features are fed into multi-label classifiers which are trained with the RNA-RBP interaction information for the three views. Finally, a voting mechanism is designed to make comprehensive decision on the results of the multi-label classifiers. Our experimental results show that the prediction performance of iDeepMV, which combines multi-view deep feature learning models with RNA-RBP interaction information, is significantly better than that of the state-of-the-art methods. iDeepMV is freely available at http://www.csbio.sjtu.edu.cn/bioinf/iDeepMV for academic use. The code is freely available at http://github.com/uchihayht/iDeepMV.
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Affiliation(s)
| | - Zhaohong Deng
- School of Artificial Intelligence and Computer Science of Jiangnan University, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (LCNBI) and ZJLab
| | - Xiaoyong Pan
- Department of Automation of Shanghai Jiao Tong University
| | | | | | - Lei Wang
- School of Biotechnology and Key Laboratory of Industrial Biotechnology Ministry in Jiangnan University
| | - Shitong Wang
- School of Artificial Intelligence and Computer Science of Jiangnan University
| | - Jing Wu
- School of Biotechnology and Key Laboratory of Industrial Biotechnology Ministry in Jiangnan University
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18
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Identification of common and dissimilar biomarkers for different cancer types from gene expressions of RNA-sequencing data. GENE REPORTS 2020. [DOI: 10.1016/j.genrep.2020.100654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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19
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Sagar A, Xue B. Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions. Protein Pept Lett 2019; 26:601-619. [PMID: 31215361 DOI: 10.2174/0929866526666190619103853] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 04/04/2019] [Accepted: 06/01/2019] [Indexed: 12/18/2022]
Abstract
The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to determine RNA-protein interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific and condition-specific, as well as these interactions are weak and frequently compete with each other, those experimental techniques can not be made full use of to discover the complete spectrum of RNA-protein interactions. To moderate these issues, continuous efforts have been devoted to developing high quality computational techniques to study the interactions between RNAs and proteins. Many important progresses have been achieved with the application of novel techniques and strategies, such as machine learning techniques. Especially, with the development and application of CLIP techniques, more and more experimental data on RNA-protein interaction under specific biological conditions are available. These CLIP data altogether provide a rich source for developing advanced machine learning predictors. In this review, recent progresses on computational predictors for RNA-protein interaction were summarized in the following aspects: dataset, prediction strategies, and input features. Possible future developments were also discussed at the end of the review.
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Affiliation(s)
- Amit Sagar
- Department of Cell Biology, Microbiology and Molecular Biology, School of Natural Sciences and Mathematics, College of Arts and Sciences, University of South Florida, Tampa, Florida 33620, United States
| | - Bin Xue
- Department of Cell Biology, Microbiology and Molecular Biology, School of Natural Sciences and Mathematics, College of Arts and Sciences, University of South Florida, Tampa, Florida 33620, United States
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20
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Zuo Y, Chang Y, Huang S, Zheng L, Yang L, Cao G. iDEF-PseRAAC: Identifying the Defensin Peptide by Using Reduced Amino Acid Composition Descriptor. Evol Bioinform Online 2019; 15:1176934319867088. [PMID: 31391777 PMCID: PMC6669840 DOI: 10.1177/1176934319867088] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 07/08/2019] [Indexed: 11/18/2022] Open
Abstract
Defensins as 1 of major classes of host defense peptides play a significant role in the innate immunity, which are extremely evolved in almost all living organisms. Developing high-throughput computational methods can accurately help in designing drugs or medical means to defense against pathogens. To take up such a challenge, an up-to-date server based on rigorous benchmark dataset, referred to as iDEF-PseRAAC, was designed for predicting the defensin family in this study. By extracting primary sequence compositions based on different types of reduced amino acid alphabet, it was calculated that the best overall accuracy of the selected feature subset was achieved to 92.38%. Therefore, we can conclude that the information provided by abundant types of amino acid reduction will provide efficient and rational methodology for defensin identification. And, a free online server is freely available for academic users at http://bioinfor.imu.edu.cn/idpf. We hold expectations that iDEF-PseRAAC may be a promising weapon for the function annotation about the defensins protein.
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Affiliation(s)
- Yongchun Zuo
- College of Veterinary Medicine, Inner Mongolia Agricultural University, Hohhot, China.,State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Yu Chang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Shenghui Huang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Guifang Cao
- College of Veterinary Medicine, Inner Mongolia Agricultural University, Hohhot, China
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21
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Poursheikhali Asghari M, Abdolmaleki P. Prediction of RNA- and DNA-Binding Proteins Using Various Machine Learning Classifiers. Avicenna J Med Biotechnol 2019; 11:104-111. [PMID: 30800250 PMCID: PMC6359699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Nucleic acid-binding proteins play major roles in different biological processes, such as transcription, splicing and translation. Therefore, the nucleic acid-binding function prediction of proteins is a step toward full functional annotation of proteins. The aim of our research was the improvement of nucleic-acid binding function prediction. METHODS In the current study, nine machine-learning algorithms were used to predict RNA- and DNA-binding proteins and also to discriminate between RNA-binding proteins and DNA-binding proteins. The electrostatic features were utilized for prediction of each function in corresponding adapted protein datasets. The leave-one-out cross-validation process was used to measure the performance of employed classifiers. RESULTS Radial basis function classifier gave the best results in predicting RNA- and DNA-binding proteins in comparison with other classifiers applied. In discriminating between RNA- and DNA-binding proteins, multilayer perceptron classifier was the best one. CONCLUSION Our findings show that the prediction of nucleic acid-binding function based on these simple electrostatic features can be improved by applied classifiers. Moreover, a reasonable progress to distinguish between RNA- and DNA-binding proteins has been achieved.
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Affiliation(s)
| | - Parviz Abdolmaleki
- Corresponding author: Parviz Abdolmaleki, Ph.D., Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran, Tel: +98 21 82883404, Fax: +98 21 82884457, E-mail:,
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22
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Shen WJ, Cui W, Chen D, Zhang J, Xu J. RPiRLS: Quantitative Predictions of RNA Interacting with Any Protein of Known Sequence. Molecules 2018; 23:molecules23030540. [PMID: 29495575 PMCID: PMC6017498 DOI: 10.3390/molecules23030540] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Revised: 02/24/2018] [Accepted: 02/25/2018] [Indexed: 02/05/2023] Open
Abstract
RNA-protein interactions (RPIs) have critical roles in numerous fundamental biological processes, such as post-transcriptional gene regulation, viral assembly, cellular defence and protein synthesis. As the number of available RNA-protein binding experimental data has increased rapidly due to high-throughput sequencing methods, it is now possible to measure and understand RNA-protein interactions by computational methods. In this study, we integrate a sequence-based derived kernel with regularized least squares to perform prediction. The derived kernel exploits the contextual information around an amino acid or a nucleic acid as well as the repetitive conserved motif information. We propose a novel machine learning method, called RPiRLS to predict the interaction between any RNA and protein of known sequences. For the RPiRLS classifier, each protein sequence comprises up to 20 diverse amino acids but for the RPiRLS-7G classifier, each protein sequence is represented by using 7-letter reduced alphabets based on their physiochemical properties. We evaluated both methods on a number of benchmark data sets and compared their performances with two newly developed and state-of-the-art methods, RPI-Pred and IPMiner. On the non-redundant benchmark test sets extracted from the PRIDB, the RPiRLS method outperformed RPI-Pred and IPMiner in terms of accuracy, specificity and sensitivity. Further, RPiRLS achieved an accuracy of 92% on the prediction of lncRNA-protein interactions. The proposed method can also be extended to construct RNA-protein interaction networks. The RPiRLS web server is freely available at http://bmc.med.stu.edu.cn/RPiRLS.
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Affiliation(s)
- Wen-Jun Shen
- Department of Bioinformatics, Shantou University Medical College, Shantou 515000, Guangdong, China.
| | - Wenjuan Cui
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China.
| | - Danze Chen
- Department of Bioinformatics, Shantou University Medical College, Shantou 515000, Guangdong, China.
| | - Jieming Zhang
- Department of Bioinformatics, Shantou University Medical College, Shantou 515000, Guangdong, China.
| | - Jianzhen Xu
- Department of Bioinformatics, Shantou University Medical College, Shantou 515000, Guangdong, China.
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Tripathi P, Pandey PN. A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou's pseudo amino acid composition. J Theor Biol 2017; 424:49-54. [DOI: 10.1016/j.jtbi.2017.04.027] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Revised: 04/24/2017] [Accepted: 04/27/2017] [Indexed: 10/19/2022]
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24
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Zhang X, Liu S. RBPPred: predicting RNA-binding proteins from sequence using SVM. Bioinformatics 2016; 33:854-862. [DOI: 10.1093/bioinformatics/btw730] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2016] [Accepted: 11/16/2016] [Indexed: 11/13/2022] Open
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25
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DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues. PLoS One 2016; 11:e0167345. [PMID: 27907159 PMCID: PMC5132331 DOI: 10.1371/journal.pone.0167345] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Accepted: 11/12/2016] [Indexed: 12/24/2022] Open
Abstract
DNA-binding proteins are fundamentally important in cellular processes. Several computational-based methods have been developed to improve the prediction of DNA-binding proteins in previous years. However, insufficient work has been done on the prediction of DNA-binding proteins from protein sequence information. In this paper, a novel predictor, DNABP (DNA-binding proteins), was designed to predict DNA-binding proteins using the random forest (RF) classifier with a hybrid feature. The hybrid feature contains two types of novel sequence features, which reflect information about the conservation of physicochemical properties of the amino acids, and the binding propensity of DNA-binding residues and non-binding propensities of non-binding residues. The comparisons with each feature demonstrated that these two novel features contributed most to the improvement in predictive ability. Furthermore, to improve the prediction performance of the DNABP model, feature selection using the minimum redundancy maximum relevance (mRMR) method combined with incremental feature selection (IFS) was carried out during the model construction. The results showed that the DNABP model could achieve 86.90% accuracy, 83.76% sensitivity, 90.03% specificity and a Matthews correlation coefficient of 0.727. High prediction accuracy and performance comparisons with previous research suggested that DNABP could be a useful approach to identify DNA-binding proteins from sequence information. The DNABP web server system is freely available at http://www.cbi.seu.edu.cn/DNABP/.
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Chrysostomou C, Seker H. Prediction of protein allergenicity based on signal-processing bioinformatics approach. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2014:808-11. [PMID: 25570082 DOI: 10.1109/embc.2014.6943714] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Current bioinformatics tools accomplish high accuracies in classifying allergenic protein sequences with high homology and generally perform poorly with low homology protein sequences. Although some homologous regions explained Immunoglobulin E (IgE) cross-reactivity in groups of allergens, no universal molecular structure could be associated with allergenicity. In addition, studies have showed that cross-reactivity is not directly linked to the homology between protein sequences. Therefore, a new homology independent method needs to be developed to determine if a protein is an allergen or not. The aim of this study is therefore to differentiate sets of allergenic and non-allergenic proteins using a signal-processing based bioinformatics approach. In this paper, a new method was proposed for characterisation and classification of allergenic protein sequences. For this method hydrophobicity amino acid index was used to encode proteins to numerical sequences and Discrete Fourier Transform to extract features for each protein. Finally, a classifier was constructed based on Support Vector Machines. In order to demonstrate the applicability of the proposed method 857 allergen and 1000 non-allergen proteins were collected from UniProt online database. The results obtained from the proposed method yielded: MCC: 0.752 ± 0.007, Specificity: 0.912 ± 0.005, Sensitivity: 0.835 ± 0.008 and Total Accuracy: 87.65% ± 0.004.
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Ghosh P, Mathew OK, Sowdhamini R. RStrucFam: a web server to associate structure and cognate RNA for RNA-binding proteins from sequence information. BMC Bioinformatics 2016; 17:411. [PMID: 27717309 PMCID: PMC5054549 DOI: 10.1186/s12859-016-1289-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Accepted: 09/29/2016] [Indexed: 11/25/2022] Open
Abstract
Background RNA-binding proteins (RBPs) interact with their cognate RNA(s) to form large biomolecular assemblies. They are versatile in their functionality and are involved in a myriad of processes inside the cell. RBPs with similar structural features and common biological functions are grouped together into families and superfamilies. It will be useful to obtain an early understanding and association of RNA-binding property of sequences of gene products. Here, we report a web server, RStrucFam, to predict the structure, type of cognate RNA(s) and function(s) of proteins, where possible, from mere sequence information. Results The web server employs Hidden Markov Model scan (hmmscan) to enable association to a back-end database of structural and sequence families. The database (HMMRBP) comprises of 437 HMMs of RBP families of known structure that have been generated using structure-based sequence alignments and 746 sequence-centric RBP family HMMs. The input protein sequence is associated with structural or sequence domain families, if structure or sequence signatures exist. In case of association of the protein with a family of known structures, output features like, multiple structure-based sequence alignment (MSSA) of the query with all others members of that family is provided. Further, cognate RNA partner(s) for that protein, Gene Ontology (GO) annotations, if any and a homology model of the protein can be obtained. The users can also browse through the database for details pertaining to each family, protein or RNA and their related information based on keyword search or RNA motif search. Conclusions RStrucFam is a web server that exploits structurally conserved features of RBPs, derived from known family members and imprinted in mathematical profiles, to predict putative RBPs from sequence information. Proteins that fail to associate with such structure-centric families are further queried against the sequence-centric RBP family HMMs in the HMMRBP database. Further, all other essential information pertaining to an RBP, like overall function annotations, are provided. The web server can be accessed at the following link: http://caps.ncbs.res.in/rstrucfam. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1289-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Pritha Ghosh
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bellary Road, Bangalore, Karnataka, 560 065, India
| | - Oommen K Mathew
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bellary Road, Bangalore, Karnataka, 560 065, India.,SASTRA University, Tirumalaisamudram, Thanjavur, 613401, Tamil Nadu, India
| | - Ramanathan Sowdhamini
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bellary Road, Bangalore, Karnataka, 560 065, India.
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Li YH, Xu JY, Tao L, Li XF, Li S, Zeng X, Chen SY, Zhang P, Qin C, Zhang C, Chen Z, Zhu F, Chen YZ. SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity. PLoS One 2016; 11:e0155290. [PMID: 27525735 PMCID: PMC4985167 DOI: 10.1371/journal.pone.0155290] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Accepted: 04/27/2016] [Indexed: 12/20/2022] Open
Abstract
Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine learning method for predicting protein functional families from protein sequences irrespective of similarity, which complemented those similarity-based and other methods in predicting diverse classes of proteins including the distantly-related proteins and homologous proteins of different functions. Since its publication in 2003, we made major improvements to SVM-Prot with (1) expanded coverage from 54 to 192 functional families, (2) more diverse protein descriptors protein representation, (3) improved predictive performances due to the use of more enriched training datasets and more variety of protein descriptors, (4) newly integrated BLAST analysis option for assessing proteins in the SVM-Prot predicted functional families that were similar in sequence to a query protein, and (5) newly added batch submission option for supporting the classification of multiple proteins. Moreover, 2 more machine learning approaches, K nearest neighbor and probabilistic neural networks, were added for facilitating collective assessment of protein functions by multiple methods. SVM-Prot can be accessed at http://bidd2.nus.edu.sg/cgi-bin/svmprot/svmprot.cgi.
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Affiliation(s)
- Ying Hong Li
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, China
| | - Jing Yu Xu
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, China
- School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China
| | - Lin Tao
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, China
- Bioinformatics and Drug Discovery group, Department of Pharmacy, National University of Singapore, Singapore, 117543, Singapore
| | - Xiao Feng Li
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, China
| | - Shuang Li
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, China
| | - Xian Zeng
- Bioinformatics and Drug Discovery group, Department of Pharmacy, National University of Singapore, Singapore, 117543, Singapore
| | - Shang Ying Chen
- Bioinformatics and Drug Discovery group, Department of Pharmacy, National University of Singapore, Singapore, 117543, Singapore
| | - Peng Zhang
- Bioinformatics and Drug Discovery group, Department of Pharmacy, National University of Singapore, Singapore, 117543, Singapore
| | - Chu Qin
- Bioinformatics and Drug Discovery group, Department of Pharmacy, National University of Singapore, Singapore, 117543, Singapore
| | - Cheng Zhang
- Bioinformatics and Drug Discovery group, Department of Pharmacy, National University of Singapore, Singapore, 117543, Singapore
| | - Zhe Chen
- Zhejiang Key Laboratory of Gastro-intestinal Pathophysiology, Zhejiang Hospital of Traditional Chinese Medicine, Zhejiang Chinese Medical University, Hangzhou, P. R. China
| | - Feng Zhu
- Innovative Drug Research and Bioinformatics Group, Innovative Drug Research Centre and School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, China
| | - Yu Zong Chen
- Bioinformatics and Drug Discovery group, Department of Pharmacy, National University of Singapore, Singapore, 117543, Singapore
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Computational Prediction of RNA-Binding Proteins and Binding Sites. Int J Mol Sci 2015; 16:26303-17. [PMID: 26540053 PMCID: PMC4661811 DOI: 10.3390/ijms161125952] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Revised: 10/20/2015] [Accepted: 10/23/2015] [Indexed: 11/19/2022] Open
Abstract
Proteins and RNA interaction have vital roles in many cellular processes such as protein synthesis, sequence encoding, RNA transfer, and gene regulation at the transcriptional and post-transcriptional levels. Approximately 6%–8% of all proteins are RNA-binding proteins (RBPs). Distinguishing these RBPs or their binding residues is a major aim of structural biology. Previously, a number of experimental methods were developed for the determination of protein–RNA interactions. However, these experimental methods are expensive, time-consuming, and labor-intensive. Alternatively, researchers have developed many computational approaches to predict RBPs and protein–RNA binding sites, by combining various machine learning methods and abundant sequence and/or structural features. There are three kinds of computational approaches, which are prediction from protein sequence, prediction from protein structure, and protein-RNA docking. In this paper, we review all existing studies of predictions of RNA-binding sites and RBPs and complexes, including data sets used in different approaches, sequence and structural features used in several predictors, prediction method classifications, performance comparisons, evaluation methods, and future directions.
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Ma X, Guo J, Xiao K, Sun X. PRBP: Prediction of RNA-Binding Proteins Using a Random Forest Algorithm Combined with an RNA-Binding Residue Predictor. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:1385-1393. [PMID: 26671809 DOI: 10.1109/tcbb.2015.2418773] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The prediction of RNA-binding proteins is an incredibly challenging problem in computational biology. Although great progress has been made using various machine learning approaches with numerous features, the problem is still far from being solved. In this study, we attempt to predict RNA-binding proteins directly from amino acid sequences. A novel approach, PRBP predicts RNA-binding proteins using the information of predicted RNA-binding residues in conjunction with a random forest based method. For a given protein, we first predict its RNA-binding residues and then judge whether the protein binds RNA or not based on information from that prediction. If the protein cannot be identified by the information associated with its predicted RNA-binding residues, then a novel random forest predictor is used to determine if the query protein is a RNA-binding protein. We incorporated features of evolutionary information combined with physicochemical features (EIPP) and amino acid composition feature to establish the random forest predictor. Feature analysis showed that EIPP contributed the most to the prediction of RNA-binding proteins. The results also showed that the information from the RNA-binding residue prediction improved the overall performance of our RNA-binding protein prediction. It is anticipated that the PRBP method will become a useful tool for identifying RNA-binding proteins. A PRBP Web server implementation is freely available at http://www.cbi.seu.edu.cn/PRBP/.
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Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection. BIOMED RESEARCH INTERNATIONAL 2015; 2015:425810. [PMID: 26543860 PMCID: PMC4620426 DOI: 10.1155/2015/425810] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2015] [Accepted: 09/21/2015] [Indexed: 11/17/2022]
Abstract
The prediction of RNA-binding proteins is one of the most challenging problems in computation biology. Although some studies have investigated this problem, the accuracy of prediction is still not sufficient. In this study, a highly accurate method was developed to predict RNA-binding proteins from amino acid sequences using random forests with the minimum redundancy maximum relevance (mRMR) method, followed by incremental feature selection (IFS). We incorporated features of conjoint triad features and three novel features: binding propensity (BP), nonbinding propensity (NBP), and evolutionary information combined with physicochemical properties (EIPP). The results showed that these novel features have important roles in improving the performance of the predictor. Using the mRMR-IFS method, our predictor achieved the best performance (86.62% accuracy and 0.737 Matthews correlation coefficient). High prediction accuracy and successful prediction performance suggested that our method can be a useful approach to identify RNA-binding proteins from sequence information.
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Yousef A, Moghadam Charkari N. SFM: A novel sequence-based fusion method for disease genes identification and prioritization. J Theor Biol 2015. [DOI: 10.1016/j.jtbi.2015.07.010] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Ren H, Shen Y. RNA-binding residues prediction using structural features. BMC Bioinformatics 2015; 16:249. [PMID: 26254826 PMCID: PMC4529986 DOI: 10.1186/s12859-015-0691-0] [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] [Received: 01/03/2015] [Accepted: 07/31/2015] [Indexed: 01/25/2023] Open
Abstract
Background RNA-protein complexes play an essential role in many biological processes. To explore potential functions of RNA-protein complexes, it’s important to identify RNA-binding residues in proteins. Results In this work, we propose a set of new structural features for RNA-binding residue prediction. A set of template patches are first extracted from RNA-binding interfaces. To construct structural features for a residue, we compare its surrounding patches with each template patch and use the accumulated distances as its structural features. These new features provide sufficient structural information of surrounding surface of a residue and they can be used to measure the structural similarity between the surface surrounding two residues. The new structural features, together with other sequence features, are used to predict RNA-binding residues using ensemble learning technique. Conclusions The experimental results reveal the effectiveness of the proposed structural features. In addition, the clustering results on template patches exhibit distinct structural patterns of RNA-binding sites, although the sequences of template patches in the same cluster are not conserved. We speculate that RNAs may have structure preferences when binding with proteins.
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Affiliation(s)
- Huizhu Ren
- 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Key Laboratory of Hormones and Development (Ministry of Health), Metabolic Diseases Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300070, China.
| | - Ying Shen
- School of Software Engineering, Tongji University, Shanghai, 201804, China. .,Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information, Ministry of Education, Nanjing University of Science and Technology, Nanjing, 210094, P.R. China.
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Yousef A, Charkari NM. A novel method based on physicochemical properties of amino acids and one class classification algorithm for disease gene identification. J Biomed Inform 2015; 56:300-6. [DOI: 10.1016/j.jbi.2015.06.018] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Revised: 06/04/2015] [Accepted: 06/26/2015] [Indexed: 10/23/2022]
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Pérez-Cano L, Fernández-Recio J. Dissection and prediction of RNA-binding sites on proteins. Biomol Concepts 2015; 1:345-55. [PMID: 25962008 DOI: 10.1515/bmc.2010.037] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
RNA-binding proteins are involved in many important regulatory processes in cells and their study is essential for a complete understanding of living organisms. They show a large variability from both structural and functional points of view. However, several recent studies performed on protein-RNA crystal structures have revealed interesting common properties. RNA-binding sites usually constitute patches of positively charged or polar residues that make most of the specific and non-specific contacts with RNA. Negatively charged or aliphatic residues are less frequent at protein-RNA interfaces, although they can also be found either forming aliphatic and positive-negative pairs in protein RNA-binding sites or contacting RNA through their main chains. Aromatic residues found within these interfaces are usually involved in specific base recognition at RNA single-strand regions. This specific recognition, in combination with structural complementarity, represents the key source for specificity in protein-RNA association. From all this knowledge, a variety of computational methods for prediction of RNA-binding sites have been developed based either on protein sequence or on protein structure. Some reported methods are really successful in the identification of RNA-binding proteins or the prediction of RNA-binding sites. Given the growing interest in the field, all these studies and prediction methods will undoubtedly contribute to the identification and comprehension of protein-RNA interactions.
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CISAPS: Complex Informational Spectrum for the Analysis of Protein Sequences. Adv Bioinformatics 2015; 2015:909765. [PMID: 25632276 PMCID: PMC4302972 DOI: 10.1155/2015/909765] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2014] [Revised: 11/27/2014] [Accepted: 12/04/2014] [Indexed: 11/23/2022] Open
Abstract
Complex informational spectrum analysis for protein sequences (CISAPS) and its web-based server are developed and presented. As recent studies show, only the use of the absolute spectrum in the analysis of protein sequences using the informational spectrum analysis is proven to be insufficient. Therefore, CISAPS is developed to consider and provide results in three forms including absolute, real, and imaginary spectrum. Biologically related features to the analysis of influenza A subtypes as presented as a case study in this study can also appear individually either in the real or imaginary spectrum. As the results presented, protein classes can present similarities or differences according to the features extracted from CISAPS web server. These associations are probable to be related with the protein feature that the specific amino acid index represents. In addition, various technical issues such as zero-padding and windowing that may affect the analysis are also addressed. CISAPS uses an expanded list of 611 unique amino acid indices where each one represents a different property to perform the analysis. This web-based server enables researchers with little knowledge of signal processing methods to apply and include complex informational spectrum analysis to their work.
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Prediction of Protein-RNA Interactions Using Sequence and Structure Descriptors**This work was partially supported by the National Natural Science Foundation of China (NSFC) Grant No. 31100949, the Scientific Research Foundation for the Returned Overseas Chinese Scholars, Ministry of Education of China, the Fundamental Research Funds of Shandong University Grant No. 2014TB006, University of Rochester Center for AIDS Research Grant P30 AI078498 (NIH/NIAID) and NIH R01 Grant GM100788-01. ACTA ACUST UNITED AC 2015. [DOI: 10.1016/j.ifacol.2015.12.090] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Gupta Y, Witte M, Möller S, Ludwig RJ, Restle T, Zillikens D, Ibrahim SM. ptRNApred: computational identification and classification of post-transcriptional RNA. Nucleic Acids Res 2014; 42:e167. [PMID: 25303994 PMCID: PMC4267668 DOI: 10.1093/nar/gku918] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
UNLABELLED Non-coding RNAs (ncRNAs) are known to play important functional roles in the cell. However, their identification and recognition in genomic sequences remains challenging. In silico methods, such as classification tools, offer a fast and reliable way for such screening and multiple classifiers have already been developed to predict well-defined subfamilies of RNA. So far, however, out of all the ncRNAs, only tRNA, miRNA and snoRNA can be predicted with a satisfying sensitivity and specificity. We here present ptRNApred, a tool to detect and classify subclasses of non-coding RNA that are involved in the regulation of post-transcriptional modifications or DNA replication, which we here call post-transcriptional RNA (ptRNA). It (i) detects RNA sequences coding for post-transcriptional RNA from the genomic sequence with an overall sensitivity of 91% and a specificity of 94% and (ii) predicts ptRNA-subclasses that exist in eukaryotes: snRNA, snoRNA, RNase P, RNase MRP, Y RNA or telomerase RNA. AVAILABILITY The ptRNApred software is open for public use on http://www.ptrnapred.org/.
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Affiliation(s)
- Yask Gupta
- Department of Dermatology, University of Lübeck, 23538 Lübeck, Germany
| | - Mareike Witte
- Department of Dermatology, University of Lübeck, 23538 Lübeck, Germany
| | - Steffen Möller
- Department of Dermatology, University of Lübeck, 23538 Lübeck, Germany
| | - Ralf J Ludwig
- Department of Dermatology, University of Lübeck, 23538 Lübeck, Germany
| | - Tobias Restle
- Institute for Molecular Medicine, University of Lübeck, 23538 Lübeck, Germany
| | - Detlef Zillikens
- Department of Dermatology, University of Lübeck, 23538 Lübeck, Germany
| | - Saleh M Ibrahim
- Department of Dermatology, University of Lübeck, 23538 Lübeck, Germany
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Zhao H, Yang Y, Janga SC, Kao CC, Zhou Y. Prediction and validation of the unexplored RNA-binding protein atlas of the human proteome. Proteins 2014; 82:640-7. [PMID: 24123256 PMCID: PMC3949140 DOI: 10.1002/prot.24441] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2013] [Revised: 09/13/2013] [Accepted: 09/26/2013] [Indexed: 12/13/2022]
Abstract
Detecting protein-RNA interactions is challenging both experimentally and computationally because RNAs are large in number, diverse in cellular location and function, and flexible in structure. As a result, many RNA-binding proteins (RBPs) remain to be identified. Here, a template-based, function-prediction technique SPOT-Seq for RBPs is applied to human proteome and its result is validated by a recent proteomic experimental discovery of 860 mRNA-binding proteins (mRBPs). The coverage (or sensitivity) is 42.6% for 1217 known RBPs annotated in the Gene Ontology and 43.6% for 860 newly discovered human mRBPs. Consistent sensitivity indicates the robust performance of SPOT-Seq for predicting RBPs. More importantly, SPOT-Seq detects 2418 novel RBPs in human proteome, 291 of which were validated by the newly discovered mRBP set. Among 291 validated novel RBPs, 61 are not homologous to any known RBPs. Successful validation of predicted novel RBPs permits us to further analysis of their phenotypic roles in disease pathways. The dataset of 2418 predicted novel RBPs along with confidence levels and complex structures is available at http://sparks-lab.org (in publications) for experimental confirmations and hypothesis generation.
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Affiliation(s)
- Huiying Zhao
- School of Informatics, Indiana University Purdue University, Indianapolis, Indiana, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 719 Indiana Ave Ste 319, Walker Plaza Building, Indianapolis, Indiana 46202, USA
| | - Yuedong Yang
- School of Informatics, Indiana University Purdue University, Indianapolis, Indiana, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 719 Indiana Ave Ste 319, Walker Plaza Building, Indianapolis, Indiana 46202, USA
- Institute for Glycomics and School of Informatics and Communication Technology, Griffith University, Parklands Dr., Southport, QLD4215, Australia
| | - Sarath Chandra Janga
- School of Informatics, Indiana University Purdue University, Indianapolis, Indiana, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 719 Indiana Ave Ste 319, Walker Plaza Building, Indianapolis, Indiana 46202, USA
| | - C. Cheng Kao
- Department of Molecular & Cellular Biochemistry, Indiana University, Bloomington, Indiana, 47405, USA
| | - Yaoqi Zhou
- School of Informatics, Indiana University Purdue University, Indianapolis, Indiana, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 719 Indiana Ave Ste 319, Walker Plaza Building, Indianapolis, Indiana 46202, USA
- Institute for Glycomics and School of Informatics and Communication Technology, Griffith University, Parklands Dr., Southport, QLD4215, Australia
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Das Roy R, Dash D. Selection of relevant features from amino acids enables development of robust classifiers. Amino Acids 2014; 46:1343-51. [PMID: 24604165 DOI: 10.1007/s00726-014-1697-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2013] [Accepted: 02/14/2014] [Indexed: 12/30/2022]
Abstract
Machine learning (ML) has been extensively applied to develop models and to understand high-throughput data of biological processes. However, new ML models, trained with novel experimental results, are required to build regularly for more precise predictions. ML methods can build models from numeric data, whereas biological data are generally textual (DNA, protein sequences) or images and needs feature calculation algorithms to generate quantitative features. Programming skills along with domain knowledge are required to develop these algorithms. Therefore, the process of knowledge discovery through ML is decelerated due to lack of generic tools to construct features and to build models directly from the data. Hence, we developed a schema that calculates about 5,000 features, selects relevant features and develops protein classifiers from the training data. To demonstrate the general applicability and robustness of our method, fungal adhesins and nuclear receptor proteins were used for building classifiers which outperformed existing classifiers when tested on independent data. Next, we built a classifier for mitochondrial proteins of Plasmodium falciparum which causes human malaria because the latest corresponding classifiers are not publically accessible. Our classifier attained 98.18 % accuracy and 0.95 Matthews correlation coefficient by fivefold cross-validation and outperformed existing classifiers on independent test set. We implemented this schema as user-friendly and open source application Pro-Gyan ( http://code.google.com/p/pro-gyan/ ), to build and share executable classifiers without programming knowledge.
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Affiliation(s)
- Rishi Das Roy
- GN Ramachandran Knowledge Centre for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, Mall Road, Delhi, 110007, India,
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Incorporating significant amino acid pairs and protein domains to predict RNA splicing-related proteins with functional roles. J Comput Aided Mol Des 2014; 28:49-60. [PMID: 24442949 DOI: 10.1007/s10822-014-9706-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2013] [Accepted: 01/07/2014] [Indexed: 12/20/2022]
Abstract
Machinery of pre-mRNA splicing is carried out through the interaction of RNA sequence elements and a variety of RNA splicing-related proteins (SRPs) (e.g. spliceosome and splicing factors). Alternative splicing, which is an important post-transcriptional regulation in eukaryotes, gives rise to multiple mature mRNA isoforms, which encodes proteins with functional diversities. However, the regulation of RNA splicing is not yet fully elucidated, partly because SRPs have not yet been exhaustively identified and the experimental identification is labor-intensive. Therefore, we are motivated to design a new method for identifying SRPs with their functional roles in the regulation of RNA splicing. The experimentally verified SRPs were manually curated from research articles. According to the functional annotation of Splicing Related Gene Database, the collected SRPs were further categorized into four functional groups including small nuclear Ribonucleoprotein, Splicing Factor, Splicing Regulation Factor and Novel Spliceosome Protein. The composition of amino acid pairs indicates that there are remarkable differences among four functional groups of SRPs. Then, support vector machines (SVMs) were utilized to learn the predictive models for identifying SRPs as well as their functional roles. The cross-validation evaluation presents that the SVM models trained with significant amino acid pairs and functional domains could provide a better predictive performance. In addition, the independent testing demonstrates that the proposed method could accurately identify SRPs in mammals/plants as well as effectively distinguish between SRPs and RNA-binding proteins. This investigation provides a practical means to identifying potential SRPs and a perspective for exploring the regulation of RNA splicing.
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Zhao H, Yang Y, Zhou Y. Prediction of RNA binding proteins comes of age from low resolution to high resolution. MOLECULAR BIOSYSTEMS 2013; 9:2417-25. [PMID: 23872922 PMCID: PMC3870025 DOI: 10.1039/c3mb70167k] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Networks of protein-RNA interactions is likely to be larger than protein-protein and protein-DNA interaction networks because RNA transcripts are encoded tens of times more than proteins (e.g. only 3% of human genome coded for proteins), have diverse function and localization, and are controlled by proteins from birth (transcription) to death (degradation). This massive network is evidenced by several recent experimental discoveries of large numbers of previously unknown RNA-binding proteins (RBPs). Meanwhile, more than 400 non-redundant protein-RNA complex structures (at 25% sequence identity or less) have been deposited into the protein databank. These sequences and structural resources for RBPs provide ample data for the development of computational techniques dedicated to RBP prediction, as experimentally determining RNA-binding functions is time-consuming and expensive. This review compares traditional machine-learning based approaches with emerging template-based methods at several levels of prediction resolution ranging from two-state binding/non-binding prediction, to binding residue prediction and protein-RNA complex structure prediction. The analysis indicates that the two approaches are complementary and their combinations may lead to further improvements.
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Affiliation(s)
- Huiying Zhao
- School of Informatics, Indiana University Purdue University, Indianapolis, Indiana 46202, USA.
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Hosseinzadeh F, Kayvanjoo AH, Ebrahimi M, Goliaei B. Prediction of lung tumor types based on protein attributes by machine learning algorithms. SPRINGERPLUS 2013; 2:238. [PMID: 23888262 PMCID: PMC3710575 DOI: 10.1186/2193-1801-2-238] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2013] [Accepted: 03/21/2013] [Indexed: 01/15/2023]
Abstract
Early diagnosis of lung cancers and distinction between the tumor types (Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC) are very important to increase the survival rate of patients. Herein, we propose a diagnostic system based on sequence-derived structural and physicochemical attributes of proteins that involved in both types of tumors via feature extraction, feature selection and prediction models. 1497 proteins attributes computed and important features selected by 12 attribute weighting models and finally machine learning models consist of seven SVM models, three ANN models and two NB models applied on original database and newly created ones from attribute weighting models; models accuracies calculated through 10-fold cross and wrapper validation (just for SVM algorithms). In line with our previous findings, dipeptide composition, autocorrelation and distribution descriptor were the most important protein features selected by bioinformatics tools. The algorithms performances in lung cancer tumor type prediction increased when they applied on datasets created by attribute weighting models rather than original dataset. Wrapper-Validation performed better than X-Validation; the best cancer type prediction resulted from SVM and SVM Linear models (82%). The best accuracy of ANN gained when Neural Net model applied on SVM dataset (88%). This is the first report suggesting that the combination of protein features and attribute weighting models with machine learning algorithms can be effectively used to predict the type of lung cancer tumors (SCLC and NSCLC).
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Affiliation(s)
- Faezeh Hosseinzadeh
- Laboratory of biophysics and molecular biology, Institute of Biophysics and Biochemistry (IBB), University of Tehran, Tehran, Iran
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Wang Y, Chen X, Liu ZP, Huang Q, Wang Y, Xu D, Zhang XS, Chen R, Chen L. De novo prediction of RNA–protein interactions from sequence information. ACTA ACUST UNITED AC 2013; 9:133-42. [DOI: 10.1039/c2mb25292a] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Jahandideh S, Srinivasasainagendra V, Zhi D. Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection. J Theor Biol 2012; 312:65-75. [PMID: 22884576 DOI: 10.1016/j.jtbi.2012.07.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2012] [Revised: 07/09/2012] [Accepted: 07/13/2012] [Indexed: 01/11/2023]
Abstract
RNA-protein interaction plays an important role in various cellular processes, such as protein synthesis, gene regulation, post-transcriptional gene regulation, alternative splicing, and infections by RNA viruses. In this study, using Gene Ontology Annotated (GOA) and Structural Classification of Proteins (SCOP) databases an automatic procedure was designed to capture structurally solved RNA-binding protein domains in different subclasses. Subsequently, we applied tuned multi-class SVM (TMCSVM), Random Forest (RF), and multi-class ℓ1/ℓq-regularized logistic regression (MCRLR) for analysis and classifying RNA-binding protein domains based on a comprehensive set of sequence and structural features. In this study, we compared prediction accuracy of three different state-of-the-art predictor methods. From our results, TMCSVM outperforms the other methods and suggests the potential of TMCSVM as a useful tool for facilitating the multi-class prediction of RNA-binding protein domains. On the other hand, MCRLR by elucidating importance of features for their contribution in predictive accuracy of RNA-binding protein domains subclasses, helps us to provide some biological insights into the roles of sequences and structures in protein-RNA interactions.
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Affiliation(s)
- Samad Jahandideh
- Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA.
| | - Vinodh Srinivasasainagendra
- Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Degui Zhi
- Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA.
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BATUWITA RUKSHAN, PALADE VASILE. ADJUSTED GEOMETRIC-MEAN: A NOVEL PERFORMANCE MEASURE FOR IMBALANCED BIOINFORMATICS DATASETS LEARNING. J Bioinform Comput Biol 2012; 10:1250003. [DOI: 10.1142/s0219720012500035] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
One common and challenging problem faced by many bioinformatics applications, such as promoter recognition, splice site prediction, RNA gene prediction, drug discovery and protein classification, is the imbalance of the available datasets. In most of these applications, the positive data examples are largely outnumbered by the negative data examples, which often leads to the development of sub-optimal prediction models having high negative recognition rate (Specificity = SP) and low positive recognition rate (Sensitivity = SE). When class imbalance learning methods are applied, usually, the SE is increased at the expense of reducing some amount of the SP. In this paper, we point out that in these data-imbalanced bioinformatics applications, the goal of applying class imbalance learning methods would be to increase the SE as high as possible by keeping the reduction of SP as low as possible. We explain that the existing performance measures used in class imbalance learning can still produce sub-optimal models with respect to this classification goal. In order to overcome these problems, we introduce a new performance measure called Adjusted Geometric-mean (AGm). The experimental results obtained on ten real-world imbalanced bioinformatics datasets demonstrates that the AGm metric can achieve a lower rate of reduction of SP than the existing performance metrics, when increasing the SE through class imbalance learning methods. This characteristic of AGm metric makes it more suitable for achieving the proposed classification goal in imbalanced bioinformatics datasets learning.
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Affiliation(s)
- RUKSHAN BATUWITA
- University of Oxford, Department of Computer Science, Oxford, OX1 3QD, United Kingdom
| | - VASILE PALADE
- University of Oxford, Department of Computer Science, Oxford, OX1 3QD, United Kingdom
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Hosseinzadeh F, Ebrahimi M, Goliaei B, Shamabadi N. Classification of lung cancer tumors based on structural and physicochemical properties of proteins by bioinformatics models. PLoS One 2012; 7:e40017. [PMID: 22829872 PMCID: PMC3400626 DOI: 10.1371/journal.pone.0040017] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2012] [Accepted: 05/30/2012] [Indexed: 12/03/2022] Open
Abstract
Rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important in diagnosis of this disease. Furthermore sequence-derived structural and physicochemical descriptors are very useful for machine learning prediction of protein structural and functional classes, classifying proteins and the prediction performance. Herein, in this study is the classification of lung tumors based on 1497 attributes derived from structural and physicochemical properties of protein sequences (based on genes defined by microarray analysis) investigated through a combination of attribute weighting, supervised and unsupervised clustering algorithms. Eighty percent of the weighting methods selected features such as autocorrelation, dipeptide composition and distribution of hydrophobicity as the most important protein attributes in classification of SCLC, NSCLC and COMMON classes of lung tumors. The same results were observed by most tree induction algorithms while descriptors of hydrophobicity distribution were high in protein sequences COMMON in both groups and distribution of charge in these proteins was very low; showing COMMON proteins were very hydrophobic. Furthermore, compositions of polar dipeptide in SCLC proteins were higher than NSCLC proteins. Some clustering models (alone or in combination with attribute weighting algorithms) were able to nearly classify SCLC and NSCLC proteins. Random Forest tree induction algorithm, calculated on leaves one-out and 10-fold cross validation) shows more than 86% accuracy in clustering and predicting three different lung cancer tumors. Here for the first time the application of data mining tools to effectively classify three classes of lung cancer tumors regarding the importance of dipeptide composition, autocorrelation and distribution descriptor has been reported.
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Affiliation(s)
- Faezeh Hosseinzadeh
- Student at Laboratory of Biophysics and Molecular Biology, Institute of Biophysics and Biochemistry, University of Tehran, Tehran, Iran
| | - Mansour Ebrahimi
- Department of Biology at Basic science School & Bioinformatics Research Group, Green Research Center, University of Qom, Qom, Iran
| | - Bahram Goliaei
- Department of Medical Physics, Iran University of Medical Science, Tehran, Iran
| | - Narges Shamabadi
- Bioinformatics Research Group, Green Research Center, University of Qom, Qom, Iran
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Fernandez M, Kumagai Y, Standley DM, Sarai A, Mizuguchi K, Ahmad S. Prediction of dinucleotide-specific RNA-binding sites in proteins. BMC Bioinformatics 2011; 12 Suppl 13:S5. [PMID: 22373260 PMCID: PMC3278845 DOI: 10.1186/1471-2105-12-s13-s5] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
Background Regulation of gene expression, protein synthesis, replication and assembly of many viruses involve RNA–protein interactions. Although some successful computational tools have been reported to recognize RNA binding sites in proteins, the problem of specificity remains poorly investigated. After the nucleotide base composition, the dinucleotide is the smallest unit of RNA sequence information and many RNA-binding proteins simply bind to regions enriched in one dinucleotide. Interaction preferences of protein subsequences and dinucleotides can be inferred from protein-RNA complex structures, enabling a training-based prediction approach. Results We analyzed basic statistics of amino acid-dinucleotide contacts in protein-RNA complexes and found their pairing preferences could be identified. Using a standard approach to represent protein subsequences by their evolutionary profile, we trained neural networks to predict multiclass target vectors corresponding to 16 possible contacting dinucleotide subsequences. In the cross-validation experiments, the accuracies of the optimum network, measured as areas under the curve (AUC) of the receiver operating characteristic (ROC) graphs, were in the range of 65-80%. Conclusions Dinucleotide-specific contact predictions have also been extended to the prediction of interacting protein and RNA fragment pairs, which shows the applicability of this method to predict targets of RNA-binding proteins. A web server predicting the 16-dimensional contact probability matrix directly from a user-defined protein sequence was implemented and made available at: http://tardis.nibio.go.jp/netasa/srcpred.
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Hsu JBK, Bretaña NA, Lee TY, Huang HD. Incorporating evolutionary information and functional domains for identifying RNA splicing factors in humans. PLoS One 2011; 6:e27567. [PMID: 22110674 PMCID: PMC3217973 DOI: 10.1371/journal.pone.0027567] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2011] [Accepted: 10/19/2011] [Indexed: 11/19/2022] Open
Abstract
Regulation of pre-mRNA splicing is achieved through the interaction of RNA sequence elements and a variety of RNA-splicing related proteins (splicing factors). The splicing machinery in humans is not yet fully elucidated, partly because splicing factors in humans have not been exhaustively identified. Furthermore, experimental methods for splicing factor identification are time-consuming and lab-intensive. Although many computational methods have been proposed for the identification of RNA-binding proteins, there exists no development that focuses on the identification of RNA-splicing related proteins so far. Therefore, we are motivated to design a method that focuses on the identification of human splicing factors using experimentally verified splicing factors. The investigation of amino acid composition reveals that there are remarkable differences between splicing factors and non-splicing proteins. A support vector machine (SVM) is utilized to construct a predictive model, and the five-fold cross-validation evaluation indicates that the SVM model trained with amino acid composition could provide a promising accuracy (80.22%). Another basic feature, amino acid dipeptide composition, is also examined to yield a similar predictive performance to amino acid composition. In addition, this work presents that the incorporation of evolutionary information and domain information could improve the predictive performance. The constructed models have been demonstrated to effectively classify (73.65% accuracy) an independent data set of human splicing factors. The result of independent testing indicates that in silico identification could be a feasible means of conducting preliminary analyses of splicing factors and significantly reducing the number of potential targets that require further in vivo or in vitro confirmation.
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Affiliation(s)
- Justin Bo-Kai Hsu
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu, Taiwan
| | - Neil Arvin Bretaña
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan
| | - Tzong-Yi Lee
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan
- * E-mail: (T-YL); (H-DH)
| | - Hsien-Da Huang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu, Taiwan
- Department of Biological Science and Technology, National Chiao Tung University, Hsin-Chu, Taiwan
- Core Facility for Structural Bioinformatics, National Chiao Tung University, Hsin-Chu, Taiwan
- * E-mail: (T-YL); (H-DH)
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