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Pradhan UK, Naha S, Das R, Gupta A, Parsad R, Meher PK. RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotes. Comput Struct Biotechnol J 2024; 23:1631-1640. [PMID: 38660008 PMCID: PMC11039349 DOI: 10.1016/j.csbj.2024.04.034] [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: 02/16/2024] [Revised: 04/12/2024] [Accepted: 04/12/2024] [Indexed: 04/26/2024] Open
Abstract
RNA-binding proteins (RBPs) are central to key functions such as post-transcriptional regulation, mRNA stability, and adaptation to varied environmental conditions in prokaryotes. While the majority of research has concentrated on eukaryotic RBPs, recent developments underscore the crucial involvement of prokaryotic RBPs. Although computational methods have emerged in recent years to identify RBPs, they have fallen short in accurately identifying prokaryotic RBPs due to their generic nature. To bridge this gap, we introduce RBProkCNN, a novel machine learning-driven computational model meticulously designed for the accurate prediction of prokaryotic RBPs. The prediction process involves the utilization of eight shallow learning algorithms and four deep learning models, incorporating PSSM-based evolutionary features. By leveraging a convolutional neural network (CNN) and evolutionarily significant features selected through extreme gradient boosting variable importance measure, RBProkCNN achieved the highest accuracy in five-fold cross-validation, yielding 98.04% auROC and 98.19% auPRC. Furthermore, RBProkCNN demonstrated robust performance with an independent dataset, showcasing a commendable 95.77% auROC and 95.78% auPRC. Noteworthy is its superior predictive accuracy when compared to several state-of-the-art existing models. RBProkCNN is available as an online prediction tool (https://iasri-sg.icar.gov.in/rbprokcnn/), offering free access to interested users. This tool represents a substantial contribution, enriching the array of resources available for the accurate and efficient prediction of prokaryotic RBPs.
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Affiliation(s)
- Upendra Kumar Pradhan
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Sanchita Naha
- Division of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Ritwika Das
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Ajit Gupta
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Rajender Parsad
- ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Prabina Kumar Meher
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
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Pradhan UK, Meher PK, Naha S, Pal S, Gupta S, Gupta A, Parsad R. RBPLight: a computational tool for discovery of plant-specific RNA-binding proteins using light gradient boosting machine and ensemble of evolutionary features. Brief Funct Genomics 2023; 22:401-410. [PMID: 37158175 DOI: 10.1093/bfgp/elad016] [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: 12/05/2022] [Revised: 04/12/2023] [Accepted: 04/21/2023] [Indexed: 05/10/2023] Open
Abstract
RNA-binding proteins (RBPs) are essential for post-transcriptional gene regulation in eukaryotes, including splicing control, mRNA transport and decay. Thus, accurate identification of RBPs is important to understand gene expression and regulation of cell state. In order to detect RBPs, a number of computational models have been developed. These methods made use of datasets from several eukaryotic species, specifically from mice and humans. Although some models have been tested on Arabidopsis, these techniques fall short of correctly identifying RBPs for other plant species. Therefore, the development of a powerful computational model for identifying plant-specific RBPs is needed. In this study, we presented a novel computational model for locating RBPs in plants. Five deep learning models and ten shallow learning algorithms were utilized for prediction with 20 sequence-derived and 20 evolutionary feature sets. The highest repeated five-fold cross-validation accuracy, 91.24% AU-ROC and 91.91% AU-PRC, was achieved by light gradient boosting machine. While evaluated using an independent dataset, the developed approach achieved 94.00% AU-ROC and 94.50% AU-PRC. The proposed model achieved significantly higher accuracy for predicting plant-specific RBPs as compared to the currently available state-of-art RBP prediction models. Despite the fact that certain models have already been trained and assessed on the model organism Arabidopsis, this is the first comprehensive computer model for the discovery of plant-specific RBPs. The web server RBPLight was also developed, which is publicly accessible at https://iasri-sg.icar.gov.in/rbplight/, for the convenience of researchers to identify RBPs in plants.
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Affiliation(s)
- Upendra K Pradhan
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Prabina K Meher
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Sanchita Naha
- Division of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Soumen Pal
- Division of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Sagar Gupta
- CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur (HP) 176061, India
| | - Ajit Gupta
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
| | - Rajender Parsad
- ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
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3
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Song Y, Yuan Q, Zhao H, Yang Y. Accurately identifying nucleic-acid-binding sites through geometric graph learning on language model predicted structures. Brief Bioinform 2023; 24:bbad360. [PMID: 37824738 DOI: 10.1093/bib/bbad360] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 09/18/2023] [Accepted: 09/18/2023] [Indexed: 10/14/2023] Open
Abstract
The interactions between nucleic acids and proteins are important in diverse biological processes. The high-quality prediction of nucleic-acid-binding sites continues to pose a significant challenge. Presently, the predictive efficacy of sequence-based methods is constrained by their exclusive consideration of sequence context information, whereas structure-based methods are unsuitable for proteins lacking known tertiary structures. Though protein structures predicted by AlphaFold2 could be used, the extensive computing requirement of AlphaFold2 hinders its use for genome-wide applications. Based on the recent breakthrough of ESMFold for fast prediction of protein structures, we have developed GLMSite, which accurately identifies DNA- and RNA-binding sites using geometric graph learning on ESMFold predicted structures. Here, the predicted protein structures are employed to construct protein structural graph with residues as nodes and spatially neighboring residue pairs for edges. The node representations are further enhanced through the pre-trained language model ProtTrans. The network was trained using a geometric vector perceptron, and the geometric embeddings were subsequently fed into a common network to acquire common binding characteristics. Finally, these characteristics were input into two fully connected layers to predict binding sites with DNA and RNA, respectively. Through comprehensive tests on DNA/RNA benchmark datasets, GLMSite was shown to surpass the latest sequence-based methods and be comparable with structure-based methods. Moreover, the prediction was shown useful for inferring nucleic-acid-binding proteins, demonstrating its potential for protein function discovery. The datasets, codes, and trained models are available at https://github.com/biomed-AI/nucleic-acid-binding.
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Affiliation(s)
- Yidong Song
- Key Laboratory of Machine Intelligence and Advanced Computing of MOE, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
| | - Qianmu Yuan
- Key Laboratory of Machine Intelligence and Advanced Computing of MOE, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
| | - Huiying Zhao
- Key Laboratory of Machine Intelligence and Advanced Computing of MOE, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
| | - Yuedong Yang
- Key Laboratory of Machine Intelligence and Advanced Computing of MOE, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
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Hong X, Tong X, Xie J, Liu P, Liu X, Song Q, Liu S, Liu S. An updated dataset and a structure-based prediction model for protein-RNA binding affinity. Proteins 2023; 91:1245-1253. [PMID: 37186412 DOI: 10.1002/prot.26503] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 03/08/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023]
Abstract
Understanding the process of protein-RNA interaction is essential for structural biology. The thermodynamic process is an important part to uncover the protein-RNA interaction mechanism. The regulatory networks between protein and RNA in organisms are dominated by the binding or dissociation in the cells. Therefore, determining the binding affinity for protein-RNA complexes can help us to understand the regulation mechanism of protein-RNA interaction. Since it is time-consuming and labor-intensive to determine the binding affinity for protein-RNA complexes by experimental methods, it is necessary and urgent to develop computational methods to predict that. To develop a binding affinity prediction model, first we update the dataset of protein-RNA binding affinity benchmark (PRBAB), which includes 145 complexes now. Second, we extract the structural features based on complex structure, and then we analyze and select the representative structural features to train the regression model. Third, we random select the subset from the PRBAB2.0 to fit the protein-RNA binding affinity determined by experiment. In the end, we tested our model on the nonredundant PDBbind dataset, and the results showed that Pearson correlation coefficient r = .57 and RMSE = 2.51 kcal/mol. The Pearson correlation coefficient achieves 0.7 while removing 5 complex structures with modified residues/nucleotides and metal ions. While testing on ProNAB, the results showed that 71.60% of the prediction achieves Pearson correlation coefficient r = .61 and RMSE = 1.56 kcal/mol with experiment values.
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Affiliation(s)
- Xu Hong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiaoxue Tong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Juan Xie
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Pinyu Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xudong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qi Song
- Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei University of Technology, Wuhan, China
| | - Sen Liu
- Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei University of Technology, Wuhan, China
| | - Shiyong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
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5
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Johansen N, Hu H, Quon G. Projecting RNA measurements onto single cell atlases to extract cell type-specific expression profiles using scProjection. Nat Commun 2023; 14:5192. [PMID: 37626024 PMCID: PMC10457395 DOI: 10.1038/s41467-023-40744-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
Multi-modal single cell RNA assays capture RNA content as well as other data modalities, such as spatial cell position or the electrophysiological properties of cells. Compared to dedicated scRNA-seq assays however, they may unintentionally capture RNA from multiple adjacent cells, exhibit lower RNA sequencing depth compared to scRNA-seq, or lack genome-wide RNA measurements. We present scProjection, a method for mapping individual multi-modal RNA measurements to deeply sequenced scRNA-seq atlases to extract cell type-specific, single cell gene expression profiles. We demonstrate several use cases of scProjection, including identifying spatial motifs from spatial transcriptome assays, distinguishing RNA contributions from neighboring cells in both spatial and multi-modal single cell assays, and imputing expression measurements of un-measured genes from gene markers. scProjection therefore combines the advantages of both multi-modal and scRNA-seq assays to yield precise multi-modal measurements of single cells.
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Affiliation(s)
- Nelson Johansen
- Graduate Group in Computer Science, University of California, Davis, Davis, CA, USA.
| | - Hongru Hu
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, Davis, CA, USA
| | - Gerald Quon
- Graduate Group in Computer Science, University of California, Davis, Davis, CA, USA.
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, Davis, CA, USA.
- Department of Molecular and Cellular Biology, University of California, Davis, Davis, CA, USA.
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Yan K, Feng J, Huang J, Wu H. iDRPro-SC: identifying DNA-binding proteins and RNA-binding proteins based on subfunction classifiers. Brief Bioinform 2023:bbad251. [PMID: 37405873 DOI: 10.1093/bib/bbad251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/10/2023] [Accepted: 06/12/2023] [Indexed: 07/07/2023] Open
Abstract
Nucleic acid-binding proteins are proteins that interact with DNA and RNA to regulate gene expression and transcriptional control. The pathogenesis of many human diseases is related to abnormal gene expression. Therefore, recognizing nucleic acid-binding proteins accurately and efficiently has important implications for disease research. To address this question, some scientists have proposed the method of using sequence information to identify nucleic acid-binding proteins. However, different types of nucleic acid-binding proteins have different subfunctions, and these methods ignore their internal differences, so the performance of the predictor can be further improved. In this study, we proposed a new method, called iDRPro-SC, to predict the type of nucleic acid-binding proteins based on the sequence information. iDRPro-SC considers the internal differences of nucleic acid-binding proteins and combines their subfunctions to build a complete dataset. Additionally, we used an ensemble learning to characterize and predict nucleic acid-binding proteins. The results of the test dataset showed that iDRPro-SC achieved the best prediction performance and was superior to the other existing nucleic acid-binding protein prediction methods. We have established a web server that can be accessed online: http://bliulab.net/iDRPro-SC.
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Affiliation(s)
- Ke Yan
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Jiawei Feng
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Jing Huang
- Huajian Yutong Technology (Beijing) Co., Ltd
- State Key Laboratory of Media Convergence Production Technology and Systems, Beijing China,100803
- Xinhua New Media Culture Communication Co., Ltd
| | - Hao Wu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
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7
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Wang N, Zhang J, Liu B. IDRBP-PPCT: Identifying Nucleic Acid-Binding Proteins Based on Position-Specific Score Matrix and Position-Specific Frequency Matrix Cross Transformation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2284-2293. [PMID: 33780341 DOI: 10.1109/tcbb.2021.3069263] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) are two important nucleic acid-binding proteins (NABPs), which play important roles in biological processes such as replication, translation and transcription of genetic material. Some proteins (DRBPs) bind to both DNA and RNA, also play a key role in gene expression. Identification of DBPs, RBPs and DRBPs is important to study protein-nucleic acid interactions. Computational methods are increasingly being proposed to automatically identify DNA- or RNA-binding proteins based only on protein sequences. One challenge is to design an effective protein representation method to convert protein sequences into fixed-dimension feature vectors. In this study, we proposed a novel protein representation method called Position-Specific Scoring Matrix (PSSM) and Position-Specific Frequency Matrix (PSFM) Cross Transformation (PPCT) to represent protein sequences. This method contains the evolutionary information in PSSM and PSFM, and their correlations. A new computational predictor called IDRBP-PPCT was proposed by combining PPCT and the two-layer framework based on the random forest algorithm to identify DBPs, RBPs and DRBPs. The experimental results on the independent dataset and the tomato genome proved the effectiveness of the proposed method. A user-friendly web-server of IDRBP-PPCT was constructed, which is freely available at http://bliulab.net/IDRBP-PPCT.
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8
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Zhang J, Yan K, Chen Q, Liu B. PreRBP-TL: prediction of species-specific RNA-binding proteins based on transfer learning. Bioinformatics 2022; 38:2135-2143. [PMID: 35176130 DOI: 10.1093/bioinformatics/btac106] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 11/18/2021] [Accepted: 02/15/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION RNA-binding proteins (RBPs) play crucial roles in post-transcriptional regulation. Accurate identification of RBPs helps to understand gene expression, regulation, etc. In recent years, some computational methods were proposed to identify RBPs. However, these methods fail to accurately identify RBPs from some specific species with limited data, such as bacteria. RESULTS In this study, we introduce a computational method called PreRBP-TL for identifying species-specific RBPs based on transfer learning. The weights of the prediction model were initialized by pretraining with the large general RBP dataset and then fine-tuned with the small species-specific RPB dataset by using transfer learning. The experimental results show that the PreRBP-TL achieves better performance for identifying the species-specific RBPs from Human, Arabidopsis, Escherichia coli and Salmonella, outperforming eight state-of-the-art computational methods. It is anticipated PreRBP-TL will become a useful method for identifying RBPs. AVAILABILITY AND IMPLEMENTATION For the convenience of researchers to identify RBPs, the web server of PreRBP-TL was established, freely available at http://bliulab.net/PreRBP-TL. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jun Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
| | - Ke Yan
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Qingcai Chen
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
| | - Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China.,School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.,Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing 100081, China
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9
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Gaither J, Lin YH, Bundschuh R. RBPBind: Quantitative prediction of Protein-RNA interactions. J Mol Biol 2022; 434:167515. [DOI: 10.1016/j.jmb.2022.167515] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 02/21/2022] [Accepted: 02/22/2022] [Indexed: 10/19/2022]
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Xie J, Zhang X, Zheng J, Hong X, Tong X, Liu X, Xue Y, Wang X, Zhang Y, Liu S. Two novel RNA-binding proteins identification through computational prediction and experimental validation. Genomics 2021; 114:149-160. [PMID: 34921931 DOI: 10.1016/j.ygeno.2021.12.003] [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: 01/14/2021] [Revised: 08/05/2021] [Accepted: 12/13/2021] [Indexed: 11/16/2022]
Abstract
Since RBPs play important roles in the cell, it's particularly important to find new RBPs. We performed iRIP-seq and CLIP-seq to verify two proteins, CLIP1 and DMD, predicted by RBPPred whether are RBPs or not. The experimental results confirm that these two proteins have RNA-binding activity. We identified significantly enriched binding motifs UGGGGAGG, CUUCCG and CCCGU for CLIP1 (iRIP-seq), DMD (iRIP-seq) and DMD (CLIP-seq), respectively. The computational KEGG and GO analysis show that the CLIP1 and DMD share some biological processes and functions. Besides, we found that the SNPs between DMD and its RNA partners may be associated with Becker muscular dystrophy, Duchenne muscular dystrophy, Dilated cardiomyopathy 3B and Cardiovascular phenotype. Among the thirteen cancers data, CLIP1 and another 300 oncogenes always co-occur, and 123 of these 300 genes interact with CLIP1. These cancers may be associated with the mutations occurred in both CLIP1 and the genes it interacts with.
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Affiliation(s)
- Juan Xie
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xiaoli Zhang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Jinfang Zheng
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xu Hong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xiaoxue Tong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xudong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yaqiang Xue
- Laboratory for Genome Regulation and Human Health, ABLife Inc., Wuhan, Hubei 430075, China
| | - Xuelian Wang
- ABLife BioBigData Institute, Wuhan, Hubei 430075, China
| | - Yi Zhang
- ABLife BioBigData Institute, Wuhan, Hubei 430075, China
| | - Shiyong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
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11
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Zhang J, Chen Q, Liu B. DeepDRBP-2L: A New Genome Annotation Predictor for Identifying DNA-Binding Proteins and RNA-Binding Proteins Using Convolutional Neural Network and Long Short-Term Memory. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1451-1463. [PMID: 31722485 DOI: 10.1109/tcbb.2019.2952338] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) are two kinds of crucial proteins, which are associated with various cellule activities and some important diseases. Accurate identification of DBPs and RBPs facilitate both theoretical research and real world application. Existing sequence-based DBP predictors can accurately identify DBPs but incorrectly predict many RBPs as DBPs, and vice versa, resulting in low prediction precision. Moreover, some proteins (DRBPs) interacting with both DNA and RNA play important roles in gene expression and cannot be identified by existing computational methods. In this study, a two-level predictor named DeepDRBP-2L was proposed by combining Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM). It is the first computational method that is able to identify DBPs, RBPs and DRBPs. Rigorous cross-validations and independent tests showed that DeepDRBP-2L is able to overcome the shortcoming of the existing methods and can go one further step to identify DRBPs. Application of DeepDRBP-2L to tomato genome further demonstrated its performance. The webserver of DeepDRBP-2L is freely available at http://bliulab.net/DeepDRBP-2L.
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12
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Palomino‐Hernandez O, Margreiter MA, Rossetti G. Challenges in RNA Regulation in Huntington's Disease: Insights from Computational Studies. Isr J Chem 2020. [DOI: 10.1002/ijch.202000021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Oscar Palomino‐Hernandez
- Computational Biomedicine, Institute of Neuroscience and Medicine (INM-9)/Instute for advanced simulations (IAS-5)Forschungszentrum Juelich 52425 Jülich Germany
- Faculty 1RWTH Aachen 52425 Aachen Germany
- Computation-based Science and Technology Research CenterThe Cyprus Institute Nicosia 2121 Cyprus
- Institute of Life ScienceThe Hebrew University of Jerusalem Jerusalem 91904 Israel
| | - Michael A. Margreiter
- Computational Biomedicine, Institute of Neuroscience and Medicine (INM-9)/Instute for advanced simulations (IAS-5)Forschungszentrum Juelich 52425 Jülich Germany
- Faculty 1RWTH Aachen 52425 Aachen Germany
| | - Giulia Rossetti
- Computational Biomedicine, Institute of Neuroscience and Medicine (INM-9)/Instute for advanced simulations (IAS-5)Forschungszentrum Juelich 52425 Jülich Germany
- Jülich Supercomputing Centre (JSC)Forschungszentrum Jülich 52425 Jülich Germany
- Department of Hematology, Oncology, Hemostaseology and Stem Cell Transplantation University Hospital AachenRWTH Aachen University Pauwelsstraße 30 52074 Aachen Germany
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13
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Corley M, Burns MC, Yeo GW. How RNA-Binding Proteins Interact with RNA: Molecules and Mechanisms. Mol Cell 2020; 78:9-29. [PMID: 32243832 PMCID: PMC7202378 DOI: 10.1016/j.molcel.2020.03.011] [Citation(s) in RCA: 337] [Impact Index Per Article: 84.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 01/13/2020] [Accepted: 03/09/2020] [Indexed: 12/17/2022]
Abstract
RNA-binding proteins (RBPs) comprise a large class of over 2,000 proteins that interact with transcripts in all manner of RNA-driven processes. The structures and mechanisms that RBPs use to bind and regulate RNA are incredibly diverse. In this review, we take a look at the components of protein-RNA interaction, from the molecular level to multi-component interaction. We first summarize what is known about protein-RNA molecular interactions based on analyses of solved structures. We additionally describe software currently available for predicting protein-RNA interaction and other resources useful for the study of RBPs. We then review the structure and function of seventeen known RNA-binding domains and analyze the hydrogen bonds adopted by protein-RNA structures on a domain-by-domain basis. We conclude with a summary of the higher-level mechanisms that regulate protein-RNA interactions.
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Affiliation(s)
- Meredith Corley
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Margaret C Burns
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA; Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Gene W Yeo
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA; Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA.
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14
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Qiu J, Bernhofer M, Heinzinger M, Kemper S, Norambuena T, Melo F, Rost B. ProNA2020 predicts protein-DNA, protein-RNA, and protein-protein binding proteins and residues from sequence. J Mol Biol 2020; 432:2428-2443. [PMID: 32142788 DOI: 10.1016/j.jmb.2020.02.026] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 02/17/2020] [Accepted: 02/23/2020] [Indexed: 11/29/2022]
Abstract
The intricate details of how proteins bind to proteins, DNA, and RNA are crucial for the understanding of almost all biological processes. Disease-causing sequence variants often affect binding residues. Here, we described a new, comprehensive system of in silico methods that take only protein sequence as input to predict binding of protein to DNA, RNA, and other proteins. Firstly, we needed to develop several new methods to predict whether or not proteins bind (per-protein prediction). Secondly, we developed independent methods that predict which residues bind (per-residue). Not requiring three-dimensional information, the system can predict the actual binding residue. The system combined homology-based inference with machine learning and motif-based profile-kernel approaches with word-based (ProtVec) solutions to machine learning protein level predictions. This achieved an overall non-exclusive three-state accuracy of 77% ± 1% (±one standard error) corresponding to a 1.8 fold improvement over random (best classification for protein-protein with F1 = 91 ± 0.8%). Standard neural networks for per-residue binding residue predictions appeared best for DNA-binding (Q2 = 81 ± 0.9%) followed by RNA-binding (Q2 = 80 ± 1%) and worst for protein-protein binding (Q2 = 69 ± 0.8%). The new method, dubbed ProNA2020, is available as code through github (https://github.com/Rostlab/ProNA2020.git) and through PredictProtein (www.predictprotein.org).
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Affiliation(s)
- Jiajun Qiu
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748, Garching, Munich, Germany; TUM Graduate School, Center of Doctoral Studies in Informatics and Its Applications (CeDoSIA), Garching, 85748, Germany.
| | - Michael Bernhofer
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748, Garching, Munich, Germany; TUM Graduate School, Center of Doctoral Studies in Informatics and Its Applications (CeDoSIA), Garching, 85748, Germany
| | - Michael Heinzinger
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748, Garching, Munich, Germany; TUM Graduate School, Center of Doctoral Studies in Informatics and Its Applications (CeDoSIA), Garching, 85748, Germany
| | - Sofie Kemper
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748, Garching, Munich, Germany
| | - Tomas Norambuena
- Molecular Bioinformatics Laboratory, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Francisco Melo
- Molecular Bioinformatics Laboratory, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile; Institute of Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Burkhard Rost
- Department of Informatics, I12-Chair of Bioinformatics and Computational Biology, Technical University of Munich (TUM), Boltzmannstrasse 3, 85748, Garching, Munich, Germany; Columbia University, Department of Biochemistry and Molecular Biophysics, 701 West, 168th Street, New York, NY, 10032, USA; Institute of Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748, Garching/Munich, Germany; Germany & Institute for Food and Plant Sciences (WZW) Weihenstephan, Alte Akademie 8, 85354 Freising, Germany
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15
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Nithin C, Mukherjee S, Bahadur RP. A structure-based model for the prediction of protein-RNA binding affinity. RNA (NEW YORK, N.Y.) 2019; 25:1628-1645. [PMID: 31395671 PMCID: PMC6859855 DOI: 10.1261/rna.071779.119] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Accepted: 08/05/2019] [Indexed: 05/28/2023]
Abstract
Protein-RNA recognition is highly affinity-driven and regulates a wide array of cellular functions. In this study, we have curated a binding affinity data set of 40 protein-RNA complexes, for which at least one unbound partner is available in the docking benchmark. The data set covers a wide affinity range of eight orders of magnitude as well as four different structural classes. On average, we find the complexes with single-stranded RNA have the highest affinity, whereas the complexes with the duplex RNA have the lowest. Nevertheless, free energy gain upon binding is the highest for the complexes with ribosomal proteins and the lowest for the complexes with tRNA with an average of -5.7 cal/mol/Å2 in the entire data set. We train regression models to predict the binding affinity from the structural and physicochemical parameters of protein-RNA interfaces. The best fit model with the lowest maximum error is provided with three interface parameters: relative hydrophobicity, conformational change upon binding and relative hydration pattern. This model has been used for predicting the binding affinity on a test data set, generated using mutated structures of yeast aspartyl-tRNA synthetase, for which experimentally determined ΔG values of 40 mutations are available. The predicted ΔGempirical values highly correlate with the experimental observations. The data set provided in this study should be useful for further development of the binding affinity prediction methods. Moreover, the model developed in this study enhances our understanding on the structural basis of protein-RNA binding affinity and provides a platform to engineer protein-RNA interfaces with desired affinity.
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Affiliation(s)
- Chandran Nithin
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Sunandan Mukherjee
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Ranjit Prasad Bahadur
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
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16
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Bressin A, Schulte-Sasse R, Figini D, Urdaneta EC, Beckmann BM, Marsico A. TriPepSVM: de novo prediction of RNA-binding proteins based on short amino acid motifs. Nucleic Acids Res 2019; 47:4406-4417. [PMID: 30923827 PMCID: PMC6511874 DOI: 10.1093/nar/gkz203] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/20/2019] [Accepted: 03/18/2019] [Indexed: 12/26/2022] Open
Abstract
In recent years, hundreds of novel RNA-binding proteins (RBPs) have been identified, leading to the discovery of novel RNA-binding domains. Furthermore, unstructured or disordered low-complexity regions of RBPs have been identified to play an important role in interactions with nucleic acids. However, these advances in understanding RBPs are limited mainly to eukaryotic species and we only have limited tools to faithfully predict RNA-binders in bacteria. Here, we describe a support vector machine-based method, called TriPepSVM, for the prediction of RNA-binding proteins. TriPepSVM applies string kernels to directly handle protein sequences using tri-peptide frequencies. Testing the method in human and bacteria, we find that several RBP-enriched tri-peptides occur more often in structurally disordered regions of RBPs. TriPepSVM outperforms existing applications, which consider classical structural features of RNA-binding or homology, in the task of RBP prediction in both human and bacteria. Finally, we predict 66 novel RBPs in Salmonella Typhimurium and validate the bacterial proteins ClpX, DnaJ and UbiG to associate with RNA in vivo.
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Affiliation(s)
- Annkatrin Bressin
- Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany
| | - Roman Schulte-Sasse
- Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany
| | - Davide Figini
- IRI Life Sciences, Humboldt University Berlin, Philippstrasse 13, 10115 Berlin, Germany
| | - Erika C Urdaneta
- IRI Life Sciences, Humboldt University Berlin, Philippstrasse 13, 10115 Berlin, Germany
| | - Benedikt M Beckmann
- IRI Life Sciences, Humboldt University Berlin, Philippstrasse 13, 10115 Berlin, Germany
| | - Annalisa Marsico
- Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany.,Free University of Berlin, Takustrasse 9, 14195 Berlin, Germany.,Institute of Computational Biology (ICB), Helmholtz Zentrum Munich, Ingolstaedter Landstr. 1 85764 Neuherberg, Germany
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17
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Deng L, Yang W, Liu H. PredPRBA: Prediction of Protein-RNA Binding Affinity Using Gradient Boosted Regression Trees. Front Genet 2019; 10:637. [PMID: 31428122 PMCID: PMC6688581 DOI: 10.3389/fgene.2019.00637] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 06/18/2019] [Indexed: 01/24/2023] Open
Abstract
Protein-RNA interactions play essential roles in many biological aspects. Quantifying the binding affinity of protein-RNA complexes is helpful to the understanding of protein-RNA recognition mechanisms and identification of strong binding partners. Due to experimentally measured protein-RNA binding affinity data available is still limited to date, there is a pressing demand for accurate and reliable computational approaches. In this paper, we propose a computational approach, PredPRBA, which can effectively predict protein-RNA binding affinity using gradient boosted regression trees. We build a dataset of protein-RNA binding affinity that includes 103 protein-RNA complex structures manually collected from related literature. Then, we generate 37 kinds of sequence and structural features and explore the relationship between the features and protein-RNA binding affinity. We find that the binding affinity mainly depends on the structure of RNA molecules. According to the type of RNA associated with proteins composed of the protein-RNA complex, we split the 103 protein-RNA complexes into six categories. For each category, we build a gradient boosted regression tree (GBRT) model based on the generated features. We perform a comprehensive evaluation for the proposed method on the binding affinity dataset using leave-one-out cross-validation. We show that PredPRBA achieves correlations ranging from 0.723 to 0.897 among six categories, which is significantly better than other typical regression methods and the pioneer protein-RNA binding affinity predictor SPOT-Seq-RNA. In addition, a user-friendly web server has been developed to predict the binding affinity of protein-RNA complexes. The PredPRBA webserver is freely available at http://PredPRBA.denglab.org/.
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Affiliation(s)
- Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha, China.,School of Software, Xinjiang University, Urumqi, China
| | - Wenyi Yang
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hui Liu
- Lab of Information Management, Changzhou University, Changzhou, China
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18
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Wang YZ, Li J, Zhang S, Huang B, Yao G, Zhang J. An RNA Scoring Function for Tertiary Structure Prediction Based on Multi-Layer Neural Networks. Mol Biol 2019. [DOI: 10.1134/s0026893319010175] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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19
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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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20
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Kulandaisamy A, Srivastava A, Kumar P, Nagarajan R, Priya SB, Gromiha MM. Identification and Analysis of Key Residues in Protein-RNA Complexes. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1436-1444. [PMID: 29993582 DOI: 10.1109/tcbb.2018.2834387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Protein-RNA complexes play important roles in various biological processes. The functions of protein-RNA complexes are dictated by their interactions, binding, stability, and affinity. In this work, we have identified the key residues (KRs), which are involved in both stability and binding. We found that 42 percent of considered proteins share common binding and stabilizing residues, whereas these residues are distinct in 58 percent of the proteins. Overall, 5 percent of stabilizing and 3 percent of binding residues serve as key residues. These residues are enriched with the combination of polar, charged, aliphatic, and aromatic residues. Analysis on subclasses of protein-RNA complexes based on protein structural class, function and RNA type showed that regulatory proteins, and complexes with single stranded RNA and rRNA have appreciable number of key residues. Specifically, Arg, Tyr, and Thr are preferred in most of the subclasses of protein-RNA complexes. In addition, residues with similar chemical behavior have different preferences to be KRs, such that Arg, Tyr, Val, and Thr are preferred over Lys, Trp, Ile, and Ser, respectively. Atomic level contacts revealed that charged and polar-nonpolar contacts are dominant in enzymes, polar in structural, and nonpolar in regulatory proteins. On the other hand, polar-nonpolar contacts are enriched in all these classes of protein-RNA complexes. Further, the influence of sequence and structural features such as conservation score, surrounding hydrophobicity, solvent accessibility, secondary structure, and long-range order in key residues are also discussed. We envisage that the present study provides insights to understand the structural and functional aspects of protein-RNA complexes.
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21
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Dvir S, Argoetti A, Mandel-Gutfreund Y. Ribonucleoprotein particles: advances and challenges in computational methods. Curr Opin Struct Biol 2018; 53:124-130. [PMID: 30172766 DOI: 10.1016/j.sbi.2018.08.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 08/07/2018] [Indexed: 01/16/2023]
Abstract
RNA-binding proteins (RBPs) interact with RNA to form Ribonucleoprotein Particles (RNPs). The interaction between RBPs and their RNA partners are traditionally thought to be mediated by highly conserved RNA-binding domains (RBDs). Recently, high-throughput studies led to the discovery of hundreds of novel proteins and domains, of which many do not follow the classical definition of RNA-binding. Despite technological innovations, experimental screenings are currently limited to the detection of specific types of RNPs, underscoring the importance of computational methods for predicting novel RBPs and RNA interacting residues and interfaces. Here, we discuss major challenges in computational prediction of RBPs and RBDs and outline new strategies to circumvent current limitations of experimental techniques.
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Affiliation(s)
- Shlomi Dvir
- Faculty of Biology, Technion-Israel Institute of Technology, Haifa 32000, Israel
| | - Amir Argoetti
- Faculty of Biology, Technion-Israel Institute of Technology, Haifa 32000, Israel
| | - Yael Mandel-Gutfreund
- Faculty of Biology, Technion-Israel Institute of Technology, Haifa 32000, Israel; Department of Computer Science, Technion-Israel Institute of Technology, Haifa 32000, Israel.
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22
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Sharan M, Förstner KU, Eulalio A, Vogel J. APRICOT: an integrated computational pipeline for the sequence-based identification and characterization of RNA-binding proteins. Nucleic Acids Res 2017; 45:e96. [PMID: 28334975 PMCID: PMC5499795 DOI: 10.1093/nar/gkx137] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Accepted: 02/27/2017] [Indexed: 11/14/2022] Open
Abstract
RNA-binding proteins (RBPs) have been established as core components of several post-transcriptional gene regulation mechanisms. Experimental techniques such as cross-linking and co-immunoprecipitation have enabled the identification of RBPs, RNA-binding domains (RBDs) and their regulatory roles in the eukaryotic species such as human and yeast in large-scale. In contrast, our knowledge of the number and potential diversity of RBPs in bacteria is poorer due to the technical challenges associated with the existing global screening approaches. We introduce APRICOT, a computational pipeline for the sequence-based identification and characterization of proteins using RBDs known from experimental studies. The pipeline identifies functional motifs in protein sequences using position-specific scoring matrices and Hidden Markov Models of the functional domains and statistically scores them based on a series of sequence-based features. Subsequently, APRICOT identifies putative RBPs and characterizes them by several biological properties. Here we demonstrate the application and adaptability of the pipeline on large-scale protein sets, including the bacterial proteome of Escherichia coli. APRICOT showed better performance on various datasets compared to other existing tools for the sequence-based prediction of RBPs by achieving an average sensitivity and specificity of 0.90 and 0.91 respectively. The command-line tool and its documentation are available at https://pypi.python.org/pypi/bio-apricot.
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Affiliation(s)
- Malvika Sharan
- Institute of Molecular Infection Biology, University of Würzburg, 97080 Würzburg, Germany
| | - Konrad U Förstner
- Core Unit Systems Medicine, University of Würzburg, 97080 Würzburg, Germany
| | - Ana Eulalio
- Institute of Molecular Infection Biology, University of Würzburg, 97080 Würzburg, Germany
| | - Jörg Vogel
- Institute of Molecular Infection Biology, University of Würzburg, 97080 Würzburg, Germany
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23
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Walia RR, El-Manzalawy Y, Honavar VG, Dobbs D. Sequence-Based Prediction of RNA-Binding Residues in Proteins. Methods Mol Biol 2017; 1484:205-235. [PMID: 27787829 PMCID: PMC5796408 DOI: 10.1007/978-1-4939-6406-2_15] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Identifying individual residues in the interfaces of protein-RNA complexes is important for understanding the molecular determinants of protein-RNA recognition and has many potential applications. Recent technical advances have led to several high-throughput experimental methods for identifying partners in protein-RNA complexes, but determining RNA-binding residues in proteins is still expensive and time-consuming. This chapter focuses on available computational methods for identifying which amino acids in an RNA-binding protein participate directly in contacting RNA. Step-by-step protocols for using three different web-based servers to predict RNA-binding residues are described. In addition, currently available web servers and software tools for predicting RNA-binding sites, as well as databases that contain valuable information about known protein-RNA complexes, RNA-binding motifs in proteins, and protein-binding recognition sites in RNA are provided. We emphasize sequence-based methods that can reliably identify interfacial residues without the requirement for structural information regarding either the RNA-binding protein or its RNA partner.
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Affiliation(s)
| | - Yasser El-Manzalawy
- College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, 16802, USA
| | - Vasant G Honavar
- College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, 16802, USA
| | - Drena Dobbs
- Genetics, Development and Cell Biology Department, Iowa State University, 3112 Molecular Biology Building, Ames, IA, 50011-3650, USA.
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24
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Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. Molecular Docking at a Glance. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.
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Affiliation(s)
| | | | - Siavoush Dastmalchi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Iran & School of Pharmacy, Tabriz University of Medical Sciences, Iran
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25
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Chen J, Xie ZR, Wu Y. Understand protein functions by comparing the similarity of local structural environments. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2016; 1865:142-152. [PMID: 27884635 DOI: 10.1016/j.bbapap.2016.11.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Revised: 11/03/2016] [Accepted: 11/17/2016] [Indexed: 12/20/2022]
Abstract
The three-dimensional structures of proteins play an essential role in regulating binding between proteins and their partners, offering a direct relationship between structures and functions of proteins. It is widely accepted that the function of a protein can be determined if its structure is similar to other proteins whose functions are known. However, it is also observed that proteins with similar global structures do not necessarily correspond to the same function, while proteins with very different folds can share similar functions. This indicates that function similarity is originated from the local structural information of proteins instead of their global shapes. We assume that proteins with similar local environments prefer binding to similar types of molecular targets. In order to testify this assumption, we designed a new structural indicator to define the similarity of local environment between residues in different proteins. This indicator was further used to calculate the probability that a given residue binds to a specific type of structural neighbors, including DNA, RNA, small molecules and proteins. After applying the method to a large-scale non-redundant database of proteins, we show that the positive signal of binding probability calculated from the local structural indicator is statistically meaningful. In summary, our studies suggested that the local environment of residues in a protein is a good indicator to recognize specific binding partners of the protein. The new method could be a potential addition to a suite of existing template-based approaches for protein function prediction.
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Affiliation(s)
- Jiawen Chen
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY 10461, United States
| | - Zhong-Ru Xie
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY 10461, United States
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY 10461, United States.
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26
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Paz I, Kligun E, Bengad B, Mandel-Gutfreund Y. BindUP: a web server for non-homology-based prediction of DNA and RNA binding proteins. Nucleic Acids Res 2016; 44:W568-74. [PMID: 27198220 PMCID: PMC4987955 DOI: 10.1093/nar/gkw454] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Accepted: 05/11/2016] [Indexed: 12/12/2022] Open
Abstract
Gene expression is a multi-step process involving many layers of regulation. The main regulators of the pathway are DNA and RNA binding proteins. While over the years, a large number of DNA and RNA binding proteins have been identified and extensively studied, it is still expected that many other proteins, some with yet another known function, are awaiting to be discovered. Here we present a new web server, BindUP, freely accessible through the website http://bindup.technion.ac.il/, for predicting DNA and RNA binding proteins using a non-homology-based approach. Our method is based on the electrostatic features of the protein surface and other general properties of the protein. BindUP predicts nucleic acid binding function given the proteins three-dimensional structure or a structural model. Additionally, BindUP provides information on the largest electrostatic surface patches, visualized on the server. The server was tested on several datasets of DNA and RNA binding proteins, including proteins which do not possess DNA or RNA binding domains and have no similarity to known nucleic acid binding proteins, achieving very high accuracy. BindUP is applicable in either single or batch modes and can be applied for testing hundreds of proteins simultaneously in a highly efficient manner.
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Affiliation(s)
- Inbal Paz
- Department of Biology, Technion-Israel Institute of Technology, Technion City, Haifa 32000, Israel
| | - Efrat Kligun
- Department of Biology, Technion-Israel Institute of Technology, Technion City, Haifa 32000, Israel
| | - Barak Bengad
- Department of Biology, Technion-Israel Institute of Technology, Technion City, Haifa 32000, Israel
| | - Yael Mandel-Gutfreund
- Department of Biology, Technion-Israel Institute of Technology, Technion City, Haifa 32000, Israel
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27
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Chauvot de Beauchene I, de Vries SJ, Zacharias M. Binding Site Identification and Flexible Docking of Single Stranded RNA to Proteins Using a Fragment-Based Approach. PLoS Comput Biol 2016; 12:e1004697. [PMID: 26815409 PMCID: PMC4729675 DOI: 10.1371/journal.pcbi.1004697] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Accepted: 12/08/2015] [Indexed: 11/18/2022] Open
Abstract
Protein-RNA docking is hampered by the high flexibility of RNA, and particularly single-stranded RNA (ssRNA). Yet, ssRNA regions typically carry the specificity of protein recognition. The lack of methodology for modeling such regions limits the accuracy of current protein-RNA docking methods. We developed a fragment-based approach to model protein-bound ssRNA, based on the structure of the protein and the sequence of the RNA, without any prior knowledge of the RNA binding site or the RNA structure. The conformational diversity of each fragment is sampled by an exhaustive RNA fragment library that was created from all the existing experimental structures of protein-ssRNA complexes. A systematic and detailed analysis of fragment-based ssRNA docking was performed which constitutes a proof-of-principle for the fragment-based approach. The method was tested on two 8-homo-nucleotide ssRNA-protein complexes and was able to identify the binding site on the protein within 10 Å. Moreover, a structure of each bound ssRNA could be generated in close agreement with the crystal structure with a mean deviation of ~1.5 Å except for a terminal nucleotide. This is the first time a bound ssRNA could be modeled from sequence with high precision.
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Affiliation(s)
| | - Sjoerd J. de Vries
- Physik-Department T38, Technische Universität München, Garching, Germany
| | - Martin Zacharias
- Physik-Department T38, Technische Universität München, Garching, Germany
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28
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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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Detecting protein-protein interactions with a novel matrix-based protein sequence representation and support vector machines. BIOMED RESEARCH INTERNATIONAL 2015; 2015:867516. [PMID: 26000305 PMCID: PMC4426769 DOI: 10.1155/2015/867516] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Revised: 01/09/2015] [Accepted: 01/09/2015] [Indexed: 11/27/2022]
Abstract
Proteins and their interactions lie at the heart of most underlying biological processes. Consequently, correct detection of protein-protein interactions (PPIs) is of fundamental importance to understand the molecular mechanisms in biological systems. Although the convenience brought by high-throughput experiment in technological advances makes it possible to detect a large amount of PPIs, the data generated through these methods is unreliable and may not be completely inclusive of all possible PPIs. Targeting at this problem, this study develops a novel computational approach to effectively detect the protein interactions. This approach is proposed based on a novel matrix-based representation of protein sequence combined with the algorithm of support vector machine (SVM), which fully considers the sequence order and dipeptide information of the protein primary sequence. When performed on yeast PPIs datasets, the proposed method can reach 90.06% prediction accuracy with 94.37% specificity at the sensitivity of 85.74%, indicating that this predictor is a useful tool to predict PPIs. Achieved results also demonstrate that our approach can be a helpful supplement for the interactions that have been detected experimentally.
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Unzippers, resolvers and sensors: a structural and functional biochemistry tale of RNA helicases. Int J Mol Sci 2015; 16:2269-93. [PMID: 25622248 PMCID: PMC4346836 DOI: 10.3390/ijms16022269] [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: 11/24/2014] [Revised: 01/09/2015] [Accepted: 01/12/2015] [Indexed: 12/28/2022] Open
Abstract
The centrality of RNA within the biological world is an irrefutable fact that currently attracts increasing attention from the scientific community. The panoply of functional RNAs requires the existence of specific biological caretakers, RNA helicases, devoted to maintain the proper folding of those molecules, resolving unstable structures. However, evolution has taken advantage of the specific position and characteristics of RNA helicases to develop new functions for these proteins, which are at the interface of the basic processes for transference of information from DNA to proteins. RNA helicases are involved in many biologically relevant processes, not only as RNA chaperones, but also as signal transducers, scaffolds of molecular complexes, and regulatory elements. Structural biology studies during the last decade, founded in X-ray crystallography, have characterized in detail several RNA-helicases. This comprehensive review summarizes the structural knowledge accumulated in the last two decades within this family of proteins, with special emphasis on the structure-function relationships of the most widely-studied families of RNA helicases: the DEAD-box, RIG-I-like and viral NS3 classes.
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