1
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Sun C, Feng Y. EPDRNA: A Model for Identifying DNA-RNA Binding Sites in Disease-Related Proteins. Protein J 2024; 43:513-521. [PMID: 38491248 DOI: 10.1007/s10930-024-10183-3] [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] [Accepted: 02/02/2024] [Indexed: 03/18/2024]
Abstract
Protein-DNA and protein-RNA interactions are involved in many biological processes and regulate many cellular functions. Moreover, they are related to many human diseases. To understand the molecular mechanism of protein-DNA binding and protein-RNA binding, it is important to identify which residues in the protein sequence bind to DNA and RNA. At present, there are few methods for specifically identifying the binding sites of disease-related protein-DNA and protein-RNA. In this study, so we combined four machine learning algorithms into an ensemble classifier (EPDRNA) to predict DNA and RNA binding sites in disease-related proteins. The dataset used in model was collated from UniProt and PDB database, and PSSM, physicochemical properties and amino acid type were used as features. The EPDRNA adopted soft voting and achieved the best AUC value of 0.73 at the DNA binding sites, and the best AUC value of 0.71 at the RNA binding sites in 10-fold cross validation in the training sets. In order to further verify the performance of the model, we assessed EPDRNA for the prediction of DNA-binding sites and the prediction of RNA-binding sites on the independent test dataset. The EPDRNA achieved 85% recall rate and 25% precision on the protein-DNA interaction independent test set, and achieved 82% recall rate and 27% precision on the protein-RNA interaction independent test set. The online EPDRNA webserver is freely available at http://www.s-bioinformatics.cn/epdrna .
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Affiliation(s)
- CanZhuang Sun
- College of Science, Inner Mongolia Agriculture University, Hohhot, 010018, People's Republic of China
| | - YongE Feng
- College of Science, Inner Mongolia Agriculture University, Hohhot, 010018, People's Republic of China.
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2
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Jia P, Zhang F, Wu C, Li M. A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond. Brief Bioinform 2024; 25:bbae162. [PMID: 38739759 PMCID: PMC11089422 DOI: 10.1093/bib/bbae162] [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: 01/01/2024] [Revised: 02/17/2024] [Accepted: 03/31/2024] [Indexed: 05/16/2024] Open
Abstract
Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.
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Affiliation(s)
- Pengzhen Jia
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
| | - Fuhao Zhang
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
- College of Information Engineering, Northwest A&F University, No. 3 Taicheng Road, Yangling, Shaanxi 712100, China
| | - Chaojin Wu
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
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3
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Zhang F, Li M, Zhang J, Kurgan L. HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins. Nucleic Acids Res 2023; 51:e25. [PMID: 36629262 PMCID: PMC10018345 DOI: 10.1093/nar/gkac1253] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 11/22/2022] [Accepted: 12/15/2022] [Indexed: 01/12/2023] Open
Abstract
The sequence-based predictors of RNA-binding residues (RBRs) are trained on either structure-annotated or disorder-annotated binding regions. A recent study of predictors of protein-binding residues shows that they are plagued by high levels of cross-predictions (protein binding residues are predicted as nucleic acid binding) and that structure-trained predictors perform poorly for the disorder-annotated regions and vice versa. Consequently, we analyze a representative set of the structure and disorder trained predictors of RBRs to comprehensively assess quality of their predictions. Our empirical analysis that relies on a new and low-similarity benchmark dataset reveals that the structure-trained predictors of RBRs perform well for the structure-annotated proteins while the disorder-trained predictors provide accurate results for the disorder-annotated proteins. However, these methods work only modestly well on the opposite types of annotations, motivating the need for new solutions. Using an empirical approach, we design HybridRNAbind meta-model that generates accurate predictions and low amounts of cross-predictions when tested on data that combines structure and disorder-annotated RBRs. We release this meta-model as a convenient webserver which is available at https://www.csuligroup.com/hybridRNAbind/.
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Affiliation(s)
- Fuhao Zhang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Min Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
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4
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PRIME-3D2D is a 3D2D model to predict binding sites of protein-RNA interaction. Commun Biol 2020; 3:384. [PMID: 32678300 PMCID: PMC7366699 DOI: 10.1038/s42003-020-1114-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 06/29/2020] [Indexed: 11/08/2022] Open
Abstract
Protein-RNA interaction participates in many biological processes. So, studying protein–RNA interaction can help us to understand the function of protein and RNA. Although the protein–RNA 3D3D model, like PRIME, was useful in building 3D structural complexes, it can’t be used genome-wide, due to lacking RNA 3D structures. To take full advantage of RNA secondary structures revealed from high-throughput sequencing, we present PRIME-3D2D to predict binding sites of protein–RNA interaction. PRIME-3D2D is almost as good as PRIME at modeling protein–RNA complexes. PRIME-3D2D can be used to predict binding sites on PDB data (MCC = 0.75/0.70 for binding sites in protein/RNA) and transcription-wide (MCC = 0.285 for binding sites in RNA). Testing on PDB and yeast transcription-wide data show that PRIME-3D2D performs better than other binding sites predictor. So, PRIME-3D2D can be used to predict the binding sites both on PDB and genome-wide, and it’s freely available. Xie et al. report a new computational method PRIME-3D2D to predict binding sites of protein–RNA interaction by considering protein 3D structure and RNA 2D structure. It is freely available, performs better than other binding sites predictor and is as good as PRIME to model protein–RNA complex.
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5
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Nguyen BP, Nguyen QH, Doan-Ngoc GN, Nguyen-Vo TH, Rahardja S. iProDNA-CapsNet: identifying protein-DNA binding residues using capsule neural networks. BMC Bioinformatics 2019; 20:634. [PMID: 31881828 PMCID: PMC6933727 DOI: 10.1186/s12859-019-3295-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 11/26/2019] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Since protein-DNA interactions are highly essential to diverse biological events, accurately positioning the location of the DNA-binding residues is necessary. This biological issue, however, is currently a challenging task in the age of post-genomic where data on protein sequences have expanded very fast. In this study, we propose iProDNA-CapsNet - a new prediction model identifying protein-DNA binding residues using an ensemble of capsule neural networks (CapsNets) on position specific scoring matrix (PSMM) profiles. The use of CapsNets promises an innovative approach to determine the location of DNA-binding residues. In this study, the benchmark datasets introduced by Hu et al. (2017), i.e., PDNA-543 and PDNA-TEST, were used to train and evaluate the model, respectively. To fairly assess the model performance, comparative analysis between iProDNA-CapsNet and existing state-of-the-art methods was done. RESULTS Under the decision threshold corresponding to false positive rate (FPR) ≈ 5%, the accuracy, sensitivity, precision, and Matthews's correlation coefficient (MCC) of our model is increased by about 2.0%, 2.0%, 14.0%, and 5.0% with respect to TargetDNA (Hu et al., 2017) and 1.0%, 75.0%, 45.0%, and 77.0% with respect to BindN+ (Wang et al., 2010), respectively. With regards to other methods not reporting their threshold settings, iProDNA-CapsNet also shows a significant improvement in performance based on most of the evaluation metrics. Even with different patterns of change among the models, iProDNA-CapsNets remains to be the best model having top performance in most of the metrics, especially MCC which is boosted from about 8.0% to 220.0%. CONCLUSIONS According to all evaluation metrics under various decision thresholds, iProDNA-CapsNet shows better performance compared to the two current best models (BindN and TargetDNA). Our proposed approach also shows that CapsNet can potentially be used and adopted in other biological applications.
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Affiliation(s)
- Binh P. Nguyen
- School of Mathematics and Statistics, Victoria University of Wellington, Gate 7, Kelburn Parade, Wellington, 6140 New Zealand
| | - Quang H. Nguyen
- School of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet, Hanoi, 100000 Vietnam
| | - Giang-Nam Doan-Ngoc
- School of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet, Hanoi, 100000 Vietnam
| | - Thanh-Hoang Nguyen-Vo
- School of Mathematics and Statistics, Victoria University of Wellington, Gate 7, Kelburn Parade, Wellington, 6140 New Zealand
| | - Susanto Rahardja
- School of Marine Science and Technology, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an, 710072 China
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6
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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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7
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A deep learning framework to predict binding preference of RNA constituents on protein surface. Nat Commun 2019; 10:4941. [PMID: 31666519 PMCID: PMC6821705 DOI: 10.1038/s41467-019-12920-0] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 10/08/2019] [Indexed: 12/21/2022] Open
Abstract
Protein-RNA interaction plays important roles in post-transcriptional regulation. However, the task of predicting these interactions given a protein structure is difficult. Here we show that, by leveraging a deep learning model NucleicNet, attributes such as binding preference of RNA backbone constituents and different bases can be predicted from local physicochemical characteristics of protein structure surface. On a diverse set of challenging RNA-binding proteins, including Fem-3-binding-factor 2, Argonaute 2 and Ribonuclease III, NucleicNet can accurately recover interaction modes discovered by structural biology experiments. Furthermore, we show that, without seeing any in vitro or in vivo assay data, NucleicNet can still achieve consistency with experiments, including RNAcompete, Immunoprecipitation Assay, and siRNA Knockdown Benchmark. NucleicNet can thus serve to provide quantitative fitness of RNA sequences for given binding pockets or to predict potential binding pockets and binding RNAs for previously unknown RNA binding proteins.
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8
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Zhu YH, Hu J, Song XN, Yu DJ. DNAPred: Accurate Identification of DNA-Binding Sites from Protein Sequence by Ensembled Hyperplane-Distance-Based Support Vector Machines. J Chem Inf Model 2019; 59:3057-3071. [PMID: 30943723 DOI: 10.1021/acs.jcim.8b00749] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Accurate identification of protein-DNA binding sites is significant for both understanding protein function and drug design. Machine-learning-based methods have been extensively used for the prediction of protein-DNA binding sites. However, the data imbalance problem, in which the number of nonbinding residues (negative-class samples) is far larger than that of binding residues (positive-class samples), seriously restricts the performance improvements of machine-learning-based predictors. In this work, we designed a two-stage imbalanced learning algorithm, called ensembled hyperplane-distance-based support vector machines (E-HDSVM), to improve the prediction performance of protein-DNA binding sites. The first stage of E-HDSVM designs a new iterative sampling algorithm, called hyperplane-distance-based under-sampling (HD-US), to extract multiple subsets from the original imbalanced data set, each of which is used to train a support vector machine (SVM). Unlike traditional sampling algorithms, HD-US selects samples by calculating the distances between the samples and the separating hyperplane of the SVM. The second stage of E-HDSVM proposes an enhanced AdaBoost (EAdaBoost) algorithm to ensemble multiple trained SVMs. As an enhanced version of the original AdaBoost algorithm, EAdaBoost overcomes the overfitting problem. Stringent cross-validation and independent tests on benchmark data sets demonstrated the superiority of E-HDSVM over several popular imbalanced learning algorithms. Based on the proposed E-HDSVM algorithm, we further implemented a sequence-based protein-DNA binding site predictor, called DNAPred, which is freely available at http://csbio.njust.edu.cn/bioinf/dnapred/ for academic use. The computational experimental results showed that our predictor achieved an average overall accuracy of 91.7% and a Mathew's correlation coefficient of 0.395 on five benchmark data sets and outperformed several state-of-the-art sequence-based protein-DNA binding site predictors.
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Affiliation(s)
- Yi-Heng Zhu
- School of Computer Science and Engineering , Nanjing University of Science and Technology , Xiaolingwei 200 , Nanjing 210094 , P. R. China
| | - Jun Hu
- College of Information Engineering , Zhejiang University of Technology , Hangzhou 310023 , P. R. China
| | - Xiao-Ning Song
- School of Internet of Things , Jiangnan University , 1800 Lihu Road , Wuxi 214122 , P. R. China
| | - Dong-Jun Yu
- School of Computer Science and Engineering , Nanjing University of Science and Technology , Xiaolingwei 200 , Nanjing 210094 , P. R. China
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9
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Song J, Liu G, Wang R, Sun L, Zhang P. A novel method for predicting RNA-interacting residues in proteins using a combination of feature-based and sequence template-based methods. BIOTECHNOL BIOTEC EQ 2019. [DOI: 10.1080/13102818.2019.1612275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
Affiliation(s)
- Jiazhi Song
- Department of Computational intelligence College of Computer Science and Technology, Jilin University, Changchun, PR China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, PR China
| | - Guixia Liu
- Department of Computational intelligence College of Computer Science and Technology, Jilin University, Changchun, PR China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, PR China
| | - Rongquan Wang
- Department of Computational intelligence College of Computer Science and Technology, Jilin University, Changchun, PR China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, PR China
| | - Liyan Sun
- Department of Computational intelligence College of Computer Science and Technology, Jilin University, Changchun, PR China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, PR China
| | - Ping Zhang
- Department of Computational intelligence College of Computer Science and Technology, Jilin University, Changchun, PR China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, PR China
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10
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Moore KS, 't Hoen PAC. Computational approaches for the analysis of RNA-protein interactions: A primer for biologists. J Biol Chem 2018; 294:1-9. [PMID: 30455357 DOI: 10.1074/jbc.rev118.004842] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
RNA-binding proteins (RBPs) play important roles in the control of gene expression and the coordination of different layers of post-transcriptional regulation. Interactions between certain RBPs and mRNA transcripts are notoriously difficult to predict, as any given protein-RNA interaction may rely not only on RNA sequence, but also on three-dimensional RNA structures, competitive inhibition from other RBPs, and input from cellular signaling pathways. Advanced and high-throughput technologies for the identification of RNA-protein interactions have come to the rescue, but the identification of binding sites and downstream functional effects of RBPs from the resulting data can be challenging. In this review, we discuss statistical inference and machine-learning approaches and tools relevant for the study of RBPs and the analysis of large-scale RNA-protein interaction datasets. This primer is intended for life scientists who are interested in incorporating these tools into their own research. We begin with the demystification of regression models, as used in the analysis of next-generation sequencing data, and progress to a discussion of Hidden Markov Models, which are of particular value in analyzing cross-linking followed by immunoprecipitation data. We then continue with examples of machine learning techniques, such as support vector machines and gradient tree boosting. We close with a brief discussion of current trends in the field, including deep learning architectures.
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Affiliation(s)
- Kat S Moore
- Department of Hematopoiesis, Sanquin, and Landsteiner Laboratory AMC/UvA, 1066 CX Amsterdam
| | - Peter A C 't Hoen
- Centre for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands.
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11
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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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12
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Improving conditional random field model for prediction of protein-RNA residue-base contacts. QUANTITATIVE BIOLOGY 2018. [DOI: 10.1007/s40484-018-0136-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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13
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Tamaki S, Tomita M, Suzuki H, Kanai A. Systematic Analysis of the Binding Surfaces between tRNAs and Their Respective Aminoacyl tRNA Synthetase Based on Structural and Evolutionary Data. Front Genet 2018; 8:227. [PMID: 29358943 PMCID: PMC5766645 DOI: 10.3389/fgene.2017.00227] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 12/15/2017] [Indexed: 12/23/2022] Open
Abstract
To determine the mechanism underlying the flow of genetic information, it is important to understand the relationship between a tRNA and its binding enzyme, a member of the aminoacyl-tRNA synthetase (aaRS) family. We have developed a novel method to project the interacting regions of tRNA-aaRS complexes, obtained from their three-dimensional structures, onto two-dimensional space. The interacting surface between each tRNA and its aaRS was successfully identified by determining these interactions with an atomic distance threshold of 3.3 Å. We analyzed their interactions, using 60 mainly bacterial and eukaryotic tRNA-aaRS complexes, and showed that the tRNA sequence regions that interacted most strongly with each aaRS are the anticodon loop and the CCA terminal region, followed by the D-stem. A sequence conservation analysis of the canonical tRNAs was conducted in 83 bacterial, 182 archaeal, and 150 eukaryotic species. Our results show that the three tRNA regions that interact with the aaRS and two additional loop regions (D-loop and TΨC-loop) known to be important for formation of the tRNA L-shaped structure are broadly conserved. We also found sequence conservations near the tRNA discriminator in the Bacteria and Archaea, and an enormous number of noncanonical tRNAs in the Eukaryotes. This is the first global view of tRNA evolution based on its structure and an unprecedented number of sequence data.
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Affiliation(s)
- Satoshi Tamaki
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.,Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Japan
| | - Masaru Tomita
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.,Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Japan.,Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan
| | - Haruo Suzuki
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.,Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan
| | - Akio Kanai
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.,Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Japan.,Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan
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14
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Zhang J, Ma Z, Kurgan L. Comprehensive review and empirical analysis of hallmarks of DNA-, RNA- and protein-binding residues in protein chains. Brief Bioinform 2017; 20:1250-1268. [DOI: 10.1093/bib/bbx168] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Revised: 11/15/2017] [Indexed: 11/13/2022] Open
Abstract
Abstract
Proteins interact with a variety of molecules including proteins and nucleic acids. We review a comprehensive collection of over 50 studies that analyze and/or predict these interactions. While majority of these studies address either solely protein–DNA or protein–RNA binding, only a few have a wider scope that covers both protein–protein and protein–nucleic acid binding. Our analysis reveals that binding residues are typically characterized with three hallmarks: relative solvent accessibility (RSA), evolutionary conservation and propensity of amino acids (AAs) for binding. Motivated by drawbacks of the prior studies, we perform a large-scale analysis to quantify and contrast the three hallmarks for residues that bind DNA-, RNA-, protein- and (for the first time) multi-ligand-binding residues that interact with DNA and proteins, and with RNA and proteins. Results generated on a well-annotated data set of over 23 000 proteins show that conservation of binding residues is higher for nucleic acid- than protein-binding residues. Multi-ligand-binding residues are more conserved and have higher RSA than single-ligand-binding residues. We empirically show that each hallmark discriminates between binding and nonbinding residues, even predicted RSA, and that combining them improves discriminatory power for each of the five types of interactions. Linear scoring functions that combine these hallmarks offer good predictive performance of residue-level propensity for binding and provide intuitive interpretation of predictions. Better understanding of these residue-level interactions will facilitate development of methods that accurately predict binding in the exponentially growing databases of protein sequences.
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15
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Hu J, Li Y, Zhang M, Yang X, Shen HB, Yu DJ. Predicting Protein-DNA Binding Residues by Weightedly Combining Sequence-Based Features and Boosting Multiple SVMs. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:1389-1398. [PMID: 27740495 DOI: 10.1109/tcbb.2016.2616469] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Protein-DNA interactions are ubiquitous in a wide variety of biological processes. Correctly locating DNA-binding residues solely from protein sequences is an important but challenging task for protein function annotations and drug discovery, especially in the post-genomic era where large volumes of protein sequences have quickly accumulated. In this study, we report a new predictor, named TargetDNA, for targeting protein-DNA binding residues from primary sequences. TargetDNA uses a protein's evolutionary information and its predicted solvent accessibility as two base features and employs a centered linear kernel alignment algorithm to learn the weights for weightedly combining the two features. Based on the weightedly combined feature, multiple initial predictors with SVM as classifiers are trained by applying a random under-sampling technique to the original dataset, the purpose of which is to cope with the severe imbalance phenomenon that exists between the number of DNA-binding and non-binding residues. The final ensembled predictor is obtained by boosting the multiple initially trained predictors. Experimental simulation results demonstrate that the proposed TargetDNA achieves a high prediction performance and outperforms many existing sequence-based protein-DNA binding residue predictors. The TargetDNA web server and datasets are freely available at http://csbio.njust.edu.cn/bioinf/TargetDNA/ for academic use.
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16
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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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Protein-RNA interactions: structural biology and computational modeling techniques. Biophys Rev 2016; 8:359-367. [PMID: 28510023 DOI: 10.1007/s12551-016-0223-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Accepted: 09/20/2016] [Indexed: 12/30/2022] Open
Abstract
RNA-binding proteins are functionally diverse within cells, being involved in RNA-metabolism, translation, DNA damage repair, and gene regulation at both the transcriptional and post-transcriptional levels. Much has been learnt about their interactions with RNAs through structure determination techniques and computational modeling. This review gives an overview of the structural data currently available for protein-RNA complexes, and discusses the technical issues facing structural biologists working to solve their structures. The review focuses on three techniques used to solve the 3-dimensional structure of protein-RNA complexes at atomic resolution, namely X-ray crystallography, solution nuclear magnetic resonance (NMR) and cryo-electron microscopy (cryo-EM). The review then focuses on the main computational modeling techniques that use these atomic resolution data: discussing the prediction of RNA-binding sites on unbound proteins, docking proteins, and RNAs, and modeling the molecular dynamics of the systems. In conclusion, the review looks at the future directions this field of research might take.
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18
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Huang BFF, Boutros PC. The parameter sensitivity of random forests. BMC Bioinformatics 2016; 17:331. [PMID: 27586051 PMCID: PMC5009551 DOI: 10.1186/s12859-016-1228-x] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Accepted: 08/26/2016] [Indexed: 02/07/2023] Open
Abstract
Background The Random Forest (RF) algorithm for supervised machine learning is an ensemble learning method widely used in science and many other fields. Its popularity has been increasing, but relatively few studies address the parameter selection process: a critical step in model fitting. Due to numerous assertions regarding the performance reliability of the default parameters, many RF models are fit using these values. However there has not yet been a thorough examination of the parameter-sensitivity of RFs in computational genomic studies. We address this gap here. Results We examined the effects of parameter selection on classification performance using the RF machine learning algorithm on two biological datasets with distinct p/n ratios: sequencing summary statistics (low p/n) and microarray-derived data (high p/n). Here, p, refers to the number of variables and, n, the number of samples. Our findings demonstrate that parameterization is highly correlated with prediction accuracy and variable importance measures (VIMs). Further, we demonstrate that different parameters are critical in tuning different datasets, and that parameter-optimization significantly enhances upon the default parameters. Conclusions Parameter performance demonstrated wide variability on both low and high p/n data. Therefore, there is significant benefit to be gained by model tuning RFs away from their default parameter settings. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1228-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Barbara F F Huang
- Informatics and Bio-computing Program, Ontario Institute for Cancer Research, Toronto, Canada
| | - Paul C Boutros
- Informatics and Bio-computing Program, Ontario Institute for Cancer Research, Toronto, Canada. .,Department of Medical Biophysics, University of Toronto, Toronto, Canada. .,Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada. .,MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario, M5G 0A3, Canada.
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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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