1
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Harini K, Sekijima M, Gromiha MM. PRA-Pred: Structure-based prediction of protein-RNA binding affinity. Int J Biol Macromol 2024; 259:129490. [PMID: 38224813 DOI: 10.1016/j.ijbiomac.2024.129490] [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: 12/10/2023] [Revised: 01/10/2024] [Accepted: 01/12/2024] [Indexed: 01/17/2024]
Abstract
Understanding crucial factors that affect the binding affinity of protein-RNA complexes is vital for comprehending their recognition mechanisms. This study involved compiling experimentally measured binding affinity (ΔG) values of 217 protein-RNA complexes and extracting numerous structure-based features, considering RNA, protein, and interactions between protein and RNA. Our findings indicate the significance of RNA base-step parameters, interaction energies, number of atomic contacts in the complex, hydrogen bonds, and contact potentials in understanding the binding affinity. Further, we observed that these factors are influenced by the type of RNA strand and the function of the protein in a protein-RNA complex. Multiple regression equations were developed for different classes of complexes to perform the prediction of the binding affinity between the protein and RNA. We evaluated the models using the jack-knife test and achieved an overall correlation 0.77 between the experimental and predicted binding affinities with a mean absolute error of 1.02 kcal/mol. Furthermore, we introduced a web server, PRA-Pred, intended for the prediction of protein-RNA binding affinity, and it is freely accessible through https://web.iitm.ac.in/bioinfo2/prapred/. We propose that our approach could function as a potential resource for investigating protein-RNA recognitions and developing therapeutic strategies.
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Affiliation(s)
- K Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - M Sekijima
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama, 226-8501, Japan; Department of Computer Science, National University of Singapore, Singapore.
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2
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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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3
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Wu Z, Basu S, Wu X, Kurgan L. qNABpredict: Quick, accurate, and taxonomy-aware sequence-based prediction of content of nucleic acid binding amino acids. Protein Sci 2023; 32:e4544. [PMID: 36519304 PMCID: PMC9798252 DOI: 10.1002/pro.4544] [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: 09/16/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 12/23/2022]
Abstract
Protein sequence-based predictors of nucleic acid (NA)-binding include methods that predict NA-binding proteins and NA-binding residues. The residue-level tools produce more details but suffer high computational cost since they must predict every amino acid in the input sequence and rely on multiple sequence alignments. We propose an alternative approach that predicts content (fraction) of the NA-binding residues, offering more information than the protein-level prediction and much shorter runtime than the residue-level tools. Our first-of-its-kind content predictor, qNABpredict, relies on a small, rationally designed and fast-to-compute feature set that represents relevant characteristics extracted from the input sequence and a well-parametrized support vector regression model. We provide two versions of qNABpredict, a taxonomy-agnostic model that can be used for proteins of unknown taxonomic origin and more accurate taxonomy-aware models that are tailored to specific taxonomic kingdoms: archaea, bacteria, eukaryota, and viruses. Empirical tests on a low-similarity test dataset show that qNABpredict is 100 times faster and generates statistically more accurate content predictions when compared to the content extracted from results produced by the residue-level predictors. We also show that qNABpredict's content predictions can be used to improve results generated by the residue-level predictors. We release qNABpredict as a convenient webserver and source code at http://biomine.cs.vcu.edu/servers/qNABpredict/. This new tool should be particularly useful to predict details of protein-NA interactions for large protein families and proteomes.
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Affiliation(s)
- Zhonghua Wu
- School of Mathematical Sciences and LPMCNankai UniversityTianjinChina
| | - Sushmita Basu
- Department of Computer ScienceVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Xuantai Wu
- School of Mathematical Sciences and LPMCNankai UniversityTianjinChina
| | - Lukasz Kurgan
- Department of Computer ScienceVirginia Commonwealth UniversityRichmondVirginiaUSA
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4
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Biró B, Zhao B, Kurgan L. Complementarity of the residue-level protein function and structure predictions in human proteins. Comput Struct Biotechnol J 2022; 20:2223-2234. [PMID: 35615015 PMCID: PMC9118482 DOI: 10.1016/j.csbj.2022.05.003] [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: 02/21/2022] [Revised: 05/02/2022] [Accepted: 05/02/2022] [Indexed: 11/24/2022] Open
Abstract
Sequence-based predictors of the residue-level protein function and structure cover a broad spectrum of characteristics including intrinsic disorder, secondary structure, solvent accessibility and binding to nucleic acids. They were catalogued and evaluated in numerous surveys and assessments. However, methods focusing on a given characteristic are studied separately from predictors of other characteristics, while they are typically used on the same proteins. We fill this void by studying complementarity of a representative collection of methods that target different predictions using a large, taxonomically consistent, and low similarity dataset of human proteins. First, we bridge the gap between the communities that develop structure-trained vs. disorder-trained predictors of binding residues. Motivated by a recent study of the protein-binding residue predictions, we empirically find that combining the structure-trained and disorder-trained predictors of the DNA-binding and RNA-binding residues leads to substantial improvements in predictive quality. Second, we investigate whether diverse predictors generate results that accurately reproduce relations between secondary structure, solvent accessibility, interaction sites, and intrinsic disorder that are present in the experimental data. Our empirical analysis concludes that predictions accurately reflect all combinations of these relations. Altogether, this study provides unique insights that support combining results produced by diverse residue-level predictors of protein function and structure.
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Affiliation(s)
- Bálint Biró
- Institute of Genetics and Biotechnology, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
| | - Bi Zhao
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
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5
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Zhao B, Katuwawala A, Oldfield CJ, Dunker AK, Faraggi E, Gsponer J, Kloczkowski A, Malhis N, Mirdita M, Obradovic Z, Söding J, Steinegger M, Zhou Y, Kurgan L. DescribePROT: database of amino acid-level protein structure and function predictions. Nucleic Acids Res 2021; 49:D298-D308. [PMID: 33119734 PMCID: PMC7778963 DOI: 10.1093/nar/gkaa931] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/11/2020] [Accepted: 10/05/2020] [Indexed: 12/30/2022] Open
Abstract
We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.
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Affiliation(s)
- Bi Zhao
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Akila Katuwawala
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | | | - A Keith Dunker
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Eshel Faraggi
- Battelle Center for Mathematical Medicine at the Nationwide Children's Hospital, and Department of Pediatrics, The Ohio State University, Columbus, OH, USA
| | - Jörg Gsponer
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
| | - Andrzej Kloczkowski
- Battelle Center for Mathematical Medicine at the Nationwide Children's Hospital, and Department of Pediatrics, The Ohio State University, Columbus, OH, USA
| | - Nawar Malhis
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
| | - Milot Mirdita
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Zoran Obradovic
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Johannes Söding
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Martin Steinegger
- School of Biological Sciences and Institute of Molecular Biology & Genetics, Seoul National University, Seoul, Republic of Korea
| | - Yaoqi Zhou
- Institute for Glycomics, Griffith University, Gold Coast, Queensland, Australia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
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6
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Zhang J, Chen Q, Liu B. iDRBP_MMC: Identifying DNA-Binding Proteins and RNA-Binding Proteins Based on Multi-Label Learning Model and Motif-Based Convolutional Neural Network. J Mol Biol 2020; 432:5860-5875. [DOI: 10.1016/j.jmb.2020.09.008] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 08/12/2020] [Accepted: 09/04/2020] [Indexed: 11/28/2022]
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7
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Wang K, Hu G, Wu Z, Su H, Yang J, Kurgan L. Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type. Int J Mol Sci 2020; 21:E6879. [PMID: 32961749 PMCID: PMC7554811 DOI: 10.3390/ijms21186879] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 09/15/2020] [Accepted: 09/17/2020] [Indexed: 02/07/2023] Open
Abstract
With close to 30 sequence-based predictors of RNA-binding residues (RBRs), this comparative survey aims to help with understanding and selection of the appropriate tools. We discuss past reviews on this topic, survey a comprehensive collection of predictors, and comparatively assess six representative methods. We provide a novel and well-designed benchmark dataset and we are the first to report and compare protein-level and datasets-level results, and to contextualize performance to specific types of RNAs. The methods considered here are well-cited and rely on machine learning algorithms on occasion combined with homology-based prediction. Empirical tests reveal that they provide relatively accurate predictions. Virtually all methods perform well for the proteins that interact with rRNAs, some generate accurate predictions for mRNAs, snRNA, SRP and IRES, while proteins that bind tRNAs are predicted poorly. Moreover, except for DRNApred, they confuse DNA and RNA-binding residues. None of the six methods consistently outperforms the others when tested on individual proteins. This variable and complementary protein-level performance suggests that users should not rely on applying just the single best dataset-level predictor. We recommend that future work should focus on the development of approaches that facilitate protein-level selection of accurate predictors and the consensus-based prediction of RBRs.
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Affiliation(s)
- Kui Wang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China; (K.W.); (Z.W.); (H.S.); (J.Y.)
| | - Gang Hu
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin 300071, China;
| | - Zhonghua Wu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China; (K.W.); (Z.W.); (H.S.); (J.Y.)
| | - Hong Su
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China; (K.W.); (Z.W.); (H.S.); (J.Y.)
| | - Jianyi Yang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China; (K.W.); (Z.W.); (H.S.); (J.Y.)
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
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8
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Gao J, Miao Z, Zhang Z, Wei H, Kurgan L. Prediction of Ion Channels and their Types from Protein Sequences: Comprehensive Review and Comparative Assessment. Curr Drug Targets 2020; 20:579-592. [PMID: 30360734 DOI: 10.2174/1389450119666181022153942] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2018] [Revised: 10/03/2018] [Accepted: 10/04/2018] [Indexed: 12/20/2022]
Abstract
BACKGROUND Ion channels are a large and growing protein family. Many of them are associated with diseases, and consequently, they are targets for over 700 drugs. Discovery of new ion channels is facilitated with computational methods that predict ion channels and their types from protein sequences. However, these methods were never comprehensively compared and evaluated. OBJECTIVE We offer first-of-its-kind comprehensive survey of the sequence-based predictors of ion channels. We describe eight predictors that include five methods that predict ion channels, their types, and four classes of the voltage-gated channels. We also develop and use a new benchmark dataset to perform comparative empirical analysis of the three currently available predictors. RESULTS While several methods that rely on different designs were published, only a few of them are currently available and offer a broad scope of predictions. Support and availability after publication should be required when new methods are considered for publication. Empirical analysis shows strong performance for the prediction of ion channels and modest performance for the prediction of ion channel types and voltage-gated channel classes. We identify a substantial weakness of current methods that cannot accurately predict ion channels that are categorized into multiple classes/types. CONCLUSION Several predictors of ion channels are available to the end users. They offer practical levels of predictive quality. Methods that rely on a larger and more diverse set of predictive inputs (such as PSIONplus) are more accurate. New tools that address multi-label prediction of ion channels should be developed.
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Affiliation(s)
- Jianzhao Gao
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China
| | - Zhen Miao
- College of Life Sciences, Nankai University, Tianjin, China
| | - Zhaopeng Zhang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China
| | - Hong Wei
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, United States
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9
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PSIONplus m Server for Accurate Multi-Label Prediction of Ion Channels and Their Types. Biomolecules 2020; 10:biom10060876. [PMID: 32517331 PMCID: PMC7355608 DOI: 10.3390/biom10060876] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 05/28/2020] [Accepted: 06/04/2020] [Indexed: 11/26/2022] Open
Abstract
Computational prediction of ion channels facilitates the identification of putative ion channels from protein sequences. Several predictors of ion channels and their types were developed in the last quindecennial. While they offer reasonably accurate predictions, they also suffer a few shortcomings including lack of availability, parallel prediction mode, single-label prediction (inability to predict multiple channel subtypes), and incomplete scope (inability to predict subtypes of the voltage-gated channels). We developed a first-of-its-kind PSIONplusm method that performs sequential multi-label prediction of ion channels and their subtypes for both voltage-gated and ligand-gated channels. PSIONplusm sequentially combines the outputs produced by three support vector machine-based models from the PSIONplus predictor and is available as a webserver. Empirical tests show that PSIONplusm outperforms current methods for the multi-label prediction of the ion channel subtypes. This includes the existing single-label methods that are available to the users, a naïve multi-label predictor that combines results produced by multiple single-label methods, and methods that make predictions based on sequence alignment and domain annotations. We also found that the current methods (including PSIONplusm) fail to accurately predict a few of the least frequently occurring ion channel subtypes. Thus, new predictors should be developed when a larger quantity of annotated ion channels will be available to train predictive models.
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10
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Katuwawala A, Ghadermarzi S, Kurgan L. Computational prediction of functions of intrinsically disordered regions. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2019; 166:341-369. [PMID: 31521235 DOI: 10.1016/bs.pmbts.2019.04.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Intrinsically disorder regions (IDRs) are abundant in nature, particularly among Eukaryotes. While they facilitate a wide spectrum of cellular functions including signaling, molecular assembly and recognition, translation, transcription and regulation, only several hundred IDRs are annotated functionally. This annotation gap motivates the development of fast and accurate computational methods that predict IDR functions directly from protein sequences. We introduce and describe a comprehensive collection of 25 methods that provide accurate predictions of IDRs that interact with proteins and nucleic acids, that function as flexible linkers and that moonlight multiple functions. Virtually all of these predictors can be accessed online and many were developed in the last few years. They utilize a wide range of predictive architectures and take advantage of modern machine learning algorithms. Our empirical analysis shows that predictors that are available as webservers enjoy high rates of citations, attesting to their practical value and popularity. The most cited methods include DISOPRED3, ANCHOR, alpha-MoRFpred, MoRFpred, fMoRFpred and MoRFCHiBi. We present two case studies to demonstrate that predictions produced by these computational tools are relatively easy to interpret and that they deliver valuable functional clues. However, the current computational tools cover a relatively narrow range of disorder functions. Further development efforts that would cover a broader range of functions should be pursued. We demonstrate that a sufficient amount of functionally annotated IDRs that are associated with several other disorder functions is already available and can be used to design and validate novel predictors.
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Affiliation(s)
- Akila Katuwawala
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
| | - Sina Ghadermarzi
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States.
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11
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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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12
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Jung Y, El-Manzalawy Y, Dobbs D, Honavar VG. Partner-specific prediction of RNA-binding residues in proteins: A critical assessment. Proteins 2018; 87:198-211. [PMID: 30536635 PMCID: PMC6389706 DOI: 10.1002/prot.25639] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 10/10/2018] [Accepted: 11/29/2018] [Indexed: 01/06/2023]
Abstract
RNA-protein interactions play essential roles in regulating gene expression. While some RNA-protein interactions are "specific", that is, the RNA-binding proteins preferentially bind to particular RNA sequence or structural motifs, others are "non-RNA specific." Deciphering the protein-RNA recognition code is essential for comprehending the functional implications of these interactions and for developing new therapies for many diseases. Because of the high cost of experimental determination of protein-RNA interfaces, there is a need for computational methods to identify RNA-binding residues in proteins. While most of the existing computational methods for predicting RNA-binding residues in RNA-binding proteins are oblivious to the characteristics of the partner RNA, there is growing interest in methods for partner-specific prediction of RNA binding sites in proteins. In this work, we assess the performance of two recently published partner-specific protein-RNA interface prediction tools, PS-PRIP, and PRIdictor, along with our own new tools. Specifically, we introduce a novel metric, RNA-specificity metric (RSM), for quantifying the RNA-specificity of the RNA binding residues predicted by such tools. Our results show that the RNA-binding residues predicted by previously published methods are oblivious to the characteristics of the putative RNA binding partner. Moreover, when evaluated using partner-agnostic metrics, RNA partner-specific methods are outperformed by the state-of-the-art partner-agnostic methods. We conjecture that either (a) the protein-RNA complexes in PDB are not representative of the protein-RNA interactions in nature, or (b) the current methods for partner-specific prediction of RNA-binding residues in proteins fail to account for the differences in RNA partner-specific versus partner-agnostic protein-RNA interactions, or both.
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Affiliation(s)
- Yong Jung
- Bioinformatics and Genomics Graduate Program, Pennsylvania State University, University Park, Pennsylvania.,Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, Pennsylvania.,The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania
| | - Yasser El-Manzalawy
- Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, Pennsylvania.,Clinical and Translational Sciences Institute, Pennsylvania State University, University Park, Pennsylvania.,College of Information Sciences and Technology, Pennsylvania State University, Pennsylvania
| | - Drena Dobbs
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa.,Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, Iowa
| | - Vasant G Honavar
- Bioinformatics and Genomics Graduate Program, Pennsylvania State University, University Park, Pennsylvania.,Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, Pennsylvania.,Institute for Cyberscience, Pennsylvania State University, University Park, Pennsylvania.,Clinical and Translational Sciences Institute, Pennsylvania State University, University Park, Pennsylvania.,The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania.,College of Information Sciences and Technology, Pennsylvania State University, Pennsylvania
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13
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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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14
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Su H, Liu M, Sun S, Peng Z, Yang J. Improving the prediction of protein–nucleic acids binding residues via multiple sequence profiles and the consensus of complementary methods. Bioinformatics 2018; 35:930-936. [DOI: 10.1093/bioinformatics/bty756] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Revised: 08/02/2018] [Accepted: 08/28/2018] [Indexed: 12/31/2022] Open
Affiliation(s)
- Hong Su
- School of Mathematical Sciences, Nankai University, Tianjin, China
| | - Mengchen Liu
- School of Mathematical Sciences, Nankai University, Tianjin, China
| | - Saisai Sun
- School of Mathematical Sciences, Nankai University, Tianjin, China
| | - Zhenling Peng
- Center for Applied Mathematics, Tianjin University, Tianjin, China
| | - Jianyi Yang
- School of Mathematical Sciences, Nankai University, Tianjin, China
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15
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Chowdhury S, Zhang J, Kurgan L. In Silico Prediction and Validation of Novel RNA Binding Proteins and Residues in the Human Proteome. Proteomics 2018; 18:e1800064. [PMID: 29806170 DOI: 10.1002/pmic.201800064] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Revised: 05/05/2018] [Indexed: 12/22/2022]
Abstract
Deciphering a complete landscape of protein-RNA interactions in the human proteome remains an elusive challenge. We computationally elucidate RNA binding proteins (RBPs) using an approach that complements previous efforts. We employ two modern complementary sequence-based methods that provide accurate predictions from the structured and the intrinsically disordered sequences, even in the absence of sequence similarity to the known RBPs. We generate and analyze putative RNA binding residues on the whole proteome scale. Using a conservative setting that ensures low, 5% false positive rate, we identify 1511 putative RBPs that include 281 known RBPs and 166 RBPs that were previously predicted. We empirically demonstrate that these overlaps are statistically significant. We also validate the putative RBPs based on two major hallmarks of their RNA binding residues: high levels of evolutionary conservation and enrichment in charged amino acids. Moreover, we show that the novel RBPs are significantly under-annotated functionally which coincides with the fact that they were not yet found to interact with RNAs. We provide two examples of our novel putative RBPs for which there is recent evidence of their interactions with RNAs. The dataset of novel putative RBPs and RNA binding residues for the future hypothesis generation is provided in the Supporting Information.
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Affiliation(s)
- Shomeek Chowdhury
- Dr. Vikram Sarabhai Institute of Cell and Molecular Biology, Maharaja Sayajirao University of Baroda, Gujarat, 390005, India.,Department of Computer Science, Virginia Commonwealth University, Richmond, VA, 23284, USA
| | - Jian Zhang
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, 23284, USA.,School of Computer and Information Technology, Xinyang Normal University, Xinyang, 464000, P. R. China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, 23284, USA
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16
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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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17
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Yan J, Kurgan L. DRNApred, fast sequence-based method that accurately predicts and discriminates DNA- and RNA-binding residues. Nucleic Acids Res 2017; 45:e84. [PMID: 28132027 PMCID: PMC5449545 DOI: 10.1093/nar/gkx059] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Accepted: 01/24/2017] [Indexed: 01/18/2023] Open
Abstract
Protein-DNA and protein-RNA interactions are part of many diverse and essential cellular functions and yet most of them remain to be discovered and characterized. Recent research shows that sequence-based predictors of DNA-binding residues accurately find these residues but also cross-predict many RNA-binding residues as DNA-binding, and vice versa. Most of these methods are also relatively slow, prohibiting applications on the whole-genome scale. We describe a novel sequence-based method, DRNApred, which accurately and in high-throughput predicts and discriminates between DNA- and RNA-binding residues. DRNApred was designed using a new dataset with both DNA- and RNA-binding proteins, regression that penalizes cross-predictions, and a novel two-layered architecture. DRNApred outperforms state-of-the-art predictors of DNA- or RNA-binding residues on a benchmark test dataset by substantially reducing the cross predictions and predicting arguably higher quality false positives that are located nearby the native binding residues. Moreover, it also more accurately predicts the DNA- and RNA-binding proteins. Application on the human proteome confirms that DRNApred reduces the cross predictions among the native nucleic acid binders. Also, novel putative DNA/RNA-binding proteins that it predicts share similar subcellular locations and residue charge profiles with the known native binding proteins. Webserver of DRNApred is freely available at http://biomine.cs.vcu.edu/servers/DRNApred/.
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Affiliation(s)
- Jing Yan
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6G 2V4, Canada
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, 23284, USA
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18
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Sequence-based discrimination of protein-RNA interacting residues using a probabilistic approach. J Theor Biol 2017; 418:77-83. [DOI: 10.1016/j.jtbi.2017.01.040] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Revised: 01/06/2017] [Accepted: 01/27/2017] [Indexed: 11/22/2022]
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19
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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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20
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Abstract
Intrinsically disordered proteins and regions (IDPs and IDRs) are involved in a wide range of cellular functions and they often facilitate interactions with RNAs, DNAs, and proteins. Although many computational methods can predict IDPs and IDRs in protein sequences, only a few methods predict their functions and these functions primarily concern protein binding. We describe how to use the first computational method DisoRDPbind for high-throughput prediction of multiple functions of disordered regions. Our method predicts the RNA-, DNA-, and protein-binding residues located in IDRs in the input protein sequences. DisoRDPbind provides accurate predictions and is sufficiently fast to make predictions for full genomes. Our method is implemented as a user-friendly webserver that is freely available at http://biomine.ece.ualberta.ca/DisoRDPbind/ . We overview our predictor, discuss how to run the webserver, and show how to interpret the corresponding results. We also demonstrate the utility of our method based on two case studies, human BRCA1 protein that binds various proteins and DNA, and yeast 60S ribosomal protein L4 that interacts with proteins and RNA.
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21
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MUPPIRALA USHA, LEWIS BENJAMINA, MANN CARLAM, DOBBS DRENA. A MOTIF-BASED METHOD FOR PREDICTING INTERFACIAL RESIDUES IN BOTH THE RNA AND PROTEIN COMPONENTS OF PROTEIN-RNA COMPLEXES. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2016; 21:445-455. [PMID: 26776208 PMCID: PMC4721245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Efforts to predict interfacial residues in protein-RNA complexes have largely focused on predicting RNA-binding residues in proteins. Computational methods for predicting protein-binding residues in RNA sequences, however, are a problem that has received relatively little attention to date. Although the value of sequence motifs for classifying and annotating protein sequences is well established, sequence motifs have not been widely applied to predicting interfacial residues in macromolecular complexes. Here, we propose a novel sequence motif-based method for "partner-specific" interfacial residue prediction. Given a specific protein-RNA pair, the goal is to simultaneously predict RNA binding residues in the protein sequence and protein-binding residues in the RNA sequence. In 5-fold cross validation experiments, our method, PS-PRIP, achieved 92% Specificity and 61% Sensitivity, with a Matthews correlation coefficient (MCC) of 0.58 in predicting RNA-binding sites in proteins. The method achieved 69% Specificity and 75% Sensitivity, but with a low MCC of 0.13 in predicting protein binding sites in RNAs. Similar performance results were obtained when PS-PRIP was tested on two independent "blind" datasets of experimentally validated protein- RNA interactions, suggesting the method should be widely applicable and valuable for identifying potential interfacial residues in protein-RNA complexes for which structural information is not available. The PS-PRIP webserver and datasets are available at: http://pridb.gdcb.iastate.edu/PSPRIP/.
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MESH Headings
- Amino Acid Motifs
- Amino Acid Sequence
- Base Sequence
- Binding Sites/genetics
- Computational Biology/methods
- Computational Biology/statistics & numerical data
- Databases, Nucleic Acid/statistics & numerical data
- Databases, Protein/statistics & numerical data
- Escherichia coli Proteins/chemistry
- Escherichia coli Proteins/genetics
- Escherichia coli Proteins/metabolism
- Models, Molecular
- Protein Binding
- RNA/chemistry
- RNA/genetics
- RNA/metabolism
- RNA, Bacterial/chemistry
- RNA, Bacterial/genetics
- RNA, Bacterial/metabolism
- RNA, Ribosomal, 16S/chemistry
- RNA, Ribosomal, 16S/genetics
- RNA, Ribosomal, 16S/metabolism
- RNA-Binding Proteins/chemistry
- RNA-Binding Proteins/genetics
- RNA-Binding Proteins/metabolism
- Ribosomal Proteins/chemistry
- Ribosomal Proteins/genetics
- Ribosomal Proteins/metabolism
- Software
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Affiliation(s)
- USHA MUPPIRALA
- Genome Informatics Facility, Iowa State University, Ames, Iowa, 50011, USA
| | - BENJAMIN A LEWIS
- Department of Computer Science, Truman State University, Kirksville, Missouri, 63501, USA,
| | - CARLA M. MANN
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, 50011, USA,
| | - DRENA DOBBS
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, Iowa, 50011, USA,
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22
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Miao Z, Westhof E. A Large-Scale Assessment of Nucleic Acids Binding Site Prediction Programs. PLoS Comput Biol 2015; 11:e1004639. [PMID: 26681179 PMCID: PMC4683125 DOI: 10.1371/journal.pcbi.1004639] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Accepted: 10/30/2015] [Indexed: 11/18/2022] Open
Abstract
Computational prediction of nucleic acid binding sites in proteins are necessary to disentangle functional mechanisms in most biological processes and to explore the binding mechanisms. Several strategies have been proposed, but the state-of-the-art approaches display a great diversity in i) the definition of nucleic acid binding sites; ii) the training and test datasets; iii) the algorithmic methods for the prediction strategies; iv) the performance measures and v) the distribution and availability of the prediction programs. Here we report a large-scale assessment of 19 web servers and 3 stand-alone programs on 41 datasets including more than 5000 proteins derived from 3D structures of protein-nucleic acid complexes. Well-defined binary assessment criteria (specificity, sensitivity, precision, accuracy…) are applied. We found that i) the tools have been greatly improved over the years; ii) some of the approaches suffer from theoretical defects and there is still room for sorting out the essential mechanisms of binding; iii) RNA binding and DNA binding appear to follow similar driving forces and iv) dataset bias may exist in some methods.
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Affiliation(s)
- Zhichao Miao
- Architecture et Réactivité de l'ARN, Université de Strasbourg, Institut de Biologie Moléculaire et Cellulaire du CNRS, Strasbourg, France
| | - Eric Westhof
- Architecture et Réactivité de l'ARN, Université de Strasbourg, Institut de Biologie Moléculaire et Cellulaire du CNRS, Strasbourg, France
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23
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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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24
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Peng Z, Kurgan L. High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder. Nucleic Acids Res 2015; 43:e121. [PMID: 26109352 PMCID: PMC4605291 DOI: 10.1093/nar/gkv585] [Citation(s) in RCA: 113] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Revised: 04/24/2015] [Accepted: 05/24/2015] [Indexed: 01/05/2023] Open
Abstract
Intrinsically disordered proteins and regions (IDPs and IDRs) lack stable 3D structure under physiological conditions in-vitro, are common in eukaryotes, and facilitate interactions with RNA, DNA and proteins. Current methods for prediction of IDPs and IDRs do not provide insights into their functions, except for a handful of methods that address predictions of protein-binding regions. We report first-of-its-kind computational method DisoRDPbind for high-throughput prediction of RNA, DNA and protein binding residues located in IDRs from protein sequences. DisoRDPbind is implemented using a runtime-efficient multi-layered design that utilizes information extracted from physiochemical properties of amino acids, sequence complexity, putative secondary structure and disorder and sequence alignment. Empirical tests demonstrate that it provides accurate predictions that are competitive with other predictors of disorder-mediated protein binding regions and complementary to the methods that predict RNA- and DNA-binding residues annotated based on crystal structures. Application in Homo sapiens, Mus musculus, Caenorhabditis elegans and Drosophila melanogaster proteomes reveals that RNA- and DNA-binding proteins predicted by DisoRDPbind complement and overlap with the corresponding known binding proteins collected from several sources. Also, the number of the putative protein-binding regions predicted with DisoRDPbind correlates with the promiscuity of proteins in the corresponding protein-protein interaction networks. Webserver: http://biomine.ece.ualberta.ca/DisoRDPbind/.
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Affiliation(s)
- Zhenling Peng
- Center for Applied Mathematics, Tianjin University, Tianjin, 300072, P.R. China Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, T6G 2V4, Canada
| | - Lukasz Kurgan
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, T6G 2V4, Canada
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25
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Solem AC, Halvorsen M, Ramos SBV, Laederach A. The potential of the riboSNitch in personalized medicine. WILEY INTERDISCIPLINARY REVIEWS-RNA 2015; 6:517-32. [PMID: 26115028 PMCID: PMC4543445 DOI: 10.1002/wrna.1291] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2014] [Revised: 03/25/2015] [Accepted: 05/13/2015] [Indexed: 01/28/2023]
Abstract
RNA conformation plays a significant role in stability, ligand binding, transcription, and translation. Single nucleotide variants (SNVs) have the potential to disrupt specific structural elements because RNA folds in a sequence-specific manner. A riboSNitch is an element of RNA structure with a specific function that is disrupted by an SNV or a single nucleotide polymorphism (SNP; or polymorphism; SNVs occur with low frequency in the population, <1%). The riboSNitch is analogous to a riboswitch, where binding of a small molecule rather than mutation alters the structure of the RNA to control gene regulation. RiboSNitches are particularly relevant to interpreting the results of genome-wide association studies (GWAS). Often GWAS identify SNPs associated with a phenotype mapping to noncoding regions of the genome. Because a majority of the human genome is transcribed, significant subsets of GWAS SNPs are putative riboSNitches. The extent to which the transcriptome is tolerant of SNP-induced structure change is still poorly understood. Recent advances in ultra high-throughput structure probing begin to reveal the structural complexities of mutation-induced structure change. This review summarizes our current understanding of SNV and SNP-induced structure change in the human transcriptome and discusses the importance of riboSNitch discovery in interpreting GWAS results and massive sequencing projects.
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Affiliation(s)
- Amanda C Solem
- Department of Biology, University of North Carolina, Chapel Hill, NC, USA
| | - Matthew Halvorsen
- Department of Biology, University of North Carolina, Chapel Hill, NC, USA.,Institute for Genomic Medicine, Columbia University, New York, NY, USA
| | - Silvia B V Ramos
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC, USA
| | - Alain Laederach
- Department of Biology, University of North Carolina, Chapel Hill, NC, USA.,Bioinformatics and Computational Biology Program, University of North Carolina, Chapel Hill, NC, USA
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26
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Miao Z, Westhof E. Prediction of nucleic acid binding probability in proteins: a neighboring residue network based score. Nucleic Acids Res 2015; 43:5340-51. [PMID: 25940624 PMCID: PMC4477668 DOI: 10.1093/nar/gkv446] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Revised: 04/23/2015] [Accepted: 04/24/2015] [Indexed: 11/13/2022] Open
Abstract
We describe a general binding score for predicting the nucleic acid binding probability in proteins. The score is directly derived from physicochemical and evolutionary features and integrates a residue neighboring network approach. Our process achieves stable and high accuracies on both DNA- and RNA-binding proteins and illustrates how the main driving forces for nucleic acid binding are common. Because of the effective integration of the synergetic effects of the network of neighboring residues and the fact that the prediction yields a hierarchical scoring on the protein surface, energy funnels for nucleic acid binding appear on protein surfaces, pointing to the dynamic process occurring in the binding of nucleic acids to proteins.
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Affiliation(s)
- Zhichao Miao
- Architecture et Réactivité de l'ARN, Université de Strasbourg, Institut de biologie moléculaire et cellulaire du CNRS, 15 Rue Descartes, 67000 Strasbourg, France
| | - Eric Westhof
- Architecture et Réactivité de l'ARN, Université de Strasbourg, Institut de biologie moléculaire et cellulaire du CNRS, 15 Rue Descartes, 67000 Strasbourg, France
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27
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Yan J, Friedrich S, Kurgan L. A comprehensive comparative review of sequence-based predictors of DNA- and RNA-binding residues. Brief Bioinform 2015; 17:88-105. [DOI: 10.1093/bib/bbv023] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Indexed: 01/07/2023] Open
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28
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Dufour D, Marti-Renom MA. Software for predicting the 3D structure of RNA molecules. WILEY INTERDISCIPLINARY REVIEWS: COMPUTATIONAL MOLECULAR SCIENCE 2015. [DOI: 10.1002/wcms.1198] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- David Dufour
- Genome Biology Group, Centre Nacional d'Anàlisi Genòmica (CNAG) and Gene Regulacion, Stem Cells and Cancer Program; Centre de Regulació Genòmica (CRG); Barcelona Spain
| | - Marc A. Marti-Renom
- Institució Catalana de Recerca i Estudis Avançats (ICREA); Genome Biology Group, Centre Nacional d'Anàlisi Genòmica (CNAG) and Gene Regulacion, Stem Cells and Cancer Program, Centre de Regulació Genòmica (CRG); Barcelona Spain
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29
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Yang XX, Deng ZL, Liu R. RBRDetector: Improved prediction of binding residues on RNA-binding protein structures using complementary feature- and template-based strategies. Proteins 2014; 82:2455-71. [DOI: 10.1002/prot.24610] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2014] [Revised: 04/28/2014] [Accepted: 05/09/2014] [Indexed: 11/05/2022]
Affiliation(s)
- Xiao-Xia Yang
- Agricultural Bioinformatics Key Laboratory of Hubei Province; College of Informatics; Huazhong Agricultural University; Wuhan 430070 People's Republic of China
| | - Zhi-Luo Deng
- Agricultural Bioinformatics Key Laboratory of Hubei Province; College of Informatics; Huazhong Agricultural University; Wuhan 430070 People's Republic of China
| | - Rong Liu
- Agricultural Bioinformatics Key Laboratory of Hubei Province; College of Informatics; Huazhong Agricultural University; Wuhan 430070 People's Republic of China
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30
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Li ZC, Lai YH, Chen LL, Xie Y, Dai Z, Zou XY. Identifying functions of protein complexes based on topology similarity with random forest. MOLECULAR BIOSYSTEMS 2014; 10:514-25. [PMID: 24389559 DOI: 10.1039/c3mb70401g] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Elucidating the functions of protein complexes is critical for understanding disease mechanisms, diagnosis and therapy. In this study, based on the concept that protein complexes with similar topology may have similar functions, we firstly model protein complexes as weighted graphs with nodes representing the proteins and edges indicating interaction between proteins. Secondly, we use topology features derived from the graphs to characterize protein complexes based on the graph theory. Finally, we construct a predictor by using random forest and topology features to identify the functions of protein complexes. Effectiveness of the current method is evaluated by identifying the functions of mammalian protein complexes. And then the predictor is also utilized to identify the functions of protein complexes retrieved from human protein-protein interaction networks. We identify some protein complexes with significant roles in the occurrence of tumors, vesicles and retinoblastoma. It is anticipated that the current research has an important impact on pathogenesis and the pharmaceutical industry. The source code of Matlab and the dataset are freely available on request from the authors.
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Affiliation(s)
- Zhan-Chao Li
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, People's Republic of China.
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31
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Yang Y, Zhao H, Wang J, Zhou Y. SPOT-Seq-RNA: predicting protein-RNA complex structure and RNA-binding function by fold recognition and binding affinity prediction. Methods Mol Biol 2014; 1137:119-30. [PMID: 24573478 PMCID: PMC3937850 DOI: 10.1007/978-1-4939-0366-5_9] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
RNA-binding proteins (RBPs) play key roles in RNA metabolism and post-transcriptional regulation. Computational methods have been developed separately for prediction of RBPs and RNA-binding residues by machine-learning techniques and prediction of protein-RNA complex structures by rigid or semiflexible structure-to-structure docking. Here, we describe a template-based technique called SPOT-Seq-RNA that integrates prediction of RBPs, RNA-binding residues, and protein-RNA complex structures into a single package. This integration is achieved by combining template-based structure-prediction software, SPARKS X, with binding affinity prediction software, DRNA. This tool yields reasonable sensitivity (46 %) and high precision (84 %) for an independent test set of 215 RBPs and 5,766 non-RBPs. SPOT-Seq-RNA is computationally efficient for genome-scale prediction of RBPs and protein-RNA complex structures. Its application to human genome study has revealed a similar sensitivity and ability to uncover hundreds of novel RBPs beyond simple homology. The online server and downloadable version of SPOT-Seq-RNA are available at http://sparks-lab.org/server/SPOT-Seq-RNA/.
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Affiliation(s)
- Yuedong Yang
- School of Informatics, Indiana University Purdue University, Indianapolis, IN, USA
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