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Pradhan UK, Meher PK, Naha S, Das R, Gupta A, Parsad R. ProkDBP: Toward more precise identification of prokaryotic DNA binding proteins. Protein Sci 2024; 33:e5015. [PMID: 38747369 PMCID: PMC11094783 DOI: 10.1002/pro.5015] [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: 12/08/2023] [Revised: 04/18/2024] [Accepted: 04/21/2024] [Indexed: 05/19/2024]
Abstract
Prokaryotic DNA binding proteins (DBPs) play pivotal roles in governing gene regulation, DNA replication, and various cellular functions. Accurate computational models for predicting prokaryotic DBPs hold immense promise in accelerating the discovery of novel proteins, fostering a deeper understanding of prokaryotic biology, and facilitating the development of therapeutics targeting for potential disease interventions. However, existing generic prediction models often exhibit lower accuracy in predicting prokaryotic DBPs. To address this gap, we introduce ProkDBP, a novel machine learning-driven computational model for prediction of prokaryotic DBPs. For prediction, a total of nine shallow learning algorithms and five deep learning models were utilized, with the shallow learning models demonstrating higher performance metrics compared to their deep learning counterparts. The light gradient boosting machine (LGBM), coupled with evolutionarily significant features selected via random forest variable importance measure (RF-VIM) yielded the highest five-fold cross-validation accuracy. The model achieved the highest auROC (0.9534) and auPRC (0.9575) among the 14 machine learning models evaluated. Additionally, ProkDBP demonstrated substantial performance with an independent dataset, exhibiting higher values of auROC (0.9332) and auPRC (0.9371). Notably, when benchmarked against several cutting-edge existing models, ProkDBP showcased superior predictive accuracy. Furthermore, to promote accessibility and usability, ProkDBP (https://iasri-sg.icar.gov.in/prokdbp/) is available as an online prediction tool, enabling free access to interested users. This tool stands as a significant contribution, enhancing the repertoire of resources for accurate and efficient prediction of prokaryotic DBPs.
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Affiliation(s)
- Upendra Kumar Pradhan
- Division of Statistical GeneticsICAR‐Indian Agricultural Statistics Research Institute, PUSANew DelhiIndia
| | - Prabina Kumar Meher
- Division of Statistical GeneticsICAR‐Indian Agricultural Statistics Research Institute, PUSANew DelhiIndia
| | - Sanchita Naha
- Division of Computer ApplicationsICAR‐Indian Agricultural Statistics Research Institute, PUSANew DelhiIndia
| | - Ritwika Das
- Division of Agricultural BioinformaticsICAR‐Indian Agricultural Statistics Research Institute, PUSANew DelhiIndia
| | - Ajit Gupta
- Division of Statistical GeneticsICAR‐Indian Agricultural Statistics Research Institute, PUSANew DelhiIndia
| | - Rajender Parsad
- ICAR‐Indian Agricultural Statistics Research Institute, PUSANew DelhiIndia
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2
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Ahmed SH, Bose DB, Khandoker R, Rahman MS. StackDPP: a stacking ensemble based DNA-binding protein prediction model. BMC Bioinformatics 2024; 25:111. [PMID: 38486135 PMCID: PMC10941422 DOI: 10.1186/s12859-024-05714-9] [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: 12/08/2022] [Accepted: 02/20/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND DNA-binding proteins (DNA-BPs) are the proteins that bind and interact with DNA. DNA-BPs regulate and affect numerous biological processes, such as, transcription and DNA replication, repair, and organization of the chromosomal DNA. Very few proteins, however, are DNA-binding in nature. Therefore, it is necessary to develop an efficient predictor for identifying DNA-BPs. RESULT In this work, we have proposed new benchmark datasets for the DNA-binding protein prediction problem. We discovered several quality concerns with the widely used benchmark datasets, PDB1075 (for training) and PDB186 (for independent testing), which necessitated the preparation of new benchmark datasets. Our proposed datasets UNIPROT1424 and UNIPROT356 can be used for model training and independent testing respectively. We have retrained selected state-of-the-art DNA-BP predictors in the new dataset and reported their performance results. We also trained a novel predictor using the new benchmark dataset. We extracted features from various feature categories, then used a Random Forest classifier and Recursive Feature Elimination with Cross-validation (RFECV) to select the optimal set of 452 features. We then proposed a stacking ensemble architecture as our final prediction model. Named Stacking Ensemble Model for DNA-binding Protein Prediction, or StackDPP in short, our model achieved 0.92, 0.92 and 0.93 accuracy in 10-fold cross-validation, jackknife and independent testing respectively. CONCLUSION StackDPP has performed very well in cross-validation testing and has outperformed all the state-of-the-art prediction models in independent testing. Its performance scores in cross-validation testing generalized very well in the independent test set. The source code of the model is publicly available at https://github.com/HasibAhmed1624/StackDPP . Therefore, we expect this generalized model can be adopted by researchers and practitioners to identify novel DNA-binding proteins.
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Affiliation(s)
- Sheikh Hasib Ahmed
- Department of CSE, BUET, ECE Building, West Palashi, Dhaka, 1000, Bangladesh
| | | | - Rafi Khandoker
- Department of CSE, BUET, ECE Building, West Palashi, Dhaka, 1000, Bangladesh
| | - M Saifur Rahman
- Department of CSE, BUET, ECE Building, West Palashi, Dhaka, 1000, Bangladesh.
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3
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Levine J, Lobyntseva A, Shazman S, Hakim F, Gozes I. Longitudinal Genotype-Phenotype (Vineland Questionnaire) Characterization of 15 ADNP Syndrome Cases Highlights Mutated Protein Length and Structural Characteristics Correlation with Communicative Abilities Accentuated in Males. J Mol Neurosci 2024; 74:15. [PMID: 38282129 DOI: 10.1007/s12031-024-02189-4] [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] [Accepted: 01/08/2024] [Indexed: 01/30/2024]
Abstract
Activity-dependent neuroprotective protein (ADNP) is essential for neurodevelopment and de novo mutations in ADNP cause the ADNP syndrome. From brain pathologies point of view, tauopathy has been demonstrated at a young age, implying stunted development coupled with early/accelerated neurodegeneration. Given potential genotype-phenotype differences and age-dependency, we have assessed here a cohort of 15 individuals (1-27-year-old), using 1-3 longitudinal parent (caretaker) interview/s (Vineland 3 questionnaire) over several years. Our results indicated developmental delays, or even developmental arrests, coupled with potential spurts of development at early ages. Severe outcomes correlated with the truncating high impact mutation, in other words, the remaining mutated protein length as well as with the tested individual age, corroborating the hypothesis of developmental delays coupled with accelerated aging. A significant correlation was noted between mutated protein length and communication, implying a high impact of ADNP on communicative skills. Additionally, correlations were discovered between the two previously described epi-genetic signatures in ADNP emphasizing aberrant acquisition of motor behaviors, with truncating mutations around the nuclear localization signal being mostly affected. Finally, all individuals seem to acquire an age equivalent of 1-6 years, requiring disease modification treatment, such as the ADNP-derived drug candidate, NAP (davunetide), which has recently shown efficacy in women suffering from the neurodegenerative disorder, progressive supranuclear palsy (PSP), a late-onset tauopathy.
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Affiliation(s)
- Jospeh Levine
- The Elton Laboratory for Molecular Neuroendocrinology, Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Sagol School of Neuroscience and Adams Super Center for Brain Studies, Tel Aviv University, Tel Aviv, 6997801, Israel
- Psychiatric Division, Ben Gurion University of the Negev, Beersheba, Israel
| | - Alexandra Lobyntseva
- The Elton Laboratory for Molecular Neuroendocrinology, Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Sagol School of Neuroscience and Adams Super Center for Brain Studies, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Shula Shazman
- Department of Mathematics and Computer Science, The Open University of Israel, Ra'anana, Israel
| | | | - Illana Gozes
- The Elton Laboratory for Molecular Neuroendocrinology, Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Sagol School of Neuroscience and Adams Super Center for Brain Studies, Tel Aviv University, Tel Aviv, 6997801, Israel.
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4
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Mohanty P, Kapoor U, Sundaravadivelu Devarajan D, Phan TM, Rizuan A, Mittal J. Principles Governing the Phase Separation of Multidomain Proteins. Biochemistry 2022; 61:2443-2455. [PMID: 35802394 PMCID: PMC9669140 DOI: 10.1021/acs.biochem.2c00210] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
A variety of membraneless organelles, often termed "biological condensates", play an important role in the regulation of cellular processes such as gene transcription, translation, and protein quality control. On the basis of experimental and theoretical investigations, liquid-liquid phase separation (LLPS) has been proposed as a possible mechanism for the origin of biological condensates. LLPS requires multivalent macromolecules that template the formation of long-range, intermolecular interaction networks and results in the formation of condensates with defined composition and material properties. Multivalent interactions driving LLPS exhibit a wide range of modes from highly stereospecific to nonspecific and involve both folded and disordered regions. Multidomain proteins serve as suitable macromolecules for promoting phase separation and achieving disparate functions due to their potential for multivalent interactions and regulation. Here, we aim to highlight the influence of the domain architecture and interdomain interactions on the phase separation of multidomain protein condensates. First, the general principles underlying these interactions are illustrated on the basis of examples of multidomain proteins that are predominantly associated with nucleic acid binding and protein quality control and contain both folded and disordered regions. Next, the examples showcase how LLPS properties of folded and disordered regions can be leveraged to engineer multidomain constructs that form condensates with the desired assembly and functional properties. Finally, we highlight the need for improvements in coarse-grained computational models that can provide molecular-level insights into multidomain protein condensates in conjunction with experimental efforts.
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Affiliation(s)
- Priyesh Mohanty
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843
| | - Utkarsh Kapoor
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843
| | | | - Tien Minh Phan
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843
| | - Azamat Rizuan
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843
| | - Jeetain Mittal
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843
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5
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Pal A, Chakrabarti P, Dey S. ProDFace: A web-tool for the dissection of protein-DNA interfaces. Front Mol Biosci 2022; 9:978310. [PMID: 36148013 PMCID: PMC9486321 DOI: 10.3389/fmolb.2022.978310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 08/09/2022] [Indexed: 11/30/2022] Open
Abstract
Protein-DNA interactions play a crucial role in gene expression and regulation. Identifying the DNA binding surface of proteins has long been a challenge–in comparison to protein-protein interactions, limited progress has been made in the development of efficient DNA binding site prediction and protein-DNA docking methods. Here we present ProDFace, a web tool that characterizes the binding region of a protein-DNA complex based on amino acid propensity, hydrogen bond (HB) donor capacity (number of solvent accessible HB donor groups), sequence conservation at the interface core and rim region, and geometry. The program takes as input the structure of a protein-DNA complex in PDB (Protein Data Bank) format, and outputs various physicochemical and geometric parameters of the interface, as well as conservation of the interface residues in the protein component. Values are provided for the whole interface, and after dissecting it into core and rim regions. Details of water mediated HBs between protein and DNA, potential HB donor groups present at the binding surface of protein, and conserved interface residues are also provided as downloadable text files. These parameters can be useful in evaluating and validating protein-DNA docking solutions, structures derived from simulation as well as solutions from the available prediction tools, and facilitate the development of more efficient prediction methods. The web-tool is freely available at structbioinfo.iitj.ac.in/resources/bioinfo/pd_interface.
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Affiliation(s)
- Arumay Pal
- School of Bioengineering, Vellore Institute of Technology, Bhopal, India
| | | | - Sucharita Dey
- Department of Bioscience and Bioengineering, Indian Institute of Technology Jodhpur, Karwar, India
- *Correspondence: Sucharita Dey,
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6
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Feric M, Misteli T. Function moves biomolecular condensates in phase space. Bioessays 2022; 44:e2200001. [PMID: 35243657 PMCID: PMC9277701 DOI: 10.1002/bies.202200001] [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: 01/03/2022] [Revised: 02/19/2022] [Accepted: 02/22/2022] [Indexed: 11/08/2022]
Abstract
Phase separation underlies the formation of biomolecular condensates. We hypothesize the cellular processes that occur within condensates shape their structural features. We use the example of transcription to discuss structure-function relationships in condensates. Various types of transcriptional condensates have been reported across the evolutionary spectrum in the cell nucleus as well as in mitochondrial and bacterial nucleoids. In vitro and in vivo observations suggest that transcriptional activity of condensates influences their supramolecular structure, which in turn affects their function. Condensate organization thus becomes driven by differences in miscibility among the DNA and proteins of the transcription machinery and the RNA transcripts they generate. These considerations are in line with the notion that cellular processes shape the structural properties of condensates, leading to a dynamic, mutual interplay between structure and function in the cell.
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Affiliation(s)
- Marina Feric
- National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Tom Misteli
- National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
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7
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DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features. Appl Bionics Biomech 2022; 2022:5483115. [PMID: 35465187 PMCID: PMC9020926 DOI: 10.1155/2022/5483115] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/25/2021] [Accepted: 02/05/2022] [Indexed: 12/29/2022] Open
Abstract
In the domain of genome annotation, the identification of DNA-binding protein is one of the crucial challenges. DNA is considered a blueprint for the cell. It contained all necessary information for building and maintaining the trait of an organism. It is DNA, which makes a living thing, a living thing. Protein interaction with DNA performs an essential role in regulating DNA functions such as DNA repair, transcription, and regulation. Identification of these proteins is a crucial task for understanding the regulation of genes. Several methods have been developed to identify the binding sites of DNA and protein depending upon the structures and sequences, but they were costly and time-consuming. Therefore, we propose a methodology named “DNAPred_Prot”, which uses various position and frequency-dependent features from protein sequences for efficient and effective prediction of DNA-binding proteins. Using testing techniques like 10-fold cross-validation and jackknife testing an accuracy of 94.95% and 95.11% was yielded, respectively. The results of SVM and ANN were also compared with those of a random forest classifier. The robustness of the proposed model was evaluated by using the independent dataset PDB186, and an accuracy of 91.47% was achieved by it. From these results, it can be predicted that the suggested methodology performs better than other extant methods for the identification of DNA-binding proteins.
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8
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Wei J, Chen S, Zong L, Gao X, Li Y. Protein-RNA interaction prediction with deep learning: structure matters. Brief Bioinform 2022; 23:bbab540. [PMID: 34929730 PMCID: PMC8790951 DOI: 10.1093/bib/bbab540] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/14/2021] [Accepted: 11/22/2021] [Indexed: 12/11/2022] Open
Abstract
Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Because of the limitation of the previous database, especially the lack of protein structure data, most of the existing computational methods rely heavily on the sequence data, with only a small portion of the methods utilizing the structural information. Recently, AlphaFold has revolutionized the entire protein and biology field. Foreseeably, the protein-RNA interaction prediction will also be promoted significantly in the upcoming years. In this work, we give a thorough review of this field, surveying both the binding site and binding preference prediction problems and covering the commonly used datasets, features and models. We also point out the potential challenges and opportunities in this field. This survey summarizes the development of the RNA-binding protein-RNA interaction field in the past and foresees its future development in the post-AlphaFold era.
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Affiliation(s)
- Junkang Wei
- Department of Computer Science and Engineering (CSE), The Chinese
University of Hong Kong (CUHK), 999077, Hong Kong SAR, China
| | - Siyuan Chen
- Computational Bioscience Research Center (CBRC),
King Abdullah University of Science and Technology (KAUST),
23955-6900, Thuwal, Saudi Arabia
| | - Licheng Zong
- Department of Computer Science and Engineering (CSE), The Chinese
University of Hong Kong (CUHK), 999077, Hong Kong SAR, China
| | - Xin Gao
- Computational Bioscience Research Center (CBRC),
King Abdullah University of Science and Technology (KAUST),
23955-6900, Thuwal, Saudi Arabia
| | - Yu Li
- Department of Computer Science and Engineering (CSE), The Chinese
University of Hong Kong (CUHK), 999077, Hong Kong SAR, China
- The CUHK Shenzhen Research Institute, Hi-Tech Park, 518057,
Shenzhen, China
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9
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Jia Y, Huang S, Zhang T. KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest. Front Genet 2021; 12:811158. [PMID: 34912382 PMCID: PMC8667860 DOI: 10.3389/fgene.2021.811158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 11/15/2021] [Indexed: 02/04/2023] Open
Abstract
DNA-binding protein (DBP) is a protein with a special DNA binding domain that is associated with many important molecular biological mechanisms. Rapid development of computational methods has made it possible to predict DBP on a large scale; however, existing methods do not fully integrate DBP-related features, resulting in rough prediction results. In this article, we develop a DNA-binding protein identification method called KK-DBP. To improve prediction accuracy, we propose a feature extraction method that fuses multiple PSSM features. The experimental results show a prediction accuracy on the independent test dataset PDB186 of 81.22%, which is the highest of all existing methods.
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Affiliation(s)
- Yuran Jia
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianjiao Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
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10
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Zhang Y, Ni J, Gao Y. RF-SVM: Identification of DNA-binding proteins based on comprehensive feature representation methods and support vector machine. Proteins 2021; 90:395-404. [PMID: 34455627 DOI: 10.1002/prot.26229] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 08/10/2021] [Accepted: 08/24/2021] [Indexed: 01/07/2023]
Abstract
Protein-DNA interactions play an important role in biological progress, such as DNA replication, repair, and modification processes. In order to have a better understanding of its functions, the one of the most important steps is the identification of DNA-binding proteins. We propose a DNA-binding protein predictor, namely, RF-SVM, which contains four types features, that is, pseudo amino acid composition (PseAAC), amino acid distribution (AAD), adjacent amino acid composition frequency (ACF) and Local-DPP. Random Forest algorithm is utilized for selecting top 174 features, which are established the predictor model with the support vector machine (SVM) on training dataset UniSwiss-Tr. Finally, RF-SVM method is compared with other existing methods on test dataset UniSwiss-Tst. The experimental results demonstrated that RF-SVM has accuracy of 84.25%. Meanwhile, we discover that the physicochemical properties of amino acids for OOBM770101(H), CIDH920104(H), MIYS990104(H), NISK860101(H), VINM940103(H), and SNEP660101(A) have contribution to predict DNA-binding proteins. The main code and datasets can gain in https://github.com/NiJianWei996/RF-SVM.
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Affiliation(s)
- Yanping Zhang
- Department of Mathematics, School of Science, Hebei University of Engineering, Handan, China
| | - Jianwei Ni
- Department of Mathematics, School of Science, Hebei University of Engineering, Handan, China
| | - Ya Gao
- Department of Mathematics, School of Science, Hebei University of Engineering, Handan, China
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11
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Li G, Du X, Li X, Zou L, Zhang G, Wu Z. Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning. PeerJ 2021; 9:e11262. [PMID: 33986992 PMCID: PMC8101451 DOI: 10.7717/peerj.11262] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 03/22/2021] [Indexed: 12/12/2022] Open
Abstract
DNA-binding proteins (DBPs) play pivotal roles in many biological functions such as alternative splicing, RNA editing, and methylation. Many traditional machine learning (ML) methods and deep learning (DL) methods have been proposed to predict DBPs. However, these methods either rely on manual feature extraction or fail to capture long-term dependencies in the DNA sequence. In this paper, we propose a method, called PDBP-Fusion, to identify DBPs based on the fusion of local features and long-term dependencies only from primary sequences. We utilize convolutional neural network (CNN) to learn local features and use bi-directional long-short term memory network (Bi-LSTM) to capture critical long-term dependencies in context. Besides, we perform feature extraction, model training, and model prediction simultaneously. The PDBP-Fusion approach can predict DBPs with 86.45% sensitivity, 79.13% specificity, 82.81% accuracy, and 0.661 MCC on the PDB14189 benchmark dataset. The MCC of our proposed methods has been increased by at least 9.1% compared to other advanced prediction models. Moreover, the PDBP-Fusion also gets superior performance and model robustness on the PDB2272 independent dataset. It demonstrates that the PDBP-Fusion can be used to predict DBPs from sequences accurately and effectively; the online server is at http://119.45.144.26:8080/PDBP-Fusion/.
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Affiliation(s)
- Guobin Li
- School of Artificial Intelligence and Big Data, Hefei University, Hefei, China
| | - Xiuquan Du
- School of Computer Science and Technology, Anhui University, Hefei, China
| | - Xinlu Li
- School of Artificial Intelligence and Big Data, Hefei University, Hefei, China
| | - Le Zou
- School of Artificial Intelligence and Big Data, Hefei University, Hefei, China
| | - Guanhong Zhang
- School of Artificial Intelligence and Big Data, Hefei University, Hefei, China
| | - Zhize Wu
- School of Artificial Intelligence and Big Data, Hefei University, Hefei, China
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12
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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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13
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Hu S, Ma R, Wang H. An improved deep learning method for predicting DNA-binding proteins based on contextual features in amino acid sequences. PLoS One 2019; 14:e0225317. [PMID: 31725778 PMCID: PMC6855455 DOI: 10.1371/journal.pone.0225317] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Accepted: 11/02/2019] [Indexed: 11/23/2022] Open
Abstract
As the number of known proteins has expanded, how to accurately identify DNA binding proteins has become a significant biological challenge. At present, various computational methods have been proposed to recognize DNA-binding proteins from only amino acid sequences, such as SVM, DNABP and CNN-RNN. However, these methods do not consider the context in amino acid sequences, which makes it difficult for them to adequately capture sequence features. In this study, a new method that coordinates a bidirectional long-term memory recurrent neural network and a convolutional neural network, called CNN-BiLSTM, is proposed to identify DNA binding proteins. The CNN-BiLSTM model can explore the potential contextual relationships of amino acid sequences and obtain more features than can traditional models. The experimental results show that the CNN-BiLSTM achieves a validation set prediction accuracy of 96.5%—7.8% higher than that of SVM, 9.6% higher than that of DNABP and 3.7% higher than that of CNN-RNN. After testing on 20,000 independent samples provided by UniProt that were not involved in model training, the accuracy of CNN-BiLSTM reached 94.5%—12% higher than that of SVM, 4.9% higher than that of DNABP and 4% higher than that of CNN-RNN. We visualized and compared the model training process of CNN-BiLSTM with that of CNN-RNN and found that the former is capable of better generalization from the training dataset, showing that CNN-BiLSTM has a wider range of adaptations to protein sequences. On the test set, CNN-BiLSTM has better credibility because its predicted scores are closer to the sample labels than are those of CNN-RNN. Therefore, the proposed CNN-BiLSTM is a more powerful method for identifying DNA-binding proteins.
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Affiliation(s)
- Siquan Hu
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China
- Sichuan Jiuzhou Video Technology Co., Ltd, Mianyang, China
- * E-mail:
| | - Ruixiong Ma
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China
| | - Haiou Wang
- School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China
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14
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Wang W, Langlois R, Langlois M, Genchev GZ, Wang X, Lu H. Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins. Front Genet 2019; 10:729. [PMID: 31543893 PMCID: PMC6729729 DOI: 10.3389/fgene.2019.00729] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 07/11/2019] [Indexed: 12/27/2022] Open
Abstract
Function annotation efforts provide a foundation to our understanding of cellular processes and the functioning of the living cell. This motivates high-throughput computational methods to characterize new protein members of a particular function. Research work has focused on discriminative machine-learning methods, which promise to make efficient, de novo predictions of protein function. Furthermore, available function annotation exists predominantly for individual proteins rather than residues of which only a subset is necessary for the conveyance of a particular function. This limits discriminative approaches to predicting functions for which there is sufficient residue-level annotation, e.g., identification of DNA-binding proteins or where an excellent global representation can be divined. Complete understanding of the various functions of proteins requires discovery and functional annotation at the residue level. Herein, we cast this problem into the setting of multiple-instance learning, which only requires knowledge of the protein’s function yet identifies functionally relevant residues and need not rely on homology. We developed a new multiple-instance leaning algorithm derived from AdaBoost and benchmarked this algorithm against two well-studied protein function prediction tasks: annotating proteins that bind DNA and RNA. This algorithm outperforms certain previous approaches in annotating protein function while identifying functionally relevant residues involved in binding both DNA and RNA, and on one protein-DNA benchmark, it achieves near perfect classification.
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Affiliation(s)
- Wenchuan Wang
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, Chinas
| | - Robert Langlois
- Department of Bioengineering and Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States
| | - Marina Langlois
- Department of Bioengineering and Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States
| | - Georgi Z Genchev
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, Chinas.,Department of Bioengineering and Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States.,Bulgarian Institute for Genomics and Precision Medicine, Sofia, Bulgaria
| | - Xiaolei Wang
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, Chinas.,Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Hui Lu
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, Chinas.,Department of Bioengineering and Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States.,Center for Biomedical Informatics, Shanghai Children's Hospital, Shanghai, China
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15
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Blanco JD, Radusky L, Climente-González H, Serrano L. FoldX accurate structural protein-DNA binding prediction using PADA1 (Protein Assisted DNA Assembly 1). Nucleic Acids Res 2019; 46:3852-3863. [PMID: 29608705 PMCID: PMC5934639 DOI: 10.1093/nar/gky228] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 03/20/2018] [Indexed: 12/20/2022] Open
Abstract
The speed at which new genomes are being sequenced highlights the need for genome-wide methods capable of predicting protein–DNA interactions. Here, we present PADA1, a generic algorithm that accurately models structural complexes and predicts the DNA-binding regions of resolved protein structures. PADA1 relies on a library of protein and double-stranded DNA fragment pairs obtained from a training set of 2103 DNA–protein complexes. It includes a fast statistical force field computed from atom-atom distances, to evaluate and filter the 3D docking models. Using published benchmark validation sets and 212 DNA–protein structures published after 2016 we predicted the DNA-binding regions with an RMSD of <1.8 Å per residue in >95% of the cases. We show that the quality of the docked templates is compatible with FoldX protein design tool suite to identify the crystallized DNA molecule sequence as the most energetically favorable in 80% of the cases. We highlighted the biological potential of PADA1 by reconstituting DNA and protein conformational changes upon protein mutagenesis of a meganuclease and its variants, and by predicting DNA-binding regions and nucleotide sequences in proteins crystallized without DNA. These results opens up new perspectives for the engineering of DNA–protein interfaces.
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Affiliation(s)
- Javier Delgado Blanco
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Leandro Radusky
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Héctor Climente-González
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain
| | - Luis Serrano
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluis Companys 23, 08010 Barcelona, Spain
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16
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Abstract
Background:DNA-binding proteins, binding to DNA, widely exist in living cells, participating in many cell activities. They can participate some DNA-related cell activities, for instance DNA replication, transcription, recombination, and DNA repair.Objective:Given the importance of DNA-binding proteins, studies for predicting the DNA-binding proteins have been a popular issue over the past decades. In this article, we review current machine-learning methods which research on the prediction of DNA-binding proteins through feature representation methods, classifiers, measurements, dataset and existing web server.Method:The prediction methods of DNA-binding protein can be divided into two types, based on amino acid composition and based on protein structure. In this article, we accord to the two types methods to introduce the application of machine learning in DNA-binding proteins prediction.Results:Machine learning plays an important role in the classification of DNA-binding proteins, and the result is better. The best ACC is above 80%.Conclusion:Machine learning can be widely used in many aspects of biological information, especially in protein classification. Some issues should be considered in future work. First, the relationship between the number of features and performance must be explored. Second, many features are used to predict DNA-binding proteins and propose solutions for high-dimensional spaces.
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Affiliation(s)
- Kaiyang Qu
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Leyi Wei
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- College of Intelligence and Computing, Tianjin University, Tianjin, China
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17
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Zhang J, Liu B. A Review on the Recent Developments of Sequence-based Protein Feature Extraction Methods. Curr Bioinform 2019. [DOI: 10.2174/1574893614666181212102749] [Citation(s) in RCA: 96] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:Proteins play a crucial role in life activities, such as catalyzing metabolic reactions, DNA replication, responding to stimuli, etc. Identification of protein structures and functions are critical for both basic research and applications. Because the traditional experiments for studying the structures and functions of proteins are expensive and time consuming, computational approaches are highly desired. In key for computational methods is how to efficiently extract the features from the protein sequences. During the last decade, many powerful feature extraction algorithms have been proposed, significantly promoting the development of the studies of protein structures and functions.Objective:To help the researchers to catch up the recent developments in this important field, in this study, an updated review is given, focusing on the sequence-based feature extractions of protein sequences.Method:These sequence-based features of proteins were grouped into three categories, including composition-based features, autocorrelation-based features and profile-based features. The detailed information of features in each group was introduced, and their advantages and disadvantages were discussed. Besides, some useful tools for generating these features will also be introduced.Results:Generally, autocorrelation-based features outperform composition-based features, and profile-based features outperform autocorrelation-based features. The reason is that profile-based features consider the evolutionary information, which is useful for identification of protein structures and functions. However, profile-based features are more time consuming, because the multiple sequence alignment process is required.Conclusion:In this study, some recently proposed sequence-based features were introduced and discussed, such as basic k-mers, PseAAC, auto-cross covariance, top-n-gram etc. These features did make great contributions to the developments of protein sequence analysis. Future studies can be focus on exploring the combinations of these features. Besides, techniques from other fields, such as signal processing, natural language process (NLP), image processing etc., would also contribute to this important field, because natural languages (such as English) and protein sequences share some similarities. Therefore, the proteins can be treated as documents, and the features, such as k-mers, top-n-grams, motifs, can be treated as the words in the languages. Techniques from these filed will give some new ideas and strategies for extracting the features from proteins.
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Affiliation(s)
- Jun Zhang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong 518055, China
| | - Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong 518055, China
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18
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Mishra A, Pokhrel P, Hoque MT. StackDPPred: a stacking based prediction of DNA-binding protein from sequence. Bioinformatics 2018; 35:433-441. [DOI: 10.1093/bioinformatics/bty653] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 07/18/2018] [Indexed: 12/12/2022] Open
Affiliation(s)
- Avdesh Mishra
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA
| | - Pujan Pokhrel
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA
| | - Md Tamjidul Hoque
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA
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19
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DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC. J Theor Biol 2018; 452:22-34. [PMID: 29753757 DOI: 10.1016/j.jtbi.2018.05.006] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Revised: 04/21/2018] [Accepted: 05/04/2018] [Indexed: 11/21/2022]
Abstract
A DNA-binding protein (DNA-BP) is a protein that can bind and interact with a DNA. Identification of DNA-BPs using experimental methods is expensive as well as time consuming. As such, fast and accurate computational methods are sought for predicting whether a protein can bind with a DNA or not. In this paper, we focus on building a new computational model to identify DNA-BPs in an efficient and accurate way. Our model extracts meaningful information directly from the protein sequences, without any dependence on functional domain or structural information. After feature extraction, we have employed Random Forest (RF) model to rank the features. Afterwards, we have used Recursive Feature Elimination (RFE) method to extract an optimal set of features and trained a prediction model using Support Vector Machine (SVM) with linear kernel. Our proposed method, named as DNA-binding Protein Prediction model using Chou's general PseAAC (DPP-PseAAC), demonstrates superior performance compared to the state-of-the-art predictors on standard benchmark dataset. DPP-PseAAC achieves accuracy values of 93.21%, 95.91% and 77.42% for 10-fold cross-validation test, jackknife test and independent test respectively. The source code of DPP-PseAAC, along with relevant dataset and detailed experimental results, can be found at https://github.com/srautonu/DNABinding. A publicly accessible web interface has also been established at: http://77.68.43.135:8080/DPP-PseAAC/.
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20
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HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features. BIOMED RESEARCH INTERNATIONAL 2017; 2017:4590609. [PMID: 29270430 PMCID: PMC5706079 DOI: 10.1155/2017/4590609] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 10/22/2017] [Indexed: 12/21/2022]
Abstract
DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.
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21
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Festuccia N, Gonzalez I, Owens N, Navarro P. Mitotic bookmarking in development and stem cells. Development 2017; 144:3633-3645. [DOI: 10.1242/dev.146522] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The changes imposed on the nucleus, chromatin and its regulators during mitosis lead to the dismantlement of most gene regulatory processes. However, an increasing number of transcriptional regulators are being identified as capable of binding their genomic targets during mitosis. These so-called ‘mitotic bookmarking factors’ encompass transcription factors and chromatin modifiers that are believed to convey gene regulatory information from mother to daughter cells. In this Primer, we review mitotic bookmarking processes in development and stem cells and discuss the interest and potential importance of this concept with regard to epigenetic regulation and cell fate transitions involving cellular proliferation.
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Affiliation(s)
- Nicola Festuccia
- Epigenetics of Stem Cells, Department of Developmental and Stem Cell Biology, Institut Pasteur, CNRS UMR3738, 25 rue du Docteur Roux, 75015 Paris, France
| | - Inma Gonzalez
- Epigenetics of Stem Cells, Department of Developmental and Stem Cell Biology, Institut Pasteur, CNRS UMR3738, 25 rue du Docteur Roux, 75015 Paris, France
| | - Nick Owens
- Epigenetics of Stem Cells, Department of Developmental and Stem Cell Biology, Institut Pasteur, CNRS UMR3738, 25 rue du Docteur Roux, 75015 Paris, France
| | - Pablo Navarro
- Epigenetics of Stem Cells, Department of Developmental and Stem Cell Biology, Institut Pasteur, CNRS UMR3738, 25 rue du Docteur Roux, 75015 Paris, France
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22
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Zhang L, Duan X, He N, Chen X, Shi J, Li W, Xu L, Li H. Exposure to lethal levels of benzo[a]pyrene or cadmium trigger distinct protein expression patterns in earthworms (Eisenia fetida). THE SCIENCE OF THE TOTAL ENVIRONMENT 2017; 595:733-742. [PMID: 28407590 DOI: 10.1016/j.scitotenv.2017.04.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 03/30/2017] [Accepted: 04/01/2017] [Indexed: 06/07/2023]
Abstract
UNLABELLED Different pollutants induce distinct toxic responses in earthworms (Eisenia fetida). Here, we used proteomics techniques to compare the responses of E. fetida to exposure to the 10% lethal concentration (14d-LC10) of benzo[a]pyrene (BaP) or cadmium (Cd) in natural red soil (China). BaP exposure markedly induced the expression of oxidation-reduction proteins, whereas Cd exposure mainly induced the expression of proteins involved in transcription- and translation-related processes. Furthermore, calmodulin-binding proteins were differentially expressed upon exposure to different pollutants. The calcium (Ca2+)-binding cytoskeletal element myosin was down-regulated upon BaP treatment, whereas the Ca2+-binding cytoskeletal element tropomyosin-1 was up-regulated upon Cd treatment. Some proteins exhibited opposite responses to the two pollutants. For instance, catalase (CAT) and heat shock protein 70 were up-regulated upon BaP treatment and down-regulated upon Cd treatment. A significant (p<0.05, one-way ANOVA with least-significant difference (LSD) test) increase in the level of reactive oxygen species (ROS) and CAT activity further showed that BaP mainly induces oxidative stress. Real-time PCR analysis showed that mRNA expression often did not correlate well with protein expression in earthworms subjected to Cd or BaP treatment. In addition, the expression of the gene encoding the protein metallothionein, which was not detected in the protein analysis, was induced upon Cd treatment, but slightly reduced upon BaP treatment. Therefore, BaP and Cd have distinct effects on the protein profile of E. Fetida with BaP markedly inducing ROS activity, and Cd mainly triggering genotoxicity. CAPSULE SUMMARY Distinct patterns of protein expression are induced in earthworms upon exposure to different pollutants; BaP markedly induces high levels of ROS, while Cd resultes in genotoxicity.
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Affiliation(s)
- Lihao Zhang
- College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, People's Republic of China; Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing 210014, People's Republic of China
| | - Xiaochen Duan
- College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, People's Republic of China; College of Resources, Environment, and Planning, Dezhou University, Dezhou 253023, People's Republic of China
| | - Nannan He
- College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
| | - Xu Chen
- College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
| | - Jinli Shi
- College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, People's Republic of China
| | - Weiming Li
- Nanjing Scientific Institute of Vegetables and Flowers, Nanjing 210095, People's Republic of China
| | - Li Xu
- College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, People's Republic of China; Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing 210014, People's Republic of China.
| | - Huixin Li
- College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, People's Republic of China.
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23
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Improved detection of DNA-binding proteins via compression technology on PSSM information. PLoS One 2017; 12:e0185587. [PMID: 28961273 PMCID: PMC5621689 DOI: 10.1371/journal.pone.0185587] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Accepted: 09/17/2017] [Indexed: 12/04/2022] Open
Abstract
Since the importance of DNA-binding proteins in multiple biomolecular functions has been recognized, an increasing number of researchers are attempting to identify DNA-binding proteins. In recent years, the machine learning methods have become more and more compelling in the case of protein sequence data soaring, because of their favorable speed and accuracy. In this paper, we extract three features from the protein sequence, namely NMBAC (Normalized Moreau-Broto Autocorrelation), PSSM-DWT (Position-specific scoring matrix—Discrete Wavelet Transform), and PSSM-DCT (Position-specific scoring matrix—Discrete Cosine Transform). We also employ feature selection algorithm on these feature vectors. Then, these features are fed into the training SVM (support vector machine) model as classifier to predict DNA-binding proteins. Our method applys three datasets, namely PDB1075, PDB594 and PDB186, to evaluate the performance of our approach. The PDB1075 and PDB594 datasets are employed for Jackknife test and the PDB186 dataset is used for the independent test. Our method achieves the best accuracy in the Jacknife test, from 79.20% to 86.23% and 80.5% to 86.20% on PDB1075 and PDB594 datasets, respectively. In the independent test, the accuracy of our method comes to 76.3%. The performance of independent test also shows that our method has a certain ability to be effectively used for DNA-binding protein prediction. The data and source code are at https://doi.org/10.6084/m9.figshare.5104084.
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24
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Zhang J, Liu B. PSFM-DBT: Identifying DNA-Binding Proteins by Combing Position Specific Frequency Matrix and Distance-Bigram Transformation. Int J Mol Sci 2017; 18:ijms18091856. [PMID: 28841194 PMCID: PMC5618505 DOI: 10.3390/ijms18091856] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 08/19/2017] [Accepted: 08/22/2017] [Indexed: 12/30/2022] Open
Abstract
DNA-binding proteins play crucial roles in various biological processes, such as DNA replication and repair, transcriptional regulation and many other biological activities associated with DNA. Experimental recognition techniques for DNA-binding proteins identification are both time consuming and expensive. Effective methods for identifying these proteins only based on protein sequences are highly required. The key for sequence-based methods is to effectively represent protein sequences. It has been reported by various previous studies that evolutionary information is crucial for DNA-binding protein identification. In this study, we employed four methods to extract the evolutionary information from Position Specific Frequency Matrix (PSFM), including Residue Probing Transformation (RPT), Evolutionary Difference Transformation (EDT), Distance-Bigram Transformation (DBT), and Trigram Transformation (TT). The PSFMs were converted into fixed length feature vectors by these four methods, and then respectively combined with Support Vector Machines (SVMs); four predictors for identifying these proteins were constructed, including PSFM-RPT, PSFM-EDT, PSFM-DBT, and PSFM-TT. Experimental results on a widely used benchmark dataset PDB1075 and an independent dataset PDB186 showed that these four methods achieved state-of-the-art-performance, and PSFM-DBT outperformed other existing methods in this field. For practical applications, a user-friendly webserver of PSFM-DBT was established, which is available at http://bioinformatics.hitsz.edu.cn/PSFM-DBT/.
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Affiliation(s)
- Jun Zhang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China.
| | - Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China.
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25
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Jia Z, Li L, Chakravorty A, Alexov E. Treating ion distribution with Gaussian-based smooth dielectric function in DelPhi. J Comput Chem 2017; 38:1974-1979. [PMID: 28602026 PMCID: PMC5495612 DOI: 10.1002/jcc.24831] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 03/21/2017] [Accepted: 04/22/2017] [Indexed: 11/06/2022]
Abstract
The standard treatment of ions in the framework of the Poisson-Boltzmann equation relies on molecular surfaces, which are commonly constructed along with the Stern layer. The molecular surface determines where ions can be present. In the Gaussian-based smooth dielectric function in DelPhi, smooth boundaries between the solute and solvent take the place of molecular surface. Therefore, this invokes the question of how to model mobile ions in the water phase without a definite solute-solvent boundary. This article reports a natural extension of the Gaussian-based smooth dielectric function approach that treats mobile ions via Boltzmann distribution with an added desolvation penalty. Thus, ion concentration near macromolecules is governed by the local electrostatic potential and the desolvation penalty (from being partially desolvated). The approach is tested against the experimental salt dependence of binding free energy on 7 protein-protein complexes and 12 DNA-protein complexes, resulting in Pearson correlations of 0.95 and 0.88, respectively. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Zhe Jia
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina, United States, 29634
| | - Lin Li
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina, United States, 29634
| | - Arghya Chakravorty
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina, United States, 29634
| | - Emil Alexov
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina, United States, 29634
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26
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Wei L, Tang J, Zou Q. Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2016.06.026] [Citation(s) in RCA: 196] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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ThiN as a Versatile Domain of Transcriptional Repressors and Catalytic Enzymes of Thiamine Biosynthesis. J Bacteriol 2017; 199:JB.00810-16. [PMID: 28115546 DOI: 10.1128/jb.00810-16] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Accepted: 01/14/2017] [Indexed: 01/21/2023] Open
Abstract
Thiamine biosynthesis is commonly regulated by a riboswitch mechanism; however, the enzymatic steps and regulation of this pathway in archaea are poorly understood. Haloferax volcanii, one of the representative archaea, uses a eukaryote-like Thi4 (thiamine thiazole synthase) for the production of the thiazole ring and condenses this ring with a pyrimidine moiety synthesized by an apparent bacterium-like ThiC (2-methyl-4-amino-5-hydroxymethylpyrimidine [HMP] phosphate synthase) branch. Here we found that archaeal Thi4 and ThiC were encoded by leaderless transcripts, ruling out a riboswitch mechanism. Instead, a novel ThiR transcription factor that harbored an N-terminal helix-turn-helix (HTH) DNA binding domain and C-terminal ThiN (TMP synthase) domain was identified. In the presence of thiamine, ThiR was found to repress the expression of thi4 and thiC by a DNA operator sequence that was conserved across archaeal phyla. Despite having a ThiN domain, ThiR was found to be catalytically inactive in compensating for the loss of ThiE (TMP synthase) function. In contrast, bifunctional ThiDN, in which the ThiN domain is fused to an N-terminal ThiD (HMP/HMP phosphate [HMP-P] kinase) domain, was found to be interchangeable for ThiE function and, thus, active in thiamine biosynthesis. A conserved Met residue of an extended α-helix near the active-site His of the ThiN domain was found to be important for ThiDN catalytic activity, whereas the corresponding Met residue was absent and the α-helix was shorter in ThiR homologs. Thus, we provide new insight into residues that distinguish catalytic from noncatalytic ThiN domains and reveal that thiamine biosynthesis in archaea is regulated by a transcriptional repressor, ThiR, and not by a riboswitch.IMPORTANCE Thiamine pyrophosphate (TPP) is a cofactor needed for the enzymatic activity of many cellular processes, including central metabolism. In archaea, thiamine biosynthesis is an apparent chimera of eukaryote- and bacterium-type pathways that is not well defined at the level of enzymatic steps or regulatory mechanisms. Here we find that ThiN is a versatile domain of transcriptional repressors and catalytic enzymes of thiamine biosynthesis in archaea. Our study provides new insight into residues that distinguish catalytic from noncatalytic ThiN domains and reveals that archaeal thiamine biosynthesis is regulated by a ThiN domain transcriptional repressor, ThiR, and not by a riboswitch.
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Ponnuraj K, Saravanan KM. Dihedral angle preferences of DNA and RNA binding amino acid residues in proteins. Int J Biol Macromol 2017; 97:434-439. [PMID: 28099891 DOI: 10.1016/j.ijbiomac.2017.01.068] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 01/12/2017] [Accepted: 01/13/2017] [Indexed: 11/30/2022]
Abstract
A protein can interact with DNA or RNA molecules to perform various cellular processes. Identifying or analyzing DNA/RNA binding site amino acid residues is important to understand molecular recognition process. It is quite possible to accurately model DNA/RNA binding amino acid residues in experimental protein-DNA/RNA complex by using the electron density map whereas, locating/modeling the binding site amino acid residues in the predicted three dimensional structures of DNA/RNA binding proteins is still a difficult task. Considering the above facts, in the present work, we have carried out a comprehensive analysis of dihedral angle preferences of DNA and RNA binding site amino acid residues by using a classical Ramachandran map. We have computed backbone dihedral angles of non-DNA/RNA binding residues and used as control dataset to make a comparative study. The dihedral angle preference of DNA and RNA binding site residues of twenty amino acid type is presented. Our analysis clearly revealed that the dihedral angles (φ, ψ) of DNA/RNA binding amino acid residues prefer to occupy (-89° to -60°, -59° to -30°) bins. The results presented in this paper will help to model/locate DNA/RNA binding amino acid residues with better accuracy.
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Affiliation(s)
- Karthe Ponnuraj
- Centre of Advanced Study in Crystallography & Biophysics, University of Madras, Guindy Campus, Chennai 600 025, Tamilnadu, India
| | - Konda Mani Saravanan
- Centre of Advanced Study in Crystallography & Biophysics, University of Madras, Guindy Campus, Chennai 600 025, Tamilnadu, India.
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29
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Dutta S, Madan S, Parikh H, Sundar D. An ensemble micro neural network approach for elucidating interactions between zinc finger proteins and their target DNA. BMC Genomics 2016; 17:1033. [PMID: 28155662 PMCID: PMC5260015 DOI: 10.1186/s12864-016-3323-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND The ability to engineer zinc finger proteins binding to a DNA sequence of choice is essential for targeted genome editing to be possible. Experimental techniques and molecular docking have been successful in predicting protein-DNA interactions, however, they are highly time and resource intensive. Here, we present a novel algorithm designed for high throughput prediction of optimal zinc finger protein for 9 bp DNA sequences of choice. In accordance with the principles of information theory, a subset identified by using K-means clustering was used as a representative for the space of all possible 9 bp DNA sequences. The modeling and simulation results assuming synergistic mode of binding obtained from this subset were used to train an ensemble micro neural network. Synergistic mode of binding is the closest to the DNA-protein binding seen in nature, and gives much higher quality predictions, while the time and resources increase exponentially in the trade off. Our algorithm is inspired from an ensemble machine learning approach, and incorporates the predictions made by 100 parallel neural networks, each with a different hidden layer architecture designed to pick up different features from the training dataset to predict optimal zinc finger proteins for any 9 bp target DNA. RESULTS The model gave an accuracy of an average 83% sequence identity for the testing dataset. The BLAST e-value are well within the statistical confidence interval of E-05 for 100% of the testing samples. The geometric mean and median value for the BLAST e-values were found to be 1.70E-12 and 7.00E-12 respectively. For final validation of approach, we compared our predictions against optimal ZFPs reported in literature for a set of experimentally studied DNA sequences. The accuracy, as measured by the average string identity between our predictions and the optimal zinc finger protein reported in literature for a 9 bp DNA target was found to be as high as 81% for DNA targets with a consensus sequence GCNGNNGCN reported in literature. Moreover, the average string identity of our predictions for a catalogue of over 100 9 bp DNA for which the optimal zinc finger protein has been reported in literature was found to be 71%. CONCLUSIONS Validation with experimental data shows that our tool is capable of domain adaptation and thus scales well to datasets other than the training set with high accuracy. As synergistic binding comes the closest to the ideal mode of binding, our algorithm predicts biologically relevant results in sync with the experimental data present in the literature. While there have been disjointed attempts to approach this problem synergistically reported in literature, there is no work covering the whole sample space. Our algorithm allows designing zinc finger proteins for DNA targets of the user's choice, opening up new frontiers in the field of targeted genome editing. This algorithm is also available as an easy to use web server, ZifNN, at http://web.iitd.ac.in/~sundar/ZifNN/ .
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Affiliation(s)
- Shayoni Dutta
- Department of Biochemical Engineering and Biotechnology, DBT-AIST International Laboratory for Advanced Biomedicine (DAILAB), Indian Institute of Technology Delhi, New Delhi, 110016 India
| | - Spandan Madan
- Department of Biochemical Engineering and Biotechnology, DBT-AIST International Laboratory for Advanced Biomedicine (DAILAB), Indian Institute of Technology Delhi, New Delhi, 110016 India
| | - Harsh Parikh
- Department of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi, 110016 India
| | - Durai Sundar
- Department of Biochemical Engineering and Biotechnology, DBT-AIST International Laboratory for Advanced Biomedicine (DAILAB), Indian Institute of Technology Delhi, New Delhi, 110016 India
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Identification of DNA binding proteins using evolutionary profiles position specific scoring matrix. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.03.025] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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31
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Zhou J, Xu R, He Y, Lu Q, Wang H, Kong B. PDNAsite: Identification of DNA-binding Site from Protein Sequence by Incorporating Spatial and Sequence Context. Sci Rep 2016; 6:27653. [PMID: 27282833 PMCID: PMC4901350 DOI: 10.1038/srep27653] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Accepted: 05/18/2016] [Indexed: 02/01/2023] Open
Abstract
Protein-DNA interactions are involved in many fundamental biological processes essential for cellular function. Most of the existing computational approaches employed only the sequence context of the target residue for its prediction. In the present study, for each target residue, we applied both the spatial context and the sequence context to construct the feature space. Subsequently, Latent Semantic Analysis (LSA) was applied to remove the redundancies in the feature space. Finally, a predictor (PDNAsite) was developed through the integration of the support vector machines (SVM) classifier and ensemble learning. Results on the PDNA-62 and the PDNA-224 datasets demonstrate that features extracted from spatial context provide more information than those from sequence context and the combination of them gives more performance gain. An analysis of the number of binding sites in the spatial context of the target site indicates that the interactions between binding sites next to each other are important for protein-DNA recognition and their binding ability. The comparison between our proposed PDNAsite method and the existing methods indicate that PDNAsite outperforms most of the existing methods and is a useful tool for DNA-binding site identification. A web-server of our predictor (http://hlt.hitsz.edu.cn:8080/PDNAsite/) is made available for free public accessible to the biological research community.
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Affiliation(s)
- Jiyun Zhou
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.,Department of Computing, the Hong Kong Polytechnic University, Hong Kong
| | - Ruifeng Xu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.,Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China
| | - Yulan He
- School of Engineering and Applied Science, Aston University, UK
| | - Qin Lu
- Department of Computing, the Hong Kong Polytechnic University, Hong Kong
| | - Hongpeng Wang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Bing Kong
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
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Molecular Pathophysiology of Fragile X-Associated Tremor/Ataxia Syndrome and Perspectives for Drug Development. THE CEREBELLUM 2016; 15:599-610. [DOI: 10.1007/s12311-016-0800-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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33
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Paz I, Kligun E, Bengad B, Mandel-Gutfreund Y. BindUP: a web server for non-homology-based prediction of DNA and RNA binding proteins. Nucleic Acids Res 2016; 44:W568-74. [PMID: 27198220 PMCID: PMC4987955 DOI: 10.1093/nar/gkw454] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Accepted: 05/11/2016] [Indexed: 12/12/2022] Open
Abstract
Gene expression is a multi-step process involving many layers of regulation. The main regulators of the pathway are DNA and RNA binding proteins. While over the years, a large number of DNA and RNA binding proteins have been identified and extensively studied, it is still expected that many other proteins, some with yet another known function, are awaiting to be discovered. Here we present a new web server, BindUP, freely accessible through the website http://bindup.technion.ac.il/, for predicting DNA and RNA binding proteins using a non-homology-based approach. Our method is based on the electrostatic features of the protein surface and other general properties of the protein. BindUP predicts nucleic acid binding function given the proteins three-dimensional structure or a structural model. Additionally, BindUP provides information on the largest electrostatic surface patches, visualized on the server. The server was tested on several datasets of DNA and RNA binding proteins, including proteins which do not possess DNA or RNA binding domains and have no similarity to known nucleic acid binding proteins, achieving very high accuracy. BindUP is applicable in either single or batch modes and can be applied for testing hundreds of proteins simultaneously in a highly efficient manner.
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Affiliation(s)
- Inbal Paz
- Department of Biology, Technion-Israel Institute of Technology, Technion City, Haifa 32000, Israel
| | - Efrat Kligun
- Department of Biology, Technion-Israel Institute of Technology, Technion City, Haifa 32000, Israel
| | - Barak Bengad
- Department of Biology, Technion-Israel Institute of Technology, Technion City, Haifa 32000, Israel
| | - Yael Mandel-Gutfreund
- Department of Biology, Technion-Israel Institute of Technology, Technion City, Haifa 32000, Israel
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Liu B, Wang S, Dong Q, Li S, Liu X. Identification of DNA-binding proteins by combining auto-cross covariance transformation and ensemble learning. IEEE Trans Nanobioscience 2016; 15:328-334. [PMID: 28113908 DOI: 10.1109/tnb.2016.2555951] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. With the rapid development of next generation of sequencing technique, the number of protein sequences is unprecedentedly increasing. Thus it is necessary to develop computational methods to identify the DNA-binding proteins only based on the protein sequence information. In this study, a novel method called iDNA-KACC is presented, which combines the Support Vector Machine (SVM) and the auto-cross covariance transformation. The protein sequences are first converted into profile-based protein representation, and then converted into a series of fixed-length vectors by the auto-cross covariance transformation with Kmer composition. The sequence order effect can be effectively captured by this scheme. These vectors are then fed into Support Vector Machine (SVM) to discriminate the DNA-binding proteins from the non DNA-binding ones. iDNA-KACC achieves an overall accuracy of 75.16% and Matthew correlation coefficient of 0.5 by a rigorous jackknife test. Its performance is further improved by employing an ensemble learning approach, and the improved predictor is called iDNA-KACC-EL. Experimental results on an independent dataset shows that iDNA-KACC-EL outperforms all the other state-of-the-art predictors, indicating that it would be a useful computational tool for DNA binding protein identification. .
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35
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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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36
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DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representation. Sci Rep 2015; 5:15479. [PMID: 26482832 PMCID: PMC4611492 DOI: 10.1038/srep15479] [Citation(s) in RCA: 82] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Accepted: 09/28/2015] [Indexed: 02/01/2023] Open
Abstract
DNA-binding proteins play an important role in most cellular processes. Therefore, it is necessary to develop an efficient predictor for identifying DNA-binding proteins only based on the sequence information of proteins. The bottleneck for constructing a useful predictor is to find suitable features capturing the characteristics of DNA binding proteins. We applied PseAAC to DNA binding protein identification, and PseAAC was further improved by incorporating the evolutionary information by using profile-based protein representation. Finally, Combined with Support Vector Machines (SVMs), a predictor called iDNAPro-PseAAC was proposed. Experimental results on an updated benchmark dataset showed that iDNAPro-PseAAC outperformed some state-of-the-art approaches, and it can achieve stable performance on an independent dataset. By using an ensemble learning approach to incorporate more negative samples (non-DNA binding proteins) in the training process, the performance of iDNAPro-PseAAC was further improved. The web server of iDNAPro-PseAAC is available at http://bioinformatics.hitsz.edu.cn/iDNAPro-PseAAC/.
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37
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Frye SA, Lång E, Beyene GT, Balasingham SV, Homberset H, Rowe AD, Ambur OH, Tønjum T. The Inner Membrane Protein PilG Interacts with DNA and the Secretin PilQ in Transformation. PLoS One 2015; 10:e0134954. [PMID: 26248334 PMCID: PMC4527729 DOI: 10.1371/journal.pone.0134954] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2014] [Accepted: 07/15/2015] [Indexed: 11/19/2022] Open
Abstract
Expression of type IV pili (Tfp), filamentous appendages emanating from the bacterial surface, is indispensable for efficient neisserial transformation. Tfp pass through the secretin pore consisting of the membrane protein PilQ. PilG is a polytopic membrane protein, conserved in Gram-positive and Gram-negative bacteria, that is required for the biogenesis of neisserial Tfp. PilG null mutants are devoid of pili and non-competent for transformation. Here, recombinant full-length, truncated and mutated variants of meningococcal PilG were overexpressed, purified and characterized. We report that meningococcal PilG directly binds DNA in vitro, detected by both an electromobility shift analysis and a solid phase overlay assay. PilG DNA binding activity was independent of the presence of the consensus DNA uptake sequence. PilG-mediated DNA binding affinity was mapped to the N-terminus and was inactivated by mutation of residues 43 to 45. Notably, reduced meningococcal transformation of DNA in vivo was observed when PilG residues 43 to 45 were substituted by alanine in situ, defining a biologically significant DNA binding domain. N-terminal PilG also interacted with the N-terminal region of PilQ, which previously was shown to bind DNA. Collectively, these data suggest that PilG and PilQ in concert bind DNA during Tfp-mediated transformation.
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Affiliation(s)
- Stephan A. Frye
- Department of Microbiology, Oslo University Hospital, Oslo, Norway
| | - Emma Lång
- Department of Microbiology, Oslo University Hospital, Oslo, Norway
| | | | | | | | | | - Ole Herman Ambur
- Department of Microbiology, Oslo University Hospital, Oslo, Norway
| | - Tone Tønjum
- Department of Microbiology, Oslo University Hospital, Oslo, Norway
- Department of Microbiology, University of Oslo, Oslo, Norway
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SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues. PLoS One 2015; 10:e0133260. [PMID: 26176857 PMCID: PMC4503397 DOI: 10.1371/journal.pone.0133260] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2015] [Accepted: 06/25/2015] [Indexed: 11/19/2022] Open
Abstract
Protein-nucleic acid interactions are central to various fundamental biological processes. Automated methods capable of reliably identifying DNA- and RNA-binding residues in protein sequence are assuming ever-increasing importance. The majority of current algorithms rely on feature-based prediction, but their accuracy remains to be further improved. Here we propose a sequence-based hybrid algorithm SNBRFinder (Sequence-based Nucleic acid-Binding Residue Finder) by merging a feature predictor SNBRFinderF and a template predictor SNBRFinderT. SNBRFinderF was established using the support vector machine whose inputs include sequence profile and other complementary sequence descriptors, while SNBRFinderT was implemented with the sequence alignment algorithm based on profile hidden Markov models to capture the weakly homologous template of query sequence. Experimental results show that SNBRFinderF was clearly superior to the commonly used sequence profile-based predictor and SNBRFinderT can achieve comparable performance to the structure-based template methods. Leveraging the complementary relationship between these two predictors, SNBRFinder reasonably improved the performance of both DNA- and RNA-binding residue predictions. More importantly, the sequence-based hybrid prediction reached competitive performance relative to our previous structure-based counterpart. Our extensive and stringent comparisons show that SNBRFinder has obvious advantages over the existing sequence-based prediction algorithms. The value of our algorithm is highlighted by establishing an easy-to-use web server that is freely accessible at http://ibi.hzau.edu.cn/SNBRFinder.
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39
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An overview of the prediction of protein DNA-binding sites. Int J Mol Sci 2015; 16:5194-215. [PMID: 25756377 PMCID: PMC4394471 DOI: 10.3390/ijms16035194] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2014] [Revised: 02/21/2015] [Accepted: 02/27/2015] [Indexed: 02/06/2023] Open
Abstract
Interactions between proteins and DNA play an important role in many essential biological processes such as DNA replication, transcription, splicing, and repair. The identification of amino acid residues involved in DNA-binding sites is critical for understanding the mechanism of these biological activities. In the last decade, numerous computational approaches have been developed to predict protein DNA-binding sites based on protein sequence and/or structural information, which play an important role in complementing experimental strategies. At this time, approaches can be divided into three categories: sequence-based DNA-binding site prediction, structure-based DNA-binding site prediction, and homology modeling and threading. In this article, we review existing research on computational methods to predict protein DNA-binding sites, which includes data sets, various residue sequence/structural features, machine learning methods for comparison and selection, evaluation methods, performance comparison of different tools, and future directions in protein DNA-binding site prediction. In particular, we detail the meta-analysis of protein DNA-binding sites. We also propose specific implications that are likely to result in novel prediction methods, increased performance, or practical applications.
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40
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Xu R, Zhou J, Wang H, He Y, Wang X, Liu B. Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation. BMC SYSTEMS BIOLOGY 2015; 9 Suppl 1:S10. [PMID: 25708928 PMCID: PMC4331676 DOI: 10.1186/1752-0509-9-s1-s10] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation. There have been several computational methods proposed in the literature to deal with the DNA-binding protein identification. However, most of them can't provide an invaluable knowledge base for our understanding of DNA-protein interactions. RESULTS We firstly presented a new protein sequence encoding method called PSSM Distance Transformation, and then constructed a DNA-binding protein identification method (SVM-PSSM-DT) by combining PSSM Distance Transformation with support vector machine (SVM). First, the PSSM profiles are generated by using the PSI-BLAST program to search the non-redundant (NR) database. Next, the PSSM profiles are transformed into uniform numeric representations appropriately by distance transformation scheme. Lastly, the resulting uniform numeric representations are inputted into a SVM classifier for prediction. Thus whether a sequence can bind to DNA or not can be determined. In benchmark test on 525 DNA-binding and 550 non DNA-binding proteins using jackknife validation, the present model achieved an ACC of 79.96%, MCC of 0.622 and AUC of 86.50%. This performance is considerably better than most of the existing state-of-the-art predictive methods. When tested on a recently constructed independent dataset PDB186, SVM-PSSM-DT also achieved the best performance with ACC of 80.00%, MCC of 0.647 and AUC of 87.40%, and outperformed some existing state-of-the-art methods. CONCLUSIONS The experiment results demonstrate that PSSM Distance Transformation is an available protein sequence encoding method and SVM-PSSM-DT is a useful tool for identifying the DNA-binding proteins. A user-friendly web-server of SVM-PSSM-DT was constructed, which is freely accessible to the public at the web-site on http://bioinformatics.hitsz.edu.cn/PSSM-DT/.
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Affiliation(s)
- Ruifeng Xu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Jiyun Zhou
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Hongpeng Wang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Yulan He
- School of Engineering & Applied Science, Aston University, Birmingham, UK
| | - Xiaolong Wang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
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Samant M, Jethva M, Hasija Y. INTERACT-O-FINDER: A Tool for Prediction of DNA-Binding Proteins Using Sequence Features. Int J Pept Res Ther 2014. [DOI: 10.1007/s10989-014-9446-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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42
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Xu R, Zhou J, Liu B, He Y, Zou Q, Wang X, Chou KC. Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach. J Biomol Struct Dyn 2014; 33:1720-30. [PMID: 25252709 DOI: 10.1080/07391102.2014.968624] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
DNA-binding proteins are crucial for various cellular processes and hence have become an important target for both basic research and drug development. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to establish an automated method for rapidly and accurately identifying DNA-binding proteins based on their sequence information alone. Owing to the fact that all biological species have developed beginning from a very limited number of ancestral species, it is important to take into account the evolutionary information in developing such a high-throughput tool. In view of this, a new predictor was proposed by incorporating the evolutionary information into the general form of pseudo amino acid composition via the top-n-gram approach. It was observed by comparing the new predictor with the existing methods via both jackknife test and independent data-set test that the new predictor outperformed its counterparts. It is anticipated that the new predictor may become a useful vehicle for identifying DNA-binding proteins. It has not escaped our notice that the novel approach to extract evolutionary information into the formulation of statistical samples can be used to identify many other protein attributes as well.
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Affiliation(s)
- Ruifeng Xu
- a School of Computer Science and Technology , Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town , Xili, Shenzhen 518055 , Guangdong , China
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Liu B, Xu J, Fan S, Xu R, Zhou J, Wang X. PseDNA-Pro: DNA-Binding Protein Identification by Combining Chou’s PseAAC and Physicochemical Distance Transformation. Mol Inform 2014; 34:8-17. [DOI: 10.1002/minf.201400025] [Citation(s) in RCA: 135] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2014] [Accepted: 05/27/2014] [Indexed: 11/06/2022]
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44
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newDNA-Prot: Prediction of DNA-binding proteins by employing support vector machine and a comprehensive sequence representation. Comput Biol Chem 2014; 52:51-9. [PMID: 25240115 DOI: 10.1016/j.compbiolchem.2014.09.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2014] [Revised: 09/05/2014] [Accepted: 09/06/2014] [Indexed: 11/21/2022]
Abstract
Identification of DNA-binding proteins is essential in studying cellular activities as the DNA-binding proteins play a pivotal role in gene regulation. In this study, we propose newDNA-Prot, a DNA-binding protein predictor that employs support vector machine classifier and a comprehensive feature representation. The sequence representation are categorized into 6 groups: primary sequence based, evolutionary profile based, predicted secondary structure based, predicted relative solvent accessibility based, physicochemical property based and biological function based features. The mRMR, wrapper and two-stage feature selection methods are employed for removing irrelevant features and reducing redundant features. Experiments demonstrate that the two-stage method performs better than the mRMR and wrapper methods. We also perform a statistical analysis on the selected features and results show that more than 95% of the selected features are statistically significant and they cover all 6 feature groups. The newDNA-Prot method is compared with several state of the art algorithms, including iDNA-Prot, DNAbinder and DNA-Prot. The results demonstrate that newDNA-Prot method outperforms the iDNA-Prot, DNAbinder and DNA-Prot methods. More specific, newDNA-Prot improves the runner-up method, DNA-Prot for around 10% on several evaluation measures. The proposed newDNA-Prot method is available at http://sourceforge.net/projects/newdnaprot/
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Liu B, Xu J, Lan X, Xu R, Zhou J, Wang X, Chou KC. iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition. PLoS One 2014; 9:e106691. [PMID: 25184541 PMCID: PMC4153653 DOI: 10.1371/journal.pone.0106691] [Citation(s) in RCA: 208] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2014] [Accepted: 07/31/2014] [Indexed: 11/18/2022] Open
Abstract
Playing crucial roles in various cellular processes, such as recognition of specific nucleotide sequences, regulation of transcription, and regulation of gene expression, DNA-binding proteins are essential ingredients for both eukaryotic and prokaryotic proteomes. With the avalanche of protein sequences generated in the postgenomic age, it is a critical challenge to develop automated methods for accurate and rapidly identifying DNA-binding proteins based on their sequence information alone. Here, a novel predictor, called "iDNA-Prot|dis", was established by incorporating the amino acid distance-pair coupling information and the amino acid reduced alphabet profile into the general pseudo amino acid composition (PseAAC) vector. The former can capture the characteristics of DNA-binding proteins so as to enhance its prediction quality, while the latter can reduce the dimension of PseAAC vector so as to speed up its prediction process. It was observed by the rigorous jackknife and independent dataset tests that the new predictor outperformed the existing predictors for the same purpose. As a user-friendly web-server, iDNA-Prot|dis is accessible to the public at http://bioinformatics.hitsz.edu.cn/iDNA-Prot_dis/. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step protocol guide is provided on how to use the web-server to get their desired results without the need to follow the complicated mathematic equations that are presented in this paper just for the integrity of its developing process. It is anticipated that the iDNA-Prot|dis predictor may become a useful high throughput tool for large-scale analysis of DNA-binding proteins, or at the very least, play a complementary role to the existing predictors in this regard.
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Affiliation(s)
- Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Shanghai Key Laboratory of Intelligent Information Processing, Shanghai, China
- Gordon Life Science Institute, Belmont, Massachusetts, United States of America
- * E-mail: (BL); (KCC)
| | - Jinghao Xu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Xun Lan
- Stanford University, Stanford, California, United States of America
| | - Ruifeng Xu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Jiyun Zhou
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Xiaolong Wang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Belmont, Massachusetts, United States of America
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
- * E-mail: (BL); (KCC)
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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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47
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enDNA-Prot: identification of DNA-binding proteins by applying ensemble learning. BIOMED RESEARCH INTERNATIONAL 2014; 2014:294279. [PMID: 24977146 PMCID: PMC4058174 DOI: 10.1155/2014/294279] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Revised: 05/05/2014] [Accepted: 05/05/2014] [Indexed: 12/03/2022]
Abstract
DNA-binding proteins are crucial for various cellular processes, such as recognition of specific nucleotide, regulation of transcription, and regulation of gene expression. Developing an effective model for identifying DNA-binding proteins is an urgent research problem. Up to now, many methods have been proposed, but most of them focus on only one classifier and cannot make full use of the large number of negative samples to improve predicting performance. This study proposed a predictor called enDNA-Prot for DNA-binding protein identification by employing the ensemble learning technique. Experiential results showed that enDNA-Prot was comparable with DNA-Prot and outperformed DNAbinder and iDNA-Prot with performance improvement in the range of 3.97–9.52% in ACC and 0.08–0.19 in MCC. Furthermore, when the benchmark dataset was expanded with negative samples, the performance of enDNA-Prot outperformed the three existing methods by 2.83–16.63% in terms of ACC and 0.02–0.16 in terms of MCC. It indicated that enDNA-Prot is an effective method for DNA-binding protein identification and expanding training dataset with negative samples can improve its performance. For the convenience of the vast majority of experimental scientists, we developed a user-friendly web-server for enDNA-Prot which is freely accessible to the public.
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48
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Zhao H, Wang J, Zhou Y, Yang Y. Predicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome. PLoS One 2014; 9:e96694. [PMID: 24792350 PMCID: PMC4008587 DOI: 10.1371/journal.pone.0096694] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2014] [Accepted: 04/10/2014] [Indexed: 12/25/2022] Open
Abstract
As more and more protein sequences are uncovered from increasingly inexpensive sequencing techniques, an urgent task is to find their functions. This work presents a highly reliable computational technique for predicting DNA-binding function at the level of protein-DNA complex structures, rather than low-resolution two-state prediction of DNA-binding as most existing techniques do. The method first predicts protein-DNA complex structure by utilizing the template-based structure prediction technique HHblits, followed by binding affinity prediction based on a knowledge-based energy function (Distance-scaled finite ideal-gas reference state for protein-DNA interactions). A leave-one-out cross validation of the method based on 179 DNA-binding and 3797 non-binding protein domains achieves a Matthews correlation coefficient (MCC) of 0.77 with high precision (94%) and high sensitivity (65%). We further found 51% sensitivity for 82 newly determined structures of DNA-binding proteins and 56% sensitivity for the human proteome. In addition, the method provides a reasonably accurate prediction of DNA-binding residues in proteins based on predicted DNA-binding complex structures. Its application to human proteome leads to more than 300 novel DNA-binding proteins; some of these predicted structures were validated by known structures of homologous proteins in APO forms. The method [SPOT-Seq (DNA)] is available as an on-line server at http://sparks-lab.org.
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Affiliation(s)
- Huiying Zhao
- School of Informatics, Indiana University Purdue University, Indianapolis, Indiana, United States of America
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Jihua Wang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
- Shandong Provincial Key Laboratory of Functional Macromolecular Biophysics, Dezhou University, Dezhou, Shandong, China
| | - Yaoqi Zhou
- School of Informatics, Indiana University Purdue University, Indianapolis, Indiana, United States of America
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
- Shandong Provincial Key Laboratory of Functional Macromolecular Biophysics, Dezhou University, Dezhou, Shandong, China
- Institute for Glycomics and School of Information and Communication Technique, Griffith University, Southport, Queensland, Australia
- * E-mail: (YZ); (YY)
| | - Yuedong Yang
- School of Informatics, Indiana University Purdue University, Indianapolis, Indiana, United States of America
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
- Institute for Glycomics and School of Information and Communication Technique, Griffith University, Southport, Queensland, Australia
- * E-mail: (YZ); (YY)
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49
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Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and Gaussian naïve Bayes. PLoS One 2014; 9:e86703. [PMID: 24475169 PMCID: PMC3901691 DOI: 10.1371/journal.pone.0086703] [Citation(s) in RCA: 112] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2013] [Accepted: 12/10/2013] [Indexed: 11/22/2022] Open
Abstract
Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naïve Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function. As a result, the proposed DBPPred yields the highest average accuracy of 0.791 and average MCC of 0.583 according to the five-fold cross validation with ten runs on the training benchmark dataset PDB594. Subsequently, blind tests on the independent dataset PDB186 by the proposed model trained on the entire PDB594 dataset and by other five existing methods (including iDNA-Prot, DNA-Prot, DNAbinder, DNABIND and DBD-Threader) were performed, resulting in that the proposed DBPPred yielded the highest accuracy of 0.769, MCC of 0.538, and AUC of 0.790. The independent tests performed by the proposed DBPPred on completely a large non-DNA binding protein dataset and two RNA binding protein datasets also showed improved or comparable quality when compared with the relevant prediction methods. Moreover, we observed that majority of the selected features by the proposed method are statistically significantly different between the mean feature values of the DNA-binding and the non DNA-binding proteins. All of the experimental results indicate that the proposed DBPPred can be an alternative perspective predictor for large-scale determination of DNA-binding proteins.
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50
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Cirillo D, Livi CM, Agostini F, Tartaglia GG. Discovery of protein–RNA networks. ACTA ACUST UNITED AC 2014; 10:1632-42. [DOI: 10.1039/c4mb00099d] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
We review the latest advances and future challenges in experimental and computational investigation of protein–RNA networks.
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Affiliation(s)
- Davide Cirillo
- Gene Function and Evolution
- Centre for Genomic Regulation (CRG)
- 08003 Barcelona, Spain
- Universitat Pompeu Fabra (UPF)
- 08003 Barcelona, Spain
| | - Carmen Maria Livi
- Gene Function and Evolution
- Centre for Genomic Regulation (CRG)
- 08003 Barcelona, Spain
- Universitat Pompeu Fabra (UPF)
- 08003 Barcelona, Spain
| | - Federico Agostini
- Gene Function and Evolution
- Centre for Genomic Regulation (CRG)
- 08003 Barcelona, Spain
- Universitat Pompeu Fabra (UPF)
- 08003 Barcelona, Spain
| | - Gian Gaetano Tartaglia
- Gene Function and Evolution
- Centre for Genomic Regulation (CRG)
- 08003 Barcelona, Spain
- Universitat Pompeu Fabra (UPF)
- 08003 Barcelona, Spain
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