1
|
Zheng J, Yang X, Huang Y, Yang S, Wuchty S, Zhang Z. Deep learning-assisted prediction of protein-protein interactions in Arabidopsis thaliana. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 114:984-994. [PMID: 36919205 DOI: 10.1111/tpj.16188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 02/20/2023] [Accepted: 03/09/2023] [Indexed: 05/27/2023]
Abstract
Currently, the experimentally identified interactome of Arabidopsis (Arabidopsis thaliana) is still far from complete, suggesting that computational prediction methods can complement experimental techniques. Motivated by the prosperity and success of deep learning algorithms and natural language processing techniques, we introduce an integrative deep learning framework, DeepAraPPI, allowing us to predict protein-protein interactions (PPIs) of Arabidopsis utilizing sequence, domain and Gene Ontology (GO) information. Our current DeepAraPPI comprises: (i) a word2vec encoding-based Siamese recurrent convolutional neural network (RCNN) model; (ii) a Domain2vec encoding-based multiple-layer perceptron (MLP) model; and (iii) a GO2vec encoding-based MLP model. Finally, DeepAraPPI combines the prediction results of the three individual predictors through a logistic regression model. Compiling high-quality positive and negative training and test samples by applying strict filtering strategies, DeepAraPPI shows superior performance compared with existing state-of-the-art Arabidopsis PPI prediction methods. DeepAraPPI also provides better cross-species predictive ability in rice (Oryza sativa) than traditional machine learning methods, although the overall performance in cross-species prediction remains to be improved. DeepAraPPI is freely accessible at http://zzdlab.com/deeparappi/. In the meantime, we have also made the source code and data sets of DeepAraPPI available at https://github.com/zjy1125/DeepAraPPI.
Collapse
Affiliation(s)
- Jingyan Zheng
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Xiaodi Yang
- Department of Hematology, Peking University First Hospital, Beijing, 100034, China
| | - Yan Huang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Shiping Yang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Stefan Wuchty
- Department of Computer Science, University of Miami, Miami, FL, 33146, USA
- Department of Biology, University of Miami, Miami, FL, 33146, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, 33136, USA
- Institute of Data Science and Computing, University of Miami, Miami, FL, 33146, USA
| | - Ziding Zhang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| |
Collapse
|
2
|
Guala D, Ogris C, Müller N, Sonnhammer ELL. Genome-wide functional association networks: background, data & state-of-the-art resources. Brief Bioinform 2019; 21:1224-1237. [PMID: 31281921 PMCID: PMC7373183 DOI: 10.1093/bib/bbz064] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 04/29/2019] [Accepted: 05/04/2019] [Indexed: 02/06/2023] Open
Abstract
The vast amount of experimental data from recent advances in the field of high-throughput biology begs for integration into more complex data structures such as genome-wide functional association networks. Such networks have been used for elucidation of the interplay of intra-cellular molecules to make advances ranging from the basic science understanding of evolutionary processes to the more translational field of precision medicine. The allure of the field has resulted in rapid growth of the number of available network resources, each with unique attributes exploitable to answer different biological questions. Unfortunately, the high volume of network resources makes it impossible for the intended user to select an appropriate tool for their particular research question. The aim of this paper is to provide an overview of the underlying data and representative network resources as well as to mention methods of integration, allowing a customized approach to resource selection. Additionally, this report will provide a primer for researchers venturing into the field of network integration.
Collapse
Affiliation(s)
- Dimitri Guala
- Science for Life Laboratory, Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Box 1031, 17121 Solna, Sweden
| | - Christoph Ogris
- Computational Cell Maps, Institute of Computational Biology, Helmholtz Center Munich, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Nikola Müller
- Computational Cell Maps, Institute of Computational Biology, Helmholtz Center Munich, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Erik L L Sonnhammer
- Science for Life Laboratory, Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Box 1031, 17121 Solna, Sweden
| |
Collapse
|
3
|
Yao H, Wang X, Chen P, Hai L, Jin K, Yao L, Mao C, Chen X. Predicted Arabidopsis Interactome Resource and Gene Set Linkage Analysis: A Transcriptomic Analysis Resource. PLANT PHYSIOLOGY 2018; 177. [PMID: 29530937 PMCID: PMC5933134 DOI: 10.1104/pp.18.00144] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
An advanced functional understanding of omics data is important for elucidating the design logic of physiological processes in plants and effectively controlling desired traits in plants. We present the latest versions of the Predicted Arabidopsis Interactome Resource (PAIR) and of the gene set linkage analysis (GSLA) tool, which enable the interpretation of an observed transcriptomic change (differentially expressed genes [DEGs]) in Arabidopsis (Arabidopsis thaliana) with respect to its functional impact for biological processes. PAIR version 5.0 integrates functional association data between genes in multiple forms and infers 335,301 putative functional interactions. GSLA relies on this high-confidence inferred functional association network to expand our perception of the functional impacts of an observed transcriptomic change. GSLA then interprets the biological significance of the observed DEGs using established biological concepts (annotation terms), describing not only the DEGs themselves but also their potential functional impacts. This unique analytical capability can help researchers gain deeper insights into their experimental results and highlight prospective directions for further investigation. We demonstrate the utility of GSLA with two case studies in which GSLA uncovered how molecular events may have caused physiological changes through their collective functional influence on biological processes. Furthermore, we showed that typical annotation-enrichment tools were unable to produce similar insights to PAIR/GSLA. The PAIR version 5.0-inferred interactome and GSLA Web tool both can be accessed at http://public.synergylab.cn/pair/.
Collapse
Affiliation(s)
- Heng Yao
- Institute of Pharmaceutical Biotechnology, Faculty of Medicine, Zhejiang University, Hangzhou, People's Republic of China, 310058
- State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou, People's Republic of China, 310058
| | - Xiaoxuan Wang
- Institute of Pharmaceutical Biotechnology, Faculty of Medicine, Zhejiang University, Hangzhou, People's Republic of China, 310058
| | - Pengcheng Chen
- Institute of Pharmaceutical Biotechnology, Faculty of Medicine, Zhejiang University, Hangzhou, People's Republic of China, 310058
| | - Ling Hai
- Institute of Pharmaceutical Biotechnology, Faculty of Medicine, Zhejiang University, Hangzhou, People's Republic of China, 310058
| | - Kang Jin
- Institute of Pharmaceutical Biotechnology, Faculty of Medicine, Zhejiang University, Hangzhou, People's Republic of China, 310058
| | - Lixia Yao
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905
| | - Chuanzao Mao
- State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou, People's Republic of China, 310058
| | - Xin Chen
- Institute of Pharmaceutical Biotechnology, Faculty of Medicine, Zhejiang University, Hangzhou, People's Republic of China, 310058
- State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou, People's Republic of China, 310058
| |
Collapse
|
4
|
Wang J, Zhou Y, Li X, Meng X, Fan M, Chen H, Xue J, Chen M. Genome-Wide Analysis of the Distinct Types of Chromatin Interactions in Arabidopsis thaliana. PLANT & CELL PHYSIOLOGY 2017; 58:57-70. [PMID: 28064247 DOI: 10.1093/pcp/pcw194] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Accepted: 06/15/2016] [Indexed: 06/06/2023]
Abstract
The three-dimensional shapes of chromosomes regulate gene expression and genome function. Our knowledge of the role of chromatin interaction is evolving rapidly. Here, we present a study of global chromatin interaction patterns in Arabidopsis thaliana. High-throughput experimental techniques have been developed to map long-range interactions within chromatin. We have integrated data from multiple experimental sources including Hi-C, BS-seq, ChIP-chip and ChIP-seq data for 17 epigenetic marks and 35 transcription factors. We identified seven groups of interacting loci, which can be distinguished by their epigenetic profiles. Furthermore, the seven groups of interacting loci can be divided into three types of chromatin linkages based on expression status. We observed that two interacting loci sometimes share common epigenetic and transcription factor-binding profiles. Different groups of loci display very different relationships between epigenetic marks and the binding of transcription factors. Distinctive types of chromatin linkages exhibit different gene expression profiles. Our study unveils an entirely unexplored regulatory interaction, linking epigenetic profiles, transcription factor binding and the three-dimensional spatial organization of the Arabidopsis nuclear genome.
Collapse
Affiliation(s)
- Jingjing Wang
- Department of Bioinformatics, The State Key Laboratory of Plant Physiology and Biochemistry, Institute of Plant Science, PR China
- James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou, PR China
- College of Life Sciences, Zhejiang University, Hangzhou, PR China
| | - Yincong Zhou
- Department of Bioinformatics, The State Key Laboratory of Plant Physiology and Biochemistry, Institute of Plant Science, PR China
- College of Life Sciences, Zhejiang University, Hangzhou, PR China
| | - Xue Li
- Department of Bioinformatics, The State Key Laboratory of Plant Physiology and Biochemistry, Institute of Plant Science, PR China
- James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou, PR China
- College of Life Sciences, Zhejiang University, Hangzhou, PR China
| | - Xianwen Meng
- Department of Bioinformatics, The State Key Laboratory of Plant Physiology and Biochemistry, Institute of Plant Science, PR China
- College of Life Sciences, Zhejiang University, Hangzhou, PR China
| | - Miao Fan
- Department of Bioinformatics, The State Key Laboratory of Plant Physiology and Biochemistry, Institute of Plant Science, PR China
- Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, PR China
- College of Life Sciences, Zhejiang University, Hangzhou, PR China
| | - Hongjun Chen
- Department of Bioinformatics, The State Key Laboratory of Plant Physiology and Biochemistry, Institute of Plant Science, PR China
- College of Life Sciences, Zhejiang University, Hangzhou, PR China
| | - Jitong Xue
- Department of Bioinformatics, The State Key Laboratory of Plant Physiology and Biochemistry, Institute of Plant Science, PR China
- James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou, PR China
- College of Life Sciences, Zhejiang University, Hangzhou, PR China
| | - Ming Chen
- Department of Bioinformatics, The State Key Laboratory of Plant Physiology and Biochemistry, Institute of Plant Science, PR China
- James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou, PR China
- College of Life Sciences, Zhejiang University, Hangzhou, PR China
| |
Collapse
|
5
|
Yue J, Xu W, Ban R, Huang S, Miao M, Tang X, Liu G, Liu Y. PTIR: Predicted Tomato Interactome Resource. Sci Rep 2016; 6:25047. [PMID: 27121261 PMCID: PMC4848565 DOI: 10.1038/srep25047] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 04/08/2016] [Indexed: 01/18/2023] Open
Abstract
Protein-protein interactions (PPIs) are involved in almost all biological processes and form the basis of the entire interactomics systems of living organisms. Identification and characterization of these interactions are fundamental to elucidating the molecular mechanisms of signal transduction and metabolic pathways at both the cellular and systemic levels. Although a number of experimental and computational studies have been performed on model organisms, the studies exploring and investigating PPIs in tomatoes remain lacking. Here, we developed a Predicted Tomato Interactome Resource (PTIR), based on experimentally determined orthologous interactions in six model organisms. The reliability of individual PPIs was also evaluated by shared gene ontology (GO) terms, co-evolution, co-expression, co-localization and available domain-domain interactions (DDIs). Currently, the PTIR covers 357,946 non-redundant PPIs among 10,626 proteins, including 12,291 high-confidence, 226,553 medium-confidence, and 119,102 low-confidence interactions. These interactions are expected to cover 30.6% of the entire tomato proteome and possess a reasonable distribution. In addition, ten randomly selected PPIs were verified using yeast two-hybrid (Y2H) screening or a bimolecular fluorescence complementation (BiFC) assay. The PTIR was constructed and implemented as a dedicated database and is available at http://bdg.hfut.edu.cn/ptir/index.html without registration.
Collapse
Affiliation(s)
- Junyang Yue
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Wei Xu
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Rongjun Ban
- School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China
| | - Shengxiong Huang
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Min Miao
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Xiaofeng Tang
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Guoqing Liu
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Yongsheng Liu
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
- Ministry of Education Key Laboratory for Bio-resource and Eco-environment, College of Life Science, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610064, China
| |
Collapse
|
6
|
Chilli leaf curl virus infection highlights the differential expression of genes involved in protein homeostasis and defense in resistant chilli plants. Appl Microbiol Biotechnol 2015; 99:4757-70. [DOI: 10.1007/s00253-015-6415-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2014] [Revised: 01/15/2015] [Accepted: 01/17/2015] [Indexed: 01/22/2023]
|
7
|
Carazzolle MF, de Carvalho LM, Slepicka HH, Vidal RO, Pereira GAG, Kobarg J, Vaz Meirelles G. IIS--Integrated Interactome System: a web-based platform for the annotation, analysis and visualization of protein-metabolite-gene-drug interactions by integrating a variety of data sources and tools. PLoS One 2014; 9:e100385. [PMID: 24949626 PMCID: PMC4065059 DOI: 10.1371/journal.pone.0100385] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2013] [Accepted: 05/27/2014] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND High-throughput screening of physical, genetic and chemical-genetic interactions brings important perspectives in the Systems Biology field, as the analysis of these interactions provides new insights into protein/gene function, cellular metabolic variations and the validation of therapeutic targets and drug design. However, such analysis depends on a pipeline connecting different tools that can automatically integrate data from diverse sources and result in a more comprehensive dataset that can be properly interpreted. RESULTS We describe here the Integrated Interactome System (IIS), an integrative platform with a web-based interface for the annotation, analysis and visualization of the interaction profiles of proteins/genes, metabolites and drugs of interest. IIS works in four connected modules: (i) Submission module, which receives raw data derived from Sanger sequencing (e.g. two-hybrid system); (ii) Search module, which enables the user to search for the processed reads to be assembled into contigs/singlets, or for lists of proteins/genes, metabolites and drugs of interest, and add them to the project; (iii) Annotation module, which assigns annotations from several databases for the contigs/singlets or lists of proteins/genes, generating tables with automatic annotation that can be manually curated; and (iv) Interactome module, which maps the contigs/singlets or the uploaded lists to entries in our integrated database, building networks that gather novel identified interactions, protein and metabolite expression/concentration levels, subcellular localization and computed topological metrics, GO biological processes and KEGG pathways enrichment. This module generates a XGMML file that can be imported into Cytoscape or be visualized directly on the web. CONCLUSIONS We have developed IIS by the integration of diverse databases following the need of appropriate tools for a systematic analysis of physical, genetic and chemical-genetic interactions. IIS was validated with yeast two-hybrid, proteomics and metabolomics datasets, but it is also extendable to other datasets. IIS is freely available online at: http://www.lge.ibi.unicamp.br/lnbio/IIS/.
Collapse
Affiliation(s)
- Marcelo Falsarella Carazzolle
- Laboratório Nacional de Biociências, Centro Nacional de Pesquisa em Energia e Materiais, Campinas, São Paulo, Brazil
- Laboratório de Genômica e Expressão, Departamento de Genética e Evolução, Instituto de Biologia, Unicamp, Campinas, São Paulo, Brazil
| | - Lucas Miguel de Carvalho
- Laboratório Nacional de Biociências, Centro Nacional de Pesquisa em Energia e Materiais, Campinas, São Paulo, Brazil
| | - Hugo Henrique Slepicka
- Laboratório Nacional de Luz Síncrotron, Centro Nacional de Pesquisa em Energia e Materiais, Campinas, São Paulo, Brazil
| | - Ramon Oliveira Vidal
- Laboratório de Genômica e Expressão, Departamento de Genética e Evolução, Instituto de Biologia, Unicamp, Campinas, São Paulo, Brazil
| | | | - Jörg Kobarg
- Laboratório Nacional de Biociências, Centro Nacional de Pesquisa em Energia e Materiais, Campinas, São Paulo, Brazil
| | - Gabriela Vaz Meirelles
- Laboratório Nacional de Biociências, Centro Nacional de Pesquisa em Energia e Materiais, Campinas, São Paulo, Brazil
| |
Collapse
|
8
|
Schmitt T, Ogris C, Sonnhammer ELL. FunCoup 3.0: database of genome-wide functional coupling networks. Nucleic Acids Res 2013; 42:D380-8. [PMID: 24185702 PMCID: PMC3965084 DOI: 10.1093/nar/gkt984] [Citation(s) in RCA: 90] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
We present an update of the FunCoup database (http://FunCoup.sbc.su.se) of functional couplings, or functional associations, between genes and gene products. Identifying these functional couplings is an important step in the understanding of higher level mechanisms performed by complex cellular processes. FunCoup distinguishes between four classes of couplings: participation in the same signaling cascade, participation in the same metabolic process, co-membership in a protein complex and physical interaction. For each of these four classes, several types of experimental and statistical evidence are combined by Bayesian integration to predict genome-wide functional coupling networks. The FunCoup framework has been completely re-implemented to allow for more frequent future updates. It contains many improvements, such as a regularization procedure to automatically downweight redundant evidences and a novel method to incorporate phylogenetic profile similarity. Several datasets have been updated and new data have been added in FunCoup 3.0. Furthermore, we have developed a new Web site, which provides powerful tools to explore the predicted networks and to retrieve detailed information about the data underlying each prediction.
Collapse
Affiliation(s)
- Thomas Schmitt
- Stockholm Bioinformatics Centre, Science for Life Laboratory, Box 1031, Solna SE-17121, Sweden, Department of Biochemistry and Biophysics, Stockholm University and Swedish eScience Research Center
| | | | | |
Collapse
|
9
|
Bassel GW, Gaudinier A, Brady SM, Hennig L, Rhee SY, De Smet I. Systems analysis of plant functional, transcriptional, physical interaction, and metabolic networks. THE PLANT CELL 2012; 24:3859-75. [PMID: 23110892 PMCID: PMC3517224 DOI: 10.1105/tpc.112.100776] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2012] [Revised: 08/21/2012] [Accepted: 10/11/2012] [Indexed: 05/19/2023]
Abstract
Physiological responses, developmental programs, and cellular functions rely on complex networks of interactions at different levels and scales. Systems biology brings together high-throughput biochemical, genetic, and molecular approaches to generate omics data that can be analyzed and used in mathematical and computational models toward uncovering these networks on a global scale. Various approaches, including transcriptomics, proteomics, interactomics, and metabolomics, have been employed to obtain these data on the cellular, tissue, organ, and whole-plant level. We summarize progress on gene regulatory, cofunction, protein interaction, and metabolic networks. We also illustrate the main approaches that have been used to obtain these networks, with specific examples from Arabidopsis thaliana, and describe the pros and cons of each approach.
Collapse
Affiliation(s)
- George W. Bassel
- School of Biosciences, University of Birmingham, Birmingham B15 2TT, United Kingdom
- Division of Plant and Crop Sciences, School of Biosciences and Centre for Plant Integrative Biology, University of Nottingham, Loughborough LE12 5RD, United Kingdom
| | - Allison Gaudinier
- Department of Plant Biology and Genome Center, University of California, Davis, California 95616
| | - Siobhan M. Brady
- Department of Plant Biology and Genome Center, University of California, Davis, California 95616
| | - Lars Hennig
- Department of Plant Biology and Forest Genetics, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, SE-75007 Uppsala, Sweden
| | - Seung Y. Rhee
- Department of Plant Biology, Carnegie Institution for Science, Stanford, California 94305
| | - Ive De Smet
- Division of Plant and Crop Sciences, School of Biosciences and Centre for Plant Integrative Biology, University of Nottingham, Loughborough LE12 5RD, United Kingdom
| |
Collapse
|
10
|
Valencia-Morales MDP, Camas-Reyes JA, Cabrera-Ponce JL, Alvarez-Venegas R. The Arabidopsis thaliana SET-domain-containing protein ASHH1/SDG26 interacts with itself and with distinct histone lysine methyltransferases. JOURNAL OF PLANT RESEARCH 2012; 125:679-692. [PMID: 22438063 DOI: 10.1007/s10265-012-0485-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2011] [Accepted: 02/23/2012] [Indexed: 05/31/2023]
Abstract
Polycomb group (PcG) and trithorax group (trxG) proteins are key regulators of homeotic genes and have central roles in cell proliferation, growth and development. In animals, PcG and trxG proteins form higher order protein complexes that contain SET domain proteins with histone methyltransferase activity, and are responsible for the different types of lysine methylation at the N-terminal tails of the core histone proteins. However, whether H3K4 methyltransferase complexes exist in Arabidopsis thaliana remains unknown. Here, we make use of the yeast two-hybrid system and the bimolecular fluorescence complementation assay to provide evidence for the self-association of the Arabidopsis thaliana SET-domain-containing protein SET DOMAIN GROUP 26 (SDG26), also known as ABSENT, SMALL, OR HOMEOTIC DISCS 1 HOMOLOG 1 (ASHH1). In addition, we show that the ASHH1 protein associates with SET-domain-containing sequences from two distinct histone lysine methyltransferases, the ARABIDOPSIS HOMOLOG OF TRITHORAX-1 (ATX1) and ASHH2 proteins. Furthermore, after screening a cDNA library we found that ASHH1 interacts with two proteins from the heat shock protein 40 kDa (Hsp40/DnaJ) superfamily, thus connecting the epigenetic network with a system sensing external cues. Our findings suggest that trxG complexes in Arabidopsis thaliana could involve different sets of histone lysine methyltransferases, and that these complexes may be engaged in multiple developmental processes in Arabidopsis.
Collapse
Affiliation(s)
- María del Pilar Valencia-Morales
- Departamento de Ingeniería Genética, CINVESTAV Unidad Irapuato, Km. 9.6 Libramiento Norte, Carretera Irapuato-León, C.P. 36821, Irapuato, Guanajuato, Mexico
| | | | | | | |
Collapse
|
11
|
Zhu P, Gu H, Jiao Y, Huang D, Chen M. Computational identification of protein-protein interactions in rice based on the predicted rice interactome network. GENOMICS PROTEOMICS & BIOINFORMATICS 2012; 9:128-37. [PMID: 22196356 PMCID: PMC5054448 DOI: 10.1016/s1672-0229(11)60016-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2011] [Accepted: 07/04/2011] [Indexed: 01/29/2023]
Abstract
Plant protein-protein interaction networks have not been identified by large-scale experiments. In order to better understand the protein interactions in rice, the Predicted Rice Interactome Network (PRIN; http://bis.zju.edu.cn/prin/) presented 76,585 predicted interactions involving 5,049 rice proteins. After mapping genomic features of rice (GO annotation, subcellular localization prediction, and gene expression), we found that a well-annotated and biologically significant network is rich enough to capture many significant functional linkages within higher-order biological systems, such as pathways and biological processes. Furthermore, we took MADS-box domain-containing proteins and circadian rhythm signaling pathways as examples to demonstrate that functional protein complexes and biological pathways could be effectively expanded in our predicted network. The expanded molecular network in PRIN has considerably improved the capability of these analyses to integrate existing knowledge and provide novel insights into the function and coordination of genes and gene networks.
Collapse
Affiliation(s)
- Pengcheng Zhu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | | | | | | | | |
Collapse
|
12
|
Abstract
The study of protein-protein interactions (PPIs) is essential to uncover unknown functions of proteins at the molecular level and to gain insight into complex cellular networks. Affinity purification and mass spectrometry (AP-MS), yeast two-hybrid, imaging approaches and numerous diverse databases have been developed as strategies to analyze PPIs. The past decade has seen an increase in the number of identified proteins with the development of MS and large-scale proteome analyses. Consequently, the false-positive protein identification rate has also increased. Therefore, the general consensus is to confirm PPI data using one or more independent approaches for an accurate evaluation. Furthermore, identifying minor PPIs is fundamental for understanding the functions of transient interactions and low-abundance proteins. Besides establishing PPI methodologies, we are now seeing the development of new methods and/or improvements in existing methods, which involve identifying minor proteins by MS, multidimensional protein identification technology or OFFGEL electrophoresis analyses, one-shot analysis with a long column or filter-aided sample preparation methods. These advanced techniques should allow thousands of proteins to be identified, whereas in-depth proteomic methods should permit the identification of transient binding or PPIs with weak affinity. Here, the current status of PPI analysis is reviewed and some advanced techniques are discussed briefly along with future challenges for plant proteomics.
Collapse
Affiliation(s)
- Yoichiro Fukao
- Plant Global Educational Project, Nara Institute of Science and Technology, Ikoma, Japan
| |
Collapse
|
13
|
Thalapati S, Batchu AK, Neelamraju S, Ramanan R. Os11Gsk gene from a wild rice, Oryza rufipogon improves yield in rice. Funct Integr Genomics 2012; 12:277-89. [PMID: 22367483 DOI: 10.1007/s10142-012-0265-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2011] [Revised: 01/27/2012] [Accepted: 02/07/2012] [Indexed: 12/11/2022]
Abstract
Chromosomal segments from wild rice species Oryza rufipogon, introgressed into an elite indica rice restorer line (KMR3) using molecular markers, resulted in significant increase in yield. Here we report the transcriptome analysis of flag leaves and fully emerged young panicles of one of the high yielding introgression lines IL50-7 in comparison to KMR3. A 66-fold upregulated gene Os11Gsk, which showed no transcript in KMR3 was highly expressed in O. rufipogon and IL50-7. A 5-kb genomic region including Os11Gsk and its flanking regions could be PCR amplified only from IL50-7, O. rufipogon, japonica varieties of rice-Nipponbare and Kitaake but not from the indica varieties, KMR3 and Taichung Native-1. Three sister lines of IL50-7 yielding higher than KMR3 showed presence of Os11Gsk, whereas the gene was absent in three other ILs from the same cross having lower yield than KMR3, indicating an association of the presence of Os11Gsk with high yield. Southern analysis showed additional bands in the genomic DNA of O. rufipogon and IL50-7 with Os11Gsk probe. Genomic sequence analysis of ten highly co-expressed differentially regulated genes revealed that two upregulated genes in IL50-7 were derived from O. rufipogon and most of the downregulated genes were either from KMR3 or common to KMR3, IL50-7, and O. rufipogon. Thus, we show that Os11Gsk is a wild rice-derived gene introduced in KMR3 background and increases yield either by regulating expression of functional genes sharing homology with it or by causing epigenetic modifications in the introgression line.
Collapse
Affiliation(s)
- Sudhakar Thalapati
- Biotechnology Unit, Directorate of Rice Research, Rajendranagar, Hyderabad 500 030, India
| | | | | | | |
Collapse
|
14
|
Yang J, Osman K, Iqbal M, Stekel DJ, Luo Z, Armstrong SJ, Franklin FCH. Inferring the Brassica rapa Interactome Using Protein-Protein Interaction Data from Arabidopsis thaliana. FRONTIERS IN PLANT SCIENCE 2012; 3:297. [PMID: 23293649 PMCID: PMC3537189 DOI: 10.3389/fpls.2012.00297] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2012] [Accepted: 12/11/2012] [Indexed: 05/06/2023]
Abstract
Following successful completion of the Brassica rapa sequencing project, the next step is to investigate functions of individual genes/proteins. For Arabidopsis thaliana, large amounts of protein-protein interaction (PPI) data are available from the major PPI databases (DBs). It is known that Brassica crop species are closely related to A. thaliana. This provides an opportunity to infer the B. rapa interactome using PPI data available from A. thaliana. In this paper, we present an inferred B. rapa interactome that is based on the A. thaliana PPI data from two resources: (i) A. thaliana PPI data from three major DBs, BioGRID, IntAct, and TAIR. (ii) ortholog-based A. thaliana PPI predictions. Linking between B. rapa and A. thaliana was accomplished in three complementary ways: (i) ortholog predictions, (ii) identification of gene duplication based on synteny and collinearity, and (iii) BLAST sequence similarity search. A complementary approach was also applied, which used known/predicted domain-domain interaction data. Specifically, since the two species are closely related, we used PPI data from A. thaliana to predict interacting domains that might be conserved between the two species. The predicted interactome was investigated for the component that contains known A. thaliana meiotic proteins to demonstrate its usability.
Collapse
Affiliation(s)
- Jianhua Yang
- University of BirminghamBirmingham, UK
- *Correspondence: Jianhua Yang and F. Chris H. Franklin, University of Birmingham, B152TT Birmingham, UK. e-mail: ,
| | - Kim Osman
- University of BirminghamBirmingham, UK
| | | | | | - Zewei Luo
- University of BirminghamBirmingham, UK
| | | | - F. Chris H. Franklin
- University of BirminghamBirmingham, UK
- *Correspondence: Jianhua Yang and F. Chris H. Franklin, University of Birmingham, B152TT Birmingham, UK. e-mail: ,
| |
Collapse
|
15
|
Mochida K, Shinozaki K. Advances in omics and bioinformatics tools for systems analyses of plant functions. PLANT & CELL PHYSIOLOGY 2011; 52:2017-38. [PMID: 22156726 PMCID: PMC3233218 DOI: 10.1093/pcp/pcr153] [Citation(s) in RCA: 118] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Omics and bioinformatics are essential to understanding the molecular systems that underlie various plant functions. Recent game-changing sequencing technologies have revitalized sequencing approaches in genomics and have produced opportunities for various emerging analytical applications. Driven by technological advances, several new omics layers such as the interactome, epigenome and hormonome have emerged. Furthermore, in several plant species, the development of omics resources has progressed to address particular biological properties of individual species. Integration of knowledge from omics-based research is an emerging issue as researchers seek to identify significance, gain biological insights and promote translational research. From these perspectives, we provide this review of the emerging aspects of plant systems research based on omics and bioinformatics analyses together with their associated resources and technological advances.
Collapse
Affiliation(s)
- Keiichi Mochida
- RIKEN Biomass Engineering Program, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045 Japan.
| | | |
Collapse
|
16
|
Mochida K, Shinozaki K. Advances in omics and bioinformatics tools for systems analyses of plant functions. PLANT & CELL PHYSIOLOGY 2011. [PMID: 22156726 DOI: 10.1093/pcp/pc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Omics and bioinformatics are essential to understanding the molecular systems that underlie various plant functions. Recent game-changing sequencing technologies have revitalized sequencing approaches in genomics and have produced opportunities for various emerging analytical applications. Driven by technological advances, several new omics layers such as the interactome, epigenome and hormonome have emerged. Furthermore, in several plant species, the development of omics resources has progressed to address particular biological properties of individual species. Integration of knowledge from omics-based research is an emerging issue as researchers seek to identify significance, gain biological insights and promote translational research. From these perspectives, we provide this review of the emerging aspects of plant systems research based on omics and bioinformatics analyses together with their associated resources and technological advances.
Collapse
Affiliation(s)
- Keiichi Mochida
- RIKEN Biomass Engineering Program, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045 Japan.
| | | |
Collapse
|
17
|
Gu H, Zhu P, Jiao Y, Meng Y, Chen M. PRIN: a predicted rice interactome network. BMC Bioinformatics 2011; 12:161. [PMID: 21575196 PMCID: PMC3118165 DOI: 10.1186/1471-2105-12-161] [Citation(s) in RCA: 131] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2010] [Accepted: 05/16/2011] [Indexed: 12/22/2022] Open
Abstract
Background Protein-protein interactions play a fundamental role in elucidating the molecular mechanisms of biomolecular function, signal transductions and metabolic pathways of living organisms. Although high-throughput technologies such as yeast two-hybrid system and affinity purification followed by mass spectrometry are widely used in model organisms, the progress of protein-protein interactions detection in plants is rather slow. With this motivation, our work presents a computational approach to predict protein-protein interactions in Oryza sativa. Results To better understand the interactions of proteins in Oryza sativa, we have developed PRIN, a Predicted Rice Interactome Network. Protein-protein interaction data of PRIN are based on the interologs of six model organisms where large-scale protein-protein interaction experiments have been applied: yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans), fruit fly (Drosophila melanogaster), human (Homo sapiens), Escherichia coli K12 and Arabidopsis thaliana. With certain quality controls, altogether we obtained 76,585 non-redundant rice protein interaction pairs among 5,049 rice proteins. Further analysis showed that the topology properties of predicted rice protein interaction network are more similar to yeast than to the other 5 organisms. This may not be surprising as the interologs based on yeast contribute nearly 74% of total interactions. In addition, GO annotation, subcellular localization information and gene expression data are also mapped to our network for validation. Finally, a user-friendly web interface was developed to offer convenient database search and network visualization. Conclusions PRIN is the first well annotated protein interaction database for the important model plant Oryza sativa. It has greatly extended the current available protein-protein interaction data of rice with a computational approach, which will certainly provide further insights into rice functional genomics and systems biology. PRIN is available online at http://bis.zju.edu.cn/prin/.
Collapse
Affiliation(s)
- Haibin Gu
- Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | | | | | | | | |
Collapse
|
18
|
Lin M, Zhou X, Shen X, Mao C, Chen X. The predicted Arabidopsis interactome resource and network topology-based systems biology analyses. THE PLANT CELL 2011; 23:911-22. [PMID: 21441435 PMCID: PMC3082272 DOI: 10.1105/tpc.110.082529] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2010] [Revised: 12/30/2010] [Accepted: 03/10/2011] [Indexed: 05/17/2023]
Abstract
Predicted interactions are a valuable complement to experimentally reported interactions in molecular mechanism studies, particularly for higher organisms, for which reported experimental interactions represent only a small fraction of their total interactomes. With careful engineering consideration of the lessons from previous efforts, the predicted arabidopsis interactome resource (PAIR; ) presents 149,900 potential molecular interactions, which are expected to cover approximately 24% of the entire interactome with approximately 40% precision. This study demonstrates that, although PAIR still has limited coverage, it is rich enough to capture many significant functional linkages within and between higher-order biological systems, such as pathways and biological processes. These inferred interactions can nicely power several network topology-based systems biology analyses, such as gene set linkage analysis, protein function prediction, and identification of regulatory genes demonstrating insignificant expression changes. The drastically expanded molecular network in PAIR has considerably improved the capability of these analyses to integrate existing knowledge and suggest novel insights into the function and coordination of genes and gene networks.
Collapse
Affiliation(s)
- Mingzhi Lin
- State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou 310058, People’s Republic of China
- Department of Bioinformatics, Zhejiang University, Hangzhou 310058, People’s Republic of China
| | - Xi Zhou
- Department of Bioinformatics, Zhejiang University, Hangzhou 310058, People’s Republic of China
| | - Xueling Shen
- Institute of Biochemistry, Zhejiang University, Hangzhou 310058, People’s Republic of China
| | - Chuanzao Mao
- State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou 310058, People’s Republic of China
| | - Xin Chen
- State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou 310058, People’s Republic of China
- Department of Bioinformatics, Zhejiang University, Hangzhou 310058, People’s Republic of China
- Institute of Biochemistry, Zhejiang University, Hangzhou 310058, People’s Republic of China
| |
Collapse
|
19
|
Xu F, Li G, Zhao C, Li Y, Li P, Cui J, Deng Y, Shi T. Global protein interactome exploration through mining genome-scale data in Arabidopsis thaliana. BMC Genomics 2010; 11 Suppl 2:S2. [PMID: 21047383 PMCID: PMC2975419 DOI: 10.1186/1471-2164-11-s2-s2] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Background Many essential cellular processes, such as cellular metabolism, transport, cellular metabolism and most regulatory mechanisms, rely on physical interactions between proteins. Genome-wide protein interactome networks of yeast, human and several other animal organisms have already been established, but this kind of network reminds to be established in the field of plant. Results We first predicted the protein protein interaction in Arabidopsis thaliana with methods, including ortholog, SSBP, gene fusion, gene neighbor, phylogenetic profile, coexpression, protein domain, and used Naïve Bayesian approach next to integrate the results of these methods and text mining data to build a genome-wide protein interactome network. Furthermore, we adopted the data of GO enrichment analysis, pathway, published literature to validate our network, the confirmation of our network shows the feasibility of using our network to predict protein function and other usage. Conclusions Our interactome is a comprehensive genome-wide network in the organism plant Arabidopsis thaliana, and provides a rich resource for researchers in related field to study the protein function, molecular interaction and potential mechanism under different conditions.
Collapse
Affiliation(s)
- Feng Xu
- College of Life Sciences, the Northeast Forestry University, Harbin, Heilongjiang 150040, China.
| | | | | | | | | | | | | | | |
Collapse
|
20
|
Abstract
The predicted Arabidopsis interactome resource (PAIR, http://www.cls.zju.edu.cn/pair/), comprised of 5990 experimentally reported molecular interactions in Arabidopsis thaliana together with 145 494 predicted interactions, is currently the most comprehensive data set of the Arabidopsis interactome with high reliability. PAIR predicts interactions by a fine-tuned support vector machine model that integrates indirect evidences for interaction, such as gene co-expressions, domain interactions, shared GO annotations, co-localizations, phylogenetic profile similarities and homologous interactions in other organisms (interologs). These predictions were expected to cover 24% of the entire Arabidopsis interactome, and their reliability was estimated to be 44%. Two independent example data sets were used to rigorously validate the prediction accuracy. PAIR features a user-friendly query interface, providing rich annotation on the relationships between two proteins. A graphical interaction network browser has also been integrated into the PAIR web interface to facilitate mining of specific pathways.
Collapse
Affiliation(s)
- Mingzhi Lin
- Department of Bioinformatics and Institute of Biochemistry, Zhejiang University, Hangzhou, PR China
| | | | | |
Collapse
|
21
|
He F, Zhou Y, Zhang Z. Deciphering the Arabidopsis floral transition process by integrating a protein-protein interaction network and gene expression data. PLANT PHYSIOLOGY 2010; 153:1492-505. [PMID: 20530214 PMCID: PMC2923896 DOI: 10.1104/pp.110.153650] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2010] [Accepted: 06/03/2010] [Indexed: 05/18/2023]
Abstract
In a plant, the progression from vegetative growth to reproductive growth is called the floral transition. Over the past several decades, the floral transition has been shown to be determined not by a single gene but by a complicated gene network. This important biological process, however, has not been investigated at a genome-wide network level. We collected Arabidopsis (Arabidopsis thaliana) protein-protein interaction data from several public databases and compiled them into a genome-wide Arabidopsis interactome. Then, we integrated gene expression profiles during the Arabidopsis floral transition process into the established protein-protein interaction network to identify two types of anticorrelated modules associated with vegetative and reproductive growth. Generally, the vegetative modules are conserved in plants, while the reproductive modules are more specific to advanced plants. The existence of floral transition switches demonstrates that vegetative and reproductive processes might be coordinated by the interacting interface of these modules. Our work also provides many candidates for mediating the interactions between these modules, which may play important roles during the Arabidopsis vegetative/reproductive switch.
Collapse
|
22
|
Lee K, Thorneycroft D, Achuthan P, Hermjakob H, Ideker T. Mapping plant interactomes using literature curated and predicted protein-protein interaction data sets. THE PLANT CELL 2010; 22:997-1005. [PMID: 20371643 PMCID: PMC2879763 DOI: 10.1105/tpc.109.072736] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Most cellular processes are enabled by cohorts of interacting proteins that form dynamic networks within the plant proteome. The study of these networks can provide insight into protein function and provide new avenues for research. This article informs the plant science community of the currently available sources of protein interaction data and discusses how they can be useful to researchers. Using our recently curated IntAct Arabidopsis thaliana protein-protein interaction data set as an example, we discuss potentials and limitations of the plant interactomes generated to date. In addition, we present our efforts to add value to the interaction data by using them to seed a proteome-wide map of predicted protein subcellular locations.
Collapse
Affiliation(s)
- KiYoung Lee
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon 443-749, Korea.
| | | | | | | | | |
Collapse
|
23
|
Handling Missing Features with Boosting Algorithms for Protein–Protein Interaction Prediction. LECTURE NOTES IN COMPUTER SCIENCE 2010. [DOI: 10.1007/978-3-642-15120-0_11] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
|
24
|
Brandão MM, Dantas LL, Silva-Filho MC. AtPIN: Arabidopsis thaliana protein interaction network. BMC Bioinformatics 2009; 10:454. [PMID: 20043823 PMCID: PMC2810305 DOI: 10.1186/1471-2105-10-454] [Citation(s) in RCA: 85] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2009] [Accepted: 12/31/2009] [Indexed: 12/13/2022] Open
Abstract
Background Protein-protein interactions (PPIs) constitute one of the most crucial conditions to sustain life in living organisms. To study PPI in Arabidopsis thaliana we have developed AtPIN, a database and web interface for searching and building interaction networks based on publicly available protein-protein interaction datasets. Description All interactions were divided into experimentally demonstrated or predicted. The PPIs in the AtPIN database present a cellular compartment classification (C3) which divides the PPI into 4 classes according to its interaction evidence and subcellular localization. It has been shown in the literature that a pair of genuine interacting proteins are generally expected to have a common cellular role and proteins that have common interaction partners have a high chance of sharing a common function. In AtPIN, due to its integrative profile, the reliability index for a reported PPI can be postulated in terms of the proportion of interaction partners that two proteins have in common. For this, we implement the Functional Similarity Weight (FSW) calculation for all first level interactions present in AtPIN database. In order to identify target proteins of cytosolic glutamyl-tRNA synthetase (Cyt-gluRS) (AT5G26710) we combined two approaches, AtPIN search and yeast two-hybrid screening. Interestingly, the proteins glutamine synthetase (AT5G35630), a disease resistance protein (AT3G50950) and a zinc finger protein (AT5G24930), which has been predicted as target proteins for Cyt-gluRS by AtPIN, were also detected in the experimental screening. Conclusions AtPIN is a friendly and easy-to-use tool that aggregates information on Arabidopsis thaliana PPIs, ontology, and sub-cellular localization, and might be a useful and reliable strategy to map protein-protein interactions in Arabidopsis. AtPIN can be accessed at http://bioinfo.esalq.usp.br/atpin.
Collapse
Affiliation(s)
- Marcelo M Brandão
- Departamento de Genética, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, 13400-970 Piracicaba, SP, Brazil.
| | | | | |
Collapse
|