<i>In silico</i> structural-functional characterization of three differentially expressed resistance gene analogs identified in <i>Dalbergia sissoo</i> against dieback disease reveals their role in immune response regulation

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Abstract

Plant immunity includes enemy recognition, signal transduction, and defensive response against pathogens. We experimented to identify the genes that contribute resistance against dieback disease to Dalbergia sissoo, an economically important timber tree. In this study, we investigated the role of three differentially expressed genes identified in the dieback-induced transcriptome in Dalbergia sissoo. The transcriptome was probed using DOP-rtPCR analysis. The identified RGAs were characterized in silico as the contributors of disease resistance that switch on under dieback stress. Their predicted fingerprints revealed involvement in stress response. Ds-DbRCaG-02-Rga.a, Ds-DbRCaG-04-Rga.b, and Ds-DbRCaG-06-Rga.c showed structural homology with the Transthyretin-52 domain, EAL associated YkuI_C domain, and Src homology-3 domain respectively, which are the attributes of signaling proteins possessing a role in regulating immune responses in plants. Based on in-silico structural and functional characterization, they were predicted to have a role in immune response regulation in D. sissoo.

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SWISS-MODEL: homology modelling of protein structures and complexes

Andrew Waterhouse, Martino Bertoni, Stefan Bienert … (2018)

Abstract Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.

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The Phyre2 web portal for protein modeling, prediction and analysis.

Lawrence A Kelley, Stefans Mezulis, Christopher M Yates … (2015)

Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30 min and 2 h after submission.

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Protein Identification and Analysis Tools on the ExPASy Server

Elisabeth Gasteiger, Christine Hoogland, Alexandre Gattiker … (2005)

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Author and article information

Contributors

Siddra Ijaz: URI : https://loop.frontiersin.org/people/268294

Imran Ul Haq: URI : https://loop.frontiersin.org/people/1990195

Hafiza Arooj Razzaq: URI : https://loop.frontiersin.org/people/2212283

Hayssam M. Ali: URI : https://loop.frontiersin.org/people/436410

Journal

Journal ID (nlm-ta): Front Plant Sci

Journal ID (iso-abbrev): Front Plant Sci

Journal ID (publisher-id): Front. Plant Sci.

Title: Frontiers in Plant Science

Publisher: Frontiers Media S.A.

ISSN (Electronic): 1664-462X

Publication date (Electronic): 16 October 2023

Publication date Collection: 2023

Volume: 14

Electronic Location Identifier: 1134806

Affiliations

[1] ¹ Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture , Faisalabad, Pakistan

[2] ² Department of Plant Pathology, University of Agriculture , Faisalabad, Pakistan

[3] ³ Department of Botany and Microbiology, College of Science, King Saud University , Riyadh, Saudi Arabia

[4] ⁴ Department of Plant Pathology, University of California Davis , Davis, CA, United States

Author notes

Edited by: Pallem Chowdappa, Central Plantation Crops Research Institute (ICAR), India

Reviewed by: Enrique Castano, Centro de Investigación Científica de Yucatán, Mexico; Deepu Mathew, Kerala Agricultural University, India; Jiban Shrestha, Nepal Agricultural Research Council, Nepal

*Correspondence: Imran Ul Haq, imran_1614@ 123456uaf.edu.pk

Article

DOI: 10.3389/fpls.2023.1134806

PMC ID: 10613980

SO-VID: 6029986f-d913-45a8-8fb2-d5d32e40b1af

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

History

Date received : 30 December 2022

Date accepted : 19 September 2023

Page count

Figures: 4, Tables: 1, Equations: 0, References: 57, Pages: 10, Words: 5139

Funding

Researchers Supporting Project number (RSP2023R123), King Saud University, Riyadh, Saudi Arabia., is funding support for publication.

Custom metadata

section-in-acceptance Plant Pathogen Interactions

ScienceOpen disciplines: Plant science & Botany

Keywords: nucleotide-binding site,leucine-rich repeats,protein fingerprints,homology modeling,dalbergia sissoo

Data availability:

ScienceOpen disciplines: Plant science & Botany

Keywords: nucleotide-binding site, leucine-rich repeats, protein fingerprints, homology modeling, dalbergia sissoo

In silico structural-functional characterization of three differentially expressed resistance gene analogs identified in Dalbergia sissoo against dieback disease reveals their role in immune response regulation

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