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In silico approaches for unearthing bacterial quorum-sensing inhibitors against pathogenic bacteria

The bacterial phenotypic traits of biofilm formation, bioluminescence, swarming motility, and even virulence are being highly influenced by the phenomenon of cell density-dependent gene regulation a.k.a. quorum sensing (QS) through which the bacteria communicate within themselves. Essentially, QS is...

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Main Authors: Pawar, Shrikant, Bramha Chari, P. V., Lahiri, Chandrajit *
Format: Book Section
Published: Springer Nature 2019
Subjects:
QH301 Biology
QR355 Virology
Online Access:http://eprints.sunway.edu.my/1190/
http://doi.org/10.1007/978-981-32-9409-7_6
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spelling my.sunway.eprints.11902019-12-20T03:00:00Z http://eprints.sunway.edu.my/1190/ In silico approaches for unearthing bacterial quorum-sensing inhibitors against pathogenic bacteria Pawar, Shrikant Bramha Chari, P. V. Lahiri, Chandrajit * QH301 Biology QR355 Virology The bacterial phenotypic traits of biofilm formation, bioluminescence, swarming motility, and even virulence are being highly influenced by the phenomenon of cell density-dependent gene regulation a.k.a. quorum sensing (QS) through which the bacteria communicate within themselves. Essentially, QS is an intracellular signaling system which are different for the different gram characters of bacteria. While gram-negative bacteria use chemical autoinducer molecules like acyl-homoserine lactones (AHLs) for such signaling, the gram-positive bacteria use peptide-based signaling systems. These quorum-sensing peptides (QSPs) can initiate a signaling cascade of events via two-component system or even by direct binding to transcription factors. After the detection of QSPs by bacteria, response regulators or transcriptional factors are activated, which further stimulates change in the target gene expression. Owing to the therapeutic potential of the AHLs and QSPs as drug targets, different in silico approaches were utilized for the identification of inhibitors and their modeling which can help in combatingthe respective bacterial pathogenicity. Thus, certain group of researchers also developed machine learning tools based on support vector machine (SVM) and hidden Markov models (HMM) for the identification of novel and effective biofilm inhibitory peptides (BIPs), while others used in silico approaches for predicting and designing of antibiofilm peptides usingbidirectional recursive neural network (BRNN) and Random Forest (RF) algorithms. Moreover, biological network visualization techniques and analysis enabled the identification of QSPs in different bacteria using related information from the curated databases. To this end, identification of the binding pocket(s), motif search, and other physicochemical properties will help in predicting the three-dimensional structure of such target. Furthermore, ultra-high-throughput screening is another approach which unveils QS inhibitors (QSI) based on the characterization of natural products and screening for naturally occurring enzymes. This review specifically focuses on all such in silico approaches in predicting QSI in different bacterial species. Such in silico QSI predictions and their docking onto QS targets can help to shape up a promising future for making newer therapeutic options available against different pathogenic bacteria. Springer Nature Bramha Chari, P. V. 2019 Book Section PeerReviewed Pawar, Shrikant and Bramha Chari, P. V. and Lahiri, Chandrajit * (2019) In silico approaches for unearthing bacterial quorum-sensing inhibitors against pathogenic bacteria. In: Implication of Quorum Sensing and Biofilm Formation in Medicine, Agriculture and Food Industry. Springer Nature, Singapore, pp. 67-83. ISBN 978-981-32-9409-7 http://doi.org/10.1007/978-981-32-9409-7_6 doi:10.1007/978-981-32-9409-7_6
institution Sunway University
building Sunway Campus Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Sunway University
content_source Sunway Institutional Repository
url_provider http://eprints.sunway.edu.my/
topic QH301 Biology
QR355 Virology
spellingShingle QH301 Biology
QR355 Virology
Pawar, Shrikant
Bramha Chari, P. V.
Lahiri, Chandrajit *
In silico approaches for unearthing bacterial quorum-sensing inhibitors against pathogenic bacteria
description The bacterial phenotypic traits of biofilm formation, bioluminescence, swarming motility, and even virulence are being highly influenced by the phenomenon of cell density-dependent gene regulation a.k.a. quorum sensing (QS) through which the bacteria communicate within themselves. Essentially, QS is an intracellular signaling system which are different for the different gram characters of bacteria. While gram-negative bacteria use chemical autoinducer molecules like acyl-homoserine lactones (AHLs) for such signaling, the gram-positive bacteria use peptide-based signaling systems. These quorum-sensing peptides (QSPs) can initiate a signaling cascade of events via two-component system or even by direct binding to transcription factors. After the detection of QSPs by bacteria, response regulators or transcriptional factors are activated, which further stimulates change in the target gene expression. Owing to the therapeutic potential of the AHLs and QSPs as drug targets, different in silico approaches were utilized for the identification of inhibitors and their modeling which can help in combatingthe respective bacterial pathogenicity. Thus, certain group of researchers also developed machine learning tools based on support vector machine (SVM) and hidden Markov models (HMM) for the identification of novel and effective biofilm inhibitory peptides (BIPs), while others used in silico approaches for predicting and designing of antibiofilm peptides usingbidirectional recursive neural network (BRNN) and Random Forest (RF) algorithms. Moreover, biological network visualization techniques and analysis enabled the identification of QSPs in different bacteria using related information from the curated databases. To this end, identification of the binding pocket(s), motif search, and other physicochemical properties will help in predicting the three-dimensional structure of such target. Furthermore, ultra-high-throughput screening is another approach which unveils QS inhibitors (QSI) based on the characterization of natural products and screening for naturally occurring enzymes. This review specifically focuses on all such in silico approaches in predicting QSI in different bacterial species. Such in silico QSI predictions and their docking onto QS targets can help to shape up a promising future for making newer therapeutic options available against different pathogenic bacteria.
author2 Bramha Chari, P. V.
author_facet Bramha Chari, P. V.
Pawar, Shrikant
Bramha Chari, P. V.
Lahiri, Chandrajit *
format Book Section
author Pawar, Shrikant
Bramha Chari, P. V.
Lahiri, Chandrajit *
author_sort Pawar, Shrikant
title In silico approaches for unearthing bacterial quorum-sensing inhibitors against pathogenic bacteria
title_short In silico approaches for unearthing bacterial quorum-sensing inhibitors against pathogenic bacteria
title_full In silico approaches for unearthing bacterial quorum-sensing inhibitors against pathogenic bacteria
title_fullStr In silico approaches for unearthing bacterial quorum-sensing inhibitors against pathogenic bacteria
title_full_unstemmed In silico approaches for unearthing bacterial quorum-sensing inhibitors against pathogenic bacteria
title_sort in silico approaches for unearthing bacterial quorum-sensing inhibitors against pathogenic bacteria
publisher Springer Nature
publishDate 2019
url http://eprints.sunway.edu.my/1190/
http://doi.org/10.1007/978-981-32-9409-7_6
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score 13.244745

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