Cargando…

Exploring Prediction of Antimicrobial Resistance Based on Protein Solvent Accessibility Variation

Antimicrobial resistance (AMR) is a significant and growing public health threat. Sequencing of bacterial isolates is becoming more common, and therefore automatic identification of resistant bacterial strains is of pivotal importance for efficient, wide-spread AMR detection. To support this approac...

Descripción completa

Detalles Bibliográficos
Autores principales: Marini, Simone, Oliva, Marco, Slizovskiy, Ilya B., Noyes, Noelle Robertson, Boucher, Christina, Prosperi, Mattia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7862766/
https://www.ncbi.nlm.nih.gov/pubmed/33552147
http://dx.doi.org/10.3389/fgene.2021.564186
_version_ 1783647360897777664
author Marini, Simone
Oliva, Marco
Slizovskiy, Ilya B.
Noyes, Noelle Robertson
Boucher, Christina
Prosperi, Mattia
author_facet Marini, Simone
Oliva, Marco
Slizovskiy, Ilya B.
Noyes, Noelle Robertson
Boucher, Christina
Prosperi, Mattia
author_sort Marini, Simone
collection PubMed
description Antimicrobial resistance (AMR) is a significant and growing public health threat. Sequencing of bacterial isolates is becoming more common, and therefore automatic identification of resistant bacterial strains is of pivotal importance for efficient, wide-spread AMR detection. To support this approach, several AMR databases and gene identification algorithms have been recently developed. A key problem in AMR detection, however, is the need for computational approaches detecting potential novel AMR genes or variants, which are not included in the reference databases. Toward this direction, here we study the relation between AMR and relative solvent accessibility (RSA) of protein variants from an in silico perspective. We show how known AMR protein variants tend to correspond to exposed residues, while on the contrary their susceptible counterparts tend to be buried. Based on these findings, we develop RSA-AMR, a novel relative solvent accessibility-based AMR scoring system. This scoring system can be applied to any protein variant to estimate its propensity of altering the relative solvent accessibility, and potentially conferring (or hindering) AMR. We show how RSA-AMR score can be integrated with existing AMR detection algorithms to expand their range of applicability into detecting potential novel AMR variants, and provide a ten-fold increase in Specificity. The two main limitations of RSA-AMR score is that it is designed on single point changes, and a limited number of variants was available for model learning.
format Online
Article
Text
id pubmed-7862766
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-78627662021-02-06 Exploring Prediction of Antimicrobial Resistance Based on Protein Solvent Accessibility Variation Marini, Simone Oliva, Marco Slizovskiy, Ilya B. Noyes, Noelle Robertson Boucher, Christina Prosperi, Mattia Front Genet Genetics Antimicrobial resistance (AMR) is a significant and growing public health threat. Sequencing of bacterial isolates is becoming more common, and therefore automatic identification of resistant bacterial strains is of pivotal importance for efficient, wide-spread AMR detection. To support this approach, several AMR databases and gene identification algorithms have been recently developed. A key problem in AMR detection, however, is the need for computational approaches detecting potential novel AMR genes or variants, which are not included in the reference databases. Toward this direction, here we study the relation between AMR and relative solvent accessibility (RSA) of protein variants from an in silico perspective. We show how known AMR protein variants tend to correspond to exposed residues, while on the contrary their susceptible counterparts tend to be buried. Based on these findings, we develop RSA-AMR, a novel relative solvent accessibility-based AMR scoring system. This scoring system can be applied to any protein variant to estimate its propensity of altering the relative solvent accessibility, and potentially conferring (or hindering) AMR. We show how RSA-AMR score can be integrated with existing AMR detection algorithms to expand their range of applicability into detecting potential novel AMR variants, and provide a ten-fold increase in Specificity. The two main limitations of RSA-AMR score is that it is designed on single point changes, and a limited number of variants was available for model learning. Frontiers Media S.A. 2021-01-22 /pmc/articles/PMC7862766/ /pubmed/33552147 http://dx.doi.org/10.3389/fgene.2021.564186 Text en Copyright © 2021 Marini, Oliva, Slizovskiy, Noyes, Boucher and Prosperi. http://creativecommons.org/licenses/by/4.0/ 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.
spellingShingle Genetics
Marini, Simone
Oliva, Marco
Slizovskiy, Ilya B.
Noyes, Noelle Robertson
Boucher, Christina
Prosperi, Mattia
Exploring Prediction of Antimicrobial Resistance Based on Protein Solvent Accessibility Variation
title Exploring Prediction of Antimicrobial Resistance Based on Protein Solvent Accessibility Variation
title_full Exploring Prediction of Antimicrobial Resistance Based on Protein Solvent Accessibility Variation
title_fullStr Exploring Prediction of Antimicrobial Resistance Based on Protein Solvent Accessibility Variation
title_full_unstemmed Exploring Prediction of Antimicrobial Resistance Based on Protein Solvent Accessibility Variation
title_short Exploring Prediction of Antimicrobial Resistance Based on Protein Solvent Accessibility Variation
title_sort exploring prediction of antimicrobial resistance based on protein solvent accessibility variation
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7862766/
https://www.ncbi.nlm.nih.gov/pubmed/33552147
http://dx.doi.org/10.3389/fgene.2021.564186
work_keys_str_mv AT marinisimone exploringpredictionofantimicrobialresistancebasedonproteinsolventaccessibilityvariation
AT olivamarco exploringpredictionofantimicrobialresistancebasedonproteinsolventaccessibilityvariation
AT slizovskiyilyab exploringpredictionofantimicrobialresistancebasedonproteinsolventaccessibilityvariation
AT noyesnoellerobertson exploringpredictionofantimicrobialresistancebasedonproteinsolventaccessibilityvariation
AT boucherchristina exploringpredictionofantimicrobialresistancebasedonproteinsolventaccessibilityvariation
AT prosperimattia exploringpredictionofantimicrobialresistancebasedonproteinsolventaccessibilityvariation