Exploring Prediction of Antimicrobial Resistance Based on Protein Solvent Accessibility Variation

Simone Marini, Marco Oliva, Ilya B. Slizovskiy, Noelle Robertson Noyes, Christina Boucher, Mattia Prosperi

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number564186
JournalFrontiers in Genetics
Volume12
DOIs
StatePublished - Jan 22 2021

Bibliographical note

Funding Information:
Funding. This work was supported by NIH NIAID 1R01AI141810-01 and University of Florida?s Creating the Healthiest Generation ? Moonshot initiative, supported by the UF Office of the Provost, UF Office of Research, UF Health, UF College of Medicine, and UF Clinical and Translational Science Institute.

Funding Information:
This work was supported by NIH NIAID 1R01AI141810-01 and University of Florida’s Creating the Healthiest Generation –

Publisher Copyright:
© Copyright © 2021 Marini, Oliva, Slizovskiy, Noyes, Boucher and Prosperi.

Keywords

  • AMR
  • RSA
  • antimicrobial resistance
  • protein variant
  • relative solvent accessibility
  • scoring
  • secondary structure

PubMed: MeSH publication types

  • Journal Article

Fingerprint

Dive into the research topics of 'Exploring Prediction of Antimicrobial Resistance Based on Protein Solvent Accessibility Variation'. Together they form a unique fingerprint.

Cite this