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.
Highlights
Antimicrobial resistance (AMR) is a significant and growing public health threat
In this work (a) we find how relative solvent accessibility (RSA) consistently characterizes known CARD resistant and susceptible variants, with resistant variants more likely to be exposed, and susceptible variants more likely to be buried; (b) we show how the variants in AMR MEGARes proteins with the potential of hindering the resistance mechanism tend to be denoted by RSA variation; (c) we elaborate an AMR protein variant scoring system based on RSA (RSA-AMR score), and we show it can be used to expand the range of applicability of an existing AMR detection algorithm
Our analysis showed a strong RSA distribution shift related to the AMR-conferring protein variants (Figure 2 and Table 1), present in 3 out of 4 RSA classes
Summary
Treating infections caused by resistant organisms is clinically challenging, and sometimes impossible; even when resistant infections can be treated with alternative antibiotics, these treatments are often costly both in terms of healthcare costs as well as increased morbidity in treated patients For these same reasons, AMR is a significant challenge in veterinary and plant health, and the need to develop novel AMR treatments is deemed very urgent (Nelson et al, 2019). Efficient identification of AMR is of pivotal importance in order to control the spread of AMR and contain its impact To address this need, several AMR databases and identification algorithms have been recently developed, including MEGARes (Doster et al, 2020) and CARD (Alcock et al, 2020). Curated records in AMR databases typically list whole resistant gene accessions, i.e., genes resistant to specific molecules or AMR classes/mechanisms; or housekeeping genes associated with specific AMR-conferring amino acid variants.
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