ReviewComputational and structural biotechnology journal2020
Combining structure and genomics to understand antimicrobial resistance.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 27 citations in OpenAlex.
- Medicinal Plants: A Promising Therapeutic Approach for Addressing Antimicrobial Resistance.International journal of molecular sciences · 2026Review
- PIPENN-EMB ensemble net and protein embeddings generalise protein interface prediction beyond homology.Scientific reports · 2025Article
- Prediction of pyrazinamide resistance inJAC-antimicrobial resistance · 2024Article
- Hybrid-Capture Target Enrichment in Human Pathogens: Identification, Evolution, Biosurveillance, and Genomic Epidemiology.Pathogens (Basel, Switzerland) · 2024Review
- Article
- AI-Driven Enhancements in Drug Screening and Optimization.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects.Frontiers in cellular and infection microbiology · 2024Review
- Using Graph-Based Signatures to Guide Rational Antibody Engineering.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures.Briefings in bioinformatics · 2022Article
- Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction.International journal of molecular sciences · 2021Article
- mmCSM-PPI: predicting the effects of multiple point mutations on protein-protein interactions.Nucleic acids research · 2021Article
- Insects, Rodents, and Pets as Reservoirs, Vectors, and Sentinels of Antimicrobial Resistance.Antibiotics (Basel, Switzerland) · 2021Review
- Distinguishing between PTEN clinical phenotypes through mutation analysis.Computational and structural biotechnology journal · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
Antimicrobials against bacterial, viral and parasitic pathogens have transformed human and animal health. Nevertheless, their widespread use (and misuse) has led to the emergence of antimicrobial resistance (AMR) which poses a potentially catastrophic threat to public health and animal husbandry. There are several routes, both intrinsic and acquired, by which AMR can develop. One major route is through non-synonymous single nucleotide polymorphisms (nsSNPs) in coding regions. Large scale genomic studies using high-throughput sequencing data have provided powerful new ways to rapidly detect and respond to such genetic mutations linked to AMR. However, these studies are limited in their mechanistic insight. Computational tools can rapidly and inexpensively evaluate the effect of mutations on protein function and evolution. Subsequent insights can then inform experimental studies, and direct existing or new computational methods. Here we review a range of sequence and structure-based computational tools, focussing on tools successfully used to investigate mutational effect on drug targets in clinically important pathogens, particularly
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.