A bio-informatics approach to identify new drug targets in multidrug-resistant bacteria
Ramsden, I.; Chiam, A. J.; de Jong-Hoogland, D.; Ulmschneider, M. B.
Show abstract
Antibiotic resistance poses a global health crisis. In order to develop new antibiotic agents, it is crucial to identify drug targets in multidrug-resistant bacteria. Criteria for such a target are an -helical, essential membrane protein, that is non-homologues with the human membrane proteome, and present across multiple bacterial species. Using a stepwise subtractive genomics approach, the membrane protein F0F1 ATP synthase subunit C was identified as a non-human analogues drug target that is present in 11 bacterial species.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Comprehensive Genome Based Analysis of Vibrio parahaemolyticus for Identifying Novel Drug and Vaccine Molecules: Subtractive Proteomics and Vaccinomics Approach 93%
- Discovery of Novel Targets for Important Human and Plant Fungal Pathogens via Automated Computational Pipeline HitList 92%
- Inhibiting the copper efflux system in microbes as a novel approach for developing antibiotics 92%
Similar papers in this journal
- The Solvation of the E. coli CheY Phosphorylation SiteMapped by XFMS 91%
- Assessing Protein Surface-Based Scoring for Interpreting Genomic Variants 91%
- Possible link between higher transmissibility of B.1.617 and B.1.1.7 variants of SARS-CoV-2 and increased structural stability of its spike protein and hACE2 affinity 91%
Similar papers in this journal
- Identification and Characterization of Outer Membrane Proteins and Membrane Spanning Protein Complexes in Brucella melitensis 95%
- Generalizable strategy to analyze domains in the context of parent protein architecture: A CheW case study 92%
- Identification of Sequence Determinants for the ABHD14 Enzymes 92%
Similar papers in this journal
- Designing BH3-mimetic Peptide Inhibitors for the Viral Bcl-2 Homologs A179L and BHRF1: Importance of long-range electrostatic interactions 91%
- NRPreTo: A Machine Learning Based Nuclear Receptor and Subfamily Prediction Tool 91%
- Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-based Classifiers 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.