RESUMEN
The effect of peptide-to-peptoid substitutions on the passive membrane permeability of an N-methylated cyclic hexapeptide is examined. In general, substitutions maintained permeability but increased conformational heterogeneity. Diversification with nonproteinogenic side chains increased permeability up to 3-fold. Additionally, the conformational impact of peptoid substitutions within a ß-turn are explored. Based on these results, the strategic incorporation of peptoid residues into cyclic peptides can maintain or improve cell permeability, while increasing access to diverse side-chain functionality.
Asunto(s)
Células Epiteliales/efectos de los fármacos , Péptidos/farmacología , Permeabilidad/efectos de los fármacos , Animales , Línea Celular , Perros , Células Epiteliales/metabolismo , Espectroscopía de Resonancia Magnética , Modelos Moleculares , Conformación Molecular , Simulación de Dinámica Molecular , Péptidos/química , Relación Estructura-ActividadRESUMEN
Investigating the mechanisms of action (MOAs) of bioactive compounds and the deconvolution of their cellular targets is an important and challenging undertaking. Drug resistance in model organisms such as S. cerevisiae has long been a means for discovering drug targets and MOAs. Strains are selected for resistance to a drug of interest, and the resistance mutations can often be mapped to the drug's molecular target using classical genetic techniques. Here we demonstrate the use of next generation sequencing (NGS) to identify mutations that confer resistance to two well-characterized drugs, benomyl and rapamycin. Applying NGS to pools of drug-resistant mutants, we develop a simple system for ranking single nucleotide polymorphisms (SNPs) based on their prevalence in the pool, and for ranking genes based on the number of SNPs that they contain. We clearly identified the known targets of benomyl (TUB2) and rapamycin (FPR1) as the highest-ranking genes under this system. The highest-ranking SNPs corresponded to specific amino acid changes that are known to confer resistance to these drugs. We also found that by screening in a pdr1Δ null background strain that lacks a transcription factor regulating the expression of drug efflux pumps, and by pre-screening mutants in a panel of unrelated anti-fungal agents, we were able to mitigate against the selection of multi-drug resistance (MDR) mutants. We call our approach "Mutagenesis to Uncover Targets by deep Sequencing", or "MUTseq", and show through this proof-of-concept study its potential utility in characterizing MOAs and targets of novel compounds.
