Biology subjects

DuPai, C. D.

Publications and source records attributed to DuPai, C. D..

3 recordsLinked to original sources

bioRxiv2022

Designing β-hairpin peptide macrocycles for antibiotic potential

Peptide macrocycles are a rapidly emerging new class of therapeutic, yet the design of their structure and activity remains challenging. This is especially true for those with {beta}-hairpin structure due to weak folding properties and a propensity for aggregation. Here we use proteomic analysis and common antimicrobial features to design a large peptide library with macrocyclic {beta}-hairpin structure. Using an activity-driven high-throughput screen we identify dozens of peptides killing bacteria through selective membrane disruption and analyze their biochemical features via machine learning. Active peptides contain a unique constrained structure and are highly enriched for cationic charge with arginine in their turn region. Our results provide a synthetic strategy for structured macrocyclic peptide design and discovery, while also elucidating characteristics important for {beta}-hairpin antimicrobial peptide activity. Brief SummaryWe design, screen, and computationally analyze a synthetic macrocyclic {beta}-hairpin peptide library for antibiotic potential.

biochemistry↗

bioRxiv2020

A systematic analysis of the beta hairpin motif in the Protein Data Bank

The beta hairpin motif is a ubiquitous protein structural motif that can be found in molecules across the tree of life. This motif, which is also popular in synthetically designed proteins and peptides, is known for its stability and adaptability to broad functions. Here we systematically probe all 49,000 unique beta hairpin substructures contained within the Protein Data Bank (PDB) to uncover key characteristics correlated with stable beta hairpin structure, including amino acid biases and enriched inter-strand contacts. We also establish a set of broad design principles that can be applied to the generation of libraries encoding proteins or peptides containing beta hairpin structures. ImportanceThe beta hairpin motif is a common protein structural motif that is known for its stability and varied activity in diverse proteins. Here we use nearly fifty thousand beta hairpin substructures from the Protein Data Bank to systematically analyze and identify key characteristics of the beta hairpin motif. Ultimately, we provide a set of design principles for the generation of synthetic libraries encoding proteins containing beta hairpin structures.

bioinformatics↗

bioRxiv2020

A comprehensive co-expression network analysis in Vibrio cholerae

Research into the evolution and pathogenesis of Vibrio cholerae has benefited greatly from the generation of high throughput sequencing data to drive molecular analyses. The steady accumulation of these datasets now provides a unique opportunity for in silico hypothesis generation via co-expression analysis. Here we leverage all published V. cholerae RNA-sequencing data, in combination with select data from other platforms, to generate a gene co-expression network that validates known gene interactions and identifies novel genetic partners across the entire V. cholerae genome. This network provides direct insights into genes influencing pathogenicity, metabolism, and transcriptional regulation, further clarifies results from previous sequencing experiments in V. cholerae (e.g. Tn-seq and ChIP-seq), and expands upon micro-array based findings in related gram-negative bacteria. ImportanceCholera is a devastating illness that kills tens of thousands of people annually. Vibrio cholerae, the causative agent of cholera, is an important model organism to investigate both bacterial pathogenesis and the impact of horizontal gene transfer on the emergence and dissemination of new virulent strains. Despite this importance, roughly one third of V. cholerae genes are functionally un-annotated, leaving large gaps in our understanding of this microbe. Through co-expression network analysis of existing RNA-sequencing data, this work develops an approach to uncover novel gene-gene relationships and contextualize genes with no known function, which will advance our understanding of V. cholerae virulence and evolution.

microbiology↗