Patricia L. Clark

The paper explains how autotransporter proteins avoid a kinetic trap during secretion by using a computational framework to reveal hidden conformational states.

The paper introduces a platform to characterize GPCR expression and uses machine learning to identify structural features affecting their biosynthesis.

This study identifies an on-pathway intermediate in the folding of a large protein, revealing a two-step folding mechanism and its role in navigating kinetic competition between folding and misfolding.

The paper shows that the direction and speed of polypeptide chain synthesis affect protein folding outcomes, highlighting the impact of sequential appearance on folding success.

The paper evaluates the detection of amino acid substitutions in shotgun proteomics by creating a ground-truth library using mixed bacterial proteomes and identifies sources of bias in substitution identification.

The paper shows that synonymous codon substitutions in E. coli can upregulate an upstream gene through cryptic transcription, challenging the view that synonymous mutations have no phenotypic effects.

The paper evaluates five computational codon usage models for their ability to predict translational slowdowns using ribosome footprint data from S. cerevisiae, finding strong associations but also highlighting inconsistencies in RFP data.