J. Meiler

The paper investigates whether gain-of-function mutations in KCNQ1 cause super-trafficking of the potassium channel to the plasma membrane, finding increased surface levels and altered channel activity.

This paper argues for using ROC cost functions in deep learning for virtual screening to address class imbalance and decision threshold challenges, showing improved performance on drug discovery datasets.

The paper analyzes structural and evolutionary features of cholesterol recognition sites in integral membrane proteins to distinguish specific from nonspecific interactions.

The paper develops AmphiScan, a Rosetta protocol to predict amphipathic helix-membrane interactions, achieving high accuracy in predicting coordinates and membrane-embedded residues.

The paper identifies critical residues in Gβγ proteins for their interaction with the SNARE complex through peptide screening and computational docking.

The paper describes a method using Rosetta with sparse NMR data to predict protein structures, achieving correct folds and high-resolution models in some cases.

This paper develops and validates structural models of human KCNQ1 potassium channels to understand mutation-induced dysfunction and guide drug design.

This paper demonstrates that multi-state protein design using the RECON algorithm predicts sequences optimal for conformational change, showing increased sequence conservation and resemblance to evolutionary sequence space.

RosettaNMR improves protein structure prediction by integrating paramagnetic NMR restraints with chemical shifts and NOE data, achieving high accuracy in 50% of benchmark cases.

This study examines the molecular evolution of placental galectins across mammals and humans, identifying signatures of natural selection and structural differences in a common haplotype.

This study identifies that most KCNQ1 mutations causing long QT syndrome destabilize the voltage sensor domain, leading to mistrafficking and proteasomal degradation.