Jens Meiler

This study shows AlphaFold2 contact distance distributions can approximate conformational Boltzmann distributions and predict mutation effects in some protein systems.

The paper examines how mutations in unstable membrane proteins like GnRHR form distinct epistatic interactions depending on their destabilizing mechanisms and severity.

This paper introduces a method using dark nanodiscs and nanoDSF to measure membrane protein thermostability, showing a biphasic unfolding pattern for DsbB with two distinct melting temperatures.

The paper introduces a chirality-aware graph neural network for drug discovery that improves predictive performance and interpretability in quantitative structure activity relationship modeling.

The paper uses personalized structural biology to analyze de novo KCNC2 variants in a DEE patient, revealing distinct functional effects of two nearby variants.

The paper characterizes fitness landscapes of fluorescent proteins and finds that flat landscapes are less suitable for machine learning-based protein design.

The paper presents a framework for ensuring reproducibility in bio-macromolecular modeling through automated benchmarks and a test server, demonstrating its utility for Rosetta and other tools.

The paper uses Rosetta comparative modeling to generate structural models of ΔF508 CFTR and its variants, revealing how the mutation destabilizes the protein and how drugs like VX-809 stabilize it.

This paper introduces DiGePred, a machine learning tool that identifies potential digenic disease gene pairs, achieving high precision and recall in evaluating rare genetic disorders.

The paper introduces MASSP, a deep-learning system that simultaneously predicts secondary structure, transmembrane segments, and topology of proteins, outperforming existing methods in accuracy and applicability.

This study investigates the effects of a DAT mutation linked to DTDS, showing impaired dopamine function and behavioral deficits in Drosophila, with potential therapeutic implications.

The paper describes the clinical, neuroimaging, and molecular features of TECPR2-associated hereditary sensory and autonomic neuropathy with intellectual disability, providing variant interpretation guidelines and carrier frequency estimates.

This paper introduces a new Rosetta protocol for modeling GPCRs with improved accuracy, enabling modeling of nearly 90% of the GPCR family using low-identity templates.

The paper predicts animal species susceptibility to SARS-CoV-2 by analyzing ACE2 receptor sequences and structures to identify key residues that determine infection risk.

The paper assesses how well existing human protein 3D structures represent genetic and functional diversity across the 1000 Genomes Project, finding significant underrepresentation of African ancestry sequences.

This paper introduces RosettaDEER, a method that simulates DEER distance distributions and decay traces to improve protein fold prediction using sparse experimental data.

This study evaluates KCNQ1 variants using high-throughput electrophysiology, classifying 70% of unstudied variants and showing allele frequency is not a reliable predictor of function.

This paper extends a protein-ligand docking method to incorporate protein mutagenesis-derived SARs, improving binding mode prediction for ligand series.

The paper analyzes the spatial distribution of missense variants in human protein structures to classify variants of unknown significance, finding disease-causing variants cluster more than benign ones.