J. Meiler

A Quantum Lens on Molecular Design: A Machine-Learned Energy Function from Interacting Quantum Atoms.

The paper introduces a machine learning framework to predict accurate energy functions derived from the Interacting Quantum Atoms (IQA) scheme, enabling rapid and interpretable analysis of molecular interactions.

Divergent Folding-Mediated Epistasis Among Unstable Membrane Protein Variants

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

Modeling membrane geometries implicitly in Rosetta

The paper modifies Rosetta's membrane energy function to model membrane geometries beyond flat bilayers, improving structure refinement and design for membrane proteins.

Approximating conformational Boltzmann distributions with AlphaFold2 predictions

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

Discovery of post-translationally modified self-peptides that promote hypertension

The paper identifies post-translationally modified self-peptides linked to hypertension using a combined computational and experimental approach.

Dark nanodiscs as a model membrane for evaluating membrane protein thermostability by differential scanning fluorimetry

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.

Impact of Post-Translational Modification on MHC Peptide Binding and TCR Engagement

This study investigates how post-translational modifications affect MHC peptide binding and TCR engagement, revealing their impact on antigen presentation and potential implications for autoimmune diseases and tumor neoantigens.

Biasing AlphaFold2 to predict GPCRs and Kinases with user-defined functional or structural properties

The paper extends AlphaFold2 to predict GPCRs and Kinases by incorporating user-defined functional or structural features, achieving accurate models with biased conformational states.

Evolutionarily diverse caveolins share a common structural framework built around amphipathic discs

The paper predicts structures for 73 caveolins across animal diversity and identifies conserved structural elements and amphipathic disc assembly, revealing an ancient structural framework predating Metazoa.

Rosetta 's Predictive Ability for Low-Affinity Ligand Binding in Fragment-Based Drug Discovery

This study evaluates RosettaLigand's ability to predict low-affinity ligand binding in fragment-based drug discovery using NMR data and finds it ranks binders with an AUC of 0.74.

Elucidation of Global Trends in the Effects of VX-661 and VX-445 on the Expression of Clinical CFTR Variants

This study compares the effects of VX-661 and VX-445 on 129 CFTR variants using deep mutational scanning, finding distinct patterns of expression rescue based on variant destabilization and binding pocket locations.

Acquired secondary HER2 mutations enhance HER2/MAPK signaling and promote resistance to HER2 kinase inhibition in HER2-mutant breast cancer

This study identifies secondary HER2 mutations that confer resistance to HER2 kinase inhibitors in HER2-mutant breast cancer by enhancing HER2 signaling and reducing drug binding.

Pseudocontact shift NMR data obtained from a non-canonical amino acid-linked lanthanide tag improves integral membrane protein structure prediction

The paper improves integral membrane protein structure prediction by integrating paramagnetic NMR restraints with RosettaNMR, achieving better model accuracy and sampling for DsbB.

Template-free prediction of a new monotopic membrane protein fold and oligomeric assembly by Alphafold2

This study uses AlphaFold2 to predict the structure of caveolin-1, revealing its new fold and oligomeric assembly, but notes limitations in predicting higher-order complexes.

Molecular simulations reveal a mechanism for enhanced allosteric coupling between voltage-sensor and pore domains in KCNQ1 explaining its activation by ML277

Computational epitope mapping of class I fusion proteins using Bayes classification

This paper introduces AxIEM, a Bayesian classifier for predicting epitopes on class I viral fusion proteins, showing improved accuracy over existing methods.

Structural characterization of a breast cancer-associated mutation in caveolin-1

This study examines how a breast cancer-associated mutation in CAV1, P132L, affects its structure and oligomerization, revealing its impact on caveolae formation.

Integrated AlphaFold2 and DEER investigation of the conformational dynamics of a pH-dependent APC antiporter

This paper investigates the conformational dynamics of the pH-dependent APC antiporter GadC using DEER spectroscopy and AlphaFold2 models, revealing acid-induced structural changes and substrate-specific dynamics.

First crystal structure of a non-canonical amino acid linked to a paramagnetic lanthanide tag facilitates protein structure determination using NMR-derived restraints

The paper presents the first crystal structure of a non-canonical amino acid linked to a paramagnetic lanthanide tag, showing how it improves NMR-derived structural modeling of proteins.

Allele-specific activation, enzyme kinetics, and inhibitor sensitivities of EGFR exon 19 deletion mutations in lung cancer

The paper identifies allele-specific functional differences among EGFR exon 19 deletion variants, showing how sequence and structure motifs affect oncogenic activity, dimerization, and TKI sensitivity.

Molecular Basis for Variations in the Sensitivity of Pathogenic Rhodopsin Variants to 9-cis-Retinal

This study uses deep mutational scanning to analyze how 123 pathogenic rhodopsin variants respond to 9-cis-retinal, identifying variants with diminished membrane expression and varying sensitivity to retinal.

Molecular architecture of the human caveolin-1 complex

The paper uses cryo-electron microscopy to reveal the structure of the human caveolin-1 complex, showing it forms a disc with a flat membrane-binding surface.

Benchmarking Peptide Structure Prediction with AlphaFold2

This study benchmarks AlphaFold2's ability to predict peptide structures, finding it effective for some scaffolds but less so for peptides with kinks, turns, or flexible regions.

Modeling of protein conformational changes with Rosetta guided by limited experimental data

This paper introduces ConfChangeMover (CCM), a Rosetta tool for modeling protein conformational changes using sparse experimental data, and shows it outperforms existing methods.

Towards Generalizable Predictions for the Effects of Mutations on G-Protein Coupled Receptor Expression

This paper trains neural networks to predict how mutations affect plasma membrane expression of GPCRs, showing structural features alone can generalize across related proteins.

Predicting the Functional Impact of KCNQ1 Variants with Artificial Neural Networks

This paper develops ANN models to predict the functional impact of KCNQ1 variants using biophysical features, outperforming models based solely on evolutionary features.

Sampling the conformational landscapes of transporters and receptors with AlphaFold2

This study uses AlphaFold2 to generate multiple conformations of transporters and GPCRs, showing that reducing MSA depth improves conformational sampling accuracy.

An active learning framework improves tumor variant interpretation

This study applies active learning to improve variant interpretation for tumor genomes, showing better performance than traditional methods in predicting variant effects on cancer treatment.

Mapping of the fibrinogen-binding site on the staphylocoagulase C-terminal repeat region

This paper identifies the fibrinogen-binding site on the C-terminal repeat region of staphylocoagulase using biophysical methods and shows conserved residues and stoichiometry across single and multi-repeat constructs.

Simultaneous selection of nanobodies for accessible epitopes on immune cells in the tumor microenvironment

This study introduces INSPIRE-seq, a method to identify nanobodies targeting immune cells in the tumor microenvironment, revealing a dendritic cell-binding nanobody that interacts with PHB2.