M. Feig

Structural basis of transcription: RNA Polymerase II substrate binding and metal coordination at 3.0 A using a free-electron laser

This paper presents the structure of ATP-bound RNA Polymerase II at 3.0 Å resolution using a free-electron laser, revealing new insights into substrate binding and metal coordination in the active site.

One particle per residue is sufficient to describe all-atom protein structures

This paper shows that single beads per amino acid residue can accurately reconstruct all-atom protein structures using machine learning, with minimal information loss.

The Effect of Polymer Length in Phase Separation

This study investigates the thermodynamics of liquid-liquid phase separation in short polymer systems, showing that condensation depends on polymer length and concentration, with a negative free energy indicating stability.

Modeling concentration-dependent phase separation processes involving peptides and RNA via residue-based coarse-graining

The paper introduces a residue-based coarse-grained model, COCOMO, to simulate concentration-dependent phase separation of peptides and RNA, showing it predicts experimental data and is computationally efficient.

Characterization of RNA Polymerase II Trigger Loop Mutations using Molecular Dynamics Simulations and Machine Learning

This study uses molecular dynamics and machine learning to analyze RNA Polymerase II trigger loop mutations, finding that ML can predict phenotypes from sequences and MD data reveals structural changes linked to mutation effects.

Characterizing Transient Protein-Protein Interactions by Trp-Cys Quenching and Computer Simulations

This paper introduces Trp-Cys quenching experiments combined with simulations to study transient protein interactions under crowded conditions, showing they can distinguish interaction types and preferences.

Direct Generation of Protein Conformational Ensembles via Machine Learning

This paper shows that machine learning can generate protein conformational ensembles without simulation, using a transformer-based GAN trained on simulation data.

Multi-state Modeling of G-protein Coupled Receptors at Experimental Accuracy

This paper introduces a multi-state prediction protocol for GPCRs using AlphaFold2, achieving high accuracy in predicting both active and inactive states and outperforming existing methods in ligand docking and competition benchmarks.

Inhibitory proteins block substrate access by occupying the active site cleft of Bacillus subtilis intramembrane metalloprotease SpoIVFB

Charge-Driven Phase Condensation of RNA and Proteins Suggests Broad Role of Phase Separation in Cytoplasmic Environments

This study shows that RNA and positively charged proteins can form phase-separated condensates through electrostatic complementarity, suggesting a broad role of phase separation in cytoplasmic environments.

Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Proteins by Machine Learning and Physics-Based Refinement

The paper predicts and refines SARS-CoV-2 protein structures using a machine learning pipeline and molecular dynamics refinement, comparing results with existing models.

High-Accuracy Protein Structures By Combining Machine-Learning With Physics-Based Refinement

This paper combines machine-learning models with physics-based refinement to improve protein structure prediction, achieving higher accuracy than existing methods.

The phage L capsid decoration protein has a novel OB-fold and an unusual capsid binding strategy

The paper determines the structure of the phage L Dec protein and its binding strategy to the capsid using cryo-EM and NMR.