Protein Structure and Dynamics

This study demonstrates that the REHT method efficiently samples membrane-bound conformations of the Osh4 ALPS peptide, revealing a preference for horizontal over vertical conformations.

This study uses MD simulations to show that self-crowding in FUS LCD condensates slows large-amplitude motions more than small-scale motions.

This paper introduces a Graph Partitioned GNN (GP-GNN) to predict protein liquid-liquid phase separation by focusing on subgraphs containing intrinsically disordered regions.

The paper introduces TED, a resource that segments and classifies over 370 million domains from the AlphaFold Protein Structure Database, expanding known structural domains and uncovering new structural interactions.

The paper introduces a diffusion model to generate diverse conformations of P-type ATPases, demonstrating high accuracy in capturing functional states.

The paper models the structure of the active NOX2 complex using AlphaFold2 and experimental data, revealing the role of disordered regions in subunit assembly.

This paper characterizes the conformational landscape of KaiB, revealing its slow interconversion between states and how evolution tunes this process to match circadian rhythms.

This study identifies the PALB2 DNA-binding domain as an intrinsically disordered region that promotes DNA compaction through oligomerization, revealing a novel strand exchange mechanism.

This paper introduces DyNoPy, a method combining coevolution analysis and molecular dynamics to predict functionally important residues in proteins, demonstrating its application on beta-lactamases.

The paper investigates how ESM-2, a protein language model, predicts protein contacts by analyzing coevolving residue statistics and local sequence context.

BFVD provides a large repository of predicted viral protein structures, enhancing annotation of bacteriophage proteins compared to sequence-based methods.

This study compares ABFE and MSM methods for calculating binding free energies of a small molecule to an IDP, finding that MSM provides more consistent results.

This study investigates the role of intrinsically disordered regions (IDRs) in ER-phagy by modeling and simulating their membrane interactions and remodeling effects.

ANTIPASTI predicts antibody binding affinity using Normal Mode correlation maps and a Convolutional Neural Network, revealing structural and energetic patterns that influence binding.

This study evaluates machine learning-assisted directed evolution strategies across diverse protein fitness landscapes, finding that focused training with zero-shot predictors outperforms random sampling in challenging landscapes.

This study uses molecular dynamics simulations to investigate how 2-deoxy-ATP modifies the structure and dynamics of myosin in the interacting heads motif state.

This paper introduces EuDockScore, a scoring model for protein-protein interfaces using euclidean graph neural networks, and provides variants for antibody-antigen docking and re-ranking AlphaFold-Multimer outputs.

The paper identifies proton loading sites in Complex I by analyzing protonation microstates using MCCE and Molecular Dynamics simulations.

This study uses atomistic modeling to show that sequence length in elastin-like polypeptides determines their conformational state, with shorter sequences remaining coil-like and longer ones forming globule states.

This study investigates A22's interactions with prokaryotic MreB and eukaryotic actin using computational and experimental methods, revealing strong binding and toxicity to eukaryotic cells.

The paper introduces a coarse-grained model for multi-domain proteins that accurately simulates conformational properties and phase separation behavior.

The paper develops a computational pipeline to annotate functional tunnels in enzyme-ligand complexes and validates it using molecular systems, showing it matches molecular dynamics simulations in speed and accuracy.

The paper quantifies how mutations in JUN affect its binding to 54 human basic leucine zipper transcription factors, revealing that specificity is encoded by both positive and negative determinants.

This study uses molecular dynamics to analyze inhibitor binding to DHFR, showing that reduced activity correlates with multiple-bound states on the protein surface.

This study uses molecular dynamics simulations to investigate how phosphorylation patterns and salt concentration affect RNA Polymerase II CTD clustering behavior.

This study uses statistical coupling analysis and molecular dynamics to show that dynamically coupled residues in DHFR influence evolution and allosteric communication.

This study investigates the dynamic self-association of eIF4B's intrinsically disordered region using experiments and simulations, revealing its role in forming oligomers and phase separation.

This study uses NMR to analyze the structure, dynamics, and interactions of N-myc's transactivation domain, revealing phosphorylation sites and binding interactions.

The paper develops a coarse-grained modeling approach to study Mincle receptor interactions with glycolipids, identifying key parameters like loop flexibility and calcium ion regulation.

AFEXplorer improves protein structure prediction by incorporating user-defined constraints, enhancing the generation of functional conformations.