Protein Structure and Dynamics

This study benchmarks six residue-resolution coarse-grained models to evaluate their accuracy in simulating the phase behavior and material properties of hnRNPA1 LCD condensates.

The paper evaluates the diversity of non-covalent protein-carbohydrate interfaces using the DIONYSUS database, revealing evolutionary convergence and demonstrating the utility of CBS structure-based approaches for functional annotation.

The paper reports a 2.5 Å cryo-EM structure of budding yeast F-actin and compares it with mammalian and avian F-actin, identifying key structural differences.

This study shows phosphorylation at Ser118 of the estrogen receptor's disordered N-terminal domain alters long-range hydrophobic interactions, leading to conformational changes that activate transcriptional function.

This study uses DeepTDA to design collective variables for enhanced sampling simulations of RNA-peptide binding, revealing multiple binding and unbinding events and calculating the free energy surface.

FlowPacker predicts protein side-chain conformations using torsional flow matching and equivariant graph attention, outperforming existing methods and handling missing coordinates and multimeric targets.

The paper investigates how structural ensembles of disordered activation domains correlate with transcriptional activity through mutational analysis of HIF-1 and CITED2.

The paper identifies a critical polar interaction in the viral fusion protein gB using machine learning and experiments, showing its role in stabilizing the pre-fusion conformation and blocking membrane fusion.

This paper shows that a transformer model can predict future states of physicochemical systems faster than traditional simulations by learning from time-discretized molecular dynamics data.

The paper analyzes protein loop regions in human and E. coli proteomes to explore their structural, functional, and evolutionary characteristics.

The paper applies a novel active learning strategy, DEW-DROP, to improve protein structure prediction efficiency and data selection for two protein datasets.

This study uses AlphaFold2 to screen proteins that bind to the open conformation of Drosophila filamin Cheerio, identifying 132 hits that specifically recognize the open state.

This study compares AlphaFold 2 predictions with experimental structures of nuclear receptors, finding high accuracy in stable conformations but limitations in capturing flexible regions and ligand-binding pocket variability.

The paper uses MD simulations to study helical stability in polyXY sequences at IDR boundaries, finding they form dynamic helices that may aid in protein flexibility.

This paper introduces a framework combining a protein GPT model with a custom discriminator to prioritize diverse and functional protein sequences for de novo design.

This paper introduces DeepUMQA-PA, a deep learning method to estimate residue-wise quality scores for protein complex models, showing improvements over existing methods on benchmark datasets.

This paper designs 3-helix bundle peptides and proteins with controlled topology and stability using computational methods and experimental validation.

The paper evaluates AlphaFold 2's performance on repeat proteins, finding it predicts confident but unrealistic beta-solenoid structures, unlike other methods.

The paper introduces Masala, an open-source software suite designed to extend and improve upon Rosetta for synthetic heteropolymer design, offering modular architecture and performance improvements.

GenDiS3 provides a census of protein domain superfamilies' prevalence across sequence databases, using computational tools to link sequences to known structures and explore evolutionary and functional diversity.

This study uses single-molecule FRET to show urea activates adenylate kinase by promoting an open conformation and reducing AMP inhibition, revealing the interplay between conformational dynamics and substrate binding.

The paper identifies and characterizes PDI family members in sorghum to explore their domain architecture, structure, and functionality, highlighting structural differences from human PDIs.

This study uses molecular dynamics simulations to analyze the transport mechanism of Ala-Ala through PepTSo, revealing conformational changes and free energy barriers influenced by substrate binding.

The paper summarizes HADDOCK's performance in CAPRI rounds 47-55, highlighting the effectiveness of integrating HADDOCK with AI-driven methods like AlphaFold2 for protein complex structure prediction.

This study characterizes IDE conformational dynamics using cryo-EM and MD simulations, identifying key residues and mechanisms that modulate enzyme activity.

This study investigates nearest-neighbor cooperativity in TRAP by engineering dTRAP variants and using cryo-EM and thermodynamic analyses to reveal structural and thermodynamic mechanisms of ligand binding cooperativity.

The paper evaluates how evolutionary models predict protein stability using deep mutational scanning data and finds that simple models perform as well as complex ones when combined with structural features.

GATE estimates protein complex model accuracy using graph transformers on pairwise similarity graphs, achieving high correlation and low ranking loss on CASP15 and CASP16 datasets.

This study investigates how the N642H mutation in STAT5B stabilizes the parallel dimer through changes in hydrogen bonding, using molecular dynamics simulations and HDX mass spectrometry.

MDTAP automates the analysis of permeation events across membrane proteins and nucleic acids from MD trajectories, providing visualization and insights into solute/solvent transport mechanisms.