Protein Structure and Dynamics

STRAND improves RNA-protein complex backbone predictions using a diffusion model, building on existing tools like AlphaFold 3 and ProRNA3D-single.

This paper introduces an uncertainty-aware discrete diffusion model for protein design, improving sequence generation from 3D structures by incorporating position-specific uncertainty and pretrained models.

The paper shows that the direction and speed of polypeptide chain synthesis affect protein folding outcomes, highlighting the impact of sequential appearance on folding success.

This study compares two charge-change correction strategies for {lambda}-dynamics free energy calculations, finding that a simplified AC approach is more generalizable than CI for protein mutations.

This paper evaluates current biomolecular structure prediction models using physics-based criteria, revealing widespread errors in capturing atomic interactions and conformational preferences.

StruCloze reconstructs atomistic structures from coarse-grained models and inpaints missing regions in biomolecules, achieving state-of-the-art accuracy and transferability.

The paper investigates how protein conformational dynamics reveal hidden post-translational modification sites and their functional relevance across timescales.

This study introduces RelaVDEP, a virtual directed evolution pipeline using reinforcement learning to accelerate protein mutation discovery, showing improved catalytic activity in PETase through novel mutations.

This paper designs a protein inhibitor for TEM-171 β-lactamase using deep learning and simulations, showing high stability and binding affinity.

The paper highlights discrepancies between AlphaFold predictions and UniProt data, showing how bioinformatics resources can quickly become outdated.

This paper introduces SIMPLIFE, a computational workflow for designing optimal insertion sites for conjugation tags in proteins, demonstrating its use in augmenting T7 RNA polymerase function.

The paper constructs allosteric maps for five human proteins to study how allostery evolves, finding conserved core interactions and protein-specific extensions.

This study shows blue light or high pressure activates AsLOV2 by expelling structured water, causing conformational changes and suggesting hydration water drives protein mechanics.

The paper evaluates how AlphaFold2-Multimer, AlphaFold3, Boltz-1, and Chai-1 predict protein-peptide complex structures using experimentally resolved datasets, finding biases and insights into MSA usage.

This study compares the structural features of manganese catalase with other ferritin subtypes, highlighting differences in monomer structure, interface arrangement, and cage architecture that support its catalytic function.

FluxMD provides a GPU-accelerated framework to analyze protein mechanical responses by generating stress barcodes from ligand-protein interactions, demonstrating its effectiveness with GPX4.

The paper benchmarks pKa prediction methods for OmpF channel using Constant pH Molecular Dynamics and finds existing methods fail to capture membrane-embedded channel specificities.

FunCLAN categorises protein complex conformations, measures their similarities, and solves the chain equivalence problem using sequence similarity and superposition transformations.

PLANET-MD predicts dynamic protein properties from sequence or structure with high accuracy and speed, enabling comprehensive proteome-scale analysis of protein dynamics.

The paper quantifies how amino acid composition and peptide conformation affect hydration shell structure using molecular dynamics and SAXS simulations, showing distinct differences between globular and intrinsically disordered proteins.

The paper introduces PIONEER2, a method that improves protein-protein interface prediction by integrating structural homology and deep learning, outperforming AlphaFold3 in certain scenarios.

This paper identifies three prediction modes in low-pLDDT regions of AlphaFold2 structures and develops a tool to annotate and visualize them for improved interpretation.

ProGuide generates structural models of protein conformations using DEER-derived distance restraints, revealing novel conformational states in the angiotensin II type 1 receptor.

The paper uses AlphaFold to predict structures of host-pathogen PPIs, revealing molecular mimicry modes and convergent evolution of interaction interfaces.

This study evaluates AlphaFold 3's ability to model natural variants of Helicobacter pylori KatA, showing high accuracy for conservative substitutions but variable results for flexible or interface sites.

The paper presents a fine-tuned Alphafold2 model that better captures geometric diversity in de novo designed proteins compared to standard methods.

The paper introduces a method to apply a distance-based cutoff to hydrodynamic interactions in Brownian dynamics simulations while maintaining a positive definite diffusion tensor.

The paper introduces an information-theoretic framework to identify key tuning residues in proteins by denoising sequence data, validated across multiple datasets with significant performance improvements.

This paper introduces bio-inspired collective variables to improve protein folding simulations, accurately resolving free energy landscapes and identifying intermediates like the dry molten globule.

This paper shows that residue-level fluctuations in proteins encode geometric bits communicated through sparse 3D networks of shared entropy, validated across multiple experimental data modalities.