Protein Structure and Dynamics

This study identifies drugable pockets on c-MYC using simulations, showing 10074-G5 binds stably, offering a new approach to target this undruggable protein.

The paper investigates how two autism-associated mutations in the Homer1 EVH1 domain affect its internal dynamics and binding properties using experimental and computational methods.

PRISM enables sequence-independent, one-shot fine-tuning for simultaneous protein domain segmentation and classification, improving accuracy for complex and novel architectures.

The paper investigates how protein entanglement misfolding affects proteasomal degradation in human cells, finding that entangled proteins are more likely to be ubiquitinated and degraded.

This study designs compact protein binders targeting BabA, an H. pylori adhesin, using a structure-guided computational pipeline integrating structural, sequence, and evolutionary analyses.

SynFit improves protein fitness prediction by learning shared and property-specific sequence representations, outperforming existing methods in multi-objective optimization.

This study develops a method to estimate protein melting temperatures using small-ladder replica exchange simulations, showing improved efficiency with iterative temperature ladders.

The paper quantifies differences in cation-π interaction strengths between Arg and Lys in biomolecular condensates, showing Arg is more effective than Lys due to higher dehydration penalty of Lys.

This study determines the crystal structure of the C-terminal cysteine knot domain of Aedes albopictus vitellogenin, revealing a monomeric, evolutionarily divergent architecture that challenges prior assumptions about its oligomerization role.

This paper parameterizes 186 common metabolites for the Martini 3 force field to enable realistic simulations of metabolite interactions in biological systems.

The paper predicts homooligomeric phage protein structures using a computational framework, confirming predictions for dimers and trimers and showing high accuracy in classifying homooligomeric states.

This study examines how charge patterning affects the conformational behavior and dynamics of intrinsically disordered proteins using experimental techniques and simulations.

This study shows that disordered protein sequences encode characteristic small molecule binding pathways, identified through molecular dynamics simulations and machine learning.

The paper develops a stickiness scale for disordered proteins based on biophysical experiments and compares it to existing hydropathy scales.

The paper introduces a framework to decompose protein folding flux into pathway-specific kinetic contributions, revealing hidden kinetic hierarchies in folding mechanisms.

This study identifies nanobody CDRs using deep learning and structural analysis, finding that their disorder and conformational diversity are adaptive features.

The paper evaluates and ranks protein backbone generation methods using a unified benchmark, identifying top-performing models in both unconditional and conditional generation.

This paper introduces a deep learning method to identify conformational binding signatures in protein dynamics from molecular simulations.

SuBMIT generates input files for molecular dynamics simulations of coarse-grained structure-based models using GROMACS and OpenMM/OpenSMOG.

CASPE is a lightweight platform for site-directed protein engineering that improves the identification of beneficial mutations for thermostability and pH tolerance.

The paper uses AlphaFold and molecular dynamics to model Saccharomyces killer toxins, revealing structural homology and functional insights into their mechanisms.

The paper measures protein stability for 1.8 million domains using a new experimental method and develops models that accurately predict stability with high precision.

The paper maps the functional landscape of the human intrinsically disordered proteome using evolutionary signatures to classify and predict IDR functions.

This study evaluates deep learning models' ability to predict protein flexibility by comparing their structural ensembles with experimental data, finding AlphaFold3 performs best.

ConforFlux enhances protein structure prediction by sampling diverse conformations through trunk repulsion, improving success rates and coverage across multiple models and targets.

The paper presents a computational model of the S. cerevisiae INO80 chromatin remodeling complex using integrative methods, revealing the structure and dynamics of key modules and introducing the INTEGRATOR workflow tool.

This study designs peptides that localize at biomolecular condensate interfaces using a computational pipeline combining simulations, machine learning, and optimization, revealing charge-dependent localization mechanisms.

The paper uses simulations to identify CaM conformations under different calcium and salt conditions, showing how these factors influence its conformational dynamics.

The paper investigates how allostery evolves in the ArsR superfamily by showing that functional diversification arises from internal dynamics rather than large conformational changes.

This paper designs synthetic transmembrane subunits to stabilize and expand membrane proteins, demonstrating their utility in structural biology and disease treatment.