protein folding & dynamics

This paper introduces a jump-diffusion model to explain cohesin extrusion and CTCF barrier function by integrating active and passive motion mechanisms.

This paper designs de novo ligases that achieve site-specific ubiquitylation of a target protein with high selectivity and efficiency.

The paper identifies structural adaptations in Microviricetes capsids that enable salinity tolerance by analyzing amino acid residues linked to ocean and lake environments.

This study designs de novo amide ligases with a cysteine catalytic triad to selectively form amide bonds across diverse substrates, demonstrating high selectivity and applicability in synthesizing bioactive molecules.

The paper develops a computational model to show that Hsp70 chaperones only delay, not prevent, protein aggregation, depending on client-specific kinetic and thermodynamic properties.

This paper introduces HV-MultiScaleGNN, a graph neural network framework for predicting host-virus protein-protein interactions using network topology, achieving strong performance across multiple evaluation metrics.

The paper develops a method combining evolutionary and protein language model representations to predict protein stability changes with thermodynamic consistency, improving accuracy and physical consistency.

The paper introduces a method to identify key players in rare conformational transitions of proteins by analyzing transition corridors and testing their impact through computational and mutational approaches.

This study investigates how ribosome-mediated protein folding imposes sequence constraints by analyzing mutations in E. coli DHFR using deep mutational scanning and fluorogenic sensors.

The paper uses a bioinformatic pipeline to analyze hypothetical proteins in Rickettsia akari, identifying candidates with potential roles in virulence and host interaction.

This paper introduces Cadense, a machine learning framework that generates antibody conformational ensembles from sequence, capturing dynamic properties and improving docking accuracy.

The paper introduces MaaD-TAD, an all-atom model of a duplicated chromatin domain, using sequence homology and cohesin placement to reconstruct DNA structure where Hi-C data are missing.

The paper introduces mE-DIA, a multi-enzyme DIA-MS workflow that improves proteoform detection in proteomics by integrating analysis and interpretation into a single toolset.

LinearKnot approximates CParty's hierarchical pseudoknot analysis with linear time complexity, achieving comparable accuracy for RNA structure prediction and ensemble calculations.

This study identifies developmentally-regulated alternative splicing of CACNA1C during human neurodevelopment and shows it affects CaV1.2 channel function.

This study identifies noncanonical RNA junctions and hidden coding potential in PEDV using integrated transcriptomic and translatomic approaches.

The paper uses simulations to show that V2Rpp binding broadens the conformational landscape of arrestin-2 beyond C-tail detachment, with interdomain rotation angles being less favored without V2Rpp.

This paper introduces a workflow that decouples quantification and identification in label-free top-down proteomics, improving proteome coverage and quantification precision.

The paper uses integrative modeling to reveal inter-domain flexibility in F-box proteins, showing varying conformational dynamics across different F-box proteins.

The paper introduces a PyTorch-based tool for fast proteome-scale local frustration analysis, achieving 70-13,000x speedup over the original FrustratometeR.

The paper analyzes six-residue Omega-loops in protein complexes, showing their structural constraints, sequence conservation patterns, and functional significance through sequence design and mutation mapping.

This study uses nonequilibrium molecular dynamics to show frequency-dependent structural changes in amyloid fibrils under terahertz pulses, identifying two electric-field thresholds for disruption.

The paper introduces MIST, a framework that integrates single-molecule trajectory data with cellular context to improve molecular phenotyping in E. coli.

This study shows that AlphaFold2 distance maps can reveal multi-state protein structures, enabling prediction of alternative conformations with high accuracy.

This study maps the Ras/Nore1A binding landscape under different states and mutations, revealing how oncogenic changes alter interaction dynamics and provide insights for inhibitor design.

The paper develops a scalable assay and a protein language model to predict monomeric yield of proteins in E. coli, showing it can be learned and designed.

This paper investigates the structural feasibility of squalene interacting with the KEAP1 Kelch domain using molecular docking and provides a hypothesis for its role in NRF2 regulation under stress.

The paper decodes protein N-glycosylation sites from protein language model embeddings using a zero-shot framework, revealing biological insights and disease associations.

The paper investigates how the Spiral-Pincer scaffold regulates ATM kinase activation by oxidative stress, showing it restricts access to an oxidation-competent conformation.

The paper uses AI to model 109,282 human protein interactions, maps pathogenic variants to these structures, and identifies interface mutations in disease contexts.