Protein Structure and Dynamics

Accelerating protein design by scaling experimental characterization

This paper introduces scalable experimental and computational workflows to accelerate protein design by improving throughput, reducing costs, and enabling rapid validation of thousands of protein designs.

Dynamics-aware geometric learning predicts disease-associated molecular perturbations

This study introduces DynGeo-Pheno, a geometric deep learning framework that predicts disease-associated phosphosites and missense mutations by integrating protein dynamics and structural information.

IDRs lead the way: Cooperativity between intrinsically disordered regions and structured interaction domains drives enrichment in transcription condensates

The paper shows that intrinsically disordered regions (IDRs) alone are insufficient to explain the enrichment of full-length transcription proteins in condensates, suggesting a need for structured domains in binding interactions.

NMR assignments and secondary structure analysis of the human 5MP1 C-terminal domain

This study provides NMR resonance assignments and secondary structure analysis of the human 5MP1 C-terminal domain, revealing structural discrepancies between experimental data and AlphaFold predictions.

Switching Functional DNA-Binding Modes by Tuning Protein Order-Disorder Equilibria

This study shows how phosphorylation tuning affects Nhp6A's conformational equilibria and DNA binding modes, revealing a conserved mechanism for chromatin regulation.

Physically Grounded Generative Modeling of All-Atom Biomolecular Dynamics

BioKinema generates physically accurate all-atom biomolecular trajectories at lower computational cost than traditional MD simulations, enabling dynamic analysis of protein and protein-ligand systems.

β-lactoglobulin a new whey: Computational redesign improves stability and nutritional composition

The paper uses computational methods to redesign β-lactoglobulin, improving its stability and BCAA content, with experimental validation of the results.

FlyPredictome: A structural atlas of predicted protein-protein interactions in Drosophila

FlyPredictome scores 1.5 million AlphaFold-Multimer predictions of Drosophila protein pairs with a contact-aware integrated Local Interaction Score (iLIS), yielding residue-level interfaces enriched for phenotype-associated missense alleles.

Pi-Ensemble: Sequence-guided generation of interpolated protein conformational ensembles

Pi-Ensemble generates protein conformational ensembles between two structural states using sequence-guided interpolation, producing physically plausible intermediates without additional training.

Bidirectional allosteric ligand regulation in a central glycolytic enzyme

This study resolves a long-standing discrepancy between the structural and functional properties of PFK by showing how activator and inhibitor binding differentially reweights its conformational ensemble.

Temperature-Dependent Rotamer Population Shifts Govern Tryptophan Fluorescence in Proteins

This study investigates how temperature affects tryptophan fluorescence in proteins by analyzing rotamer population shifts in bovine serum albumin using molecular dynamics and quantum mechanical methods.

Aligning protein generative models with experimental fitness via Direct Preference Optimization

This paper aligns a protein generative model with experimental fitness using Direct Preference Optimization, improving stability prediction and enabling new applications.

Redesign of energetically frustrated regions rescues function in defective T4 clamp loaders

The paper identifies and redesigns energetically frustrated regions in a defective T4 clamp loader to restore function through conformational balance and stability.

Phosphorylation and pocket-binding ligands rewire the coupled networks in multidomain protein condensate

This study shows how phosphorylation and pocket-binding ligands regulate condensate organization in multidomain proteins like HDAC6 through distinct electrostatic and interaction mechanisms.

Systematic identification of pH-sensing amyloid core motifs reveals a widespread mechanism for reversible protein assembly upon stress

The paper identifies pH-sensing amyloid core motifs in yeast and human proteomes, showing they enable reversible protein assembly under stress.

FrustrAI-Seq: Scaling Local Energetic Frustration to the Protein Sequence Space

A fine-tuned ProtT5 protein language model predicts per-residue energetic frustration from sequence (Pearson r 0.86 on unseen CATH topologies) and profiles the human proteome in ~17 minutes on one GPU.

Tubulin E-hook Hexamers Reveal Charge Dependent Compaction and Transient Secondary Structure Signatures

The paper presents quantum-mechanically optimized structures of tubulin E-hook hexamers and analyzes their structural properties using computational methods.

ContrastQA: A Label-guided Graph Contrastive Learning-based approach for protein complex structure quality assessment

ContrastQA improves protein complex structure quality assessment using label-guided graph contrastive learning, achieving lower ranking losses than existing methods on CASP16 datasets.

Pro4S: prediction of protein solubility by fusing sequence, structure, and surface

Pro4S predicts protein solubility by integrating sequence, structure, and surface data, improving accuracy and reducing non-expressed proteins in de novo design.

Nucleotide-binding motifs nucleated folding of the first enzymes

This study investigates the emergence of two key cofactor-binding protein folds by analyzing their metabolic roles, structural organization, and folding mechanisms.

ESMDynamic: A Fast and Accurate Prediction of Protein Dynamic Contact Maps from Single Sequences

ESMDynamic predicts protein dynamic contact maps from sequences, outperforming existing methods with less computation and enabling large-scale analysis of conformational variability.

Ab initio side-chain sampling with PUD+ enables high-fidelity protein dynamics across AI-driven and classical simulations

This study shows that ab initio sampling of dipeptide side-chain conformations improves force field accuracy and enables high-fidelity protein dynamics in both AI-driven and classical simulations.

Hydration Energetics Shape Antibody Discrimination between Sulfotyrosine and Phosphotyrosine

This study shows how PSG2 antibody discriminates between sulfotyrosine and phosphotyrosine through a balance of direct interactions and desolvation effects.

An Artificial Intelligence Model for Translating Natural Language into Functional de Novo Proteins

This paper introduces MP4, a transformer-based model that translates natural language into functional de novo proteins, achieving high performance in sequence realism, fold quality, and function alignment.

Learning millisecond protein dynamics from what is missing in NMR spectra

Trained on missing NMR amide assignments in the Biological Magnetic Resonance Data Bank, Dyna-1 (ESM-3 layer 22 embeddings) predicts micro-to-millisecond conformational exchange seen in relaxation data.

Toward Robust Characterization of Dynamic Binding Pockets: Lessons from the HBV Capsid Assembly Modulator Site

The paper develops and validates a method to quantify dynamic protein binding pockets using the HBV capsid assembly modulator site as a model, demonstrating robust performance across conformational states.

Gaussian Accelerated Molecular Dynamics in GROMACS

This paper implements Gaussian accelerated molecular dynamics in GROMACS, showing improved sampling of conformational and binding free-energy basins compared to conventional MD simulations.

A ligand-property-guided computational framework for prioritizing de novo protein binders for small molecules

This paper develops a computational framework to prioritize de novo protein binders for small molecules based on ligand physicochemical properties and evaluates its effectiveness across different ligand types.

Biasing Conformational Sampling in AlphaFold 3 and Boltz-2 via Pair Representation Scaling

Multiplying the AlphaFold 3 or Boltz-2 pair representation by a single scalar (1+β) at the Pairformer input, without retraining, broadens sampled ensembles toward alternative states across 86 two-state targets.

Benchmarking AI-generated structural ensembles of membrane proteins against physics-based modelling

This study benchmarks AI-generated structural ensembles of membrane proteins against physics-based modeling, showing BioEmu can generate plausible conformations for large membrane proteins with lower computational cost.