Protein Structure and Dynamics

This paper shows how substrate specificity in a designed RAS-targeting protease is linked to active site and distal motions, revealing mechanisms for enhanced specificity.

This paper introduces ProCEDiS, a framework that generates representative protein conformations using neural surrogates and AlphaFold2, validated on four protein systems.

GraphESMStable predicts protein mutation-induced thermal stability changes using a deep learning framework combining pre-trained sequence models and graph neural networks, achieving state-of-the-art performance.

The paper introduces TEDLH, a library of domain HMMs derived from TED annotations, enabling sensitive detection of remote homologues and expanding coverage of sequence space and CATH superfamily diversity.

This study identifies three binding sites of a small molecule activator on Neurolysin using MD simulations and Markov state modeling, revealing how these sites modulate conformational dynamics and catalytic activity.

This study introduces NapshiftC, a neural network that uses NMR chemical shifts to improve coarse-grained simulations of intrinsically disordered proteins by incorporating secondary structure.

This paper designs de novo protein nanoparticles with integrated functional motifs, demonstrating their ability to form structured assemblies and elicit immune responses in mice.

This paper shows how evolution improves enzyme function by shifting the rate-limiting step from substrate binding to sub-state interconversion in β-lactamase OXA-48.

This study investigates the temperature dependence of triosephosphate isomerase activity using experiments and simulations, revealing non-Arrhenius behavior and dynamical differences between enzymes from different organisms.

This paper introduces DREAM, a framework that enables protein design by inverting all-atom diffusion predictors, allowing gradient-based optimization of complex modalities like cyclic peptides.

DASH samples function-related metastable states of proteins, revealing new folding states and allosteric sites, verified by experiments and crystal structures.

The paper quantifies noise suppression in protein allostery by analyzing communication pathways and their topology-aware weighting across multiple proteins.

The paper designs protein-based inhibitors to efficiently inhibit and remove chymosin from whey, enabling sustainable cheese production and enzyme control.

The paper evaluates improvements in all-atom force fields for modeling flexible peptides by comparing conformational ensembles to experimental data.

This paper investigates whether protein structure improves mutation-induced stability prediction, finding that sequence embeddings dominate and structure provides limited additive value in most cases.

The paper introduces MONDE·T, a database and webserver for non-canonical amino acids in the PDB, providing structural analysis and visualization tools.

The paper uses simulations to study how proteins partition into MED1 and FUS condensates, identifying sequence features that predict partitioning and developing a sequence-based predictor.

This study uses molecular dynamics and correlated motion analysis to reveal distal thermal activation pathways in soybean lipoxygenase, showing preferential communication through Leu546 over Leu754.

The paper compares structural networks of remote homologous protein domains from different architectures to assess how domain combinations influence structural and evolutionary changes.

This study uses molecular dynamics simulations to show how the S810L mutation in the MR alters ligand-specific agonist/antagonist behavior by changing side-chain network interactions.

This paper designs globular proteins that drive biomolecular condensation and demonstrates their use in forming dynamic condensates in cells.

ProFam is an open-source protein family language model that improves fitness prediction and design by leveraging evolutionary context through homologous sequences.

This study identifies a sequence-encoded mechanism in plant proteins that enables thermoresponsive phase separation through low-complexity domains.

This paper uses computational methods to study the structural disorder of Dsup and its interaction with the human nucleosome, revealing dynamic electrostatic shielding effects.

The paper investigates MALT1 activation dynamics using a combination of AlphaFold, MD simulations, and NMR analyses, revealing how ionic strength influences its conformational states.

This study designs a protein that precisely controls Gremlin-1 antagonism by releasing BMPs, revealing the role of interface rigidity in antagonist function.

LoMuS improves protein stability prediction using a multimodal deep learning model that combines physicochemical descriptors with low-rank adapted embeddings from a protein language model.

This study uses molecular dynamics simulations to show that CDRs significantly influence antibody framework dynamics through CDR-FW correlation networks, highlighting their impact on structure and function.

This paper introduces PS and IIOS, AI tools for analyzing and engineering molecular interactions by decomposing binding interfaces with stage-resolved force-wise insights.

This paper evaluates AlphaFold 3 and other methods in predicting α-synuclein polymorphs using a reference set of known structures and finds they can generate fibril-like models, though performance varies with template use.