Protein Structure and Dynamics

The paper improves collective variables for biased sampling by iteratively refining them using enhanced sampling data and a probabilistic clustering model.

The paper measures catalytic constants for diverse Adenylate Kinase sequences and mutants, revealing distinct catalytic peaks and challenging existing hypotheses about thermophilic enzyme kinetics.

ExEnDiff generates protein conformations using experimental data as a prior, improving diversity and physical realism over existing methods.

This paper introduces RBPseg, a method to predict high-confidence tail fiber structures using segmentation and deep learning, and validates it with experimental data and structural classification.

This paper introduces BADASS, an optimization algorithm for protein design that improves fitness prediction and sequence diversity compared to existing methods.

This paper analyzes sequence-dynamics relationships in subtilisin-like serine proteases to understand thermal adaptation mechanisms, revealing structural features that contribute to thermophilic and psychrophilic stability.

This paper introduces HELM, a hybrid clustering method combining k-means efficiency with hierarchical flexibility, and demonstrates its application to protein-DNA and protein folding simulations.

The paper improves protein complex structure prediction by integrating AlphaFold2 and 3 with enhanced methods in MULTICOM4, achieving high TM-scores and DockQ scores in CASP16.

The paper compresses protein sequence and structure representations, showing that compressed embeddings retain structural accuracy and enable downstream applications.

This study uses molecular dynamics simulations to show that covalent adducts of small molecules with the disordered androgen receptor transactivation domain lead to heterogeneous, collapsed conformations.

The paper introduces a method to identify key residues driving conformational changes in proteins using multiple perturbations, with applications in biosensor design.

The paper analyzes short-range cross-links to reveal their correlation with protein secondary structure and introduces X-SPAN for integrating XL-MS data with AlphaFold predictions.

DPCstruct classifies AlphaFold2-predicted protein structures into domain clusters, recovering known folds and identifying novel structural motifs.

The paper introduces a simple model using residue conservation and solvent accessibility to predict mutational effects, showing it performs as well as complex deep learning models.

This study uses molecular dynamics simulations to analyze REV-ERBβ ligand-binding domain dynamics and antagonist effects on NCoR peptide binding, informing rational drug design.

The paper develops a method to predict intermolecular interactions between intrinsically disordered regions and partner proteins using chemical physics principles.

This paper designs dynamic proteins with switch-like behavior using deep learning, validated by structure solving and simulations showing controllable conformational changes.

The paper identifies two co-factors that enhance Pfu DNA polymerase performance using NMR-based hot spot residue screening without prior structural data.

This study evaluates AlphaFold's ability to model nanobody-peptide interactions, finding moderate success in some cases but limitations when translating to complex contexts.

This paper introduces a method called 'temperature vaulting' to screen inhibitors targeting kinases in their non-native state, demonstrating its effectiveness with the inhibitor FINDY (1) against DYRK1A.

This study quantifies the cooperativity of protein backbone hydrogen bonds using quantum chemistry calculations and evaluates force fields' ability to reproduce cooperative effects.

The paper uses molecular simulations to study how phosphorylation by CK1δ affects TDP-43 condensates and aggregation, finding that phosphorylation patterns depend on sequence and charge rather than position.

The paper demonstrates the feasibility of engineering a transmembrane histidine kinase with a de novo metal-binding sensor domain that transmits binding information.

ASMC is a redesigned workflow for analyzing enzyme active sites, improving clustering and prediction through updated methods and unified code.

This paper introduces a new computational method for simulating protein conformational transitions in explicit environments and demonstrates its application across multiple protein types.

This paper designs a de novo ATPase with a P-loop motif, showing high thermal stability and ATPase activity, but not matching natural enzymes' efficiency.

The paper introduces COBY, a coarse-grained system builder for creating complex membrane systems with improved accuracy using Martini molecule models.

This study models DNA binding modes of ComP using multiple tools, identifying two robust binding modes that enhance understanding of ComP-DUS interactions.

This paper uses machine learning to classify molecular dynamics simulations of viral capsid assembly intermediates, distinguishing between apo and CAM-bound states with high accuracy.

SHINE improves pathway ensemble analysis by integrating n-ary similarity, cheminformatics tools, and hierarchical clustering for MD simulations.