Protein Structure and Dynamics

This study shows IP6 binding influences the kinetic switch of the HIV-1 capsid's TVGG motif between coil and helix conformations, essential for pentamer formation.

This study quantifies charge-based transient interactions in intrinsically disordered proteins, showing they shape conformational behavior and are enriched in specific biological functions.

The paper introduces IgStrand, a universal residue numbering scheme for Ig-folds to enable systematic annotation and analysis of Ig-proteomes and Ig-interactomes across species.

This study uses molecular dynamics simulations and machine learning to analyze how mutations affect Smlt1473's binding to substrates, identifying key residues involved in substrate interaction.

DeepAssembly2 improves protein complex structure prediction using domain-domain interactions and integrates new features and models to enhance accuracy.

The paper shows how peptide backbones enhance pairwise interactions in dilute phases but weaken them in dense phases, enabling percolation in peptide-based mimics of protein condensates.

The paper develops and validates coarse-grained models of enzymes using a top-down approach, showing agreement between simulated and experimental structural measures.

This study quantitatively analyzes E. coli moonlighting proteins to identify patterns in amino acid composition and structural-functional relationships.

CONSTRUCT identifies functional or structurally important regions in protein tertiary structure by weighting substitution rates with neighboring sites' rates.

The paper introduces a Discard-and-Restart MD algorithm to efficiently sample protein transition states, enhancing structure-based drug discovery by reducing simulation times by up to 2000x.

This paper introduces LigandMPNN, a deep learning method for protein sequence design that models non-protein atomic contexts, achieving better performance than existing methods in recovering interacting residues.

This study shows that the conformational behavior of α-synuclein's disordered amino terminus varies depending on its structural context and environmental conditions.

PUNCH2 is a novel IDR predictor that improves accuracy using sequence embeddings and convolutional layers, outperforming existing methods on the CAID2 dataset.

The paper identifies a conserved arginine residue critical for maintaining interface integrity in septin protofilaments across species using molecular dynamics simulations and experimental validation.

This study improves the Martini 3 force field to accurately model protein-excipient interactions in mAb formulations using arginine and glutamate as excipients.

This study evaluates AlphaFold's ability to predict missing residues and structural disorder in proteins using PDB data and compares it with traditional disorder prediction methods.

The paper develops a machine learning model to predict phase separation propensities of disordered proteins from sequence, identifying 5% of human IDRs likely to undergo homotypic phase separation.

This study improves antibody-antigen complex prediction using AI-augmented physics-based docking, showing better performance than AlphaFold2 but lower than AlphaFold3.

This study investigates clinically relevant cytochrome c variants using bioinformatics and molecular dynamics to reveal structural and functional changes linked to thrombocytopenia 4.

The paper introduces a computational method to predict protein evolution by reconstructing a protein's evolutionary history and suggesting future amino acid substitutions to improve desired properties.

The paper introduces the Protein Design Archive (PDA), a database of designed proteins, and analyzes its contents to show increasing complexity and diversity in protein design over time.

The paper investigates the unfolding pathways of the yeast protein Yfh1 under pressure and different temperatures using high-pressure NMR, revealing similarities and differences in the mechanisms of denaturation.

This study identifies a nutrient-responsive quaternary structure remodeling in metazoan PASK through structural and evolutionary analysis, linking nutrient sensing to catalytic activation.

This paper develops a deep learning method to identify progress coordinates during weighted ensemble simulations, improving folding rate constant estimation by >3-fold in protein folding simulations.

This study models the full-length APOBEC3B structure and finds that NTD-CTD interactions increase active site opening frequency.

The paper determines the solution structure of the Z0 domain of BCL11A and identifies sequence patterns distinguishing DNA-binding from non-binding zinc-fingers.

This paper develops a transferable model to predict biophysical properties of IDPs, showing agreement with experimental data on size, conformation, and SAXS profiles.

This study analyzes the molecular features of PRAME and BAP1 proteins in melanoma using bioinformatics methods to identify potential therapeutic targets.

This study uses deep learning and coevolutionary analysis to explore how sequence variation and structural dynamics influence thermal adaptation in cytosolic malate dehydrogenase.

The paper uses deep learning to rapidly identify missense mutations in intrinsically disordered proteins that significantly alter their structure.