Protein Structure and Dynamics

Biomolecular Condensates Dictate the Folding Landscape of Proteins

This study investigates how protein folding landscapes change within biomolecular condensates using atomistic simulations and Bayesian optimization models, revealing sequence-dependent folding behavior influenced by multivalent interactions and crowding.

The Role of Linker Length and Composition in Actin Binding and Bundling by Palladin

This study investigates how the length and composition of an intrinsically disordered linker in Palladin affect its actin binding and bundling capabilities.

AFsample3: Generating and selecting multipleconformational states with Alphafold3

AFsample3 improves protein conformational sampling by generating and selecting multiple states, outperforming AlphaFold3 and AFsample2 in accuracy and diversity.

PathDiffusion: modeling protein folding pathway using evolution-guided diffusion

PathDiffusion models protein folding pathways using evolution-guided diffusion, accurately reconstructing folding events and demonstrating versatility across multiple applications.

Leveraging Unified Sequence-Structure Representations for Enhanced Protein Stability Prediction

This paper introduces ProStab-Former, a deep learning model for protein stability prediction that improves upon existing methods by integrating sequence and structure information, achieving competitive performance and efficient mutation prediction.

Targeted Computational Design of an Interleukin-7 Superkine with Enhanced Folding Efficiency and Immunotherapeutic Efficacy

This study computationally designs an IL-7 superkine, Neo-7, with improved folding efficiency and immunotherapeutic efficacy in mice.

Extending Conformational Ensemble Prediction to Multidomain Proteins and Protein Complex

This paper introduces IDPFold2, a framework for predicting conformational ensembles of proteins, including multidomain proteins and complexes, and shows it outperforms existing methods.

Substrate specificity in a designed RAS-targeting protease is coupled to active site and distal motions

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.

Constructing the ensemble of representative structures for a protein via neural-surrogate-guided MSA recombination

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

RNA polymerase II CTD Ser5 phosphorylation induces competing effects of expansion and compaction

This study uses molecular dynamics simulations to show that Ser5 phosphorylation of RNA Polymerase II CTD causes both expansion and compaction through electrostatic repulsion and cis-Pro6 stabilization.

Balancing Speed and Precision in Protein Folding: A Comparison of AlphaFold2, ESMFold, and OmegaFold

The paper compares AlphaFold2, ESMFold, and OmegaFold on new PDB structures, finding AlphaFold2 most accurate but slower, while ESMFold and OmegaFold offer faster predictions with varying strengths in specific cases.

The blobulator: a toolkit for identification and visual exploration of hydrophobic modularity in protein sequences

The blobulator toolkit identifies and visualizes hydrophobic modules in protein sequences using the blobulation algorithm, with applications across different protein types.

Improved protein binder design using -pairing targeted RFdiffusion

Conditioning RFdiffusion on edge beta-strand pairing yields specific binders to polar targets, with in silico success above 9.2% versus 0.98% for hotspot-only conditioning.

Assessing Structural Prediction Accuracy for Nanobody-Small Molecule Complexes

This study evaluates structure prediction tools for nanobody-small molecule complexes, finding AlphaFold3 performs best, with pLDDT indicating CDR accuracy and co-folding improving predictions.

Multi-objective optimization for designing structurally similar proteins with diverse sequences

GraphESMStable: A Deep Learning Framework for Protein Stability Prediction Fusing Pre-trained Sequence Models and Graph Neural Networks

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.

Computational design of conformation-biasing mutations to alter protein functions

Contrastive scoring of protein variants across alternative backbone structures with inverse folding models (Conformational Biasing) predicts mutations that shift conformational equilibria and tune effector binding, catalysis, and labeling specificity.

H+ ions and ATP reshape the conformational landscape of an RNA recognition motif and regulate its fibrillation

This study examines how H+ ions and ATP affect the conformational dynamics and aggregation behavior of FUS-RRM, revealing their role as kinetic regulators of fibrillation.

TEDLH: Domain HMMs for sensitive detection of remote homologues

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.

Conformation Driven Enhancement of Neurolysin Activity in Presence of a Small Molecule Activator

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.

Phosphoglycerate Kinase Can Adopt a Topologically Misfolded Form that is More Stable than its Native State

This study identifies a topologically misfolded form of E. coli phosphoglycerate kinase that is more stable than its native state and resists denaturation.

Towards In Situ Dynamics of DNA-bound Full-Length p53 Tetramer

This study generates the first wild-type full-length p53 tetramer model bound to DNA and analyzes its conformational dynamics and mutations in cancer mutants.

Capturing secondary structure in coarse grained intrinsically disordered proteins with simulations driven by chemical shifts.

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.

NetMD: Unsupervised Synchronization of Molecular Dynamics Trajectories via Graph Embedding and Time Warping

NetMD synchronizes and analyzes molecular dynamics trajectories using graph embedding and time warping to reveal shared dynamics and deviations across conditions.

Sticky Interactions Govern Sequence-Dependent Dynamics in Biomolecular Condensates

This study uses molecular dynamics simulations to show that sequence-dependent electrostatic interactions govern condensate viscosity and diffusivity in IDPs.

De novo design of protein nanoparticles with integrated functional motifs

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

PymolFold: A PyMOL Plugin for API-driven Structure Prediction and Quality Assessment

PymolFold integrates SOTA protein structure predictors into PyMOL, enabling users to predict, visualize, and assess protein structures with minimal technical barriers.

CyclicMPNN: Stable Cyclic Peptide Sequence Generation

The paper introduces CyclicMPNN, a model that generates energetically stable cyclic peptide sequences by fine-tuning ProteinMPNN with structural and in silico data, improving success rates in folding into generated backbones.

Structome-TM: Complementing dataset assembly for structural phylogenetics by addressing size-based biases

Structome-TM assembles datasets for structural phylogenetics by prioritizing local structural similarity to address size-based biases in homologous structure identification.

Folding the unfoldable 2: using AlphaFold and ESMFold to explore spurious proteins

The paper evaluates if newer structure prediction methods can better distinguish spurious proteins from real ones, finding improved discrimination beyond 100 amino acids.