Protein Structure and Dynamics

GLOM TGITO: multiscale protein modelling and multiview landscape-guided sequence discovery

The paper introduces GLOM-TGITO, a method for protein design that combines multiscale modeling with multiview landscape-guided optimization, achieving high accuracy and novel sequence discovery.

Differentiable Spin-Center Diffusion Model Enables DEER-Guided Generative Pipeline of Weighted Protein Ensembles

This paper introduces a differentiable spin-center diffusion model to generate weighted protein ensembles from DEER spectroscopy data, enabling thermodynamic interpretation of conformational ensembles.

EpiRank: partner-agnostic surface-patch retrieval for epitope prediction of protein binding interfaces

EpiRank evaluates epitope prediction by ranking surface patches on single-chain geometry, finding it correctly identifies real epitopes 64% of the time but with significant baseline overlap.

A tale of two states: AlphaFold binding pockets on the apo-holo continuum

The paper analyzes AlphaFold's predicted binding pockets to determine their similarity to apo or holo states, finding most are holo-like and showing how state affects docking success.

PHENOMS: Open-Source Software for Rust-Accelerated Hydrogen-Bond Analysis in Molecular Dynamics Simulations

PHENOMS analyzes hydrogen-bond networks in molecular dynamics simulations using Rust-accelerated code, achieving significant speedup over Python-based tools and demonstrating its utility with HDX-MS data.

VoroTIDE: Protein domain segmentation through independent hierarchical searches using Voronoi tessellation and physical self-containment

VoroTIDE segments protein domains using Voronoi contact areas and self-containment scores, achieving strong performance on benchmark datasets.

IDPForge: Deep Learning of Proteins with Global and Local Regions of Disorder

IDPForge generates all-atom ensembles of intrinsically disordered proteins and regions using a transformer diffusion model, showing good agreement with experimental data.

Semidilute Polymer Scaling Controls PEG-induced Protein Phase Separation

This study determines when PEG mass concentration can measure crowding strength across different polymer chain lengths using BSA and PEG as a depletion system.

Optimization of the NFκB-DNA ensemble leads to high affinity binding of partial consensus sites

The paper investigates how NFκB (RelA/p50) achieves high-affinity binding to partial consensus DNA sequences through structural rearrangements and dynamic interactions.

De novo Protein Generating Model via Structure-Aware Graph Neural Networks

This paper introduces a protein conformation prediction model optimized for structural accuracy using a structure-aware GNN, achieving results comparable to sequence-focused methods without sequence recovery supervision.

Neopep: Reinforcement Learning From Machine-Learned Force Field Energy for Non-Canonical Peptide Structure Prediction

Neopep uses reinforcement learning with machine-learned force field energy to predict structures of non-canonical amino acid-containing peptides, achieving lower RMSD than existing methods.

Site Specific Hydration Contributes to the Thermal Stability of Proteins and Protein-Complexes

This study investigates how site-specific hydration affects the thermal stability of proteins and protein complexes using D2O and experimental methods, revealing that stabilization depends on both hydration extent and water interaction sites.

Who contributes to whom: following the evidence behind protein language-model predictions of backbone variability

ESMfluc predicts local backbone variability in proteins using attention-based evidence decomposition, showing how sequence positions contribute to predictions of single or multi-PB states.

Discrete-time quantum walks reveal critical residues involved in long-range interactions in proteins

The paper uses discrete-time quantum walks to predict critical residues in proteins involved in long-range interactions, validated experimentally on AsLOV2.

Predicting conformational changes induced by biomolecular interactions

This paper introduces K-Fold, a method that improves prediction of conformational changes in protein complexes by modeling structural transitions from unbound to bound states.

Iterative Refinement for Structural Optimization of De Novo Protein Sequences

The paper introduces an iterative refinement method to improve de novo protein sequence structural accuracy by substituting amino acids based on structural metrics, achieving significant reductions in RMSD.

Cross-family learning transfers thermal adaptation between enzymes

This paper shows that thermal adaptation can be transferred between unrelated enzymes using a generative framework, improving thermostability without evolutionary homology.

An evolutionary strategy for highly efficient secretion of multidomain Staphylococcal protein A: N- to C-terminal stability gradient

The paper measures folding thermodynamics of SpA domains and identifies a stability gradient that facilitates efficient secretion.

De novo design of light-controlled conformational protein switches

This paper computationally designs light-responsive proteins that undergo conformational changes upon light activation, enabling precise control of biological functions.

An Interactive Bidirectional De Novo Protein Design System

This paper introduces an interactive server that combines a structure-aware GNN and ESMFold for bidirectional protein design, enabling users to analyze and validate design hypotheses without software installation.

From {triangleup}{triangleup}G Prediction to Mutation Prioritization with Conformal Prediction

The paper introduces ProbJanusDDG to prioritize mutations for experimental follow-up by using conformal prediction intervals to assess uncertainty in predicted stability changes.

Computational Design and Evaluation of De Novo Protein Minibinders Targeting Specific Sites of STAT3

The paper designs and evaluates de novo protein minibinders targeting specific STAT3 sites using AI and AlphaFold3, identifying high-confidence binders with novel sequences.

Accurate prediction of protein stability changes from single mutations using self-distillation and antisymmetric constraint strategies

DPStab predicts protein stability changes from single mutations using self-distillation and antisymmetric constraints, achieving state-of-the-art performance in ΔΔG and ΔTm prediction.

A structural screen for candidate polyphosphate binding surfaces in human membrane associated proteins

The paper develops a computational workflow to identify candidate polyphosphate binding surfaces in human membrane-associated proteins using structural and sequence-based approaches.

Rapid and Reliable Structural Modeling of Adaptive Immune Receptors Using an Optimized AlphaFold3 workflow

This paper optimizes AlphaFold3 for rapid and reliable structural modeling of antibodies and T-cell receptors, achieving 45-fold faster MSA phase and 3.6-fold faster inference.

Integrated Framework for Probing Multimodal Protein Foundation Models with Structure-Functional Interpretability Analysis in Detection of Allosteric Binding Sites

The paper evaluates multimodal protein foundation models for allosteric binding site detection, finding that dataset properties, not model complexity, primarily determine performance.

Protein Folding Interference Enables Selective Cyclin D1 Degradation

This paper identifies a folding intermediate of Cyclin D1 as a target for selective degradation, showing that pharmacological interference with this state inhibits tumor growth and overcomes resistance to CDK4/6 inhibitors.

Decoding the Physical Mechanisms of Protein-Small Molecule Association by Multiscale Kinetic Monte-Carlo Simulations

This paper develops a multiscale kinetic Monte-Carlo framework to simulate protein-small molecule association and evaluates its accuracy against experimental data.

Projected Gradient Descent for Hallucination-Based Protein Binder Design

This paper evaluates projected gradient descent (PGD) for hallucination-based protein binder design, finding it improves design efficiency and diversity compared to softmax-based methods.

Astrolabe: Programmable all-atom biomolecular design through cross-context coordination and structural intervention

Astrolabe is a programmable framework for biomolecular design that coordinates molecular contexts and structures, achieving high pass rates in binder benchmarks and enabling complex assembly designs.