Protein Structure and Dynamics

AlphaFast: High-throughput AlphaFold 3 via GPU-accelerated MSA construction

AlphaFast accelerates AlphaFold 3 by using GPU-accelerated MSA construction, achieving significant speedups and cost reductions while maintaining structural accuracy.

Large-scale exploration of protein space by automated NMR

This paper develops a scalable NMR pipeline to characterize protein structure and dynamics, finding that designed proteins adopt intended folds and exhibit unexpected dynamics not captured by current models.

ProtFlow: Flow Matching-based Protein Sequence Design with Comprehensive Protein Semantic Distribution Learning and High-quality Generation

ProtFlow generates high-quality protein sequences by learning the full semantic distribution of protein space, outperforming existing methods in generating antimicrobial peptides with broad activity.

Ion Mobility Mass Spectrometry Guided Modeling with AlphaFold and Rosetta Improves Protein Complex Structure Prediction

The paper improves protein complex structure prediction by integrating ion mobility mass spectrometry data with AlphaFold and Rosetta, achieving better RMSD values for many complexes.

dsRBD Redesign: A Targeted Strategy for Inhibition of RNA Helicase DHX9

This paper redesigns dsRBD domains to inhibit DHX9 helicase activity by targeting its autoregulatory interface, offering a computationally efficient strategy for therapeutic development.

Solvent Isotope Effect on the Stability of a Heterodimeric Protein

The paper investigates how solvent isotopes affect protein stability using double-chain monellin, finding that wild-type protein is more stabilized in D2O due to hydrophobic interactions.

IntelliFold-2: Surpassing AlphaFold 3 via Architectural Refinement and Structural Consistency

IntelliFold-2 improves protein structure prediction accuracy and robustness over AlphaFold 3 using architectural refinements and structural consistency, with strong gains in antibody-antigen and protein-ligand interactions.

Disulphide and sequence-encoded conformational priors guide nanobody structure prediction

This paper introduces NbForge, a nanobody folding model that improves prediction of HCDR3 blueprint and disulphide bonds, achieving accuracy comparable to large predictors with faster speed.

Mapping Bidirectional Allosteric Communication in Arf GTPases

The paper maps a bidirectional allosteric pathway in Arf GTPases that controls access to a functional molten globule state through the GDP ligand, rather than directly affecting switching rates.

Towards Understanding the Drivers of Antibody-Antigen Binding

The paper uses FTMap to analyze 50 antibody-antigen complexes, finding that paratopes are hot spot-rich and that Trp/Tyr residues drive hot spot formation.

Reading TEA leaves for de novo protein design

This paper introduces a method for fast de novo protein design using a structure-inspired alphabet and Metropolis sampling to generate sequences without known homologues.

Metadiffusion: inference-time meta-energy biasing of biomolecular diffusion models

Metadiffusion introduces a meta-energy biasing layer to diffusion models to generate diverse biomolecular conformations without retraining, matching molecular dynamics simulations.

DNA binding drives phase separation of the Gcn4 bZIP domain and reveals its conformational ensemble in the diluted and condensate phases

Out-of-equilibrium selection pressure enhances inference from protein sequence data

The paper shows that fluctuating selection pressures improve coevolution-based inference of protein structure and function from sequence data.

Rapid and Interpretable Protein Contact Map Prediction Using a Pattern-Matching Strategy

The paper introduces a pattern-matching strategy to predict protein contact maps from sequences, achieving high correlation with experimental data using minimal computational resources.

Membrane-coupled conformational switching of VISTA regulates immune checkpoint signaling via CC' loop accessibility

This study uses molecular dynamics simulations to show that VISTA's conformational switching between Up and Down states regulates its immune checkpoint function through CC' loop accessibility.

Trainable subnetworks reveal insights into structure knowledge organization in protein language models

This study introduces trainable subnetworks to analyze how protein language models factorize structural information and shows that structure prediction is sensitive to these factorized representations.

MemConverter: An Iterative Pipeline for Reprogramming Protein Localization in Membrane or Aqueous Solution

MemConverter reprograms protein localization between membrane and aqueous environments using MCP-guided sequence design and AlphaFold2 refinement, achieving stable integration or solubility.

Decoupling Structure and Property in Discrete Protein Diffusion: Alignment Dynamics and Collapse Mechanisms

This paper investigates the temporal dynamics of discrete protein diffusion models, revealing a decoupling between structural alignment and property optimization, and identifies a structural collapse regime due to Coulombic repulsion.

How minor sequence changes enable mechanistic diversity in MFS transporters? An atomic-level rationale for symport emergence in NarU

The paper uses molecular dynamics simulations to show how minor sequence changes in NarU enable symport instead of antiport compared to NarK.

Investigating the Conformational Flexibility of Staphylokinase Across Multiple Time Scales

This study investigates the conformational flexibility of staphylokinase variants to explain their reduced immunogenicity and altered properties.

Design to Data for mutants of β-glucosidase B from Paenibacillus polymyxa: Y333F, A88E, L219Q, A408H, Y173L, E340S, and Y422F

This study evaluates seven single-point mutants of β-glucosidase B using computational design and experimental analysis, finding weak correlations between predicted thermodynamic stability and enzyme properties.

Atomic resolution ensembles of intrinsically disordered proteins with Alphafold

This paper introduces bAIes, a Bayesian framework for generating accurate atomic-resolution ensembles of intrinsically disordered proteins using AlphaFold2 and molecular mechanics, showing improved accuracy over existing methods.

A free energy landscape screen reveals the disordered conformational ensemble of tropoelastin

The paper introduces FELS, a method to screen free energy landscapes for disordered proteins, and applies it to tropoelastin, revealing its conformational ensemble and resolving conflicting models.

Exploring the Potential of AlphaFold Distograms for Predicting Binding-induced Hinge Motions

The paper evaluates if AlphaFold distograms can predict binding-induced hinge motions in proteins, showing they capture flexibility not seen in predicted structures.

Designing protein/non-protein binding interactions using a full-atom diffusion model

The paper introduces AtomPaint, a full-atom diffusion model for designing protein/non-protein binding interactions, showing it outperforms random guessing in sequence recovery.

Resolving Conformational Heterogeneity in IntrinsicallyDisordered Proteins via Experimentally Guided Multi-Replica Simulations

The paper introduces MARS, a simulation framework that resolves conformational heterogeneity in IDPs by integrating experimental data from SAXS and PRE, revealing two major states in ER-NTD.

MViewEMA: Efficient Global Accuracy Estimation for Protein Complex Structural Models Using Multi-View Representation Learning

MViewEMA improves protein complex model accuracy estimation with multi-view representation learning, achieving higher efficiency and performance than previous methods.

Xeno amino acid alphabets form peptides with familiar secondary structure

The paper shows that peptides made from non-standard amino acids can form protein-like secondary structures, expanding the known sequence-structure space beyond Earth's 20 amino acids.

Multi-scale structural similarity embedding search across entire proteomes

This paper introduces a scalable method for searching 3D protein structure similarity across entire proteomes using deep learning and vector databases.