protein structure

Rapid generation of antibody conformational ensembles using machine learning

This paper introduces Cadense, a machine learning framework that generates antibody conformational ensembles from sequence, capturing dynamic properties and improving docking accuracy.

Realism and resolution: How AI-predicted protein structures differ from experimental structures

The paper introduces WhatRes, a tool to evaluate AI-predicted protein structures by comparing their geometry to experimental structures across resolution and realism.

Glycine-Proline driven flexibility and interface-dependent conservation of six-residue {Omega}-loops in protein complexes

The paper analyzes six-residue Omega-loops in protein complexes, showing their structural constraints, sequence conservation patterns, and functional significance through sequence design and mutation mapping.

Frequency-Dependent Structural Responses of Amyloid-β Fibrils to Terahertz Pulses: A Nonequilibrium Molecular Dynamics Study

This study uses nonequilibrium molecular dynamics to show frequency-dependent structural changes in amyloid fibrils under terahertz pulses, identifying two electric-field thresholds for disruption.

Predicting the alternative conformation of a known protein structure based on the distance map of AlphaFold2

This study shows that AlphaFold2 distance maps can reveal multi-state protein structures, enabling prediction of alternative conformations with high accuracy.

RPFace: From biased structural benchmarks to generalizable RNA-protein interface prediction

RPFace predicts RNA-protein interfaces directly from sequence, outperforming existing methods on RNA interfaces and generalizing to in vivo data without pretraining.

FoldAbS: Repurposing the Protein Folding Model as a Foundation Encoder for Antibody Screening

FoldAbS repurposes protein folding models as foundation encoders to improve antibody screening by evaluating specificity, affinity, and developability in a unified framework.

AlphaFold 3 Prediction Accuracy and Reliability across RNA Structures and Protein-RNA Interfaces

This study evaluates AlphaFold 3's accuracy and reliability in predicting RNA structures and protein-RNA interfaces, finding that confidence scores do not always correlate with correct predictions.

All-Atom Structure Tokenizer: AtomLM-9b for Biomolecular Structure Prediction

The paper introduces AtomLM-9b, a language model using an all-atom structure tokenizer to predict protein structures with performance comparable to AlphaFold 3.

Mapping and Probing the Substrate Scope of a Ketoamide-Forming Splicease by Computational Design

The paper uses computational methods to design and evaluate splicease variants for generating diverse ketoamide substrates, finding that top-scoring peptides are spliced more efficiently than single-point mutants.

AI-based structural modelling and pathogenic variant mapping across the human protein interactome

The paper uses AI to model 109,282 human protein interactions, maps pathogenic variants to these structures, and identifies interface mutations in disease contexts.

Res ipSAE adhuc loquuntur: What the directionality of predicted aligned error in cofolding models can tell us about scoring diverse molecular complexes

This paper introduces ipSAE, a directional scoring framework for assessing confidence in predicted biomolecular complexes, showing improved performance over existing methods in protein-ligand and protein-DNA interactions.

ToxINsight: An Integrated Web Platform for Sequence, Structure, and Ligand Binding Analysis of Snake Venom Toxins

ToxINsight provides a web platform for analyzing snake venom toxin sequences, structures, ligand interactions, and geographical distributions to aid in antivenom development.

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.

Protein co-scientist aided simulations of the structural and functional impact of O-GlcNAc Transferase TPR domain deletions and amino acid substitutions associated with X-Linked Intellectual Disability

The paper uses computational methods to model structural and functional impacts of TPR domain deletions and amino acid substitutions in OGT linked to X-Linked Intellectual Disability.

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.

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.

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.

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.

Can Folding Models Tell Binders from Bluffers? Evidence from POISK: The Patent-Derived Antibody Dataset

The paper benchmarks structure prediction models on a new antibody dataset to assess their ability to distinguish binders from decoys, finding moderate success but limitations in epitope localization.

Design of full-length agonist and antagonist antibodies for hard targets

This paper designs full-length agonist and antagonist antibodies for hard targets using a computational pipeline, achieving nanomolar affinities and functional activity.

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.

UnibindMD: An Integrated AI and Molecular Dynamics Platform for De Novo Peptide Binder Design

UnibindMD integrates AI and molecular dynamics to design validated peptide binders by combining multiple stages and validating predictions through dynamics simulations.

NuFold RNA Structure Database: Large-Scale RNA Tertiary Structure Prediction with Confidence Metrics

The paper introduces NuFold, a tool for large-scale RNA tertiary structure prediction, and provides a database with precomputed structures and confidence metrics.

Towards a Unified Framework for Protein Interaction Site Prediction Using Hierarchical Protein Representations

PRIME-ISP predicts interaction sites across diverse biomolecular interactions using a hierarchical structural framework, outperforming sequence-based methods and matching specialized approaches without task-specific features.

DEEP: Extracting Latent Energetic Landscapes from Protein Structure Predictors

The paper introduces DEEP-Boltz, a framework that extracts energetic potentials from protein structure predictors, outperforming existing methods in predicting mutation-induced stability changes.

Structural modelling predicts that fibrocystin destabilisation is a pathogenic cause of ARPKD

The paper uses structural modeling to show that fibrocystin destabilization due to PKHD1 missense mutations is a key pathogenic mechanism in ARPKD, suggesting stabilization as a therapeutic target.

Design of enantioselective photoenzymes by scaffolding mirror image catalytic sites

This paper designs enantioselective photoenzymes using deep learning methods to precisely position a photosensitizer and substrate, achieving high stereocontrol and regiocontrol.

MIEN: a deep-learning model for pocket-conditioned glycan pose and conformation discrimination

MIEN improves glycan pose ranking by distinguishing experimentally observed conformations from non-native ones in protein-carbohydrate interactions.