Protein Structure and Dynamics

This paper introduces AFLF, a method to generate diverse protein conformations from sequence using AlphaFold's latent space, showing it captures structural fluctuations and binding sites.

The paper integrates the MARTINI2 force field into HADDOCK3 to enable faster modelling of large biomolecular complexes with reduced computational costs while maintaining accuracy.

The paper introduces PROTEUS, a method to assess protein conformational plasticity using a generative model, showing it correlates with molecular dynamics data and identifies functionally plastic proteins.

The paper visualizes an invisible chimeric intermediate in the fold-switching pathway of a metamorphic protein using NMR techniques and shows its role as an on-pathway intermediate.

AlphaUnfold assesses structural fragility in AlphaFold3 models using high-pressure MD, finding lower pLDDT correlates with higher structural drift and RMSF.

This study presents X-ray structures of detemir in hexameric and dodecameric forms, revealing structural dynamics and identifying key residues involved in conformational changes and stability.

This study benchmarks AlphaFold2 and OpenFold3 on protein-peptide complexes, finding AlphaFold2 outperforms OpenFold3 in success rate and high-quality model proportion.

This study uses molecular dynamics simulations to investigate how vitrification affects the conformational ensemble of Trp-cage miniprotein and develops a thermodynamic inference framework to recover equilibrium populations from vitrified samples.

This paper improves AlphaFold3 by using customized MSAs and templates, achieving higher accuracy in structure prediction for monomers, multimers, and protein-ligand complexes.

This study combines normal modes with weighted ensemble simulations to generate unbiased protein pathways efficiently.

This paper develops a CHARMM-based force field for oligourea foldamers to simulate their structural integrity and performance in desalination applications.

The paper identifies a molecular grammar that predicts environmental sensitivity in intrinsically disordered protein regions using sequence features and experimental validation.

GeomMotif evaluates protein generation models' ability to preserve arbitrary structural fragments, revealing differences in performance based on structural and physicochemical context.

This paper introduces Surleton, a framework for inverse protein folding that jointly models backbone and surface geometry, improving sequence recovery and predictive confidence on benchmark datasets.

The paper uses FAMPNN to redesign SrBDH1 for increased thermostability while maintaining stereoselectivity.

The paper investigates whether proteins with native entanglements are more prone to misfolding and associated with disease, finding significant statistical associations.

The paper assesses how mutation-induced biophysical destabilization in the human structural protein interactome correlates with cancer-driving potential using fold difference metrics.

The paper introduces EntDetect, a tool to detect non-covalent lasso entanglements in protein structures, simulations, and mass spectrometry data, and demonstrates its application in identifying misfolded states.

This paper shows how ionic strength affects the structural disorder and oligomerization of the Phd transcription factor, revealing its ability to function as a conformational rheostat.

The paper redesigns calmodulin to improve its function while preserving conformational dynamics, showing that structural flexibility is essential for physiological regulation.

The paper examines age-related structural changes in yeast proteins using mass spectrometry and simulations, finding that entanglement misfolding contributes to proteome aging.

ABB4-STEROIDS generates antibody conformational ensembles with state-of-the-art accuracy, outperforming existing methods in experimental benchmarks.

This paper presents a generative framework for designing intrinsically disordered proteins using protein language models, showing that large datasets are essential for accurate conformational control.

This study identifies structural differences in EndoG proteins between two Xenopus species adapted to different temperatures, suggesting temperature-driven adaptive evolution.

The paper identifies and models interactions between transactivation domains and arginine-glycine-rich regions using proteome-scale data and simulations, revealing electrostatic mechanisms driving TF-RBP coupling.

The paper introduces ioNERDSS, a Python tool that converts 3D atomic structures into coarse-grained models for simulating macromolecular self-assembly.

This paper introduces IARA, a deep learning model that rapidly identifies optimal binding interfaces for protein binder design, reducing computational costs.

The paper identifies context-dependent sequence features that improve prediction of de novo binder success beyond structure and affinity.

This paper introduces a hybrid physics-deep learning framework for designing small-molecule binding proteins, achieving experimental success in binding affinity and structural accuracy.

The paper benchmarks MSA pairing strategies for protein-protein complex structure prediction and finds that increased MSA depth, not specific pairing, improves accuracy.