Protein Structure and Dynamics

The paper classifies over 542,000 Swiss-Prot protein structure predictions into ECOD domains, expanding domain coverage and revealing novel evolutionary groups.

PySteMoDA provides an automated tool for analyzing steered molecular dynamics simulations, improving accuracy and accessibility in computational biochemistry and biophysics.

This paper designs a protein family that binds and senses small molecules using deep learning and physics-based methods, achieving nanomolar sensitivity for cortisol.

This study uses holographic imaging to show that Bovine Serum Albumin phase separation depends on higher-order oligomers and is tunable by pH.

ProTact improves protein-protein interaction prediction using a geometric graph neural network with physics-informed constraints, outperforming existing methods on benchmark datasets.

This study investigates how different crowding agents affect protein phase separation using NMR, revealing residue-level environmental influences and highlighting the importance of crowder identity.

ProteinMCP accelerates and democratizes protein engineering by automating workflows and integrating 38 tools, achieving a comprehensive fitness modeling task in 11 minutes.

NEAT-DNA is a coarse-grained DNA model that improves physical realism and sequence specificity for large-scale simulations of genome structure.

This study benchmarks deep learning methods for predicting antibody-antigen complex structures, finding that increased sampling improves accuracy but model selection remains challenging, with AlphaFold3 performing best overall.

This study uses molecular dynamics simulations to show that the FUS RGG3 IDR forms a dynamically organized dense phase with defined interaction motifs and multi-scale topology.

This paper introduces a coarse-grained simulation method to predict the viscosity of antibody solutions and their response to excipients.

This paper introduces Martini3-NMR, a framework that integrates NMR data into coarse-grained simulations to improve the accuracy of protein conformational ensembles.

DynMoCo identifies modular substructures in protein dynamics from molecular simulations by detecting time-evolving communities of residues.

This study reveals that ezrin activation involves spontaneous FERM-CTD dissociation triggered by PI(4,5)P2 binding, with phosphorylation stabilizing the open conformation.

The paper shows that function can be conserved in transcriptional activation domains without sequence conservation through evolutionary turnover of key residues.

BiHiTo introduces a tokenizer inspired by biomolecular hierarchies to improve structure reconstruction and dynamic conformation exploration, achieving better performance than existing methods.

MAProt improves protein design by combining structure-based and language model approaches using multi-agent reinforcement learning with Pareto-based optimization, achieving better performance across multiple tasks.

ProAR generates molecular dynamics trajectories with reduced error and improved conformational accuracy compared to existing methods.

This paper introduces KAMD, an enhanced sampling method that improves molecular dynamics efficiency for domain/motif level processes while maintaining equilibrium properties.

This study investigates the pH-dependent structural dynamics of neuropeptide Y using NMR and constant pH simulations, revealing pH-induced changes in helical core length and protonation states.

The paper introduces an algorithm to accurately count strands in bacterial outer membrane beta-barrels, achieving 97% accuracy on 571,760 structures from AlphaFold2.

This study identifies molecular determinants of improved CGRP peptide binding kinetics using enhanced simulations of the CLR:RAMP1 complex and its engineered variant ssCGRP.

This paper determines the solution structure of the CH-domain zinc finger from Makorin-3, revealing its metal-binding properties and potential role in protein interactions.

This study shows that pathogenic missense mutations affect Menin function through dynamic changes and allostery, not just structural destabilization.

The paper examines hydration changes in a protein complex across pH values using cryo-EM and simulations, showing significant water loss and its link to metal release.

The paper investigates how sequence variation affects the dynamics of intrinsically disordered proteins, finding that reconfiguration times are largely independent of sequence despite changes in chain dimensions.

The paper introduces intDesc-AbMut, a tool to automate the analysis of antibody mutations' effects on environmental interactions, achieving high accuracy in predicting crystal-like environments.

The paper evaluates nine LLPS predictors across diverse protein landscapes, finding significant variability in performance and highlighting the need for diverse benchmarking data.

The paper evaluates AlphaFold and ESMFold by introducing adversarial mutations and finds structural invariance and unreliable confidence metrics, suggesting reliance on templates rather than biophysical principles.

IDPFunNet predicts IDR functions using a hybrid deep learning model, outperforming existing methods in binding prediction and DFL identification across multiple benchmarks.