Protein Structure and Dynamics

This paper shows how oppositely charged IDPPs can form synthetic condensates with tunable properties through sequence-encoded phase behavior and micropolarity.

The paper engineers a covalent linkage into a de novo enzyme, revealing that the modified protein rescues cells through upregulating siderophore biosynthesis rather than catalyzing iron release.

The paper shows that full-length Staphylococcal protein A fails to fold stably despite its individual domains being stable, challenging assumptions about modular protein folding and AI prediction accuracy.

The paper uses solution scattering and simulations to show that Dhh1's modular architecture allows structural heterogeneity while maintaining functional specificity.

This study improves nanobody structure prediction by using synthetic data and a classifier to filter structural accuracy, achieving better results than existing methods with lower computational cost.

This study shows that the pKa values of buried ionizable amino acids in proteins correlate with the thermodynamic stability of the protein, indicating that they reflect conformational transition energetics rather than local electrostatic properties.

This paper introduces a D-NEMD method to identify allosteric positions in proteins by analyzing long-range structural responses to alanine substitutions, validated on KPC-2 beta-lactamase.

This study investigates how lipid membrane interactions affect the conformational dynamics and energy landscape of membrane-bound TNF, showing that membrane association stabilizes the symmetric state over asymmetric conformations.

This paper examines whether diffusion models can extract physics-like energies for protein docking, finding they can recover energy funnels comparable to Rosetta in 9 out of 25 cases.

The paper combines NMR data with AlphaFlow to identify excited state conformers of pro-IL-18, revealing structural insights from transient states.

The paper identifies that AlphaFold has a higher false-positive rate for homodimers than heterodimers and improves detection using a logistic regression model incorporating structural and homology data.

FuzzAletheia maps dynamic interaction patterns of IDP-ligand complexes to guide hit-to-lead optimization, achieving a 10-fold activity enhancement in a p53 ligand.

The paper uses crystallographic ensembles to quantify conformational entropy changes in SARS-CoV-2 Macrodomain upon ligand binding, linking structural heterogeneity to binding thermodynamics.

The paper expands a Design to Data dataset with kinetic and thermal analysis of BglB variants, showing a weak link between Foldit energy and thermal stability.

This paper introduces a method to optimise protein structures more efficiently than traditional methods, achieving results comparable to constrained alpha carbon optimisation in less time.

This paper develops Slab-PTM, a module for coarse-grained models to simulate post-translational modifications in intrinsically disordered proteins, showing improved accuracy in phase behavior predictions.

AlphaFlex generates atomistic conformational ensembles for human proteins with intrinsically disordered regions, integrating AlphaFold models and implicit bilayers for transmembrane proteins.

LoL-align improves protein structure alignment by maximizing a local log-odds score, outperforming TMalign and DALI in sensitivity and speed.

This study shows that general-purpose LLMs like DeepSeek V3.2 can generate protein sequences with desired structures and improve solubility without biological training.

This study uses AI and simulations to analyze human rhomboid proteases, revealing that orphan members have narrower gates requiring more energy to open, explaining their substrate limitations.

The paper introduces CCK*, a suite of algorithms for de novo design of L- and D-peptides, integrating data-driven and physics-based methods to expand design space across chiral spaces.

This study investigates how small molecules stabilize oligomers of the androgen receptor activation domain using molecular dynamics simulations and NMR spectroscopy, revealing mechanisms that inform inhibitor design.

An all-atom diffusion model unifying biomolecular structure prediction and sequence-structure generation designs experimentally validated nanobodies, mini-proteins, and linear or cyclic peptides across 26 diverse targets.

This study identifies an allosteric pathway in laccase that enables multi-substrate recognition through conformational selection, revealed via molecular dynamics and machine learning.

The paper introduces a framework using MSA-Transformer to generate hybrid proteins between specified source and target proteins, producing sequences with higher consistency and plausibility than random baselines.

This paper introduces a Markov State Model to describe small molecule interactions with disordered proteins, identifying dynamic binding paths and druggable regions.

CIRPIN identifies protein homologs related by circular permutation using a permutation-invariant graph neural network, uncovering thousands of novel pairs and revealing natural PDZ domain permutations.

The study investigates whether synthetic crowders suppress cryptic pocket opening in a viral protein, finding negligible effects from excluded volume effects.

This paper identifies residues critical for succinimide formation in MjGATase and shows how their presence affects thermal stability.

The paper develops a pairwise amino acid energy matrix to estimate Boltzmann statistical energy for proteins from 3D structures and applies it to analyze drug binding effects.