Journal of Computer-Aided Molecular Design - The papain-like protease of SARS-CoV-2 (PLpro2) is integral to viral polyprotein cleavage and the modulation of host immune responses, positioning it as...

Nature Communications - Here, the authors present bAIes, which integrates AlphaFold2 information into a molecular dynamics framework to efficiently generate atomic-resolution ensembles of...

Proteins often exhibit subdiffusive configurational dynamics, the origins of which
are still unresolved. We investigate the impact of non-Markovian...

Identifying how drugs interact with proteins is fundamental to understanding their therapeutic effects and side effects. While numerous chemical proteomics methods exist for determining protein targets of drugs, each exhibits “blind spots,” necessitating…

Interactions between proteins and metal cations are central to biochemical processes and shape protein structures. SilE, an intrinsically disordered protein involved in bacterial silver resistance, folds into α-helices upon binding Ag+ ions. Focusing on the…

Heat shock protein 90 (Hsp90) is a key cancer drug target, yet the highly flexible N-terminal ATP-binding pocket yields seemingly conflicting crystallographic and NMR inhibitor-binding models, complicating rational design. Here we integrate >100 μs of all…

Nature Communications - Riboswitches are RNA elements that regulate gene expression through dynamic changes in secondary structure. Here, the authors reveal how the glycine tandem riboswitch...

Abstract. Recent advances in artificial intelligence have led to the development of generative models for de novo protein design. We compared 13 state-of-t

Nature - De novo designed proteins that target the transmembrane domain of G-protein-coupled receptors, created using iterative structural predictions, are able to act as agonist-positive, negative...

Antimicrobial peptides (AMPs) are emerging as potent alternatives to conventional antibiotics, yet their diverse nature due to divergent mechanisms of action hinders rational design. Here, we present an electrostatics-stratified computational framework that…

Protein engineering is limited by the inefficient search through a high-dimensional sequence space to find combinations of synergistic mutations. Traditional approaches use stepwise mutation stacking, whereas machine learning methods require extensive ...

Accurate identification of protein–ligand binding sites is an essential step in structure-based drug discovery. Herein, we present SwinSite, a deep learning framework that leverages a hybrid architecture combining 3D convolutional neural networks and hierarchical…