PURA syndrome-causing mutations impair PUR-domain integrity and affect P-body association.

Mutations in the human gene cause the neurodevelopmental PURA syndrome. In contrast to several other monogenetic disorders, almost all reported mutations in this nucleic acid-binding protein result in the full disease penetrance. In this study, we observed that patient mutations across PURA impair its previously reported co-localization with processing bodies.

NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics.

Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational cost arising from the vast number of parameters compared with traditional molecular mechanics. To tackle this issue, we introduce an optimized implementation of the hybrid method (NNP/MM), which combines a neural network potential (NNP) and molecular mechanics (MM).

PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks.

Deep learning has been successfully applied to structure-based protein-ligand affinity prediction, yet the black box nature of these models raises some questions. In a previous study, we presented K, a convolutional neural network that predicted the binding affinity of a given protein-ligand complex while reaching state-of-the-art performance. However, it was unclear what this model was learning.

Publisher Correction: GPCRmd uncovers the dynamics of the 3D-GPCRome.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

GPCRmd uncovers the dynamics of the 3D-GPCRome.

G-protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new three-dimensional (3D) molecular structures of GPCRs (3D-GPCRome) over the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique for exploring the conformational landscape of proteins at an atomic level.

SkeleDock: A Web Application for Scaffold Docking in PlayMolecule.

SkeleDock is a scaffold docking algorithm which uses the structure of a protein-ligand complex as a template to model the binding mode of a chemically similar system. This algorithm was evaluated in the D3R Grand Challenge 4 pose prediction challenge, where it achieved competitive performance. Furthermore, we show that if crystallized fragments of the target ligand are available then SkeleDock can outperform rDock docking software at predicting the binding mode.

LigVoxel: inpainting binding pockets using 3D-convolutional neural networks.

Motivation: Structure-based drug discovery methods exploit protein structural information to design small molecules binding to given protein pockets. This work proposes a purely data driven, structure-based approach for imaging ligands as spatial fields in target protein pockets. We use an end-to-end deep learning framework trained on experimental protein-ligand complexes with the intention of mimicking a chemist's intuition at manually placing atoms when designing a new compound.

Membrane cholesterol effect on the 5-HT receptor: Insights into the lipid-induced modulation of an antipsychotic drug target.

The serotonin 5-hydroxytryptamine 2A (5-HT ) receptor is a G-protein-coupled receptor (GPCR) relevant for the treatment of CNS disorders. In this regard, neuronal membrane composition in the brain plays a crucial role in the modulation of the receptor functioning. Since cholesterol is an essential component of neuronal membranes, we have studied its effect on the 5-HT receptor dynamics through all-atom MD simulations.