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Yuret, D.

Publications and source records attributed to Yuret, D..

2 recordsLinked to original sources

Dynamic Coupling and Entropy Changes in KRAS G12D Mutation: Insights into Molecular Flexibility, Allostery and Function

The oncogenic G12D mutation in KRAS is a major driver of cancer progression, yet the complete mechanism by which this mutation alters protein dynamics and function remains incompletely understood. Here, we investigate how the G12D mutation alters KRASs conformational landscape and residue-residue interactions using molecular dynamics simulations coupled with entropy calculations and mutual information (MI) analysis. We demonstrate that the mutation increases local entropy at key functional residues (D12, Y32, G60, and Q61), and introduces new peaks to the Ramachandran angles, disrupting the precise structural alignment necessary for GTP hydrolysis. Notably, while individual residue entropy increases, joint entropy analysis shows a complex reorganization pattern. MI analysis identifies enhanced dynamic coupling between distant residues, suggesting that the mutation establishes new long-range interactions that stabilize the active state. These findings show how G12D mutation redefines KRASs dynamic network, leading to persistent activation through enhanced residue coupling rather than mere local disruption. Our results suggest novel therapeutic strategies focused on modulating protein dynamics rather than targeting specific binding sites, potentially offering new approaches to combat KRAS-driven cancers. HighlightsO_LIThe G12D mutation increases entropy and mutual information between residue pairs, resulting in more correlated motions due to enhanced flexibility. C_LIO_LICharacteristic features of local and long-range allostery in wild-type (WT) and G12D KRAS are determined using structural proximity and mutual information analysis. C_LIO_LIThe dynamics of KRAS interactions with its natural ligand atoms and the coordination of bridging water molecules provide insights into the changes induced by the G12D mutation. C_LI

bioinformatics↗

DeepInterface: Protein-protein interface validation using 3D Convolutional Neural Networks

MotivationProtein-protein interactions are crucial in almost all biological processes. Proteins interact through their interfaces. It is important to determine how proteins interact through interfaces to understand protein binding mechanisms and to predict new protein-protein interactions.\n\nResultsWe present DeepInterface, a deep learning based method which predicts, for a given protein complex, if the interface between the proteins of a complex is a true interface or not. The model is a 3-dimensional convolutional neural networks model and the positive datasets are obtained from all complexes in the Protein Data Bank, the negative datasets are the incorrect solutions of the docking decoys. The model analyzes a given interface structure and outputs the probability of the given structure being an interface. The accuracy of the model for several interface data sets, including PIFACE, PPI4DOCK, DOCKGROUND is approximately 88% in the validation dataset and 75% in the test dataset. The method can be used to improve the accuracy of template based PPI predictions.

bioinformatics↗