DeepPhosPPI: a deep learning framework with attention-CNN and transformer for predicting phosphorylation effects on protein-protein interactions.

Protein phosphorylation regulates protein function and cellular signaling pathways, and is strongly associated with diseases, including neurodegenerative disorders and cancer. Phosphorylation plays a critical role in regulating protein activity and cellular signaling by modulating protein-protein interactions (PPIs). It alters binding affinities and interaction networks, thereby influencing biological processes and maintaining cellular homeostasis.

Numerical simulation and governance framework for multi stakeholder symbiotic evolution in digital innovation ecosystems.

The symbiotic evolution of stakeholders within digital innovation ecosystems (DIES) is crucial for achieving ecological sustainability. Thus, we integrate the Lotka-Volterra symbiotic evolution model into digital DIES research by constructing both three-actor and four-actor dynamic interaction frameworks and simulating multiple symbiotic evolution scenarios. This study indicates that: (1) The principal actors in DIES are core digital enterprises, innovation partners, digital product users, and digital platforms, with their interdependencies governed by symbiotic coefficients.

MR2CPPIS: Accurate prediction of protein-protein interaction sites based on multi-scale Res2Net with coordinate attention mechanism.

Proteins play a vital role in various biological processes and achieve their functions through protein-protein interactions (PPIs). Thus, accurate identification of PPI sites is essential. Traditional biological methods for identifying PPIs are costly, labor-intensive, and time-consuming.

A CNN-LSTM Ensemble Model for Predicting Protein-Protein Interaction Binding Sites.

Proteins commonly perform biological functions through protein-protein interactions (PPIs). The knowledge of PPI sites is imperative for the understanding of protein functions, disease mechanisms, and drug design. Traditional biological experimental methods for studying PPI sites still incur considerable drawbacks, including long experimental time and high labor costs.

Heterologous expression of fungal AcGDH alleviates ammonium toxicity and suppresses photorespiration, thereby improving drought tolerance in rice.

Drought stress can significantly affect plant growth and agricultural productivity. Thus, it is essential to explore and identify the optimal genes for the improvement of crop drought tolerance. Here, a fungal NADP(H)-dependent glutamate dehydrogenase gene (AcGDH) was isolated from Aspergillus candidus, and heterologously expressed in rice.