Banghao Wu

This paper designs an engineered PET hydrolase, VenusPETase, using deep learning to enhance PET depolymerization efficiency and thermostability, achieving high TPA yields and complete degradation of commercial PET.

This paper uses a protein language model and structural representation tree to discover improved PET hydrolases, identifying KbPETase with higher thermal stability and degradation activity.

ProDMM is a multimodal sequence modeling framework that captures interdependencies between protein motifs, coding DNA, and regulatory elements to predict phenotypes and generate optimized regulatory sequences.

This study uses a large language model to design an antibody with enhanced alkali resistance, validated for mass production.

This study introduces Venus-TIGER, a deep learning model that identifies critical amino acids for protein expression, showing improved accuracy and transferability across datasets.

This study introduces a lightweight graph neural network for protein engineering that predicts fitness and guides the design of effective mutants with improved physicochemical properties.

The paper investigates the onset of anharmonicity in a protein and its surface water around 200 K, finding they are decoupled, with the protein's transition being intrinsic and the water's being a resolution effect.