Gang Xu

ProtmRNA transfers knowledge from protein language models to mRNA sequences, achieving competitive performance with less computational resources.

ProtRNA transfers knowledge from protein language models to RNA using cross-modality transfer learning, achieving comparable or better performance with less data and parameters.

OPUS-GO provides sequence-level and residue-level annotations for proteins and RNA sequences using a biological language model and modified MIL strategy, improving accuracy and interpretability.

OPUS-Design improves protein sequence design by combining 3DCNN and protein language models, achieving high sequence recovery rates and successfully designing a thermal-tolerant mutant of T4 lysozyme.

OPUS-BFactor predicts normalized protein B-factors using sequence or structural information, outperforming existing methods on benchmark datasets.

OPUS-Fold3 is a gradient-based protein folding and docking framework that generates accurate 3D structures under specified constraints, outperforming pyRosetta in side-chain modeling.

OPUS-Mut improves side-chain modeling for mutation effect analysis, showing good agreement with experimental results and correlating structural shifts with functional changes.

OPUS-Rota4 improves protein side-chain modeling by integrating deep learning predictors and constraints, achieving better accuracy than Alphafold2 on CASP14.

OPUS-X is an open-source toolkit for protein structure prediction that includes modules for torsion angles, secondary structure, solvent accessibility, contact maps, and 3D folding, outperforming existing methods in some aspects.