Yunda Si

NUMonomer predicts nucleic acid structures directly from sequence, outperforming existing methods without auxiliary inputs.

This paper introduces InvDNA, a deep learning method for designing single-stranded DNA sequences from backbone coordinates, achieving better sequence recovery and structural accuracy than existing methods.

PLMGraph-Inter improves inter-protein contact prediction by integrating geometric graphs and protein language models, outperforming existing methods and enhancing docking performance.

This study introduces DRN-1D2D_Inter, a deep learning method for inter-protein contact prediction that outperforms existing methods using only sequence data.

This study improves protein complex structure prediction by using taxonomically restricted interolog MSAs with AlphaFold2, achieving higher success rates than existing methods.

This paper introduces IDR-DCA, a statistical framework for detecting significant evolutionary couplings, showing improved performance over traditional methods in contact prediction and RNA structure prediction.

The paper introduces a new protein contact prediction model, DRN-1D2D, which uses hybrid residual blocks and a singularity-enhanced loss function, achieving better performance than existing methods.