Qing Shao

The paper benchmarks ESM-2 model precision configurations for variant-effect prediction, finding that average performance metrics hide critical failures and that asymmetric activation quantization improves accuracy.

The paper identifies PLK1-mediated phosphorylation of PHGDH as a mechanism driving serine metabolism reprogramming in advanced prostate cancer, increasing reliance on exogenous serine uptake.

This paper introduces DPLM, a dynamics-aware protein language model that integrates molecular dynamics simulations with sequence data to improve predictions of protein properties.

This study introduces ProtLoc-GRPO, a reinforcement learning method that improves subcellular localization prediction by optimizing PPI networks, achieving a 7% macro-F1 score improvement.

This study shows that PLK1-mediated PDHA1 phosphorylation impairs mitochondrial function and promotes Cr(VI)-induced lung cancer progression by disrupting energy metabolism and apoptosis.

This paper introduces S-PLM, a 3D structure-aware protein language model that improves prediction tasks by integrating structural information through efficient fine-tuning.

S-PLM improves protein language models by integrating 3D structure information through contrastive learning, outperforming sequence-only models in clustering and classification tasks.

This study shows that PLK1 phosphorylates AHR in LUAD, promoting metastasis through DIO2-TH signaling.

The paper evaluates AlphaFold2's fairness in predicting protein 3D structures by analyzing PLDDT score variability across amino acid types, secondary structures, and sequence lengths, revealing systematic biases.