Zhongyue Zhang

Integrating Millions of Years of Evolutionary Information into Protein Structure Models for Function Prediction

This paper introduces ESMSCOP, a contrast-aware pre-training framework that integrates evolutionary and structural information for improved protein function prediction.

OriGene: A Self-Evolving Virtual Disease Biologist Automating Therapeutic Target Discovery

OriGene identifies therapeutic targets by integrating diverse data modalities and outperforms human experts and models in accuracy and robustness.

Capturing Natural Evolution in Function-guided RNA Design via Genomic Foundation Models

The paper introduces a zero-shot RNA design method using genomic foundation models, achieving improved fluorescence and binding affinity in RNA aptamers.

EnzymeCAGE: A Geometric Foundation Model for Enzyme Retrieval with Evolutionary Insights

EnzymeCAGE is a geometric foundation model trained on enzyme-reaction pairs to predict enzyme function, de-orphan reactions, identify catalytic sites, and reconstruct biosynthetic pathways.

AlphaFold3, a secret sauce for predicting mutational effects on protein-protein interactions

This paper benchmarks AlphaFold3 against SKEMPI to assess its ability to predict mutational effects on protein-protein interactions and compares it with force field and deep learning methods.