RESEARCH AREAS

RESEARCH INTERESTS

• AI and deep learning

• Protein and RNA structure prediction

• Protein and peptide design

• AI-based drug discovery

RESEARCH PROJECTS

Protein folding and protein structure prediction

Deciphering the structure and function of proteins is pivotal in modern biology and medicine. Our work focuses on pioneering computational methods, integrating advanced AI techniques and physics-based force fields, to precisely model protein structure and function. A key objective is uncovering the fundamental interplay between protein sequence, structure, and function.

• Computational Biology

AI-based protein design and drug discovery

Nature proteins exhibit limited structural folds and functions, shaped over billions of years of evolution. This project seeks to leverage AI and deep learning to craft novel protein sequences, surpassing natural constraints. The computationally designed proteins and peptides hold promise as drugs, offering innovative treatments for diverse human diseases, including cancer and Alzheimer's.

• Computational Biology

RNA structure prediction and small-molecule drug design

Non-coding RNAs, single-chain nucleotide biomolecules, play crucial roles as regulators of diverse biological processes. Our development of advanced AI and deep-learning algorithms, for precise RNA structure prediction and small molecule interactions, promises over a 10-fold expansion in drug design possibilities, surpassing traditional protein-targeting drug discovery approaches.

• Artificial Intelligence

Deep Learning-Based Pipeline for High-Accuracy RNA Structure Prediction

Predicting RNA structures is crucial for drug discovery, but current methods lack accuracy. This project uses AI trained on vast RNA data to create precise 3D models from RNA sequences. This could significantly improve drug development and impact human health.

• Artificial Intelligence

RESEARCH GROUPS

TEACHING INNOVATIONS

SELECTED PUBLICATIONS

• (A full list of Dr. Yang Zhang's publications can be seen at https://scholar.google.com/citations?user=MtBs-kMAAAAJ)
• W Zheng, Q Wuyun, Y Li, C Zhang, PL Freddolino, Y Zhang. Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data. Nature Methods, https://doi.org/10.1038/s41592-023-02130-4 (2024).
• R Pearce, X Huang, GS Omenn, Y Zhang. De novo protein fold design through sequence-independent fragment assembly simulations. PNAS, 120: e2208275120 (2023).
• Y Li, C Zhang, C Feng, R Pearce, PL Freddolino, Y Zhang. Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction. Nature Communications, 14: 5745 (2023).
• C Zhang, M Shine, AM Pyle, Y Zhang. US-align: Universal structure alignment of proteins, nucleic acids and macromolecular complexes. Nature Methods, 19: 1109-1115 (2022).
• X Zhou, W Zheng, Y Li, R Pearce, C Zhang, EW Bell, G Zhang, Y Zhang. I-TASSER-MTD: A deep-learning based platform for multi-domain protein structure and function prediction. Nature Protocols, 17: 2326-2353 (2022).
• X Zhou, Y Li, C Zhang, W Zheng, G Zhang, Y Zhang. Progressive assembly of multi-domain protein structures from cryo-EM density maps. Nature Computational Science, 2: 265-275 (2022).
• X Zhang, B Zhang, PL Freddolino, Y Zhang. CR-I-TASSER: Assemble protein structures from cryo-EM density maps using deep convolutional neural networks. Nature Methods, 19: 195-204 (2022).
• SM Mortuz, W Zheng, C Zhang, Y Li, R Pearce, Y Zhang. Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions. Nature Communications, 12: 5011 (2021).
• P Yang, W Zheng, K Ning, Y Zhang. Decoding the link of microbiome niches with homologous sequences enables accurately targeted protein structure prediction. PNAS, 118: e2110828118 (2021).

AWARDS & HONOURS

MODULES TAUGHT