KAUST researchers used cryogenic electron microscopy (cryo-EM) to study the 3D structure of protein complexes involved in DNA replication and repair. They investigated the interaction between the Y-family TLS polymerase Pol K and mono-ubiquitylated PCNA. The study revealed that DNA binding is required for Pol K to form a rigid, active complex with PCNA. Why it matters: Understanding these structural interactions may provide insights into cancer development and drug resistance mechanisms.
Xiao Wang from Purdue University presented research on Adversarial Contrastive Learning (AdCo) and Cooperative-adversarial Contrastive Learning (CaCo) for improved self-supervised learning. He also discussed CryoREAD, a framework for building DNA/RNA structures from cryo-EM maps, and future work in deep learning for drug discovery. Wang's algorithms have impacted molecular biology, leading to new structure discoveries published in journals like Cell and Nature Microbiology. Why it matters: The research advances AI techniques for crucial tasks in molecular biology and drug discovery, with potential applications for institutions in the GCC region focused on healthcare and biotechnology.
Daisuke Kihara from Purdue University presented a seminar at MBZUAI on using deep learning for biomolecular structure modeling. His lab is developing 3D structure modeling methods, especially for cryo-electron microscopy (cryo-EM) data. They are also working on RNA structure prediction and peptide docking using deep neural networks inspired by AlphaFold2. Why it matters: Applying advanced deep learning techniques to biomolecular structure prediction can accelerate drug discovery and our understanding of molecular functions.