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.
KAUST Discovery Associate Professor Stefan Arold has established KAUST's first structural biology lab specializing in determining the atomic 3D structure of proteins and other biological macromolecules. The lab setup involved challenges such as assembling instruments and continuing research, but the Bioscience Core Lab at KAUST and support from colleagues aided in the process. Arold's research focuses on understanding protein function through an integrated 'hybrid' approach to analyze 3D structure and function of proteins. Why it matters: This new lab enhances KAUST's capabilities in molecular biophysics and structural biology, enabling advanced research into the functions of proteins and their implications for health and disease.
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.
KAUST researchers have determined the atomic 3D structure of a key protein involved in plant stress signaling using X-ray crystallography at the SOLEIL synchrotron in France. Postdoctoral fellow Umar Farook Shahul Hameed optimized a tiny crystal of the plant enzyme for over six months. The team used the EIGER 9M detector to capture the weak diffraction pattern from the crystal. Why it matters: Understanding the interactions between proteins that communicate plant stress could lead to engineering more stress-tolerant crops, enhancing food security.
KAUST research engineer Samy Ould-Chikh is collaborating with the Néel Institute-CNRS at the European Synchrotron Radiation Facility (ESRF) in France. They are using the ESRF's high-energy synchrotron light source to study the inner structure of matter at the atomic and molecular levels. Ould-Chikh's research focuses on catalysis and functional materials, with an emphasis on renewable energy and photocatalysis. Why it matters: This collaboration highlights KAUST's engagement with leading international research institutions to advance materials science and energy research.
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.
A KAUST team discovered a simple method to fabricate microspheres using block copolymer self-assembly. The resulting particles have pH-responsive gates and a highly porous structure, granting them ultrahigh protein sorption capacity. The team leveraged their expertise in block copolymers and self-assembly to achieve this. Why it matters: This new method and the resulting particles have potential applications in biotechnology, medicine, and catalysis, advancing materials science in the region.
A KAUST alumnus presented research on using large language models for complex disease modeling and drug discovery. LLMs were trained on insurance claims of 123 million US people to model diseases and predict genetic parameters. Protein language models were developed to discover remote homologs and functional biomolecules, while RNA language models were used for RNA structure prediction and reverse design. Why it matters: This work highlights the potential of LLMs to accelerate computational biology research and drug development, with a KAUST connection.