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.
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 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.
KAUST research scientist Dr. Ram Karan won two awards at the International Congress of Extremophiles 2018 for his work on extremozymes from Red Sea brine pools. His research focuses on understanding how life is possible under extreme conditions using culture-independent methods to evaluate the structure and function of polyextremophilic enzymes. Crystal structure analysis provided insights into how enzymes adapt to extreme conditions. Why it matters: This research provides insights into the possibilities of life in extreme conditions and has implications for astrobiology.
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 DeepMind researcher presented work on incorporating symmetries into machine learning models, with applications to lattice-QCD and molecular dynamics. The work includes permutation and translation-invariant normalizing flows for free-energy estimation in molecular dynamics. They also presented U(N) and SU(N) Gauge-equivariant normalizing flows for pure Gauge simulations and its extensions to incorporate fermions in lattice-QCD. Why it matters: Applying symmetry principles to generative models could improve AI's ability to model complex physical systems relevant to materials science and other fields in the region.