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 developed a new methodology for high-resolution transmission electron microscopy (TEM) imaging of beam-sensitive materials. The method addresses challenges in acquiring images with low electron doses, aligning images, and determining defocus values. The processes incorporate two provisional patents and are applicable to aligning nanosized crystals and noisy images with periodic features. Why it matters: This advancement enables the study of delicate materials like MOFs at atomic resolution, with broad applications in materials science and nanotechnology.
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 and Thermo Fisher Scientific launched an Electron Microscopy Center of Excellence on May 9. The Center expands the existing partnership between KAUST and Thermo Fisher, focusing on instrument performance and R&D collaboration. It features the FEI Titan Themis Z scanning transmission electron microscope, the first installation globally. Why it matters: The center will provide advanced materials science research capabilities to KAUST researchers, industry partners, and Saudi Arabia, enhancing scientific discovery and technological advancement in the region.
KAUST hosted the Electron Microscopy Frontiers conference from December 9-11, marking five years of electron microscopy activities at the university. The symposium featured over 20 speakers from international universities and research institutions. It aimed to foster scientific collaborations and showcase KAUST's growing role as a leading electron microscopy laboratory in the Middle East. Why it matters: The conference signals KAUST's ambition to become a hub for advanced microscopy research and collaboration in the region, potentially driving innovation in materials science and bioscience.
KAUST researchers have captured the initial unwinding of DNA using cryo-electron microscopy and deep learning. The study details 15 atomic states describing how the Simian Virus 40 Large Tumor Antigen helicase unwinds DNA, revealing the coordinated roles of DNA, helicases, and ATP. The research elucidates the fundamental mechanisms of DNA replication, a cornerstone of growth and reproduction. Why it matters: This detailed understanding of helicase function could lead to advances in nanotechnology and our understanding of genetic processes.
KAUST researchers, in collaboration with Nanyang Technological University, have discovered a unique chiral structure in gold nanowires. The nanowires exhibit a Boerdijk-Coxeter-Bernal (BCB) helix structure, achieved through a seed-mediated substrate growth method, reaching a minimum diameter of 3 nanometers. High-resolution transmission electron microscopy (HRTEM) at KAUST was crucial in revealing the structure. Why it matters: This breakthrough in chiral metallic nanowire production could lead to advancements in chemical separation, sensing, and catalysis due to the unique properties of chiral crystals.
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.