SARS-CoV-2 Interactome 3D: A Web interface for 3D visualization and analysis of SARS-CoV-2-human mimicry and interactions.

SUMMARY: We present a web-based server for navigating and visualizing possible interactions between SARS-CoV-2 and human host proteins. The interactions are obtained from HMI_Pred which relies on the rationale that virus proteins mimic host proteins. The structural alignment of the viral protein with one side of the human protein-protein interface determines the mimicry. The mimicked human proteins and predicted interactions, and the binding sites are presented. The user can choose one of the 18 SARS-CoV-2 protein structures and visualize the potential 3D complexes it forms with human proteins. The mimicked interface is also provided. The user can superimpose two interacting human proteins in order to see whether they bind to the same site or different sites on the viral protein. The server also tabulates all available mimicked interactions together with their match scores and number of aligned residues. This is the first server listing and cataloging all interactions between SARS-CoV-2 and human protein structures, enabled by our innovative interface mimicry strategy. AVAILABILITY AND IMPLEMENTATION: The server is available at https://interactome.ku.edu.tr/sars/.

AI-Based Metamaterial Design.

The use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed.