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Highlights of Coronavirus Structural Studies

6 Apr 2022

Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization (PNAS)

The ability of viruses to mutate and evade the human immune system and neutralizing antibodies remains an obstacle to antiviral and vaccine development. Many neutralizing antibodies, including some approved for emergency use authorization (EUA), reduced or lost activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Here, we introduce a geometric deep learning algorithm that efficiently enhances antibody affinity to achieve broader and more potent neutralizing activity against such variants. We demonstrate the utility of our approach on a human antibody P36-5D2, which is effective against SARS-CoV-2 Alpha, Beta, and Gamma but not Delta. We show that our geometric neural network model optimizes this antibody's complementaritydetermining region (CDR) sequences to improve its binding affinity against multiple SARS-CoV-2 variants. Through iterative optimization of the CDR regions and experimental measurements, we enable expanded antibody breadth and improved potency by similar to 10to 600-fold against SARS-CoV-2 variants, including Delta. We have also demonstrated that our approach can identify CDR changes that alleviate the impact of two Omicron mutations on the epitope. These results highlight the power of our deep learning approach in antibody optimization and its potential application to engineering other protein molecules. Our optimized antibodies can potentially be developed into antibody drug candidates for current and emerging SARS-CoV-2 variants.

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Reader's Corner Archive

19 Apr 2023

Structures and mechanisms of tRNA methylation by METTL1–WDR4 (Nature)

Specific, regulated modification of RNAs is important for proper gene expression(1,2). tRNAs are rich with various chemical modifications that affect their stability and function(3,4). 7-Methylguanosine (m(7)G) at tRNA position 46 is a conserved modification that modulates steady-state tRNA levels to affect cell growth(5,6). The METTL1-WDR4 complex generates m(7)G46 in humans, and dysregulation of METTL1-WDR4 has been linked to brain malformation and multiple cancers(7-22). Here we show how METTL1 and WDR4 cooperate to recognize RNA substrates and catalyse methylation. A crystal structure of METTL1-WDR4 and cryo-electron microscopy structures of METTL1-WDR4-tRNA show that the composite protein surface recognizes the tRNA elbow through shape complementarity. The cryo-electron microscopy structures of METTL1-WDR4-tRNA with S-adenosylmethionine or S-adenosylhomocysteine along with METTL1 crystal structures provide additional insights into the catalytic mechanism by revealing the active site in multiple states. The METTL1 N terminus couples cofactor binding with conformational changes in the tRNA, the catalytic loop and the WDR4 C terminus, acting as the switch to activate m(7)G methylation. Thus, our structural models explain how post-translational modifications of the METTL1 N terminus can regulate methylation. Together, our work elucidates the core and regulatory mechanisms underlying m(7)G modification by METTL1, providing the framework to understand its contribution to biology and disease.

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