Dual neutralization of influenza virus hemagglutinin and neuraminidase by a bispecific antibody leads to improved antiviral activity.

Targeting multiple viral proteins is pivotal for sustained suppression of highly mutable viruses. In recent years, broadly neutralizing antibodies that target the influenza virus hemagglutinin and neuraminidase glycoproteins have been developed, and antibody monotherapy has been tested in preclinical and clinical studies to treat or prevent influenza virus infection. However, the impact of dual neutralization of the hemagglutinin and neuraminidase on the course of infection, as well as its therapeutic potential, has not been thoroughly tested. For this purpose, we generated a bispecific antibody that neutralizes both the hemagglutinin and the neuraminidase of influenza viruses. We demonstrated that this bispecific antibody has a dual-antiviral activity as it blocks infection and prevents the release of progeny viruses from the infected cells. We show that dual neutralization of the hemagglutinin and the neuraminidase by a bispecific antibody is advantageous over monoclonal antibody combination as it resulted an improved neutralization capacity and augmented the antibody effector functions. Notably, the bispecific antibody showed enhanced antiviral activity in influenza virus-infected mice, reduced mice mortality, and limited the virus mutation profile upon antibody administration. Thus, dual neutralization of the hemagglutinin and neuraminidase could be effective in controlling influenza virus infection.

Integrative modeling meets deep learning: Recent advances in modeling protein assemblies.

Recent progress in protein structure prediction based on deep learning revolutionized the field of Structural Biology. Beyond single proteins, it also enabled high-throughput prediction of structures of protein-protein interactions. Despite the success in predicting complex structures, large macromolecular assemblies still require specialized approaches. Here we describe recent advances in modeling macromolecular assemblies using integrative and hierarchical approaches. We highlight applications that predict protein-protein interactions and challenges in modeling complexes based on the interaction networks, including the prediction of complex stoichiometry and heterogeneity.

CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2.

Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score >0.7) 72% of the complexes among the top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding Protein Data Bank entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold's high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.

Predicting structures of large protein assemblies using combinatorial assembly algorithm and AlphaFold2.

Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score > 0.7) 72% of the complexes among the Top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding PDB entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold's high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.