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ABSTRACT Ubiquitous and abundant in ecosystems and microbiomes, gokushoviruses constitute a Microviridae subfamily, distantly related to bacteriophages ΦX174, α3, and G4. A high-resolution cryo-EM structure of gokushovirus ΦEC6098 was determined, and the atomic model was built de novo . Although gokushoviruses lack external scaffolding and spike proteins, which extensively interact with the ΦX174 capsid protein, the core of the ΦEC6098 coat protein (VP1) displayed a similar structure. There are, however, key differences. At each ΦEC6098 icosahedral 3-fold axis, a long insertion loop formed mushroom-like protrusions, which have been noted in lower-resolution gokushovirus structures. Hydrophobic interfaces at the bottom of these protrusions may confer stability to the capsid shell. In ΦX174, the N-terminus of the capsid protein resides directly atop the 3-fold axes of symmetry; however, the ΦEC6098 N-terminus stretched across the inner surface of the capsid shell, reaching nearly to the 5-fold axis of the neighboring pentamer. Thus, this extended N-terminus interconnected pentamers on the inside of the capsid shell, presumably promoting capsid assembly, a function performed by the ΦX174 external scaffolding protein. There were also key differences between the ΦX174-like DNA-binding J proteins and its ΦEC6098 homologue VP8. As seen with the J proteins, C-terminal VP8 residues were bound into a pocket within the major capsid protein; however, its N-terminal residues were disordered, likely due to flexibility. We show that the combined location and interaction of VP8’s C-terminus and a portion of VP1’s N-terminus are reminiscent of those seen with the ΦX174 and α3 J proteins. IMPORTANCE There is a dramatic structural and morphogenetic divide within the Microviridae . The well-studied ΦX174-like viruses have prominent spikes at their icosahedral vertices, which are absent in gokushoviruses. Instead, gokushovirus major coat proteins form extensive mushroom-like protrusions at the 3-fold axes of symmetry. In addition, gokushoviruses lack an external scaffolding protein, the more critical of the two ΦX174 assembly proteins, but retain an internal scaffolding protein. The ΦEC6098 virion suggests that key external scaffolding functions are likely performed by coat protein domains unique to gokushoviruses. Thus, within one family, different assembly paths have been taken, demonstrating how a two-scaffolding protein system can evolve into a one-scaffolding protein system, or vice versa. 

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device, coupled with label-free Raman spectroscopy, holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning (ML) approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such an ML approach for analyzing Raman spectra of human and avian viruses. A convolutional neural network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A versus type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and nonenveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus [IBV]) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups—for example, amide, amino acid, and carboxylic acid—we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids, and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses.