Use of a neural network secondary structure prediction to define targets for mutagenesis of herpes simplex virus glycoprotein B.

Herpes simplex virus glycoprotein B (HSV gB) is essential for penetration of virus into cells, for cell-to-cell spread of virus, and for cell-cell fusion. Every member of the family Herpesviridae has a gB homolog, underlining its importance. The antigenic structure of gB has been studied extensively, but little is known about which regions of the protein are important for its roles in virus entry and spread. In contrast to successes with other HSV glycoproteins, attempts to map functional domains of gB by insertion mutagenesis have been largely frustrated by the misfolding of most mutants. The present study shows that this problem can be overcome by targeting mutations to the loop regions that connect alpha-helices and beta-strands, avoiding the helices and strands themselves. The positions of loops in the primary sequence were predicted by the PHD neural network procedure, using a multiple sequence alignment of 19 alphaherpesvirus gB sequences as input. Comparison of the prediction with a panel of insertion mutants showed that all mutants with insertions in predicted alpha-helices or beta-strands failed to fold correctly and consequently had no activity in virus entry; in contrast, half the mutants with insertions in predicted loops were able to fold correctly. There are 27 predicted loops of four or more residues in gB; targeting of mutations to these regions will minimize the number of misfolded mutants and maximize the likelihood of identifying functional domains of the protein.