2.3.2 ǀ Subcellular localization, structure, function and
various properties of cpOAS1 protein
Subcellular localization was predicted by PSORT II
(https://psort.hgc.jp/form2.html). Subcellular localization of
cpOAS1 protein was also confirmed by using deep learning (Neural
networks algorithm) by the DeepLoc-1.0 server
(https://services.healthtech.dtu.dk/service.php?DeepLoc-1.0). Signal
peptide was evaluated using SignalP 5.033(https://services.healthtech.dtu.dk/service.php?SignalP-5.0) and further
confirmed by Signal-3L 3.0
(http://www.csbio.sjtu.edu.cn/bioinf/Signal-3L/). The
transmembrane helices were analyzed by the TMHMM server v.
2.034(https://services.healthtech.dtu.dk/service.php?TMHMM-2.0). The
secondary structural feature of cpOAS1 protein was computed by the SOPMA
(Self Optimized Prediction Method with Alignment)35software by keeping window width 17 and threshold 8
(https://npsa-prabi.ibcp.fr/cgibin/npsa_automat.pl?page=/NPSA/npsa_sopma.html).
The accessible surface area (ASA), structure, disorder, and phi/psi
dihedral angles of amino acids were predicted by the NetSurfP2.0
server36(https://services.healthtech.dtu.dk/service.php?NetSurfP-2.0).
Rossmann fold sequence domains and their specificity for the cofactors
FAD, NAD or NADP were predicted by Cofactory - 1.037(https://services.healthtech.dtu.dk/service.php?Cofactory-1.0).
Anti-oxidative properties of cpOAS1 were predicted by AnOxPePred - 1.0
tool using convolutional neural network38(https://services.healthtech.dtu.dk/service.php?AnOxPePred-1.0).
2.3.3 ǀ Immunological features of
cpOAS1
cpOAS1 B cell epitopes
(BepiPred Linear
Epitope Prediction 2.0) and T cell epitopes (immunogenicity prediction)
with a 50% threshold were predicted using IEDB analysis. Under the
BepiPred-2.0 server, a random forest algorithm trained on epitopes and
non-epitope amino acids determined from crystal structures was used.