Antimicrobial peptides
(AMPs) are at the focus of attention due
to their therapeutic importance and developing computational tools
for the identification of efficient antibiotics from the primary structure.
Here, we utilized the 13CNMR spectral of amino acids and
clustered them into various groups. These clusters were used to build
feature vectors for the AMP sequences based on the composition, transition,
and distribution of cluster members. These features, along with the
physicochemical properties of AMPs were exploited to learn computational
models to predict active AMPs solely from their sequences. Naïve
Bayes (NB), k-nearest neighbors (KNN), support-vector machine (SVM),
random forest (RF), and eXtreme Gradient Boosting (XGBoost) were employed
to build the classification system using the collected AMP datasets
from the CAMP, LAMP, ADAM, and AntiBP databases. Our results were
validated and compared with the CAMP and ADAM prediction systems and
indicated that the synergistic combination of the 13CNMR
features with the physicochemical descriptors enables the proposed
ensemble mechanism to improve the prediction performance of active
AMP sequences. Our web-based AMP prediction platform, IAMPE, is available
at .
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