  Artificial Immune System (AIS-MACA) a novel computational intelligence
technique is can be used for strengthening the automated protein prediction
system with more adaptability and incorporating more parallelism to the system.
Most of the existing approaches are sequential which will classify the input
into four major classes and these are designed for similar sequences. AIS-MACA
is designed to identify ten classes from the sequences that share twilight zone
similarity and identity with the training sequences with mixed and hybrid
variations. This method also predicts three states (helix, strand, and coil)
for the secondary structure. Our comprehensive design considers 10 feature
selection methods and 4 classifiers to develop MACA (Multiple Attractor
Cellular Automata) based classifiers that are build for each of the ten
classes. We have tested the proposed classifier with twilight-zone and
1-high-similarity benchmark datasets with over three dozens of modern competing
predictors shows that AIS-MACA provides the best overall accuracy that ranges
between 80% and 89.8% depending on the dataset.
