Enhanced HACBLalign Method using Transitional Pattern Search and Pre-Trained Classification Model for Protein Remote Homology Detection and Fold Recognition
Abstract:
One
of the most important tasks to predict the structure of proteins is Protein
Remote Homology Detection and Fold Recognition. To do this, a Hierarchical
Attention-based Convolutional Neural Network with Bidirectional Long Short-Term
Memory called the HACBLalign algorithm was proposed by the authors, which
performs Multiple Sequence Alignments (MSAs), extracts features, and recognizes
protein homologies. But, when the quantity of Protein Sequences (PSs)
increases, the number of times the decision-making system runs also increases. To
avoid this issue, this article proposes an Enhanced HACBLalign (EHACBLalign) method
using Transitional Pattern Search (TPS) and pre-trained classification for Protein
Remote Homology Detection and Fold Recognition. During the alignment stage, the
intermediate sequences such as Hit Regions (HRs) are identified by the TPS. Then,
the HRs are extended in middle layers and utilized as a query in all TPS
iterations. Besides, the HACBLalign algorithm is applied in all intermediate
layers for generating pairwise alignments. Moreover, each pairwise alignment
between intermediate sequences is merged to get the final alignment. Further,
various characteristics are obtained from the chosen alignment and learned by
the pre-trained Convolutional Neural Network (CNN) with a softmax function for recognizing
protein remote homologies precisely. This enhances the performance of the decision-making
system for large-scale PS databases. Finally, the test outcomes exhibit that
the EHACBLalign realizes a 94.6%, 94.1%, and 93.4% accuracy on SCOP 1.53, SCOP
1.67, and superfamily corpora, respectively in Protein Remote Homology
Detection and Fold Recognition.
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