Title: Deep Robust Framework for Protein Function Prediction using Variable-Length Protein Sequences,
Abstract: Amino acid sequence portrays most intrinsic form of a protein and expresses
primary structure of protein. The order of amino acids in a sequence enables a
protein to acquire a particular stable conformation that is responsible for the
functions of the protein. This relationship between a sequence and its function
motivates the need to analyse the sequences for predicting protein functions.
Early generation computational methods using BLAST, FASTA, etc. perform
function transfer based on sequence similarity with existing databases and are
computationally slow. Although machine learning based approaches are fast, they
fail to perform well for long protein sequences (i.e., protein sequences with
more than 300 amino acid residues). In this paper, we introduce a novel method
for construction of two separate feature sets for protein sequences based on
analysis of 1) single fixed-sized segments and 2) multi-sized segments, using
bi-directional long short-term memory network. Further, model based on proposed
feature set is combined with the state of the art Multi-lable Linear
Discriminant Analysis (MLDA) features based model to improve the accuracy.
Extensive evaluations using separate datasets for biological processes and
molecular functions demonstrate promising results for both single-sized and
multi-sized segments based feature sets. While former showed an improvement of
+3.37% and +5.48%, the latter produces an improvement of +5.38% and +8.00%
respectively for two datasets over the state of the art MLDA based classifier.
After combining two models, there is a significant improvement of +7.41% and
+9.21% respectively for two datasets compared to MLDA based classifier.
Specifically, the proposed approach performed well for the long protein
sequences and superior overall performance.