Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks.
The supervised learning of recurrent neural networks well-suited for prediction of protein secondary structures from the underlying amino acids sequence is studied. Modular reciprocal recurrent neural networks (MRR-NN) are proposed to model the strong correlations between adjacent secondary structure elements. Besides, a multilayer bidirectional recurrent neural network (MBR-NN) is introduced to capture the long-range intramolecular interactions between amino acids in formation of the secondary structure. The final modular prediction system is devised based on the interactive integration of the MRR-NN and the MBR-NN structures to arbitrarily engage the neighboring effects of the secondary structure types concurrent with memorizing the sequential dependencies of amino acids along the protein chain. The advanced combined network augments the percentage accuracy (Q₃) to 79.36% and boosts the segment overlap (SOV) up to 70.09% when tested on the PSIPRED dataset in three-fold cross-validation.
Babaei
Sepideh
S
Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Geranmayeh
Amir
A
Seyyedsalehi
Seyyed Ali
SA
D000465:Algorithms; D020816:Amino Acid Motifs / Q000235:genetics* / Q000502:physiology; D003199:Computer Systems; D030562:Databases, Protein; D006801:Humans; D007858:Learning; D008958:Models, Molecular; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry; D016415:Sequence Alignment / Q000295:instrumentation* / Q000379:methods; D020539:Sequence Analysis, Protein / Q000295:instrumentation* / Q000379:methods; D012984:Software; D013223:Statistics as Topic
D011506:Proteins