Prediction of Secondary Structure of Proteins Using Sliding Window and Backpropagation Algorithm

Prediction of protein secondary structure plays a vital role in structural biology. Computational methodology is the initial step in bioinformatics to predict the 3-D secondary structure from a primary sequence and structure homology. This problem lies in the category of NP problem, and thus its time and space complexity is very high. In this paper, in a model for secondary structure prediction of proteins using sliding window and MADALINE, a multilayer feedforward network is proposed. The algorithm starts with encoding of amino acid sequence, which after passing through window is given as input to the neural network. The resultant data is in numeric format and translated back to actual secondary structure. It is observed from the results that the proposed technique provides better prediction with an accuracy more than 75%.

[1]  Mathias J. Krause,et al.  Three-dimensional protein structure prediction based on memetic algorithms , 2018, Comput. Oper. Res..

[2]  D. Ramyachitra,et al.  MODCSA-CA: A multi objective diversity controlled self adaptive cuckoo algorithm for protein structure prediction , 2017 .

[3]  Vijander Singh,et al.  Development of soft sensor for neural network based control of distillation column. , 2013, ISA transactions.

[4]  N. Gopalakrishna Kini,et al.  Ab initio protein structure prediction using GPU computing , 2016 .

[5]  Mario Inostroza-Ponta,et al.  APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction , 2015, Comput. Biol. Chem..

[6]  Luís C. Lamb,et al.  Three-dimensional protein structure prediction: Methods and computational strategies , 2014, Comput. Biol. Chem..

[7]  Vijander Singh,et al.  Levenberg–Marquardt-Based Non-Invasive Blood Glucose Measurement System , 2018 .

[8]  Liang Kong,et al.  Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou's pseudo amino acid composition. , 2014, Journal of theoretical biology.

[9]  J. Jung,et al.  Protein structure prediction. , 2001, Current opinion in chemical biology.

[10]  Jesús S. Aguilar-Ruiz,et al.  Soft computing methods for the prediction of protein tertiary structures: A survey , 2015, Appl. Soft Comput..

[11]  Shivani Agarwal,et al.  Prediction of Secondary Structure of Protein Using Support Vector Machine , 2014 .

[12]  Yan Li,et al.  A protein structural classes prediction method based on PSI-BLAST profile. , 2014, Journal of theoretical biology.

[13]  Andrzej Kloczkowski,et al.  Protein secondary structure prediction using a small training set (compact model) combined with a Complex-valued neural network approach , 2016, BMC Bioinformatics.

[14]  Jyotshna Dongardive,et al.  Reaching optimized parameter set: protein secondary structure prediction using neural network , 2016, Neural Computing and Applications.

[15]  A. A. Ibrahim,et al.  Using Neural Networks to Predict Secondary Structure for Protein Folding , 2017 .

[16]  Emir Buza,et al.  A hybrid method for prediction of protein secondary structure based on multiple artificial neural networks , 2017, 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).