Prediction of Protein Secondary Structure With Clonal Selection Algorithm and Multilayer Perceptron

The recent studies indicate that the protein secondary structure provides very important advantages in determining the function of a protein, treating numerous diseases and drug design. Determining the secondary structure in the laboratory environment is both costly and challenging. Therefore, the prediction of protein secondary structure has been an important study field of bioinformatics and computational biology for many years. The aim of this paper was to provide a contribution to the prediction of protein secondary structure using the nature-inspired methods. The data in the first phase were trained with clonal selection algorithm (CSA) which was modeled by being inspired by the live immune system. The classification was then performed with multilayer perceptron which is one of the deep learning methods modeled by being inspired by the biological nervous system. The results obtained indicated that training of the data with CSA prior to classification contributed positively to classification success.

[1]  T. Sejnowski,et al.  Predicting the secondary structure of globular proteins using neural network models. , 1988, Journal of molecular biology.

[2]  M. Can.  Conformational Parameters for Amino Acids in Helical, β-Sheet, and Random Coil Regions Calculated from Proteins: After 40 Years , 2015, SOCO 2015.

[3]  Satoru Hayamizu,et al.  Prediction of protein secondary structure by the hidden Markov model , 1993, Comput. Appl. Biosci..

[4]  Süleyman Bilgin,et al.  A Study on the Effects of Sympathetic Skin Response Parameters in Diagnosis of Fibromyalgia Using Artificial Neural Networks , 2016, Journal of Medical Systems.

[5]  Nor Ashidi Mat Isa,et al.  Intelligent Medical Disease Diagnosis Using Improved Hybrid Genetic Algorithm - Multilayer Perceptron Network , 2013, Journal of Medical Systems.

[6]  V. Romanov,et al.  Protein misfolding and aggregation in neurodegenerative diseases: a review of pathogeneses, novel detection strategies, and potential therapeutics , 2019, Reviews in the neurosciences.

[7]  Jaap Heringa,et al.  Protein secondary structure prediction. , 2010, Methods in molecular biology.

[8]  M. Karplus,et al.  Protein secondary structure prediction with a neural network. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Gregg H. Gunsch,et al.  An artificial immune system architecture for computer security applications , 2002, IEEE Trans. Evol. Comput..

[10]  Dipankar Dasgupta,et al.  An Overview of Artificial Immune Systems and Their Applications , 1993 .

[11]  Ya Li,et al.  Protein secondary structure optimization using an improved artificial bee colony algorithm based on AB off-lattice model , 2014, Eng. Appl. Artif. Intell..

[12]  Heder S. Bernardino,et al.  Artificial Immune Systems for Optimization , 2009, Nature-Inspired Algorithms for Optimisation.

[13]  Jian Peng,et al.  Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields , 2015, Scientific Reports.

[14]  Vincenzo Cutello,et al.  An Immune Algorithm for Protein Structure Prediction on Lattice Models , 2007, IEEE Transactions on Evolutionary Computation.

[15]  Hitesh Shah,et al.  Protein Secondary Structure Prediction Using Support Vector Machines (SVMs) , 2013, 2013 International Conference on Machine Intelligence and Research Advancement.

[16]  Raghu Nandan Sengupta,et al.  Bankruptcy Prediction Using Artificial Immune Systems , 2007, ICARIS.

[17]  Jianlin Cheng,et al.  A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction , 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[18]  Victor Hugo C. De Albuquerque,et al.  Health of Things Algorithms for Malignancy Level Classification of Lung Nodules , 2018, IEEE Access.

[19]  Diptendu Sinha Roy,et al.  Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization , 2017, Journal of healthcare engineering.

[20]  Rajani K. Mudi,et al.  Prediction of protein secondary Structure using Probability Based Features and a Hybrid System , 2013, J. Bioinform. Comput. Biol..

[21]  J. Mesirov,et al.  Hybrid system for protein secondary structure prediction. , 1992, Journal of molecular biology.

[22]  Babita Pandey,et al.  Protein Secondary Structure Prediction using Feed Forward Artificial Neural Network and Perceptron , 2017 .

[23]  Vincenzo Cutello,et al.  On discrete models and immunological algorithms for protein structure prediction , 2011, Natural Computing.

[24]  J. Garnier,et al.  Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. , 1978, Journal of molecular biology.

[25]  Leandro Nunes de Castro,et al.  Artificial Immune Systems: Part I-Basic Theory and Applications , 1999 .

[26]  Jian Zhou,et al.  Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction , 2014, ICML.

[27]  Jonathan Timmis,et al.  An immune-inspired swarm aggregation algorithm for self-healing swarm robotic systems , 2016, Biosyst..

[28]  Bernard F. Buxton,et al.  Secondary structure prediction with support vector machines , 2003, Bioinform..

[29]  Lingxi Peng,et al.  An Automated Diagnosis System of Liver Disease using Artificial Immune and Genetic Algorithms , 2013, Journal of Medical Systems.

[30]  Juliette Martin,et al.  Analysis of an optimal hidden Markov model for secondary structure prediction , 2006, BMC Structural Biology.

[31]  Wen-Lian Hsu,et al.  HYPROSP: a hybrid protein secondary structure prediction algorithm--a knowledge-based approach. , 2004, Nucleic acids research.

[32]  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).

[33]  Zhang Yi,et al.  Protein secondary structure prediction by using deep learning method , 2017, Knowl. Based Syst..

[34]  L. Allison.  Fundamental Molecular Biology , 2007 .