Prediction of protein structural classes by neural network (original) (raw)
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Protein Eight Secondary Structure Classes Prediction Using Artificial Neural Networks
Protein is considered the backbone of any human being. Protein is responsible for many functionalities in the human body, these functionalities differ according to the way protein amino acids (amino acids are the raw elements of protein) bond together. Then the protein forms its secondary,tertiary and quaternary structures from the amino acid structure (primary sequence) by forming hydrogen bonds. Many machine learning techniques have been used through the past decade to try to predict the protein secondary structure. The most commonly used paradigm was the Artificial Neural Networks. A lot of research was conducted in this field. This paper presents the usage of Artificial Neural Networks to predict the protein secondary structure. The difference this paper proposes is predicting the eight classes of secondary structure not only the three main classes named: alpha, beta and coil. The maximum accuracy reached is 71% which is better that other discussed methods.
Protein Structure Prediction using Artificial Neural Network
2011
Protein secondary structure prediction is a problem related to structural bioinformatics which deals with the prediction and analysis of macromolecules i.e. DNA, RNA and protein. It is an important step towards elucidating its three dimensional structure, as well as its function. Secondary structure of a protein can be predicted from its primary structures i.e. from the amino acid sequences or from the residues though challenges exists. For these four methods are used. These are Statistical Approach, Nearest Neighbor method, Neural Network Approach and Hidden Markov Model Approach. The Artificial Neural Network (ANN) approach for prediction of protein secondary structure is the most successful one among all the methods used. In this method, ANNs are trained to make them capable of performing recognition of amino acid patterns in known secondary structure units and these patterns are used to distinguish between the different types of secondary structures. This work is related to the prediction of secondary structure of proteins employing artificial neural network though it is restricted initially to three structures only.
Protein structure prediction system based on artificial neural networks
1993
Methods based on the neural network techniques are among the most accurate in the secondary structure prediction of globular proteins. Here the same principles have been used for the tertiary structure prediction problem. The map of dihedral dp and V angles is divided into 10 by 10 squares each spanning 36 by 36 degrees. By predicting the classification of each residue in the protein chain in this map a rough tertiary structure can be deduced. A complete prediction system running on a cluster of workstations and a graphical user interface was developed. Keywords: artificial neural networks, protein structure prediction, distributed computing.
Local structural motifs of protein backbones are classified by self-organizing neural networks
"Protein Engineering, Design and Selection", 1996
Important and relevant information is expected to be encoded in local structural elements of proteins. An unsupervised learning algorithm (Kohonen algorithm) was applied to the representation and unbiased classification of local backbone structures contained in a set of proteins. Training yielded a two-dimensional Kohonen feature map with 100 different structural motifs including certain helical and strand structures. All motifs were represented in a <}>-ijf-plot and some of them as a threedimensional model. The course of structural motifs along the backbone of four selected proteins (cytochrome b 5 , cytochrome b s62 , lysozyme, y crystallin) was investigated in detail. Trajectories and histograms visualizing the abundance of characteristic motifs allowed for the distinction between different types of protein overall folds. It is demonstrated how the histograms may be used to construct a structural similarity matrix for proteins. The Kohonen algorithm provides a simple procedure for classification of local protein structures independent of any a priori knowledge of leading structural motifs. Training of the Kohonen network leads to the generation of 'consensus structures' serving for the task of classification. Keywords: feature map/Kohonen network/protein similarity/ protein structure/structural universe
Protein Structure Prediction using Certain Dimension Reduction Techniques and ANN
communicated to 3rd International …, 2012
Protein structure prediction is becoming a major research area in the field of bioinformatics. The structure of a protein can be determined using experimental techniques and soft computing techniques. This paper shows an approach to predict the structure of a protein using soft computing techniques. We have used an Artificial Neural Network (ANN) and also certain image processing and statistical techniques to formulate a structure for protein prediction. It also describes some dimension reduction techniques such as Principal Component Analysis (PCA) and Self Organizing Map (SOM) and their use in the structure prediction of protein. The present technique is superior to the technique described in in terms of computational complexity.
Artificial Neural Network aided Protein Structure Prediction
International Journal of …, 2012
Protein structure prediction plays a vital role in drug design and biotechnology. Understanding protein structures is necessary to determine the function of a protein and its interaction with DNA, RNA and Enzymes. Experimental techniques such as NMR Spectroscopy and X-ray Crystallography have been the main source of information about protein structures. But these conventional methods are now replaced by Machine learning methods such as Artificial Neural Network (ANN) and Support Vector Machine (SVM)s. In this paper, ANNs are used as a two level classifier to estimate the tertiary structure of proteins. ANNs are trained to make them capable of recognizing the primary sequences and DSSP codes of protein structures and their association with the secondary structure is derived. Based on majority selection, the final secondary structure is evaluated. These secondary structures can be further used as inputs to classify between the basic tertiary folds and subclasses of tertiary folds.
Knowing that predicting the secondary structure of protein can help deeply in the protein functionality understanding and helps in multiple diseases diagnoses. Moreover, it can help in giving an accurate prediction to the tertiary structures. This forces any researcher to try multiple experiments to find ways to enhance the prediction accuracy. Through the past decade, many machine learning techniques have been used to predict the secondary structures. The main used technique was neural networks. This paper presents three different experiments that make use of artificial neural networks. The first uses a single neural network with different number of hidden layers and nodes. The second combines the output of two neural networks to enhance the accuracy. Last, the third compared to the previous two predicts not only the broad classes of secondary structure (namely; alpha, beta and coil) but predicts all the classes, then combines the result. All the experiments are based on a data set pulled out from the Rcsb protein data bank. The results of all experiments show that the highest accuracy is reached when encoding the primary sequence using binary format and use Feed-Forward network. The accuracy reached around 86% when predicting Beta strands or sheets only. Combining the results of two neural networks showed accuracy of about 83%. Moreover, merging the results of alpha and beta predictors didn't show high significance.
The usage of Neural Networks Paradigm in the prediction of protein secondary structure
—The process of predicting the secondary structure of protein is crucial in understanding the protein functionality. It is very important in understanding the protein functions and in diagnosing any disorder. This paper shows the usage of neural networks paradigm in the prediction process. It discusses the difference between applying feed forward and back propagation algorithms on the prediction accuracy. The results show that the highest accuracy is reached when presenting the primary sequence in binary format and use Feed-Forward network. The accuracy reached around 86% when predicting Beta strands or sheets only. Moreover, it was seen that predicting alpha and beta each alone and combining the results didn't show significant accuracy enhancement.