Protein Structure Prediction by Fusion,Bayesian Methods

2011 ◽  
pp. 1330-1336
Author(s):  
Somasheker Akkaladevi ◽  
Ajay K. Katangur ◽  
Xin Luo
Keyword(s):  
Neural Network ◽  
Amino Acids ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Prediction Accuracy ◽  
Protein Secondary Structure ◽  
Decision Fusion ◽  
Amino Acid Residues ◽  
Fusion Methods ◽  
Bayesian Inference Method

Prediction of protein secondary structure (alpha-helix, beta-sheet, coil) from primary sequence of amino acids is a very challenging and difficult task, and the problem has been approached from several angles. A protein is a sequence of amino acid residues and can thus be considered as a one dimensional chain of ‘beads’ where each bead correspond to one of the 20 different amino acid residues known to occur in proteins. The length of most protein sequence ranges from 50 residues to about 1000 residues but longer proteins are also known, e.g. myosin, the major protein of muscle fibers, consists of 1800 residues (Altschul et al. 1997). Many techniques were used many researchers to predict the protein secondary structure, but the most commonly used technique for protein secondary structure prediction is the neural network (Qian et al. 1988). This chapter discusses a new method combining profile-based neural networks (Rost et al. 1993b), Simulated Annealing (SA) (Akkaladevi et al. 2005; Simons et al. 1997), Genetic algorithm (GA) (Akkaladevi et al. 2005) and the decision fusion algorithms (Akkaladevi et al. 2005). Researchers used the neural network (Hopfield 1982) combined with GA and SA algorithms, and then applied the two decision fusion methods; committee method and the correlation methods and obtained improved results on the prediction accuracy (Akkaladevi et al. 2005). Sequence profiles of amino acids are fed as input to the profile-based neural network. The two decision fusion methods improved the prediction accuracy, but noticeably one method worked better in some cases and the other method for some other sequence profiles of amino acids as input (Akkaladevi et al. 2005). Instead of compromising on some of the good solutions that could have generated from either approach, a combination of these two approaches is used for obtaining better prediction accuracy. This criterion is the basis for the Bayesian inference method (Anandalingam et al. 1989; Schmidler et al. 2000; Simons et al. 1997). The results obtained show that the prediction accuracy improves by more than 2% using the combination of the decision fusion approach and the Bayesian inference method.

scholarly journals OCLSTM: Optimized convolutional and long short-term memory neural network model for protein secondary structure prediction

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0245982
Author(s):  
Yawu Zhao ◽  
Yihui Liu
Keyword(s):  
Neural Network ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Short Term Memory ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Short Term ◽  
Protein Secondary Structure Prediction ◽  
Term Memory ◽  
Long Short Term Memory

Protein secondary structure prediction is extremely important for determining the spatial structure and function of proteins. In this paper, we apply an optimized convolutional neural network and long short-term memory neural network models to protein secondary structure prediction, which is called OCLSTM. We use an optimized convolutional neural network to extract local features between amino acid residues. Then use the bidirectional long short-term memory neural network to extract the remote interactions between the internal residues of the protein sequence to predict the protein structure. Experiments are performed on CASP10, CASP11, CASP12, CB513, and 25PDB datasets, and the good performance of 84.68%, 82.36%, 82.91%, 84.21% and 85.08% is achieved respectively. Experimental results show that the model can achieve better results.

scholarly journals Protein secondary structure prediction with context convolutional neural network

RSC Advances ◽  
2019 ◽  
Vol 9 (66) ◽  
pp. 38391-38396 ◽  
Author(s):  
Shiyang Long ◽  
Pu Tian
Keyword(s):  
Neural Network ◽  
Secondary Structure ◽  
Convolutional Neural Network ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Protein Secondary Structure Prediction ◽  
P Protein

Protein secondary structure prediction using context convolutional neural network.

scholarly journals Protein Secondary Structure Prediction

2019 ◽  
Vol 8 (9) ◽  
pp. 1370-1373
Keyword(s):  
Amino Acids ◽  
Comparative Analysis ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Three Dimensional ◽  
Protein Secondary Structure ◽  
Two Dimensional ◽  
Ionic Bond

Proteins are made up of basic units called amino acids which are held together by bonds namely hydrogen and ionic bond. The way in which the amino acids are sequenced has been categorized into two dimensional and three dimensional structures. The main advantage of predicting secondary structure is to produce tertiary structure likelihoods that are in great demand for continuous detection of proteins. This paper reviews the different methods adopted for predicting the protein secondary structure and provides a comparative analysis of accuracies obtained from various input datasets [1].

scholarly journals Prediction of 8-state protein secondary structures by 1D-Inception and BD-LSTM

2019 ◽  
Author(s):  
Aminur Rab Ratul ◽  
Marcel Turcotte ◽  
M. Hamed Mozaffari ◽  
WonSook Lee
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
3D Structure ◽  
Protein Secondary Structure ◽  
Research Area ◽  
Protein Secondary Structures ◽  
Precise Prediction

AbstractProtein secondary structure is crucial to create an information bridge between the primary structure and the tertiary (3D) structure. Precise prediction of 8-state protein secondary structure (PSS) significantly utilized in the structural and functional analysis of proteins in bioinformatics. In this recent period, deep learning techniques have been applied in this research area and raise the Q8 accuracy remarkably. Nevertheless, from a theoretical standpoint, there still lots of room for improvement, specifically in 8-state (Q8) protein secondary structure prediction. In this paper, we presented two deep learning architecture, namely 1D-Inception and BD-LSTM, to improve the performance of 8-classes PSS prediction. The input of these two architectures is a carefully constructed feature matrix from the sequence features and profile features of the proteins. Firstly, 1D-Inception is a Deep convolutional neural network-based approach that was inspired by the InceptionV3 model and containing three inception modules. Secondly, BD-LSTM is a recurrent neural network model which including bidirectional LSTM layers. Our proposed 1D-Inception method achieved 76.65%, 71.18%, 76.86%, and 74.07% Q8 accuracy respectively on benchmark CullPdb6133, CB513, CASP10, and CASP11 datasets. Moreover, BD-LSTM acquired 74.71%, 69.49%, 74.07%, and 72.37% state-8 accuracy after evaluated on CullPdb6133, CB513, CASP10, and CASP11 datasets, respectively. Both these architectures enable the efficient processing of local and global interdependencies between amino acids to make an accurate prediction of each class is very beneficial in the deep neural network. To the best of our knowledge, experiment results of the 1D-Inception model demonstrate that it outperformed all the state-of-art methods on the benchmark CullPdb6133, CB513, and CASP10 datasets.

Prediction of Protein Secondary-Structure by Monte Carlo Simulation

2008 ◽  
Vol 59 (2) ◽  
pp. 199-204
Author(s):  
Alexandru Woinaroschy
Keyword(s):  
Amino Acids ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Practical Importance ◽  
Protein Secondary Structure ◽  
Large Data ◽  
Cancer Disease

Proteins have four structural categories. The primary structure is the amino-acid sequence of the polypeptide chain. The secondary structure is the conformation, representing of the backbone (a-helix or b-sheet). The knowledge of protein structure has a paramount theoretical and practical importance (e.g. cancer disease) and a huge effort of research was devoted to this subject. Despite the fact that several methods were developed for protein secondary-structure prediction, there are no consensuses of their results. In this paper was proposed an new, original, method to investigate the influence of the number of amino acids and the percentage contents in the twenty amino acids for the prediction of protein secondary-structure, respectively Monte Carlo simulation using a multilayer neural networks. The method is very promising in connection with the use of large data bases.

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