Literature Survey of Protein Secondary Structure Prediction

Dengan wujudnya projek jujukan DNA secara besar-besaran, teknik yang tepat untuk meramalkan struktur protein diperlukan. Masalah meramalkan struktur protein daripada jujukan DNA pada dasarnya masih belum dapat diselesaikan walaupun kajian intensif telah dilakukan selama lebih daripada tiga dekad. Dalam kertas kerja ini, teori asas struktur protein akan dibincangkan sebagai panduan umum bagi kajian peramalan struktur protein sekunder. Analisis jujukan terkini serta prinsi p yang digunakan dalam teknik-teknik tersebut akan diterangkan. Kata kunci: peramalan stuktur sekunder protein; rangkaian neural. In the wake of large-scale DNA sequencing projects, accurate tools are needed to predict protein structures. The problem of predicting protein structure from DNA sequence remains fundamentally unsolved even after more than three decades of intensive research. In this paper, fundamental theory of the protein structure of the protein structure will be presented as a general guide to protein secondary structure prediction research. An overview of the state-of-theart in sequence analysis and some princi ples of the methods invloved wil be described. Key words: protein secondary structure prediction;neural networks.

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Prediction of Protein Secondary Structure

Jurnal Teknologi ◽

10.11113/jt.v35.605 ◽

2012 ◽

Author(s):

Satya Nanda Vel Arjunan ◽

Safaai Deris ◽

Rosli Md Illias

Keyword(s):

Protein Structure ◽

Secondary Structure ◽

Structure Prediction ◽

Large Scale ◽

Secondary Structure Prediction ◽

State Of The Art ◽

Protein Structures ◽

Protein Secondary Structure ◽

Protein Secondary Structure Prediction ◽

General Guide

Dengan wujudnya projek jujukan DNA secara besar–besaran, teknik yang tepat untuk meramalkan struktur protein diperlukan. Masalah meramalkan struktur protein daripada jujukan DNA pada dasarnya masih belum dapat diselesaikan walaupun kajian intensif telah dilakukan selama lebih daripada tiga dekad. Dalam kertas kerja ini, teori asas struktur protein akan dibincangkan sebagai panduan umum bagi kajian peramalan struktur protein sekunder. Analisis jujukan terkini serta prinsip yang digunakan dalam teknik–teknik tersebut akan diterangkan. Kata kunci: Peramalan struktur sekunder protein; Rangkaian Neural In the wake of large-scale DNA sequencing projects, accurate tools are needed to predict protein structures. The problem of predicting protein structure from DNA sequence remains fundamentally unsolved even after more than three decades of intensive research. In this paper, fundamental theory of the protein structure will be presented as a general guide to protein secondary structure prediction research. An overview of the state–of–the–art in sequence analysis and some principles of the methods involved wil be described. Key words: Protein secondary structure prediction; Neural networks

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Improving protein structure prediction by deep learning and computational optimization

10.32469/10355/76251 ◽

2019 ◽

Author(s):

◽

Jie Hou

Keyword(s):

Deep Learning ◽

Protein Structure ◽

Secondary Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Protein Structures ◽

Protein Secondary Structure ◽

Scattering Data ◽

Protein Secondary Structure Prediction

Protein structure prediction is one of the most important scientific problems in the field of bioinformatics and computational biology. The availability of protein three-dimensional (3D) structure is crucial for studying biological and cellular functions of proteins. The importance of four major sub-problems in protein structure prediction have been clearly recognized. Those include, first, protein secondary structure prediction, second, protein fold recognition, third, protein quality assessment, and fourth, multi-domain assembly. In recent years, deep learning techniques have proved to be a highly effective machine learning method, which has brought revolutionary advances in computer vision, speech recognition and bioinformatics. In this dissertation, five contributions are described. First, DNSS2, a method for protein secondary structure prediction using one-dimensional deep convolution network. Second, DeepSF, a method of applying deep convolutional network to classify protein sequence into one of thousands known folds. Third, CNNQA and DeepRank, two deep neural network approaches to systematically evaluate the quality of predicted protein structures and select the most accurate model as the final protein structure prediction. Fourth, MULTICOM, a protein structure prediction system empowered by deep learning and protein contact prediction. Finally, SAXSDOM, a data-assisted method for protein domain assembly using small-angle X-ray scattering data. All the methods are available as software tools or web servers which are freely available to the scientific community.

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Hermes: an ensemble machine learning architecture for protein secondary structure prediction

10.1101/640656 ◽

2019 ◽

Author(s):

Larry Bliss ◽

Ben Pascoe ◽

Samuel K Sheppard

Keyword(s):

Machine Learning ◽

Protein Structure ◽

Secondary Structure ◽

Structure Prediction ◽

Cross Validation ◽

Secondary Structure Prediction ◽

Protein Structures ◽

Lower Boundary ◽

Protein Secondary Structure ◽

Homologous Proteins

AbstractMotivationProtein structure predictions, that combine theoretical chemistry and bioinformatics, are an increasingly important technique in biotechnology and biomedical research, for example in the design of novel enzymes and drugs. Here, we present a new ensemble bi-layered machine learning architecture, that directly builds on ten existing pipelines providing rapid, high accuracy, 3-State secondary structure prediction of proteins.ResultsAfter training on 1348 solved protein structures, we evaluated the model with four independent datasets: JPRED4 - compiled by the authors of the successful predictor with the same name, and CASP11, CASP12 & CASP13 - assembled by the Critical Assessment of protein Structure Prediction consortium who run biannual experiments focused on objective testing of predictors. These rigorous, pre-established protocols included 7-fold cross-validation and blind testing. This led to a mean Hermes accuracy of 95.5%, significantly (p<0.05) better than the ten previously published models analysed in this paper. Furthermore, Hermes yielded a reduction in standard deviation, lower boundary outliers, and reduced dependency on solved structures of homologous proteins, as measured by NEFF score. This architecture provides advantages over other pipelines, while remaining accessible to users at any level of bioinformatics experience.Availability and ImplementationThe source code for Hermes is freely available at: https://github.com/HermesPrediction/Hermes. This page also includes the cross-validation with corresponding models, and all training/testing data presented in this study with predictions and accuracy.

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By using MADALINE Learning with Back Propagation and Keras to Predict the Protein Secondary Structure

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b4964.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 4878-4882

Keyword(s):

Protein Structure ◽

Secondary Structure ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Protein Structures ◽

Back Propagation ◽

Protein Secondary Structure ◽

Sigmoid Function ◽

Crucial Component ◽

Main Motive

Understanding of intermediate protein structure prediction serves as a crucial component to find the function of residues of amino acid. In this paper, focus on the intermediate protein structure by using feed forward and feedback method and enhancing the concept of sliding window. Prediction of secondary structure is a very cosmic problem of bioinformatics. This can be reduced by predicting or unfold the protein structures if it is unfolded so that can give the great results in medical sciences. Our main motive is to improve the accuracy of secondary structures and minimize the error .Experimentally, use the Multilayer ADALINE network for learning and KERAS TENSORFLOW use for train the weight matrix and sigmoid function for calculating the resultant with back propagation. Resultant of this paper results provides more prominent results as compare to already existing methods. Those improve the accuracy of secondary structure prediction

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The Future of Protein Secondary Structure Prediction Was Invented by Oleg Ptitsyn

Biomolecules ◽

10.3390/biom10060910 ◽

2020 ◽

Vol 10 (6) ◽

pp. 910

Author(s):

Daniel Rademaker ◽

Jarek van Dijk ◽

Willem Titulaer ◽

Joanna Lange ◽

Gert Vriend ◽

...

Keyword(s):

Secondary Structure ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Protein Structures ◽

Protein Secondary Structure ◽

Prediction Method ◽

Research Field ◽

Protein Secondary Structure Prediction ◽

Learning Methods ◽

Average Percentage

When Oleg Ptitsyn and his group published the first secondary structure prediction for a protein sequence, they started a research field that is still active today. Oleg Ptitsyn combined fundamental rules of physics with human understanding of protein structures. Most followers in this field, however, use machine learning methods and aim at the highest (average) percentage correctly predicted residues in a set of proteins that were not used to train the prediction method. We show that one single method is unlikely to predict the secondary structure of all protein sequences, with the exception, perhaps, of future deep learning methods based on very large neural networks, and we suggest that some concepts pioneered by Oleg Ptitsyn and his group in the 70s of the previous century likely are today’s best way forward in the protein secondary structure prediction field.

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