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

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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Literature Survey of Protein Secondary Structure Prediction

Jurnal Teknologi ◽

10.11113/jt.v34.642 ◽

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 ◽

Protein Structures ◽

Protein Secondary Structure ◽

Fundamental Theory ◽

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 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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Protein Secondary Structure Prediction: A Review of Progress and Directions

Current Bioinformatics ◽

10.2174/1574893614666191017104639 ◽

2020 ◽

Vol 15 (2) ◽

pp. 90-107 ◽

Cited By ~ 2

Author(s):

Tomasz Smolarczyk ◽

Irena Roterman-Konieczna ◽

Katarzyna Stapor

Keyword(s):

Secondary Structure ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Protein Secondary Structure ◽

Research Field ◽

Data Sets ◽

Protein Secondary Structure Prediction ◽

Data Set ◽

Class Prediction ◽

Recent Advances

Background: Over the last few decades, a search for the theory of protein folding has grown into a full-fledged research field at the intersection of biology, chemistry and informatics. Despite enormous effort, there are still open questions and challenges, like understanding the rules by which amino acid sequence determines protein secondary structure. Objective: In this review, we depict the progress of the prediction methods over the years and identify sources of improvement. Methods: The protein secondary structure prediction problem is described followed by the discussion on theoretical limitations, description of the commonly used data sets, features and a review of three generations of methods with the focus on the most recent advances. Additionally, methods with available online servers are assessed on the independent data set. Results: The state-of-the-art methods are currently reaching almost 88% for 3-class prediction and 76.5% for an 8-class prediction. Conclusion: This review summarizes recent advances and outlines further research directions.

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