Improving protein structure prediction by deep learning and computational optimization

Protein Structures ◽

Protein Secondary Structure Prediction

Scattering Data ◽

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.

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 ◽

Protein Structures ◽

Fundamental Theory ◽

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.

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 ◽

State Of The Art ◽

Protein Structures ◽

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

Review for "DNSS2 : Improved ab initio protein secondary structure prediction using advanced deep learning architectures"

10.1002/prot.26007/v2/review2 ◽

2020 ◽

Keyword(s):

Deep Learning ◽

Secondary Structure ◽

Ab Initio ◽

Structure Prediction ◽

Learning Architectures

Decision letter for "DNSS2 : Improved ab initio protein secondary structure prediction using advanced deep learning architectures"

10.1002/prot.26007/v2/decision1 ◽

2020 ◽

Keyword(s):

Deep Learning ◽

Secondary Structure ◽

Ab Initio ◽

Structure Prediction ◽

Learning Architectures

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 ◽

Protein Structures ◽

Lower Boundary ◽

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.