Evolution teaches neural networks to predict protein structure

2007 ◽  
pp. 207-223 ◽  
Author(s):  
Burkhard Rost
Keyword(s):  
Neural Networks ◽  
Protein Structure ◽  
Predict Protein Structure

Prediction of Structural and Functional Aspects of Protein

2014 ◽  
pp. 317-333
Author(s):  
Arun G. Ingale
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Sequence Information ◽  
Predict Protein Structure ◽  
Basic Ideas

To predict the structure of protein from a primary amino acid sequence is computationally difficult. An investigation of the methods and algorithms used to predict protein structure and a thorough knowledge of the function and structure of proteins are critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this chapter sheds light on the methods used for protein structure prediction. This chapter covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, it presents an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction, giving unique insight into the future applications of the modeled protein structures. In this chapter, current protein structure prediction methods are reviewed for a milieu on structure prediction, the prediction of structural fundamentals, tertiary structure prediction, and functional imminent. The basic ideas and advances of these directions are discussed in detail.

scholarly journals Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)

2019 ◽  
Vol 87 (12) ◽  
pp. 1141-1148 ◽  
Author(s):  
Andrew W. Senior ◽  
Richard Evans ◽  
John Jumper ◽  
James Kirkpatrick ◽  
Laurent Sifre ◽  
...  
Keyword(s):  
Neural Networks ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Deep Neural Networks ◽  
Critical Assessment

Using PconsC4 and PconsFold2 to Predict Protein Structure

2019 ◽  
Vol 66 (1) ◽  
Author(s):  
Claudio Bassot ◽  
David Menendez Hurtado ◽  
Arne Elofsson
Keyword(s):  
Protein Structure ◽  
Predict Protein Structure

Optimal neural networks for protein-structure prediction

1993 ◽  
Vol 48 (2) ◽  
pp. 1502-1515 ◽  
Author(s):  
Teresa Head-Gordon ◽  
Frank H. Stillinger
Keyword(s):  
Neural Networks ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction

Neural networks applied to protein structure

1991 ◽  
Vol 88 ◽  
pp. 2729-2729
Author(s):  
RMJ Cotterill
Keyword(s):  
Neural Networks ◽  
Protein Structure

TopModel: Template-Based Protein Structure Prediction at Low Sequence Identity Using Top-Down Consensus and Deep Neural Networks

2020 ◽  
Vol 16 (3) ◽  
pp. 1953-1967 ◽  
Author(s):  
Daniel Mulnaes ◽  
Nicola Porta ◽  
Rebecca Clemens ◽  
Irina Apanasenko ◽  
Jens Reiners ◽  
...  
Keyword(s):  
Neural Networks ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Deep Neural Networks ◽  
Top Down ◽  
Sequence Identity

Neural Networks Applied to Protein Structure

1991 ◽  
Author(s):  
Henrik Bohr ◽  
Jacob Bohr ◽  
So̸ren Brunak ◽  
Rodney M. J. Cotterill ◽  
Henrik Fredholm ◽  
...  
Keyword(s):  
Neural Networks ◽  
Protein Structure

scholarly journals Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks

2018 ◽  
Vol 35 (14) ◽  
pp. 2403-2410 ◽  
Author(s):  
Jack Hanson ◽  
Kuldip Paliwal ◽  
Thomas Litfin ◽  
Yuedong Yang ◽  
Yaoqi Zhou
Keyword(s):  
Neural Networks ◽  
Protein Structure ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Rapid Expansion ◽  
Solvent Accessible Surface Area ◽  
Supplementary Information ◽  
Test Set ◽  
Contact Maps ◽  
Unseen Data

Abstract Motivation Sequence-based prediction of one dimensional structural properties of proteins has been a long-standing subproblem of protein structure prediction. Recently, prediction accuracy has been significantly improved due to the rapid expansion of protein sequence and structure libraries and advances in deep learning techniques, such as residual convolutional networks (ResNets) and Long-Short-Term Memory Cells in Bidirectional Recurrent Neural Networks (LSTM-BRNNs). Here we leverage an ensemble of LSTM-BRNN and ResNet models, together with predicted residue-residue contact maps, to continue the push towards the attainable limit of prediction for 3- and 8-state secondary structure, backbone angles (θ, τ, ϕ and ψ), half-sphere exposure, contact numbers and solvent accessible surface area (ASA). Results The new method, named SPOT-1D, achieves similar, high performance on a large validation set and test set (≈1000 proteins in each set), suggesting robust performance for unseen data. For the large test set, it achieves 87% and 77% in 3- and 8-state secondary structure prediction and 0.82 and 0.86 in correlation coefficients between predicted and measured ASA and contact numbers, respectively. Comparison to current state-of-the-art techniques reveals substantial improvement in secondary structure and backbone angle prediction. In particular, 44% of 40-residue fragment structures constructed from predicted backbone Cα-based θ and τ angles are less than 6 Å root-mean-squared-distance from their native conformations, nearly 20% better than the next best. The method is expected to be useful for advancing protein structure and function prediction. Availability and implementation SPOT-1D and its data is available at: http://sparks-lab.org/. Supplementary information Supplementary data are available at Bioinformatics online.

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