PaleAle 5.0: prediction of protein relative solvent accessibility by deep learning

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Title: PaleAle 5.0: prediction of protein relative solvent accessibility by deep learning
Authors: Kaleel, ManazTorrisi, MirkoMooney, CatherinePollastri, Gianluca
Permanent link: http://hdl.handle.net/10197/11324
Date: 6-Aug-2019
Online since: 2020-03-20T11:59:18Z
Abstract: Predicting the three-dimensional structure of proteins is a long-standing challenge of computational biology, as the structure (or lack of a rigid structure) is well known to determine a protein's function. Predicting relative solvent accessibility (RSA) of amino acids within a protein is a significant step towards resolving the protein structure prediction challenge especially in cases in which structural information about a protein is not available by homology transfer. Today, arguably the core of the most powerful prediction methods for predicting RSA and other structural features of proteins is some form of deep learning, and all the state-of-the-art protein structure prediction tools rely on some machine learning algorithm. In this article we present a deep neural network architecture composed of stacks of bidirectional recurrent neural networks and convolutional layers which is capable of mining information from long-range interactions within a protein sequence and apply it to the prediction of protein RSA using a novel encoding method that we shall call "clipped". The final system we present, PaleAle 5.0, which is available as a public server, predicts RSA into two, three and four classes at an accuracy exceeding 80% in two classes, surpassing the performances of all the other predictors we have benchmarked.
Funding Details: Irish Research Council
metadata.dc.description.othersponsorship: UCD School of Computer Science and Informatics
Type of material: Journal Article
Publisher: Springer
Journal: Amino Acids
Volume: 51
Issue: 9
Start page: 1289
End page: 1296
Copyright (published version): 2019 Springer
Keywords: Protein structure predictionSolvent accessibilityDeep learningEvolutionary information
DOI: 10.1007/s00726-019-02767-6
Language: en
Status of Item: Peer reviewed
Appears in Collections:Computer Science Research Collection

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