Deeper Profiles and Cascaded Recurrent and Convolutional Neural Networks for state-of-the-art Protein Secondary Structure Prediction

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Title: Deeper Profiles and Cascaded Recurrent and Convolutional Neural Networks for state-of-the-art Protein Secondary Structure Prediction
Authors: Torrisi, MirkoKaleel, ManazPollastri, Gianluca
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Date: 26-Aug-2019
Online since: 2020-09-22T14:24:36Z
Abstract: Protein Secondary Structure prediction has been a central topic of research in Bioinformatics for decades. In spite of this, even the most sophisticated ab initio SS predictors are not able to reach the theoretical limit of three-state prediction accuracy (88-90%), while only a few predict more than the 3 traditional Helix, Strand and Coil classes. In this study we present tests on different models trained both on single sequence and evolutionary profile-based inputs and develop a new state-of-the-art system with Porter 5. Porter 5 is composed of ensembles of cascaded Bidirectional Recurrent Neural Networks and Convolutional Neural Networks, incorporates new input encoding techniques and is trained on a large set of protein structures. Porter 5 achieves 84% accuracy (81% SOV) when tested on 3 classes and 73% accuracy (70% SOV) on 8 classes on a large independent set. In our tests Porter 5 is 2% more accurate than its previous version and outperforms or matches the most recent predictors of secondary structure we tested. When Porter 5 is retrained on SCOPe based sets that eliminate homology between training/testing samples we obtain similar results. Porter is available as a web server and standalone program at alongside all the datasets and alignments.
Funding Details: Irish Research Council
Type of material: Journal Article
Publisher: Springer
Journal: Scientific Reports
Volume: 9
Issue: 1
Copyright (published version): 2019 the Authors
Keywords: Protein structure predictionSecondary structureDeep learningMachine learningBioinformaticsStructural bioinformatics
DOI: 10.1038/s41598-019-48786-x
Language: en
Status of Item: Peer reviewed
Appears in Collections:Computer Science Research Collection

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