Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction

Abstract Protein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MS...

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Autores principales:	Aashish Jain, Genki Terashi, Yuki Kagaya, Sai Raghavendra Maddhuri Venkata Subramaniya, Charles Christoffer, Daisuke Kihara
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4b802a2160964401b3d88deddd0fef82
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id	oai:doaj.org-article:4b802a2160964401b3d88deddd0fef82
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spelling	oai:doaj.org-article:4b802a2160964401b3d88deddd0fef822021-12-02T18:15:34ZAnalyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction10.1038/s41598-021-87204-z2045-2322https://doaj.org/article/4b802a2160964401b3d88deddd0fef822021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87204-zhttps://doaj.org/toc/2045-2322Abstract Protein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MSAs). In this work we present AttentiveDist, a novel approach that uses different MSAs generated with different E-values in a single model to increase the co-evolutionary information provided to the model. To determine the importance of each MSA’s feature at the inter-residue level, we added an attention layer to the deep neural network. We show that combining four MSAs of different E-value cutoffs improved the model prediction performance as compared to single E-value MSA features. A further improvement was observed when an attention layer was used and even more when additional prediction tasks of bond angle predictions were added. The improvement of distance predictions were successfully transferred to achieve better protein tertiary structure modeling.Aashish JainGenki TerashiYuki KagayaSai Raghavendra Maddhuri Venkata SubramaniyaCharles ChristofferDaisuke KiharaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Aashish Jain Genki Terashi Yuki Kagaya Sai Raghavendra Maddhuri Venkata Subramaniya Charles Christoffer Daisuke Kihara Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction
description	Abstract Protein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MSAs). In this work we present AttentiveDist, a novel approach that uses different MSAs generated with different E-values in a single model to increase the co-evolutionary information provided to the model. To determine the importance of each MSA’s feature at the inter-residue level, we added an attention layer to the deep neural network. We show that combining four MSAs of different E-value cutoffs improved the model prediction performance as compared to single E-value MSA features. A further improvement was observed when an attention layer was used and even more when additional prediction tasks of bond angle predictions were added. The improvement of distance predictions were successfully transferred to achieve better protein tertiary structure modeling.
format	article
author	Aashish Jain Genki Terashi Yuki Kagaya Sai Raghavendra Maddhuri Venkata Subramaniya Charles Christoffer Daisuke Kihara
author_facet	Aashish Jain Genki Terashi Yuki Kagaya Sai Raghavendra Maddhuri Venkata Subramaniya Charles Christoffer Daisuke Kihara
author_sort	Aashish Jain
title	Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction
title_short	Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction
title_full	Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction
title_fullStr	Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction
title_full_unstemmed	Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction
title_sort	analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4b802a2160964401b3d88deddd0fef82
work_keys_str_mv	AT aashishjain analyzingeffectofquadruplemultiplesequencealignmentsondeeplearningbasedproteininterresiduedistanceprediction AT genkiterashi analyzingeffectofquadruplemultiplesequencealignmentsondeeplearningbasedproteininterresiduedistanceprediction AT yukikagaya analyzingeffectofquadruplemultiplesequencealignmentsondeeplearningbasedproteininterresiduedistanceprediction AT sairaghavendramaddhurivenkatasubramaniya analyzingeffectofquadruplemultiplesequencealignmentsondeeplearningbasedproteininterresiduedistanceprediction AT charleschristoffer analyzingeffectofquadruplemultiplesequencealignmentsondeeplearningbasedproteininterresiduedistanceprediction AT daisukekihara analyzingeffectofquadruplemultiplesequencealignmentsondeeplearningbasedproteininterresiduedistanceprediction
_version_	1718378331036975104

Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction

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