SeRenDIP: SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions

Qingzhen Hou, Paul F.G. De Geest, Christian J. Griffioen, Sanne Abeln, Jaap Heringa, K. Anton Feenstra

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

MOTIVATION: Interpretation of ubiquitous protein sequence data has become a bottleneck in biomolecular research, due to a lack of structural and other experimental annotation data for these proteins. Prediction of protein interaction sites from sequence may be a viable substitute. We therefore recently developed a sequence-based random-forest method for protein-protein interface prediction, which yielded a significantly increased performance than other methods on both homomeric and heteromeric protein-protein interactions. Here we present a webserver that implements this method efficiently.

RESULTS: With the aim of accelerating our previous approach, we obtained sequence conservation profiles by re-mastering the alignment of homologous sequences found by PSI-BLAST. This yielded a more than ten-fold speedup and at least the same accuracy, as reported previously for our method; these results allowed us to offer the method as a webserver. The web-server interface is targeted to the non-expert user. The input is simply a sequence of the protein of interest, and the output a table with scores indicating the likelihood of having an interaction interface at a certain position. As the method is sequence-based and not sensitive to the type of protein interaction, we expect this webserver to be of interest to many biological researchers in academia and in industry.

AVAILABILITY: Webserver, source code and datasets are available at www.ibi.vu.nl/programs/serendipwww/.

Original languageEnglish
Pages (from-to)4794-4796
Number of pages3
JournalBioinformatics
Volume35
Issue number22
Early online date22 May 2019
DOIs
Publication statusPublished - 15 Nov 2019

Fingerprint

Web Server
Proteins
Protein
Prediction
Interaction
Random Forest
Protein-protein Interaction
Protein Sequence
Substitute
Molecular Sequence Annotation
Annotation
Profile
Conservation
Likelihood
Table
Alignment
Speedup
Fold
Sequence Homology
Industry

Bibliographical note

© The Author(s) (2019). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Cite this

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title = "SeRenDIP: SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions",
abstract = "MOTIVATION: Interpretation of ubiquitous protein sequence data has become a bottleneck in biomolecular research, due to a lack of structural and other experimental annotation data for these proteins. Prediction of protein interaction sites from sequence may be a viable substitute. We therefore recently developed a sequence-based random-forest method for protein-protein interface prediction, which yielded a significantly increased performance than other methods on both homomeric and heteromeric protein-protein interactions. Here we present a webserver that implements this method efficiently.RESULTS: With the aim of accelerating our previous approach, we obtained sequence conservation profiles by re-mastering the alignment of homologous sequences found by PSI-BLAST. This yielded a more than ten-fold speedup and at least the same accuracy, as reported previously for our method; these results allowed us to offer the method as a webserver. The web-server interface is targeted to the non-expert user. The input is simply a sequence of the protein of interest, and the output a table with scores indicating the likelihood of having an interaction interface at a certain position. As the method is sequence-based and not sensitive to the type of protein interaction, we expect this webserver to be of interest to many biological researchers in academia and in industry.AVAILABILITY: Webserver, source code and datasets are available at www.ibi.vu.nl/programs/serendipwww/.",
author = "Qingzhen Hou and {De Geest}, {Paul F.G.} and Griffioen, {Christian J.} and Sanne Abeln and Jaap Heringa and Feenstra, {K. Anton}",
note = "{\circledC} The Author(s) (2019). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.",
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SeRenDIP : SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions. / Hou, Qingzhen; De Geest, Paul F.G.; Griffioen, Christian J.; Abeln, Sanne; Heringa, Jaap; Feenstra, K. Anton.

In: Bioinformatics, Vol. 35, No. 22, 15.11.2019, p. 4794-4796.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - SeRenDIP

T2 - SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions

AU - Hou, Qingzhen

AU - De Geest, Paul F.G.

AU - Griffioen, Christian J.

AU - Abeln, Sanne

AU - Heringa, Jaap

AU - Feenstra, K. Anton

N1 - © The Author(s) (2019). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

PY - 2019/11/15

Y1 - 2019/11/15

N2 - MOTIVATION: Interpretation of ubiquitous protein sequence data has become a bottleneck in biomolecular research, due to a lack of structural and other experimental annotation data for these proteins. Prediction of protein interaction sites from sequence may be a viable substitute. We therefore recently developed a sequence-based random-forest method for protein-protein interface prediction, which yielded a significantly increased performance than other methods on both homomeric and heteromeric protein-protein interactions. Here we present a webserver that implements this method efficiently.RESULTS: With the aim of accelerating our previous approach, we obtained sequence conservation profiles by re-mastering the alignment of homologous sequences found by PSI-BLAST. This yielded a more than ten-fold speedup and at least the same accuracy, as reported previously for our method; these results allowed us to offer the method as a webserver. The web-server interface is targeted to the non-expert user. The input is simply a sequence of the protein of interest, and the output a table with scores indicating the likelihood of having an interaction interface at a certain position. As the method is sequence-based and not sensitive to the type of protein interaction, we expect this webserver to be of interest to many biological researchers in academia and in industry.AVAILABILITY: Webserver, source code and datasets are available at www.ibi.vu.nl/programs/serendipwww/.

AB - MOTIVATION: Interpretation of ubiquitous protein sequence data has become a bottleneck in biomolecular research, due to a lack of structural and other experimental annotation data for these proteins. Prediction of protein interaction sites from sequence may be a viable substitute. We therefore recently developed a sequence-based random-forest method for protein-protein interface prediction, which yielded a significantly increased performance than other methods on both homomeric and heteromeric protein-protein interactions. Here we present a webserver that implements this method efficiently.RESULTS: With the aim of accelerating our previous approach, we obtained sequence conservation profiles by re-mastering the alignment of homologous sequences found by PSI-BLAST. This yielded a more than ten-fold speedup and at least the same accuracy, as reported previously for our method; these results allowed us to offer the method as a webserver. The web-server interface is targeted to the non-expert user. The input is simply a sequence of the protein of interest, and the output a table with scores indicating the likelihood of having an interaction interface at a certain position. As the method is sequence-based and not sensitive to the type of protein interaction, we expect this webserver to be of interest to many biological researchers in academia and in industry.AVAILABILITY: Webserver, source code and datasets are available at www.ibi.vu.nl/programs/serendipwww/.

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