Prediction of DNA-binding propensity of proteins by the ball-histogram method

Andrea Szabóová, Ondřej Kuželka, Sergio Morales E., Filip Železný, Jakub Tolar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We contribute a novel, ball-histogram approach to DNA-binding propensity prediction of proteins. Unlike state-of-the-art methods based on constructing an ad-hoc set of features describing the charged patches of the proteins, the ball-histogram technique enables a systematic, Monte-Carlo exploration of the spatial distribution of charged amino acids, capturing joint probabilities of specified amino acids occurring in certain distances from each other. This exploration yields a model for the prediction of DNA binding propensity. We validate our method in prediction experiments, achieving favorable accuracies. Moreover, our method also provides interpretable features involving spatial distributions of selected amino acids.

Original languageEnglish (US)
Title of host publicationBioinformatics Research and Applications - 7th International Symposium, ISBRA 2011, Proceedings
Pages358-367
Number of pages10
DOIs
StatePublished - May 16 2011
Event7th International Symposium on Bioinformatics Research and Applications, ISBRA 2011 - Changsha, China
Duration: May 27 2011May 29 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6674 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Symposium on Bioinformatics Research and Applications, ISBRA 2011
CountryChina
CityChangsha
Period5/27/115/29/11

Fingerprint

Histogram
Amino Acids
Amino acids
Ball
DNA
Proteins
Protein
Spatial Distribution
Spatial distribution
Prediction
Patch
Experiment
Experiments
Model

Cite this

Szabóová, A., Kuželka, O., Morales E., S., Železný, F., & Tolar, J. (2011). Prediction of DNA-binding propensity of proteins by the ball-histogram method. In Bioinformatics Research and Applications - 7th International Symposium, ISBRA 2011, Proceedings (pp. 358-367). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6674 LNBI). https://doi.org/10.1007/978-3-642-21260-4_34

Prediction of DNA-binding propensity of proteins by the ball-histogram method. / Szabóová, Andrea; Kuželka, Ondřej; Morales E., Sergio; Železný, Filip; Tolar, Jakub.

Bioinformatics Research and Applications - 7th International Symposium, ISBRA 2011, Proceedings. 2011. p. 358-367 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6674 LNBI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Szabóová, A, Kuželka, O, Morales E., S, Železný, F & Tolar, J 2011, Prediction of DNA-binding propensity of proteins by the ball-histogram method. in Bioinformatics Research and Applications - 7th International Symposium, ISBRA 2011, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6674 LNBI, pp. 358-367, 7th International Symposium on Bioinformatics Research and Applications, ISBRA 2011, Changsha, China, 5/27/11. https://doi.org/10.1007/978-3-642-21260-4_34
Szabóová A, Kuželka O, Morales E. S, Železný F, Tolar J. Prediction of DNA-binding propensity of proteins by the ball-histogram method. In Bioinformatics Research and Applications - 7th International Symposium, ISBRA 2011, Proceedings. 2011. p. 358-367. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-21260-4_34
Szabóová, Andrea ; Kuželka, Ondřej ; Morales E., Sergio ; Železný, Filip ; Tolar, Jakub. / Prediction of DNA-binding propensity of proteins by the ball-histogram method. Bioinformatics Research and Applications - 7th International Symposium, ISBRA 2011, Proceedings. 2011. pp. 358-367 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{b83902bd5540496db15e83df27c5f447,
title = "Prediction of DNA-binding propensity of proteins by the ball-histogram method",
abstract = "We contribute a novel, ball-histogram approach to DNA-binding propensity prediction of proteins. Unlike state-of-the-art methods based on constructing an ad-hoc set of features describing the charged patches of the proteins, the ball-histogram technique enables a systematic, Monte-Carlo exploration of the spatial distribution of charged amino acids, capturing joint probabilities of specified amino acids occurring in certain distances from each other. This exploration yields a model for the prediction of DNA binding propensity. We validate our method in prediction experiments, achieving favorable accuracies. Moreover, our method also provides interpretable features involving spatial distributions of selected amino acids.",
author = "Andrea Szab{\'o}ov{\'a} and Ondřej Kuželka and {Morales E.}, Sergio and Filip Železn{\'y} and Jakub Tolar",
year = "2011",
month = "5",
day = "16",
doi = "10.1007/978-3-642-21260-4_34",
language = "English (US)",
isbn = "9783642212598",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "358--367",
booktitle = "Bioinformatics Research and Applications - 7th International Symposium, ISBRA 2011, Proceedings",

}

TY - GEN

T1 - Prediction of DNA-binding propensity of proteins by the ball-histogram method

AU - Szabóová, Andrea

AU - Kuželka, Ondřej

AU - Morales E., Sergio

AU - Železný, Filip

AU - Tolar, Jakub

PY - 2011/5/16

Y1 - 2011/5/16

N2 - We contribute a novel, ball-histogram approach to DNA-binding propensity prediction of proteins. Unlike state-of-the-art methods based on constructing an ad-hoc set of features describing the charged patches of the proteins, the ball-histogram technique enables a systematic, Monte-Carlo exploration of the spatial distribution of charged amino acids, capturing joint probabilities of specified amino acids occurring in certain distances from each other. This exploration yields a model for the prediction of DNA binding propensity. We validate our method in prediction experiments, achieving favorable accuracies. Moreover, our method also provides interpretable features involving spatial distributions of selected amino acids.

AB - We contribute a novel, ball-histogram approach to DNA-binding propensity prediction of proteins. Unlike state-of-the-art methods based on constructing an ad-hoc set of features describing the charged patches of the proteins, the ball-histogram technique enables a systematic, Monte-Carlo exploration of the spatial distribution of charged amino acids, capturing joint probabilities of specified amino acids occurring in certain distances from each other. This exploration yields a model for the prediction of DNA binding propensity. We validate our method in prediction experiments, achieving favorable accuracies. Moreover, our method also provides interpretable features involving spatial distributions of selected amino acids.

UR - http://www.scopus.com/inward/record.url?scp=79955869476&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79955869476&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-21260-4_34

DO - 10.1007/978-3-642-21260-4_34

M3 - Conference contribution

SN - 9783642212598

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 358

EP - 367

BT - Bioinformatics Research and Applications - 7th International Symposium, ISBRA 2011, Proceedings

ER -