Accuracy in predicting secondary structure of ionic channels

Bogumil Konopka; Witold Dyrka; Jean-Christophe Nebel; Malgorzata Kotulska

doi:10.1007/978-3-642-03958-4_27

Accuracy in predicting secondary structure of ionic channels

Bogumil Konopka
, Witold Dyrka
, Jean-Christophe Nebel
, Malgorzata Kotulska

Wrocław University of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Ionic channels are among the most difficult proteins for experimental structure determining, very few of them has been resolved. Bioinformatical tools has not been tested for this specific protein group. In the paper, prediction quality of ionic channel secondary structure is evaluated. The tests were carried out with general protein predictors and predictors only for transmembrane segments. The predictor performance was measured by the accuracy per residue Q and per segment SOV. The evaluation comparing ionic channels and other transmembrane proteins shows that ionic channels are only slightly more difficult objects for modeling than transmembrane proteins; the modeling quality is comparable with a general set of all proteins. Prediction quality showed dependence on the ratio of secondary structures in the ionic channel. Surprisingly, general purpose PSIPRED predictor outperformed other general but also dedicated transmembrane predictors under evaluation.

Original language	English
Title of host publication	New challenges in computational collective intelligence
Editors	Ngoc Thanh Nguyen, Radosław Piotr Katarzyniak, Adam Janiak
Place of Publication	Berlin, Germany
Publisher	Springer
Pages	315-326
Number of pages	12
Volume	244
ISBN (Electronic)	9783642039584
ISBN (Print)	9783642039577
DOIs	https://doi.org/10.1007/978-3-642-03958-4_27
Publication status	Published - Jul 2009
Event	1st International Conference on Computational Collective Intelligence - Wroclaw, Poland Duration: 5 Oct 2009 → 7 Oct 2009

Publication series

Name	Studies in Computational Intelligence
Publisher	Springer Nature
Number	244

Conference

Conference	1st International Conference on Computational Collective Intelligence
Country/Territory	Poland
City	Wroclaw
Period	5/10/09 → 7/10/09

Bibliographical note

The book consists of 29 chapters which have been selected and invited from the submissions to the 1st International Conference on Collective Intelligence - Semantic Web, Social Networks & Multiagent Systems (ICCCI 2009).

Keywords

Computer science and informatics

Access to Document

10.1007/978-3-642-03958-4_27

Protein Bioinformatics: Structure and Function
Nebel, J.-C. (PI), Abbass, J. (CoI), Esmaielbeiki, R. (Researcher), Laibe, J. (Researcher), Dyrka, W. (Researcher), Kotulska, M. (CoI) & Konopka, B. M. (Researcher)
7/06/04 → 10/12/21
Project: Research

Cite this

Konopka, B., Dyrka, W., Nebel, J.-C., & Kotulska, M. (2009). Accuracy in predicting secondary structure of ionic channels. In N. Thanh Nguyen, R. Piotr Katarzyniak, & A. Janiak (Eds.), New challenges in computational collective intelligence (Vol. 244, pp. 315-326). (Studies in Computational Intelligence; No. 244). Springer. https://doi.org/10.1007/978-3-642-03958-4_27

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abstract = "Ionic channels are among the most difficult proteins for experimental structure determining, very few of them has been resolved. Bioinformatical tools has not been tested for this specific protein group. In the paper, prediction quality of ionic channel secondary structure is evaluated. The tests were carried out with general protein predictors and predictors only for transmembrane segments. The predictor performance was measured by the accuracy per residue Q and per segment SOV. The evaluation comparing ionic channels and other transmembrane proteins shows that ionic channels are only slightly more difficult objects for modeling than transmembrane proteins; the modeling quality is comparable with a general set of all proteins. Prediction quality showed dependence on the ratio of secondary structures in the ionic channel. Surprisingly, general purpose PSIPRED predictor outperformed other general but also dedicated transmembrane predictors under evaluation.",

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note = "The book consists of 29 chapters which have been selected and invited from the submissions to the 1st International Conference on Collective Intelligence - Semantic Web, Social Networks \& Multiagent Systems (ICCCI 2009).; 1st International Conference on Computational Collective Intelligence ; Conference date: 05-10-2009 Through 07-10-2009",

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Konopka, B, Dyrka, W, Nebel, J-C & Kotulska, M 2009, Accuracy in predicting secondary structure of ionic channels. in N Thanh Nguyen, R Piotr Katarzyniak & A Janiak (eds), New challenges in computational collective intelligence. vol. 244, Studies in Computational Intelligence, no. 244, Springer, Berlin, Germany, pp. 315-326, 1st International Conference on Computational Collective Intelligence, Wroclaw, Poland, 5/10/09. https://doi.org/10.1007/978-3-642-03958-4_27

Accuracy in predicting secondary structure of ionic channels. / Konopka, Bogumil; Dyrka, Witold; Nebel, Jean-Christophe et al.
New challenges in computational collective intelligence. ed. / Ngoc Thanh Nguyen; Radosław Piotr Katarzyniak; Adam Janiak. Vol. 244 Berlin, Germany: Springer, 2009. p. 315-326 (Studies in Computational Intelligence; No. 244).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Accuracy in predicting secondary structure of ionic channels

AU - Konopka, Bogumil

AU - Dyrka, Witold

AU - Nebel, Jean-Christophe

AU - Kotulska, Malgorzata

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AB - Ionic channels are among the most difficult proteins for experimental structure determining, very few of them has been resolved. Bioinformatical tools has not been tested for this specific protein group. In the paper, prediction quality of ionic channel secondary structure is evaluated. The tests were carried out with general protein predictors and predictors only for transmembrane segments. The predictor performance was measured by the accuracy per residue Q and per segment SOV. The evaluation comparing ionic channels and other transmembrane proteins shows that ionic channels are only slightly more difficult objects for modeling than transmembrane proteins; the modeling quality is comparable with a general set of all proteins. Prediction quality showed dependence on the ratio of secondary structures in the ionic channel. Surprisingly, general purpose PSIPRED predictor outperformed other general but also dedicated transmembrane predictors under evaluation.

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BT - New challenges in computational collective intelligence

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T2 - 1st International Conference on Computational Collective Intelligence

Y2 - 5 October 2009 through 7 October 2009

ER -

Accuracy in predicting secondary structure of ionic channels

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Fingerprint

Projects

Protein Bioinformatics: Structure and Function

Cite this