TOPTMH: Topology predictor for transmembrane α-helices

Rezwan Ahmed; Huzefa Rangwala; George Karypis

doi:10.1007/978-3-540-87479-9_20

TOPTMH: Topology predictor for transmembrane α-helices

Rezwan Ahmed
, Huzefa Rangwala
, George Karypis

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

Alpha-helical transmembrane proteins mediate many key biological processes and represent 20%-30% of all genes in many organisms. Due to the difficulties in experimentally determining their high-resolution 3D structure, computational methods to predict the location and orientation of transmembrane helix segments using sequence information are essential. We present, TOPTMH a new transmembrane helix topology prediction method that combines support vector machines, hidden Markov models, and a widely-used rule-based scheme. The contribution of this work is the development of a prediction approach that first uses a binary SVM classifier to predict the helix residues and then it employs a pair of HMM models that incorporate the SVM predictions and hydropathy-based features to identify the entire transmembrane helix segments by capturing the structural characteristics of these proteins. TOPTMH outperforms state-of-the-art prediction methods and achieves the best performance on an independent static benchmark.

Original language	English (US)
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2008, Proceedings
Pages	23-38
Number of pages	16
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-540-87479-9_20
State	Published - 2008
Event	European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2008 - Antwerp, Belgium Duration: Sep 15 2008 → Sep 19 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	5211 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2008
Country/Territory	Belgium
City	Antwerp
Period	9/15/08 → 9/19/08

Bibliographical note

Funding Information:
This work was supported by NSF EIA-9986042, ACI-0133464, IIS-0431135, NIH RLM008713A, NIH T32GM008347, the Digital Technology Center, University of Minnesota and the Minnesota Supercomputing Institute.

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10.1007/978-3-540-87479-9_20

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Ahmed, R., Rangwala, H., & Karypis, G. (2008). TOPTMH: Topology predictor for transmembrane α-helices. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2008, Proceedings (PART 1 ed., pp. 23-38). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5211 LNAI, No. PART 1). https://doi.org/10.1007/978-3-540-87479-9_20

TOPTMH: Topology predictor for transmembrane α-helices. / Ahmed, Rezwan; Rangwala, Huzefa; Karypis, George.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2008, Proceedings. PART 1. ed. 2008. p. 23-38 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5211 LNAI, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ahmed, R, Rangwala, H & Karypis, G 2008, TOPTMH: Topology predictor for transmembrane α-helices. in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2008, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 5211 LNAI, pp. 23-38, European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2008, Antwerp, Belgium, 9/15/08. https://doi.org/10.1007/978-3-540-87479-9_20

Ahmed R, Rangwala H, Karypis G. TOPTMH: Topology predictor for transmembrane α-helices. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2008, Proceedings. PART 1 ed. 2008. p. 23-38. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-540-87479-9_20

Ahmed, Rezwan ; Rangwala, Huzefa ; Karypis, George. / TOPTMH : Topology predictor for transmembrane α-helices. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2008, Proceedings. PART 1. ed. 2008. pp. 23-38 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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abstract = "Alpha-helical transmembrane proteins mediate many key biological processes and represent 20\%-30\% of all genes in many organisms. Due to the difficulties in experimentally determining their high-resolution 3D structure, computational methods to predict the location and orientation of transmembrane helix segments using sequence information are essential. We present, TOPTMH a new transmembrane helix topology prediction method that combines support vector machines, hidden Markov models, and a widely-used rule-based scheme. The contribution of this work is the development of a prediction approach that first uses a binary SVM classifier to predict the helix residues and then it employs a pair of HMM models that incorporate the SVM predictions and hydropathy-based features to identify the entire transmembrane helix segments by capturing the structural characteristics of these proteins. TOPTMH outperforms state-of-the-art prediction methods and achieves the best performance on an independent static benchmark.",

author = "Rezwan Ahmed and Huzefa Rangwala and George Karypis",

note = "Funding Information: This work was supported by NSF EIA-9986042, ACI-0133464, IIS-0431135, NIH RLM008713A, NIH T32GM008347, the Digital Technology Center, University of Minnesota and the Minnesota Supercomputing Institute.; European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2008 ; Conference date: 15-09-2008 Through 19-09-2008",

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TOPTMH: Topology predictor for transmembrane α-helices

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