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Insights into the classification of small GTPases

In this study we used a Random Forest-based approach for an assignment of small guanosine triphosphate proteins (GTPases) to specific subgroups. Small GTPases represent an important functional group of proteins that serve as molecular switches in a wide range of fundamental cellular processes, inclu...

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Detalles Bibliográficos
Autores principales: Heider, Dominik, Hauke, Sascha, Pyka, Martin, Kessler, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3170009/
https://www.ncbi.nlm.nih.gov/pubmed/21918623
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author Heider, Dominik
Hauke, Sascha
Pyka, Martin
Kessler, Daniel
author_facet Heider, Dominik
Hauke, Sascha
Pyka, Martin
Kessler, Daniel
author_sort Heider, Dominik
collection PubMed
description In this study we used a Random Forest-based approach for an assignment of small guanosine triphosphate proteins (GTPases) to specific subgroups. Small GTPases represent an important functional group of proteins that serve as molecular switches in a wide range of fundamental cellular processes, including intracellular transport, movement and signaling events. These proteins have further gained a special emphasis in cancer research, because within the last decades a huge variety of small GTPases from different subgroups could be related to the development of all types of tumors. Using a random forest approach, we were able to identify the most important amino acid positions for the classification process within the small GTPases superfamily and its subgroups. These positions are in line with the results of earlier studies and have been shown to be the essential elements for the different functionalities of the GTPase families. Furthermore, we provide an accurate and reliable software tool (GTPasePred) to identify potential novel GTPases and demonstrate its application to genome sequences.
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spelling pubmed-31700092011-09-14 Insights into the classification of small GTPases Heider, Dominik Hauke, Sascha Pyka, Martin Kessler, Daniel Adv Appl Bioinforma Chem Original Research In this study we used a Random Forest-based approach for an assignment of small guanosine triphosphate proteins (GTPases) to specific subgroups. Small GTPases represent an important functional group of proteins that serve as molecular switches in a wide range of fundamental cellular processes, including intracellular transport, movement and signaling events. These proteins have further gained a special emphasis in cancer research, because within the last decades a huge variety of small GTPases from different subgroups could be related to the development of all types of tumors. Using a random forest approach, we were able to identify the most important amino acid positions for the classification process within the small GTPases superfamily and its subgroups. These positions are in line with the results of earlier studies and have been shown to be the essential elements for the different functionalities of the GTPase families. Furthermore, we provide an accurate and reliable software tool (GTPasePred) to identify potential novel GTPases and demonstrate its application to genome sequences. Dove Medical Press 2010-05-21 /pmc/articles/PMC3170009/ /pubmed/21918623 Text en © 2010 Heider et al, publisher and licensee Dove Medical Press Ltd. This is an Open Access article which permits unrestricted noncommercial use, provided the original work is properly cited.
spellingShingle Original Research
Heider, Dominik
Hauke, Sascha
Pyka, Martin
Kessler, Daniel
Insights into the classification of small GTPases
title Insights into the classification of small GTPases
title_full Insights into the classification of small GTPases
title_fullStr Insights into the classification of small GTPases
title_full_unstemmed Insights into the classification of small GTPases
title_short Insights into the classification of small GTPases
title_sort insights into the classification of small gtpases
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3170009/
https://www.ncbi.nlm.nih.gov/pubmed/21918623
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