Incorporating efficient radial basis function networks and significant amino acid pairs for predicting GTP binding sites in transport proteins

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Background: Guanonine-protein (G-protein) is known as molecular switches inside cells, and is very important in signals transmission from outside to inside cell. Especially in transport protein, most of G-proteins play an important role in membrane trafficking; necessary for transferring proteins and other molecules to a variety of destinations outside and inside of the cell. The function of membrane trafficking is controlled by G-proteins via Guanosine triphosphate (GTP) binding sites. The GTP binding sites active G-proteins initiated to membrane vesicles by interacting with specific effector proteins. Without the interaction from GTP binding sites, G-proteins could not be active in membrane trafficking and consequently cause many diseases, i.e., cancer, Parkinson... Thus it is very important to identify GTP binding sites in membrane trafficking, in particular, and in transport protein, in general. Results: We developed the proposed model with a cross-validation and examined with an independent dataset. We achieved an accuracy of 95.6% for evaluating with cross-validation and 98.7% for examining the performance with the independent data set. For newly discovered transport protein sequences, our approach performed remarkably better than similar methods such as GTPBinder, NsitePred and TargetSOS. Moreover, a friendly web server was developed for identifying GTP binding sites in transport proteins available for all users. Conclusions: We approached a computational technique using PSSM profiles and SAAPs for identifying GTP binding residues in transport proteins. When we included SAAPs into PSSM profiles, the predictive performance achieved a significant improvement in all measurement metrics. Furthermore, the proposed method could be a power tool for determining new proteins that belongs into GTP binding sites in transport proteins and can provide useful information for biologists.

Original languageEnglish
Article number501
JournalBMC Bioinformatics
Volume17
DOIs
Publication statusPublished - Dec 22 2016
Externally publishedYes

Fingerprint

Radial basis function networks
Radial Basis Function Network
Binding sites
Guanosine Triphosphate
Amino Acids
Amino acids
Carrier Proteins
Binding Sites
Proteins
Protein
G Protein
GTP-Binding Proteins
Membranes
Membrane
Cross-validation
Cell
Computational Techniques
Vesicles
Web Server
Protein Sequence

Keywords

  • GTP binding site
  • Position specific scoring matrix
  • Radial basis function network
  • Significant amino acid pairs
  • Transport protein

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

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title = "Incorporating efficient radial basis function networks and significant amino acid pairs for predicting GTP binding sites in transport proteins",
abstract = "Background: Guanonine-protein (G-protein) is known as molecular switches inside cells, and is very important in signals transmission from outside to inside cell. Especially in transport protein, most of G-proteins play an important role in membrane trafficking; necessary for transferring proteins and other molecules to a variety of destinations outside and inside of the cell. The function of membrane trafficking is controlled by G-proteins via Guanosine triphosphate (GTP) binding sites. The GTP binding sites active G-proteins initiated to membrane vesicles by interacting with specific effector proteins. Without the interaction from GTP binding sites, G-proteins could not be active in membrane trafficking and consequently cause many diseases, i.e., cancer, Parkinson... Thus it is very important to identify GTP binding sites in membrane trafficking, in particular, and in transport protein, in general. Results: We developed the proposed model with a cross-validation and examined with an independent dataset. We achieved an accuracy of 95.6{\%} for evaluating with cross-validation and 98.7{\%} for examining the performance with the independent data set. For newly discovered transport protein sequences, our approach performed remarkably better than similar methods such as GTPBinder, NsitePred and TargetSOS. Moreover, a friendly web server was developed for identifying GTP binding sites in transport proteins available for all users. Conclusions: We approached a computational technique using PSSM profiles and SAAPs for identifying GTP binding residues in transport proteins. When we included SAAPs into PSSM profiles, the predictive performance achieved a significant improvement in all measurement metrics. Furthermore, the proposed method could be a power tool for determining new proteins that belongs into GTP binding sites in transport proteins and can provide useful information for biologists.",
keywords = "GTP binding site, Position specific scoring matrix, Radial basis function network, Significant amino acid pairs, Transport protein",
author = "Le, {Nguyen Quoc Khanh} and Ou, {Yu Yen}",
year = "2016",
month = "12",
day = "22",
doi = "10.1186/s12859-016-1369-y",
language = "English",
volume = "17",
journal = "BMC Bioinformatics",
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T1 - Incorporating efficient radial basis function networks and significant amino acid pairs for predicting GTP binding sites in transport proteins

AU - Le, Nguyen Quoc Khanh

AU - Ou, Yu Yen

PY - 2016/12/22

Y1 - 2016/12/22

N2 - Background: Guanonine-protein (G-protein) is known as molecular switches inside cells, and is very important in signals transmission from outside to inside cell. Especially in transport protein, most of G-proteins play an important role in membrane trafficking; necessary for transferring proteins and other molecules to a variety of destinations outside and inside of the cell. The function of membrane trafficking is controlled by G-proteins via Guanosine triphosphate (GTP) binding sites. The GTP binding sites active G-proteins initiated to membrane vesicles by interacting with specific effector proteins. Without the interaction from GTP binding sites, G-proteins could not be active in membrane trafficking and consequently cause many diseases, i.e., cancer, Parkinson... Thus it is very important to identify GTP binding sites in membrane trafficking, in particular, and in transport protein, in general. Results: We developed the proposed model with a cross-validation and examined with an independent dataset. We achieved an accuracy of 95.6% for evaluating with cross-validation and 98.7% for examining the performance with the independent data set. For newly discovered transport protein sequences, our approach performed remarkably better than similar methods such as GTPBinder, NsitePred and TargetSOS. Moreover, a friendly web server was developed for identifying GTP binding sites in transport proteins available for all users. Conclusions: We approached a computational technique using PSSM profiles and SAAPs for identifying GTP binding residues in transport proteins. When we included SAAPs into PSSM profiles, the predictive performance achieved a significant improvement in all measurement metrics. Furthermore, the proposed method could be a power tool for determining new proteins that belongs into GTP binding sites in transport proteins and can provide useful information for biologists.

AB - Background: Guanonine-protein (G-protein) is known as molecular switches inside cells, and is very important in signals transmission from outside to inside cell. Especially in transport protein, most of G-proteins play an important role in membrane trafficking; necessary for transferring proteins and other molecules to a variety of destinations outside and inside of the cell. The function of membrane trafficking is controlled by G-proteins via Guanosine triphosphate (GTP) binding sites. The GTP binding sites active G-proteins initiated to membrane vesicles by interacting with specific effector proteins. Without the interaction from GTP binding sites, G-proteins could not be active in membrane trafficking and consequently cause many diseases, i.e., cancer, Parkinson... Thus it is very important to identify GTP binding sites in membrane trafficking, in particular, and in transport protein, in general. Results: We developed the proposed model with a cross-validation and examined with an independent dataset. We achieved an accuracy of 95.6% for evaluating with cross-validation and 98.7% for examining the performance with the independent data set. For newly discovered transport protein sequences, our approach performed remarkably better than similar methods such as GTPBinder, NsitePred and TargetSOS. Moreover, a friendly web server was developed for identifying GTP binding sites in transport proteins available for all users. Conclusions: We approached a computational technique using PSSM profiles and SAAPs for identifying GTP binding residues in transport proteins. When we included SAAPs into PSSM profiles, the predictive performance achieved a significant improvement in all measurement metrics. Furthermore, the proposed method could be a power tool for determining new proteins that belongs into GTP binding sites in transport proteins and can provide useful information for biologists.

KW - GTP binding site

KW - Position specific scoring matrix

KW - Radial basis function network

KW - Significant amino acid pairs

KW - Transport protein

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