Incorporating post translational modification information for enhancing the predictive performance of membrane transport proteins

Nguyen Quoc Khanh Le, Green Arther Sandag, Yu Yen Ou

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Transporters involved in the cellular entry and exit of ions or molecules throughout the membrane proteins and thereby play an essential role in recognizing the immune system and energy transducers. According to their relevance in proteomics, numerous studies have been conducted to analyze the transporters; especially the discrimination of their classes and subfamilies. We realized that post translational modification information had a critical role in the process of transport proteins. Therefore, in this study, we aim to incorporate post translational information with radial basis function networks to improve the predictive performance of transport proteins in major classes (channels/pores, electrochemical transporters, and active transporters) and six different families (α-type channels, β-barrel porins, pore-forming toxins, porters, P–P bond hydrolysis-driven transporters, and oxidoreduction-driven transporters). The experiment results by using PSSM profiles combined with PTM information could classify the transporters into three classes and six families with five-fold cross-validation accuracy of 87.6% and 92.5%, respectively. For the independent dataset of 444 proteins, the performance with post translational modification attained the accuracy of 82.13% and 89.34% for classifying three classes and six families, respectively. Compared with the other methods and previous works, our result shows that the predictive performance is better with the accuracy improvement by 12%. We suggest that our study could become a robust model for biologists to discriminate transport proteins with high performance and understand better the function of transport proteins. Further, the contributions of this study could be fundamental for further research that can use PTM information to enhance numerous computational biology problems.

Original languageEnglish
Pages (from-to)251-260
Number of pages10
JournalComputational Biology and Chemistry
Volume77
DOIs
Publication statusPublished - Dec 1 2018
Externally publishedYes

Fingerprint

Position-Specific Scoring Matrices
Biological membranes
Molecular biology
Membrane Transport Proteins
Radial basis function networks
Radial Basis Function Network
Radial Basis Function Neural Network
Molecular Biology
Post Translational Protein Processing
Carrier Proteins
Membrane
Pulse time modulation
Membranes
Protein
Proteins
Porins
Immune system
Computational Biology
Transducers
Membrane Protein

Keywords

  • Position specific scoring matrix
  • Post translational modification
  • Radial basis function neural networks
  • Transport protein

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Organic Chemistry
  • Computational Mathematics

Cite this

Incorporating post translational modification information for enhancing the predictive performance of membrane transport proteins. / Le, Nguyen Quoc Khanh; Sandag, Green Arther; Ou, Yu Yen.

In: Computational Biology and Chemistry, Vol. 77, 01.12.2018, p. 251-260.

Research output: Contribution to journalArticle

@article{01042ce246e24997b6b4274ef8cee982,
title = "Incorporating post translational modification information for enhancing the predictive performance of membrane transport proteins",
abstract = "Transporters involved in the cellular entry and exit of ions or molecules throughout the membrane proteins and thereby play an essential role in recognizing the immune system and energy transducers. According to their relevance in proteomics, numerous studies have been conducted to analyze the transporters; especially the discrimination of their classes and subfamilies. We realized that post translational modification information had a critical role in the process of transport proteins. Therefore, in this study, we aim to incorporate post translational information with radial basis function networks to improve the predictive performance of transport proteins in major classes (channels/pores, electrochemical transporters, and active transporters) and six different families (α-type channels, β-barrel porins, pore-forming toxins, porters, P–P bond hydrolysis-driven transporters, and oxidoreduction-driven transporters). The experiment results by using PSSM profiles combined with PTM information could classify the transporters into three classes and six families with five-fold cross-validation accuracy of 87.6{\%} and 92.5{\%}, respectively. For the independent dataset of 444 proteins, the performance with post translational modification attained the accuracy of 82.13{\%} and 89.34{\%} for classifying three classes and six families, respectively. Compared with the other methods and previous works, our result shows that the predictive performance is better with the accuracy improvement by 12{\%}. We suggest that our study could become a robust model for biologists to discriminate transport proteins with high performance and understand better the function of transport proteins. Further, the contributions of this study could be fundamental for further research that can use PTM information to enhance numerous computational biology problems.",
keywords = "Position specific scoring matrix, Post translational modification, Radial basis function neural networks, Transport protein",
author = "Le, {Nguyen Quoc Khanh} and Sandag, {Green Arther} and Ou, {Yu Yen}",
year = "2018",
month = "12",
day = "1",
doi = "10.1016/j.compbiolchem.2018.10.010",
language = "English",
volume = "77",
pages = "251--260",
journal = "Computational Biology and Chemistry",
issn = "1476-9271",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Incorporating post translational modification information for enhancing the predictive performance of membrane transport proteins

AU - Le, Nguyen Quoc Khanh

AU - Sandag, Green Arther

AU - Ou, Yu Yen

PY - 2018/12/1

Y1 - 2018/12/1

N2 - Transporters involved in the cellular entry and exit of ions or molecules throughout the membrane proteins and thereby play an essential role in recognizing the immune system and energy transducers. According to their relevance in proteomics, numerous studies have been conducted to analyze the transporters; especially the discrimination of their classes and subfamilies. We realized that post translational modification information had a critical role in the process of transport proteins. Therefore, in this study, we aim to incorporate post translational information with radial basis function networks to improve the predictive performance of transport proteins in major classes (channels/pores, electrochemical transporters, and active transporters) and six different families (α-type channels, β-barrel porins, pore-forming toxins, porters, P–P bond hydrolysis-driven transporters, and oxidoreduction-driven transporters). The experiment results by using PSSM profiles combined with PTM information could classify the transporters into three classes and six families with five-fold cross-validation accuracy of 87.6% and 92.5%, respectively. For the independent dataset of 444 proteins, the performance with post translational modification attained the accuracy of 82.13% and 89.34% for classifying three classes and six families, respectively. Compared with the other methods and previous works, our result shows that the predictive performance is better with the accuracy improvement by 12%. We suggest that our study could become a robust model for biologists to discriminate transport proteins with high performance and understand better the function of transport proteins. Further, the contributions of this study could be fundamental for further research that can use PTM information to enhance numerous computational biology problems.

AB - Transporters involved in the cellular entry and exit of ions or molecules throughout the membrane proteins and thereby play an essential role in recognizing the immune system and energy transducers. According to their relevance in proteomics, numerous studies have been conducted to analyze the transporters; especially the discrimination of their classes and subfamilies. We realized that post translational modification information had a critical role in the process of transport proteins. Therefore, in this study, we aim to incorporate post translational information with radial basis function networks to improve the predictive performance of transport proteins in major classes (channels/pores, electrochemical transporters, and active transporters) and six different families (α-type channels, β-barrel porins, pore-forming toxins, porters, P–P bond hydrolysis-driven transporters, and oxidoreduction-driven transporters). The experiment results by using PSSM profiles combined with PTM information could classify the transporters into three classes and six families with five-fold cross-validation accuracy of 87.6% and 92.5%, respectively. For the independent dataset of 444 proteins, the performance with post translational modification attained the accuracy of 82.13% and 89.34% for classifying three classes and six families, respectively. Compared with the other methods and previous works, our result shows that the predictive performance is better with the accuracy improvement by 12%. We suggest that our study could become a robust model for biologists to discriminate transport proteins with high performance and understand better the function of transport proteins. Further, the contributions of this study could be fundamental for further research that can use PTM information to enhance numerous computational biology problems.

KW - Position specific scoring matrix

KW - Post translational modification

KW - Radial basis function neural networks

KW - Transport protein

UR - http://www.scopus.com/inward/record.url?scp=85055513954&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85055513954&partnerID=8YFLogxK

U2 - 10.1016/j.compbiolchem.2018.10.010

DO - 10.1016/j.compbiolchem.2018.10.010

M3 - Article

VL - 77

SP - 251

EP - 260

JO - Computational Biology and Chemistry

JF - Computational Biology and Chemistry

SN - 1476-9271

ER -