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Protein inter-domain linker prediction using Random Forest and amino acid physiochemical properties.

Shatnawi M, Zaki N, Yoo PD - BMC Bioinformatics (2014)

Bottom Line: Such information not only enhances protein-targeted drug development but also reduces the experimental cost of protein analysis by allowing researchers to work on a set of smaller and independent units.Without applying any data balancing technique such as class weighting and data re-sampling, the proposed approach is able to accurately classify inter-domain linkers from highly imbalanced datasets.Our experimental results prove that the proposed approach is useful for domain-linker identification in highly imbalanced single- and multi-domain proteins.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Protein chains are generally long and consist of multiple domains. Domains are distinct structural units of a protein that can evolve and function independently. The accurate prediction of protein domain linkers and boundaries is often regarded as the initial step of protein tertiary structure and function predictions. Such information not only enhances protein-targeted drug development but also reduces the experimental cost of protein analysis by allowing researchers to work on a set of smaller and independent units. In this study, we propose a novel and accurate domain-linker prediction approach based on protein primary structure information only. We utilize a nature-inspired machine-learning model called Random Forest along with a novel domain-linker profile that contains physiochemical and domain-linker information of amino acid sequences.

Results: The proposed approach was tested on two well-known benchmark protein datasets and achieved 68% sensitivity and 99% precision, which is better than any existing protein domain-linker predictor. Without applying any data balancing technique such as class weighting and data re-sampling, the proposed approach is able to accurately classify inter-domain linkers from highly imbalanced datasets.

Conclusion: Our experimental results prove that the proposed approach is useful for domain-linker identification in highly imbalanced single- and multi-domain proteins.

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Representation of proten sequence by AA features and sliding window. Each sequence in the dataset is replaced by its corresponding properties. These property values are then averaged over a window that slides along the length of each protein sequence.
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Figure 1: Representation of proten sequence by AA features and sliding window. Each sequence in the dataset is replaced by its corresponding properties. These property values are then averaged over a window that slides along the length of each protein sequence.

Mentions: To extract features from a protein sequence, a sliding window technique is used. For each sequence in the protein dataset, we slide an averaging window across the sequence from the N-terminal to the C-terminal as shown in Figure 1. A number of important features of a protein, located within a sliding window, are extracted. These features include the linker index [3], AA hydrophobicity, and other AA physiochemical properties such as side-chain charge, side-chain polarity, aromaticity, size, and electronic properties.


Protein inter-domain linker prediction using Random Forest and amino acid physiochemical properties.

Shatnawi M, Zaki N, Yoo PD - BMC Bioinformatics (2014)

Representation of proten sequence by AA features and sliding window. Each sequence in the dataset is replaced by its corresponding properties. These property values are then averaged over a window that slides along the length of each protein sequence.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4290662&req=5

Figure 1: Representation of proten sequence by AA features and sliding window. Each sequence in the dataset is replaced by its corresponding properties. These property values are then averaged over a window that slides along the length of each protein sequence.
Mentions: To extract features from a protein sequence, a sliding window technique is used. For each sequence in the protein dataset, we slide an averaging window across the sequence from the N-terminal to the C-terminal as shown in Figure 1. A number of important features of a protein, located within a sliding window, are extracted. These features include the linker index [3], AA hydrophobicity, and other AA physiochemical properties such as side-chain charge, side-chain polarity, aromaticity, size, and electronic properties.

Bottom Line: Such information not only enhances protein-targeted drug development but also reduces the experimental cost of protein analysis by allowing researchers to work on a set of smaller and independent units.Without applying any data balancing technique such as class weighting and data re-sampling, the proposed approach is able to accurately classify inter-domain linkers from highly imbalanced datasets.Our experimental results prove that the proposed approach is useful for domain-linker identification in highly imbalanced single- and multi-domain proteins.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Protein chains are generally long and consist of multiple domains. Domains are distinct structural units of a protein that can evolve and function independently. The accurate prediction of protein domain linkers and boundaries is often regarded as the initial step of protein tertiary structure and function predictions. Such information not only enhances protein-targeted drug development but also reduces the experimental cost of protein analysis by allowing researchers to work on a set of smaller and independent units. In this study, we propose a novel and accurate domain-linker prediction approach based on protein primary structure information only. We utilize a nature-inspired machine-learning model called Random Forest along with a novel domain-linker profile that contains physiochemical and domain-linker information of amino acid sequences.

Results: The proposed approach was tested on two well-known benchmark protein datasets and achieved 68% sensitivity and 99% precision, which is better than any existing protein domain-linker predictor. Without applying any data balancing technique such as class weighting and data re-sampling, the proposed approach is able to accurately classify inter-domain linkers from highly imbalanced datasets.

Conclusion: Our experimental results prove that the proposed approach is useful for domain-linker identification in highly imbalanced single- and multi-domain proteins.

Show MeSH