This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research
Hot spot identification by means of classification methods employing wavelet transform-based features
Anna Tamulewicz
Politechnika Slaska
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Proteins can interact with one another by an interface composed of two proteins. Some of the interface residues - called hot spots - have the greatest impact on binding energy in a protein complex. This paper introduces the application of continuous wavelet transform based on the fast Fourier transform (CWTFT) to the analysis of hot spots in proteins.
Results: The algorithm was evaluated by using data sets containing 30 proteins. From the number of tested classifiers the best 10 models were preferred. The classifiers achieved sensitivity of 52% - 71%, specificity of 70% - 83% and accuracy of 70% - 74%.
Conclusion: The analyses show that the method combining CWTFT and classification algorithms is able to identify hot spot residues with valuable results.
Methods: The basis of the algorithm is extraction of features from spectra obtained by using CWTFT with different wavelet functions including Morlet, m^th order derivative of Gaussian, Paul and Bump wavelets. Then, the classifiers that are able to separate hot spot from non-hot spot residues according to these features are applied.
Keywords
hot spot, Resonant Recognition Model, continuous wavelet transform, digital signal processing, protein interaction, classification
Figure 1
Figure 2
Figure 3
Figure 4
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 18 Jun, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Biomedical Engineering
Learn more about our company.
This is a preprint. It has not completed peer review.
Research
Hot spot identification by means of classification methods employing wavelet transform-based features
Anna Tamulewicz
Politechnika Slaska
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 18 Jun, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Biomedical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Proteins can interact with one another by an interface composed of two proteins. Some of the interface residues - called hot spots - have the greatest impact on binding energy in a protein complex. This paper introduces the application of continuous wavelet transform based on the fast Fourier transform (CWTFT) to the analysis of hot spots in proteins.
Results: The algorithm was evaluated by using data sets containing 30 proteins. From the number of tested classifiers the best 10 models were preferred. The classifiers achieved sensitivity of 52% - 71%, specificity of 70% - 83% and accuracy of 70% - 74%.
Conclusion: The analyses show that the method combining CWTFT and classification algorithms is able to identify hot spot residues with valuable results.
Methods: The basis of the algorithm is extraction of features from spectra obtained by using CWTFT with different wavelet functions including Morlet, m^th order derivative of Gaussian, Paul and Bump wavelets. Then, the classifiers that are able to separate hot spot from non-hot spot residues according to these features are applied.
Figure 1
Figure 2
Figure 3
Figure 4
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.