Prediction of hot spots in protein–DNA binding interfaces based on discrete wavelet transform and wavelet packet transform
Abstract Background Identification of hot spots in protein–DNA binding interfaces is extremely important for understanding the underlying mechanisms of protein–DNA interactions and drug design. Since experimental methods for identifying hot spots are time-consuming and expensive, and most of the exi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-04-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-023-05263-7 |
| _version_ | 1797849821548118016 |
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| author | Yu Sun Hongwei Wu Zhengrong Xu Zhenyu Yue Ke Li |
| author_facet | Yu Sun Hongwei Wu Zhengrong Xu Zhenyu Yue Ke Li |
| author_sort | Yu Sun |
| collection | DOAJ |
| description | Abstract Background Identification of hot spots in protein–DNA binding interfaces is extremely important for understanding the underlying mechanisms of protein–DNA interactions and drug design. Since experimental methods for identifying hot spots are time-consuming and expensive, and most of the existing computational methods are based on traditional protein–DNA features to predict hot spots, unable to make full use of the effective information in the features. Results In this work, a method named WTL-PDH is proposed for hot spots prediction. To deal with the unbalanced dataset, we used the Synthetic Minority Over-sampling Technique to generate minority class samples to achieve the balance of dataset. First, we extracted the solvent accessible surface area features and structural features, and then processed the traditional features using discrete wavelet transform and wavelet packet transform to extract the wavelet energy information and wavelet entropy information, and obtained a total of 175 dimensional features. In order to obtain the best feature subset, we systematically evaluate these features in various feature selection strategies. Finally, light gradient boosting machine (LightGBM) was used to establish the model. Conclusions Our method achieved good results on independent test set with AUC, MCC and F1 scores of 0.838, 0.533 and 0.750, respectively. WTL-PDH can achieve generally better performance in predicting hot spots when compared with state-of-the-art methods. The dataset and source code are available at https://github.com/chase2555/WTL-PDH . |
| first_indexed | 2024-04-09T18:50:24Z |
| format | Article |
| id | doaj.art-3deb2908db694a488258664e352acac7 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-09T18:50:24Z |
| publishDate | 2023-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-3deb2908db694a488258664e352acac72023-04-09T11:28:33ZengBMCBMC Bioinformatics1471-21052023-04-0124111610.1186/s12859-023-05263-7Prediction of hot spots in protein–DNA binding interfaces based on discrete wavelet transform and wavelet packet transformYu Sun0Hongwei Wu1Zhengrong Xu2Zhenyu Yue3Ke Li4School of Information and Computer, Anhui Agricultural UniversitySchool of Information and Computer, Anhui Agricultural UniversitySchool of Information and Computer, Anhui Agricultural UniversitySchool of Information and Computer, Anhui Agricultural UniversitySchool of Information and Computer, Anhui Agricultural UniversityAbstract Background Identification of hot spots in protein–DNA binding interfaces is extremely important for understanding the underlying mechanisms of protein–DNA interactions and drug design. Since experimental methods for identifying hot spots are time-consuming and expensive, and most of the existing computational methods are based on traditional protein–DNA features to predict hot spots, unable to make full use of the effective information in the features. Results In this work, a method named WTL-PDH is proposed for hot spots prediction. To deal with the unbalanced dataset, we used the Synthetic Minority Over-sampling Technique to generate minority class samples to achieve the balance of dataset. First, we extracted the solvent accessible surface area features and structural features, and then processed the traditional features using discrete wavelet transform and wavelet packet transform to extract the wavelet energy information and wavelet entropy information, and obtained a total of 175 dimensional features. In order to obtain the best feature subset, we systematically evaluate these features in various feature selection strategies. Finally, light gradient boosting machine (LightGBM) was used to establish the model. Conclusions Our method achieved good results on independent test set with AUC, MCC and F1 scores of 0.838, 0.533 and 0.750, respectively. WTL-PDH can achieve generally better performance in predicting hot spots when compared with state-of-the-art methods. The dataset and source code are available at https://github.com/chase2555/WTL-PDH .https://doi.org/10.1186/s12859-023-05263-7Protein–DNA complexesHot spotSynthetic minority over-sampling techniqueDiscrete wavelet transformWavelet packet transformLight gradient boosting machine |
| spellingShingle | Yu Sun Hongwei Wu Zhengrong Xu Zhenyu Yue Ke Li Prediction of hot spots in protein–DNA binding interfaces based on discrete wavelet transform and wavelet packet transform BMC Bioinformatics Protein–DNA complexes Hot spot Synthetic minority over-sampling technique Discrete wavelet transform Wavelet packet transform Light gradient boosting machine |
| title | Prediction of hot spots in protein–DNA binding interfaces based on discrete wavelet transform and wavelet packet transform |
| title_full | Prediction of hot spots in protein–DNA binding interfaces based on discrete wavelet transform and wavelet packet transform |
| title_fullStr | Prediction of hot spots in protein–DNA binding interfaces based on discrete wavelet transform and wavelet packet transform |
| title_full_unstemmed | Prediction of hot spots in protein–DNA binding interfaces based on discrete wavelet transform and wavelet packet transform |
| title_short | Prediction of hot spots in protein–DNA binding interfaces based on discrete wavelet transform and wavelet packet transform |
| title_sort | prediction of hot spots in protein dna binding interfaces based on discrete wavelet transform and wavelet packet transform |
| topic | Protein–DNA complexes Hot spot Synthetic minority over-sampling technique Discrete wavelet transform Wavelet packet transform Light gradient boosting machine |
| url | https://doi.org/10.1186/s12859-023-05263-7 |
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