Cargando…

Prediction of hot spots in protein–DNA binding interfaces based on discrete wavelet transform and wavelet packet transform

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 co...

Descripción completa

Detalles Bibliográficos
Autores principales: Sun, Yu, Wu, Hongwei, Xu, Zhengrong, Yue, Zhenyu, Li, Ke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10074722/
https://www.ncbi.nlm.nih.gov/pubmed/37016308
http://dx.doi.org/10.1186/s12859-023-05263-7
_version_ 1785019797309751296
author Sun, Yu
Wu, Hongwei
Xu, Zhengrong
Yue, Zhenyu
Li, Ke
author_facet Sun, Yu
Wu, Hongwei
Xu, Zhengrong
Yue, Zhenyu
Li, Ke
author_sort Sun, Yu
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05263-7.
format Online
Article
Text
id pubmed-10074722
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-100747222023-04-06 Prediction of hot spots in protein–DNA binding interfaces based on discrete wavelet transform and wavelet packet transform Sun, Yu Wu, Hongwei Xu, Zhengrong Yue, Zhenyu Li, Ke BMC Bioinformatics Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05263-7. BioMed Central 2023-04-04 /pmc/articles/PMC10074722/ /pubmed/37016308 http://dx.doi.org/10.1186/s12859-023-05263-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Sun, Yu
Wu, Hongwei
Xu, Zhengrong
Yue, Zhenyu
Li, Ke
Prediction of hot spots in protein–DNA binding interfaces based on discrete wavelet transform and wavelet packet transform
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 Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10074722/
https://www.ncbi.nlm.nih.gov/pubmed/37016308
http://dx.doi.org/10.1186/s12859-023-05263-7
work_keys_str_mv AT sunyu predictionofhotspotsinproteindnabindinginterfacesbasedondiscretewavelettransformandwaveletpackettransform
AT wuhongwei predictionofhotspotsinproteindnabindinginterfacesbasedondiscretewavelettransformandwaveletpackettransform
AT xuzhengrong predictionofhotspotsinproteindnabindinginterfacesbasedondiscretewavelettransformandwaveletpackettransform
AT yuezhenyu predictionofhotspotsinproteindnabindinginterfacesbasedondiscretewavelettransformandwaveletpackettransform
AT like predictionofhotspotsinproteindnabindinginterfacesbasedondiscretewavelettransformandwaveletpackettransform