Predicting Structural Susceptibility of Proteins to Proteolytic Processing
The importance of 3D protein structure in proteolytic processing is well known. However, despite the plethora of existing methods for predicting proteolytic sites, only a few of them utilize the structural features of potential substrates as predictors. Moreover, to our knowledge, there is currently...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
|
| Series: | International Journal of Molecular Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1422-0067/24/13/10761 |
| _version_ | 1797591564495618048 |
|---|---|
| author | Evgenii V. Matveev Vyacheslav V. Safronov Gennady V. Ponomarev Marat D. Kazanov |
| author_facet | Evgenii V. Matveev Vyacheslav V. Safronov Gennady V. Ponomarev Marat D. Kazanov |
| author_sort | Evgenii V. Matveev |
| collection | DOAJ |
| description | The importance of 3D protein structure in proteolytic processing is well known. However, despite the plethora of existing methods for predicting proteolytic sites, only a few of them utilize the structural features of potential substrates as predictors. Moreover, to our knowledge, there is currently no method available for predicting the structural susceptibility of protein regions to proteolysis. We developed such a method using data from CutDB, a database that contains experimentally verified proteolytic events. For prediction, we utilized structural features that have been shown to influence proteolysis in earlier studies, such as solvent accessibility, secondary structure, and temperature factor. Additionally, we introduced new structural features, including length of protruded loops and flexibility of protein termini. To maximize the prediction quality of the method, we carefully curated the training set, selected an appropriate machine learning method, and sampled negative examples to determine the optimal positive-to-negative class size ratio. We demonstrated that combining our method with models of protease primary specificity can outperform existing bioinformatics methods for the prediction of proteolytic sites. We also discussed the possibility of utilizing this method for bioinformatics prediction of other post-translational modifications. |
| first_indexed | 2024-03-11T01:39:14Z |
| format | Article |
| id | doaj.art-940aeca391344ad9b18dc462054cdada |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-11T01:39:14Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-940aeca391344ad9b18dc462054cdada2023-11-18T16:43:08ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672023-06-0124131076110.3390/ijms241310761Predicting Structural Susceptibility of Proteins to Proteolytic ProcessingEvgenii V. Matveev0Vyacheslav V. Safronov1Gennady V. Ponomarev2Marat D. Kazanov3Skolkovo Institute of Science and Technology, Moscow 121205, RussiaFaculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow 119991, RussiaSkolkovo Institute of Science and Technology, Moscow 121205, RussiaSkolkovo Institute of Science and Technology, Moscow 121205, RussiaThe importance of 3D protein structure in proteolytic processing is well known. However, despite the plethora of existing methods for predicting proteolytic sites, only a few of them utilize the structural features of potential substrates as predictors. Moreover, to our knowledge, there is currently no method available for predicting the structural susceptibility of protein regions to proteolysis. We developed such a method using data from CutDB, a database that contains experimentally verified proteolytic events. For prediction, we utilized structural features that have been shown to influence proteolysis in earlier studies, such as solvent accessibility, secondary structure, and temperature factor. Additionally, we introduced new structural features, including length of protruded loops and flexibility of protein termini. To maximize the prediction quality of the method, we carefully curated the training set, selected an appropriate machine learning method, and sampled negative examples to determine the optimal positive-to-negative class size ratio. We demonstrated that combining our method with models of protease primary specificity can outperform existing bioinformatics methods for the prediction of proteolytic sites. We also discussed the possibility of utilizing this method for bioinformatics prediction of other post-translational modifications.https://www.mdpi.com/1422-0067/24/13/10761regulatory proteolysisproteasesprotease substratessubstrate identification |
| spellingShingle | Evgenii V. Matveev Vyacheslav V. Safronov Gennady V. Ponomarev Marat D. Kazanov Predicting Structural Susceptibility of Proteins to Proteolytic Processing International Journal of Molecular Sciences regulatory proteolysis proteases protease substrates substrate identification |
| title | Predicting Structural Susceptibility of Proteins to Proteolytic Processing |
| title_full | Predicting Structural Susceptibility of Proteins to Proteolytic Processing |
| title_fullStr | Predicting Structural Susceptibility of Proteins to Proteolytic Processing |
| title_full_unstemmed | Predicting Structural Susceptibility of Proteins to Proteolytic Processing |
| title_short | Predicting Structural Susceptibility of Proteins to Proteolytic Processing |
| title_sort | predicting structural susceptibility of proteins to proteolytic processing |
| topic | regulatory proteolysis proteases protease substrates substrate identification |
| url | https://www.mdpi.com/1422-0067/24/13/10761 |
| work_keys_str_mv | AT evgeniivmatveev predictingstructuralsusceptibilityofproteinstoproteolyticprocessing AT vyacheslavvsafronov predictingstructuralsusceptibilityofproteinstoproteolyticprocessing AT gennadyvponomarev predictingstructuralsusceptibilityofproteinstoproteolyticprocessing AT maratdkazanov predictingstructuralsusceptibilityofproteinstoproteolyticprocessing |