On the Prediction of In Vitro Arginine Glycation of Short Peptides Using Artificial Neural Networks
One of the hallmarks of diabetes is an increased modification of cellular proteins. The most prominent type of modification stems from the reaction of methylglyoxal with arginine and lysine residues, leading to structural and functional impairments of target proteins. For lysine glycation, several a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/14/5237 |
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| author | Ulices Que-Salinas Dulce Martinez-Peon Angel D. Reyes-Figueroa Ivonne Ibarra Christian Quintus Scheckhuber |
| author_facet | Ulices Que-Salinas Dulce Martinez-Peon Angel D. Reyes-Figueroa Ivonne Ibarra Christian Quintus Scheckhuber |
| author_sort | Ulices Que-Salinas |
| collection | DOAJ |
| description | One of the hallmarks of diabetes is an increased modification of cellular proteins. The most prominent type of modification stems from the reaction of methylglyoxal with arginine and lysine residues, leading to structural and functional impairments of target proteins. For lysine glycation, several algorithms allow a prediction of occurrence; thus, making it possible to pinpoint likely targets. However, according to our knowledge, no approaches have been published for predicting the likelihood of arginine glycation. There are indications that arginine and not lysine is the most prominent target for the toxic dialdehyde. One of the reasons why there is no arginine glycation predictor is the limited availability of quantitative data. Here, we used a recently published high–quality dataset of arginine modification probabilities to employ an artificial neural network strategy. Despite the limited data availability, our results achieve an accuracy of about 75% of correctly predicting the exact value of the glycation probability of an arginine–containing peptide without setting thresholds upon whether it is decided if a given arginine is modified or not. This contribution suggests a solution for predicting arginine glycation of short peptides. |
| first_indexed | 2024-03-09T13:03:59Z |
| format | Article |
| id | doaj.art-969daf05ba0f4e3aa2a69f878d2c5929 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T13:03:59Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-969daf05ba0f4e3aa2a69f878d2c59292023-11-30T21:51:22ZengMDPI AGSensors1424-82202022-07-012214523710.3390/s22145237On the Prediction of In Vitro Arginine Glycation of Short Peptides Using Artificial Neural NetworksUlices Que-Salinas0Dulce Martinez-Peon1Angel D. Reyes-Figueroa2Ivonne Ibarra3Christian Quintus Scheckhuber4Centro de Ciencias de la Tierra, Universidad Veracruzana, Xalapa 91090, VER, MexicoDepartment of Electrical and Electronic Engineering, National Technological Institute of Mexico/IT, Monterrey 67170, NL, MexicoConsejo Nacional de Ciencia y Tecnología, Av. Insurgentes Sur 1582, Col. Crédito Constructor, Benito Juárez, Mexico City 03940, DF, MexicoIndependent Researcher, Monterrey 66620, NL, MexicoDepartamento de Bioingeniería, Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Ave. Eugenio Garza Sada 2501, Monterrey 64849, NL, MexicoOne of the hallmarks of diabetes is an increased modification of cellular proteins. The most prominent type of modification stems from the reaction of methylglyoxal with arginine and lysine residues, leading to structural and functional impairments of target proteins. For lysine glycation, several algorithms allow a prediction of occurrence; thus, making it possible to pinpoint likely targets. However, according to our knowledge, no approaches have been published for predicting the likelihood of arginine glycation. There are indications that arginine and not lysine is the most prominent target for the toxic dialdehyde. One of the reasons why there is no arginine glycation predictor is the limited availability of quantitative data. Here, we used a recently published high–quality dataset of arginine modification probabilities to employ an artificial neural network strategy. Despite the limited data availability, our results achieve an accuracy of about 75% of correctly predicting the exact value of the glycation probability of an arginine–containing peptide without setting thresholds upon whether it is decided if a given arginine is modified or not. This contribution suggests a solution for predicting arginine glycation of short peptides.https://www.mdpi.com/1424-8220/22/14/5237amino acidsarginineartificial neural networkglycationmethylglyoxalmodification probability |
| spellingShingle | Ulices Que-Salinas Dulce Martinez-Peon Angel D. Reyes-Figueroa Ivonne Ibarra Christian Quintus Scheckhuber On the Prediction of In Vitro Arginine Glycation of Short Peptides Using Artificial Neural Networks Sensors amino acids arginine artificial neural network glycation methylglyoxal modification probability |
| title | On the Prediction of In Vitro Arginine Glycation of Short Peptides Using Artificial Neural Networks |
| title_full | On the Prediction of In Vitro Arginine Glycation of Short Peptides Using Artificial Neural Networks |
| title_fullStr | On the Prediction of In Vitro Arginine Glycation of Short Peptides Using Artificial Neural Networks |
| title_full_unstemmed | On the Prediction of In Vitro Arginine Glycation of Short Peptides Using Artificial Neural Networks |
| title_short | On the Prediction of In Vitro Arginine Glycation of Short Peptides Using Artificial Neural Networks |
| title_sort | on the prediction of in vitro arginine glycation of short peptides using artificial neural networks |
| topic | amino acids arginine artificial neural network glycation methylglyoxal modification probability |
| url | https://www.mdpi.com/1424-8220/22/14/5237 |
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