Development of a new methodology to predict and engineer thermostable proteins
Studies of protein thermostability change prediction due to single mutations has been of great interest because of its importance for industries, biotechnological and biomedical research. However, the search of a highly accurate predictor has been unsuccessful. In this thesis, we have developed four...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:237829 |
| Acceso en línea: | https://ddd.uab.cat/record/237829 |
| Access Level: | acceso abierto |
| Palabra clave: | Protein stability Computational tools and databases Machine learning Metapredictor Thermostability predictor Integrated prediction Estabilitat de les proteïnes Eines computacionals i bases de dades Aprenentatge automàtic, Metapredictor Predictor de termostabilitat Predicció integrada |
| Sumario: | Studies of protein thermostability change prediction due to single mutations has been of great interest because of its importance for industries, biotechnological and biomedical research. However, the search of a highly accurate predictor has been unsuccessful. In this thesis, we have developed four metapredictors, from which RF-Classifier, and its combined model with RF-Regressor, has the best performance. Its main goal is to take advantage of all the other predictors' strengths and minimize their weaknesses Besides, we have conducted three retrospective studies and a prospective one with an alpha/beta hydrolase to filter and select the most stabilizing mutations. |
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