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http://dx.doi.org/10.25673/76930
Titel: | I 3D3P : an intelligent 3D protein prediction platform |
Autor(en): | Kermani, Mohamed Hachem Boufaida, Zizette |
Körperschaft: | Martin-Luther-Universität Halle-Wittenberg |
Erscheinungsdatum: | 2022 |
Art: | Bachelorarbeit |
Sprache: | Englisch |
Zusammenfassung: | Proteins are macromolecules consisting of a chain of smaller molecules (i.e. amino acids) known as monomers. Three levels of protein structure are distinguished: primary, secondary and tertiary. Determining the three-dimensional (3D) structure of a protein when only a sequence of amino acids is given, is one of the most important and frequently studied issues in bioinformatics and computational biology. Therefore, in this paper, we propose an Intelligent 3D Protein Prediction Platform, which aims to completely determine the tertiary protein structure of a given protein primary structure (i.e. the amino acid sequence). The proposed intelligent platform is based on multiple sequence alignment and machine learning techniques to predict automatically 3D protein structures. We also present a software application and an experiment of the proposed platform, which will be used by experts for a better understanding of protein functions and activities in order to develop effective mechanisms for disease prevention, personalized medicine and treatments and other healthcare aspects. |
URI: | https://opendata.uni-halle.de//handle/1981185920/78882 http://dx.doi.org/10.25673/76930 |
Open-Access: | Open-Access-Publikation |
Nutzungslizenz: | (CC BY 4.0) Creative Commons Namensnennung 4.0 International |
Enthalten in den Sammlungen: | International Conference on Applied Innovations in IT (ICAIIT) |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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2_1 Kermani.pdf | 1.73 MB | Adobe PDF | Öffnen/Anzeigen |