Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/76930
Title: I 3D3P : an intelligent 3D protein prediction platform
Author(s): Kermani, Mohamed Hachem
Boufaida, Zizette
Granting Institution: Martin-Luther-Universität Halle-Wittenberg
Issue Date: 2022
Type: Bachelor thesis
Language: English
Abstract: 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 publication
License: (CC BY 4.0) Creative Commons Attribution 4.0(CC BY 4.0) Creative Commons Attribution 4.0
Appears in Collections:International Conference on Applied Innovations in IT (ICAIIT)

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