Study of Protein Structure Based on Prediction Mining

Abstract

One of the most important unsolved challenges in biophysics and computational biology is predicting the 3D structure of a protein given its amino acid sequence. This work aims to provide a complete overview of recent protein structure prediction efforts and progress. Related topics and methodologies are provided and addressed after the general flowchart of structure prediction. In addition, concise overviews of many commonly used prediction methods and community-wide critical assessment of protein structure prediction experiments are provided. Proteins are complex structures that are normally fundamental types of molecules that can conduct a wide range of roles in cell biology. To gain a good understanding of these complicated structures, machine learning algorithms and other existing bioinformatics models must be used to obtain a comprehensive review of proteins. In order to create essential predictions, we used four machine learning methods on our protein dataset: bayes network, multilayer perceptron, One Rule, and decision stump, which were tested on a 10-fold cross validation basis. The classification results provided by these machine learning techniques were determined to be significant. Furthermore, the SWISS model was applied to a protein sequence for the purpose of predicting protein structure and examining the sequence. Proteins fold into three-dimensional configurations called conformations, which are defined by their amino acid sequence. A protein's entire structure can be classified into four stages of complexity: primary, secondary, tertiary, and quaternary.