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Abstract
Protein structure can provide insights that help biologists to predict and understand protein functions and interactions. However, the number of known protein structures has not kept pace with the number of protein sequences determined by high-throughput sequencing. Current techniques used to determine the structure of proteins are complex and require a lot of time to analyze the experimental results, especially for large protein molecules. The limitations of these methods have motivated us to create a new approach for protein structure prediction. Here we describe a new approach to predict of protein structures and structure classes from amino acid sequences. Our prediction model performs well in comparison with previous methods when applied to the structural classification of two CATH datasets with more than 5000 protein domains. The average accuracy is 92.5% for structure classification, which is higher than that of previous research. We also used our model to predict four known protein structures with a single amino acid sequence, while many other existing methods could only obtain one possible structure for a given sequence. The results show that our method provides a new effective and reliable tool for protein structure prediction research.
Highlights
The resolution of protein three-dimensional structure is one of the most important research problems in the field of structural biology
Our method is based on the integration and analysis of torsion angle information from the Protein Data Bank (PDB) database, which contains information from over 10 million torsion angles
With the continuing development of sequencing technologies, methods are required for prediction of protein structures from amino acid sequences
Summary
The resolution of protein three-dimensional structure is one of the most important research problems in the field of structural biology. By taking into account the torsion angles between protein sequences, our algorithm improves structure prediction in general It determines the class of the most likely structure for a given amino acid sequence, but it can predict and model multiple structures of the same sequence, something many other software tools are not able to achieve this point. Our new prediction method performed well with an average of 92.5% accuracy for structure classification, which is a great improvement than Rackovsky’s previous r esearch[23]. This method was applied to a single amino acid sequence to model four different known protein structures. Our results demonstrate that this new approach is efficient and reliable on protein structure prediction, and can obtain multiple different structures for the same sequence, improve protein-folding recognition, classification of structural motifs, and refinement of sequence alignment
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