Name
Title | ||
|---|---|---|
Reconstruction and Visualization of Protein Structures by exploiting Bidirectional Neural Networks and Discrete Classes |
Abstract | ||
|---|---|---|
In recent years, Deep Learning techniques have achieved some success in bioinformatics tasks, including protein conformation prediction. In this work, we propose a Bidirectional Long Short-Term Memory (BLSTM) network system, called Human Proteins Angles Prediction (HPAP), in order to improve the prediction of dihedral angles of proteins. We have introduced a discrete subdivision in classes of 5° for protein torsion angles and four new features related to accessible surface area and volume. In total there are 73 classes (72 classes include the angles between -180° and 180°, a further class is used to code the free angles at the beginning of the sequence) with a maximum expected error of ±2.5°. We have tested three model variants in several parameter combinations. With our model, we have obtained a decrease of the mean absolute error of about 2° for the $\psi$ angle. Although our dataset is reduced in size, the accuracy of $\varphi$ and $\psi$ angles is comparable to the existing methods. Predicting angles accurately is useful for accurately reconstructing the three-dimensional structure of a protein. In this context, the prediction is limited to the $\varphi$ and $\psi$ angles and we will visualize what happens locally when a prediction is correct. In case the prediction is far from true angles, even a small error can deconstruct the backbone. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IV53921.2021.00053 | 2021 25th International Conference Information Visualisation (IV) |
Keywords | DocType | ISSN |
Protein Structure,Torsion Angle Prediction,Long Short-Term Memory,Deep Learning | Conference | 1550-6037 |
ISBN | Citations | PageRank |
978-1-6654-3828-5 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessia Auriemma Citarella | 1 | 0 | 0.34 |
| Lorenzo Porcelli | 2 | 0 | 0.34 |
| Luigi Di Biasi | 3 | 0 | 0.34 |
| Michele Risi | 4 | 403 | 40.98 |
| Genoveffa Tortora | 5 | 1477 | 151.59 |