Integrating Secondary Structures Information into Triangular Spatial Relationships (TSR) for Advanced Protein Classification
Journal:
arXiv
Published Date:
Nov 19, 2024
Abstract
Protein structures represent the key to deciphering biological functions. The
more detailed form of similarity among these proteins is sometimes overlooked
by the conventional structural comparison methods. In contrast, further
advanced methods, such as Triangular Spatial Relationship (TSR), have been
demonstrated to make finer differentiations. Still, the classical
implementation of TSR does not provide for the integration of secondary
structure information, which is important for a more detailed understanding of
the folding pattern of a protein. To overcome these limitations, we developed
the SSE-TSR approach. The proposed method integrates secondary structure
elements (SSEs) into TSR-based protein representations. This allows an enriched
representation of protein structures by considering 18 different combinations
of helix, strand, and coil arrangements. Our results show that using SSEs
improves the accuracy and reliability of protein classification to varying
degrees. We worked with two large protein datasets of 9.2K and 7.8K samples,
respectively. We applied the SSE-TSR approach and used a neural network model
for classification. Interestingly, introducing SSEs improved performance
statistics for Dataset 1, with accuracy moving from 96.0% to 98.3%. For Dataset
2, where the performance statistics were already good, further small
improvements were found with the introduction of SSE, giving an accuracy of
99.5% compared to 99.4%. These results show that SSE integration can
dramatically improve TSR key discrimination, with significant benefits in
datasets with low initial accuracies and only incremental gains in those with
high baseline performance. Thus, SSE-TSR is a powerful bioinformatics tool that
improves protein classification and understanding of protein function and
interaction.
