Similarity Analysis of 3D Structures of Proteins Based Tile-CNN

Abstract

The 3D structure of a protein is closely related to its function, and the similarity analysis between their structures can help reveal the function of proteins. However, there exist two problems arising from the analysis of 3D structures of proteins. The proteins with a similar sequence may have different structures, while the proteins with a similar structure may have different sequences. In the analysis of similarity in 3D structures of proteins, it remains difficult for the traditional methods using the spatial feature distribution and geometry or topology features of proteins to solve these problems. In this paper, a Tile-CNN network is proposed to analyze the similarity of proteins in 3D structure. In order to capture the overall and the local features as exhibited by the 3D structures of proteins, it projects 3D protein models into 2D protein images from different views and then cuts these 2D projected images using the tile strategy. After the training of proteins with these images in the Tile-CNN, the test protein model can be expressed by an analysis matrix, and then the similarity between 3D structures of proteins is computed using the root mean square distance (RMSD) for the benchmark matrix and the analysis matrix. As revealed by the experimental results, the proposed algorithm is more robust in analyzing the similarity of 3D structures of proteins and produces a satisfactory performance in solving the two aforementioned problems.

Classifying metal‐binding sites with neural networks

“…In this work, we used CNNs to determine if image classification can be used as a tool to identify metal binding sites both with and without the metal in the pocket. Similar to previous studies where they were studying protein classes or protein structure classes, we also found success in classifying structures based on 2D images (Qin et al, 2020) and specifically using ball and stick representations (Nanni et al, 2020) when classifying metal‐binding sites.…”

“…They also compared the effects of various representations of the structures and found that the ball and stick representation was one of the top performing representations (Nanni et al, 2020). Other studies used mono‐colored 3D triangle mesh models from which the 2D images were obtained from different viewpoints (Qin et al, 2020).…”