3D Reconstruction of Protein Complex Structures Using Synthesized Multi-View AFM Images

TL;DR

This paper introduces a deep learning approach using synthesized multi-view AFM images for 3D reconstruction of protein complexes, addressing dataset limitations with a virtual imaging pipeline and achieving promising results.

Contribution

The work presents a virtual AFM imaging pipeline to generate datasets for training neural networks on protein complex 3D reconstruction, a novel approach in this domain.

Findings

Achieved IoU of 0.92 on training data

Achieved IoU of 0.52 on validation data

Demonstrated effectiveness of multi-view AFM images for 3D structure prediction

Abstract

Recent developments in deep learning-based methods demonstrated its potential to predict the 3D protein structures using inputs such as protein sequences, Cryo-Electron microscopy (Cryo-EM) images of proteins, etc. However, these methods struggle to predict the protein complexes (PC), structures with more than one protein. In this work, we explore the atomic force microscope (AFM) assisted deep learning-based methods to predict the 3D structure of PCs. The images produced by AFM capture the protein structure in different and random orientations. These multi-view images can help train the neural network to predict the 3D structure of protein complexes. However, obtaining the dataset of actual AFM images is time-consuming and not a pragmatic task. We propose a virtual AFM imaging pipeline that takes a 'PDB' protein file and generates multi-view 2D virtual AFM images using volume rendering techniques. With this, we created a dataset of around 8K proteins. We train a neural network for 3D reconstruction called Pix2Vox++ using the synthesized multi-view 2D AFM images dataset. We compare the predicted structure obtained using a different number of views and get the intersection over union (IoU) value of 0.92 on the training dataset and 0.52 on the validation dataset. We believe this approach will lead to better prediction of the structure of protein complexes.