  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.
