3D Reconstruction of Protein Structures from Multi-view AFM Images using Neural Radiance Fields (NeRFs)
<|reference_start|>3D Reconstruction of Protein Structures from Multi-view AFM Images using Neural Radiance Fields (NeRFs): Recent advancements in deep learning for predicting 3D protein structures have shown promise, particularly when leveraging inputs like protein sequences and Cryo-Electron microscopy (Cryo-EM) images. However, these techniques often fall short when predicting the structures of protein complexes (PCs), which involve multiple proteins. In our study, we investigate using atomic force microscopy (AFM) combined with deep learning to predict the 3D structures of PCs. AFM generates height maps that depict the PCs in various random orientations, providing a rich information for training a neural network to predict the 3D structures. We then employ the pre-trained UpFusion model (which utilizes a conditional diffusion model for synthesizing novel views) to train an instance-specific NeRF model for 3D reconstruction. The performance of UpFusion is evaluated through zero-shot predictions of 3D protein structures using AFM images. The challenge, however, lies in the time-intensive and impractical nature of collecting actual AFM images. To address this, we use a virtual AFM imaging process that transforms a `PDB' protein file into multi-view 2D virtual AFM images via volume rendering techniques. We extensively validate the UpFusion architecture using both virtual and actual multi-view AFM images. Our results include a comparison of structures predicted with varying numbers of views and different sets of views. This novel approach holds significant potential for enhancing the accuracy of protein complex structure predictions with further fine-tuning of the UpFusion network.<|reference_end|>
@article{rade20243d,
    title={3D Reconstruction of Protein Structures from Multi-view AFM Images using
  Neural Radiance Fields (NeRFs)},
    author={Jaydeep Rade, Ethan Herron, Soumik Sarkar, Anwesha Sarkar, Adarsh
  Krishnamurthy},
    journal={CVPR 2024 Workshop Deep Learning for Geometric Computing},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06244},
    primaryClass={cs.CV}
}