Protein structures and functions are determined by a contiguous arrangement of amino acid sequences. Designing novel protein sequences and structures with desired geometry and functions is a complex task with large state spaces. Here we develop a novel protein design pipeline consisting of two deep learning algorithms, ProteinSolver and AlphaFold2. ProteinSolver is a deep graph neural network that generates amino acid sequences such that the forces between interacting amino acids are favorable and compatible with the fold while AlphaFold2 is a deep learning algorithm that predicts the protein structures from protein sequences. We present forty de novo designed binding sites of the PTP1B and P53 proteins with high precision, out of which thirty proteins are novel. Using ProteinSolver and AlphaFold2 in conjunction, we can trim the exploration of the large protein conformation space, thus expanding the ability to find novel and diverse de novo protein designs.
@article{arxiv.2208.14526,
title = {Designing novel protein structures using sequence generator and AlphaFold2},
author = {Xeerak Agha and Nihang Fu and Jianjun Hu},
journal= {arXiv preprint arXiv:2208.14526},
year = {2022}
}