  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.
