ShapeProt: Top-down Protein Design with 3D Protein Shape Generative Model
Lee, Y.; Kim, J.
Show abstract
With the fact that protein functionality is tied to its structure and shape, a protein design paradigm of generating proteins tailored to specific shape contexts has been utilized for various biological applications. Recently, researchers have shown that top-down strategies are possible with the aid of deep learning for the shape-conditioned design. However, state-of-the-art models have limitations because they do not fully consider the geometric and chemical constraints of the entire shape. In response, we propose ShapeProt, a pioneering end-to-end protein design framework that directly generates protein surfaces and generate sequences with considering the entire nature of the generated shapes. ShapeProt distinguishes itself from current protein deep learning models that primarily handle sequence or structure data because ShapeProt directly handles surfaces. ShapeProt framework employs mask-based inpainting and conditioning to generate diverse shapes at the desired location, and these shapes are then translated into sequences using a shape-conditioned language model. Drawing upon various experimental results, we first prove the feasibility of generative design directly on the three-dimensional molecular surfaces beyond sequences and structures.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.