Design, characterization and structural analysis of artificial Designer-Proteins for surface binding applications using computational approaches

Background

Proteins are highly versatile biopolymers that perform a wide array of functions in biological systems, including catalysis, molecular recognition, structural support, and signaling. Harnessing the power of protein engineering allows for the creation of “Designer-Proteins” with custom functions that do not exist in nature. Recent advances in computational tools such as Rosetta developed by David Baker’s team [Rohl et al., 2004, Methods Enzymol.; Das & Baker, 2008, Annu. Rev. Biochem.] and AlphaFold [Jumper et al., 2021, Nature] have enabled the rational design and structural prediction of proteins with unprecedented precision.

Objective

This project leverages the latest advancements in computational protein design and structural biology to develop novel Designer-Proteins for wood. The designed proteins should pave the way for a platform for bio-based binders as an alternative to common synthetic glues by investigating the role of surface-binding proteins in the assembly of biomaterials for biotechnological applications. The Designer-Proteins can be used to functionalize surfaces in biomaterials e.g., wood chips, improving properties like adhesion strength or wetting. The combination of RosettaSurface and ProteinMPNN [Dauparas, et al., 2022, Science] for rational design and AlphaFold3 for structural validation represents a cutting-edge approach to designing proteins for biotechnological and industrial applications. The project is developing a computational approach for a surface-centric computational protein design by utilizing these computational tools for protein binders. The specific objectives are: Design of artificial surface-binding proteins using RosettaSurface (I), Structural ensemble prediction and validation using AlphaFold (II), Mechanistic understanding of surface binding using atomistic and coarse-grained molecular dynamics (MD) simulations (III).

Literature:

Das, R., & Baker, D. (2008). Macromolecular modeling with rosetta. Annu. Rev. Biochem., 77(1), 363-382.

Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., … & Baker, D. (2022). Robust deep learning–based protein sequence design using ProteinMPNN. Science, 378(6615), 49-56.

Jumper, J., Evans, R., Pritzel, A., et al.: Highly accurate protein structure prediction with AlphaFold. Nature

Rohl, C.A., Strauss, C.E., Misura, K.M., Baker, D.: Protein structure prediction using Rosetta. Methods Enzymol. 383(Pt A), 66–93 (2004).