De Novo Protein Fold Design Through Sequence-Independent Fragment Assembly Simulations

De novo protein design generally consists of two steps, including structure and sequence design. However, many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. Here we developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent exposed areas that closely matched their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds that were completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses demonstrated that the major contributions to the successful fold design lay in the optimal energy force field, which contains a balanced set of fragment and secondary structure packing terms, and the REMC simulations, which utilize multiple auxiliary movements to efficiently search the conformational space. These results demonstrate FoldDesigns strong potential to explore both structural and functional space through computational design simulations that natural proteins have not reached through evolution. SignificanceNatural proteins were generated following billions of years of evolution and therefore possess limited structural folds and biological functions. There is considerable interest in de novo protein design to generate artificial proteins with novel structures and functions beyond those created by nature. However, the success rate of computational de novo protein design remains low, where extensive user-intervention and large-scale experimental optimization are typically required to achieve successful designs. To address this issue, we developed a new automated open-source program, FoldDesign, for de novo protein fold design which shows improved performance in creating high fidelity stable folds compared to other state-of-the-art methods. The success of FoldDesign should enable the creation of desired protein structures with promising clinical and industrial potential.