Biology subjects

Frank, C. J.

Publications and source records attributed to Frank, C. J..

2 recordsLinked to original sources

bioRxiv2024

Alphafold2 refinement improves designability of large de novo proteins

Recent advances in computational protein design have enabled the creation of novel proteins for a variety of purposes. The capability for producing custom-shape high-quality backbones for very large proteins would further expand the scope of protein design. To this end, here we introduce the AlphaFold2 (AF2) cycler (af2cycler) design pipeline. AF2cycler is used to refine draft protein backbone geometries of arbitrary shape created by the Chroma diffusion model or other methods, using a combination of AF2 and ProteinMPNN to achieve high designability with minimal deviations from the initial target structure. In silico testing on multiple protein designs (100-1000 amino acids) demonstrated improvements in structural integrity and designability after af2cycling confirmed by enhanced ESMFold repredictions. Experimental wet lab validation through the design of a variety of test proteins with distinct shapes, each comprising 1000 amino acids, showed structural agreement between in silico predictions and transmission electron microscopy (TEM) imaging, establishing the af2cyclers efficacy in translating designs into real-world results. Af2cycler provides a convenient and reliable protein design workflow, particularly for large proteins, with potential for expanding to applications in areas such as design of higher-order protein complexes and multi-state backbone optimization.

bioinformatics↗

bioRxiv2023

Efficient and scalable de novo protein design using a relaxed sequence space

Deep learning techniques are being used to design new proteins by creating target backbone geometries and finding sequences that can fold into those shapes. While methods like ProteinMPNN provide an efficient algorithm for generating sequences for a given protein backbone, there is still room for improving the scope and computational efficiency of backbone generation. Here, we report a backbone hallucination protocol that uses a relaxed sequence representation. Our method enables protein backbone generation using a gradient descent driven hallucination approach and offers orders-of-magnitude efficiency enhancements over previous hallucination approaches. We designed and experimentally produced over 50 proteins, most of which expressed well in E. Coli, were soluble and adopted the desired oligomeric state along with the correct composition of secondary structure as measured by CD. Exemplarily, we determined 3D electron density maps using single-particle cryo EM analysis for three single-chain de-novo proteins comprising 600 AA which closely matched with the designed shape. These have no structural analogues in the protein data bank (PDB), representing potentially novel folds or arrangement of domains. Our approach broadens the scope of de novo protein design and contributes to accessibility to a wider community.

synthetic biology↗