protein design

 

https://www.ipd.uw.edu/software/

https://www.nature.com/articles/d41586-024-02214-x

https://www.nature.com/articles/d41586-023-02227-y

https://github.com/universvm/how_to_create_a_protein

RFdiffusion generates ultra-high affinity binders through several key mechanisms:

1. Guided diffusion approach: RFdiffusion uses a diffusion model to gradually sculpt protein structures from a random distribution of atoms. This allows it to generate novel protein structures that can bind tightly to target antigens[2].

2. Fine-tuning for specific tasks: The model can be fine-tuned for tasks like binder design, allowing it to generate proteins tailored for binding to specific targets[2].

3. Conditioning on interface hotspots: RFdiffusion can be provided with information about desired binding sites on the target protein, allowing it to focus on generating binders that interact with those specific regions[2].

4. Scaffold topology control: For some design challenges, the model can be conditioned on secondary structure and block-adjacency information to control the overall topology of the binder[2].

5. Direct generation in context: Unlike some other methods, RFdiffusion can generate binding proteins directly in the context of the target protein, optimizing the interface as it builds the structure[2].

6. High shape complementarity: The "build to fit" approach of RFdiffusion allows it to create binders with very high shape complementarity to the target, which contributes to high affinity[5].

7. Flexible backbone design: RFdiffusion can generate binders while allowing flexibility in the target peptide backbone, potentially finding novel binding modes[5].

8. Iterative refinement: The model can be used to refine existing designs through partial noising and denoising, allowing for fine-tuning of the binding interface[5].

These capabilities have led to remarkable results. For example, RFdiffusion has generated binders with picomolar affinity to several helical peptides, including some that are reported to be the highest-affinity binders achieved directly by computational design without experimental optimization[5].

The power of RFdiffusion lies in its ability to simultaneously optimize the overall protein structure and the binding interface, leading to binders that are both stable and highly complementary to their targets. This approach has significantly increased the success rate of computational protein design, often requiring testing of only dozens of designs to find high-affinity binders, rather than the tens of thousands that might be needed with previous methods[2][5].

Citations:

[1] https://fold.it

[2] https://www.nature.com/articles/s41586-023-06415-8

[3] https://www.ipd.uw.edu/2022/12/a-diffusion-model-for-protein-design/

[4] https://www.bakerlab.org/2023/12/19/designing-binders-with-the-highest-affinity-ever-reported/

[5] https://www.nature.com/articles/s41586-023-06953-1