AI Tools for Protein Structures

trRosetta

They inverted this network to generate new protein sequences from scratch, aiming to design proteins with structures and functions not found in nature.By conducting Monte Carlo sampling in sequence space and optimizing the predicted structural features, they managed to produce a variety of new protein sequences.

RFdiffusion

Watson, Joseph L., et al[1] published the RFdiffusion at github in 2023. It fine-tune the RoseTTAFold[2] and designed for tasks like: protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. It is a very powerful tool according to the paper. It is based on the Denoising diffusion probabilistic models (DDPMs) which is a powerful class of machine learning models demonstrated to generate new photorealistic images in response to text prompts[3].

They use the ProteinMPNN^[4] network to subsequently design sequences encoding theses structure. The diffusion model is based on the DDPMs. It can not only design a protein from generation, but also able to predict multiple types of interactions as is shown of the left. It was based on the RoseTTAFold.

Compared with AF2

• AlphaFold2 is like a very smart detective that can figure out the 3D shape of a protein just by looking at its amino acid sequence. On the other hand, RFdiffusion is more like an architect that designs entirely new proteins with specific properties. Instead of just figuring out shapes, it creates new proteins that can do things like bind to specific molecules or perform certain reactions. This makes it incredibly useful for designing new therapies and industrial enzymes.

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1. Watson J L, Juergens D, Bennett N R, et al. De novo design of protein structure and function with RFdiffusion[J]. Nature, 2023, 620(7976): 1089-1100. ↩︎

2. Baek M, et al. Accurate prediction of protein structures and interactions using a 3-track network. Science. July 2021. ↩︎

3. Ramesh, A. et al. Zero-shot text-to-image generation. in Proc. 38th International Conference on Machine Learning Vol. 139 (eds Meila, M. & Zhang, T.) 8821–8831 (PMLR, 2021). ↩︎

4. Dauparas J, Anishchenko I, Bennett N, et al. Robust deep learning–based protein sequence design using ProteinMPNN[J]. Science, 2022, 378(6615): 49-56. ↩︎