Actualités en direct des projets (non traduites)

First, we’d like to thank all our contributors who have supported and continue to contribute to our scientific projects through Rosetta@home. We want to update you on the status of Rosetta@home projects and our future plans.

With the advancement of AI models like AlphaFold and RosettaFold for protein structure predictions, Rosetta@home has been less used for this purpose. However, researchers are now utilizing Rosetta@home for small molecule and peptide designs, where even the current state-of-the-art AI models struggle due to limitations in generalizability to novel small molecules and non-canonical peptides.

Recently, we have developed a virtual screening protocol in Rosetta, named RosettaVS, for small molecule drug discovery. This work has been published in Nature Communications (https://doi.org/10.1038/s41467-024-52061-7), demonstrating that RosettaVS is one of the best physics-based virtual screening protocols. Combined with deep learning techniques, it can effectively screen multi-billion compound libraries and discover novel compounds for pharmaceutical targets.

While deep learning models like AlphaFold and RosettaFold can predict canonical peptide structures, they cannot handle peptides with non-canonical amino acids or mixed chirality. The physics-based force field in Rosetta has specialized terms to simulate these amino acids. Rosetta will be used to sample hundreds of thousands of different conformations of the designed peptide to validate the structure.

Looking ahead, Rosetta@home will be an invaluable platform for large-scale virtual screening and peptide simulations for drug discovery. We plan to launch more virtual screening jobs and peptide simulations on Rosetta@home in the near future.

Thank you!

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