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Status: Unchecked ×Keyword: tetrahedral nanoparticles ×Clear all

3 claims from 1 paper

1. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   Robust deep learning–based protein sequence design using ProteinMPNN

   Dauparas, Anishchenko, Bennett et al. · Science · 2022

   The paper describes ProteinMPNN, a deep learning method for designing protein sequences, and reports in silico and experimental tests including rescuing designs that had previously failed.

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   1. UncheckedOn native protein backbones, ProteinMPNN recovers 52.4% of the original amino acids, against 32.9% for the Rosetta software.“On native protein backbones, ProteinMPNN has a sequence recovery of 52.4% compared with 32.9% for Rosetta.”
   2. UncheckedIn ProteinMPNN, amino acid choices at different positions can be linked across one or several protein chains, so it can suit many protein design tasks.“The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges.”
   3. Unchecked“We demonstrate the broad utility and high accuracy of ProteinMPNN using x-ray crystallography, cryo–electron microscopy, and functional studies by rescuing previously failed designs, which were made using Rosetta or AlphaFold, of protein monomers, cyclic hom…