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  • Robust deep learning–based protein sequence design using . . .
    Dauparas et al built on recent deep learning protein design approaches to develop a method called ProteinMPNN They validated designs experimentally and showed that ProteinMPNN can rescue previously failed designs made
  • PROTEIN DESIGN Robust deep learning based protein . . .
    describe a deep learning–based protein sequence design method, ProteinMPNN, that has outstanding performance in both in silico and experimental tests On n ative protein backbones, ProteinMPNN has a sequence recovery of
  • Deep learning–guided design of dynamic proteins | Science . . .
    Due to what appeared to be low in silico success rates, we decided to use ProteinMPNN, a deep learning-based design method capable of quickly generating tens of thousands of multi-state designs () This method has since been shown to successfully generate proteins where domains can be hinged open and closed upon binding a peptide
  • Self-supervised machine learning methods for protein design . . .
    We chose ProteinMPNN (), MIF-ST (), and ESM-2 (14, 28) to cover the spectrum of structure- and sequence-based ML models and allow for a head-to-head comparison In addition, we compared different ML- and biophysics-based scores with the oracle predicted fitness values to identify useful metrics for ranking sampled sequences for
  • Scalable protein design using optimization in a relaxed . . .
    The emerging de novo protein design pipeline comprises backbone design, sequence generation, and design filtering ProteinMPNN and ESMFold provide fast and reliable methods for generating sequences for given backbones and
  • Scalable protein design using optimization in a relaxed . . .
    passing neural network (ProteinMPNN) mod-ule (5) to generate candidate protein sequences for the converged backbone geometry (Fig 1C) ProteinMPNN is akey component because it was specifically trained to design protein
  • In Science Journals | Science - AAAS
    Dauparas et al built on recent deep learning protein design approaches to develop a method called ProteinMPNN They validated designs experimentally and showed that ProteinMPNN can rescue previously failed designs made
  • Target-conditioned diffusion generates potent TNFR . . .
    We generated 25,000 partially diffused backbones around each starting structure, and following ProteinMPNN, the 32 designs for each starting structure that AF2 most confidently predicted bound TNFR1 in the designed binding
  • Hallucinating symmetric protein assemblies | Science
    Dauparas et al built on recent deep learning protein design approaches to develop a method called ProteinMPNN They validated designs experimentally and showed that ProteinMPNN can rescue previously failed designs made





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