MPNN - ML for protein sequence design

One powerful method for protein sequence design with ML is using the message passing neural network: ProteinMPNN. This deep learning based method “encodes” a backbone structure and then “decodes” a sequence. The MPNN architecture is broken down and the softwares decoding process is described in the context of model performance. Techniques to increase sequence diversity are also included.
Protein Modeling and Design with PyRosetta and Machine Learning
• Protein Modeling and D...
Video from the Rosetta Commons RaMP Bootcamp (July 2023)
Instructor: Deniz Akpinaroglu (UCSF)
Structured Transformer paper: proceedings.ne... paper: www.science.or...
Credits:
Instructor: Deniz Akpinaroglu (UCSF)
RaMP Director and Rosetta Commons Director: Jeffrey Gray (JHU)
RaMP Program Administrator:Camille Mathis (JHU)
Rosetta Commons Instructional Designer: Ashley Vater (UC Davis)
Video Production: Elizabeth Bonilla (JHU)
Funding: Rosetta Commons, National Science Foundation, and Johns Hopkins University