Principles and Methods in Computational Membrane Protein Design |
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Affiliation: | 1. Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA;2. UC Berkeley – UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, USA;3. Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California, USA;1. Institute for Protein Innovation, Boston, MA 02115, USA;2. Division of Hematology/Oncology, Boston Children''s Hospital, Boston, MA, USA;3. Department of Pediatrics, Harvard Medical School, Boston, MA, USA |
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Abstract: | After decades of progress in computational protein design, the design of proteins folding and functioning in lipid membranes appears today as the next frontier. Some notable successes in the de novo design of simplified model membrane protein systems have helped articulate fundamental principles of protein folding, architecture and interaction in the hydrophobic lipid environment. These principles are reviewed here, together with the computational methods and approaches that were used to identify them. We provide an overview of the methodological innovations in the generation of new protein structures and functions and in the development of membrane-specific energy functions. We highlight the opportunities offered by new machine learning approaches applied to protein design, and by new experimental characterization techniques applied to membrane proteins. Although membrane protein design is in its infancy, it appears more reachable than previously thought. |
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Keywords: | computational protein design membrane proteins folding protein structure protein function CHAMP" },{" #name" :" keyword" ," $" :{" id" :" k0035" }," $$" :[{" #name" :" text" ," _" :" Computed Helical Anti-Membrane Protein DL" },{" #name" :" keyword" ," $" :{" id" :" k0045" }," $$" :[{" #name" :" text" ," _" :" Deep learning GAN" },{" #name" :" keyword" ," $" :{" id" :" k0055" }," $$" :[{" #name" :" text" ," _" :" Generative Adversarial Networks GPCR" },{" #name" :" keyword" ," $" :{" id" :" k0065" }," $$" :[{" #name" :" text" ," _" :" G Protein Coupled Receptor MCMC" },{" #name" :" keyword" ," $" :{" id" :" k0075" }," $$" :[{" #name" :" text" ," _" :" Markov Chain Monte Carlo MP" },{" #name" :" keyword" ," $" :{" id" :" k0085" }," $$" :[{" #name" :" text" ," _" :" Membrane proteins MSA" },{" #name" :" keyword" ," $" :{" id" :" k0095" }," $$" :[{" #name" :" text" ," _" :" Multiple Sequence Alignment NLP" },{" #name" :" keyword" ," $" :{" id" :" k009598" }," $$" :[{" #name" :" text" ," _" :" natural language processing NMR" },{" #name" :" keyword" ," $" :{" id" :" k0105" }," $$" :[{" #name" :" text" ," _" :" Nuclear Magnetic Resonance PDB" },{" #name" :" keyword" ," $" :{" id" :" k0115" }," $$" :[{" #name" :" text" ," _" :" Protein Data Bank REAMP" },{" #name" :" keyword" ," $" :{" id" :" k0125" }," $$" :[{" #name" :" text" ," _" :" Recombinantly Expressed Artificial Membrane Protein RNN" },{" #name" :" keyword" ," $" :{" id" :" k0135" }," $$" :[{" #name" :" text" ," _" :" Recurrent Neural Networks TM" },{" #name" :" keyword" ," $" :{" id" :" k0145" }," $$" :[{" #name" :" text" ," _" :" transmembrane TMB" },{" #name" :" keyword" ," $" :{" id" :" k0155" }," $$" :[{" #name" :" text" ," _" :" transmembrane beta-barrel TMD" },{" #name" :" keyword" ," $" :{" id" :" k0165" }," $$" :[{" #name" :" text" ," _" :" Transmembrane Domain TR-Rosetta" },{" #name" :" keyword" ," $" :{" id" :" k0175" }," $$" :[{" #name" :" text" ," _" :" transform-restrained Rosetta VAE" },{" #name" :" keyword" ," $" :{" id" :" k0185" }," $$" :[{" #name" :" text" ," _" :" Variational AutoEncoders |
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