bioRxiv (Cold Spring Harbor Laboratory) · 2022 · 12 citations · 15 references
Life SciencesBioinformaticsComputational BiologyStructural BioinformaticsStructural BiologyProtein DesignProtein Design ModelingProtein StructureProtein Structure ModelingProtein-ligand DockingComputational EfficiencyComputational Efficiency AnalysisComputational Protein DesignDe Novo Protein Binder DesignDe Novo Protein DesignDeep Learning Protein DesignProtein Sequence DesignSequence Design
Abstract We explore the improvement of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.
15
Highly accurate protein structure prediction with AlphaFold
John Jumper, Alexander Pritzel, Tim Green et al. · Nature · 2021 · 45.2K citations · Full text
Accurate prediction of protein structures and interactions using a three-track neural network
Minkyung Baek, Frank DiMaio, Ivan Anishchenko et al. · Science · 2021 · 5.7K citations · Full text
Andrew Leaver‐Fay, Michael D. Tyka, Steven M. Lewis et al. · Methods in enzymology on CD-ROM/Methods in enzymology · 2010 · 2K citations
Design of protein-binding proteins from the target structure alone
Longxing Cao, Brian Coventry, Inna Goreshnik et al. · Nature · 2022 · 572 citations · Full text