AFsample: improving multimer prediction with AlphaFold using massive sampling

SUMMARY: The AlphaFold2 neural network model has revolutionized structural biology with unprecedented performance. We demonstrate that by stochastically perturbing the neural network by enabling dropout at inference combined with massive sampling, it is possible to improve the quality of the generated models. We generated ∼6000 models per target compared with 25 default for AlphaFold-Multimer, with v1 and v2 multimer network models, with and without templates, and increased the number of recycles within the network. The method was benchmarked in CASP15, and compared with AlphaFold-Multimer v2 it improved the average DockQ from 0.41 to 0.55 using identical input and was ranked at the very top in the protein…

• SE
  Swedish e-Science Research Centre
• NS
  National Science Council
• CT
  Carl Tryggers Stiftelse för Vetenskaplig Forskning

  Award: 20:453
• KO
  Knut och Alice Wallenbergs Stiftelse
• V
  Vetenskapsrådet

  Award: 2020-03352