AFsample: Improving Multimer Prediction with AlphaFold using Aggressive Sampling

Abstract The AlphaFold neural network model has revolutionized structural molecular 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 around 6,000 models per target compared to 25 default for AF2-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 to AF2-multimer it improved the average DockQ from 0.41 to 0.55 using identical input and was ranked at the very top in the protein assembly category when compared to all other groups participating in CASP15. The simplicity of the method should facilitate the adaptation by the field, and the method should be useful for anyone interested in modelling multimeric structures, alternate conformations or flexible structures. Availability AFsample is available online at http://wallnerlab.org/AFsample .