AlphaFold has transformed structure prediction by enabling highly
accurate predictions on par with experimentally determined structures.
Still, for difficult cases, in particular, multimers, there is still
room for improvement. Important for the success of AlphaFold is its
ability to assess its own predictions. The basic idea for the Wallner
group in CASP15 was to exploit the excellent ranking score in AlphaFold
by massive sampling. To this end, we ran AlphaFold using six different
settings, with and without templates, and with an increased number of
recycles using both multimer v1 and v2 weights. In all cases, the
dropout layers were enabled at inference to sample the uncertainty and
increase the diversity of the generated models. A median of 4,810 models
per target was generated and almost all (35/38) received a
ranking_confidence >0.7. Compared to other groups
in CASP15, Wallner obtained the highest sum of Z-scores based on the
DockQ score, 40.8 compared to 26.3 for the second highest, much higher
than -0.2 achieved by the AlphaFold baseline method, NBIS-AF2-multimer.
The improvement over the baseline is substantial with the mean DockQ
increasing from 0.43 to 0.56, with several targets showing a DockQ score
increase by +0.6 units. Remarkable, considering Wallner and
NBIS-AF2-multimer were using identical input data. The reason for the
success can be attributed to the diversified sampling using dropout with
different settings and, in particular, the use of multimer v1, which
seems to be much more susceptible to sampling compared to v2. The method
is available here: http://wallnerlab.org/AFsample/.