Conclusion
With the extension of CAMEO to the fully automated assessment of
prediction of complexes (including protein-protein, DNA, RNA, peptides,
small molecules), we aim to encourage and facilitate the development of
automated structure prediction servers going beyond the modeling of
single chains of amino acids. In this manuscript, we identified several
challenging aspects of modeling which we believe will become more active
areas of research in the future, and that are suitable for benchmarking
with CAMEO. By assessing prediction targets with the same complexity as
experimental structures using an “opt in” mechanism for the diverse
modelling tasks, CAMEO will assist the development of new methods
tackling these specific modeling challenges. As demonstrated by
analysing the PDB releases of the last year, CAMEO will be able to
provide a diverse set of challenging blind prediction targets to enable
the community to tackle next generation modeling challenges.
We welcome feedback from the community on which of these aspects should
be prioritized and how various predictions should be numerically
evaluated in CAMEO. We encourage methods developers to register to the
beta CAMEO server to help testing and evolving these new features
according to the needs of the prediction community.