Speaker
Description
In recent years we have seen an avalanche of cryo-EM (cryogenic Electron Microscopy) publications presenting beautiful biological structures at resolution levels of even better than ~3Å! This true “resolution revolution” has culminated in the 2017 Nobel prize for Chemistry being awarded for single-particle cryo-EM. Impressive as these results may be (and continue to be), various fundamental - mainly statistical - errors have been introduced in the early days of biological electron microscopy that are now interfering with the progress of the field. For example, a generic a-priori assumption made in the derivation of virtually all current resolution criteria in cryo-EM, is that signal and noise in the data are independent of each other, and that thus the cross-terms between signal and noise can be left out of the equations. Leaving them out of the equations, however, is equivalent to stating that the signal vectors and the corresponding noise vectors are orthogonal - which is incorrect - and not equivalent to stating that the signal vectors and the noise vectors are independent - which would have been correct. There are serious consequences to using flawed metrics in comparing the results of independently conducted experiments or in using such metrics for optimisation in automatic refinement procedures. The persistence of the field to move away from these flawed metrics has now led to an accumulation of errors by building new methodologies upon shaky foundations. Ignoring the Whittaker-Shannon sampling rules, for example, and justifying the results obtained from under-sampled data by pointing at “how beautiful they look”, brings us into swampy scientific territory.
