Likelihood-based docking of models into cryo-EM maps

Abstract

Optimized docking of models into cryo-EM maps requires exploiting an understanding of the signal expected in the data to minimize the calculation time while maintaining sufficient signal. The likelihood-based rotation function used in crystallography can be employed to establish plausible orientations in a docking search. A phased likelihood translation function yields scores for the placement and rigid-body refinement of oriented models. Optimized strategies for choices of the resolution of data from the cryo-EM maps to use in the calculations and the size of search volumes are based on expected log-likelihood-gain scores computed in advance of the search calculation. Tests demonstrate that the new procedure is fast, robust and effective at placing models into even challenging cryo-EM maps.

Likelihood-based signal and noise analysis for docking of models into cryo-EM maps

“…The accompanying paper (Milla ´n et al, 2023) describes the implementation of these ideas in software tools for docking, and the success of those tools demonstrates the validity of the approach described here, including the use of eLLG to choose optimal strategies. Variation of signal power (AE S ) and noise power (AE E ) in Fourier space for (a) well ordered (centre of the cytoplasmic domain) and (b) poorly ordered (chain L) regions of E. coli respiratory complex I.…”

“…If there is a hypothesis for the location of a particular component in a full reconstruction, the rotation search can use the Fourier terms computed from a portion extracted from the full reconstruction. The use of such a procedure is essential to the subvolume searches mentioned below and is discussed in detail in the accompanying paper (Milla ´n et al, 2023).…”

“…In crystallographic MR, it has been possible to optimize the choice of search strategy by firstly knowing the absolute LLG score that is required for correct solutions to be recognized and secondly being able to predict the LLG score that can be achieved in a particular search given the quality of the model, the quality of the data and the resolution limit applied to the data (McCoy et al, 2017;Oeffner et al, 2018). The same considerations of expected LLG (or eLLG) can be applied to docking in cryo-EM, as discussed in detail in the accompanying paper (Milla ´n et al, 2023).…”

Likelihood-based signal and noise analysis for docking of models into cryo-EM maps

“…In crystallographic MR, it has been possible to optimise the choice of search strategy by, first, knowing what absolute LLG score is required for correct solutions to be recognised and, second, being able to predict the LLG score that can be achieved in a particular search, given the quality of the model, the quality of the data and the resolution limit applied to the data (McCoy et al, 2017;Oeffner et al, 2018). The same considerations of expected LLG (or eLLG) can be applied to docking in cryo-EM, as discussed in detail in the accompanying paper (Millan et al, 2023).…”

“…If we consider smaller sub-volumes of the full reconstruction (useful when searching for small components, as discussed below and in the accompanying paper (Millan et al ., 2023)), the strength of the signal will vary because the degree of local structural order varies. On the other hand, the strength of the noise should be reasonably uniform over different parts of the full reconstruction, as discussed by Palmer and Aylett (2022).…”