TEMPy: a Python library for assessment of three-dimensional electron microscopy density fits

Abstract

Three-dimensional electron microscopy is currently one of the most promising techniques used to study macromolecular assemblies. Rigid and flexible fitting of atomic models into density maps is often essential to gain further insights into the assemblies they represent. Currently, tools that facilitate the assessment of fitted atomic models and maps are needed. TEMPy (template and electron microscopy comparison using Python) is a toolkit designed for this purpose. The library includes a set of methods to assess density fits in intermediate-to-low resolution maps, both globally and locally. It also provides procedures for singlefit assessment, ensemble generation of fits, clustering, and multiple and consensus scoring, as well as plots and output files for visualization purposes to help the user in analysing rigid and flexible fits. The modular nature of TEMPy helps the integration of scoring and assessment of fits into large pipelines, making it a tool suitable for both novice and expert structural biologists.

Defining the limits and reliability of rigid-body fitting in cryo-EM maps using multi-scale image pyramids

“…Further optimization of the fit can then be tried using the Fit in Map routines in UCSF Chimera. Many advances have been made in both sensitivity and speed of cross-correlation based rigid body fitting (Bettadapura et al, 2015;Chacón and Wriggers, 2002;Derevyanko and Grudinin, 2014;Farabella et al, 2015;Garzón et al, 2007;Hoang et al, 2013;Hrabe et al, 2012;Roseman, 2000;Volkmann and Hanein, 1999;Volkmann, 2009;Wu et al, 2003). Recently, we introduced the coreweighted local cross-correlation scores in our rigid-body fitting package PowerFit .…”

Integrative modelling of cellular assemblies

“…Integrative approaches yield a model or an ensemble of models that will have minimal restraints violations. The generated models can be clustered and ranked based on a score or a consensus among multiple scores and/or size of the clusters [27]. For cross validation, part of the experimental data can be excluded during the modelling process.…”

“…TEMPy is a python package for integrative modeling primarily based on EM density [27] (but also crosslinking data). Simultaneous fitting of multiple components is carried out by a genetic algorithm, which uses the mutual information to account for the goodness-of-fit and penalises for steric clashes [34].…”

Recent developments in theCCP-EMsoftware suite

“…Flex-EM requires rigid-body definitions and these can be produced using the helper program RIBFIND (Pandurangan & Topf, 2012a,b). CCP-EM also includes the TEMPy library (Farabella et al, 2015), and task interfaces for TEMPy:DiffMap (difference map) and TEMPy:SMOC (Segment-based Manders' Overlap Coefficient) for structural validation are provided.…”

“…TEMPy implements a wide variety of scoring functions for model-to-map and map-to-map fits (Vasishtan & Topf, 2011;Farabella et al, 2015;Joseph et al, 2017), as well as other functions for map and model manipulations. It was designed as a Python library with a series of command-line scripts for useful routines.…”