If you cite TEMPy, please use:

Cragnolini T, Sahota H, Joseph AP, Sweeney A, Malhotra S, Vasishtan D, Topf M (2021a) TEMPy2: A Python library with improved 3D electron microscopy density-fitting and validation workflows. Acta Crystallogr Sect D Struct Biol 77:41–47.

Other references

  • Cragnolini et al (2021b) Automated Modeling and Validation of Protein Complexes in Cryo-EM Maps. Methods Mol Biol 2215:189–223.

  • Sinnott et al (2020) Combining Information from Crosslinks and Monolinks in the Modeling of Protein Structures. Structure 28:1061-1070.e3.

  • Joseph et al (2017) Improved metrics for comparing structures of macromolecular assemblies determined by 3D electron-microscopy. J Struct Biol 99(1): 12-26

  • Joseph et al (2016) Refinement of atomic models in high resolution EM reconstructions using Flex-EM and local assessment. Methods. 1;100:42-9

  • Farabella et al (2015) TEMPy: a Python library for assessment of three-dimensional electron microscopy density fits. J. Appl. Cryst. 48, 1314-1323

  • Vasishtan and Topf (2011) Scoring functions for cryoEM density fitting. J Struct Biol 174:333-343.

  • Pandurangan AP, Vasishtan D, Alber F, Topf M. (2015) γ-TEMPy: Simultaneous Fitting of Components in 3D-EM Maps of Their Asssembly Using a Genetic Algorithm. Structure. 23(12), 2365-2376.

  • Bullock JMA, Schwab J, Thalassinos K, Topf M. (2016). The importance of non-accessible crosslinks and solvent accessible surface distance in modelling proteins with restraints from crosslinking mass spectrometry. MCP. 5(7):2491-500.