PEM-fitter: A Coarse-Grained Method to Validate Protein Candidate Models

CryoEM maps are full of potential

Electron microscopy is based on elastic scattering due to Coulomb forces between the incident electrons and the sample; thus, electron scattering is dependent on the charge distribution in the sample. Unlike atomic scattering factors for X-rays, electron scattering factors for some atoms are strongly dependent on scattering angle, and the scattering factor for ionic oxygen is negative at low scattering angle. This phenomenon can result in a significant negative contribution to Coulomb potential maps by oxygen and can result in deviations in the positions of positive map features from atomic centers. An important factor that can also complicate the interpretation of cryoEM maps is the exquisite sensitivity of macromolecules to damage from electron irradiation, especially the carboxylates of acidic amino acids. Ideally, when compared with electron density maps derived by X-ray crystallography, Coulomb potential maps can provide additional details about the electrostatic environment and charge state of atoms. Enhancements in model building, refinement and computational simulation will be required to realize the full potential of EM-derived maps to reveal deeper insight into the electronic structure and functional properties of macromolecular complexes and their interactions with binding partners, ligands, cofactors, and drugs.

Analytical Approaches to Improve Accuracy in Solving the Protein Topology Problem

To take advantage of recent advances in genomics and proteomics it is critical that the three-dimensional physical structure of biological macromolecules be determined. Cryo-Electron Microscopy (cryo-EM) is a promising and improving method for obtaining this data, however resolution is often not sufficient to directly determine the atomic scale structure. Despite this, information for secondary structure locations is detectable. De novo modeling is a computational approach to modeling these macromolecular structures based on cryo-EM derived data. During de novo modeling a mapping between detected secondary structures and the underlying amino acid sequence must be identified. DP-TOSS (Dynamic Programming for determining the Topology Of Secondary Structures) is one tool that attempts to automate the creation of this mapping. By treating the correspondence between the detected structures and the structures predicted from sequence data as a constraint graph problem DP-TOSS achieved good accuracy in its original iteration. In this paper, we propose modifications to the scoring methodology of DP-TOSS to improve its accuracy. Three scoring schemes were applied to DP-TOSS and tested: (i) a skeleton-based scoring function; (ii) a geometry-based analytical function; and (iii) a multi-well potential energy-based function. A test of 25 proteins shows that a combination of these schemes can improve the performance of DP-TOSS to solve the topology determination problem for macromolecule proteins.