Improved De Novo Main-Chain Tracing Method Mainmast for Multi-Chain Modeling, Local Refinement, and Graphical User Interface

Abstract

The significant progress of the cryo-EM poses a pressing need for software for structural interpretation of EM maps. Particularly, protein structure modeling tools are needed for maps determined around 4 Å resolution, where finding main-chain structure and assigning the amino acid sequence into EM map are still challenging problems. We have developed a de novo modeling tool named MAINMAST (MAINchain Model trAcing from Spanning Tree, http://kiharalab.org/mainmast/) for EM maps for this resolution range (Nature Communications, 2018). MAINMAST builds main-chain traces of a protein in an EM map from a tree structure constructed by connecting points with a high density in the map without referring to known protein structures or fragments.Here, we report substantial improvements of MAINMAST in three aspects. The largest improvement is that the method now can perform automatic map segmentation and structure modeling for symmetrical multi-chain complexes. The tree structure that connects dense points are traced for multiple chains simultaneously in a symmetric fashion. Moreover, the accuracy of a model is significantly improved by a new implementation of local sequence matching and structure refinement. The local matching protocol is also useful for identifying missing regions in a structure model, i.e. regions with a low density, in an EM map. Finally, we developed a software plugin of MAINMAST for the UCSF Chimera, so that users can monitor structures at each step of a modeling procedure. The major functionalities include to generate and to display tree structures from local dense points in the map, main-chain traces, and reconstructed all-atom models. Through the interface, users can easily control parameters of MAINMAST and save and restore sessions.