cryoID

cryoID identifies proteins within unknown near-atomic resolution cryo-electron microscopy (cryoEM) density maps to determine unique protein identities from ab initio reconstructed maps of endogenous protein complexes.

Key Features:

• Implementation: Python-based bioinformatics program for computational analysis of cryoEM density maps.
• Input data: Operates on near-atomic resolution cryoEM density maps reconstructed ab initio from unidentified protein complexes.
• Sequence-to-map matching: Matches candidate protein sequences against density maps during a detailed identification phase to assign protein identity.
• Atomic modeling support: Facilitates identification and subsequent atomic modeling of proteins within cryoEM maps.
• Endogenous sample compatibility: Designed to work with protein complexes enriched from the endogenous cellular environment without requiring engineered constructs.
• Applicability to challenging systems: Applicable to membrane proteins and large multicomponent complexes, including cases involving mutations or truncations.
• Empirical demonstration: Applied to datasets from Plasmodium falciparum to obtain atomic models of multiple protein complexes.

Scientific Applications:

• Plasmodium falciparum structural studies: Identification and modeling of protein complexes involved in intraerythrocytic survival of the malaria parasite.
• Membrane protein identification: Determination of identities and models for membrane proteins from cryoEM maps.
• Large multicomponent complex analysis: Identification of individual components within heterogeneous, multicomponent assemblies.
• Structural proteomics from native samples: Enabling atomic modeling and proteome-scale identification directly from endogenous complexes.

Methodology:

Ab initio reconstruction of near-atomic resolution cryoEM maps from unidentified protein complexes, followed by a sequence-to-map identification phase that matches candidate protein sequences to density maps; implemented as a Python-based program.