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2.
Nat Commun ; 12(1): 3399, 2021 06 07.
Article in English | MEDLINE | ID: covidwho-1260942

ABSTRACT

Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we comprehensively assessed protein-protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed Protein Interface-score (PI-score), a density-independent machine learning-based metric, trained using the features of protein-protein interfaces in crystal structures. We evaluated 5873 interfaces in 1053 PDB-deposited cryo-EM models (including SARS-CoV-2 complexes), as well as the models submitted to CASP13 cryo-EM targets and the EM model challenge. We further inspected the interfaces associated with low-scores and found that some of those, especially in intermediate-to-low resolution (worse than 4 Å) structures, were not captured by density-based assessment scores. A combined score incorporating PI-score and fit-to-density score showed discriminatory power, allowing our method to provide a powerful complementary assessment tool for the ever-increasing number of complexes solved by cryo-EM.


Subject(s)
Cryoelectron Microscopy/methods , Macromolecular Substances/chemistry , Protein Interaction Domains and Motifs , Protein Interaction Mapping/methods , Protein Interaction Maps , Proteins/chemistry , Humans , Machine Learning , Macromolecular Substances/metabolism , Macromolecular Substances/ultrastructure , Models, Molecular , Neural Networks, Computer , Protein Conformation , Protein Multimerization , Proteins/metabolism , Proteins/ultrastructure , Support Vector Machine , Viral Nonstructural Proteins/chemistry , Viral Nonstructural Proteins/metabolism , Viral Nonstructural Proteins/ultrastructure
3.
PLoS Comput Biol ; 16(11): e1008444, 2020 11.
Article in English | MEDLINE | ID: covidwho-1040035

ABSTRACT

We provide a stand-alone software, the BioAFMviewer, which transforms biomolecular structures into the graphical representation corresponding to the outcome of atomic force microscopy (AFM) experiments. The AFM graphics is obtained by performing simulated scanning over the molecular structure encoded in the corresponding PDB file. A versatile molecular viewer integrates the visualization of PDB structures and control over their orientation, while synchronized simulated scanning with variable spatial resolution and tip-shape geometry produces the corresponding AFM graphics. We demonstrate the applicability of the BioAFMviewer by comparing simulated AFM graphics to high-speed AFM observations of proteins. The software can furthermore process molecular movies of conformational motions, e.g. those obtained from servers which model functional transitions within a protein, and produce the corresponding simulated AFM movie. The BioAFMviewer software provides the platform to employ the plethora of structural and dynamical data of proteins in order to help in the interpretation of biomolecular AFM experiments.


Subject(s)
Microscopy, Atomic Force/statistics & numerical data , Software , Computational Biology , Computer Graphics , Computer Simulation , Microscopy, Video/statistics & numerical data , Molecular Dynamics Simulation/statistics & numerical data , Molecular Structure , Motion Pictures , Nanotechnology , Protein Conformation , Proteins/chemistry , Proteins/ultrastructure , User-Computer Interface
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