Dataset Information

Cryo electron micrographs of Coxsackievirus A9 bound with compound 17 (CL301)

ABSTRACT:

SUBMITTER: Cristopher Mitchell

PROVIDER: EMPIAR-12396 | biostudies-other |

REPOSITORIES: biostudies-other

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Publications

SAR Analysis of Novel Coxsackie virus A9 Capsid Binders.

Tammaro Chiara C Plavec Zlatka Z Myllymäki Laura L Mitchell Cristopher C Consalvi Sara S Biava Mariangela M Ciogli Alessia A Domanska Aušra A Leppilampi Valtteri V Buckner Cienna C Manetto Simone S Sciò Pietro P Coluccia Antonio A Laajala Mira M Dondio Giulio M GM Bigogno Chiara C Marjomäki Varpu V Butcher Sarah J SJ Poce Giovanna G

Journal of medicinal chemistry 20240918 19

Enterovirus infections are common in humans, yet there are no approved antiviral treatments. In this study we concentrated on inhibition of one of the <i>Enterovirus B</i> (EV-B), namely Coxsackievirus A9 (CVA9), using a combination of medicinal chemistry, virus inhibition assays, structure determination from cryogenic electron microscopy and molecular modeling, to determine the structure activity relationships for a promising class of novel <i>N</i>-phenylbenzylamines. Of the new 29 compounds s ...[more]

PMID: 39292620

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Project description:BackgroundIdentification and selection of protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based particle picking network to automatically detect particle centers from cryoEM micrographs. This is a challenging task due to the nature of cryoEM data, having low signal-to-noise ratios with variable particle sizes, shapes, distributions, grayscale variations as well as other undesirable artifacts.ResultsWe propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. This approach, entitled Deep Regression Picker Network or "DRPnet", is simple but very effective in recognizing different particle sizes, shapes, distributions and grayscale patterns corresponding to 2D views of 3D particles. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined to reduce false particle detections by the second classification CNN. DRPnet's first CNN pretrained with only a single cryoEM dataset can be used to detect particles from different datasets without retraining. Compared to RELION template-based autopicking, DRPnet results in better particle picking performance with drastically reduced user interactions and processing time. DRPnet also outperforms the state-of-the-art particle picking networks in terms of the supervised detection evaluation metrics recall, precision, and F-measure. To further highlight quality of the picked particle sets, we compute and present additional performance metrics assessing the resulting 3D reconstructions such as number of 2D class averages, efficiency/angular coverage, Rosenthal-Henderson plots and local/global 3D reconstruction resolution.ConclusionDRPnet shows greatly improved time-savings to generate an initial particle dataset compared to manual picking, followed by template-based autopicking. Compared to other networks, DRPnet has equivalent or better performance. DRPnet excels on cryoEM datasets that have low contrast or clumped particles. Evaluating other performance metrics, DRPnet is useful for higher resolution 3D reconstructions with decreased particle numbers or unknown symmetry, detecting particles with better angular orientation coverage.