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In this article, a method is presented to estimate a new local quality measure for 3D cryoEM maps that adopts the form of a `local resolution' type of information. The algorithm (DeepRes) is based on deep-learning 3D feature detection. DeepRes is fully automatic and parameter-free, and avoids the issues of most current methods, such as their insensitivity to enhancements owing to B-factor sharpening (unless the 3D mask is changed), among others, which is an issue that has been virtually neglected in the cryoEM field until now. In this way, DeepRes can be applied to any map, detecting subtle changes in local quality after applying enhancement processes such as isotropic filters or substantially more complex procedures, such as model-based local sharpening, non-model-based methods or denoising, that may be very difficult to follow using current methods. It performs as a human observer expects. The comparison with traditional local resolution indicators is also addressed. ; The authors would like to acknowledge financial support from: the Comunidad de Madrid through grant CAM (S2017/BMD-3817), the Spanish Ministry of Economy and Competitiveness (BIO2016-76400-R) and the European Union and Horizon 2020 through INSTRUCT-ULTRA (INFRADEV-03-016-2017, Proposal 731005), iNEXT (INFRAIA-1-2014-2015, Proposal 653706) and West-Life (EINFRA-2015-1, Proposal 675858).

The source code (LocalDeblur) can be found at https://github.com/I2PC/xmipp and can be run using Scipion (http://scipion.cnb.csic.es) (release numbers greater than or equal 1.2.1) ; Recent technological advances and computational developments have allowed the reconstruction of Cryo-Electron Microscopy (cryo-EM) maps at near-atomic resolution. On a typical workflow and once the cryo-EM map has been calculated, a sharpening process is usually performed to enhance map visualization, a step that has proven very important in the key task of structural modeling. However, sharpening approaches, in general, neglects the local quality of the map, which is clearly suboptimal. Results Here, a new method for local sharpening of cryo-EM density maps is proposed. The algorithm, named LocalDeblur, is based on a local resolution-guided Wiener restoration approach of the original map. The method is fully automatic and, from the user point of view, virtually parameter-free, without requiring either a starting model or introducing any additional structure factor correction or boosting. Results clearly show a significant impact on map interpretability, greatly helping modeling. In particular, this local sharpening approach is especially suitable for maps that present a broad resolution range, as is often the case for membrane proteins or macromolecules with high flexibility, all of them otherwise very suitable and interesting specimens for cryo-EM. To our knowledge, and leaving out the use of local filters, it represents the first application of local resolution in cryo-EM sharpening ; The authors would like to acknowledge economical support from: the Comunidad de Madrid through grant CAM (S2017/BMD-3817), the Spanish Ministry of Economy and Competitiveness (BIO2016-76400-R) and the European Union and Horizon 2020 through grants INSTRUCT-ULTRA (INFRADEV-03-2016-2017, Proposal: 731005) and iNEXT (INFRAIA-1-2014-2015, Proposal: 653706)

https://3dbionotes.cnb.csic.es/ws/covid19 ; The web platform 3DBionotes-WS integrates multiple Web Services and an interactive Web Viewer to provide a unified environment in which biological annotations can be analyzed in their structural context. Since the COVID-19 outbreak, new structural data from many viral proteins have been provided at a very fast pace. This effort includes many cryogenic Electron Microscopy (cryo-EM) studies, together with more traditional ones (X-rays, NMR), using several modeling approaches and complemented with structural predictions. At the same time, a plethora of new genomics and interactomics information (including fragment screening and structure-based virtual screening efforts) have been made available from different servers. In this context we have developed 3DBionotes-COVID-19 as an answer to: (1) The need to explore multi-omics data in a unified context with a special focus on structural information and (2) the drive to incorporate quality measurements, especially in the form of advanced validation metrics for cryogenic Electron Microscopy. ; We acknowledge financial support from: CSIC (PIE/COVID-19 number 202020E079), the Comunidad de Madrid through grant CAM (S2017/BMD-3817), the Spanish Ministry of Science and Innovation through projects (SEV 2017-0712, FPU-2015/264, PID2019-104757RB-I00 / AEI / 10.13039/501100011033), the Instituto de Salud Carlos III: PT17/0009/0010 (ISCIII-SGEFI / ERDF-) and the European Union and Horizon 2020 through grant: CORBEL (INFRADEV-01-2014- 1, Proposal 654248) and EOSC Life (INFRAEOSC-04-2018, Proposal: 824087). This work was supported by Instruct-ULTRA (Grant 731005), an EU H2020 project to further develop the services of Instruct-ERIC. Contributions from the Coronavirus Structural Task Force were supported by the German Federal Ministry of Education and Research [grant no. 05K19WWA] and Deutsche Forschungsgemeinschaft [project TH2135/2-1]. The authors acknowledge the support and the use of resources of Instruct, a Landmark ESFRI project. ; Peer reviewed

Using a new consensus-based image-processing approach together with principal component analysis, the flexibility and conformational dynamics of the SARS-CoV-2 spike in the prefusion state have been analysed. These studies revealed concerted motions involving the receptor-binding domain (RBD), N-terminal domain, and subdomains 1 and 2 around the previously characterized 1-RBD-up state, which have been modeled as elastic deformations. It is shown that in this data set there are not well defined, stable spike conformations, but virtually a continuum of states. An ensemble map was obtained with minimum bias, from which the extremes of the change along the direction of maximal variance were modeled by flexible fitting. The results provide a warning of the potential image-processing classification instability of these complicated data sets, which has a direct impact on the interpretability of the results. ; The authors would like to acknowledge financial support from CSIC (PIE/COVID-19 No. 202020E079), the Comunidad de Madrid through grant CAM (S2017/BMD-3817), the Spanish Ministry of Science and Innovation through projects SEV 2017-0712, FPU-2015/264 and PID2019-104757RB-I00/AEI/FEDER, the Instituto de Salud Carlos III [PT17/0009/0010 (ISCIII-SGEFI/ERDF)], and the European Union and Horizon 2020 through grants INSTRUCT–ULTRA (INFRADEV-03-2016-2017, Proposal 731005), EOSC Life (INFRAEOSC-04-2018, Proposal 824087), HighResCells (ERC-2018-SyG, Proposal 810057), IMpaCT (WIDESPREAD-03-2018, Proposal 857203), CORBEL (INFRADEV-1-2014-1, Proposal 654248) and EOSC–Synergy (EINFRA-EOSC-5, Proposal 857647). HDT and BF were supported by NIH grant GM125769 and JSM was supported by NIH grant R01-AI127521. ; Peer reviewed