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13 pages, 4 figures, 2 tables.-- Software. ; [Background] Gene/protein recognition and normalization are important preliminary steps for many biological text mining tasks, such as information retrieval, protein-protein interactions, and extraction of semantic information, among others. Despite dedication to these problems and effective solutions being reported, easily integrated tools to perform these tasks are not readily available. ; [Results] This study proposes a versatile and trainable Java library that implements gene/protein tagger and normalization steps based on machine learning approaches. The system has been trained for several model organisms and corpora but can be expanded to support new organisms and documents. ; [Conclusions] Moara is a flexible, trainable and open-source system that is not specifically orientated to any organism and therefore does not requires specific tuning in the algorithms or dictionaries utilized. Moara can be used as a stand-alone application or can be incorporated in the workflow of a more general text mining system. ; This work has been partially funded by the Spanish grants BIO2007-67150-C03-02, S-Gen-0166/2006, PS-010000-2008-1 and European Union Grant FP7-HEALTH-F4-2008-202047. APM acknowledges the support of the Spanish Ramón y Cajal program. The authors acknowledge support from Integromics, S.L. ; Peer reviewed

Therapeutic or preventive research for coronavirus SARS-CoV2 is an extremely active topic of research since its outbreak in January 2020. In this paper we report the results from a virtual drug screening analysis that, to the best of our knowledge, is the widest work in terms of target proteins and compound library. Our study was focused on the repur- posing of currently commercialized drugs, and especially those that can interact with multiple viral proteins and several binding sites within each protein. Additionally, we performed a second virtual screening analysis in which we compared our results to the predicted binding a nities for the drugs currently in clinical trials. We show that the best molecules in our screening compares favourably to those in clinical trials, suggesting their suitability for therapeutic or preventive applications.Therapeutic or preventive research for coronavirus SARS-CoV2 is an extremely active topic of research since its outbreak in January 2020. In this paper we report the results from a virtual drug screening analysis that, to the best of our knowledge, is the widest work in terms of target proteins and compound library. Our study was focused on the repur- posing of currently commercialized drugs, and especially those that can interact with multiple viral proteins and several binding sites within each protein. Additionally, we performed a second virtual screening analysis in which we compared our results to the predicted binding a nities for the drugs currently in clinical trials. We show that the best molecules in our screening compares favourably to those in clinical trials, suggesting their suitability for therapeutic or preventive applications. ; We thank nancial support from the Spanish Ministry of Economy and Competitiveness through Grants BIO2016-76400-R(AEI/FEDER, UE), the \Comunidad Autonoma de Madrid" through Grant: S2017/BMD-3817, Instituto de Salud Carlos III, PT17/0009/0010 (ISCIII-SGEFI/ERDF), European Union (EU) and Horizon 2020 through grants: CORBEL (INFRADEV-1-2014-1, Proposal: 654248), 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), EOSC-Synergy (EINFRA-EOSC-5, Proposal: 857647), and iNEXTDiscovery (Proposal: 871037). The authors acknowledge the support and the use of resources of Instruct, a Landmark ESFRI project. ; No

16 pages, 4 figures, 4 tables.-- PMID: 19321015 [PubMed].-- PMCID: PMC2683171. ; [Background] The transmission electron microscope is used to acquire structural information of macromolecular complexes. However, as any other imaging device, it introduces optical aberrations that must be corrected if high-resolution structural information is to be obtained. The set of all aberrations are usually modeled in Fourier space by the so-called Contrast Transfer Function (CTF). Before correcting for the CTF, we must first estimate it from the electron micrographs. This is usually done by estimating a number of parameters specifying a theoretical model of the CTF. This estimation is performed by minimizing some error measure between the theoretical Power Spectrum Density (PSD) and the experimentally observed PSD. The high noise present in the micrographs, the possible local minima of the error function for estimating the CTF parameters, and the cross-talking between CTF parameters may cause errors in the estimated CTF parameters. ; [Results] In this paper, we explore the effect of these estimation errors on the theoretical CTF. For the CTF model proposed in [1] we show which are the most sensitive CTF parameters as well as the most sensitive background parameters. Moreover, we provide a methodology to reveal the internal structure of the CTF model (which parameters influence in which parameters) and to estimate the accuracy of each model parameter. Finally, we explore the effect of the variability in the detection of the CTF for CTF phase and amplitude correction. ; [Conclusion] We show that the estimation errors for the CTF detection methodology proposed in [1] does not show a significant deterioration of the CTF correction capabilities of subsequent algorithms. All together, the methodology described in this paper constitutes a powerful tool for the quantitative analysis of CTF models that can be applied to other models different from the one analyzed here. ; This work was funded by the European Union (projects FP6-502828 and UE-512092), the 3DEM European network (LSHG-CT-2004-502828) and the ANR (PCV06-142771), the Spanish Ministerio de Educación y Ciencias (CSD2006-0023, BIO2007-67150-C01 and BIO2007-67150-C03), the Spanish Fondo de Investigación Sanitaria (04/0683), Univ. San Pablo CEU (USP-PPC 04/07) and the Comunidad de Madrid (S-GEN-0166-2006). The project described was supported by Award Number R01HL070472 from the National Heart, Lung, And Blood Institute. ; Peer reviewed

© The Author(s) 2020 ; The introduction of local resolution has enormously helped the understanding of cryo-EM maps. Still, for any given pixel it is a global, aggregated value, that makes impossible the individual analysis of the contribution of the different projection directions. We introduce MonoDir, a fully automatic, parameter-free method that, starting only from the final cryo-EM map, decomposes local resolution into the different projection directions, providing a detailed level of analysis of the final map. Many applications of directional local resolution are possible, and we concentrate here on map quality and validation. ; J.M.C. thanks Dr. Richard Henderson for discussions in the context of the use of radial and tangential resolution maps. J.L.V., J.M.C., C.O.S.S. were supported by the Spanish Ministry of Economy and Competitiveness through Grants BIO2016-76400-R(AEI/ FEDER, UE), the "Comunidad Autónoma de Madrid" through Grant: S2017/BMD-3817, and the use of resources of Instruct-ERIC. H.D.T. was supported by NIH grant R01GM114051. J.V. was supported by grants from NSERC Discovery Grant (RGPIN2018-04813) and FRQNT New University Researchers Start-Up (NC-253837). European Union (EU) Horizon 2020 through grants EOSC Life (INFRAEOSC-04-2018, Proposal: 824087), HighResCells (ERC - 2018 - SyG, Proposal: 810057).

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).

Versión preprint disponible en: http://hdl.handle.net/10261/214335 ; Cryo-EM maps are valuable sources of information for protein structure modeling. However, due to the loss of contrast at high frequencies, they generally need to be post-processed to improve their interpretability. Most popular approaches, based on global B-factor correction, suffer from limitations. For instance, they ignore the heterogeneity in the map local quality that reconstructions tend to exhibit. Aiming to overcome these problems, we present DeepEMhancer, a deep learning approach designed to perform automatic post-processing of cryo-EM maps. Trained on a dataset of pairs of experimental maps and maps sharpened using their respective atomic models, DeepEMhancer has learned how to post-process experimental maps performing masking-like and sharpening-like operations in a single step. DeepEMhancer was evaluated on a testing set of 20 different experimental maps, showing its ability to reduce noise levels and obtain more detailed versions of the experimental maps. Additionally, we illustrated the benefits of DeepEMhancer on the structure of the SARS-CoV-2 RNA polymerase. ; The authors would like to acknowledge economical support from: The Spanish Ministry of Science and Innovation through Grants: Proyectos de I+D+i - RTI Tipo A PID2019-108850RA-I00, SEV 2017-0712, PID2019-104757RB-I00/ AEI/10.13039/501100011033; the "Comunidad Autónoma de Madrid" through Grant S2017/BMD-3817; CSIC: PIE/COVID-19 number 202020E079; European Union (EU) and Horizon 2020 through grants EOSC Life (INFRAEOSC-04-2018, Proposal: 824087) and HighResCells (ERC - 2018- SyG, Proposal: 810057). J.V. acknowledges economical support from the Ramón y Cajal 2018 program (RYC2018-024087-I). ; Peer reviewed

11 pages, 4 figures.-- PMID: 19397789 [PubMed].-- PMCID: PMC2694187. ; [Background] Tilt series are commonly used in electron tomography as a means of collecting three-dimensional information from two-dimensional projections. A common problem encountered is the projection alignment prior to 3D reconstruction. Current alignment techniques usually employ gold particles or image derived markers to correctly align the images. When these markers are not present, correlation between adjacent views is used to align them. However, sequential pairwise correlation is prone to bias and the resulting alignment is not always optimal. ; [Results] In this paper we introduce an algorithm to find regions of the tilt series which can be tracked within a subseries of the tilt series. These regions act as landmarks allowing the determination of the alignment parameters. We show our results with synthetic data as well as experimental cryo electron tomography. ; [Conclusion] Our algorithm is able to correctly align a single-tilt tomographic series without the help of fiducial markers thanks to the detection of thousands of small image patches that can be tracked over a short number of images in the series. ; This work was funded by the European Union (projects FP6-502828 and UE-512092), the 3DEM European network (LSHG-CT-2004-502828) and the ANR (PCV06-142771), the Spanish Ministerio de Educación y Ciencias (CSD2006-0023, BIO2007-67150-C01 and BIO2007-67150-C03), the Spanish Fondo de Investigación Sanitaria (04/0683) and the Comunidad de Madrid (S-GEN-0166-2006). The project described was supported by Award Number R01HL070472 from the National Heart, Lung, And Blood Institute. ; Peer reviewed

© The Author(s) 2019. ; We have used cryo Electron Tomography, proteomics and immunolabeling to study centrosomes isolated from the young lamb thymus, an efficient source of quiescent differentiated cells. We compared the proteome of thymocyte centrosomes to data published for KE37 cells, focusing on proteins associated with centriole disengagement and centrosome separation. The data obtained enhances our understanding of the protein system joining the centrioles, a system comprised of a branched network of fibers linked to an apparently amorphous density that was partially characterized here. A number of proteins were localized to the amorphous density by immunolabeling (C-NAP1, cohesin SMC1, condensin SMC4 and NCAPD2), yet not DNA. In conjuction, these data not only extend our understanding of centrosomes but they will help refine the model that focus on the protein system associated with the centriolar junction. ; This work was partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO) grants BIO2013-44647-R and AIC-A-2011-0638 (to JMC), BIO2016-76400-R (JMC, RM), BFU2014-54181 (to JLC, FJC, MJR), the Consolider project CSD2006-00023 (JMC, JB), and by the Madrid regional government grant S2010/BMD-2305 (to JMC, JB, SIG, AA, MF). The proteomic analysis was performed at the Centro Nacional de Biotecnología (CNB-CSIC) Proteomics Facility, which is supported by grant PT13/0001 (ProteoRed, PRB2-ISCIII, which also funds AP)

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

We have developed a new data collection method and processing framework in full field cryo soft X-ray tomography to computationally extend the depth of field (DOF) of a Fresnel zone plate lens. Structural features of 3D-reconstructed eukaryotic cells that are affected by DOF artifacts in standard reconstruction are now recovered. This approach, based on focal series projections, is easily applicable with closed expressions to select specific data acquisition parameters. ; This work was partially supported by MINECO grants BFU2014-54181 to JLC and AIC-A-2011-0638, BIO2013-44647-R and BIO2016-76400-R to JMC, Madrid. Regional government grants S2013/MIT-2850 to JLC and S2010/BMD-2305 to JMC, National Science Foundation grant DMS-1114901 to GTH, the European Union through BioStruct-X Project 283570 and Horizon 2020 through grant iNEXT (INFRAIA-1-2014-2015, Proposal: 653706). ; Sí

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