29 research outputs found

    Structural and energy determinants in protein-RNA docking

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    Deciphering the structural and energetic determinants of protein-RNA interactions harbors the potential to understand key cell processes at molecular level, such as gene expression and regulation. With this purpose, computational methods like docking aim to complement current biophysical and structural biology efforts. However, the few reported docking algorithms for protein-RNA interactions show limited predictive success rates, mainly due to incomplete sampling of the conformational space of both the protein and the RNA molecules, as well as to the difficulties of the scoring function in identifying the correct docking models. Here, we have tested the predictive value of a variety of knowledge-based and energetic scoring functions on a recently published protein-RNA docking benchmark and developed a scoring function able to efficiently discriminate docking decoys. We first performed docking calculations with the bound conformation, which allowed us to analyze the problem in optimal conditions. We found that geometry-based terms and electrostatics were the most important scoring terms, while binding propensities and desolvation were much less relevant for the scoring of protein-RNA models. This is in contrast with what we observed for protein-protein docking. The results also showed an interesting dependence of the predictive rates on the flexibility of the protein molecule, which arises from the observed higher positive charge of flexible interfaces and provides hints for future development of more efficient protein-RNA docking methods.This work is supported by grant BIO2013-48213-R from Plan Nacional I+D+i (Spanish Ministry of Economy and Competitiveness). LP-C was recipient of an FPU fellowship from the Spanish Ministry of Science.Peer ReviewedPostprint (author's final draft

    LightDock: a new multi-scale approach to protein‚Äďprotein docking

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    Computational prediction of protein‚Äďprotein complex structure by docking can provide structural and mechanistic insights for protein interactions of biomedical interest. However, current methods struggle with difficult cases, such as those involving flexible proteins, low-affinity complexes or transient interactions. A major challenge is how to efficiently sample the structural and energetic landscape of the association at different resolution levels, given that each scoring function is often highly coupled to a specific type of search method. Thus, new methodologies capable of accommodating multi-scale conformational flexibility and scoring are strongly needed. We describe here a new multi-scale protein‚Äďprotein docking methodology, LightDock, capable of accommodating conformational flexibility and a variety of scoring functions at different resolution levels. Implicit use of normal modes during the search and atomic/coarse-grained combined scoring functions yielded improved predictive results with respect to state-of-the-art rigid-body docking, especially in flexible cases.B.J-G was supported by a FPI fellowship from the Spanish Ministry of Economy and Competitiveness. This work was supported by I+D+I Research Project grants BIO2013-48213-R and BIO2016-79930-R from the Spanish Ministry of Economy and Competitiveness. This work is partially supported by the European Union H2020 program through HiPEAC (GA 687698), by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology (TIN2015-65316-P) and the Departament d‚ÄôInnovaci√≥, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programaci√≥i Entorns d‚ÄôExecuci√≥ Paral¬∑lels (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

    Heterogeneous infectivity and pathogenesis of SARS-CoV-2 variants Beta, Delta and Omicron in transgenic K18-hACE2 and wildtype mice

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    The emerging SARS-CoV-2 variants of concern (VOCs) may display enhanced transmissibility, more severity and/or immune evasion; however, the pathogenesis of these new VOCs in experimental SARS-CoV-2 models or the potential infection of other animal species is not completely understood. Here we infected K18-hACE2 transgenic mice with B.1, B.1.351/Beta, B.1.617.2/Delta and BA.1.1/Omicron isolates and demonstrated heterogeneous infectivity and pathogenesis. B.1.351/Beta variant was the most pathogenic, while BA.1.1/Omicron led to lower viral RNA in the absence of major visible clinical signs. In parallel, we infected wildtype (WT) mice and confirmed that, contrary to B.1 and B.1.617.2/Delta, B.1.351/Beta and BA.1.1/Omicron can infect them. Infection in WT mice coursed without major clinical signs and viral RNA was transient and undetectable in the lungs by day 7 post-infection. In silico modeling supported these findings by predicting B.1.351/Beta receptor binding domain (RBD) mutations result in an increased affinity for both human and murine ACE2 receptors, while BA.1/Omicron RBD mutations only show increased affinity for murine ACE2.The research of CBIG consortium (constituted by IRTA-CReSA, BSC & IrsiCaixa) is supported by Grifols. We thank Foundation Dormeur for financial support for the acquisition of the QuantStudio-5 real time PCR system. C√Ā-N has a grant by Secretaria d‚ÄôUniversitats i Recerca de la Generalitat de Catalunya and Fons Social Europeu. EG-V is a research fellow from PERIS (SLT017/20/000090). This work was partially funded by grant PID2020-117145RB-I00 from the Spanish Ministry of Science and Innovation (NI-U) the Departament de Salut of the Generalitat de Catalunya (grant SLD016 to JB and Grant SLD015 to JC), the Spanish Health Institute Carlos III (Grant PI17/01518. PI20/00093 to JB and PI18/01332 to JC), Fundaci√≥ La Marat√≥ de TV3 (Project202126-30-21), CERCA Programme/Generalitat de Catalunya 2017 SGR 252, and the crowdfunding initiatives #joemcorono (https://www.yomecorono.com), BonPreu/Esclat and Correos. Funded in part by Fundaci√≥ Gl√≤ria Soler (JB). The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript.Peer Reviewed"Article signat per 27 autors/es: Ferran Tarr√©s-Freixas, Benjamin Trinit√©, Anna Pons-Gr√≠fols, Miguel Romero-Durana, Eva Riveira-Mu√Īoz, Carlos √Āvila-Nieto, M√≥nica P√©rez, Edurne Garcia-Vidal, Daniel Perez-Zsolt, Jordana Mu√Īoz-Basagoiti, D√†lia Ra√Įch-Regu√©, Nuria Izquierdo-Useros, Cristina Andr√©s, Andr√©s Ant√≥n, Tom√†s Pumarola, Ignacio Blanco, Marc Noguera-Juli√°n, Victor Guallar, Rosalba Lepore, Alfonso Valencia, Victor Urrea, J√ļlia Vergara-Alert, Bonaventura Clotet, Ester Ballana, Jorge Carrillo, Joaquim Segal√©s and Juli√† Blanco"Postprint (published version

    Heterogeneous Infectivity and Pathogenesis of SARS-CoV-2 Variants Beta, Delta and Omicron in Transgenic K18-hACE2 and Wildtype Mice

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    SARS-CoV-2; Viral load; Wildtype miceSARS-CoV-2; Carga viral; Ratones de tipo salvajeSARS-CoV-2; C√†rrega viral; Ratolins de tipus salvatgeThe emerging SARS-CoV-2 variants of concern (VOCs) may display enhanced transmissibility, more severity and/or immune evasion; however, the pathogenesis of these new VOCs in experimental SARS-CoV-2 models or the potential infection of other animal species is not completely understood. Here we infected K18-hACE2 transgenic mice with B.1, B.1.351/Beta, B.1.617.2/Delta and BA.1.1/Omicron isolates and demonstrated heterogeneous infectivity and pathogenesis. B.1.351/Beta variant was the most pathogenic, while BA.1.1/Omicron led to lower viral RNA in the absence of major visible clinical signs. In parallel, we infected wildtype (WT) mice and confirmed that, contrary to B.1 and B.1.617.2/Delta, B.1.351/Beta and BA.1.1/Omicron can infect them. Infection in WT mice coursed without major clinical signs and viral RNA was transient and undetectable in the lungs by day 7 post-infection. In silico modeling supported these findings by predicting B.1.351/Beta receptor binding domain (RBD) mutations result in an increased affinity for both human and murine ACE2 receptors, while BA.1/Omicron RBD mutations only show increased affinity for murine ACE2.The research of CBIG consortium (constituted by IRTA-CReSA, BSC & IrsiCaixa) is supported by Grifols. We thank Foundation Dormeur for financial support for the acquisition of the QuantStudio-5 real time PCR system. C√Ā-N has a grant by Secretaria d‚ÄôUniversitats i Recerca de la Generalitat de Catalunya and Fons Social Europeu. EG-V is a research fellow from PERIS (SLT017/20/000090). This work was partially funded by grant PID2020-117145RB-I00 from the Spanish Ministry of Science and Innovation (NI-U) the Departament de Salut of the Generalitat de Catalunya (grant SLD016 to JB and Grant SLD015 to JC), the Spanish Health Institute Carlos III (Grant PI17/01518. PI20/00093 to JB and PI18/01332 to JC), Fundaci√≥ La Marat√≥ de TV3 (Project202126-30-21), CERCA Programme/Generalitat de Catalunya 2017 SGR 252, and the crowdfunding initiatives #joemcorono (https://www.yomecorono.com), BonPreu/Esclat and Correos. Funded in part by Fundaci√≥ Gl√≤ria Soler (JB). The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript

    Architecture of the ESCPE-1 membrane coat

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    Recycling of membrane proteins enables the reuse of receptors, ion channels and transporters. A key component of the recycling machinery is the endosomal sorting complex for promoting exit 1 (ESCPE-1), which rescues transmembrane proteins from the endolysosomal pathway for transport to the trans-Golgi network and the plasma membrane. This rescue entails the formation of recycling tubules through ESCPE-1 recruitment, cargo capture, coat assembly and membrane sculpting by mechanisms that remain largely unknown. Herein, we show that ESCPE-1 has a single-layer coat organization and suggest how synergistic interactions between ESCPE-1 protomers, phosphoinositides and cargo molecules result in a global arrangement of amphipathic helices to drive tubule formation. Our results thus define a key process of tubule-based endosomal sorting.This work was funded by MCIN/AEI/10.13039/501100011033 (PID2020- 119132GB-I00, CEX2021-001136-S) (to A.H.), the Intramural Program of NICHD, NIH (ZIA HD001607 to J.S.B.), the Swiss National Science Foundation grant 205321 179041 (to D.C.-D.), the Human Frontiers Science Program grant RGP0017/2020 (to D.C.-D.) and the PID2021- 127309NB-I00 funded by AEI/10.13039/501100011033/ FEDER, UE (to D.C.-D.). This study made use of the Diamond Light Source proposal MX20113, ALBA synchrotron beamline BL13-XALOC, the cryo-EM facilities at the UK Electron Bio-Imaging Centre, proposals EM17171- 6 and EM17171, and the Midlands Regional Cryo-EM Facility at the Leicester Institute of Structural and Chemical Biology (LISCB). We thank C. Savva (LISCB, University of Leicester) for his help in cryo-EM data collection. With funding from the Spanish government through the Severo Ochoa Centre of Excellence’ accreditation (CEX2021-001136-S

    Susceptibility of Domestic Goat (Capra aegagrus hircus) to Experimental Infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) B.1.351/Beta Variant

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    A wide range of animal species are susceptible to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Natural and/or experimental infections have been reported in pet, zoo, farmed and wild animals. Interestingly, some SARS-CoV-2 variants, such as B.1.1.7/Alpha, B.1.351/Beta, and B.1.1.529/Omicron, were demonstrated to infect some animal species not susceptible to classical viral variants. The present study aimed to elucidate if goats (Capra aegagrus hircus) are susceptible to the B.1.351/Beta variant. First, an in silico approach was used to predict the affinity between the receptor-binding domain of the spike protein of SARS-CoV-2 B.1.351/Beta variant and angiotensin-converting enzyme 2 from goats. Moreover, we performed an experimental inoculation with this variant in domestic goat and showed evidence of infection. SARS-CoV-2 was detected in nasal swabs and tissues by RT-qPCR and/or immunohistochemistry, and seroneutralisation was confirmed via ELISA and live virus neutralisation assays. However, the viral amount and tissue distribution suggest a low susceptibility of goats to the B.1.351/Beta variant. Therefore, although monitoring livestock is advisable, it is unlikely that goats play a role as SARS-CoV-2 reservoir species, and they are not useful surrogates to study SARS-CoV-2 infection in farmed animals.info:eu-repo/semantics/publishedVersio

    Susceptibility of Domestic Goat (Capra aegagrus hircus) to Experimental Infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) B.1.351/Beta Variant

    Get PDF
    A wide range of animal species are susceptible to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Natural and/or experimental infections have been reported in pet, zoo, farmed and wild animals. Interestingly, some SARS-CoV-2 variants, such as B.1.1.7/Alpha, B.1.351/Beta, and B.1.1.529/Omicron, were demonstrated to infect some animal species not susceptible to classical viral variants. The present study aimed to elucidate if goats (Capra aegagrus hircus) are susceptible to the B.1.351/Beta variant. First, an in silico approach was used to predict the affinity between the receptor-binding domain of the spike protein of SARS-CoV-2 B.1.351/Beta variant and angiotensin-converting enzyme 2 from goats. Moreover, we performed an experimental inoculation with this variant in domestic goat and showed evidence of infection. SARS-CoV-2 was detected in nasal swabs and tissues by RT-qPCR and/or immunohistochemistry, and seroneutralisation was confirmed via ELISA and live virus neutralisation assays. However, the viral amount and tissue distribution suggest a low susceptibility of goats to the B.1.351/Beta variant. Therefore, although monitoring livestock is advisable, it is unlikely that goats play a role as SARS-CoV-2 reservoir species, and they are not useful surrogates to study SARS-CoV-2 infection in farmed animals

    Heterogeneous Infectivity and Pathogenesis of SARS-CoV-2 Variants Beta, Delta and Omicron in Transgenic K18-hACE2 and Wildtype Mice

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    Altres ajuts: Fundació La Marató de TV3 202126-30-21The emerging SARS-CoV-2 variants of concern (VOCs) may display enhanced transmissibility, more severity and/or immune evasion; however, the pathogenesis of these new VOCs in experimental SARS-CoV-2 models or the potential infection of other animal species is not completely understood. Here we infected K18-hACE2 transgenic mice with B.1, B.1.351/Beta, B.1.617.2/Delta and BA.1.1/Omicron isolates and demonstrated heterogeneous infectivity and pathogenesis. B.1.351/Beta variant was the most pathogenic, while BA.1.1/Omicron led to lower viral RNA in the absence of major visible clinical signs. In parallel, we infected wildtype (WT) mice and confirmed that, contrary to B.1 and B.1.617.2/Delta, B.1.351/Beta and BA.1.1/Omicron can infect them. Infection in WT mice coursed without major clinical signs and viral RNA was transient and undetectable in the lungs by day 7 post-infection. In silico modeling supported these findings by predicting B.1.351/Beta receptor binding domain (RBD) mutations result in an increased affinity for both human and murine ACE2 receptors, while BA.1/Omicron RBD mutations only show increased affinity for murine ACE2

    Improving the description of protein-protein association energy

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    [eng] Proteins play a crucial role in virtually every biological process taking place within our cells. Most of the times, proteins do not participate in these processes alone but forming complexes of two or more proteins. Therefore, the study of protein-protein interactions (PPIs) and complex formation has become an important field of research in the last decades due to its scientific relevance and therapeutic interest. Protein docking is one of the several computational approaches that have been applied to study protein interactions over the last years. It aims to determine the three-dimensional structure of a protein complex based on the structure of its subunits. Although the field has experienced important advances in recent years, it faces significant challenges ahead. New strategies are necessary to overcome current sampling limitations and enhance the physico-chemical description of protein-protein association, understanding its intrinsic mechanisms and identifying the most relevant residues involved, i.e., hot-spot residues. This Ph.D. thesis has focused on developing new computational tools to address some of these challenges. We have developed pyDockLite, a simplified scoring function derived from pyDock, the docking scoring function developed within our lab, which is up to 10 times faster at comparable performance. The key element in pyDockLite development is the new distance-based desolvation term, which drastically reduces the computation time required to calculate the desolvation contribution to pyDock docking energy. Based on pyDockLite, we have developed a fast rigid-body minimization algorithm, which is very efficient when the complex subunits are in their bound conformation. To model backbone flexibility we have included normal modes in the minimization algorithm. This new feature improves the results, especially for the medium-flexible and flexible cases. Most protein-protein docking protocols use scoring functions to evaluate docking poses and discriminate between good, i.e., near- native, and bad conformations. The implicit assumption is that the different energetic minima forming the docking energy landscape are represented by single docking poses which are scored individually. In this thesis, we have analyzed the concept that each energetic minima of the docking energy landscape can be formed by ensembles of docking orientations or conformations, and we have explored the consequences of scoring each minimum by such ensembles. We propose a novel ensemble-based description of the docking landscapes, integrating clustering, conformational sampling and consensus scoring, which improves docking performance. In some circumstances, we might want to have a more detailed description, at the level of residue or atoms, of the docking energy of the different states conforming the docking landscapes. We have developed a method to partition pyDock docking energy at the residue level. Interestingly, we will show how we can use this partitioned energy to identify energetically relevant residues in the binding process (hot-spots) and to estimate changes in binding affinity upon mutation to alanine, i.e., as an in-silico alanine scanning mutagenesis predictor. Regarding mutations to other residues, we have developed a new method to predict binding affinity changes upon mutation by combining MODELLER and pyDock. Results are in line with previous methods when tested on an external validation dataset. Finally, we have explored how to apply the knowledge and tools we have developed to other protein interactions such as those between proteins and RNA molecules. We present a new scoring function that combines FTDock score and pyDock electrostatics and van der Waals energy terms. This scoring function can be used to evaluate docking models of protein-RNA complexes. Our work indicates that protein-protein and protein-RNA interactions may have distinctive features that prevent the direct application of protein-protein scoring functions to protein-RNA docking studie
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