Resultados de investigación

Supplementary Figure 1. Distributions of AlphaFold2 descriptors calculated for the BoNT dataset. Supplementary Figure 2. Distributions of RoseTTAFold descriptors calculated for the BoNT dataset. Supplementary Figure 3. Distributions of MolProbity and interface descriptors calculated for the BoNT dataset Supplementary Figure 4. Global and local confidence scores for the AlphaFold2 and RoseTTAFold predictions for the Monomer dataset. (A) Supplementary Figure 5. Fragment quality and MolProbity descriptors calculated for the Monomer dataset. (A) Supplementary Figure 6. Properties of the homodimer interfaces predicted by AlphaFold2 for the Monomer dataset Supplementary Figure 7. Predictions of solvent exposed hydrophobicity and soluble expression for the Monomer dataset. (A) Supplementary Figure 8. Comparison between AlphaFold2 and RoseTTAFold predictions for the two datasets, Computational de novo protein design tailors proteins for target structures and oligomerisation states with high stability, which allows overcoming many limitations of natural proteins when redesigned for new functions. Despite significant advances in the field over the past decade, it remains challenging to predict sequences that will fold as stable monomers in solution or binders to a particular protein target; thereby requiring substantial experimental resources to identify proteins with the desired properties. To overcome this, here we leveraged the large amount of design data accumulated in the last decade, and the breakthrough in protein structure prediction from last year to investigate on improved ways of selecting promising designs before experimental testing. We collected de novo proteins from previous studies, 518 designed as monomers of different folds and 2112 as binders against the Botulinum neurotoxin, and analysed their structures with AlphaFold2, RoseTTAFold and fragment quality descriptors in combination with other properties related to surface interactions. These features showed high complementarity in rationalizing the experimental results, which allowed us to generate quite accurate machine learning models for predicting well-folded monomers and binders with a small set of descriptors. Cross-validating designs with varied orthogonal computational techniques should guide us for identifying design imperfections, rescuing designs and making more robust design selections before experimental testing., Peer reviewed

Raw microscopy data for Fig2A and G in .lif format in a single zip file., Resources available on the publisher's site: https://doi.org/10.25452/figshare.plus.19064324.v1, Marie Skłodowska-Curie grant agreement No. 754510 Ministry of Science, Innovation and Universities PGC2018-095616-B-I00 Ministry of Science, Innovation and Universities PGC2018-099562-B-I00 NIH Intramural Funding, National Cancer Institute, Peer reviewed

Raw microscopy data for Fig3B, 3E and 3S1 (.lif format) in a single zip file., Resources available on the publisher's site: https://doi.org/10.25452/figshare.plus.18309440.v1, Ministry of Science, Innovation and Universities BFU2017-83562-P, Peer reviewed

Raw microscopy data for Fig5E (.lif files) in a single zip file., Resources available on the publisher's site: https://doi.org/10.25452/figshare.plus.18348926.v1, Marie Skłodowska-Curie grant agreement No. 754510 Ministry of Science, Innovation and Universities PGC2018-095616-B-I00 Ministry of Science, Innovation and Universities PGC2018-099562-B-I00, Peer reviewed

Raw microscopy data for Fig6A, F, G, S1 and S2 (.lif or .jpg format) in a single zip file., Resources available on the publisher's site: https://doi.org/10.25452/figshare.plus.18310361.v1, Deutsche Forschungsgemeinschaft DFG PH144/4-1 and PH144/6-1, Peer reviewed

Raw microscopy data for Fig7A-B, C and D-G (.lif and .jpg files) in a single zip file., Resources available on the publisher's site: https://doi.org/10.25452/figshare.plus.18316226.v1, Deutsche Forschungsgemeinschaft DFG PH144/4-1 and PH144/6-1, Peer reviewed

Source microscopy data for Fig8A, B, C, D and S1 (.lif and .jpg file) in a single zip file., Resources available on the publisher's site: https://doi.org/10.25452/figshare.plus.18312410.v1, Deutsche Forschungsgemeinschaft DFG PH144/4-1 and PH144/6-1, Peer reviewed

Resources available on the publisher's site: https://doi.org/10.5281/zenodo.5554758, Streptococcal pyrogenic exotoxin B (SpeB) is a cysteine protease expressed during group A streptococcal infection that represents a major virulence factor. Although subject to several studies, its role during infection is still under debate, and its proteolytic properties remain insufficiently characterized. Here, we revisited this protease through a set of complementary approaches relying on state of-the-art HPLC-MS methods. After conceiving an efficient protocol to recombinantly express SpeB, the zymogen of the protease and its activation were characterized. Employing proteome-derived peptide libraries, a strong preference for hydrophobic and aromatic residues at P2 alongside negatively charged amino acids at P3′ to P6′ was revealed. To identify relevant in vivo substrates, native proteins were obtained from monocytic secretome and plasma to assess their cleavage under physiological conditions. Besides corroborating our findings concerning specificity, more than 200 cleaved proteins were identified, including proteins of the extracellular matrix, proteins of the immune system, and proteins involved in inflammation. Finally, the cleavage of IgG subclasses was studied in detail. This study precisely depicts the proteolytic properties of SpeB and provides a library of potential host substrates, including their exact cleavage positions, as a valuable source for further research to unravel the role of SpeB during streptococcal infection., Peer reviewed

Additional file 1: Table ST1. Treatments comparison by outcome in male patients. Table ST2. Treatments comparison by outcome in female patients. Table ST3. WHO classification of disease outcome. Table ST4. Panels and antibodies used for immunophenotyping. Figure SF1. Patients distribution by outcome, age, and comorbidities. Figure SF2. Distribution of male patients with comorbidities according to age and testosterone levels. Figure SF3. Longitudinal analysis of serum levels of IL-6, C-reactive protein (CRP), ferritin and lactate dehydrogenase (LDH) in male patients. Figure SF4. Bioavailable testosterone serum levels and correlation between age and sex-hormone binding globulin (SHBG). Figure SF5. Flow cytometry analysis of circulating immune subpopulations in three illustrative cases with moderate, severe survivor and severe deceased outcomes., Ministerio de Ciencia, Innovación y Universidades Consejo Superior de Investigaciones Científicas Agència de Gestió d’Ajuts Universitaris i de Recerca Conselleria d'Educació, Investigació, Cultura i Esport Instituto de Salud Carlos III, Peer reviewed

Additional file 1: Movie S1. Time-lapse movie of the system with F-actin forming and treadmilling in the grid., Ministerio de Ciencia, Innovación y Universidades. Ministerio de Ciencia, Innovación y Universidades, Peer reviewed