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Tandem repetition of domain in protein sequence occurs in all three domains of life. It creates protein diversity and adds functional complexity in organisms. In this work, we analyzed 52 streptococcal genomes and found 3748 proteins contained domain repeats. Proteins not harboring domain repeats are significantly enriched in cytoplasm, whereas proteins with domain repeats are significantly enriched in cytoplasmic membrane, cell wall and extracellular locations. Domain repetition occurs most frequently in S. pneumoniae and least in S. thermophilus and S. pyogenes. DUF1542 is the highest repeated domain in a single protein, followed by Rib, CW_binding_1, G5 and HemolysinCabind. 3D structures of 24 repeat-containing proteins were predicted to investigate the structural and functional effect of domain repetition. Several repeat-containing streptococcal cell surface proteins are known to be virulence-associated. Surface-associated tandem domain-containing proteins without experimental functional characterization may be potentially involved in the pathogenesis of streptococci and deserve further investigation.

Background and purpose: The specific attachment of amino acids to the 3' end of tRNA is a key reaction in protein biosynthesis. It is catalyzed, in general, by 20 aminoacyl-tRNA synthetases (aaRS), structurally very diverse family of enzymes. For many years we have been studying the mechanism of serylation, employed by seryl-tRNA synthetases from different organisms and/or cellular compartments, by combination of protein engineering and genetic approaches. Since none of the eukaryal SerRSs have yet been crystallized, we are using protein 3D models to place our results in a structural context. In this work we show the importance of 3D models in phylogenetic analyses. Materials and methods: The rigid body superposition method was used to compare 3D structural models of various seryl-tRNA synthetases in order to infer their phylogenetic relationships. Results: The distance matrix was constructed for the selected region (involving the active site domains) of 21 seryl-tRNA synthetases modeled upon the coordinates of the crystallized Thermus thermophilus enzyme. This was used for the construction of a 3D based phylogenetic tree, which follows the overall topologies of sequence based phylogenies. The results of structure-based phylogeny inference were used to estimate the quality of the models. Conclusion: Phylogenetic analyses based on modeled protein structures can yield biologically meaningful results, despite the limited accuracy of the models.

Background Subtilisin-like serine proteases or Subtilases in fungi are important for penetration and colonization of host. In Hypocreales, these proteins share several properties with other fungal, bacterial, plant and mammalian homologs. However, adoption of specific roles in entomopathogenesis may be governed by attainment of unique biochemical and structural features during the evolutionary course. Due to such functional shifts Subtilases coded by different family members of Hypocreales acquire distinct features according to respective hosts and lifestyle. We conducted phylogenetic and DIVERGE analyses and identified important protein residues that putatively assign functional specificity to Subtilases in fungal families/species under the order Hypocreales. Results A total of 161 Subtilases coded by 10 species from five different families under the fungal order Hypocreales was included in the analysis. Based on the presence of conserved domains, the Subtilase genes were divided into three subfamilies, Subtilisin (S08.005), Proteinase K (S08.054) and Serine-carboxyl peptidases (S53.001). These subfamilies were investigated for phylogenetic associations, protein residues under positive selection and functional divergence among paralogous clades. The observations were co-related with the life-styles of the fungal families/species. Phylogenetic and Divergence analyses of Subtilisin (S08.005) and Proteinase K (S08.054) families of proteins revealed that the paralogous clades were clear-cut representation of familial origin of the protein sequences. We observed divergence between the paralogous clades of plant-pathogenic fungi (Nectriaceae), insect-pathogenic fungi (Cordycipitaceae/Clavicipitaceae) and nematophagous fungi (Ophiocordycipitaceae). In addition, Subtilase genes from the nematode-parasitic fungus Purpureocillium lilacinum made a unique cluster which putatively indicated that the fungus might have developed distinctive mechanisms for nematode-pathogenesis. Our evolutionary genetics analysis revealed evidence of positive selection on the Subtilisin (S08.005) and Proteinase K (S08.054) protein sequences of the entomopathogenic and nematophagous species belonging to Cordycipitaceae, Clavicipitaceae and Ophiocordycipitaceae families of Hypocreales. Conclusions Our study provided new insights into the evolution of Subtilisin like serine proteases in Hypocreales, a fungal order largely consisting of biological control species. Subtilisin (S08.005) and Proteinase K (S08.054) proteins seemed to play important roles during life style modifications among different families and species of Hypocreales. Protein residues found significant in functional divergence analysis in the present study may provide support for protein engineering in future. Electronic supplementary material The online version of this article (doi:10.1186/s12862-016-0793-y) contains supplementary material, which is available to authorized users.

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).

Background Many structural bioinformatics approaches employ sequence profile-based threading techniques. To improve fold recognition rates, homology searching may include artificially evolved amino acid sequences, which were demonstrated to enhance the sensitivity of protein threading in targeting midnight zone templates. Findings We describe implementation details of eVolver, an optimization algorithm that evolves protein sequences to stabilize the respective structures by a variety of potentials, which are compatible with those commonly used in protein threading. In a case study focusing on LARG PDZ domain, we show that artificially evolved sequences have quite high capabilities to recognize the correct protein structures using standard sequence profile-based fold recognition. Conclusions Computationally design protein sequences can be incorporated in existing sequence profile-based threading approaches to increase their sensitivity. They also provide a desired linkage between protein structure and function in in silico experiments that relate to e.g. the completeness of protein structure space, the origin of folds and protein universe. eVolver is freely available as a user-friendly webserver and a well-documented stand-alone software distribution at http://www.brylinski.org/evolver.

Molecular replacement (MR) technique is to solve the phase problem in x-ray crystallography. Currently, many computational methods are available and they can provide MR solutions when a suitable 3D model of the target molecule is available. In practice, the success of the MR method is limited by various factors including unavailability of suitable 3D models and the ambiguity of crystallographic parameters. Accurate 3D modeling of the target molecule can provide a breakthrough in these cases. Recently, we have developed a protein 3D modeling method which can provide accurate protein 3D models both in backbones and side-chains. In addition, the method provides a variety of protein 3D models with structural variation. In recent blind tests, CASP7 and CASP8 experiments, the method produced the very top quality protein 3D models for high-accuracy template-based modeling targets. We combined this modeling method with a MR technique and successfully determined two protein structures, which could not be determined using conventional methods with available protein templates. It appears that high-accuracy protein 3D modeling for backbones as well as side-chains can boost up the success rate of molecular replacement technique allowing us to solve the phase problem in x-ray crystallography without requiring additional experiments with high cost efforts.