Knowledge of protein structures is essential to understand proteins' functions, evolution, dynamics, stabilities, and interactions and for data-driven protein-or drug design. Yet, experimental structure determination rates are far exceeded by that of next-generation sequencing, resulting in less than 1/1000th of proteins having an experimentally known 3D structure. Computational structure prediction seeks to alleviate this problem, and the Critical Assessment of Protein Structure Prediction (CASP) has shown the value of consensus and meta-methods that utilize complementary algorithms. However, traditionally, such methods employ majority voting during template selection and model averaging during refinement, which can drive the model away from the native fold if it is underrepresented in the ensemble. Here, we present TopModel, a fully automated meta-method for protein structure prediction. In contrast to traditional consensus and meta-methods, TopModel uses top-down consensus and deep neural networks to select templates and identify and correct wrongly modeled regions. TopModel combines a broad range of state-of-the-art methods for threading, alignment, and model quality estimation and provides a versatile workflow and toolbox for template-based structure prediction. TopModel shows a superior template selection, alignment accuracy, and model quality for template-based structure prediction on the CASP10−12 datasets compared to 12 state-of-the-art stand-alone primary predictors. TopModel was validated by prospective predictions of the nisin resistance protein (NSR) protein from Streptococcus agalactiae and LipoP from Clostridium difficile, showing far better agreement with experimental data than any of its constituent primary predictors. These results, in general, demonstrate the utility of TopModel for protein structure prediction and, in particular, show how combining computational structure prediction with sparse or low-resolution experimental data can improve the final model.
Lantibiotics are ribosomally synthesized antimicrobial peptides secreted by mainly Gram-positive bacteria. Class 1 lantibiotics mature via two modification steps introduced by a modification LanBC complex. For the lantibiotic nisin, the dehydratase NisB catalyzes the dehydration of serine and threonine residues in the so-called core peptide. Second, five (methyl)-lanthionine rings are introduced in a regio- and stereospecific manner by the cyclase NisC. Here, we characterized the assembly of the NisBC complex in vitro, which is only formed in the presence of the substrate. The complex is composed of a NisB dimer, a monomer of NisC and one prenisin molecule. Interestingly, the presence of the last lanthionine ring prevented complex formation. This stoichiometry was verified by small-angle X-ray scattering measurements, which revealed the first structural glimpse of a LanBC complex in solution.
Antimicrobial peptides, which contain (methyl)-lanthionine-rings are called lantibiotics. They are produced by several Gram-positive bacteria and are mainly active against these bacteria. Although these are highly potent antimicrobials, some human pathogenic bacteria express specific ABC transporters that confer resistance and counteract their antimicrobial activity. Two distinct ABC transporter families are known to be involved in this process. These are the Cpr- and Bce-type ABC transporter families, named after their involvement in cationic peptide resistance in Clostridium difficile, and bacitracin efflux in Bacillus subtilis, respectively. Both resistance systems differentiate to each other in terms of the proteins involved. Here, we summarize the current knowledge and describe the divergence as well as the common features present in both the systems to confer lantibiotic resistance.
Key indicators: single-crystal X-ray study; T = 296 K; mean (C-C) = 0.002 Å; R factor = 0.019; wR factor = 0.052; data-to-parameter ratio = 15.6.
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