No abstract

Lantibiotics are potent antimicrobial peptides. Nisin is the most prominent member and contains five crucial lanthionine rings. Some clinically relevant bacteria express membrane-associated resistance proteins that proteolytically inactivate nisin. However, substrate recognition and specificity of these proteins is unknown. Here, we report the first three-dimensional structure of a nisin resistance protein from Streptococcus agalactiae (SaNSR) at 2.2 Å resolution. It contains an N-terminal helical bundle, and protease cap and core domains. The latter harbors the highly conserved TASSAEM region, which lies in a hydrophobic tunnel formed by all domains. By integrative modeling, mutagenesis studies, and genetic engineering of nisin variants, a model of the SaNSR/nisin complex is generated, revealing that SaNSR recognizes the last C-terminally located lanthionine ring of nisin. This determines the substrate specificity of SaNSR and ensures the exact coordination of the nisin cleavage site at the TASSAEM region.

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.

The value of protein models obtained with automated protein structure prediction depends primarily on their accuracy. Protein model quality assessment is thus critical to select the model that can best answer biologically relevant questions from an ensemble of predictions. However, despite many advances in the field, different methods capture different types of errors, begging the question of which method to use. We introduce TopScore, a meta Model Quality Assessment Program (meta-MQAP) that uses deep neural networks to combine scores from 15 different primary predictors to predict accurate residue-wise and whole-protein error estimates. The predictions on six large independent data sets are highly correlated to superposition-independent errors in the model, achieving a Pearson’s R all 2 of 0.93 and 0.78 for whole-protein and residue-wise error predictions, respectively. This is a significant improvement over any of the investigated primary MQAPs, demonstrating that much can be gained by optimally combining different methods and using different and very large data sets.

BackgroundFeline immunodeficiency virus (FIV) is a global pathogen of Felidae species and a model system for Human immunodeficiency virus (HIV)-induced AIDS. In felids such as the domestic cat (Felis catus), APOBEC3 (A3) genes encode for single-domain A3Z2s, A3Z3 and double-domain A3Z2Z3 anti-viral cytidine deaminases. The feline A3Z2Z3 is expressed following read-through transcription and alternative splicing, introducing a previously untranslated exon in frame, encoding a domain insertion called linker. Only A3Z3 and A3Z2Z3 inhibit Vif-deficient FIV. Feline A3s also are restriction factors for HIV and Simian immunodeficiency viruses (SIV). Surprisingly, HIV-2/SIV Vifs can counteract feline A3Z2Z3.ResultsTo identify residues in feline A3s that Vifs need for interaction and degradation, chimeric human–feline A3s were tested. Here we describe the molecular direct interaction of feline A3s with Vif proteins from cat FIV and present the first structural A3 model locating these interaction regions. In the Z3 domain we have identified residues involved in binding of FIV Vif, and their mutation blocked Vif-induced A3Z3 degradation. We further identified additional essential residues for FIV Vif interaction in the A3Z2 domain, allowing the generation of FIV Vif resistant A3Z2Z3. Mutated feline A3s also showed resistance to the Vif of a lion-specific FIV, indicating an evolutionary conserved Vif–A3 binding. Comparative modelling of feline A3Z2Z3 suggests that the residues interacting with FIV Vif have, unlike Vif-interacting residues in human A3s, a unique location at the domain interface of Z2 and Z3 and that the linker forms a homeobox-like domain protruding of the Z2Z3 core. HIV-2/SIV Vifs efficiently degrade feline A3Z2Z3 by possible targeting the linker stretch connecting both Z-domains.ConclusionsHere we identified in feline A3s residues important for binding of FIV Vif and a unique protein domain insertion (linker). To understand Vif evolution, a structural model of the feline A3 was developed. Our results show that HIV Vif binds human A3s differently than FIV Vif feline A3s. The linker insertion is suggested to form a homeo-box domain, which is unique to A3s of cats and related species, and not found in human and mouse A3s. Together, these findings indicate a specific and different A3 evolution in cats and human.Electronic supplementary materialThe online version of this article (doi:10.1186/s12977-016-0274-9) contains supplementary material, which is available to authorized users.

Opine dehydrogenases catalyze the reductive condensation of pyruvate with L-amino acids. Biochemical characterization of alanopine dehydrogenase from Arenicola marina revealed that this enzyme is highly specific for L-alanine. Unbiased molecular dynamics simulations with a homology model of alanopine dehydrogenase captured the binding of L-alanine diffusing from solvent to a putative binding region near a distinct helix-kink-helix motif. These results and sequence comparisons reveal how mutations and insertions within this motif dictate the L-amino acid specificity.

Synthetic peptides derived from ethylene-insensitive protein 2 (EIN2), a central regulator of ethylene signalling, were recently shown to delay fruit ripening by interrupting protein–protein interactions in the ethylene signalling pathway. Here, we show that the inhibitory peptide NOP-1 binds to the GAF domain of ETR1 – the prototype of the plant ethylene receptor family. Site-directed mutagenesis and computational studies reveal the peptide interaction site and a plausible molecular mechanism for the ripening inhibition.

Understanding mechanisms of promiscuity is increasingly important from a fundamental and application point of view. As to enzyme structural dynamics, more promiscuous enzymes generally have been recognized to also be more flexible.However, examples for the opposite received much less attention. Here, we exploit comprehensive experimental information on the substrate promiscuity of 147 esterases tested against 96 esters together with computationally efficient rigidity analyses to understand the molecular origin of the observed promiscuity range. Unexpectedly, our data reveal that promiscuous esterases are significantly less flexible than specific ones, are significantly more thermostable, and have a significantly increased specific activity. These results may be reconciled with a model according to which structural flexibility in the case of specific esterases serves for conformational proofreading. Our results signify that an esterase sequence space can be screened by rigidity analyses for promiscuous esterases as starting points for further exploration in biotechnology and synthetic chemistry.