Accurate Prediction of DnaK-Peptide Binding via Homology Modelling and Experimental Data

Molecular chaperones are essential elements of the protein quality control machinery that governs translocation and folding of nascent polypeptides, refolding and degradation of misfolded proteins, and activation of a wide range of client proteins. The prokaryotic heat-shock protein DnaK is the E. coli representative of the ubiquitous Hsp70 family, which specializes in the binding of exposed hydrophobic regions in unfolded polypeptides. Accurate prediction of DnaK binding sites in E. coli proteins is an essential prerequisite to understand the precise function of this chaperone and the properties of its substrate proteins. In order to map DnaK binding sites in protein sequences, we have developed an algorithm that combines sequence information from peptide binding experiments and structural parameters from homology modelling. We show that this combination significantly outperforms either single approach. The final predictor had a Matthews correlation coefficient (MCC) of 0.819 when assessed over the 144 tested peptide sequences to detect true positives and true negatives. To test the robustness of the learning set, we have conducted a simulated cross-validation, where we omit sequences from the learning sets and calculate the rate of repredicting them. This resulted in a surprisingly good MCC of 0.703. The algorithm was also able to perform equally well on a blind test set of binders and non-binders, of which there was no prior knowledge in the learning sets. The algorithm is freely available at http://limbo.vib.be

The regulatory subunit of PKA-I remains partially structured and undergoes β-aggregation upon thermal denaturation

Background: The regulatory subunit (R) of cAMP-dependent protein kinase (PKA) is a modular flexible protein that responds with large conformational changes to the binding of the effector cAMP. Considering its highly dynamic nature, the protein is rather stable. We studied the thermal denaturation of full-length RIα and a truncated RIα(92-381) that contains the tandem cyclic nucleotide binding (CNB) domains A and B. Methodology/Principal Findings: As revealed by circular dichroism (CD) and differential scanning calorimetry, both RIα proteins contain significant residual structure in the heat-denatured state. As evidenced by CD, the predominantly α-helical spectrum at 25°C with double negative peaks at 209 and 222 nm changes to a spectrum with a single negative peak at 212-216 nm, characteristic of β-structure. A similar α→β transition occurs at higher temperature in the presence of cAMP. Thioflavin T fluorescence and atomic force microscopy studies support the notion that the structural transition is associated with cross-β-intermolecular aggregation and formation of non-fibrillar oligomers. Conclusions/Significance: Thermal denaturation of RIα leads to partial loss of native packing with exposure of aggregation-prone motifs, such as the B' helices in the phosphate-binding cassettes of both CNB domains. The topology of the β-sandwiches in these domains favors inter-molecular β-aggregation, which is suppressed in the ligand-bound states of RIα under physiological conditions. Moreover, our results reveal that the CNB domains persist as structural cores through heat-denaturation. © 2011 Dao et al

Prediction of peptide and protein propensity for amyloid formation

Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔGº values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation

The Assembly of Individual Chaplin Peptides from Streptomyces coelicolor into Functional Amyloid Fibrils

The self-association of proteins into amyloid fibrils offers an alternative to the natively folded state of many polypeptides. Although commonly associated with disease, amyloid fibrils represent the natural functional state of some proteins, such as the chaplins from the soil-dwelling bacterium Streptomyces coelicolor, which coat the aerial mycelium and spores rendering them hydrophobic. We have undertaken a biophysical characterisation of the five short chaplin peptides ChpD-H to probe the mechanism by which these peptides self-assemble in solution to form fibrils. Each of the five chaplin peptides produced synthetically or isolated from the cell wall is individually surface-active and capable of forming fibrils under a range of solution conditions in vitro. These fibrils contain a highly similar cross-β core structure and a secondary structure that resembles fibrils formed in vivo on the spore and mycelium surface. They can also restore the growth of aerial hyphae to a chaplin mutant strain. We show that cysteine residues are not required for fibril formation in vitro and propose a role for the cysteine residues conserved in four of the five short chaplin peptides

Aggregating sequences that occur in many proteins constitute weak spots of bacterial proteostasis

Aggregation is a sequence-specific process, nucleated by short aggregation-prone regions (APRs) that can be exploited to induce aggregation of proteins containing the same APR. Here, we find that most APRs are unique within a proteome, but that a small minority of APRs occur in many proteins. When aggregation is nucleated in bacteria by such frequently occurring APRs, it leads to massive and lethal inclusion body formation containing a large number of proteins. Buildup of bacterial resistance against these peptides is slow. In addition, the approach is effective against drug-resistant clinical isolates of Escherichiacoli and Acinetobacterbaumannii, reducing bacterial load in a murine bladder infection model. Our results indicate that redundant APRs are weak points of bacterial protein homeostasis and that targeting these may be an attractive antibacterial strategy

Molecular Interpretation of ACTH-β-Endorphin Coaggregation: Relevance to Secretory Granule Biogenesis

Peptide/protein hormones could be stored as non-toxic amyloid-like structures in pituitary secretory granules. ACTH and β-endorphin are two of the important peptide hormones that get co-stored in the pituitary secretory granules. Here, we study molecular interactions between ACTH and β-endorphin and their colocalization in the form of amyloid aggregates. Although ACTH is known to be a part of ACTH-β-endorphin aggregate, ACTH alone cannot aggregate into amyloid under various plausible conditions. Using all atom molecular dynamics simulation we investigate the early molecular interaction events in the ACTH-β-endorphin system, β-endorphin-only system and ACTH-only system. We find that β-endorphin and ACTH formed an interacting unit, whereas negligible interactions were observed between ACTH molecules in ACTH-only system. Our data suggest that ACTH is not only involved in interaction with β-endorphin but also enhances the stability of mixed oligomers of the entire system

Connecting Peptide Physicochemical and Antimicrobial Properties by a Rational Prediction Model

The increasing rate in antibiotic-resistant bacterial strains has become an imperative health issue. Thus, pharmaceutical industries have focussed their efforts to find new potent, non-toxic compounds to treat bacterial infections. Antimicrobial peptides (AMPs) are promising candidates in the fight against antibiotic-resistant pathogens due to their low toxicity, broad range of activity and unspecific mechanism of action. In this context, bioinformatics' strategies can inspire the design of new peptide leads with enhanced activity. Here, we describe an artificial neural network approach, based on the AMP's physicochemical characteristics, that is able not only to identify active peptides but also to assess its antimicrobial potency. The physicochemical properties considered are directly derived from the peptide sequence and comprise a complete set of parameters that accurately describe AMPs. Most interesting, the results obtained dovetail with a model for the AMP's mechanism of action that takes into account new concepts such as peptide aggregation. Moreover, this classification system displays high accuracy and is well correlated with the experimentally reported data. All together, these results suggest that the physicochemical properties of AMPs determine its action. In addition, we conclude that sequence derived parameters are enough to characterize antimicrobial peptides

Predicting disease-associated substitution of a single amino acid by analyzing residue interactions

<p>Abstract</p> <p>Background</p> <p>The rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues.</p> <p>Results</p> <p>We found that SAPs can be well characterized by network topological features. Mutations are probably disease-associated when they occur at a site with a high centrality value and/or high degree value in a protein structure network. We also discovered that study of the neighboring residues around a mutation site can help to determine whether the mutation is disease-related or not. We compiled a dataset from the Swiss-Prot variant pages and constructed a model to predict disease-associated SAPs based on the random forest algorithm. The values of total accuracy and MCC were 83.0% and 0.64, respectively, as determined by 5-fold cross-validation. With an independent dataset, our model achieved a total accuracy of 80.8% and MCC of 0.59, respectively.</p> <p>Conclusions</p> <p>The satisfactory performance suggests that network topological features can be used as quantification measures to determine the importance of a site on a protein, and this approach can complement existing methods for prediction of disease-associated SAPs. Moreover, the use of this method in SAP studies would help to determine the underlying linkage between SAPs and diseases through extensive investigation of mutual interactions between residues.</p

Absence of repellents in Ustilago maydis induces genes encoding small secreted proteins

The rep1 gene of the maize pathogen Ustilago maydis encodes a pre-pro-protein that is processed in the secretory pathway into 11 peptides. These so-called repellents form amphipathic amyloid fibrils at the surface of aerial hyphae. A SG200 strain in which the rep1 gene is inactivated (∆rep1 strain) is affected in aerial hyphae formation. We here assessed changes in global gene expression as a consequence of the inactivation of the rep1 gene. Microarray analysis revealed that only 31 genes in the ∆rep1 SG200 strain had a fold change in expression of ≥2. Twenty-two of these genes were up-regulated and half of them encode small secreted proteins (SSPs) with unknown functions. Seven of the SSP genes and two other genes that are over-expressed in the ∆rep1 SG200 strain encode proteins that can be classified as secreted cysteine-rich proteins (SCRPs). Interestingly, most of the SCRPs are predicted to form amyloids. The SCRP gene um00792 showed the highest up-regulation in the ∆rep1 strain. Using GFP as a reporter, it was shown that this gene is over-expressed in the layer of hyphae at the medium-air interface. Taken together, it is concluded that inactivation of rep1 hardly affects the expression profile of U. maydis, despite the fact that the mutant strain has a strong reduced ability to form aerial hyphae