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The overconsumption and inappropriate use of antibiotics is escalating antibiotic resistance development, which is now one of the 10 top threats to global health. Introducing antibiotics with a novel mode of action into clinical use is urgently needed to address this issue. Deliberately inducing aggregation of target proteins and disrupting protein homeostasis in bacteria via amyloidogenic peptides, also called Pept-ins (from peptide interferors), can be lethal to bacteria and shows considerable promise as a novel antibiotic strategy. However, the translation of Pept-ins into the clinic requires further investigation into their mechanism of action and improvement of their therapeutic window. Therefore, we performed systematic structure modifications of 2 previously discovered Pept-ins, resulting in 179 derivatives, and investigated the corresponding impact on antimicrobial potency, cellular accumulation, and ability to induce protein aggregation in bacteria, in vitro aggregation property, and toxicity on mammalian cells. Our results show that both Pept-in accumulation and aggregation of target proteins in bacteria are requisite for Pept-in mediated antimicrobial activity. Improvement of these two parameters can be achieved via increasing the number of arginine residues, increasing Pept-in aggregation propensity, optimizing the aggregate core structure, adopting β-turn linkers, or forming a disulphide bond. Correspondingly, improvement of these two parameters can enhance Pept-in antimicrobial efficacy against wildtype E. coli BL21 used in the laboratory as well as clinically isolated multidrug-resistant strain E. coli ATCC, A. baumannii, and K. pneumoniae.

Biodiversity conservation and ecosystem-service provision will increasingly depend on the existence of secondary vegetation. Our success in achieving these goals will be determined by our ability to accurately estimate the structure and diversity of such communities at broad geographic scales. We examined whether the texture (the spatial variation of the image elements) of very high-resolution satellite imagery can be used for this purpose. In 14 fallows of different ages and one mature forest stand in a seasonally dry tropical forest landscape, we estimated basal area, canopy cover, stem density, species richness, Shannon index, Simpson index, and canopy height. The first six attributes were also estimated for a subset comprising the tallest plants. We calculated 40 texture variables based on the red and the near infrared bands, and EVI and NDVI, and selected the best-fit linear models describing each vegetation attribute based on them. Basal area (R(2) = 0.93), vegetation height and cover (0.89), species richness (0.87), and stand age (0.85) were the best-described attributes by two-variable models. Cross validation showed that these models had a high predictive power, and most estimated vegetation attributes were highly accurate. The success of this simple method (a single image was used and the models were linear and included very few variables) rests on the principle that image texture reflects the internal heterogeneity of successional vegetation at the proper scale. The vegetation attributes best predicted by texture are relevant in the face of two of the gravest threats to biosphere integrity: climate change and biodiversity loss. By providing reliable basal area and fallow-age estimates, image-texture analysis allows for the assessment of carbon sequestration and diversity loss rates. New and exciting research avenues open by simplifying the analysis of the extent and complexity of successional vegetation through the spatial variation of its spectral information.

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.