We present SpotOn, a web server to identify and classify interfacial residues as Hot-Spots (HS) and Null-Spots (NS). SpotON implements a robust algorithm with a demonstrated accuracy of 0.95 and sensitivity of 0.98 on an independent test set. The predictor was developed using an ensemble machine learning approach with up-sampling of the minor class. It was trained on 53 complexes using various features, based on both protein 3D structure and sequence. The SpotOn web interface is freely available at: http://milou.science.uu.nl/services/SPOTON/.

Influenza (flu) is a contagious viral disease, which targets the human respiratory tract and spreads throughout the world each year. Every year, influenza infects around 10% of the world population and between 290,000 and 650,000 people die from it according to the World Health Organization (WHO). Influenza viruses belong to the Orthomyxoviridae family and have a negative sense eight-segment single-stranded RNA genome that encodes 11 different proteins. The only control over influenza seasonal epidemic outbreaks around the world are vaccines, annually updated according to viral strains in circulation, but, because of high rates of mutation and recurrent genetic assortment, new viral strains of influenza are constantly emerging, increasing the likelihood of pandemics. Vaccination effectiveness is limited, calling for new preventive and therapeutic approaches and a better understanding of the virus–host interactions. In particular, grasping the role of influenza non-structural protein 1 (NS1) and related known interactions in the host cell is pivotal to better understand the mechanisms of virus infection and replication, and thus propose more effective antiviral approaches. In this review, we assess the structure of NS1, its dynamics, and multiple functions and interactions, to highlight the central role of this protein in viral biology and its potential use as an effective therapeutic target to tackle seasonal and pandemic influenza.

Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set.

There is an ever-increasing global demand from consumers for high-quality foods with major emphasis placed on quality and safety attributes. One of the main demands that consumers display is for minimally processed, high-nutrition/low-energy natural foods with no or minimal chemical preservatives. The nutritional value of raspberry fruit is widely recognized. In particular, red raspberries are known to demonstrate a strong antioxidant capacity that might prove beneficial to human health by preventing free radical-induced oxidative stress. However, food products that are consumed raw, are increasingly being recognized as important vehicles for transmission of human pathogens. Food irradiation is one of the few technologies that address both food quality and safety by virtue of its ability to control spoilage and foodborne pathogenic microorganisms without significantly affecting sensory or other organoleptic attributes of the food. Food irradiation is well established as a physical, nonthermal treatment (cold pasteurization) that processes foods at or nearly at ambient temperature in the final packaging, reducing the possibility of cross contamination until the food is actually used by the consumer. The aim of this study was to evaluate effects of gamma radiation on raspberries in order to assess consequences of irradiation. Freshly packed raspberries (Rubus idaeus L.) were irradiated in a (60)Co source at several doses (0.5, 1, or 1.5 kGy). Bioburden, total phenolic content, antioxidant activity, physicochemical properties such as texture, color, pH, soluble solids content, and acidity, and sensorial parameters were assessed before and after irradiation and during storage time up to 14 d at 4°C. Characterization of raspberries microbiota showed an average bioburden value of 10(4) colony-forming units (CFU)/g and a diverse microbial population predominantly composed of two morphological types (gram-negative, oxidase-negative rods, 35%, and filamentous fungi, 41%). The inactivation studies on the raspberries mesophilic population indicated a one log reduction of microbial load (95% inactivation efficiency for 1.5 kGy), in the surviving population mainly constituted by filamentous fungi (79-98%). The total phenolic content of raspberries indicated an increase with radiation doses and a decrease with storage time. The same trend was found for raspberries' antioxidant capacity with storage time. Regarding raspberries physicochemical properties, irradiation induced a significant decrease in firmness compared with nonirradiated fruit. However, nonirradiated and irradiated fruit presented similar physicochemical and sensory properties during storage time. Further studies are needed to elucidate the benefits of irradiation as a raspberries treatment process.