Electronic noses (E-noses), are usually composed by an array of sensors with different selectivities towards classes of VOCs (Volatile Organic Compounds). These devices have been applied to a variety of fields, including environmental protection, public safety, food and beverage industries, cosmetics, and clinical diagnostics. This work demonstrates that it is possible to classify eleven VOCs from different chemical classes using a single gas sensing biomaterial that changes its optical properties in the presence of VOCs. To accomplish this, an in-house built E-nose, tailor-made for the novel class of gas sensing biomaterials, was improved and combined with powerful machine learning techniques. The device comprises a delivery system, a detection system and a data acquisition and control system. It was designed to be stable, miniaturized and easy-to-handle. The data collected was pre-processed and features and curve fitting parameters were extracted from the original response. A recursive feature selection method was applied to select the best features, and then a Support Vector Machine classifier was implemented to distinguish the eleven distinct VOCs. The results show that the followed methodology allowed the classification of all the VOCs tested with 94.6% (± 0.9%) accuracy.

Phosphoprotein-binding domains interact with cognate phosphorylated targets ruling several biological processes. The impairment of such interactions is often associated with disease development, namely cancer. The breast cancer susceptibility gene 1 (BRCA1) C-terminal (BRCT) domain is involved in the control of complex signaling networks of the DNA damage response. The capture and identification of BRCT-binding proteins and peptides may be used for the development of new diagnostic tools for diseases with abnormal phosphorylation profiles. Here we show that designed cyclic β-hairpin structures can be used as peptidomimetics of the BRCT domain, with high selectivity in binding to a target phosphorylated peptide. The amino acid residues and spatial constraints involved in the interaction between a phosphorylated peptide (GK14-P) and the BRCT domain were identified and crafted onto a 14-mer β-hairpin template in silico. Several cyclic peptides models were designed and their binding towards the target peptide and other phosphorylated peptides evaluated through virtual screening. Selected cyclic peptides were then synthesized, purified and characterized. The high affinity and selectivity of the lead cyclic peptide towards the target phosphopeptide was confirmed, and the possibility to capture it using affinity chromatography demonstrated. This work paves the way for the development of cyclic β-hairpin peptidomimetics as a novel class of affinity reagents for the highly selective identification and capture of target molecules.

Artificial olfaction is a fast-growing field aiming to mimic natural olfactory systems. Olfactory systems rely on a first step of molecular recognition in which volatile organic compounds (VOCs) bind to an array of specialized olfactory proteins. This results in electrical signals transduced to the brain where pattern recognition is performed. An efficient approach in artificial olfaction combines gas-sensitive materials with dedicated signal processing and classification tools. In this work, films of gelatin hybrid gels with a single composition that change their optical properties upon binding to VOCs were studied as gas-sensing materials in a custom-built electronic nose. The effect of films thickness was studied by acquiring signals from gelatin hybrid gel films with thicknesses between 15 and 90 μm when exposed to 11 distinct VOCs. Several features were extracted from the signals obtained and then used to implement a dedicated automatic classifier based on support vector machines for data processing. As an optical signature could be associated to each VOC, the developed algorithms classified 11 distinct VOCs with high accuracy and precision (higher than 98%), in particular when using optical signals from a single film composition with 30 μm thickness. This shows an unprecedented example of soft matter in artificial olfaction, in which a single gelatin hybrid gel, and not an array of sensing materials, can provide enough information to accurately classify VOCs with small structural and functional differences.