Assessing the impact of e-cigarettes on human barrier systems: A systematic review.

The use of e-cigarettes has grown rapidly in recent years, raising concerns about their impact on human health, particularly on critical physiological barriers such as the blood-brain barrier (BBB), alveolar-capillary barrier, and vascular systems. This systematic review evaluates the current literature on the effects of e-cigarette exposure on these barrier systems. E-cigarettes, regardless of nicotine content, have been shown to induce oxidative stress, inflammation, and disruption of tight junction proteins, leading to impaired barrier function.

Predicting ion mobility collision cross sections using projection approximation with ROSIE-PARCS webserver.

Ion mobility coupled to mass spectrometry informs on the shape and size of protein structures in the form of a collision cross section (CCSIM). Although there are several computational methods for predicting CCSIM based on protein structures, including our previously developed projection approximation using rough circular shapes (PARCS), the process usually requires prior experience with the command-line interface. To overcome this challenge, here we present a web application on the Rosetta Online Server that Includes Everyone (ROSIE) webserver to predict CCSIM from protein structure using projection approximation with PARCS.

Applications of machine learning in computer-aided drug discovery.

Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD.

Prediction of Intrinsic Disorder Using Rosetta ResidueDisorder and AlphaFold2.

The combination of deep learning and sequence data has transformed protein structure prediction and modeling, evidenced in the success of AlphaFold (AF). For this reason, many methods have been developed to take advantage of this success in areas where inaccurate structural modeling may limit computational predictiveness. For example, many methods have been developed to predict protein intrinsic disorder from sequence, including our Rosetta ResidueDisorder (RRD) approach.

Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction.

Ion mobility (IM) mass spectrometry provides structural information about protein shape and size in the form of an orientationally-averaged collision cross-section (CCS). While IM data have been used with various computational methods, they have not yet been utilized to predict monomeric protein structure from sequence. Here, we show that IM data can significantly improve protein structure determination using the modelling suite Rosetta.