PAbFold: Linear Antibody Epitope Prediction using AlphaFold2.

Defining the binding epitopes of antibodies is essential for understanding how they bind to their antigens and perform their molecular functions. However, while determining linear epitopes of monoclonal antibodies can be accomplished utilizing well-established empirical procedures, these approaches are generally labor- and time-intensive and costly. To take advantage of the recent advances in protein structure prediction algorithms available to the scientific community, we developed a calculation pipeline based on the localColabFold implementation of AlphaFold2 that can predict linear antibody epitopes by predicting the structure of the complex between antibody heavy and light chains and target peptide sequences derived from antigens.

Evaluation of the Adsorption-Accessible Surface Area of MIL-53(Al) using Cannabinoids in a Closed System.

Cannabinoids are important industrial analytes commonly assayed with high-pressure liquid chromatography (HPLC). In this study, we evaluate the suitability of MIL-53(Al), a commercially available metal-organic framework (MOF), as a stationary phase for cannabinoid separations. The suitability of an MOF for a given separation is hypothesized to be limited by the ability of a given molecule to enter the pore of the MOF.