RESUMEN
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. We found that this AlphaFold2 pipeline, which we call PAbFold, was able to accurately flag known epitope sequences for several well-known antibody targets (HA / Myc) when the target sequence was broken into small overlapping linear peptides and antibody complementarity determining regions (CDRs) were grafted onto several different antibody framework regions in the single-chain antibody fragment (scFv) format. To determine if this pipeline was able to identify the epitope of a novel antibody with no structural information publicly available, we determined the epitope of a novel anti-SARS-CoV-2 nucleocapsid targeted antibody using our method and then experimentally validated our computational results using peptide competition ELISA assays. These results indicate that the AlphaFold2-based PAbFold pipeline we developed is capable of accurately identifying linear antibody epitopes in a short time using just antibody and target protein sequences. This emergent capability of the method is sensitive to methodological details such as peptide length, AlphaFold2 neural network versions, and multiple-sequence alignment database. PAbFold is available at https://github.com/jbderoo/PAbFold.
RESUMEN
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. To evaluate the extent of possible adsorptive interactions between cannabinoids and the interior surface area of MIL-53(Al), the radii of gyration (Rg) and solvent-accessible surface areas were calculated for three cannabinoids, namely, cannabidiol, cannabinol, and Δ9-tetrahydrocannabinol, as well as the MOF. These values were used to calculate the theoretical adsorption capacity of the MOF, using four competing adsorption models. The Rg of cannabinoids (4.1 Å) is larger than one MOF pore aperture dimension (4.0 × 5.0 Å). The adsorption capacity was measured by relating a decrease in the cannabinoid concentration in acetonitrile when exposed to 100 mg of MOF. The cannabinoid uptake by the MOF was estimated using the relative standard deviation (RSD) of the soaking solution assay, as the decomposition-corrected RSD as uptake (DCRU). The DCRU was calculated as 0.007 ± 0.004 µgcannabinoids/mgMOF. These findings indicate that most of the MOF surface area was inaccessible for adsorption by cannabinoids due to size-exclusion effects. The implication of this work is that the suitability of an MOF for adsorptive separations, such as liquid chromatography, must have an upper limit for the size of the analyte. Additionally, MOFs may generally be more suitable for separations in the gas phase, where adsorbates are not hindered by the presence of a solvation shell.
