Our second publication about modeling HLA-B*57:01 mediated adverse drug reactions has been accepted by the Journal of Cheminformatics (DOI: 10.1186/s13321-018-0257-z)!
In this study, we combined three docking models developed from X-ray crystals 3VRI, 3VRJ, and 3UPR (with three unique co-binding peptides) into a virtual screening platform to predict a drug's likelihood of binding to HLA-B*57:01. The models used to create our virtual screening platform were developed in our first HLA-B*57:01 study (DOI: 10.1186/s13321-017-0202-6) which revealed that accounting for a co-binding peptide is extremely important for HLA docking performance. That study also revealed that our model's were abacavir-specific (i.e. reliable for modeling compounds similar to abacavir).
Using those new insights, we merged our three docking models into a virtual screening platform and docked the DrugBank database. DrugBank contained over 7,000 compounds at the time of this study (6,000 drugs were docked after curation). After docking DrugBank with all three models, we identified 22 compounds that may be HLA-B*57:01 binders.
An abstract is below.
Adverse drug reactions triggered by the common HLA-B*57:01 variant: virtual screening of DrugBank using 3D molecular docking
Idiosyncratic adverse drug reactions have been linked to a drug’s ability to bind with a human leukocyte antigen (HLA) protein. However, due to the thousands of HLA variants and limited structural data for drug-HLA complexes, predicting a specific drug-HLA combination represents a significant challenge. Recently, we investigated the binding mode of abacavir with the HLA-B*57:01 variant using molecular docking. Herein, we developed a new ensemble screening workflow involving three X-ray crystal derived docking procedures to screen the DrugBank database and identify potentially HLA-B*57:01 liable drugs. Then, we compared our workflow’s performance with another model recently developed by Metushi et al., which proposed seven in silico HLA-B*57:01 actives, but were later found to be experimentally inactive.
After curation, there were over 6000 approved and experimental drugs remaining in DrugBank for docking using Schrodinger’s GLIDE SP and XP scoring functions. Docking was performed with our new consensus-like ensemble workflow, relying on three different X-ray crystals (3VRI, 3VRJ, and 3UPR) in presence and absence of co-binding peptides. The binding modes of HLA-B*57:01 hit compounds for all three peptides were further explored using 3D interaction fingerprints and hierarchical clustering.
The screening resulted in 22 hit compounds forecasted to bind HLA-B*57:01 in all docking conditions (SP and XP with and without peptides P1, P2, and P3). These 22 compounds afforded 2D-Tanimoto similarities being less than 0.6 when compared to the structure of native abacavir, whereas their 3D binding mode similarities varied in a broader range (0.2–0.8). Hierarchical clustering using a Ward Linkage revealed different clustering patterns for each co-binding peptide. When we docked Metushi et al.’s seven proposed hits using our workflow, our screening platform identified six out of seven as being inactive. Molecular dynamic simulations were used to explore the stability of abacavir and acyclovir in complex with peptide P3.
This study reports on the extensive docking of the DrugBank database and the 22 HLA-B*57:01 liable candidates we identified. Importantly, comparisons between this study and the one by Metushi et al. highlighted new critical and complementary knowledge for the development of future HLA-specific in silico models.