A promising protein target for computational drug development, the human cluster of differentiation 38 (CD38), plays a crucial role in many physiological and pathological processes, primarily through the upstream regulation of factors that control cytoplasmic Ca²⁺ concentrations. Recently, a small-molecule inhibitor of CD38 was shown to slow down pathways relating to aging and DNA damage. We examined the performance of seven docking programs for their ability to model protein-ligand interactions with CD38. A test set of twelve CD38 crystal structures, containing crystallized biologically relevant substrates, were used to assess pose prediction. The rankings for each program based on the median RMSD between the native and predicted were Vina, AD4 > PLANTS, Gold, Glide, Molegro > rDock. Forty-two compounds with known affinities were docked to assess the accuracy of the programs at affinity/ranking predictions. The rankings based on scoring power were: Vina, PLANTS > Glide, Gold > Molegro >> AutoDock 4 >> rDock. Out of the top four performing programs, Glide had the only scoring function that did not appear to show bias towards overpredicting the affinity of the ligand-based on its size. Factors that affect the reliability of pose prediction and scoring are discussed. General limitations and known biases of scoring functions are examined, aided in part by using molecular fingerprints and Random Forest classifiers. This machine learning approach may be used to systematically diagnose molecular features that are correlated with poor scoring accuracy.

Human single-stranded DNA binding protein 1 (hSSB1) is critical to preserving genome stability, interacting with single-stranded DNA (ssDNA) through an oligonucleotide/oligosaccharide binding-fold. The depletion of hSSB1 in cell-line models leads to aberrant DNA repair and increased sensitivity to irradiation. hSSB1 is over-expressed in several types of cancers, suggesting that hSSB1 could be a novel therapeutic target in malignant disease. hSSB1 binding studies have focused on DNA; however, despite the availability of 3D structures, small molecules targeting hSSB1 have not been explored. Quinoline derivatives targeting hSSB1 were designed through a virtual fragment-based screening process, synthesizing them using AlphaLISA and EMSA to determine their affinity for hSSB1. In parallel, we further screened a structurally diverse compound library against hSSB1 using the same biochemical assays. Three compounds with nanomolar affinity for hSSB1 were identified, exhibiting cytotoxicity in an osteosarcoma cell line. To our knowledge, this is the first study to identify small molecules that modulate hSSB1 activity. Molecular dynamics simulations indicated that three of the compounds that were tested bound to the ssDNA-binding site of hSSB1, providing a framework for the further elucidation of inhibition mechanisms. These data suggest that small molecules can disrupt the interaction between hSSB1 and ssDNA, and may also affect the ability of cells to repair DNA damage. This test study of small molecules holds the potential to provide insights into fundamental biochemical questions regarding the OB-fold.