Sequential oligopeptides based on a pentapeptide (TKPKG) derived from tuftsin with different lengths were synthesized by stepwise solid phase methodology. These highly soluble oligomers were nontoxic on mouse spleen cells, and other biological data suggested that tuftsin-like properties were also presented. The (TKPKG)n (n=2,4,6,8) oligopeptides were not immunogenic; however, they increased sheep red blood cells (SRBC) antigen specific antibody response in mice, demonstrating their immunostimulatory effect. Chemotactic activity was also found on J774 monocyte cells, while MRC5 fibroblasts were chemotactically nonresponders to the tested forms of tuftsin. These oligomers showed unordered and flexible structure by CD measurements, confirmed by computer modeling studies indicating also a fairly good accessibility of the epsilon-amino group of each lysine residue. Data suggest that these new oligotuftsin derivatives can be considered as promising carriers for synthetic vaccine.

3D shape- or volume-based virtual screening is a broadly used approach in drug discovery. In recent years a large number of publications have appeared in which these tools were compared not only to competitive methods but to docking studies as well. Studies often showed that the effectiveness of docking could be highly variable due to a large number of possible confounding factors, while ligand-based, shape-based approaches were more consistent. Here, we describe a novel, fully flexible shape-based virtual screening algorithm that does not require previous 3D conformation or conformer generation. Due to its solid consistency it can easily be used on desktop computers by non-expert scientists. The algorithm is demonstrated in a study for the investigation of β-secretase inhibitors and benchmarked on the Directory of Useful Decoys data set.

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Molecular descriptor (2D) and three dimensional (3D) shape based similarity methods are widely used in ligand based virtual drug design. In the present study pairwise structure comparisons among a set of 4858 DTP compounds tested in the NCI60 tumor cell line anticancer drug screen were computed using chemical hashed fingerprints and 3D molecule shapes to calculate 2D and 3D similarities, respectively. Additionally, pairwise biological activity similarities were calculated by correlating the 60 element vectors of pGI50 values corresponding to the cytotoxicity of the compounds across the NCI60 panel. Subsequently, we compared the power of 2D and 3D structural similarity metrics to predict the toxicity pattern of compounds. We found that while the positive predictive value and sensitivity of 3D and molecular descriptor based approaches to predict biological activity are similar, a subset of molecule pairs yielded contradictory results. By simultaneously requiring similarity of biological activities and 3D shapes, and dissimilarity of molecular descriptor based comparisons, we identify pairs of scaffold hopping candidates displaying characteristic core structural changes such as heteroatom/heterocycle change and ring closure. Attempts to discover scaffold hopping candidates of mitoxantrone recovered known Topoisomerase II (Top2) inhibitors, and also predicted new, previously unknown chemotypes possessing in vitro Top2 inhibitory activity.

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