Aggregations of proteins are in many cases associated with neurodegenerative diseases such as Alzheimer's (AD). Small compounds capable of inhibiting protein aggregation are expected to be useful for not only in the treatment of disease but also in probing the structures of aggregated proteins. In previous studies using phage display, we found that arginine-rich short peptides consisting of four or seven amino acids bound to soluble 42-residue amyloid β (Aβ42) and inhibited globulomer (37/48 kDa oligomer) formation. In the present study, we searched for arginine-containing small molecules using the SciFinder searching service and tested their inhibitory activities against Aβ42 aggregation, by sodium dodecyl sulfate (SDS)-PAGE and thioflavine T binding assay. Commercially available Arg-Arg-7-amino-4-trifluoromethylcoumarin was found to exhibit remarkable inhibitory activities to the formation of the globulomer and the fibril of Aβ42. This chimera-type tri-peptide is expected to serve as the seed molecule of a potent inhibitor of the Aβ aggregation process.

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There have been many reports suggesting that soluble oligomers of amyloid β (Aβ) are neurotoxins causing Alzheimer's disease (AD). Although inhibition of the soluble oligomerization of Aβ is considered to be effective in the treatment of AD, almost all peptide inhibitors have been designed from the β-sheet structure (H14-D23) of Aβ(1-42). To obtain more potent peptides than the known inhibitors of the soluble-oligomer formation of Aβ(1-42), we performed random screening by phage display. After fifth-round panning of a hepta-peptide library against soluble Aβ(1-42), novel peptides containing arginine residues were enriched. These peptides were found to suppress specifically 37/48 kDa oligomer formation and to keep the monomeric form of Aβ(1-42) even after 24 h of incubation, as disclosed by SDS-PAGE and size-exclusion chromatography. Thus we succeeded in acquiring novel efficient peptides for inhibition of soluble 37/48 kDa oligomer formation of Aβ(1-42).

Structure-based virtual screening is gaining popularity in drug discovery. A number of molecular docking programs and scoring functions have been developed in the community, but they had not fulfilled the demands for the improved accuracy, yet. In order to improve the accuracy, the consensus scoring method has been developed. It combines docking scores from various scoring functions without considering characteristics of the docking scores. In this study, we adopted the concepts of the consensus scoring, and improved the docking score from each docking programs, DOCK, FRED or GOLD, for virtual screening. Instead using simple sum of score components in those docking scores, weight factors of the score components were introduced and adjusted for better predictions of active ligands during virtual screening. Several optimization processes were tested to find the best optimization methods of the docking scores using a wide variety of 113 target proteins with over 2000 diverse decoys. Finally, the optimizations improved the chance to discover the active ligands by up to 52.4% (e.g. from 36.8% to 56.1% using GOLD) for the test set. Additionally, the combination of the optimized scores using GOLD and FRED improved success rate in the test set by 77.2%, and approximately 70% of ligands for target proteins were predictable in the test set with 20 times enrichment.