A prospective compound screening contest identified broader inhibitors for Sirtuin 1

Abstract

Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using in silico methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified.

Figure 1

(a) A flowchart of the contest. Each group (G1-G16) proposed 400 compounds (cmpds) with a prioritized rank from compound library using their own methods. The proposed compounds that were not stocked-out were selected until the number of compounds reached 200 for each group. If there was duplication in the proposed compounds among different groups, these groups attained additional compounds to be assayed. For this reason, there are differences among the number of selected compounds of each group. Finally, the selected compounds were assayed. (b) The screening flow of the compounds in the experimental assay. The filtering criteria are shown in a trapezium. The number of compounds applied to each screening is shown in parenthesis. This flow was conducted twice with NAD⁺ (the first number in parenthesis) and without NAD⁺ (the second number in parenthesis). (c) IC₅₀ hits found based on TSA screening without (w/o) and with (w/) NAD⁺ (see Screening of potential inhibitors in the main text).

Figure 2

(a) Similarity of the compounds proposed from each group. The similarity scores are defined with the Tanimoto coefficient of the MACCS descriptor. The number of identical compounds proposed from different two groups is shown in Figure S7, which indicates that identical compounds were rarely proposed from different groups, except for the combinations of G6 and G12 (19 compounds) and G6 and G13 (5 compounds), which used ligand information. (b) Averaged similarity scores in each cell of (a), in which identical compounds on the diagonal are not included for averaging. (c) Assayed compounds from each group are projected to the first and second principal components (PC1: x-axis, PC2: y-axis). Hit compounds are projected to PC1 and PC2 as well. Principal component analysis was applied to the compound library using the MACCS descriptor. The cumulative variance of the PC1 and PC2 are 26% and 50%, respectively. A randomly chosen 3% of the compounds in the library are projected (gray points).

Figure 3

The similarity of each hit compound to known Sirtuin 1 inhibitors (see Novelty of the assayed and hit compounds Section) is plotted against the experimental inhibition activity. The error bar represents 95% confidence intervals estimated from IC₅₀ assays. For each point, the category of method used is presented (see Table 1). The similarity in the figure was calculated with the Tanimoto coefficient of the MACCS descriptor.