Discovering inhibitors of human sirtuin type 2: novel structural scaffolds.

A successful virtual screening experiment of novel SIRT2 inhibitors is described. Four out of 11 experimentally tested compounds showed in vitro inhibitory activity toward SIRT2 in a micromolar level, resulting in an experimental hit ratio of 36%. Two of these compounds inhibited SIRT2 with IC50 (microM) values of 51 and 91; moreover, one of the new inhibitors was comprised of an entirely new SIRT2-inhibiting structural scaffold.

N,N'-Bisbenzylidenebenzene-1,4-diamines and N,N'-Bisbenzylidenenaphthalene-1,4-diamines as Sirtuin Type 2 (SIRT2) Inhibitors.

A series of N,N'-bisbenzylidenebenzene-1,4-diamine and N,N'-bisbenzylidenenaphthalene-1,4-diamine derivatives were synthesized as inhibitors for human sirtuin type 2 (SIRT2). The design of the new compounds was based on two earlier reported hits from molecular modeling and virtual screening. The most potent compound was N,N'-bis(2-hydroxybenzylidene)benzene-1,4-diamine, which was equipotent with the most potent hit compound and well-known SIRT2 inhibitor sirtinol.

BRUTUS: optimization of a grid-based similarity function for rigid-body molecular superposition. 1. Alignment and virtual screening applications.

We have developed a fast grid-based algorithm, BRUTUS, for rigid-body molecular superposition and similarity searching. BRUTUS aligns molecules using field information derived from charge distributions and van der Waals shapes of the compounds. Molecules can have similar biological properties if their charge distributions and shapes are similar, even though they have different chemical structures; that is, BRUTUS can identify compounds possessing similar properties, regardless of their structures. In this paper, we present two applications of BRUTUS. First, BRUTUS was used to superimpose five sets of inhibitors. Second, two sets of known inhibitors were searched from a database, and the results were analyzed using self-organizing maps. We demonstrate that BRUTUS is successful in superimposing compounds using molecular fields and, importantly, is fast and accurate enough for virtual screening of chemical databases using a standard personal computer. This fast and efficient molecular-field-based algorithm is applicable for virtual screening of structurally diverse, active molecules.

An in silico approach to discovering novel inhibitors of human sirtuin type 2.

Type 2 human sirtuin (SIRT2) is a NAD(+)-dependent cytoplasmic protein that is colocalized with HDAC6 on microtubules. SIRT2 has been shown to deacetylate alpha-tubulin and to control mitotic exit in the cell cycle. To date, some small molecular inhibitors of SIRT2 have been identified; however, more inhibitors are still needed to improve the understanding of SIRT2 biological function and to discover its possible therapeutic indications. In this paper, an in silico identification procedure is described for discovering novel SIRT2 inhibitors. Molecular modeling and virtual screening were utilized to find potential compounds, which were then subjected to experimental tests for their SIRT2 inhibitory activity. Five of the 15 compounds tested in vitro showed inhibitory activity toward SIRT2, yielding a hit ratio of 33% in a micromolar level and thus demonstrating the usefulness of this procedure in finding new bioactive compounds. Two of the five compounds yielded in vitro IC(50) values of 56.7 and 74.3 microM, and these can be considered as novel inhibitors of SIRT2. On the basis of our results, a phenol moiety on the active compound is suggested to be important for SIRT2 inhibitory activity. This phenol group, together with a hydrophobic moiety and hydrogen-bonding features, is suggested to form an active SIRT2 pharmacophore.

Insights into ligand-elicited activation of human constitutive androstane receptor based on novel agonists and three-dimensional quantitative structure-activity relationship.

The human constitutive androstane receptor (CAR, NR1I3) is an important regulator of xenobiotic metabolism and other physiological processes. So far, only few CAR agonists are known and no explicit mechanism has been proposed for their action. Thus, we aimed to generate a 3D QSAR model that could explain the molecular determinants of CAR agonist action. To obtain a sufficient number of agonists that cover a wide range of activity, we applied a virtual screening approach using both structure- and ligand-based methods. We identified 27 novel human CAR agonists on which a 3D QSAR model was generated. The model, complemented by coregulator recruitment and mutagenesis results, suggests a potential activation mechanism for human CAR and may serve to predict potential activation of CAR for compounds emerging from drug development projects or for chemicals undergoing toxicological risk assessment.

N-(3-(4-Hydroxyphenyl)-propenoyl)-amino acid tryptamides as SIRT2 inhibitors.

A series of N-(3-(4-hydroxyphenyl)-propenoyl)-amino acid tryptamides was based on a previously reported new SIRT2 inhibitor from our group, and it was designed to study if the molecular size of the compound could be reduced. The most potent compounds, N-(3-(4-hydroxyphenyl)-propenoyl)-2-aminoisobutyric acid tryptamide and N-(3-(4-hydroxyphenyl)-propenoyl)-L-alanine tryptamide, were equipotent, 30% smaller in molecular weight, and slightly more selective (SIRT2/SIRT1) than the parent compound.

BRUTUS: optimization of a grid-based similarity function for rigid-body molecular superposition. II. Description and characterization.

Finding novel lead molecules is one of the primary goals in early phases of drug discovery projects. However, structurally dissimilar compounds may exhibit similar biological activity, and finding new and structurally diverse lead compounds is difficult for computer algorithms. Molecular energy fields are appropriate for finding structurally novel molecules, but they are demanding to calculate and this limits their usefulness in virtual screening of large chemical databases. In our approach, energy fields are computed only once per superposition and a simple interpolation scheme is devised to allow coarse energy field lattices having fewer grid points to be used without any significant loss of accuracy. The resulting processing speed of about 0.25 s per conformation on a 2.4 GHz Intel Pentium processor allows the method to be used for virtual screening on commonly available desktop machines. Moreover, the results indicate that grid-based superposition methods could be efficiently used for the virtual screening of compound libraries.

Comparing the quality and predictiveness between 3D QSAR models obtained from manual and automated alignment.

A set of 113 flexible cyclic urea inhibitors of human immunodeficiency virus protease (HIV-1 PR) was used to compare the quality and predictive power of CoMFA and CoMSIA models for manually or automatically aligned inhibitor set. Inhibitors that were aligned automatically with molecular docking were in agreement with information obtained from existing X-ray structures. Both alignment methods produced statistically significant CoMFA and CoMSIA models, with the best q(2) value being 0.649 and the best predictive r(2) being 0.754. The manual alignment gave statistically higher values, whereas the automated alignment gave more robust models for predicting the activities of an external inhibitor set. Both models utilized similar amino acids in the HIV-1 PR active site, supporting the idea that hydrogen bonds form between an inhibitor and the backbone carbonyl oxygens of Gly48 and Gly48' and also the backbone NH group of Asp30, Gly48, Asp29', and Gly48' of the enzyme. These results suggest that an automated inhibitor alignment can yield predictive 3D QSAR models that are well comparable to manual methods. Thus, an automated alignment method in creating 3D QSAR models is encouragable when a well-characterized structure of the target protein is available.

A structure-activity relationship study of catechol-O-methyltransferase inhibitors combining molecular docking and 3D QSAR methods.

A panel of 92 catechol-O-methyltransferase (COMT) inhibitors was used to examine the molecular interactions affecting their biological activity. COMT inhibitors are used as therapeutic agents in the treatment of Parkinson's disease, but there are limitations in the currently marketed compounds due to adverse side effects. This study combined molecular docking methods with three-dimensional structure-activity relationships (3D QSAR) to analyse possible interactions between COMT and its inhibitors, and to incite the design of new inhibitors. Comparative molecular field analysis (CoMFA) and GRID/GOLPE models were made by using bioactive conformations from docking experiments, which yielded q2 values of 0.594 and 0.636, respectively. The docking results, the COMT X-ray structure, and the 3D QSAR models are in agreement with each other. The models suggest that an interaction between the inhibitor's catechol oxygens and the Mg2+ ion in the COMT active site is important. Both hydrogen bonding with Lys144, Asn170 and Glu199, and hydrophobic contacts with Trp38, Pro174 and Leu198 influence inhibitor binding. Docking suggests that a large R1 substituent of the catechol ring can form hydrophobic contacts with side chains of Val173, Leu198, Met201 and Val203 on the COMT surface. Our models propose that increasing steric volume of e.g. the diethylamine tail of entacapone is favourable for COMT inhibitory activity.