Sanja Tomić

Novel cyano- and amidinobenzothiazole derivatives: synthesis, antitumor evaluation, and X-ray and quantitative structure-activity relationship (QSAR) analysis.

Synthesis of a series of novel cyano- and amidinobenzothiazole derivatives 3-31 is described. All studied amidino derivatives showed noticeable antiproliferative effect on several tumor cell lines. Cyano derivatives 11-17 showed considerably less pronounced activity because of their poor solubility in aqueous cell culture medium, which was confirmed by the principal components (PC) analysis. Compounds 21, 22, 28, and 29 were tested for their effects on the cell cycle and apoptosis, whereby 22 and 29, having methyl group at the C-6 position in pyridine ring, showed drastic cell cycle perturbations that were both concentration- and time-dependent and induced apoptosis. The QSAR modeling, based on the physicochemical descriptors and on the measured biological activities, indicated the relevance of molecular polarizability and particular distribution of pharmacophores on the molecular surface for activity. In conclusion, benzothiazoles containing either isopropylamidino or imidazolyl groups will be considered as starting compounds for further investigation on lead identification.

A quantitative model for predicting enzyme enantioselectivity: application to Burkholderia cepacia lipase and 3-(aryloxy)-1,2-propanediol derivatives.

We describe a new approach for predicting the enantioselectivity of enzymes towards racemic compounds. It is based on comparative binding energy (COMBINE) analysis. The approach is used to rationalise the enantioselectivity of Burkholderia cepacia lipase (BCL) towards thirteen racemic 3-(aryloxy)-1,2-propanediols in the process of acylation. According to our molecular modelling study the two 3-(aryloxy)-1,2-propanediols enantiomers bind in the BCL active site in different orientations. To derive a quantitative structure-activity relationship (QSAR), the difference in the interaction energy between two enantiomers with each amino acid residue was computed. These residue-based energy differences were then subjected to chemometric analysis and 3D QSAR models were derived. The models were able to unambiguously predict the fast-reacting enantiomer and the approximate magnitude of the enantioselectivity. The study enabled identification of interactions between the substrate and the lipase amino acid residues that play key roles in secondary alcohol enantiodifferentiation. From the results, it was possible to propose modifications of both, substrate and protein, which would directionally modify enantioselectivity of BCL towards secondary aryl-alcohols.

Classification of auxin plant hormones by interaction property similarity indices.

Although auxins were the first type of plant hormone to be identified, little is known about the molecular mechanism of this important class of plant hormones. We present a classification of a set of about 50 compounds with measured auxin activities, according to their interaction properties. Four classes of compounds were defined: strongly active, weakly active with weak antiauxin behaviour, inactive and inhibitory. All compounds were modeled in two low-energy conformations, ‘P’ and ‘T’, so as to obtain the best match to the ‘planar’ and ‘tilted’ conformations, respectively, of indole 3-acetic acid. Each set of conformers was superimposed separately using several different alignment schemes. Molecular interaction energy fields were computed for each molecule with five different chemical probes and then compared by computing similarity indices. Similarity analysis showed that the classes are on average distinguishable, with better differentiation achieved for the T conformers than the P conformers. This indicates that the T conformation might be the active one. Further, a screening was developed which could distinguish compounds with auxin activity from inactive compounds and most antiauxins using the T conformers. The classifications rationalize ambiguities in activity data found in the literature and should be of value in predicting the activities of new plant growth substances and herbicides.

Auxin Amidohydrolases From Brassica rapa Cleave The Alanine Conjugate Of Indolepropionic Acid As A Preferable Substrate: A Biochemical And Modeling Approach

Two auxin amidohydrolases, BrIAR3 and BrILL2, from Chinese cabbage [Brassica rapa L. ssp. pekinensis (Lour.) Hanelt] were produced by heterologous expression in Escherichia coli, purified, and screened for activity towards N-(indol-3-ylacetyl)-L-alanine (IAA-Ala) and the long-chain auxin-amino acid conjugates, N-[3-(indol-3-yl)propionyl]-L-alanine (IPA-Ala) and N-[4-(indol-3-yl)butyryl]-L-alanine (IBA-Ala). IPA-Ala was shown to be the favored substrate of both enzymes, but BrILL2 was approximately 15 times more active than BrIAR3. Both enzymes cleaved IBA-Ala and IAA-Ala to a lesser extent. The enzyme kinetics were measured for BrILL2 and the obtained parameters suggested similar binding affinities for the long-chain auxin-amino acid conjugates (IPA-Ala and IBA-Ala). The velocity of the hydrolyzing reaction decreased in the order IPA-Ala > IBA-Ala > IAA-Ala. In a root growth bioassay, higher growth inhibition was caused by IPA-Ala and IBA-Ala in comparison with IAA-Ala. Neither these conjugates nor the corresponding free auxins affected the expression of the BrILL2 gene. A modeling study revealed several possible modes of IPA-Ala binding to BrILL2. Based on these results, two possible scenarios for substrate hydrolysis are proposed. In one the metal binding water is activated by the carboxyl group of the substrate itself, and in the other by a glutamate residue from the active site of the enzyme.

COMBINE analysis of the specificity of binding of Ras proteins to their effectors

The small guanosine triphosphate (GTP)‐binding proteins of the Ras family are involved in many cellular pathways leading to cell growth, differentiation, and apoptosis. Understanding the interaction of Ras with other proteins is of importance not only for studying signalling mechanisms but also, because of their medical relevance as targets, for anticancer therapy. To study their selectivity and specificity, which are essential to their signal transfer function, we performed COMparative BINding Energy (COMBINE) analysis for 122 different wild‐type and mutant complexes between the Ras proteins, Ras and Rap, and their effectors, Raf and RalGDS. The COMBINE models highlighted the amino acid residues responsible for subtle differences in binding of the same effector to the two different Ras proteins, as well as more significant differences in the binding of the two different effectors (RalGDS and Raf) to Ras. The study revealed that E37, D38, and D57 in Ras are nonspecific hot spots at its effector interface, important for stabilization of both the RalGDS–Ras and Raf–Ras complexes. The electrostatic interaction between a GTP analogue and the effector, either Raf or RalGDS, also stabilizes these complexes. The Raf–Ras complexes are specifically stabilized by S39, Y40, and D54, and RalGDS–Ras complexes by E31 and D33. Binding of a small molecule in the vicinity of one of these groups of amino acid residues could increase discrimination between the Raf–Ras and RalGDS–Ras complexes. Despite the different size of the RalGDS–Ras and Raf–Ras complexes, we succeeded in building COMBINE models for one type of complex that were also predictive for the other type of protein complex. Further, using system‐specific models trained with only five complexes selected according to the results of principal component analysis, we were able to predict binding affinities for the other mutants of the particular Ras‐effector complex. As the COMBINE analysis method is able to explicitly reveal the amino acid residues that have most influence on binding affinity, it is a valuable aid for protein design. Proteins 2007.

A new approach to predict the biological activity of molecules based on similarity of their interaction fields and the logP and logD values: application to auxins.

The activity of a biological compound is dependent both on specific binding to a target receptor and its ADME (Absorption, Distribution, Metabolism, Excretion) properties. A challenge to predict biological activity is to consider both contributions simultaneously in deriving quantitative models. We present a novel approach to derive QSAR models combining similarity analysis of molecular interaction fields (MIFs) with prediction of logP and/or logD. This new classification method is applied to a set of about 100 compounds related to the auxin plant hormone. The classification based on similarity of their interaction fields is more successful for the indole than the phenoxy compounds. The classification of the phenoxy compounds is however improved by taking into account the influence of the logP and/or the logD values on biological activity. With the new combined method, the majority (8 out of 10) of the previously misclassified derivatives of phenoxy acetic acid are classified in accord with their bioassays. The recently determined crystal structure of the auxin-binding protein 1 (ABP1) enabled validation of our approach. The results of docking a few auxin related compounds with different biological activity to ABP1 correlate well with the classification based on similarity of MIFs only. Biological activity is, however, better predicted by a combined similarity of MIFs + logP/logD approach.

Auxin activity and molecular structure of 2-alkylindole-3-acetic acids

Abstract2-Methylindole-3-acetic acid (2-Me-IAA) is a known auxin, but its 2-ethyl homologue has been considered inactive. Here we show that the compound previously bioassayed as ‘2-ethylindole-3-acetic acid’ (2-Et-IAA) was, in fact, 3-(3-methylindol-2-yl)propionic acid. The proper 2-Et-IAA and its 2-(n-propyl) homologue (2-Pr-IAA) are prepared, unambiguously characterized, and their auxin activity is demonstrated in the Avena coleoptile-section straight-growth test. Their half-optimal concentrations are approximately the same as for 2-Me-IAA (2 × 10−5mol L−1), and hence about ten times larger than for unsubstituted indole-3-acetic acid (IAA) and its derivatives alkylated in positions 4, 5, 6 or 7. The optimal response elicited by 2-Et-IAA and 2-Pr-IAA is about half that observed for 2-Me-IAA. These characteristics place the three 2-alkyl-IAAs along the borderline between the classes of strong and weak auxins, thus corroborating the results of interaction similarity analysis, a mathematical approach based on the capability of auxin molecules to participate in non-bonding interactions with a generalized receptor protein. X-ray diffraction analysis shows no explicit structural features to be blamed for the decrease in auxin activity caused by attaching a 2-alkyl substituent to the IAA molecule; sterical interference of the 3-CH2COOH group and the 2-alkyl moiety is barely recognizable in the crystalline state. Quantum-chemical calculations and molecular dynamics simulations suggest that 2-alkyl-IAAs, in the absence of crystal-packing restraints, prefer conformations with the CH2-COOH bond tilted to the heterocyclic ring system. Substantially higher conformational energy (and hence lower abundance) is predicted for planar conformers which were previously shown to prevail for IAA and many of its derivatives substituted in the benzene moiety of the indole nucleus. This shift in the rotational preferences of the -CH2COOH moiety may be one of the reasons for the reduced plant-growth promoting activity of 2-alkyl-IAAs.

Combine and Free-Wilson QSAR Analysis of Nuclear Receptor-DNA Binding

Specific binding of transcription factors to DNA is crucial for gene regulation. We have studied the specificity of binding of transcription factors from the nuclear receptor family to DNA using two QSAR methods: a) a Free-Wilson like method and b) Comparative Binding Energy (COMBINE)1 analysis.