Changes in dynamics of α-chymotrypsin due to covalent inhibitors investigated by elastic incoherent neutron scattering.

An essential role of enzymes is to catalyze various chemical reactions in the human body and inhibition of the enzymatic activity by small molecules is the mechanism of action of many drugs or tool compounds used to study biological processes. Here, we investigate the effect on the dynamics of the serine protease α-chymotrypsin when in complex with two different covalently bound inhibitors using elastic incoherent neutron scattering. The results show that the inhibited enzyme displays enhanced dynamics compared to the free form. The difference was prominent at higher temperatures (240-310 K) and the type of motions that differ include both small amplitude motions, such as hydrogen atom rotations around a methyl group, and large amplitude motions, such as amino acid side chain movements. The measurements were analyzed with multivariate methods in addition to the standard univariate methods, allowing for a more in-depth analysis of the types of motions that differ between the two forms. The binding strength of an inhibitor is linked to the changes in dynamics occurring during the inhibitor-enzyme binding event and thus these results may aid in the deconvolution of this fundamental event and in the design of new inhibitors.

On the use of electronic descriptors for QSAR modelling of PCDDs, PCDFs and dioxin-like PCBs.

The electronic properties of 29 polychlorinated dibenzo-p-dioxins and dibenzofurans and dioxin-like polychlorinated biphenyls that have been included in the toxic equivalency factor system have been investigated and used to derive quantum mechanical (QM) chemical descriptors for QSAR modelling. Their utility in this context was investigated alongside descriptors based on ultraviolet absorption data and traditional 2D descriptors including log K(ow), polarizability, molecular surface properties, van der Waals volume and selected connectivity indices. The QM descriptors were calculated using the semi-empirical AM1 method and the density functional theory method B3-LYP/6-31G**. Atom-specific and molecular quantum chemical descriptors were calculated to compare the electronic properties of dioxin-like compounds regardless of their chemical class, with particular emphasis on the lateral positions. Multivariate analysis revealed differences between the chemical classes in terms of their electronic properties and also highlighted differences between congeners. The results obtained demonstrated the importance of considering molecular orbital energies, but also indicated that the ratios of the coefficients of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) at the lateral carbons were important. In addition, the digitalized UV spectra contained chemical information that provided crucial insights into dioxin-like activity.

Statistical molecular design of balanced compound libraries for QSAR modeling.

A fundamental step in preclinical drug development is the computation of quantitative structure-activity relationship (QSAR) models, i.e. models that link chemical features of compounds with activities towards a target macromolecule associated with the initiation or progression of a disease. QSAR models are computed by combining information on the physicochemical and structural features of a library of congeneric compounds, typically assembled from two or more building blocks, and biological data from one or more in vitro assays. Since the models provide information on features affecting the compounds' biological activity they can be used as guides for further optimization. However, in order for a QSAR model to be relevant to the targeted disease, and drug development in general, the compound library used must contain molecules with balanced variation of the features spanning the chemical space believed to be important for interaction with the biological target. In addition, the assays used must be robust and deliver high quality data that are directly related to the function of the biological target and the associated disease state. In this review, we discuss and exemplify the concept of statistical molecular design (SMD) in the selection of building blocks and final synthetic targets (i.e. compounds to synthesize) to generate information-rich, balanced libraries for biological testing and computation of QSAR models.

Statistical molecular design, parallel synthesis, and biological evaluation of a library of thrombin inhibitors.

A library of thrombin inhibitors has been designed using statistical molecular design. An aromatic scaffold was used, with three varied positions corresponding to three pockets at the active site of thrombin (the S-, P-, and D-pockets). The selection was performed in the building block space, and previously acquired data were included in the design procedure. The design resulted in six, four, and six building blocks for the first (S), second (P), and third (D) pockets, respectively. A second round of selection applied to the combined selected building blocks resulted in a subset of 18 compounds. The selected library was synthesized in parallel and biologically evaluated. The compounds were analyzed with respect to their inhibition (pIC(50)) of thrombin; membrane permeability, estimated by migration behavior in micellar media (CE log k') and pK(a); and specificity with respect to inhibition (K(i)) of trypsin. Multivariate QSAR studies of the responses yielded valuable results and information that could only be found using statistical molecular design in combination with multivariate analysis.

Statistical molecular design of building blocks for combinatorial chemistry.

The reduction of the size of a combinatorial library can be made in two ways, either base the selection on the building blocks (BB's) or base it on the full set of virtually constructed products. In this paper we have investigated the effects of applying statistical designs to BB sets compared to selections based on the final products. The two sets of BB's and the virtually constructed library were described by structural parameters, and the correlation between the two characterizations was investigated. Three different selection approaches were used both for the BB sets and for the products. In the first two the selection algorithms were applied directly to the data sets (D-optimal design and space-filling design), while for the third a cluster analysis preceded the selection (cluster-based design). The selections were compared using visual inspection, the Tanimoto coefficient, the Euclidean distance, the condition number, and the determinant of the resulting data matrix. No difference in efficiency was found between selections made in the BB space and in the product space. However, it is of critical importance to investigate the BB space carefully and to select an appropriate number of BB's to result in an adequate diversity. An example from the pharmaceutical industry is then presented, where selection via BB's was made using a cluster-based design.

Statistical molecular design of peptoid libraries.

Statistical experimental design provides an efficient approach for selecting the building blocks to span the structural space and increase the information content in a combinatorial library. A set of renin-inhibitors, hexapeptoids, is used to illustrate the approach. Multivariate quantitative structure-activity relationships (MQSARs) were developed relating renin inhibition to the peptoid sequences variation, parametrized by the z-scales. By using the information from the models, the number of building block sets could be reduced from six to three. Using a statistical molecular design (SMD) reduces the number of compounds from more than 100,000 down to 90. A second SMD was used for comparison, based on less prior knowledge. This gave a reduction from over 2 billion to 120 compounds.