Journal of Computer-Aided Molecular Design

Multivariate data analysis methods (Principal Component Analysis (PCA) and Partial Least Squares (PLS)) are applied to the analysis of the CoMFA (Comparative Molecular Field Analysis) data for several nucleic acids components. The data set includes nitrogenated bases, nucleosides, linear nucleotides, 3′, 5′-cyclic nucleotides and oligonucleotides. PCA is applied to study the structure of the CoMFA data and to detect possible outliers in the data set. PLS is applied to correlate the CoMFA data with either calculated AM1 proton affinities or with experimental pKa values. The possibility of making a prediction of pKa values directly from 3D structures of the monomers for polynucleotides is also shown. The influence of the superposition criteria and of conformational changes along the glycosidic bond on the pKa prediction are studied as well.

The absolute binding free energies and binding enthalpies of twelve host–guest systems in the SAMPL5 blind challenge were computed using our attach-pull-release (APR) approach. This method has previously shown good correlations between experimental and calculated binding data in retrospective studies of cucurbit[7]uril (CB7) and β-cyclodextrin (βCD) systems. In the present work, the computed binding free energies for host octa acid (OA or OAH) and tetra-endo-methyl octa-acid (TEMOA or OAMe) with guests are in good agreement with prospective experimental data, with a coefficient of determination (R2) of 0.8 and root-mean-squared error of 1.7 kcal/mol using the TIP3P water model. The binding enthalpy calculations achieve moderate correlations, with R2 of 0.5 and RMSE of 2.5 kcal/mol, for TIP3P water. Calculations using the newly developed OPC water model also show good performance. Furthermore, the present calculations semi-quantitatively capture the experimental trend of enthalpy-entropy compensation observed, and successfully predict guests with the strongest and weakest binding affinity. The most populated binding poses of all twelve systems, based on clustering analysis of 750 ns molecular dynamics (MD) trajectories, were extracted and analyzed. Computational methods using MD simulations and explicit solvent models in a rigorous statistical thermodynamic framework, like APR, can generate reasonable predictions of binding thermodynamics. Especially with continuing improvement in simulation force fields, such methods hold the promise of making substantial contributions to hit identification and lead optimization in the drug discovery process.

Four new potential agents muscarinic (allosteric modulators) were synthesized and studied by using the B3LYP density functional method. The optimum conformation and geometry structure of these compounds were determined and analyzed. Solvent effects were considered including a variable number (1–15) of explicit water molecules surrounding the compound in order to simulate the first hydration shell, as well as using the Tomasi’s polarized continuum model (PCM). A similar simultaneous analysis of the potents W84 and DUO-3O allosteric modulator of muscarinic receptors was also carried out. The effect of the hydration on the total atomic charges and several intermolecular distances of interest were also discussed. The biological activity against acetylcholine of our four synthesized bispyridinium salts was determined. Relationships/tendencies structure–activity were established. Several general conclusions were underlined.

A novel pharmacophore definition procedure is described, which uses a Monte Carlo method to superimpose molecules. Pharmacophore space is searched by a technique similar to high temperature annealing. Subsequent refinement of candidate pharmacophores by energy minimization produces low-energy conformations that may be involved in receptor binding. The method has been applied to compounds that bind to the human platelet-activating factor (PAF) receptor. Alternative binding site models for the PAF receptor are presented and discussed.

Rational drug design involves finding solutions to large combinatorial problems for which an exhaustive search is impractical. Genetic algorithms provide a novel tool for the investigation of such problems. These are a class of algorithms that mimic some of the major characteristics of Darwinian evolution. LEA has been designed in order to conceive novel small organic molecules which satisfy quantitative structure-activity relationship based rules (fitness). The fitness consists of a sum of constraints that are range properties. The algorithm takes an initial set of fragments and iteratively improves them by means of crossover and mutation operators that are related to those involved in Darwinian evolution. The basis of the algorithm, its implementation and parameterization, are described together with an application in de novo molecular design of new retinoids. The results may be promising for chemical synthesis and show that this tool may find extensive applications in de novo drug design projects.

The transient receptor potential vanilloid subtype 1 (TRPV1) is a non-selective cation channel composed of four monomers with six transmembrane helices (TM1–TM6). TRPV1 is found in the central and peripheral nervous system, and it is an important therapeutic target for pain relief. We describe here the construction of a tetrameric homology model of rat TRPV1 (rTRPV1). We experimentally evaluated by mutational analysis the contribution of residues of rTRPV1 contributing to ligand binding by the prototypical TRPV1 agonists, capsaicin and resiniferatoxin (RTX). We then performed docking analysis using our homology model. The docking results with capsaicin and RTX showed that our homology model was reliable, affording good agreement with our mutation data. Additionally, the binding mode of a simplified RTX (sRTX) ligand as predicted by the modeling agreed well with those of capsaicin and RTX, accounting for the high binding affinity of the sRTX ligand for TRPV1. Through the homology modeling, docking and mutational studies, we obtained important insights into the ligand-receptor interactions at the molecular level which should prove of value in the design of novel TRPV1 ligands.

The recently proposed WHIM (Weighted Holistic Invariant Molecular) approach [Todeschini,R., Lasagni, M. and Marengo, E., J. Chemometrics, 8 (1994) 263] has been applied tomolecular surfaces to derive new 3D theoretical descriptors, called MS-WHIM. To test theirreliability, a 3D QSAR study has been performed on a series of steroids, comparing the MS-WHIM description to both the original WHIM indices and CoMFA fields. The analysis of thestatistical models obtained shows that MS-WHIM descriptors provide meaningful quantitativestructure–activity correlations. Thus, the results obtained agree well with thoseachieved using CoMFA fields. The concise number of indices, the ease of their calculationand their invariance to the coordinate system make MS-WHIM an attractive tool for 3DQSAR studies.

Một phương pháp gần đây để ước lượng độ gắn kết của ligand đã được mở rộng. Phương pháp này sử dụng trung bình các thành phần năng lượng tương tác từ mô phỏng động lực học phân tử hoặc các kỹ thuật lấy mẫu tuần hoàn nhiệt khác. Việc kết hợp các sai lệch hệ thống từ phản ứng tuyến tính tĩnh điện, được rút ra từ các nghiên cứu rối loạn năng lượng tự do, vào biểu thức năng lượng tự do tuyệt đối của độ gắn kết đã làm tăng đáng kể độ chính xác của phương pháp này. Loại phương pháp này có thể hữu ích cho việc dự đoán tính chất gắn kết của ligand một cách tính toán, ví dụ, trong các ứng dụng thiết kế thuốc.