RESUMEN
The recently introduced non-stochastic and stochastic bond-based linear indices are been generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. These improved modified descriptors are applied to several well-known data sets to validate each one of them. Particularly, Cramer's steroid data set has become a benchmark for the assessment of novel quantitative structure activity relationship methods. This data set has been used by several researchers using 3D-QSAR approaches such as Comparative Molecular Field Analysis, Molecular Quantum Similarity Measures, Comparative Molecular Moment Analysis, E-state, Mapping Property Distributions of Molecular Surfaces, and so on. For that reason, it is selected by us for the sake of comparability. In addition, to evaluate the effectiveness of this novel approach in drug design we model the angiotensin-converting enzyme inhibitory activity of perindoprilate's sigma-stereoisomers combinatorial library, as well as codify information related to a pharmacological property highly dependent on the molecular symmetry of a set of seven pairs of chiral N-alkylated 3-(3-hydroxyphenyl)-piperidines that bind sigma-receptors. The validation of this method is achieved by comparison with earlier publications applied to the same data sets. The non-stochastic and stochastic bond-based 3D-chiral linear indices appear to provide a very interesting alternative to other more common 3D-QSAR descriptors.
Asunto(s)
Inhibidores de la Enzima Convertidora de Angiotensina/química , Diseño de Fármacos , Indoles/química , Modelos Químicos , Piperidinas/química , Inhibidores de la Enzima Convertidora de Angiotensina/farmacología , Técnicas Químicas Combinatorias , Indoles/farmacología , Piperidinas/farmacología , Relación Estructura-Actividad Cuantitativa , Receptores sigma/antagonistas & inhibidores , Receptores sigma/metabolismo , Estereoisomerismo , Procesos Estocásticos , TermodinámicaRESUMEN
The TOpological MOlecular COMputer Design (TOMOCOMD-CARDD) approach has been introduced for the classification and design of antimicrobial agents using computer-aided molecular design. For this propose, atom, atom-type, and total quadratic indices have been generalized to codify chemical structure information. In this sense, stochastic quadratic indices have been introduced for the description of the molecular structure. These stochastic fingerprints are based on a simple model for the intramolecular movement of all valence-bond electrons. In this work, a complete data set containing 1006 antimicrobial agents is collected and presented. Two structure-based antibacterial activity classification models have been generated. The models (including nonstochastic and stochastic indices) classify correctly more than 90% of 1525 compounds in training sets. These models permit the correct classification of 92.28% and 89.31% of 505 compounds in an external test sets. The TOMOCOMD-CARDD approach, also, satisfactorily compares with respect to nine of the most useful models for antimicrobial selection reported to date. Finally, a virtual screening of 87 new compounds reported in the antiinfective field with antibacterial activities is developed showing the ability of the TOMOCOMD-CARDD models to identify new leads as antibacterial.
Asunto(s)
Antibacterianos/química , Modelos Teóricos , Relación Estructura-Actividad Cuantitativa , Antibacterianos/clasificación , Simulación por Computador , Bases de Datos Factuales , Conformación Molecular , Procesos EstocásticosRESUMEN
A novel approach to bio-macromolecular design from a linear algebra point of view is introduced. A protein's total (whole protein) and local (one or more amino acid) linear indices are a new set of bio-macromolecular descriptors of relevance to protein QSAR/QSPR studies. These amino-acid level biochemical descriptors are based on the calculation of linear maps on Rn[f k(xmi):Rn-->Rn] in canonical basis. These bio-macromolecular indices are calculated from the kth power of the macromolecular pseudograph alpha-carbon atom adjacency matrix. Total linear indices are linear functional on Rn. That is, the kth total linear indices are linear maps from Rn to the scalar R[f k(xm):Rn-->R]. Thus, the kth total linear indices are calculated by summing the amino-acid linear indices of all amino acids in the protein molecule. A study of the protein stability effects for a complete set of alanine substitutions in the Arc repressor illustrates this approach. A quantitative model that discriminates near wild-type stability alanine mutants from the reduced-stability ones in a training series was obtained. This model permitted the correct classification of 97.56% (40/41) and 91.67% (11/12) of proteins in the training and test set, respectively. It shows a high Matthews correlation coefficient (MCC=0.952) for the training set and an MCC=0.837 for the external prediction set. Additionally, canonical regression analysis corroborated the statistical quality of the classification model (Rcanc=0.824). This analysis was also used to compute biological stability canonical scores for each Arc alanine mutant. On the other hand, the linear piecewise regression model compared favorably with respect to the linear regression one on predicting the melting temperature (tm) of the Arc alanine mutants. The linear model explains almost 81% of the variance of the experimental tm (R=0.90 and s=4.29) and the LOO press statistics evidenced its predictive ability (q2=0.72 and scv=4.79). Moreover, the TOMOCOMD-CAMPS method produced a linear piecewise regression (R=0.97) between protein backbone descriptors and tm values for alanine mutants of the Arc repressor. A break-point value of 51.87 degrees C characterized two mutant clusters and coincided perfectly with the experimental scale. For this reason, we can use the linear discriminant analysis and piecewise models in combination to classify and predict the stability of the mutant Arc homodimers. These models also permitted the interpretation of the driving forces of such folding process, indicating that topologic/topographic protein backbone interactions control the stability profile of wild-type Arc and its alanine mutants.
Asunto(s)
Alanina/química , Biología Computacional/métodos , Modelos Teóricos , Proteínas/química , Relación Estructura-Actividad Cuantitativa , Proteínas Represoras/química , Proteínas Virales/química , Sustitución de Aminoácidos , Modelos Lineales , Mutación , Proteínas Represoras/genética , Programas Informáticos , Temperatura , Proteínas Virales/genética , Proteínas Reguladoras y Accesorias ViralesRESUMEN
PURPOSE: Quantitative Structure-Permeability Relationships (QSPerR) of the intestinal permeability across the (Caco-2) cells monolayer could be obtained by the application of new molecular descriptors. METHOD: A novel topologic-molecular approach to computer molecular design ( TOMOCOMD-CARDD ) has been used to estimate the intestinal-epithelial transport of drug in Caco-2 cell culture. RESULTS: The Permeability Coefficients in Caco-2 cells (P) for 33 structurally diverse drugs were well described using quadratic indices of the molecular pseudograph's atom adjacency matrix as molecular descriptors. A quantitative model that discriminates the high-absorption compounds from those with moderate-poor absorption was obtained for the training data set, showing a global classification of 87.87%. In addition, two QSPerR models, through a multiple linear regression, were obtained to predict the P [apical to basolateral (AP-->BL) and basolateral to apical (BL-->AP)]. A leave- n -out and leave- one -out cross-validation procedure revealed that the discriminant and regression models respectively, had a good predictability. Furthermore, others 18 drugs were selected as a test set in order to assess the predictive power of the models and the accuracy of the final prediction was similar to achieve for the data set. Besides, the use of both regression models, in a combinative way, is possible to predict the Permeability Directional Ratio (PDR, BL-->AP/AP-->BL) value. The found models were used in virtual screening of drug intestinal permeability and a relationship between calculated P and percentage of human intestinal absorption for several compounds was established. Furthermore, this approximation permits us to obtain a good explanation of the experiment based on the molecular structural features. CONCLUSIONS: These results suggest that the proposed method is able to predict the P values and it proved to be a good tool for studying the oral absorption of drug candidates during the drug development process.