Program BABELPDB allows browsing–interrogating native–derived structural features of biological macromolecules using data obtained from the Protein Data Bank (PDB). Major features are: (1) convert from PDB to other formats, (2) add H atoms, (3) strip water molecules and (4) separate a-carbons (Ca). The coordinates obtained with BABELPDB allow characterizing the presence of Hbonds. The algorithm for detecting H-bonds is implemented in program TOPO for the theoretical simulation of the molecular shape. An example illustrates the capabilities, i.e., calculation of the fractal dimension of lysozyme (1.908) with and without (1.920) H atoms. The numbers compare with reference calculations performed with program GEPOL and results from Pfeifer et al. For proteins, Ca skeleton allows drawing the ribbons image, which determines the secondary structure.

Bond-extended stochastic and non-stochastic bilinear indices are introduced in this paper as novel bond-level molecular descriptors (MDs). These novel totals (wholemolecule) MDs are based on a bilinear maps (forms) similar to use defined in linear algebra. The proposed non-stochastic indices try to match molecular structure provided by the molecular topology by using the kth Edge(Bond)-Adjacency Matrix (Ek, designed here as non-stochastic E matrix). The stochastic parameters are computed by using the kth stochastic edge-adjacency matrix, ESk, as matrix operators of bilinear transformations. This new edge (bond)-adjacency relationships can be obtained directly from Ek and can be consider like a new matrix-transformation strategic to obtain new relation for a molecular graph. In both set of MDs, chemical information is codified by using different pair combinations of atomic weightings (in this case four atomic-labels: atomic mass, polarizability, van der Waals volume, and electronegativity). In addition, a local-fragment (bond-type) formalism was also developed. The kth bond-type bilinear indices are calculated by summing the kth bond bilinear indices of all bonds of the same bond type in the molecules. The new set of MDs can be easily and quickly calculate in our in house software TOMOCOMD-CARDD (TOpological MOlecular COMputer Design Computer-Aided –Rational– Drug Design). The reported application and utilization of these MDs for predictive capability correlations of structure with physicochemical and pharmacology properties are reviewed. Three benchmark datasets have been used to evaluate the QSPR/QSAR behavior of the new bond-level TOMOCOMD-CARDD MDs. We developed the QSPR models to describe several physicochemical properties of octane isomers (FIRST CASE) and, to analyze of the boiling point of 28 alkyl-alcohols (SECOND CASE) and to examine of the specific rate constant (log k), the partition coefficient (log P), as well as the antibacterial activity of 34 derivatives of 2-furylethylenes (THIRD CASE). For these three rounds, the quantitative models found are significant from a statistical point of view and permit a clear interpretation of the studied properties in terms of the structural features of molecules. A leave-out-out cross-validation procedure revealed that the regression models had a good predictability. The comparison with other approaches reveals good performance of the method proposed. Therefore, it is clearly demonstrated that this suitability is higher than that shown by other 2D/3D well-known sets of MDs. The approach described here appears to be a very promising structural invariant, useful for QSPR/QSAR studies and shown to provide an excellent alternative or guides for discovery and optimization of new lead compounds, reducing the time and cost of traditional procedure.

A new set of nucleotide-based biomacromolecular descriptors are presented. This novel approach to biomacromolecular design from a linear algebra point of view is relevant to nucleic acids QSAR (Quantitative Structure-Activity Relationship) studies. These biomacromolecular indices are based on the calculus of bilinear maps on Rn [ mk ( m , m ) b x y : R n x R n ?R ] in canonical basis. Nucleic acid’s bilinear indices are calculated from kth power of non-stochastic and stochastic nucleotide’s graph–theoretic electronic-contact matrices, km M and km sM , respectively. That is to say, the kth non-stochastic and stochastic nucleic acid’s bilinear indices are calculated using km M and km sM as matrix operators of bilinear transformations. Moreover, biochemical information is codified by using different pair combinations of nucleotide-base properties as weightings (experimental molar absorption coefficient 260 ? at 260 nm and PH = 7.0, first ( 1 ?E ) and second ( 2 ?E ) single excitation energies in eV, and first (f1) and second (f2) oscillator strength values (of the first singlet excitation energies) of the nucleotide DNA-RNA bases. As example of this approach, an interaction study of the antibiotic Paromomycin with the packaging region of the HIV-1 ?-RNA have been performed and it have been obtained several linear models in order to predict the interaction strength. The best linear model obtained by using nonstochastic bilinear indices explains about 91% of the variance of the experimental Log K (R = 0.95 and s = 0.08x10-4M-1) as long as the best stochastic bilinear indices-based equation account for 89% of the Log K variance (R = 0.94 and s = 0.10 x10-4M-1). The Leave-One-Out (LOO) press statistics, evidenced high predictive ability of both models (q2 = 0.86 and scv = 0.09×10-4M-1 for non-stochastic and q2 = 0.79 and scv = 0.11 x10-4M-1 for stochastic bilinear indices). The nucleic acid’s bilinear indices based models compared favourably with other nucleic acid’s indices based approaches reported nowadays. These models also permit the interpretation of the driving forces of the interaction process. In this sense, developed equations involve short-reaching (k = 3), middle-reaching (4 < k < 9) and farreaching (k = 10 or greater) nucleotide’s bilinear indices. This situation points to electronic and topologic nucleotide’s backbone interactions control of the stability profile of Paromomycin-RNA complexes. Consequently, the present approach represents a novel and rather promising way to theoretical-biology studies.

A new set of amino-acid based bio-macromolecular descriptors support on a bilinear map are presented. This novel approach to bio-macromolecular design from a linear algebra point of view is relevant to protein QSAR/QSPR studies. These biochemical descriptors are based on the computation of bilinear maps on R n [ ( , ) mk m m b x y : R n x R n ? R ] in canonical basis. Protein’s bilinear indices are calculated from kth power of non-stochastic and stochastic graph–theoretic electroniccontact matrices, km M and km sM , respectively. That is to say, the kth non-stochastic and stochastic protein’s bilinear indices are calculated using km M and km sM as matrix operators of bilinear transformations. Moreover, biochemical information is codified by using different pair combinations of amino-acid properties as weightings (z-values, sidechain isotropic surface area (ISA), amino-acids atomic charges (ECI) and hydrophathy index (Kyte-Doolittle scale; HPI). Quantitative models that discriminate near wild-type stability alanine-mutants from the reduced-stability ones in training and test series were obtained. Non-stochastic and stochastic equations permitted the correct classification of 100% (41/41) and 97.56% (40/41) of proteins in the training set, respectively. Correct classification in test sets were 91.67% for both models. In order to predict Arc alaninemutant’s melting temperature (tm), lineal regression models were developed. The linear model obtained by using non-stochastic bilinear indices explains almost 84% of the variance of the experimental tm (R = 0.91 and s = 4.50oC) as long as the stochastic bilinear indices-based equation describe 81% of the tm variance (R = 0.90 and s = 5.01oC). The Leave-one-our press statistics, evidenced high predictive ability of both models (q2 = 0.73 and scv = 4.50 oC for non-stochastic and q2 = 0.64 and scv = 5.01 oC for stochastic bilinear indices). Moreover, non-stochastic and stochastic protein’s bilinear indices produced rather linear piecewise regressions (R of 0.95 and 0.96, correspondingly) between protein-backbone descriptors and tm values for alaninemutants of Arc repressor. Both obtained break-point values were 51.87oC and characterized two mutant’s clusters as well as coincided perfectly with the experimental scale. Therefore, we can use the linear discriminant analysis and piecewise models in combination to classify and predict the stability of the mutant Arc homodimers. Protein’s bilinear indices models compared favorably with several bio-macromolecular descriptors previously reported. These models also permitted the interpretation of the driving forces of such a folding process, indicating that topologic/topographic protein’s backbone interactions control the stability profile of wild-type Arc and its alaninemutants.

Quantitative structure-affinity relationships were applied by multiple linear regression (MLR) analysis for a series of 21 monoazo dyes. Calculated 0D, 1D and 2D structural dye features were correlated to their affinity for cellulose. Variable selection was performed by the genetic algorithm. Good correlations with dye affinity and models with predictive power were obtained. Electrostatic interactions are favorable and hydrophobic disfavorable for dye binding on cellulose.

The bromination reaction of thieno[3,2-b]benzofuran was studied theoretically. Stationary points on the reaction potential energy profile including intermediates and transition states were successfully located employing hybrid DFT procedure at the B3LYP/6-31G* level of theory. The bromination proceeds in two steps at the C(2) carbon of thiophene ring. Initially, a p-complex forms between bromine molecule and thiophene ring. Further the p-complex with high activation barrier 69.9 kcal/mol transforms to s- complex intermediate. Finally the s-complex with 4.6 kcal/mol activation transforms to product 2-bromothieno[3,2-b]benzofuran.

Up to now, very few reports have been published concerning the application of multiobjective optimization (MOOP) techniques to quantitative structure–activity relationship (QSAR) studies. However, none reports the optimization of objectives related directly to the desired pharmaceutical profile of the drug. In this work, for the first time, it is proposed a MOOP method based on Derringer’s desirability function that allows conducting global QSAR studies considering simultaneously the pharmacological, pharmacokinetic and toxicological profile of a set of molecule candidates. The usefulness of the method is demonstrated by applying it to the simultaneous optimization of the analgesic, antiinflammatory, and ulcerogenic properties of a library of fifteen 3-(3-methylphenyl)-2-substituted amino-3H-quinazolin-4-one compounds. The levels of the predictor variables producing concurrently the best possible compromise between these properties is found and used to design a set of new optimized drug candidates. Our results also suggest the relevant role of the bulkiness of alkyl substituents on the C-2 position of the quinazoline ring over the ulcerogenic properties for this family of compounds. Finally, and most importantly, the desirabilitybased MOOP method proposed is a valuable tool and shall aid in the future rational design of novel successful drugs.

The study of type III RNases constitutes an important area in molecular biology. It is known that the pac1+ gene encodes a particular RNase III that shares low amino acid similarity with other genes despite having a double-stranded ribonuclease activity. Bioinformatics methods based on sequence alignment may fail when there is a low amino acidic identity percentage between query sequence and others with similar functions (remote homologues) or a similar sequence is not recorded in the database. Quantitative Structure-Activity Relationships (QSAR) applied to protein sequences may allow an alignment-independent prediction of protein function. These sequences QSAR like methods often use 1D sequence numerical parameters as the input to seek sequence-function relationships. However, previous 2D representation of sequences may uncover useful higher-order information. In the work described here we calculated for the first time the Spectral Moments of a Markov Matrix (MMM) associated with a 2D-HP-map of a protein sequence. We used MMMs values to characterize numerically 81 sequences of type III RNases and 133 proteins of a control group. We subsequently developed one MMM-QSAR and one classic Hidden Markov Model (HMM) based on the same data. The MMM-QSAR showed a discrimination power of RNAses from other proteins of 97.35% without using alignment, which is a result as good as for the known HMM techniques. We also report for the first time the isolation of a new Pac1 protein (DQ647826) from Schizosaccharomyces pombe, strain 428-4-1. The MMM-QSAR model predicts the new RNase III with the same accuracy as otherclassical alignment methods. Experimental assay of this protein confirms the predicted activity. The present results suggest that MMM-QSAR models may be used for protein function annotation avoiding sequence alignment with the same accuracy of classic HMM models.