QSAR study of steroid benchmark and dipeptides based on MEDV-13

Ensemble QSAR: a QSAR method based on conformational ensembles and metric descriptors

Quantitative structure-activity relationship (QSAR) is the most versatile tool in computer-assisted molecular design. One conceptual drawback seen in QSAR approaches is the "one chemical-one structure-one parameter value" dogma where the model development is based on physicochemical description for a single molecular conformation, while ignoring the rest of the conformational space. It is well known that molecules have several low-energy conformations populated at physiological temperature, and each conformer makes a significant impact on associated properties such as biological activity. At the level of molecular interaction, the dynamics around the molecular structure is of prime essence rather than the average structure. As a step toward understanding the role of these discrete microscopic states in biological activity, we have put together a theoretically rigorous and computationally tractable formalism coined as eQSAR. In this approach, the biological activity is modeled as a function of physicochemical description for a selected set of low-energy conformers, rather than that's for a single lowest energy conformation. Eigenvalues derived from the "Physicochemical property integrated distance matrices" (PD-matrices) that encompass both 3D structure and physicochemical properties, have been used as descriptors; is a novel addition. eQSAR is validated on three peptide datasets and explicitly elaborated for bradykinin-potentiating peptides. The conformational ensembles were generated by a simple molecular dynamics and consensus dynamics approaches. The eQSAR models are statistically significant and possess the ability to select the most biologically relevant conformation(s) with the relevant physicochemical attributes that have the greatest meaning for description of the biological activity.

Descriptors for antimicrobial peptides

INTRODUCTION

A frightening increase in the number of isolated multidrug resistant bacterial strains linked to the decline in novel antimicrobial drugs entering the market is a great cause for concern. Cationic antimicrobial peptides (AMPs) have lately been introduced as a potential new class of antimicrobial drugs, and computational methods utilizing molecular descriptors can significantly accelerate the development of new peptide drug candidates.

AREAS COVERED

This paper gives a broad overview of peptide and amino-acid scale descriptors available for AMP modeling and highlights which of these are currently being used in quantitative structure-activity relationship (QSAR) studies for AMP optimization. Additionally, some key commercial computational tools are discussed, and both successful and less successful studies are referenced, illustrating some of the challenges facing AMP scientists. Through examples of different peptide QSAR studies, this review highlights some of the missing links and illuminates some of the questions that would be interesting to challenge in a more systematic fashion.

EXPERT OPINION

Computer-aided peptide QSAR using molecular descriptors may provide the necessary edge to peptide drug discovery, enabling successful design of a new generation anti-infective drug molecules. However, if this wonderful scenario is to play out, computational chemists and peptide microbiologists would need to start playing together and not just side by side.

Prediction of blood-brain partitioning: a model based on molecular electronegativity distance vector descriptors

The objective of this paper is to build a reliable model based on the molecular electronegativity distance vector (MEDV) descriptors for predicting the blood-brain barrier (BBB) permeability and to reveal the effects of the molecular structural segments on the BBB permeability. Using 70 structurally diverse compounds, the partial least squares regression (PLSR) models between the BBB permeability and the MEDV descriptors were developed and validated by the variable selection and modeling based on prediction (VSMP) technique. The estimation ability, stability, and predictive power of a model are evaluated by the estimated correlation coefficient (r), leave-one-out (LOO) cross-validation correlation coefficient (q), and predictive correlation coefficient (R(p)). It has been found that PLSR model has good quality, r=0.9202, q=0.7956, and R(p)=0.6649 for M1 model based on the training set of 57 samples. To search the most important structural factors affecting the BBB permeability of compounds, we performed the values of the variable importance in projection (VIP) analysis for MEDV descriptors. It was found that some structural fragments in compounds, such as -CH(3), -CH(2)-, =CH-, =C, triple bond C-, -CH<, =C<, =N-, -NH-, =O, and -OH, are the most important factors affecting the BBB permeability.

ST-scale as a novel amino acid descriptor and its application in QSAM of peptides and analogues

In this study, structural topology scale (ST-scale) was recruited as a novel structural topological descriptor derived from principal component analysis on 827 structural variables of 167 amino acids. By using partial least squares (PLS), we applied ST-scale for the study of quantitative sequence-activity models (QSAMs) on three peptide datasets (58 angiotensin-converting enzyme (ACE) inhibitors, 34 antimicrobial peptides (AMPs) and 89 elastase substrates (ES)). The results of QSAMs were superior to that of the earlier studies, with determination coefficient (r(2)) and cross-validated (q(2)) equal to 0.855, 0.774; 0.79, 0.371 (OSC-PLS: 0.995, 0.848) and 0.846, 0.747, respectively. Therefore, ST-scale descriptors were considered to be competent to extract information from 827 structural variables and relate with their bioactivities.

Combined photobacterium toxicity of herbicide mixtures containing one insecticide

To test whether the dose-addition (DA) model can predict the combined toxicity of the mixtures of herbicides that coexisted with insecticide(s), we selected five herbicides (simetryn, prometon, bromacil, velpar, and diquat) and one organophosphorus insecticide (dichlorvos) as the test components. The inhibition toxicities of the six pesticides as well as those of their mixtures to Vibrio qinghaiensis sp.-Q67 were determined by using the microplate toxicity test procedure. The dose-response curves (DRCs) between the observed inhibition toxicities and the doses of the pesticides or the mixtures were modeled by using the nonlinear least square fitting. It was shown that all dose-response relationships were effectively described by the Weibull function. To fully explore the combined toxicities of mixtures including various concentration compositions, we designed three equivalent-effect concentration ratio (EECR) mixtures and six uniform design concentration ratio (UDCR) mixtures. The combined toxicity of a mixture is identified by inspecting whether the DRC predicted by the dose addition (DA) or independent action (IA) locates in the 95% confidence interval of the DRC of the mixture. Furthermore, the possible reason for the three mixtures to depart from the DA action was the very high concentration ratio of diquat in the mixtures.

New descriptors of amino acids and their application to peptide QSAR study

A new set of descriptors was derived from a matrix of three structural variables of the natural amino acid, including van der Waal's volume, net charge index and hydrophobic parameter of side residues. They were selected from many properties of amino acid residues, which have been validated being the key factors to influence the interaction between peptides and its protein receptor. They were then applied to structure characterization and QSAR analysis on bitter tasting di-peptide, agiotensin-converting enzyme inhibitor and bactericidal peptides by using multiple linear regression (MLR) method. The leave one out cross validation values (Q(2)) were 0.921, 0.943 and 0.773. The multiple correlation coefficients (R(2)) were 0.948, 0.970 and 0.926, the root mean square (RMS) error for estimated error were 0.165, 0.154 and 0.41, respectively for bitter tasting di-peptide, angiotensin-converting enzyme inhibitor and bactericidal peptides. Test sets of peptides were used to validate the quantitative model, and it was shown that all these QSAR models had good predictability for outside samples. The results showed that, in comparison with the conventional descriptors, the new set of descriptors is a useful structure characterization method for peptide QSAR analysis, which has multiple advantages, such as definite physical and chemical meaning, easy to get, and good structural characterization ability.

In silico identification of anthropogenic chemicals as ligands of zebrafish sex hormone binding globulin

Anthropogenic compounds with the capacity to interact with the steroid-binding site of sex hormone binding globulin (SHBG) pose health risks to humans and other vertebrates including fish. Building on studies of human SHBG, we have applied in silico drug discovery methods to identify potential binders for SHBG in zebrafish (Danio rerio) as a model aquatic organism. Computational methods, including; homology modeling, molecular dynamics simulations, virtual screening, and 3D QSAR analysis, successfully identified 6 non-steroidal substances from the ZINC chemical database that bind to zebrafish SHBG (zfSHBG) with low-micromolar to nanomolar affinities, as determined by a competitive ligand-binding assay. We also screened 80,000 commercial substances listed by the European Chemicals Bureau and Environment Canada, and 6 non-steroidal hits from this in silico screen were tested experimentally for zfSHBG binding. All 6 of these compounds displaced the [(3)H]5alpha-dihydrotestosterone used as labeled ligand in the zfSHBG screening assay when tested at a 33 microM concentration, and 3 of them (hexestrol, 4-tert-octylcatechol, and dihydrobenzo(a)pyren-7(8H)-one) bind to zfSHBG in the micromolar range. The study demonstrates the feasibility of large-scale in silico screening of anthropogenic compounds that may disrupt or highjack functionally important protein:ligand interactions. Such studies could increase the awareness of hazards posed by existing commercial chemicals at relatively low cost.

A new predictive model for the bioconcentration factors of polychlorinated biphenyls (PCBs) based on the molecular electronegativity distance vector (MEDV)

Polychlorinated biphenyls (PCBs) are some of the most prevalent pollutants in the total environment and receive more and more concerns as a group of ubiquitous potential persistent organic pollutants. Using the variable selection and modeling based on prediction (VSMP), the molecular electronegativity distance vector (MEDV) derived directly from the molecular topological structures was employed to develop a linear model (MI) between the bioconcentration factors (BCF) and two MEDV descriptors of 58 PCBs. The MI model showed a good estimation ability with a correlation coefficient (r) of 0.9605 and a high stability with a leave-one-out cross-validation correlation coefficient (q) of 0.9564. The MEDV-base model (MI) is easier to use than the splinoid poset method reported by Ivanciuc et al. [Ivanciuc, T., Ivanciuc, O., Klein, D.J., 2006. Modeling the bioconcentration factors and bioaccumulation factors of polychlorinated biphenyls with posetic quatitative super-structure/activity relationships (QSSAR). Mol. Divers. 10, 133-145] and gives a better statistics than molecular connectivity index (MCI)-base model developed by Hu et al. [Hu, H.Y., Xu, F.L., Li, B.G., Cao, J., Dawson, R., Tao, S., 2005. Prediction of the bioconcentration factor of PCBs in fish using the molecular connectivity index and fragment constant models. Water Environ. Res. 77, 87-97]. Main structural factors influencing the BCF of PCBs are the substructures expressed by two atomic groups >C= and -CH=. 58 PCBs were divided into an "odd set" and "even set" in order to ensure the predicted potential of the MI for the external samples. It was shown that three models, MI, MO for "odd set", and ME for "even set", can be used to predict the BCF of remaining 152 PCBs in which the experimental BCFs are not available.

Three-dimensional holograph vector of atomic interaction field (3D-HoVAIF): a novel rotation-translation invariant 3D structure descriptor and its applications to peptides

Quantitative structure-activity relationship (QSAR) study, important in drug design, mainly involves two aspects, molecular structural characterization (MSC) and construction of a statistical model. MSC focuses on transforming molecular structural and property characteristics into a group of numerical codes, dedicated to minimizing information loss during this process. In this context, common atoms in organic compounds are classified according to their families in the periodic table, and hybridization states, and on the basis of these, three nonbonding interactions (i.e. electrostatic, van der Waals and hydrophobic) are calculated, ultimately resulting in a new rotation-translation invariant, 3D-MSC, as a three-dimensional holograph vector of atomic interaction field (3D-HoVAIF). By applying 3D-HoVAIF to QSAR studies on two classical peptides including 58 angiotensin-converting enzyme (ACE) inhibitors and 48 bitter-tasting dipeptides, we get two excellent genetic algorithm-partial least squares (GA-PLS) models, with statistics r(2), q(2), root mean square error (RMSEE), and root mean square error of cross-validation (RMSCV) of 0.857, 0.811, 0.376, and 0.432 for ACE inhibitors and 0.940, 0.892, 0.153 and 0.205 for bitter-tasting dipeptides, respectively. By equally dividing the two datasets into training and test sets by D-optimal, the 3D-HoVAIF approach undergoes rigorous statistical validation. Furthermore, the superior performance of 3D-HoVAIF is confirmed in comparison with two other peptide MSC approaches referring to z-scale and ISA-ECI. For 58 ACE inhibitors, the GA-PLS model yields two principal components, with the following statistics: r(2) = 0.893, q(2) = 0.824, RMSEE = 0.349, RMSCV = 0.425, q2(ext) = 0.739, r2(ext)= 0.784, r2(0.ext) = 0.781, rf2(0.ext) = 0.77, k = 0.962, k' = 1.019, and RMSEP = 0.460; for 48 bitter-tasting dipeptides, three principal components resulted, with the statistics as: r(2) = 0.950, q(2) = 0.893, RMSEE = 0.152, RMSCV = 0.222, q2(ext)= 0.875, r2(ext) = 0.919, r2(0.ext)= 0.919, rf2(0.ext)= 0.919, k = 1.018, k' = 0.974, and RMSEP = 0.198. In addition, the relationship of ACE-inhibiting activities with bitter-tasting thresholds has been investigated by applying the above-constructed models to predictions on 400 theoretically possible dipeptides. Through analysis, the ACE-inhibiting activities are found to be prominently related to bitter-tasting intensities. Thus, it is deemed to be difficult to find such dipeptides that simultaneously satisfy pharmacodynamic action (high ACE-inhibiting activities) and comfortable tastes, suggesting that active components of dipeptides that are served as functional food to lower blood pressure are not very ideal.

Structural parameterization and functional prediction of antigenic polypeptome sequences with biological activity through quantitative sequence-activity models (QSAM) by molecular electronegativity edge-distance vector (VMED)

Only from the primary structures of peptides, a new set of descriptors called the molecular electronegativity edge-distance vector (VMED) was proposed and applied to describing and characterizing the molecular structures of oligopeptides and polypeptides, based on the electronegativity of each atom or electronic charge index (ECI) of atomic clusters and the bonding distance between atom-pairs. Here, the molecular structures of antigenic polypeptides were well expressed in order to propose the automated technique for the computerized identification of helper T lymphocyte (Th) epitopes. Furthermore, a modified MED vector was proposed from the primary structures of polypeptides, based on the ECI and the relative bonding distance of the fundamental skeleton groups. The side-chains of each amino acid were here treated as a pseudo-atom. The developed VMED was easy to calculate and able to work. Some quantitative model was established for 28 immunogenic or antigenic polypeptides (AGPP) with 14 (1-14) A(d) and 14 other restricted activities assigned as "1"(+) and "0"(-), respectively. The latter comprised 6 A(b)(15-20), 3 A(k)(21-23), 2 E(k)(24-26), 2 H-2(k)(27 and 28) restricted sequences. Good results were obtained with 90% correct classification (only 2 wrong ones for 20 training samples) and 100% correct prediction (none wrong for 8 testing samples); while contrastively 100% correct classification (none wrong for 20 training samples) and 88% correct classification (1 wrong for 8 testing samples). Both stochastic samplings and cross validations were performed to demonstrate good performance. The described method may also be suitable for estimation and prediction of classes I and II for major histocompatibility antigen (MHC) epitope of human. It will be useful in immune identification and recognition of proteins and genes and in the design and development of subunit vaccines. Several quantitative structure activity relationship (QSAR) models were developed for various oligopeptides and polypeptides including 58 dipeptides and 31 pentapeptides with angiotensin converting enzyme (ACE) inhibition by multiple linear regression (MLR) method. In order to explain the ability to characterize molecular structure of polypeptides, a molecular modeling investigation on QSAR was performed for functional prediction of polypeptide sequences with antigenic activity and heptapeptide sequences with tachykinin activity through quantitative sequence-activity models (QSAMs) by the molecular electronegativity edge-distance vector (VMED). The results showed that VMED exhibited both excellent structural selectivity and good activity prediction. Moreover, the results showed that VMED behaved quite well for both QSAR and QSAM of poly-and oligopeptides, which exhibited both good estimation ability and prediction power, equal to or better than those reported in the previous references. Finally, a preliminary conclusion was drawn: both classical and modified MED vectors were very useful structural descriptors. Some suggestions were proposed for further studies on QSAR/QSAM of proteins in various fields.

Semi-empirical topological method for prediction of the gas chromatographic relative retention times of Polybrominated Diphenyl Ethers (PBDEs)

Quantitative structure-retention relationship (QSRR) studies have proved to be a valuable approach in the prediction of the gas chromatographic relative retention time (GC-RRT) of organic chemicals. Polybrominated diphenyl ether (PBDE) congeners are now ubiquitous environmental pollutants. Of the 209 possible PBDE congeners, 126 have been synthesized and their retention-time data on seven different stationary phases has been determined [Korytár et al.:J Chromatography A 1065:239-249, (2005)]. To estimate and predict the GC-RRT values of all 209 PBDEs on different stationary phases, 17 molecular descriptors from the semi-experience algorithm in MOPAC program and the topological structures of PBDE molecules were calculated. By means of the VSMP (variable selection and modeling based on prediction) program [Liu et al.:J Chem Inf Comput Sci 43:964-969, (2003)], six optimal descriptors were selected to develop a QSRR model for the prediction of GC-RRT of PBDE. The descriptors contain some energy information (such as the energy of the lowest unoccupied molecular orbital and highest occupied molecular orbital) and topological information (the number of ortho-, meta-, and para- substituted bromine atoms) as well as the molecular weight (lnM (W)). All the models developed were cross-validated using leave-one-out (LOO). For seven GC stationary phases, the estimated correlation coefficients (r(2)) are all more than 0.985 but for the column CP-Sil 19 (r(2) = 0.9392) and LOO-validated correlation coefficients (q(2)) all more than 0.985 but for the column CP-Sil 19 (q(2) = 0.9345).

Prediction of chromatographic relative retention time of polychlorinated biphenyls from the molecular electronegativity distance vector

Using the molecular electronegativity distance vector (MEDV) descriptors derived directly from the molecular topological structures, the gas chromatographic relative retention times (RRTs) of 209 polychlorinated biphenyls (PCBs) on the SE-54 stationary phase were predicted. A five-variable regression equation with the correlation coefficient of 0.9964 and the root mean square errors of 0.0152 was developed. The descriptors included in the equation represent degree of chlorination (nCl), nonortho index (Ino), and interactions between three pairs of atom types, i.e., atom groups -C= and -C=, -C= and >C=, -C= and -Cl. It has been proved that the retention times of all 209 PCB congeners can be accurately predicted as long as there are more than 50 calibration compounds. In the same way, the MEDV descriptors are also used to develop the five- or six-variable models of RRTs of PCBs on other 18 stationary phases and the correlation coefficients in both modeling stage and LOO cross-validation step are not lower than 0.99 except two models.

Improving the performance of SOMFA by use of standard multivariate methods

Self-Organizing Molecular Field Analysis (SOMFA) comes with a built-in regression methodology, the Self-Organizing Regression (SOR), instead of relying on external methods such as PLS. In this article we present a proof of the equivalence between SOR and SIMPLS with one principal component. Thus, the modest performance of SOMFA on complex datasets can be primarily attributed to the low performance of the SOMFA regression methodology. A multi-component extension of the original SOR methodology (MCSOR) is introduced, and the performances of SOR, MCSOR and SIMPLS are compared using several datasets. The results indicate that in general the performance of SOMFA models is greatly improved if SOR is replaced with a more sophisticated regression method. The results obtained for the Cramer (CBG) dataset further underline the fact that it is a very poor benchmark dataset and should not be used to evaluate the performance of QSAR techniques.

Ligand intramolecular motions in ligand-protein interaction: ALPHA, a novel dynamic descriptor and a QSAR study with extended steroid benchmark dataset

The role of intramolecular motions in ligand-macromolecule interactions has been explored by developing and validating ALPHA, a novel QSAR (quantitative structure-activity relationship) descriptor. It is based on the spectral exponents (alpha), which measure the degree of 1/f alpha noise of coordinate fluctuations in molecular dynamics (MD) simulations. ALPHA is the first truly 'dynamic' QSAR descriptor, i.e., it can be derived directly from an MD trajectory. The performance of ALPHA was tested in detail employing the CBG (corticosteroid binding globulin) affinity of 31 benchmark steroids, supplemented with 11 steroids as an external test set. The only fair (42-50%) correlations of ALPHA with static 3D and electronic descriptors mean that ALPHA forms an independent molecular property. Furthermore, inclusion of ALPHA in the SOMFA/ESP model improves the correlation coefficient from 0.86 to 0.91, and /delta/ave from 0.46 to 0.36 for the benchmark dataset. The predictive ability of ALPHA can be interpreted as indirect evidence of the dynamic contribution to ligand-macromolecule interactions. The physical background of ALPHA is discussed and the importance of molecular motions for biological activity is anticipated.