Proteochemometric modeling reveals the interaction site for Trp9 modified alpha-MSH peptides in melanocortin receptors.

The interactions of alpha-MSH peptides with melanocortin receptors (MCRs) were located by proteochemometric modeling. Nine alpha-MSH peptide analogues were constructed by exchanging the Trp9 residue in the alpha-MSH core with the natural or artificial amino acids Arg, Asp, Cys, Gly, Leu, Nal, d-Nal, Pro, or d-Trp. The nine peptides created, and alpha-MSH itself, were evaluated for their interactions with the 4 wild-type MC(1,3-5)Rs and 15 multichimeric MCRs, each of the latter being constructed from three sequence segments, each taken from a different wild-type MC(1,3-5)R. The segments of the chimeric MCRs were selected according to the principles of statistical molecular design and were arranged so as to divide the receptors into five parts. By this approach, a set of 19 maximally diverse MC receptor proteins was obtained for which the interaction activity with the 10 peptides were measured by radioligand binding thus creating data for 190 ligand-protein pairs, which were subsequently analyzed by use of proteochemometric modeling. In proteochemometrics, the structural or physicochemical properties of both interaction partners, which represent the complementarity of the interacting entities, are used to create multivariate mathematical descriptions. (Here, physicochemical property descriptors of the receptors' and peptides' amino acids were used). A valid, highly predictive (Q2 = 0.74) and easily interpretable model was then obtained. The model was further validated by its ability to correctly predicting the affinity of alpha-MSH for new point and cassette-mutated MC4/MC1Rs, and it was then used to identify the receptor residues that are important for affording the high affinity and selectivity of alpha-MSH for the MC1R. It was revealed that these residues are located in several quite distant parts of the receptors' transmembrane cavity and must therefore cause their influence at various stages of the dynamic ligand-binding process, such as by affecting the conformation of the ligand at the vicinity of the receptor and taking part in the path of the ligand's entry into its binding pocket. Our study can be used as a template how to create high resolution proteochemometric models when there are a limited number of natural proteins and ligands available.

Prediction of indirect interactions in proteins.

BACKGROUND: Both direct and indirect interactions determine molecular recognition of ligands by proteins. Indirect interactions can be defined as effects on recognition controlled from distant sites in the proteins, e.g. by changes in protein conformation and mobility, whereas direct interactions occur in close proximity of the protein's amino acids and the ligand. Molecular recognition is traditionally studied using three-dimensional methods, but with such techniques it is difficult to predict the effects caused by mutational changes of amino acids located far away from the ligand-binding site. We recently developed an approach, proteochemometrics, to the study of molecular recognition that models the chemical effects involved in the recognition of ligands by proteins using statistical sampling and mathematical modelling. RESULTS: A proteochemometric model was built, based on a statistically designed protein library's (melanocortin receptors') interaction with three peptides and used to predict which amino acids and sequence fragments that are involved in direct and indirect ligand interactions. The model predictions were confirmed by directed mutagenesis. The predicted presumed direct interactions were in good agreement with previous three-dimensional studies of ligand recognition. However, in addition the model could also correctly predict the location of indirect effects on ligand recognition arising from distant sites in the receptors, something that three-dimensional modelling could not afford. CONCLUSION: We demonstrate experimentally that proteochemometric modelling can be used with high accuracy to predict the site of origin of direct and indirect effects on ligand recognitions by proteins.

Rough set-based proteochemometrics modeling of G-protein-coupled receptor-ligand interactions.

G-Protein-coupled receptors (GPCRs) are among the most important drug targets. Because of a shortage of 3D crystal structures, most of the drug design for GPCRs has been ligand-based. We propose a novel, rough set-based proteochemometric approach to the study of receptor and ligand recognition. The approach is validated on three datasets containing GPCRs. In proteochemometrics, properties of receptors and ligands are used in conjunction and modeled to predict binding affinity. The rough set (RS) rule-based models presented herein consist of minimal decision rules that associate properties of receptors and ligands with high or low binding affinity. The information provided by the rules is then used to develop a mechanistic interpretation of interactions between the ligands and receptors included in the datasets. The first two datasets contained descriptors of melanocortin receptors and peptide ligands. The third set contained descriptors of adrenergic receptors and ligands. All the rule models induced from these datasets have a high predictive quality. An example of a decision rule is "If R1_ligand(Ethyl) and TM helix 2 position 27(Methionine) then Binding(High)." The easily interpretable rule sets are able to identify determinative receptor and ligand parts. For instance, all three models suggest that transmembrane helix 2 is determinative for high and low binding affinity. RS models show that it is possible to use rule-based models to predict ligand-binding affinities. The models may be used to gain a deeper biological understanding of the combinatorial nature of receptor-ligand interactions.

Improved approach for proteochemometrics modeling: application to organic compound--amine G protein-coupled receptor interactions.

MOTIVATION: Proteochemometrics is a novel technology for the analysis of interactions of series of proteins with series of ligands. We have here customized it for analysis of large datasets and evaluated it for the modeling of the interaction of psychoactive organic amines with all the five known families of amine G protein-coupled receptors (GPCRs). RESULTS: The model exploited data for the binding of 22 compounds to 31 amine GPCRs, correlating chemical descriptions and cross-descriptions of compounds and receptors to binding affinity using a novel strategy. A highly valid model (q2 = 0.76) was obtained which was further validated by external predictions using data for 10 other entirely independent compounds, yielding the high q2ext = 0.67. Interpretation of the model reveals molecular interactions that govern psychoactive organic amines overall affinity for amine GPCRs, as well as their selectivity for particular amine GPCRs. The new modeling procedure allows us to obtain fully interpretable proteochemometrics models using essentially unlimited number of ligand and protein descriptors.

N-alkylated dipeptide amides and related structures as imitations of the melanocortins' active core.

Thirty-three low molecular mass structures combining both peptide and peptoid features were prepared and tested on human melanocortin receptors MC1,3-5R. Most of them displayed low micromolar activity with preference for diamines, guanidino and 2-naphthyl derivatives compared to monoacetylated, amino and 3-indolyl counterparts. Some contained L- or D-histidine residues, but the change did not influence affinity. QSAR modelling yielded excellent models for the MC3-5 receptors explaining R2Y=0.89-0.91 and predicting Q2=0.77-0.80 of the affinity variations. One compound displayed MC1R selectivity (13-fold and more). An NMR study of showed that it exists as a mixture of four rotamers at its tertiary amide bonds. Comparisons with earlier data for melanocortin core tetrapeptide analogues indicate that the novel peptide-peptoids interact with the melanocortin receptors in a different way.

Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling.

BACKGROUND: Proteochemometrics is a new methodology that allows prediction of protein function directly from real interaction measurement data without the need of 3D structure information. Several reported proteochemometric models of ligand-receptor interactions have already yielded significant insights into various forms of bio-molecular interactions. The proteochemometric models are multivariate regression models that predict binding affinity for a particular combination of features of the ligand and protein. Although proteochemometric models have already offered interesting results in various studies, no detailed statistical evaluation of their average predictive power has been performed. In particular, variable subset selection performed to date has always relied on using all available examples, a situation also encountered in microarray gene expression data analysis. RESULTS: A methodology for an unbiased evaluation of the predictive power of proteochemometric models was implemented and results from applying it to two of the largest proteochemometric data sets yet reported are presented. A double cross-validation loop procedure is used to estimate the expected performance of a given design method. The unbiased performance estimates (P2) obtained for the data sets that we consider confirm that properly designed single proteochemometric models have useful predictive power, but that a standard design based on cross validation may yield models with quite limited performance. The results also show that different commercial software packages employed for the design of proteochemometric models may yield very different and therefore misleading performance estimates. In addition, the differences in the models obtained in the double CV loop indicate that detailed chemical interpretation of a single proteochemometric model is uncertain when data sets are small. CONCLUSION: The double CV loop employed offer unbiased performance estimates about a given proteochemometric modelling procedure, making it possible to identify cases where the proteochemometric design does not result in useful predictive models. Chemical interpretations of single proteochemometric models are uncertain and should instead be based on all the models selected in the double CV loop employed here.

Proteochemometric mapping of the interaction of organic compounds with melanocortin receptor subtypes.

Proteochemometrics was applied in the analysis of the binding of organic compounds to wild-type and chimeric melanocortin receptors. Thirteen chimeric melanocortin receptors were designed based on statistical molecular design; each chimera contained parts from three of the MC(1,3-5) receptors. The binding affinities of 18 compounds were determined for these chimeric melanocortin receptors and the four wild-type melanocortin receptors. The data for 14 of these compounds were correlated to the physicochemical and structural descriptors of compounds, binary descriptors of receptor sequences, and cross-terms derived from ligand and receptor descriptors to obtain a proteochemometric model (correlation was performed using partial least-squares projections to latent structures; PLS). A well fitted mathematical model (R(2) = 0.92) with high predictive ability (Q(2) = 0.79) was obtained. In a further validation of the model, the predictive ability for ligands (Q(2)lig = 0.68) and receptors (Q(2)rec = 0.76) was estimated. The model was moreover validated by external prediction by using the data for the four additional compounds that had not at all been included in the proteochemometric model; the analysis yielded a Q(2)ext = 0.73. An interpretation of the results using PLS coefficients revealed the influence of particular properties of organic compounds on their affinity to melanocortin receptors. Three-dimensional models of melanocortin receptors were also created, and physicochemical properties of the amino acids inside the receptors' transmembrane cavity were correlated to the PLS modeling results. The importance of particular amino acids for selective binding of organic compounds was estimated and used to outline the ligand recognition site in the melanocortin receptors.

QSAR and proteo-chemometric analysis of the interaction of a series of organic compounds with melanocortin receptor subtypes.

We have created quantitative structure-activity relationship (QSAR) models describing the interaction of a series of 54 organic compounds with four melanocortin (MC) receptor subtypes, MC(1), MC(3), MC(4), and MC(5). In addition to traditional QSAR analysis, we applied our recently developed proteo-chemometrics approach. Proteo-chemometrics is based on the combined analysis of series of receptors and ligands, wherein descriptions of ligands, proteins, and so-called ligand-protein cross-terms are correlated with interaction activities. The compounds were characterized by structural descriptors, including three-dimensional grid-independent descriptors (GRINDs), topological descriptors, and geometrical descriptors. Description of receptors was obtained by computing the receptors' amino acid sequence identities. Both the QSAR and proteo-chemometrics approaches resulted in models with essentially the same statistical significance: the cross-validated correlation coefficient q(2) for the proteo-chemometric model being 0.71, while for the QSAR models the q(2)s were 0.75, 0.68, 0.63, and 0.71 for the MC(1), MC(3)(-)(5) receptor, respectively. However, the proteo-chemometrics modeling provided more detailed information about receptor-ligand interactions and determinants for receptor subtype selectivity than did QSAR.

Proteochemometrics modeling of the interaction of amine G-protein coupled receptors with a diverse set of ligands.

We have evaluated the proteochemometrics approach in the analysis of the interactions of a diverse set or organic ligands with subtypes of serotonin, dopamine, histamine, and adrenergic receptors. As used herein, proteochemometrics exploits affinity data for series of organic amines binding to wild-type amine G protein-coupled receptors, correlating it to descriptions and cross-description derived from the primary amino acid sequences of the receptors and the computed structures of the organic compounds. We show that after appropriate data preprocessing, statistically valid models that have good external predictive ability can be created. Evaluation of the models gave important quantitative insight into the mode of interactions of the amine G protein-coupled receptors with their ligands.

Classification of G-protein coupled receptors by alignment-independent extraction of principal chemical properties of primary amino acid sequences.

We have developed an alignment-independent method for classification of G-protein coupled receptors (GPCRs) according to the principal chemical properties of their amino acid sequences. The method relies on a multivariate approach where the primary amino acid sequences are translated into vectors based on the principal physicochemical properties of the amino acids and transformation of the data into a uniform matrix by applying a modified autocross-covariance transform. The application of principal component analysis to a data set of 929 class A GPCRs showed a clear separation of the major classes of GPCRs. The application of partial least squares projection to latent structures created a highly valid model (cross-validated correlation coefficient, Q(2) = 0.895) that gave unambiguous classification of the GPCRs in the training set according to their ligand binding class. The model was further validated by external prediction of 535 novel GPCRs not included in the training set. Of the latter, only 14 sequences, confined in rapidly expanding GPCR classes, were mispredicted. Moreover, 90 orphan GPCRs out of 165 were tentatively identified to GPCR ligand binding class. The alignment-independent method could be used to assess the importance of the principal chemical properties of every single amino acid in the protein sequences for their contributions in explaining GPCR family membership. It was then revealed that all amino acids in the unaligned sequences contributed to the classifications, albeit to varying extent; the most important amino acids being those that could also be determined to be conserved by using traditional alignment-based methods.