Peteris Prusis

Discovery of novel melanocortin4 receptor selective MSH analogues.

We synthesized a novel series of cyclic melanocyte stimulating hormone (MSH) analogues and tested their binding properties on cells transiently expressing the human melanocortin1 (MC1), MC3, MC4 and MC5 receptors. We discovered that compounds with 26 membered rings of [Cys4,D‐Nal7,Cys11]α‐MSH(4–11) displayed specific MC4 receptor selectivity. The preference order of the different MC receptor subtypes for the novel [Cys4D‐Nal7Cys11]α‐MSH(4–11) analogues are distinct from all other known MSH analogues, particularly as they bind the MC4 receptor with high and the MC1 receptor with low relative affinities. HS964 and HS014 have 12 and 17 fold MC4/MC3 receptor selectivity, respectively, which is much higher than for the previously described cyclic lactam and [Cys4,Cys10]α‐MSH analogues SHU9119 and HS9510. HS964 is the first substance showing higher affinity for the MC5 receptor than the MC1 receptor. HS014, which was the most potent and selective MC4 receptor ligand (Ki 3.2 nM, which is ∼300 fold higher affinity than for α‐MSH), was also demonstrated to antagonize α‐MSH stimulation of cyclic AMP in MC4 receptor transfected cells. We found that a compound with a 29 membered ring of [Cys3,Nle10,D‐Nal7,Cys11]α‐MSH(3–11) (HS010) had the highest affinity for the MC3 receptor. This is the first study to describe ligands that are truly MC4 selective and a ligand having a high affinity for the MC3 receptor. The novel compounds may be of use in clarifying the physiological roles of the MC3, MC4 and MC5 receptors.

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, Q2 = 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.

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.

Long term orexigenic effect of a novel melanocortin 4 receptor selective antagonist

We designed and synthesized several novel cyclic MSH analogues and tested their affinities for cells expressing the MC1, MC3, MC4 and MC5 receptors. One of the substances HS028 (cyclic [AcCys11, dichloro‐D‐phenylalanine14, Cys18, Asp‐NH222]‐β‐MSH11–22) showed high affinity (Ki of 0.95nM) and high (80 fold) MC4 receptor selectivity over the MC3 receptor. HS028 thus shows both higher affinity and higher selectivity for the MC4 receptor compared to the earlier first described MC4 receptor selective substance HS014. HS028 antagonised a α‐MSH induced increase in cyclic AMP production in transfected cells expressing the MC3 and MC4 receptors, whereas it seemed to be a partial agonist for the MC1 and MC5 receptors. Chronic intracerebroventricularly (i.c.v.) administration of HS028 by osmotic minipumps significantly increased both food intake and body weight in a dose dependent manner without tachyphylaxis for a period of 7 days. This is the first report demonstrating that an MC4 receptor antagonist can increase food intake and body weight during chronic administration providing further evidence that the MC4 receptor is an important mediator of long term weight homeostasis.

Modeling of the three-dimensional structure of the human melanocortin 1 receptor, using an automated method and docking of a rigid cyclic melanocyte-stimulating hormone core peptide☆

A model is presented of the melanocortin 1 receptor (MC1R), constructed by use of an unbiased, objective method. The model is created directly from data derived from multiple sequence analysis, a low-resolution EM-projection map of rhodopsin, and the approximate membrane thickness. The model agrees well with available data concerning natural mutations of MC1Rs occurring in different species. A model is also presented of the most rigid ligand for this receptor, the cyclic pentapeptide cHFRWG, shown docked in the receptor model. The receptor-ligand complex model agrees well with available experimental data. The ligand is located between transmembrane region 1 (TM1), TM2, TM3, TM6, and TM7 of the receptor. Multiple interactions occur between ligand and receptor, including interactions with Leu-48 (TM1), Ser-52 (TM1), Glu-55 (TM1), Asn-91 (TM2), Glu-94 (TM2), Thr-95 (TM2) Ile-98 (TM2), Asp-121 (TM3), Thr-124 (TM3), Phe-257 (TM6), Phe-283 (TM7), Asn-290 (TM7), and Asp-294 (TM7) of the receptor.

Proteochemometric modeling of HIV protease susceptibility

BackgroundA major obstacle in treatment of HIV is the ability of the virus to mutate rapidly into drug-resistant variants. A method for predicting the susceptibility of mutated HIV strains to antiviral agents would provide substantial clinical benefit as well as facilitate the development of new candidate drugs. Therefore, we used proteochemometrics to model the susceptibility of HIV to protease inhibitors in current use, utilizing descriptions of the physico-chemical properties of mutated HIV proteases and 3D structural property descriptions for the protease inhibitors. The descriptions were correlated to the susceptibility data of 828 unique HIV protease variants for seven protease inhibitors in current use; the data set comprised 4792 protease-inhibitor combinations.ResultsThe model provided excellent predictability (R2 = 0.92, Q2 = 0.87) and identified general and specific features of drug resistance. The models predictive ability was verified by external prediction in which the susceptibilities to each one of the seven inhibitors were omitted from the data set, one inhibitor at a time, and the data for the six remaining compounds were used to create new models. This analysis showed that the over all predictive ability for the omitted inhibitors was Q2inhibitors= 0.72.ConclusionOur results show that a proteochemometric approach can provide generalized susceptibility predictions for new inhibitors. Our proteochemometric model can directly analyze inhibitor-protease interactions and facilitate treatment selection based on viral genotype. The model is available for public use, and is located at HIV Drug Research Centre.

Binding of cyclic and linear MSH core peptides to the melanocortin receptor subtypes.

We report here the binding of 5-, 6- and 7-amino-acid-long linear and cyclic core peptides of MSH (melanocyte-stimulating hormone) to cells transiently expressing the human melanocortin MC1, MC3, MC4 and MC5 receptors. The results show that, in contrast to the natural peptides, the core peptides did not differentiate between the melanocortin MC3 and MC4 receptors. All tested cyclic peptides had much lower affinities than their corresponding linear homologues. Interestingly, the relative loss of binding due to the cyclisation did not change as the ring size decreased. Therefore, decreasing the ring size does not seem to force the peptide into a more unfavourable conformation.

Generalized modeling of enzyme–ligand interactions using proteochemometrics and local protein substructures

Modeling and understanding protein–ligand interactions is one of the most important goals in computational drug discovery. To this end, proteochemometrics uses structural and chemical descriptors from several proteins and several ligands to induce interaction‐models. Here, we present a new and generalized approach in which proteins varying greatly in terms of sequence and structure are represented by a library of local substructures. Using linear regression and rule‐based learning, we combine such local substructures with chemical descriptors from the ligands to model binding affinity for a training set of hydrolase and lyase enzymes. We evaluate the predictive performance of these models using cross validation and sets of unseen ligand with unknown three‐dimensional structure. The models are shown to generalize by outperforming models using descriptors from only proteins or only ligands, or models using global structure similarities rather than local similarities. Thus, we demonstrate that this approach is capable of describing dependencies between local structural properties and ligands in otherwise dissimilar protein structures. These dependencies are often, but not always, associated with local substructures that are in contact with the ligands. Finally, we show that strongly bound enzyme–ligand complexes require the presence of particular local substructures, while weakly bound complexes may be described by the absence of certain properties. The results demonstrate that the alignment‐independent approach using local substructures is capable of describing protein–ligand interaction for largely different proteins and hence opens up for proteochemometrics‐analysis of the interaction‐space of entire proteomes. Current approaches are limited to families of closely related proteins. families of closely related proteins. Proteins 2006.

Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling

BackgroundProteochemometrics 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.ResultsA 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.ConclusionThe 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.

Selective properties of C- and N-terminals and core residues of the melanocyte-stimulating hormone on binding to the human melanocortin receptor subtypes

We synthesised nine analogues of [Nle4,D-Phe7]alpha-MSH (melanocyte-stimulating hormone) (NDP) where (1) the N- or C-terminals were deleted or exchanged by those of beta- or gamma-MSH and (2) the core residues His6, Phe7, Arg8 and Trp9 were individually substituted by Glu6, beta-(2-naphthyl)-D-alanine (D-Nal7), Lys8 and His9, respectively. We tested these analogues in ligand binding assays with cells transiently expressing the human melanocortin MC1, MC3, MC4 and MC5 receptors. The results show that the N-terminal segment (Ser1-Tyr2-Ser3) of NDP was not important for binding to melanocortin MC1 and MC4 receptors whereas it affects binding to melanocortin MC3 and MC5 receptors. The C-terminal segment (Gly10-Lys11-Pro12-Val13) of NDP was clearly important for binding to all the four melanocortin receptor subtypes. The data indicate that the low affinity of gamma-MSH for the melanocortin MC4 receptor is due to its C-terminal (Asp10)-Arg11-Phe12). Substitution of D-Phe7 by D-Nal7 increased the affinity for the melanocortin MC4 receptor but not for the other melanocortin receptor subtypes. The other core residue substitutions lowered the affinity in a differentiated manner for each of the melanocortin receptors. These results are valuable for the molecular modelling and design of selective drugs for the melanocortin receptors.