Characterization of the structure of RAMP1 by mutagenesis and molecular modeling

Receptor activity modifying proteins (RAMPs) are a family of single-pass transmembrane proteins that dimerize with G-protein-coupled receptors. They may alter the ligand recognition properties of the receptors (particularly for the calcitonin receptor-like receptor, CLR). Very little structural information is available about RAMPs. Here, an ab initio model has been generated for the extracellular domain of RAMP1. The disulfide bond arrangement (Cys 27-Cys82, Cys40-Cys72, and Cys 57-Cys104) was determined by site-directed mutagenesis. The secondary structure (a-helices from residues 29-51, 60-80, and 87-100) was established from a consensus of predictive routines. Using these constraints, an assemblage of 25,000 structures was constructed and these were ranked using an all-atom statistical potential. The best 1000 conformations were energy minimized. The lowest scoring model was refined by molecular dynamics simulation. To validate our strategy, the same methods were applied to three proteins of known structure; PDB:1HP8, PDB:1V54 chain H (residues 21-85), and PDB:1T0P. When compared to the crystal structures, the models had root mean-square deviations of 3.8 Å, 4.1 Å, and 4.0 Å, respectively. The model of RAMP1 suggested that Phe93, Tyr 100, and Phe101 form a binding interface for CLR, whereas Trp74 and Phe92 may interact with ligands that bind to the CLR/RAMP1 heterodimer. © 2006 by the Biophysical Society.

Static energy analysis of MHC class I and class II peptide-binding affinity

Antigenic peptide is presented to a T-cell receptor (TCR) through the formation of a stable complex with a major histocompatibility complex (MHC) molecule. Various predictive algorithms have been developed to estimate a peptide's capacity to form a stable complex with a given MHC class II allele, a technique integral to the strategy of vaccine design. These have previously incorporated such computational techniques as quantitative matrices and neural networks. A novel predictive technique is described, which uses molecular modeling of predetermined crystal structures to estimate the stability of an MHC class II-peptide complex. The structures are remodeled, energy minimized, and annealed before the energetic interaction is calculated.

Diverse functional motifs within the three intracellular loops of the CGRP1 receptor

The CGRP1 receptor exists as a heterodimeric complex between a single-pass transmembrane accessory protein (RAMP1) and a family B G-protein-coupled receptor (GPCR) called the calcitonin receptor-like receptor (CLR). This study investigated the structural motifs found in the intracellular loops (ICLs) of this receptor. Molecular modeling was used to predict active and inactive conformations of each ICL. Conserved residues were altered to alanine by site-directed mutagenesis. cAMP accumulation, cell-surface expression, agonist affinity, and CGRP-stimulated receptor internalization were characterized. Within ICL1, L147 and particularly R151 were important for coupling to Gs. R151 may interact directly with the G-protein, accessing it following conformational changes involving ICL2 and ICL3. At the proximal end of ICL3, I290 and L294, probably lying on the same face of an α helix, formed a G-protein coupling motif. The largest effects on coupling were observed with I290A; additionally, it reduced CGRP affinity and impaired internalization. 1290 may interact with TM6 to stabilize the conformation of ICL3, but it could also interact directly with Gs. R314, at the distal end of ICL3, impaired G-protein coupling and to a lesser extent reduced CGRP affinity; it may stabilize the TM6-ICL3 junction by interacting with the polar headgroups of membrane phospholipids. Y215 and L214 in ICL2 are required for cell-surface expression; they form a microdomain with H216 which has the same function. This study reveals similarities between the activation of CLR and other GPCRs in the role of TM6 and ICL3 but shows that other conserved motifs differ in their function. © 2006 American Chemical Society.

The design of novel antifolates against pneumocystis carinii

The pneumonia caused by Pneumocystis carinii is ultimately responsible for the death of many acquired immunodeficiency syndrome (AIDS) patients. Large doses of trimethoprim and pyrimethamine in combination with a sulphonamide and/or pentamidine suppress the infection but produce serious side-effects and seldom prevent recurrence after treatment withdrawal. However, the partial success of the aforementioned antifolates, and also trimetrexate used alone, does suggest dihydrofolate reductase (DHFR) as a target for the development of antipneumocystis agents. From the DHFR inhibitory activities of 3'-substituted pyrimethamine analogues it was suggested that the 3'-(3'',3''-dimethyltriazen-1''-yl) substituent may be responsible for the greater activity for the P.carinii over the mammalian enzyme. Crystallographic and molecular modeling studies revealed considerable geometrical and electronic differences between the triazene and the chemically related formamidine functions that may account for the differences in DHFR inhibitory profiles. Structural and electronic parameters calculated for a series of 3'-(3'',3''-disubstitutedtriazen-1''-yl) pyrimethamine analogues did not correlate with the DHFR inhibitory activities. However, the in vitro screening against P.carinii DHFR revealed that the 3''-hydroxyethyl-3''-benzyl analogue was the most active and selective. Models of the active sites of human and P.carinii DHFRs were constructed using DHFR sequence and structural homology data which had identified key residues involved in substrate and cofactor binding. Low energy conformations of the 3'',3''-dimethyl and 3''-hydroxyethyl-3''-benzyle analogues, determined from nuclear magnetic resonance studies and theoretical calculations, were docked by superimposing the diaminopyrimidine fragment onto a previously docked pyrimethamine analogue. Enzyme kinetic data supported the 3''-hydroxyethyl-3''-benzyl moiety being located in the NADPH binding groove. The 3''-benzyl substituent was able to locate to within 3 AA of a valine residue in the active site of P.carinii DHFR thereby producing a hydrophobic contact. The equivalent residue in human DHFR is threonine, more hydrophilic and less likely to be involved in such a contact. This difference may account for the greater inhibitory activity this analogue has for P.carinii DHFR and provide a basis for future drug design. From an in vivo model of PCP in immunosuppressed rats it was established that the 3"-hydroxyethyl-3"-benzyl analogue was able to reduce the.P.carinii burden more effectively with increasing doses, without causmg any visible signs of toxicity. However, equivalent doses were not as effective as pentamidine, a current treatment of choice for Pneumocystis carinii pneumonia.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity:in silico bioinformatic step-by-step guide using quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

Toward prediction of class II mouse major histocompatibility complex peptide binding affinity:in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique

The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide−MHC binding affinity. The ISC−PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide−MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms - q2, SEP, and NC - ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made freely available online (http://www.jenner.ac.uk/MHCPred).

Towards in silico prediction of immunogenic epitopes

As torrents of new data now emerge from microbial genomics, bioinformatic prediction of immunogenic epitopes remains challenging but vital. In silico methods often produce paradoxically inconsistent results: good prediction rates on certain test sets but not others. The inherent complexity of immune presentation and recognition processes complicates epitope prediction. Two encouraging developments – data driven artificial intelligence sequence-based methods for epitope prediction and molecular modeling methods based on three-dimensional protein structures – offer hope for the future.

Clinical oxidative stress during leprosy multidrug therapy:impact of dapsone oxidation

This study aims to assess the oxidative stress in leprosy patients under multidrug therapy (MDT; dapsone, clofazimine and rifampicin), evaluating the nitric oxide (NO) concentration, catalase (CAT) and superoxide dismutase (SOD) activities, glutathione (GSH) levels, total antioxidant capacity, lipid peroxidation, and methemoglobin formation. For this, we analyzed 23 leprosy patients and 20 healthy individuals from the Amazon region, Brazil, aged between 20 and 45 years. Blood sampling enabled the evaluation of leprosy patients prior to starting multidrug therapy (called MDT 0) and until the third month of multidrug therapy (MDT 3). With regard to dapsone (DDS) plasma levels, we showed that there was no statistical difference in drug plasma levels between multibacillary (0.518±0.029 μg/mL) and paucibacillary (0.662±0.123 μg/mL) patients. The methemoglobin levels and numbers of Heinz bodies were significantly enhanced after the third MDTsupervised dose, but this treatment did not significantly change the lipid peroxidation and NO levels in these leprosy patients. In addition, CAT activity was significantly reduced in MDT-treated leprosy patients, while GSH content was increased in these patients. However, SOD and Trolox equivalent antioxidant capacity levels were similar in patients with and without treatment. These data suggest that MDT can reduce the activity of some antioxidant enzyme and influence ROS accumulation, which may induce hematological changes, such as methemoglobinemia in patients with leprosy. We also explored some redox mechanisms associated with DDS and its main oxidative metabolite DDS-NHOH and we explored the possible binding of DDS to the active site of CYP2C19 with the aid of molecular modeling software. © 2014 Schalcher et al.

AllerTOP v.2 - a server for in silico prediction of allergens

Allergy is an overreaction by the immune system to a previously encountered, ordinarily harmless substance - typically proteins - resulting in skin rash, swelling of mucous membranes, sneezing or wheezing, or other abnormal conditions. The use of modified proteins is increasingly widespread: their presence in food, commercial products, such as washing powder, and medical therapeutics and diagnostics, makes predicting and identifying potential allergens a crucial societal issue. The prediction of allergens has been explored widely using bioinformatics, with many tools being developed in the last decade; many of these are freely available online. Here, we report a set of novel models for allergen prediction utilizing amino acid E-descriptors, auto- and cross-covariance transformation, and several machine learning methods for classification, including logistic regression (LR), decision tree (DT), naïve Bayes (NB), random forest (RF), multilayer perceptron (MLP) and k nearest neighbours (kNN). The best performing method was kNN with 85.3% accuracy at 5-fold cross-validation. The resulting model has been implemented in a revised version of the AllerTOP server (http://www.ddg-pharmfac.net/AllerTOP). © Springer-Verlag 2014.

Receptor activity-modifying protein-directed G protein signaling specificity for the calcitonin gene-related peptide family of receptors

The calcitonin gene-related peptide (CGRP) family of G protein- coupled receptors (GPCRs) is formed through the association of the calcitonin receptor-like receptor (CLR) and one of three receptor activity-modifying proteins (RAMPs). Binding of one of the three peptide ligands, CGRP, adrenomedullin (AM), and intermedin/adrenomedullin 2 (AM2), is well known to result in aGαs-mediated increase in cAMP. Here we used modified yeast strains that couple receptor activation to cell growth, via chimeric yeast/Gα subunits, and HEK-293 cells to characterize the effect of different RAMP and ligand combinations on this pathway. We not only demonstrate functional couplings to both Gαs and Gαq but also identify a Gαi component to CLR signaling in both yeast and HEK-293 cells, which is absent in HEK-293S cells. We show that the CGRP family of receptors displays both ligand- and RAMPdependent signaling bias among the Gαs, Gαi, and Gαq/11 pathways. The results are discussed in the context of RAMP interactions probed through molecular modeling and molecular dynamics simulations of the RAMP-GPCR-G protein complexes. This study further highlights the importance of RAMPs to CLR pharmacology and to bias in general, as well as identifying the importance of choosing an appropriate model system for the study of GPCR pharmacology.

Modeling the peptide-T cell receptor interaction by the comparative molecular similarity indices analysis-soft independent modeling of class analogy technique

A set of 38 epitopes and 183 non-epitopes, which bind to alleles of the HLA-A3 supertype, was subjected to a combination of comparative molecular similarity indices analysis (CoMSIA) and soft independent modeling of class analogy (SIMCA). During the process of T cell recognition, T cell receptors (TCR) interact with the central section of the bound nonamer peptide; thus only positions 4−8 were considered in the study. The derived model distinguished 82% of the epitopes and 73% of the non-epitopes after cross-validation in five groups. The overall preference from the model is for polar amino acids with high electron density and the ability to form hydrogen bonds. These so-called “aggressive” amino acids are flanked by small-sized residues, which enable such residues to protrude from the binding cleft and take an active role in TCR-mediated T cell recognition. Combinations of “aggressive” and “passive” amino acids in the middle part of epitopes constitute a putative TCR binding motif

The second intracellular loop of the calcitonin gene-related peptide receptor provides molecular determinants for signal transduction and cell surface expression

The calcitonin gene-related peptide (CGRP) receptor is a heterodimer of a family B G-protein-coupled receptor, calcitonin receptor-like receptor (CLR), and the accessory protein receptor activity modifying protein 1. It couples to Gs, but it is not known which intracellular loops mediate this. We have identified the boundaries of this loop based on the relative position and length of the juxtamembrane transmembrane regions 3 and 4. The loop has been analyzed by systematic mutagenesis of all residues to alanine, measuring cAMP accumulation, CGRP affinity, and receptor expression. Unlike rhodopsin, ICL2 of the CGRP receptor plays a part in the conformational switch after agonist interaction. His-216 and Lys-227 were essential for a functional CGRP-induced cAMP response. The effect of (H216A)CLR is due to a disruption to the cell surface transport or surface stability of the mutant receptor. In contrast, (K227A)CLR had wild-type expression and agonist affinity, suggesting a direct disruption to the downstream signal transduction mechanism of the CGRP receptor. Modeling suggests that the loop undergoes a significant shift in position during receptor activation, exposing a potential G-protein binding pocket. Lys-227 changes position to point into the pocket, potentially allowing it to interact with bound G-proteins. His-216 occupies a position similar to that of Tyr-136 in bovine rhodopsin, part of the DRY motif of the latter receptor. This is the first comprehensive analysis of an entire intracellular loop within the calcitonin family of G-protein-coupled receptor. These data help to define the structural and functional characteristics of the CGRP-receptor and of family B G-protein-coupled receptors in general. © 2006 by The American Society for Biochemistry and Molecular Biology, Inc.

Data visualization during the early stages of drug discovery

Multidimensional compound optimization is a new paradigm in the drug discovery process, yielding efficiencies during early stages and reducing attrition in the later stages of drug development. The success of this strategy relies heavily on understanding this multidimensional data and extracting useful information from it. This paper demonstrates how principled visualization algorithms can be used to understand and explore a large data set created in the early stages of drug discovery. The experiments presented are performed on a real-world data set comprising biological activity data and some whole-molecular physicochemical properties. Data visualization is a popular way of presenting complex data in a simpler form. We have applied powerful principled visualization methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), to help the domain experts (screening scientists, chemists, biologists, etc.) understand and draw meaningful decisions. We also benchmark these principled methods against relatively better known visualization approaches, principal component analysis (PCA), Sammon's mapping, and self-organizing maps (SOMs), to demonstrate their enhanced power to help the user visualize the large multidimensional data sets one has to deal with during the early stages of the drug discovery process. The results reported clearly show that the GTM and HGTM algorithms allow the user to cluster active compounds for different targets and understand them better than the benchmarks. An interactive software tool supporting these visualization algorithms was provided to the domain experts. The tool facilitates the domain experts by exploration of the projection obtained from the visualization algorithms providing facilities such as parallel coordinate plots, magnification factors, directional curvatures, and integration with industry standard software. © 2006 American Chemical Society.

DVMS 1.5: A user manual (the data visualization and modeling system)

The data available during the drug discovery process is vast in amount and diverse in nature. To gain useful information from such data, an effective visualisation tool is required. To provide better visualisation facilities to the domain experts (screening scientist, biologist, chemist, etc.),we developed a software which is based on recently developed principled visualisation algorithms such as Generative Topographic Mapping (GTM) and Hierarchical Generative Topographic Mapping (HGTM). The software also supports conventional visualisation techniques such as Principal Component Analysis, NeuroScale, PhiVis, and Locally Linear Embedding (LLE). The software also provides global and local regression facilities . It supports regression algorithms such as Multilayer Perceptron (MLP), Radial Basis Functions network (RBF), Generalised Linear Models (GLM), Mixture of Experts (MoE), and newly developed Guided Mixture of Experts (GME). This user manual gives an overview of the purpose of the software tool, highlights some of the issues to be taken care while creating a new model, and provides information about how to install & use the tool. The user manual does not require the readers to have familiarity with the algorithms it implements. Basic computing skills are enough to operate the software.

miniDVMS v1.8 : A user manual(The data visualization and modeling system)

Today, the data available to tackle many scientific challenges is vast in quantity and diverse in nature. The exploration of heterogeneous information spaces requires suitable mining algorithms as well as effective visual interfaces. miniDVMS v1.8 provides a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualisation domain. The advantage of this interface is that the user is directly involved in the data mining process. Principled projection methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), are integrated with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates, and user interaction facilities, to provide this integrated visual data mining framework. The software also supports conventional visualisation techniques such as principal component analysis (PCA), Neuroscale, and PhiVis. This user manual gives an overview of the purpose of the software tool, highlights some of the issues to be taken care while creating a new model, and provides information about how to install and use the tool. The user manual does not require the readers to have familiarity with the algorithms it implements. Basic computing skills are enough to operate the software.