Multidimensional scaling reveals the main evolutionary pathways of class A G-protein-coupled receptors

Allosteric Modulators of Dopamine D2 Receptors for Fine-Tuning of Dopaminergic Neurotransmission in CNS Diseases: Overview, Pharmacology, Structural Aspects and Synthesis

Allosteric modulation of G protein-coupled receptors (GPCRs) is nowadays a hot topic in medicinal chemistry. Allosteric modulators, i.e., compounds which bind in a receptor site topologically distinct from orthosteric sites, exhibit a number of advantages. They are more selective, safer and display a ceiling effect which prevents overdosing. Allosteric modulators of dopamine D₂ receptor are potential drugs against a number of psychiatric and neurological diseases, such as schizophrenia and Parkinson's disease. In this review, an insightful summary of current research on D₂ receptor modulators is presented, ranging from their pharmacology and structural aspects of ligand-receptor interactions to their synthesis.

Bioinformatic Characterization and Molecular Evolution of the Lucina pectinata Hemoglobins

(1) Introduction: Lucina pectinata is a clam found in sulfide-rich mud environments that has three hemoglobins believed to be responsible for the transport of hydrogen sulfide (HbILp) and oxygen (HbIILp and HbIIILp) to chemoautotrophic endosymbionts. The physiological roles and evolution of these globins in sulfide-rich environments are not well understood. (2) Methods: We performed bioinformatic and phylogenetic analyses with 32 homologous mollusk globin sequences. Phylogenetics suggests a first gene duplication resulting in sulfide binding and oxygen binding genes. A more recent gene duplication gave rise to the two oxygen-binding hemoglobins. Multidimensional scaling analysis of the sequence space shows evolutionary drift of HbIILp and HbIIILp, while HbILp was closer to the Calyptogena hemoglobins. Further corroboration is seen by conservation in the coding region of hemoglobins from L. pectinata compared to those from Calyptogena. (3) Conclusions: Presence of glutamine in position E7 in organisms living in sulfide-rich environments can be considered an adaptation to prevent loss of protein function. In HbILp a substitution of phenylalanine in position B10 is accountable for its unique reactivity towards H2S. It appears that HbILp has been changing over time, apparently not subject to functional constraints of binding oxygen, and acquired a unique function for a specialized environment.

In Silico Identification of Cholesterol Binding Motifs in the Chemokine Receptor CCR3

CC motif chemokine receptor 3 (CCR3) is a Class A G protein-coupled receptor (GPCR) mainly responsible for the cellular trafficking of eosinophils. As such, it plays key roles in inflammatory conditions, such as asthma and arthritis, and the metastasis of many deadly forms of cancer. However, little is known about how CCR3 functionally interacts with its bilayer environment. Here, we investigate cholesterol binding sites in silico through Coarse-Grained Molecular Dynamics (MD) and Pylipid analysis using an extensively validated homology model based on the crystal structure of CCR5. These simulations identified several cholesterol binding sites containing Cholesterol Recognition/Interaction Amino Acid Consensus motif (CRAC) and its inversion CARC motifs in CCR3. One such site, a CARC site in TM1, in conjunction with aliphatic residues in TM7, emerged as a candidate for future investigation based on the cholesterol residency time within the binding pocket. This site forms the core of a cholesterol binding site previously observed in computational studies of CCR2 and CCR5. Most importantly, these cholesterol binding sites are conserved in other chemokine receptors and may provide clues to cholesterol regulation mechanisms in this subfamily of Class A GPCRs.

Homology Modeling of Class A G-Protein-Coupled Receptors in the Age of the Structure Boom

With 700 members, G protein-coupled receptors (GPCRs) of the rhodopsin family (class A) form the largest membrane receptor family in humans and are the target of about 30% of presently available pharmaceutical drugs. The recent boom in GPCR structures led to the structural resolution of 57 unique receptors in different states (39 receptors in inactive state only, 2 receptors in active state only and 16 receptors in different activation states). In spite of these tremendous advances, most computational studies on GPCRs, including molecular dynamics simulations, virtual screening and drug design, rely on GPCR models obtained by homology modeling. In this protocol, we detail the different steps of homology modeling with the MODELLER software, from template selection to model evaluation. The present structure boom provides closely related templates for most receptors. If, in these templates, some of the loops are not resolved, in most cases, the numerous available structures enable to find loop templates with similar length for equivalent loops. However, simultaneously, the large number of putative templates leads to model ambiguities that may require additional information based on multiple sequence alignments or molecular dynamics simulations to be resolved. Using the modeling of the human bradykinin receptor B1 as a case study, we show how several templates are managed by MODELLER, and how the choice of template(s) and of template fragments can improve the quality of the models. We also give examples of how additional information and tools help the user to resolve ambiguities in GPCR modeling.

Identification of Psychoactive Metabolites from Cannabis sativa, Its Smoke, and Other Phytocannabinoids Using Machine Learning and Multivariate Methods

Cannabis sativa is a medicinal plant having a very complex matrix composed of mainly cannabinoids and terpenoids. The literature has numerous reports, which indicate that tetrahydrocannabinol (THC) is the only major psychoactive metabolite in C. sativa. It is important to explore other metabolites having the possibility of exhibiting the psychoactive character of various degrees and also to identify metabolites targeting other receptors such as opioid, γ amino butyric acid (GABA), glycine, serotonin, and nicotine present in C. sativa, the smoke of C. sativa, and other phytocannabinoid matrices. This article aims to achieve this goal by application of batteries of computational tools such as machine learning tools and multivariate methods on physiochemical and absorption, distribution, metabolism, excretion, and toxicity (ADMET) descriptors of 468 metabolites from C. sativa, its smoke and, other phytocannabinoids. The structure-activity relationship (SAR) showed that 54 metabolites from C. sativa have high scaffold homology with THC. Its implications on the route of administration and factors affecting the SAR are discussed. C. sativa smoke has metabolites that have possibility of interacting with GABA, and glycine receptors.

Unexpected role of a conserved domain in the first extracellular loop in G protein-coupled receptor trafficking

G protein-coupled receptors are the largest superfamily of cell surface receptors in the Metazoa and play critical roles in transducing extracellular signals into intracellular responses. This action is mediated through conformational changes in the receptor following ligand binding. A number of conserved motifs have critical roles in GPCR function, and here we focus on a highly conserved motif (WxFG) in extracellular loop one (EL1). A phylogenetic analysis documents the presence of the WxFG motif in ∼90% of Class A GPCRs and the motif is represented in 17 of the 19 Class A GPCR subfamilies. Using site-directed mutagenesis, we mutagenized the conserved tryptophan residue in eight receptors which are members of disparate class A GPCR subfamilies from different taxa. The modification of the Drosophila leucokinin receptor shows that substitution of any non-aromatic amino acid for the tryptophan leads to a loss of receptor function. Additionally, leucine substitutions at this position caused similar signaling defects in the follicle-stimulating hormone receptor (FSHR), Galanin receptor (GALR1), AKH receptor (AKHR), corazonin receptor (CRZR), and muscarinic acetylcholine receptor (mACHR1). Visualization of modified receptors through the incorporation of a fluorescent tag revealed a severe reduction in plasma membrane expression, indicating aberrant trafficking of these modified receptors. Taken together, these results suggest a novel role for the WxFG motif in GPCR trafficking and receptor function.

Evolution of chemokine receptors is driven by mutations in the sodium binding site

Chemokines and their receptors (members of the GPCR super-family) are involved in a wide variety of physiological processes and diseases; thus, understanding the specificity of the chemokine receptor family could help develop new receptor specific drugs. Here, we explore the evolutionary mechanisms that led to the emergence of the chemokine receptors. Based on GPCR hierarchical classification, we analyzed nested GPCR sets with an eigen decomposition approach of the sequence covariation matrix and determined three key residues whose mutation was crucial for the emergence of the chemokine receptors and their subsequent divergence into homeostatic and inflammatory receptors. These residues are part of the allosteric sodium binding site. Their structural and functional roles were investigated by molecular dynamics simulations of CXCR4 and CCR5 as prototypes of homeostatic and inflammatory chemokine receptors, respectively. This study indicates that the three mutations crucial for the evolution of the chemokine receptors dramatically altered the sodium binding mode. In CXCR4, the sodium ion is tightly bound by four protein atoms and one water molecule. In CCR5, the sodium ion is mobile within the binding pocket and moves between different sites involving from one to three protein atoms and two to five water molecules. Analysis of chemokine receptor evolution reveals that a highly constrained sodium binding site characterized most ancient receptors, and that the constraints were subsequently loosened during the divergence of this receptor family. We discuss the implications of these findings for the evolution of the chemokine receptor functions and mechanisms of action.

Visualizing the GPCR Network: Classification and Evolution

In this study, we delineate an unsupervised clustering algorithm, minimum span clustering (MSC), and apply it to detect G-protein coupled receptor (GPCR) sequences and to study the GPCR network using a base dataset of 2770 GPCR and 652 non-GPCR sequences. High detection accuracy can be achieved with a proper dataset. The clustering results of GPCRs derived from MSC show a strong correlation between their sequences and functions. By comparing our level 1 MSC results with the GPCRdb classification, the consistency is 87.9% for the fourth level of GPCRdb, 89.2% for the third level, 98.4% for the second level, and 100% for the top level (the lowest resolution level of GPCRdb). The MSC results of GPCRs can be well explained by estimating the selective pressure of GPCRs, as exemplified by investigating the largest two subfamilies, peptide receptors (PRs) and olfactory receptors (ORs), in class A GPCRs. PRs are decomposed into three groups due to a positive selective pressure, whilst ORs remain as a single group due to a negative selective pressure. Finally, we construct and compare phylogenetic trees using distance-based and character-based methods, a combination of which could convey more comprehensive information about the evolution of GPCRs.

The G Protein-Coupled Receptor UT of the Neuropeptide Urotensin II Displays Structural and Functional Chemokine Features

The urotensinergic system was previously considered as being linked to numerous physiopathological states, including atherosclerosis, heart failure, hypertension, pre-eclampsia, diabetes, renal disease, as well as brain vascular lesions. Thus, it turns out that the actions of the urotensin II (UII)/G protein-coupled receptor UT system in animal models are currently not predictive enough in regard to their effects in human clinical trials and that UII analogs, established to target UT, were not as beneficial as expected in pathological situations. Thus, many questions remain regarding the overall signaling profiles of UT leading to complex involvement in cardiovascular and inflammatory responses as well as cancer. We address the potential UT chemotactic structural and functional definition under an evolutionary angle, by the existence of a common conserved structural feature among chemokine receptorsopioïdergic receptors and UT, i.e., a specific proline position in the transmembrane domain-2 TM2 (P2.58) likely responsible for a kink helical structure that would play a key role in chemokine functions. Even if the last decade was devoted to the elucidation of the cardiovascular control by the urotensinergic system, we also attempt here to discuss the role of UII on inflammation and migration, likely providing a peptide chemokine status for UII. Indeed, our recent work established that activation of UT by a gradient concentration of UII recruits Gαi/o and Gα13 couplings in a spatiotemporal way, controlling key signaling events leading to chemotaxis. We think that this new vision of the urotensinergic system should help considering UT as a chemotactic therapeutic target in pathological situations involving cell chemoattraction.

Interplay between Two Allosteric Sites and Their Influence on Agonist Binding in Human μ Opioid Receptor

Allostery is a widespread mechanism that allows for precise protein tuning. Its underlying mechanisms are elusive, particularly when there are multiple allosteric sites at the protein. This concerns also G-protein-coupled receptors (GPCRs), which are targets for a vast part of currently used drugs. To address this issue, we performed molecular dynamics simulations of a GPCR-human μ opioid receptor (MOR) in a native-like environment, with full agonist (R)-methadone, Na(+) ions, and a positive modulator BMS986122 in various configurations. We found that MOR's seventh transmembrane helix (TM VII) is central for allosteric signal transmission, and modulators affect its bending and rotation. The PAM stabilizes favorable agonist interactions, while Na(+) tends to disrupt agonist binding. We identified two residues involved in allosteric signal transmission: Trp 7.35 at the top and Tyr 7.53 at the bottom of TM VII.

Molecular Insights into the Transmembrane Domain of the Thyrotropin Receptor

The thyrotropin receptor (TSHR) is a G protein-coupled receptor (GPCR) that is member of the leucine-rich repeat subfamily (LGR). In the absence of crystal structure, the success of rational design of ligands targeting the receptor internal cavity depends on the quality of the TSHR models built. In this subfamily, transmembrane helices (TM) 2 and 5 are characterized by the absence of proline compared to most receptors, raising the question of the structural conformation of these helices. To gain insight into the structural properties of these helices, we carried out bioinformatics and experimental studies. Evolutionary analysis of the LGR family revealed a deletion in TM5 but provided no information on TM2. Wild type residues at positions 2.58, 2.59 or 2.60 in TM2 and/or at position 5.50 in TM5 were substituted to proline. Depending on the position of the proline substitution, different effects were observed on membrane expression, glycosylation, constitutive cAMP activity and responses to thyrotropin. Only proline substitution at position 2.59 maintained complex glycosylation and high membrane expression, supporting occurrence of a bulged TM2. The TSHR transmembrane domain was modeled by homology with the orexin 2 receptor, using a protocol that forced the deletion of one residue in the TM5 bulge of the template. The stability of the model was assessed by molecular dynamics simulations. TM5 straightened during the equilibration phase and was stable for the remainder of the simulations. Our data support a structural model of the TSHR transmembrane domain with a bulged TM2 and a straight TM5 that is specific of glycoprotein hormone receptors.

Clustering and visualizing similarity networks of membrane proteins

We proposed a fast and unsupervised clustering method, minimum span clustering (MSC), for analyzing the sequence-structure-function relationship of biological networks, and demonstrated its validity in clustering the sequence/structure similarity networks (SSN) of 682 membrane protein (MP) chains. The MSC clustering of MPs based on their sequence information was found to be consistent with their tertiary structures and functions. For the largest seven clusters predicted by MSC, the consistency in chain function within the same cluster is found to be 100%. From analyzing the edge distribution of SSN for MPs, we found a characteristic threshold distance for the boundary between clusters, over which SSN of MPs could be properly clustered by an unsupervised sparsification of the network distance matrix. The clustering results of MPs from both MSC and the unsupervised sparsification methods are consistent with each other, and have high intracluster similarity and low intercluster similarity in sequence, structure, and function. Our study showed a strong sequence-structure-function relationship of MPs. We discussed evidence of convergent evolution of MPs and suggested applications in finding structural similarities and predicting biological functions of MP chains based on their sequence information.

Sequence, structure and ligand binding evolution of rhodopsin-like G protein-coupled receptors: a crystal structure-based phylogenetic analysis

G protein-coupled receptors (GPCRs) form the largest family of membrane receptors in the human genome. Advances in membrane protein crystallization so far resulted in the determination of 24 receptors available as high-resolution atomic structures. We performed the first phylogenetic analysis of GPCRs based on the available set of GPCR structures. We present a new phylogenetic tree of known human rhodopsin-like GPCR sequences based on this structure set. We can distinguish the three separate classes of small-ligand binding GPCRs, peptide binding GPCRs, and olfactory receptors. Analyzing different structural subdomains, we found that small molecule binding receptors most likely have evolved from peptide receptor precursors, with a rhodopsin/S1PR1 ancestor, most likely an ancestral opsin, forming the link between both classes. A light-activated receptor therefore seems to be the origin of the small molecule hormone receptors of the central nervous system. We find hints for a common evolutionary path of both ligand binding site and central sodium/water binding site. Surprisingly, opioid receptors exhibit both a binding cavity and a central sodium/water binding site similar to the one of biogenic amine receptors instead of peptide receptors, making them seemingly prone to bind small molecule ligands, e.g. opiates. Our results give new insights into the relationship and the pharmacological properties of rhodopsin-like GPCRs.

Alkane hydroxylase genes in psychrophile genomes and the potential for cold active catalysis

BACKGROUND

Psychrophiles are presumed to play a large role in the catabolism of alkanes and other components of crude oil in natural low temperature environments. In this study we analyzed the functional diversity of genes for alkane hydroxylases, the enzymes responsible for converting alkanes to more labile alcohols, as found in the genomes of nineteen psychrophiles for which alkane degradation has not been reported. To identify possible mechanisms of low temperature optimization we compared putative alkane hydroxylases from these psychrophiles with homologues from nineteen taxonomically related mesophilic strains.

RESULTS

Seven of the analyzed psychrophile genomes contained a total of 27 candidate alkane hydroxylase genes, only two of which are currently annotated as alkane hydroxylase. These candidates were mostly related to the AlkB and cytochrome p450 alkane hydroxylases, but several homologues of the LadA and AlmA enzymes, significant for their ability to degrade long-chain alkanes, were also detected. These putative alkane hydroxylases showed significant differences in primary structure from their mesophile homologues, with preferences for specific amino acids and increased flexibility on loops, bends, and α-helices.

CONCLUSION

A focused analysis on psychrophile genomes led to discovery of numerous candidate alkane hydroxylase genes not currently annotated as alkane hydroxylase. Gene products show signs of optimization to low temperature, including regions of increased flexibility and amino acid preferences typical of psychrophilic proteins. These findings are consistent with observations of microbial degradation of crude oil in cold environments and identify proteins that can be targeted in rate studies and in the design of molecular tools for low temperature bioremediation.

Multi-Dimensional Scaling and MODELLER-Based Evolutionary Algorithms for Protein Model Refinement

Protein structure prediction, i.e., computationally predicting the three-dimensional structure of a protein from its primary sequence, is one of the most important and challenging problems in bioinformatics. Model refinement is a key step in the prediction process, where improved structures are constructed based on a pool of initially generated models. Since the refinement category was added to the biennial Critical Assessment of Structure Prediction (CASP) in 2008, CASP results show that it is a challenge for existing model refinement methods to improve model quality consistently. This paper presents three evolutionary algorithms for protein model refinement, in which multidimensional scaling(MDS), the MODELLER software, and a hybrid of both are used as crossover operators, respectively. The MDS-based method takes a purely geometrical approach and generates a child model by combining the contact maps of multiple parents. The MODELLER-based method takes a statistical and energy minimization approach, and uses the remodeling module in MODELLER program to generate new models from multiple parents. The hybrid method first generates models using the MDS-based method and then run them through the MODELLER-based method, aiming at combining the strength of both. Promising results have been obtained in experiments using CASP datasets. The MDS-based method improved the best of a pool of predicted models in terms of the global distance test score (GDT-TS) in 9 out of 16test targets.

Systematic characterization and comparative analysis of the rabbit immunoglobulin repertoire

Rabbits have been used extensively as a model system for the elucidation of the mechanism of immunoglobulin diversification and for the production of antibodies. We employed Next Generation Sequencing to analyze Ig germline V and J gene usage, CDR3 length and amino acid composition, and gene conversion frequencies within the functional (transcribed) IgG repertoire of the New Zealand white rabbit (Oryctolagus cuniculus). Several previously unannotated rabbit heavy chain variable (VH) and light chain variable (VL) germline elements were deduced bioinformatically using multidimensional scaling and k-means clustering methods. We estimated the gene conversion frequency in the rabbit at 23% of IgG sequences with a mean gene conversion tract length of 59±36 bp. Sequencing and gene conversion analysis of the chicken, human, and mouse repertoires revealed that gene conversion occurs much more extensively in the chicken (frequency 70%, tract length 79±57 bp), was observed to a small, yet statistically significant extent in humans, but was virtually absent in mice.

Sequence-structure based phylogeny of GPCR Class A Rhodopsin receptors

Current methods of G protein coupled receptors (GPCRs) phylogenetic classification are sequence based and therefore inappropriate for highly divergent sequences, sharing low sequence identity. In this study, sequence structure profile based alignment generated by PROMALS3D was used to understand the GPCR Class A Rhodopsin superfamily evolution using the MEGA 5 software. Phylogenetic analysis included a combination of Neighbor-Joining method and Maximum Likelihood method, with 1000 bootstrap replicates. Our study was able to identify potential ligand association for Class A Orphans and putative/unclassified Class A receptors with no cognate ligand information: GPR21 and GPR52 with fatty acids; GPR75 with Neuropeptide Y; GPR82, GPR18, GPR141 with N-arachidonylglycine; GPR176 with Free fatty acids, GPR10 with Tachykinin & Neuropeptide Y; GPR85 with ATP, ADP & UDP glucose; GPR151 with Galanin; GPR153 and GPR162 with Adrenalin, Noradrenalin; GPR146, GPR139, GPR142 with Neuromedin, Ghrelin, Neuromedin U-25 & Thyrotropin-releasing hormone; GPR171 with ATP, ADP & UDP Glucose; GPR88, GPR135, GPR161, GPR101with 11-cis-retinal; GPR83 with Tackykinin; GPR148 with Prostanoids, GPR109b, GPR81, GPR31with ATP & UTP and GPR150 with GnRH I & GnRHII. Furthermore, we suggest that this study would prove useful in re-classification of receptors, selecting templates for homology modeling and identifying ligands which may show cross reactivity with other GPCRs as signaling via multiple ligands play a significant role in disease modulation.

Structural evolution of G-protein-coupled receptors: a sequence space approach

Class A G-protein-coupled receptors (GPCRs) provide a fascinating example of evolutionary success. In this review, we discuss how metric multidimensional scaling (MDS), a multivariate analysis method, complements traditional tree-based phylogenetic methods and helps decipher the mechanisms that drove the evolution of class A GPCRs. MDS provides low-dimensional representations of a distance matrix. Applied to a multiple sequence alignment, MDS represents the sequences in a Euclidean space as points whose interdistances are as close as possible to the distances in the alignment (the so-called sequence space). We detail how to perform the MDS analysis of a multiple sequence alignment and how to analyze and interpret the resulting sequence space. We also show that the projection of supplementary data (a property of the MDS method) can be used to straightforwardly monitor the evolutionary drift of specific subfamilies. The sequence space of class A GPCRs reveals the key role of mutations at the level of the TM2 and TM5 proline residues in the evolution of class A GPCRs.

G protein-coupled receptors--recent advances

The years 2000 and 2007 witnessed milestones in current understanding of G protein-coupled receptor (GPCR) structural biology. In 2000 the first GPCR, bovine rhodopsin, was crystallized and the structure was solved, while in 2007 the structure of β(2)-adrenergic receptor, the first GPCR with diffusible ligands, was determined owing to advances in microcrystallization and an insertion of the fast-folding lysozyme into the receptor. In parallel with those crystallographic studies, the biological and biochemical characterization of GPCRs has advanced considerably because those receptors are molecular targets for many of currently used drugs. Therefore, the mechanisms of activation and signal transduction to the cell interior deduced from known GPCRs structures are of the highest importance for drug discovery. These proteins are the most diversified membrane receptors encoded by hundreds of genes in our genome. They participate in processes responsible for vision, smell, taste and neuronal transmission in response to photons or binding of ions, hormones, peptides, chemokines and other factors. Although the GPCRs share a common seven-transmembrane α-helical bundle structure their binding sites can accommodate thousands of different ligands. The ligands, including agonists, antagonists or inverse agonists change the structure of the receptor. With bound agonists they can form a complex with a suitable G protein, be phosphorylated by kinases or bind arrestin. The discovered signaling cascades invoked by arrestin independently of G proteins makes the GPCR activating scheme more complex such that a ligand acting as an antagonist for G protein signaling can also act as an agonist in arrestin-dependent signaling. Additionally, the existence of multiple ligand-dependent partial activation states as well as dimerization of GPCRs result in a 'microprocessor-like' action of these receptors rather than an 'on-off' switch as was commonly believed only a decade ago.

Action of molecular switches in GPCRs--theoretical and experimental studies

G protein coupled receptors (GPCRs), also called 7TM receptors, form a huge superfamily of membrane proteins that, upon activation by extracellular agonists, pass the signal to the cell interior. Ligands can bind either to extracellular N-terminus and loops (e.g. glutamate receptors) or to the binding site within transmembrane helices (Rhodopsin-like family). They are all activated by agonists although a spontaneous auto-activation of an empty receptor can also be observed. Biochemical and crystallographic methods together with molecular dynamics simulations and other theoretical techniques provided models of the receptor activation based on the action of so-called "molecular switches" buried in the receptor structure. They are changed by agonists but also by inverse agonists evoking an ensemble of activation states leading toward different activation pathways. Switches discovered so far include the ionic lock switch, the 3-7 lock switch, the tyrosine toggle switch linked with the nPxxy motif in TM7, and the transmission switch. The latter one was proposed instead of the tryptophan rotamer toggle switch because no change of the rotamer was observed in structures of activated receptors. The global toggle switch suggested earlier consisting of a vertical rigid motion of TM6, seems also to be implausible based on the recent crystal structures of GPCRs with agonists. Theoretical and experimental methods (crystallography, NMR, specific spectroscopic methods like FRET/BRET but also single-molecule-force-spectroscopy) are currently used to study the effect of ligands on the receptor structure, location of stable structural segments/domains of GPCRs, and to answer the still open question on how ligands are binding: either via ensemble of conformational receptor states or rather via induced fit mechanisms. On the other hand the structural investigations of homoand heterodimers and higher oligomers revealed the mechanism of allosteric signal transmission and receptor activation that could lead to design highly effective and selective allosteric or ago-allosteric drugs.

Bios2mds: an R package for comparing orthologous protein families by metric multidimensional scaling

BACKGROUND

The distance matrix computed from multiple alignments of homologous sequences is widely used by distance-based phylogenetic methods to provide information on the evolution of protein families. This matrix can also be visualized in a low dimensional space by metric multidimensional scaling (MDS). Applied to protein families, MDS provides information complementary to the information derived from tree-based methods. Moreover, MDS gives a unique opportunity to compare orthologous sequence sets because it can add supplementary elements to a reference space.

RESULTS

The R package bios2mds (from BIOlogical Sequences to MultiDimensional Scaling) has been designed to analyze multiple sequence alignments by MDS. Bios2mds starts with a sequence alignment, builds a matrix of distances between the aligned sequences, and represents this matrix by MDS to visualize a sequence space. This package also offers the possibility of performing K-means clustering in the MDS derived sequence space. Most importantly, bios2mds includes a function that projects supplementary elements (a.k.a. "out of sample" elements) onto the space defined by reference or "active" elements. Orthologous sequence sets can thus be compared in a straightforward way. The data analysis and visualization tools have been specifically designed for an easy monitoring of the evolutionary drift of protein sub-families.

CONCLUSIONS

The bios2mds package provides the tools for a complete integrated pipeline aimed at the MDS analysis of multiple sets of orthologous sequences in the R statistical environment. In addition, as the analysis can be carried out from user provided matrices, the projection function can be widely used on any kind of data.