Computing highly correlated positions using mutual information and graph theory for G protein-coupled receptors

Prediction of Allosteric Communication Pathways in Proteins

MOTIVATION

Allostery in proteins is an essential phenomenon in biological processes. In this paper, we present a computational model to predict paths of maximum information transfer between active and allosteric sites. In this information theoretic study, we use mutual information as the measure of information transfer, where transition probability of information from one residue to its contacting neighbors is proportional to the magnitude of mutual information between the two residues. Starting from a given residue and using a Hidden Markov Model, we successively determine the neighboring residues that eventually lead to a path of optimum information transfer. The Gaussian approximation of mutual information between residue pairs is adopted. The limits of validity of this approximation are discussed in terms of a nonlinear theory of mutual information and its reduction to the Gaussian form.

RESULTS

Predictions of the model are tested on six widely studied cases, CheY Bacterial Chemotaxis, B-cell Lymphoma extra-large Bcl-xL, Human proline isomerase cyclophilin A (CypA), Dihydrofolate reductase DHFR, HRas GTPase, and Caspase-1. The communication transmission rendering the propagation of local fluctuations from the active sites throughout the structure in multiple paths correlate well with the known experimental data. Distinct paths originating from the active site may likely represent a multi functionality such as involving more than one allosteric site and/or preexistence of some other functional states. Our model is computationally fast and simple, and can give allosteric communication pathways, which are crucial for the understanding and control of protein functionality.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Molecular evolution of a collage of cholesterol interaction motifs in transmembrane helix V of the serotonin1A receptor

The human serotonin_1A receptor is a representative member of the superfamily of G protein-coupled receptors (GPCRs) and an important drug target for neurological disorders. Using a combination of biochemical, biophysical and molecular dynamics simulation approaches, we and others have shown that membrane cholesterol modulates the organization, dynamics and function of vertebrate serotonin_1A receptors. Previous studies have shown that the cytoplasmic portion of transmembrane helix V (TM V) and the extramembraneous intracellular loop 3 are critical for G-protein coupling, phosphorylation and desensitization of the receptor. We have recently resolved a collage of putative cholesterol interaction motifs from the amino acid sequence overlapping this region. In this paper, we explore the sequence plasticity of this fragment that may have adapted to altered membrane lipidome, after vertebrates evolved from primordial invertebrates. Since invertebrates have lower levels of membrane cholesterol relative to vertebrates, we compared TM V sequence fragments from invertebrate serotonin₁ receptors with vertebrate orthologs to infer the sequence plasticity in TM V. We report that the average number of cholesterol interaction motifs in TM V for diverse phyla represents an increasing trend that could mirror vertebrate evolution from primordial invertebrates. By statistical modeling, we propose that the collage of cholesterol interaction motifs in TM V of the human serotonin_1A receptor may have evolved from rudimentary collages, reminiscent of primordial invertebrate orthologs. Taken together, we propose that a repertoire of cholesterol-philic nonsynonymous substitutions may have enhanced collage complexity in TM V during vertebrate evolution.

A collage of cholesterol interaction motifs in the serotonin1A receptor: An evolutionary implication for differential cholesterol interaction

The serotonin_1A receptor is a representative member of the G protein-coupled receptor (GPCR) superfamily and acts as an important drug target. In our previous work, we comprehensively demonstrated that membrane cholesterol is necessary in the organization, dynamics and function of the serotonin_1A receptor. In this context, analysis of high-resolution GPCR crystal structures in general and in silico studies of the serotonin_1A receptor in particular, have suggested the presence of cholesterol interaction sites (hotspots) in various regions of the receptor. In this work, we have identified an evolutionarily conserved collage of four categories of cholesterol interaction motifs associated with transmembrane helix V and the adjacent intracellular loop 3 fragment of the vertebrate serotonin_1A receptor. This collage of motifs represents a total of twenty diverse context-dependent cholesterol interaction configurations. We envision that the gamut of cholesterol interaction sites, characterized by sequence plasticity in cholesterol interaction, could be relevant in receptor-cholesterol interaction in membranes of varying cholesterol content and organization, as found in diverse cell types. We conclude that an evolutionarily conserved mechanism of GPCR-cholesterol interaction allows the serotonin_1A receptor to adapt to diverse membrane cholesterol levels during natural evolution.

Protein sites with more coevolutionary connections tend to evolve slower, while more variable protein families acquire higher coevolutionary connections

Background: Amino acid exchanges within proteins sometimes compensate for one another and could therefore be co-evolved. It is essential to investigate the intricate relationship between the extent of coevolution and the evolutionary variability exerted at individual protein sites, as well as the whole protein. Methods: In this study, we have used a reliable set of coevolutionary connections (sites within 10Å spatial distance) and investigated their correlation with the evolutionary diversity within the respective protein sites. Results: Based on our observations, we propose an interesting hypothesis that higher numbers of coevolutionary connections are associated with lesser evolutionary variable protein sites, while higher numbers of the coevolutionary connections can be observed for a protein family that has higher evolutionary variability. Our findings also indicate that highly coevolved sites located in a solvent accessible state tend to be less evolutionary variable. This relationship reverts at the whole protein level where cytoplasmic and extracellular proteins show moderately higher anti-correlation between the number of coevolutionary connections and the average evolutionary conservation of the whole protein. Conclusions: Observations and hypothesis presented in this study provide intriguing insights towards understanding the critical relationship between coevolutionary and evolutionary changes observed within proteins. Our observations encourage further investigation to find out the reasons behind subtle variations in the relationship between coevolutionary connectivity and evolutionary diversity for proteins located at various cellular localizations and/or involved in different molecular-biological functions.

Computational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and Communication

Allosteric interactions in the Hsp70 proteins are linked with their regulatory mechanisms and cellular functions. Despite significant progress in structural and functional characterization of the Hsp70 proteins fundamental questions concerning modularity of the allosteric interaction networks and hierarchy of signaling pathways in the Hsp70 chaperones remained largely unexplored and poorly understood. In this work, we proposed an integrated computational strategy that combined atomistic and coarse-grained simulations with coevolutionary analysis and network modeling of the residue interactions. A novel aspect of this work is the incorporation of dynamic residue correlations and coevolutionary residue dependencies in the construction of allosteric interaction networks and signaling pathways. We found that functional sites involved in allosteric regulation of Hsp70 may be characterized by structural stability, proximity to global hinge centers and local structural environment that is enriched by highly coevolving flexible residues. These specific characteristics may be necessary for regulation of allosteric structural transitions and could distinguish regulatory sites from nonfunctional conserved residues. The observed confluence of dynamics correlations and coevolutionary residue couplings with global networking features may determine modular organization of allosteric interactions and dictate localization of key mediating sites. Community analysis of the residue interaction networks revealed that concerted rearrangements of local interacting modules at the inter-domain interface may be responsible for global structural changes and a population shift in the DnaK chaperone. The inter-domain communities in the Hsp70 structures harbor the majority of regulatory residues involved in allosteric signaling, suggesting that these sites could be integral to the network organization and coordination of structural changes. Using a network-based formalism of allostery, we introduced a community-hopping model of allosteric communication. Atomistic reconstruction of signaling pathways in the DnaK structures captured a direction-specific mechanism and molecular details of signal transmission that are fully consistent with the mutagenesis experiments. The results of our study reconciled structural and functional experiments from a network-centric perspective by showing that global properties of the residue interaction networks and coevolutionary signatures may be linked with specificity and diversity of allosteric regulation mechanisms.

The diversity of allosteric mechanisms in the Hsp70 proteins could range from modulation of the inter-domain interactions and conformational dynamics to fine-tuning of the Hsp70 interactions with co-chaperones. The goal of this study is to present a systematic computational analysis of the dynamic and evolutionary factors underlying allosteric structural transformations of the Hsp70 proteins. We investigated the relationship between functional dynamics, residue coevolution, and network organization of residue interactions in the Hsp70 proteins. The results of this study revealed that conformational dynamics of the Hsp70 proteins may be linked with coevolutionary propensities and mutual information dependencies of the protein residues. Modularity and connectivity of allosteric interactions in the Hsp70 chaperones are coordinated by stable functional sites that feature unique coevolutionary signatures and high network centrality. The emergence of the inter-domain communities that are coordinated by functional centers and include highly coevolving residues could facilitate structural transitions through cooperative reorganization of the local interacting modules. We determined that the differences in the modularity of the residue interactions and organization of coevolutionary networks in DnaK may be associated with variations in their allosteric mechanisms. The network signatures of the DnaK structures are characteristic of a population-shift allostery that allows for coordinated structural rearrangements of local communities. A dislocation of mediating centers and insufficient coevolutionary coupling between functional regions may render a reduced cooperativity and promote a limited entropy-driven allostery in the Sse1 chaperone that occurs without structural changes. The results of this study showed that a network-centric framework and a community-hopping model of allosteric communication pathways may provide novel insights into molecular and evolutionary principles of allosteric regulation in the Hsp70 proteins.

Graph analysis of β2 adrenergic receptor structures: a "social network" of GPCR residues

Purpose

G protein-coupled receptors (GPCRs) are a superfamily of membrane proteins of vast pharmaceutical interest. Here, we describe a graph theory-based analysis of the structure of the β₂ adrenergic receptor (β₂ AR), a prototypical GPCR. In particular, we illustrate the network of direct and indirect interactions that link each amino acid residue to any other residue of the receptor.

Methods

Networks of interconnected amino acid residues in proteins are analogous to social networks of interconnected people. Hence, they can be studied through the same analysis tools typically employed to analyze social networks – or networks in general – to reveal patterns of connectivity, influential members, and dynamicity. We focused on the analysis of closeness-centrality, which is a measure of the overall connectivity distance of the member of a network to all other members.

Results

The residues endowed with the highest closeness-centrality are located in the middle of the seven transmembrane domains (TMs). In particular, they are mostly located in the middle of TM2, TM3, TM6 or TM7, while fewer of them are located in the middle of TM1, TM4 or TM5. At the cytosolic end of TM6, the centrality detected for the active structure is markedly lower than that detected for the corresponding residues in the inactive structures. Moreover, several residues acquire centrality when the structures are analyzed in the presence of ligands. Strikingly, there is little overlap between the residues that acquire centrality in the presence of the ligand in the blocker-bound structures and the agonist-bound structures.

Conclusions

Our results reflect the fact that the receptor resembles a bow tie, with a rather tight knot of closely interconnected residues and two ends that fan out in two opposite directions: one toward the extracellular space, which hosts the ligand binding cavity, and one toward the cytosol, which hosts the G protein binding cavity. Moreover, they underscore how interaction network is by the conformational rearrangements concomitant with the activation of the receptor and by the presence of agonists or blockers.

Electronic supplementary material

The online version of this article (doi:10.1186/2193-9616-1-16) contains supplementary material, which is available to authorized users.

A coevolutionary residue network at the site of a functionally important conformational change in a phosphohexomutase enzyme family

Coevolution analyses identify residues that co-vary with each other during evolution, revealing sequence relationships unobservable from traditional multiple sequence alignments. Here we describe a coevolutionary analysis of phosphomannomutase/phosphoglucomutase (PMM/PGM), a widespread and diverse enzyme family involved in carbohydrate biosynthesis. Mutual information and graph theory were utilized to identify a network of highly connected residues with high significance. An examination of the most tightly connected regions of the coevolutionary network reveals that most of the involved residues are localized near an interdomain interface of this enzyme, known to be the site of a functionally important conformational change. The roles of four interface residues found in this network were examined via site-directed mutagenesis and kinetic characterization. For three of these residues, mutation to alanine reduces enzyme specificity to ∼10% or less of wild-type, while the other has ∼45% activity of wild-type enzyme. An additional mutant of an interface residue that is not densely connected in the coevolutionary network was also characterized, and shows no change in activity relative to wild-type enzyme. The results of these studies are interpreted in the context of structural and functional data on PMM/PGM. Together, they demonstrate that a network of coevolving residues links the highly conserved active site with the interdomain conformational change necessary for the multi-step catalytic reaction. This work adds to our understanding of the functional roles of coevolving residue networks, and has implications for the definition of catalytically important residues.

Three-dimensional structures of membrane proteins from genomic sequencing

We show that amino acid covariation in proteins, extracted from the evolutionary sequence record, can be used to fold transmembrane proteins. We use this technique to predict previously unknown 3D structures for 11 transmembrane proteins (with up to 14 helices) from their sequences alone. The prediction method (EVfold_membrane) applies a maximum entropy approach to infer evolutionary covariation in pairs of sequence positions within a protein family and then generates all-atom models with the derived pairwise distance constraints. We benchmark the approach with blinded de novo computation of known transmembrane protein structures from 23 families, demonstrating unprecedented accuracy of the method for large transmembrane proteins. We show how the method can predict oligomerization, functional sites, and conformational changes in transmembrane proteins. With the rapid rise in large-scale sequencing, more accurate and more comprehensive information on evolutionary constraints can be decoded from genetic variation, greatly expanding the repertoire of transmembrane proteins amenable to modeling by this method.

Molecular evolution of the transmembrane domains of G protein-coupled receptors

G protein-coupled receptors (GPCRs) are a superfamily of integral membrane proteins vital for signaling and are important targets for pharmaceutical intervention in humans. Previously, we identified a group of ten amino acid positions (called key positions), within the seven transmembrane domain (7TM) interhelical region, which had high mutual information with each other and many other positions in the 7TM. Here, we estimated the evolutionary selection pressure at those key positions. We found that the key positions of receptors for small molecule natural ligands were under strong negative selection. Receptors naturally activated by lipids had weaker negative selection in general when compared to small molecule-activated receptors. Selection pressure varied widely in peptide-activated receptors. We used this observation to predict that a subgroup of orphan GPCRs not under strong selection may not possess a natural small-molecule ligand. In the subgroup of MRGX1-type GPCRs, we identified a key position, along with two non-key positions, under statistically significant positive selection.

Homology model and docking studies on porcine β₂ adrenoceptor: description of two binding sites

The affinity of the classical β(2) adrenoceptor-selective inverse agonist ICI118,551 is notoriously lower for porcine β(2) adrenoceptors (p(2)βAR) than for human β(2) adrenoceptors (hβ(2)AR) but molecular mechanisms for this difference are still unclear. Homology 3-D models of pβ(2)AR can be useful in predicting similarities and differences, which might in turn increase the comparative understanding of ligand interactions with the hβ(2)AR. In this work, the pβ(2)AR amino acid sequence was used to carry out homology modeling. The selected pβ(2)AR 3-D structure was structurally and energetically optimized and used as a model for further theoretical study. The homology model of pβ(2)AR has a 3-D structure very similar to the crystal structures of recently studied hβ(2)AR. This was also corroborated by sequence identity, RMSD, Ramachandran map, TM-score and docking results. Upon performing molecular docking simulations with the AutoDock4.0.1 program on pβ(2)AR, it was found that a set of well-known β(2)AR ligands reach two distinct binding sites on pβ(2)AR. Whereas one of these sites is similar to that reported on the hβ(2)AR crystal structure, the other can explain some important experimental observations. Additionally, the theoretical affinity estimated for ICI118,551 closely agrees with affinities estimated from experimental in vitro data. The experimental differences between the human/porcine β(2)ARs in relation to ligand affinity can in part be elucidated by observations in this molecular modeling study.

Chemogenomic analysis of G-protein coupled receptors and their ligands deciphers locks and keys governing diverse aspects of signalling

Understanding the molecular mechanism of signalling in the important super-family of G-protein-coupled receptors (GPCRs) is causally related to questions of how and where these receptors can be activated or inhibited. In this context, it is of great interest to unravel the common molecular features of GPCRs as well as those related to an active or inactive state or to subtype specific G-protein coupling. In our underlying chemogenomics study, we analyse for the first time the statistical link between the properties of G-protein-coupled receptors and GPCR ligands. The technique of mutual information (MI) is able to reveal statistical inter-dependence between variations in amino acid residues on the one hand and variations in ligand molecular descriptors on the other. Although this MI analysis uses novel information that differs from the results of known site-directed mutagenesis studies or published GPCR crystal structures, the method is capable of identifying the well-known common ligand binding region of GPCRs between the upper part of the seven transmembrane helices and the second extracellular loop. The analysis shows amino acid positions that are sensitive to either stimulating (agonistic) or inhibitory (antagonistic) ligand effects or both. It appears that amino acid positions for antagonistic and agonistic effects are both concentrated around the extracellular region, but selective agonistic effects are cumulated between transmembrane helices (TMHs) 2, 3, and ECL2, while selective residues for antagonistic effects are located at the top of helices 5 and 6. Above all, the MI analysis provides detailed indications about amino acids located in the transmembrane region of these receptors that determine G-protein signalling pathway preferences.

Learning generative models for protein fold families

We introduce a new approach to learning statistical models from multiple sequence alignments (MSA) of proteins. Our method, called GREMLIN (Generative REgularized ModeLs of proteINs), learns an undirected probabilistic graphical model of the amino acid composition within the MSA. The resulting model encodes both the position-specific conservation statistics and the correlated mutation statistics between sequential and long-range pairs of residues. Existing techniques for learning graphical models from MSA either make strong, and often inappropriate assumptions about the conditional independencies within the MSA (e.g., Hidden Markov Models), or else use suboptimal algorithms to learn the parameters of the model. In contrast, GREMLIN makes no a priori assumptions about the conditional independencies within the MSA. We formulate and solve a convex optimization problem, thus guaranteeing that we find a globally optimal model at convergence. The resulting model is also generative, allowing for the design of new protein sequences that have the same statistical properties as those in the MSA. We perform a detailed analysis of covariation statistics on the extensively studied WW and PDZ domains and show that our method out-performs an existing algorithm for learning undirected probabilistic graphical models from MSA. We then apply our approach to 71 additional families from the PFAM database and demonstrate that the resulting models significantly out-perform Hidden Markov Models in terms of predictive accuracy.

Structural models of TREK channels and their gating mechanism

Mechanosensitive TREK channels belong to the family of K2P channels, a family of widely distributed, well modulated channels that uniquely have two similar or identical subunits, each with two TM1-P-TM2 motifs. Our goal is to build viable structural models of TREK channels, as representatives of K2P channels family. The structures available to be used as templates belong to the 2TM channels superfamily. These have low sequence similarity and different structural features: four symmetrically arranged subunits, each having one TM1-P-TM2 motif. Our model building strategy used two subunits of the template (KcsA) to build one subunit of the target (TREK-1). Our models of the Closed channel were adjusted to differ substantially from those of the template, e.g., TM2 of the 2nd repeat is near the axis of the pore whereas TM2 of the 1st repeat is far from the axis. Segments linking the two repeats and immediately following the last TM segment were modeled ab initio as α-helices based on helical periodicities of hydrophobic and hydrophilic residues, highly conserved and poorly conserved residues, and statistically related positions from multiple sequence alignments. The models were further refined by two-fold symmetry-constrained MD simulations using a protocol we developed previously. We also built models of the Open state and suggest a possible tension-activated gating mechanism characterized by helical motion with two-fold symmetry. Our models are consistent with deletion/truncation mutagenesis and thermodynamic analysis of gating described in the accompanying paper.

Design, synthesis and in vitro evaluation of (R)-4-(2-(tert-butylamino)-1-hydroxyethyl)-2-(hydroxymethyl)phenyl hydrogen phenylboronate: a novel salbutamol derivative with high intrinsic efficacy on the β2 adrenoceptor

We tested a set of boron containing arylethanolamine derivatives on the human and guinea pig β(2) adrenoceptor (β(2)AR) 3-D structures by docking methodology. The compound with the highest affinity based on docking analysis, (R)-4-(2-(tert-butylamino)-1-hydroxyethyl)-2-(hydroxymethyl)phenyl hydrogen phenylboronate (boronterol) was synthesized, characterized and tested in guinea pig tracheal rings at basal tone and with histamine-induced contractions. Boronterol was at least eightfold more potent than salbutamol as a smooth muscle relaxant drug (judged by the EC(50) values) and showed a similar maximal relaxant effect as isoproterenol. ICI118,551 showed competitive antagonism on the relaxing effect of boronterol. These results suggest the β(2)AR agonist action of boronterol.

Validation of coevolving residue algorithms via pipeline sensitivity analysis: ELSC and OMES and ZNMI, oh my!

Correlated amino acid substitution algorithms attempt to discover groups of residues that co-fluctuate due to either structural or functional constraints. Although these algorithms could inform both ab initio protein folding calculations and evolutionary studies, their utility for these purposes has been hindered by a lack of confidence in their predictions due to hard to control sources of error. To complicate matters further, naive users are confronted with a multitude of methods to choose from, in addition to the mechanics of assembling and pruning a dataset. We first introduce a new pair scoring method, called ZNMI (Z-scored-product Normalized Mutual Information), which drastically improves the performance of mutual information for co-fluctuating residue prediction. Second and more important, we recast the process of finding coevolving residues in proteins as a data-processing pipeline inspired by the medical imaging literature. We construct an ensemble of alignment partitions that can be used in a cross-validation scheme to assess the effects of choices made during the procedure on the resulting predictions. This pipeline sensitivity study gives a measure of reproducibility (how similar are the predictions given perturbations to the pipeline?) and accuracy (are residue pairs with large couplings on average close in tertiary structure?). We choose a handful of published methods, along with ZNMI, and compare their reproducibility and accuracy on three diverse protein families. We find that (i) of the algorithms tested, while none appear to be both highly reproducible and accurate, ZNMI is one of the most accurate by far and (ii) while users should be wary of predictions drawn from a single alignment, considering an ensemble of sub-alignments can help to determine both highly accurate and reproducible couplings. Our cross-validation approach should be of interest both to developers and end users of algorithms that try to detect correlated amino acid substitutions.

Docking studies on a refined human beta(2) adrenoceptor model yield theoretical affinity values in function with experimental values for R-ligands, but not for S-antagonists

G-protein coupled receptors (GPCR) belong to the largest group of membrane proteins involved in signal transduction. These receptors are implicated in diverse physiological and pathological events. The human beta(2) adrenergic receptor (hbeta(2)AR) is one of the few GPCRs whose 3-D structures are available on the Protein Data Bank. Because there is great interest by drug developers for hbeta(2)AR as a target, it is necessary to study its ligand-recognition process at the atomic level. The hbeta(2)AR can recognize both R/S enantiomeric ligands, R-agonists result in a greater activation than do S-agonists (eutomers and distomers for activation, respectively), according to experimental results. In this work is reported the ligand recognition on a refined hbeta(2)AR-structure of a set of well-known R/S-ligands by means of docking studies. Data obtained in silico were analyzed and compared with those reported in vitro. The theoretical affinity values were reproduced for agonists, but not for antagonist (or inverse agonists). However, theoretical data for R-antagonists are in function to experimental data. The theoretical results confirm the role of amino acids previously reported by mutagenesis studies due to their important roles in drug affinity and stereoselectivity.

Structural and functional roles of coevolved sites in proteins

BACKGROUND

Understanding the residue covariations between multiple positions in protein families is very crucial and can be helpful for designing protein engineering experiments. These simultaneous changes or residue coevolution allow protein to maintain its overall structural-functional integrity while enabling it to acquire specific functional modifications. Despite the significant efforts in the field there is still controversy in terms of the preferable locations of coevolved residues on different regions of protein molecules, the strength of coevolutionary signal and role of coevolution in functional diversification.

METHODOLOGY

In this paper we study the scale and nature of residue coevolution in maintaining the overall functionality and structural integrity of proteins. We employed a large scale study to investigate the structural and functional aspects of coevolved residues. We found that the networks representing the coevolutionary residue connections within our dataset are in general of 'small-world' type as they have clustering coefficient values higher than random networks and also show smaller mean shortest path lengths similar and/or lower than random and regular networks. We also found that altogether 11% of functionally important sites are coevolved with any other sites. Active sites are found more frequently to coevolve with any other sites (15%) compared to protein (11%) and ligand (9%) binding sites. Metal binding and active sites are also found to be more frequently coevolved with other metal binding and active sites, respectively. Analysis of the coupling between coevolutionary processes and the spatial distribution of coevolved sites reveals that a high fraction of coevolved sites are located close to each other. Moreover, approximately 80% of charge compensatory substitutions within coevolved sites are found at very close spatial proximity (<or= 5A), pointing to the possible preservation of salt bridges in evolution.

CONCLUSION

Our findings show that a noticeable fraction of functionally important sites undergo coevolution and also point towards compensatory substitutions as a probable coevolutionary mechanism within spatially proximal coevolved functional sites.