A regulator of G protein signaling interaction surface linked to effector specificity

CovET: A covariation-evolutionary trace method that identifies protein structure-function modules

Measuring the relative effect that any two sequence positions have on each other may improve protein design or help better interpret coding variants. Current approaches use statistics and machine learning but rarely consider phylogenetic divergences which, as shown by Evolutionary Trace studies, provide insight into the functional impact of sequence perturbations. Here, we reframe covariation analyses in the Evolutionary Trace framework to measure the relative tolerance to perturbation of each residue pair during evolution. This approach (CovET) systematically accounts for phylogenetic divergences: at each divergence event, we penalize covariation patterns that belie evolutionary coupling. We find that while CovET approximates the performance of existing methods to predict individual structural contacts, it performs significantly better at finding structural clusters of coupled residues and ligand binding sites. For example, CovET found more functionally critical residues when we examined the RNA recognition motif and WW domains. It correlates better with large-scale epistasis screen data. In the dopamine D2 receptor, top CovET residue pairs recovered accurately the allosteric activation pathway characterized for Class A G protein-coupled receptors. These data suggest that CovET ranks highest the sequence position pairs that play critical functional roles through epistatic and allosteric interactions in evolutionarily relevant structure-function motifs. CovET complements current methods and may shed light on fundamental molecular mechanisms of protein structure and function.

Strategies towards Targeting Gαi/s Proteins: Scanning of Protein-Protein Interaction Sites To Overcome Inaccessibility

Heterotrimeric G proteins are classified into four subfamilies and play a key role in signal transduction. They transmit extracellular signals to intracellular effectors subsequent to the activation of G protein-coupled receptors (GPCRs), which are targeted by over 30 % of FDA-approved drugs. However, addressing G proteins as drug targets represents a compelling alternative, for example, when G proteins act independently of the corresponding GPCRs, or in cases of complex multifunctional diseases, when a large number of different GPCRs are involved. In contrast to Gαq, efforts to target Gαi/s by suitable chemical compounds has not been successful so far. Here, a comprehensive analysis was conducted examining the most important interface regions of Gαi/s with its upstream and downstream interaction partners. By assigning the existing compounds and the performed approaches to the respective interfaces, the druggability of the individual interfaces was ranked to provide perspectives for selective targeting of Gαi/s in the future.

Identification of evolutionarily stable functional and immunogenic sites across the SARS-CoV-2 proteome and the greater coronavirus family

Since the first recognized case of COVID-19, more than 100 million people have been infected worldwide. Global efforts in drug and vaccine development to fight the disease have yielded vaccines and drug candidates to cure COVID-19. However, the spread of SARS-CoV-2 variants threatens the continued efficacy of these treatments. In order to address this, we interrogate the evolutionary history of the entire SARS-CoV-2 proteome to identify evolutionarily conserved functional sites that can inform the search for treatments with broader coverage across the coronavirus family. Combining this information with the mutations observed in the current COVID-19 outbreak, we systematically and comprehensively define evolutionarily stable sites that may provide useful drug and vaccine targets and which are less likely to be compromised by the emergence of new virus strains. Several experimentally-validated effective drugs interact with these proposed target sites. In addition, the same evolutionary information can prioritize cross reactive antigens that are useful in directing multi-epitope vaccine strategies to illicit broadly neutralizing immune responses to the betacoronavirus family. Although the results are focused on SARS-CoV-2, these approaches stem from evolutionary principles that are agnostic to the organism or infective agent.

Identification of evolutionarily stable functional and immunogenic sites across the SARS-CoV-2 proteome and the greater coronavirus family

Since the first recognized case of COVID-19, more than 100 million people have been infected worldwide. Global efforts in drug and vaccine development to fight the disease have yielded vaccines and drug candidates to cure COVID-19. However, the spread of SARS-CoV-2 variants threatens the continued efficacy of these treatments. In order to address this, we interrogate the evolutionary history of the entire SARS-CoV-2 proteome to identify evolutionarily conserved functional sites that can inform the search for treatments with broader coverage across the coronavirus family. Combining this information with the mutations observed in the current COVID-19 outbreak, we systematically and comprehensively define evolutionarily stable sites that may provide useful drug and vaccine targets and which are less likely to be compromised by the emergence of new virus strains. Several experimentally-validated effective drugs interact with these proposed target sites. In addition, the same evolutionary information can prioritize cross reactive antigens that are useful in directing multi-epitope vaccine strategies to illicit broadly neutralizing immune responses to the betacoronavirus family. Although the results are focused on SARS-CoV-2, these approaches stem from evolutionary principles that are agnostic to the organism or infective agent.

Residues and residue pairs of evolutionary importance differentially direct signaling bias of D2 dopamine receptors

The D2 dopamine receptor and the serotonin 5-hydroxytryptamine 2A receptor (5-HT2A) are closely-related G-protein-coupled receptors (GPCRs) from the class A bioamine subfamily. Despite structural similarity, they respond to distinct ligands through distinct downstream pathways, whose dysregulation is linked to depression, bipolar disorder, addiction, and psychosis. They are important drug targets, and it is important to understand how their bias toward G-protein versus β-arrestin signaling pathways is regulated. Previously, evolution-based computational approaches, difference Evolutionary Trace and Evolutionary Trace-Mutual information (ET-Mip), revealed residues and residue pairs that, when switched in the D2 receptor to the corresponding residues from 5-HT2A, altered ligand potency and G-protein activation efficiency. We have tested these residue swaps for their ability to trigger recruitment of β-arrestin2 in response to dopamine or serotonin. The results reveal that the selected residues modulate agonist potency, maximal efficacy, and constitutive activity of β-arrestin2 recruitment. Whereas dopamine potency for most variants was similar to that for WT and lower than for G-protein activation, potency in β-arrestin2 recruitment for N124H^3.42 was more than 5-fold higher. T205M^5.54 displayed high constitutive activity, enhanced dopamine potency, and enhanced efficacy in β-arrestin2 recruitment relative to WT, and L379F^6.41 was virtually inactive. These striking differences from WT activity were largely reversed by a compensating mutation (T205M^5.54/L379F^6.41) at residues previously identified by ET-Mip as functionally coupled. The observation that the signs and relative magnitudes of the effects of mutations in several cases are at odds with their effects on G-protein activation suggests that they also modulate signaling bias.

Predicting protein interface residues using easily accessible on-line resources

It has been more than a decade since the completion of the Human Genome Project that provided us with a complete list of human proteins. The next obvious task is to figure out how various parts interact with each other. On that account, we review 10 methods for protein interface prediction, which are freely available as web servers. In addition, we comparatively evaluate their performance on a common data set comprising different quality target structures. We find that using experimental structures and high-quality homology models, structure-based methods outperform those using only protein sequences, with global template-based approaches providing the best performance. For moderate-quality models, sequence-based methods often perform better than those structure-based techniques that rely on fine atomic details. We note that post-processing protocols implemented in several methods quantitatively improve the results only for experimental structures, suggesting that these procedures should be tuned up for computer-generated models. Finally, we anticipate that advanced meta-prediction protocols are likely to enhance interface residue prediction. Notwithstanding further improvements, easily accessible web servers already provide the scientific community with convenient resources for the identification of protein-protein interaction sites.

Determinants of endogenous ligand specificity divergence among metabotropic glutamate receptors

To determine the structural origins of diverse ligand response specificities among metabotropic glutamate receptors (mGluRs), we combined computational approaches with mutagenesis and ligand response assays to identify specificity-determining residues in the group I receptor, mGluR1, and the group III receptors, mGluR4 and mGluR7. Among these, mGluR1 responds to L-glutamate effectively, whereas it binds weakly to another endogenous ligand, L-serine-O-phosphate (L-SOP), which antagonizes the effects of L-glutamate. In contrast, mGluR4 has in common with other group III mGluR that it is activated with higher potency and efficacy by L-SOP. mGluR7 differs from mGluR4 and other group III mGluR in that L-glutamate and L-SOP activate it with low potency and efficacy. Enhanced versions of the evolutionary trace (ET) algorithm were used to identify residues that when swapped between mGluR1 and mGluR4 increased the potency of L-SOP inhibition relative to the potency of L-glutamate activation in mGluR1 mutants and others that diminished the potency/efficacy of L-SOP for mGluR4 mutants. In addition, combining ET identified swaps from mGluR4 with one identified by computational docking produced mGluR7 mutants that respond with dramatically enhanced potency/efficacy to L-SOP. These results reveal that an early functional divergence between group I/II and group III involved variation at positions primarily at allosteric sites located outside of binding pockets, whereas a later divergence within group III occurred through sequence variation both at the ligand-binding pocket and at loops near the dimerization interface and interlobe hinge region. They also demonstrate the power of ET for identifying allosteric determinants of evolutionary importance.

Prediction and redesign of protein-protein interactions

Understanding the molecular basis of protein function remains a central goal of biology, with the hope to elucidate the role of human genes in health and in disease, and to rationally design therapies through targeted molecular perturbations. We review here some of the computational techniques and resources available for characterizing a critical aspect of protein function - those mediated by protein-protein interactions (PPI). We describe several applications and recent successes of the Evolutionary Trace (ET) in identifying molecular events and shapes that underlie protein function and specificity in both eukaryotes and prokaryotes. ET is a part of analytical approaches based on the successes and failures of evolution that enable the rational control of PPI.

Prediction and experimental validation of enzyme substrate specificity in protein structures

Structural Genomics aims to elucidate protein structures to identify their functions. Unfortunately, the variation of just a few residues can be enough to alter activity or binding specificity and limit the functional resolution of annotations based on sequence and structure; in enzymes, substrates are especially difficult to predict. Here, large-scale controls and direct experiments show that the local similarity of five or six residues selected because they are evolutionarily important and on the protein surface can suffice to identify an enzyme activity and substrate. A motif of five residues predicted that a previously uncharacterized Silicibacter sp. protein was a carboxylesterase for short fatty acyl chains, similar to hormone-sensitive-lipase-like proteins that share less than 20% sequence identity. Assays and directed mutations confirmed this activity and showed that the motif was essential for catalysis and substrate specificity. We conclude that evolutionary and structural information may be combined on a Structural Genomics scale to create motifs of mixed catalytic and noncatalytic residues that identify enzyme activity and substrate specificity.

Accounting for epistatic interactions improves the functional analysis of protein structures

Motivation: The constraints under which sequence, structure and function coevolve are not fully understood. Bringing this mutual relationship to light can reveal the molecular basis of binding, catalysis and allostery, thereby identifying function and rationally guiding protein redesign. Underlying these relationships are the epistatic interactions that occur when the consequences of a mutation to a protein are determined by the genetic background in which it occurs. Based on prior data, we hypothesize that epistatic forces operate most strongly between residues nearby in the structure, resulting in smooth evolutionary importance across the structure.

Methods and Results: We find that when residue scores of evolutionary importance are distributed smoothly between nearby residues, functional site prediction accuracy improves. Accordingly, we designed a novel measure of evolutionary importance that focuses on the interaction between pairs of structurally neighboring residues. This measure that we term pair-interaction Evolutionary Trace yields greater functional site overlap and better structure-based proteome-wide functional predictions.

Conclusions: Our data show that the structural smoothness of evolutionary importance is a fundamental feature of the coevolution of sequence, structure and function. Mutations operate on individual residues, but selective pressure depends in part on the extent to which a mutation perturbs interactions with neighboring residues. In practice, this principle led us to redefine the importance of a residue in terms of the importance of its epistatic interactions with neighbors, yielding better annotation of functional residues, motivating experimental validation of a novel functional site in LexA and refining protein function prediction.

Contact:lichtarge@bcm.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Evolutionary trace for prediction and redesign of protein functional sites

The evolutionary trace (ET) is the single most validated approach to identify protein functional determinants and to target mutational analysis, protein engineering and drug design to the most relevant sites of a protein. It applies to the entire proteome; its predictions come with a reliability score; and its results typically reach significance in most protein families with 20 or more sequence homologs. In order to identify functional hot spots, ET scans a multiple sequence alignment for residue variations that correlate with major evolutionary divergences. In case studies this enables the selective separation, recoding, or mimicry of functional sites and, on a large scale, this enables specific function predictions based on motifs built from select ET-identified residues. ET is therefore an accurate, scalable and efficient method to identify the molecular determinants of protein function and to direct their rational perturbation for therapeutic purposes. Public ET servers are located at: http://mammoth.bcm.tmc.edu/.

Separation of recombination and SOS response in Escherichia coli RecA suggests LexA interaction sites

RecA plays a key role in homologous recombination, the induction of the DNA damage response through LexA cleavage and the activity of error-prone polymerase in Escherichia coli. RecA interacts with multiple partners to achieve this pleiotropic role, but the structural location and sequence determinants involved in these multiple interactions remain mostly unknown. Here, in a first application to prokaryotes, Evolutionary Trace (ET) analysis identifies clusters of evolutionarily important surface amino acids involved in RecA functions. Some of these clusters match the known ATP binding, DNA binding, and RecA-RecA homo-dimerization sites, but others are novel. Mutation analysis at these sites disrupted either recombination or LexA cleavage. This highlights distinct functional sites specific for recombination and DNA damage response induction. Finally, our analysis reveals a composite site for LexA binding and cleavage, which is formed only on the active RecA filament. These new sites can provide new drug targets to modulate one or more RecA functions, with the potential to address the problem of evolution of antibiotic resistance at its root.

In eubacteria, genome integrity is in large part orchestrated by RecA, which directly participates in recombination, induction of DNA damage response through LexA repressor cleavage and error-prone DNA synthesis. Yet, most of the interaction sites necessary for these vital processes are largely unknown. By comparing divergences among RecA sequences and computing putative functional regions, we discovered four functional sites of RecA. Targeted point-mutations were then tested for both recombination and DNA damage induction and reveal distinct RecA functions at each one of these sites. In particular, one new set of mutants is deficient in promoting LexA cleavage and yet maintains the ability to induce the DNA damage response. These results reveal specific amino acid determinants of the RecA–LexA interaction and suggest that LexA binds RecA_i and RecA_i+6 at a composite site on the RecA filament, which could explain the role of the active filament during LexA cleavage.

Regulators of G-protein signaling and their Gα substrates: promises and challenges in their use as drug discovery targets

Because G-protein coupled receptors (GPCRs) continue to represent excellent targets for the discovery and development of small-molecule therapeutics, it is posited that additional protein components of the signal transduction pathways emanating from activated GPCRs themselves are attractive as drug discovery targets. This review considers the drug discovery potential of two such components: members of the "regulators of G-protein signaling" (RGS protein) superfamily, as well as their substrates, the heterotrimeric G-protein α subunits. Highlighted are recent advances, stemming from mouse knockout studies and the use of "RGS-insensitivity" and fast-hydrolysis mutations to Gα, in our understanding of how RGS proteins selectively act in (patho)physiologic conditions controlled by GPCR signaling and how they act on the nucleotide cycling of heterotrimeric G-proteins in shaping the kinetics and sensitivity of GPCR signaling. Progress is documented regarding recent activities along the path to devising screening assays and chemical probes for the RGS protein target, not only in pursuits of inhibitors of RGS domain-mediated acceleration of Gα GTP hydrolysis but also to embrace the potential of finding allosteric activators of this RGS protein action. The review concludes in considering the Gα subunit itself as a drug target, as brought to focus by recent reports of activating mutations to GNAQ and GNA11 in ocular (uveal) melanoma. We consider the likelihood of several strategies for antagonizing the function of these oncogene alleles and their gene products, including the use of RGS proteins with Gα(q) selectivity.

Integrating energy calculations with functional assays to decipher the specificity of G protein-RGS protein interactions

The diverse Regulator of G protein Signaling (RGS) family sets the timing of G protein signaling. To understand how the structure of RGS proteins determines their common ability to inactivate G proteins and their selective G protein recognition, we combined structure-based energy calculations with biochemical measurements of RGS activity. We found a previously unidentified group of variable 'Modulatory' residues that reside at the periphery of the RGS domain-G protein interface and fine-tune G protein recognition. Mutations of Modulatory residues in high-activity RGS proteins impaired RGS function, whereas redesign of low-activity RGS proteins in critical Modulatory positions yielded complete gain of function. Therefore, RGS proteins combine a conserved core interface with peripheral Modulatory residues to selectively optimize G protein recognition and inactivation. Finally, we show that our approach can be extended to analyze interaction specificity across other large protein families.

N-terminal half of the cGMP phosphodiesterase gamma-subunit contributes to stabilization of the GTPase-accelerating protein complex

In the visual signal terminating transition state, the cyclic GMP phosphodiesterase (PDE6) inhibitory γ-subunit (PDEγ) stimulates GTPase activity of the α-subunit of transducin (αt) by enhancing the interaction between αt and its regulator of G protein signaling (RGS9), which is constitutively bound to the type 5 G protein β-subunit (β5). Although it is known from a crystal structure of partial molecules that the PDEγ C terminus contacts with both αt and RGS9, contributions from the intrinsically disordered PDEγ N-terminal half remain unclear. In this study, we were able to investigate this issue using a photolabel transfer strategy that allows for mapping the interface of full-length proteins. We observed label transfer from PDEγ N-terminal positions 50, 30, and 16 to RGS9·β5 in the GTPase-accelerating protein (GAP) complex composed of PDEγ·αt·RGS9·β5. In support of a direct PDEγ N-terminal interaction with RGS9·β5, the PDEγ N-terminal peptide PDEγ(1-61) abolished label transfer to RGS9·β5, and another N-terminal peptide, PDEγ(10-30), disassembled the GAP complex in label transfer and pulldown experiments. Furthermore, we determined that the PDEγ C-terminal interaction with αt was enhanced whereas the N-terminal interaction was weakened upon changing the αt conformation from the signaling state to the transition state. This "rearrangement" of PDEγ domain interactions with αt appears to facilitate the interaction of the PDEγ N-terminal half with RGS9·β5 and hence its contribution to optimal stabilization of the GAP complex.

Accurate protein structure annotation through competitive diffusion of enzymatic functions over a network of local evolutionary similarities

High-throughput Structural Genomics yields many new protein structures without known molecular function. This study aims to uncover these missing annotations by globally comparing select functional residues across the structural proteome. First, Evolutionary Trace Annotation, or ETA, identifies which proteins have local evolutionary and structural features in common; next, these proteins are linked together into a proteomic network of ETA similarities; then, starting from proteins with known functions, competing functional labels diffuse link-by-link over the entire network. Every node is thus assigned a likelihood z-score for every function, and the most significant one at each node wins and defines its annotation. In high-throughput controls, this competitive diffusion process recovered enzyme activity annotations with 99% and 97% accuracy at half-coverage for the third and fourth Enzyme Commission (EC) levels, respectively. This corresponds to false positive rates 4-fold lower than nearest-neighbor and 5-fold lower than sequence-based annotations. In practice, experimental validation of the predicted carboxylesterase activity in a protein from Staphylococcus aureus illustrated the effectiveness of this approach in the context of an increasingly drug-resistant microbe. This study further links molecular function to a small number of evolutionarily important residues recognizable by Evolutionary Tracing and it points to the specificity and sensitivity of functional annotation by competitive global network diffusion. A web server is at http://mammoth.bcm.tmc.edu/networks.

Sequence and structure continuity of evolutionary importance improves protein functional site discovery and annotation

Protein functional sites control most biological processes and are important targets for drug design and protein engineering. To characterize them, the evolutionary trace (ET) ranks the relative importance of residues according to their evolutionary variations. Generally, top-ranked residues cluster spatially to define evolutionary hotspots that predict functional sites in structures. Here, various functions that measure the physical continuity of ET ranks among neighboring residues in the structure, or in the sequence, are shown to inform sequence selection and to improve functional site resolution. This is shown first, in 110 proteins, for which the overlap between top-ranked residues and actual functional sites rose by 8% in significance. Then, on a structural proteomic scale, optimized ET led to better 3D structure-function motifs (3D templates) and, in turn, to enzyme function prediction by the Evolutionary Trace Annotation (ETA) method with better sensitivity of (40% to 53%) and positive predictive value (93% to 94%). This suggests that the similarity of evolutionary importance among neighboring residues in the sequence and in the structure is a universal feature of protein evolution. In practice, this yields a tool for optimizing sequence selections for comparative analysis and, via ET, for better predictions of functional site and function. This should prove useful for the efficient mutational redesign of protein function and for pharmaceutical targeting.

Evolution: a guide to perturb protein function and networks

Protein interactions give rise to networks that control cell fate in health and disease; selective means to probe these interactions are therefore of wide interest. We discuss here Evolutionary Tracing (ET), a comparative method to identify protein functional sites and to guide experiments that selectively block, recode, or mimic their amino acid determinants. These studies suggest, in principle, a scalable approach to perturb individual links in protein networks.

An angiotensin II type 1 receptor activation switch patch revealed through evolutionary trace analysis

Seven transmembrane (7TM) or G protein-coupled receptors constitute a large superfamily of cell surface receptors sharing a structural motif of seven transmembrane spanning alpha helices. Their activation mechanism most likely involves concerted movements of the transmembrane helices, but remains to be completely resolved. Evolutionary Trace (ET) analysis is a computational method, which identifies clusters of functionally important residues by integrating information on evolutionary important residue variations with receptor structure. Combined with known mutational data, ET predicted a patch of residues in the cytoplasmic parts of TM2, TM3, and TM6 to form an activation switch that is common to all family A 7TM receptors. We tested this hypothesis in the rat Angiotensin II (Ang II) type 1a (AT1a) receptor. The receptor has important roles in the cardiovascular system, but has also frequently been applied as a model for 7TM receptor activation and signaling. Six mutations: F66A, L67R, L70R, L119R, D125A, and I245F were targeted to the putative switch and assayed for changes in activation state by their ligand binding, signaling, and trafficking properties. All but one receptor mutant (that was not expressed well) displayed phenotypes associated with changed activation state, such as increased agonist affinity or basal activity, promiscuous activation, or constitutive internalization highlighting the importance of testing different signaling pathways. We conclude that this evolutionary important patch mediates interactions important for maintaining the inactive state. More broadly, these observations in the AT1 receptor are consistent with computational predictions of a generic role for this patch in 7TM receptor activation.

Identification of functionally important residues/domains in membrane proteins using an evolutionary approach coupled with systematic mutational analysis

Structure-function studies of membrane proteins present a unique challenge to researchers due to the numerous technical difficulties associated with their expression, purification and structural characterization. In the absence of structural information, rational identification of putative functionally important residues/regions is difficult. Phylogenetic relationships could provide valuable information about the functional significance of a particular residue or region of a membrane protein. Evolutionary Trace (ET) analysis is a method developed to utilize this phylogenetic information to predict functional sites in proteins. In this method, residues are ranked according to conservation or divergence through evolution, based on the hypothesis that mutations at key positions should coincide with functional evolutionary divergences. This information can be used as the basis for a systematic mutational analysis of identified residues, leading to the identification of functionally important residues and/or domains in membrane proteins, in the absence of structural information apart from the primary amino acid sequence. This approach is potentially useful in the context of the auditory system, as several key processes in audition involve the action of membrane proteins, many of which are novel and not well characterized structurally or functionally to date.

Activation of leukemia-associated RhoGEF by Galpha13 with significant conformational rearrangements in the interface

The transient protein-protein interactions induced by guanine nucleotide-dependent conformational changes of G proteins play central roles in G protein-coupled receptor-mediated signaling systems. Leukemia-associated RhoGEF (LARG), a guanine nucleotide exchange factor for Rho, contains an RGS homology (RH) domain and Dbl homology/pleckstrin homology (DH/PH) domains and acts both as a GTPase-activating protein (GAP) and an effector for Galpha(13). However, the molecular mechanism of LARG activation upon Galpha(13) binding is not yet well understood. In this study, we analyzed the Galpha(13)-LARG interaction using cellular and biochemical methods, including a surface plasmon resonance (SPR) analysis. The results obtained using various LARG fragments demonstrated that active Galpha(13) interacts with LARG through the RH domain, DH/PH domains, and C-terminal region. However, an alanine substitution at the RH domain contact position in Galpha(13) resulted in a large decrease in affinity. Thermodynamic analysis revealed that binding of Galpha(13) proceeds with a large negative heat capacity change (DeltaCp degrees ), accompanied by a positive entropy change (DeltaS degrees ). These results likely indicate that the binding of Galpha(13) with the RH domain triggers conformational rearrangements between Galpha(13) and LARG burying an exposed hydrophobic surface to create a large complementary interface, which facilitates complex formation through both GAP and effector interfaces, and activates the RhoGEF. We propose that LARG activation is regulated by an induced-fit mechanism through the GAP interface of Galpha(13).

PCR differential display-based identification of regulator of G protein signaling 10 as the target gene in human colon cancer cells induced by black tea polyphenol theaflavin monogallate

We have previously reported that black tea polyphenol theaflavin monogallate (TF-2) suppressed COX-2 and induced apoptosis in human colon cancer cells [Lu, J.B., Ho, C.-T., Ghai, G., Chen, K.Y., 2000. Differential effects of theaflavin monogallates on cell growth, apoptosis and Cox-2 gene expression in cancerous versus normal cells. Cancer Res. 60, 6465-6471.]. We now extended the study by using PCR-based differential display to search for genes that were selectively induced by TF-2. Here we report the identification of Regulator of G-binding protein signaling 10 (RGS10) as the target gene, which was induced as early as 4 h after the TF-2 treatment. We then examined the effect of TF-2 on several other RGS genes and found that, in addition to RGS10, TF-2 induced the expression of RGS14, but not RGS4. Other tea polyphenols, including theaflavin-3,3'-digallate (TF-3) and (-) epigallocatechin-3-gallate (EGCG), also induced the expression of RGS10 and RGS14, but not RGS4. However, theaflavin (TF-1), which does not contain the gallate moiety, was ineffective. These results showed for the first time that tea polyphenols can induce the expression of selective RGS genes and that the gallate moiety may be important in this induction. In view of the role of RGS in modulating G-protein mediated signal transduction pathways, our findings may be significant since dysregulation of G-signaling is prominently implicated in carcinogenesis.

Predicting DNA-binding amino acid residues from electrostatic stabilization upon mutation to Asp/Glu and evolutionary conservation

Binding of polyanionic DNA depends on the cluster of electropositive atoms in the binding site of a DNA-binding protein. Such a cluster of electropositive protein atoms would be electrostatically unfavorable without stabilizing interactions from the respective electronegative DNA atoms and would likely be evolutionary conserved due to its critical biological role. Consequently, our strategy for predicting DNA-binding residues is based on detecting a cluster of evolutionary conserved surface residues that are electrostatically stabilized upon mutation to negatively charged Asp/Glu residues. The method requires as input the protein structure and sufficient sequence homologs to define each residue's relative conservation, and it yields as output experimentally testable residues that are predicted to bind DNA. By incorporating characteristic DNA-binding site features (i.e., electrostatic strain and amino acid conservation), the new method yields a prediction accuracy of 83%, which is much higher than methods based on only electrostatic strain (57%) or conservation alone (50%). It is also less sensitive to protein conformational changes upon DNA binding than methods that mainly depend on the 3D protein structure.

Character and evolution of protein-protein interfaces

Protein-protein interactions create the macromolecular assemblies and sequential signaling pathways essential for cell function. Their number far exceeds the number of proteins themselves and their experimental characterization, while improving, remains relatively slow. For these reasons, novel computational methods have important roles to play in understanding the physical basis of protein interactions, and in constraining the molecular basis of their specificity. This paper discusses methods based on multiple sequence alignments of protein homologues and phylogenetic trees.

Sumoylated RGS-Rz proteins act as scaffolds for Mu-opioid receptors and G-protein complexes in mouse brain

The RGSZ1 and RGSZ2 proteins, members of the RGS-Rz subfamily of GTPase-activating proteins (GAP), are involved in Mu-opioid receptor desensitization. The expression of these proteins, as well as of their main target the Gz protein, is virtually restricted to the nervous tissue. In synaptosomal membranes, these Rz proteins undergo post-translational modifications such as glycosylation and phosphorylation, and they may covalently attach to small ubiquitin-like modifier (SUMO) proteins. While RGSZ1 exists in conjugated and non-conjugated forms, RGSZ2 is mostly conjugated to SUMO-1, SUMO-2 and SUMO-3 proteins. These sumoylated forms of the GAPs readily associated with Mu-opioid receptors but they associated only poorly with Delta receptors. Furthermore, G alpha i2 and G alpha z subunits co-precipitated with the sumoylated forms of RGSZ1/Z2 proteins, but to a lesser extent with the Ser phosphorylated SUMO-free form of RGSZ1. Upon Mu-opioid receptor activation, there is a strong increase in the association of G alpha proteins with RGSZ2 proteins that persists for intervals longer than 24 h. This effect probably accounts for their role in Mu-opioid receptor desensitization. Only a moderate increase was observed with RGSZ1, the non-sumoylated form of which probably acts as an efficient GAP for these G alpha subunits. Therefore, sumoylation regulates the biological activity of RGS-Rz proteins and it is likely that it serves to switch their behavior, from that of a GAP for activated G alpha subunits to that of a scaffold protein for specific signaling proteins.

Structural basis of effector regulation and signal termination in heterotrimeric Galpha proteins

This chapter addresses, from a molecular structural perspective gained from examination of x-ray crystallographic and biochemical data, the mechanisms by which GTP-bound Galpha subunits of heterotrimeric G proteins recognize and regulate effectors. The mechanism of GTP hydrolysis by Galpha and rate acceleration by GAPs are also considered. The effector recognition site in all Galpha homologues is formed almost entirely of the residues extending from the C-terminal half of alpha2 (Switch II) together with the alpha3 helix and its junction with the beta5 strand. Effector binding does not induce substantial changes in the structure of Galpha*GTP. Effectors are structurally diverse. Different effectors may recognize distinct subsets of effector-binding residues of the same Galpha protein. Specificity may also be conferred by differences in the main chain conformation of effector-binding regions of Galpha subunits. Several Galpha regulatory mechanisms are operative. In the regulation of GMP phospodiesterase, Galphat sequesters an inhibitory subunit. Galphas is an allosteric activator and inhibitor of adenylyl cyclase, and Galphai is an allosteric inhibitor. Galphaq does not appear to regulate GRK, but is rather sequestered by it. GTP hydrolysis terminates the signaling state of Galpha. The binding energy of GTP that is used to stabilize the Galpha:effector complex is dissipated in this reaction. Chemical steps of GTP hydrolysis, specifically, formation of a dissociative transition state, is rate limiting in Ras, a model G protein GTPase, even in the presence of a GAP; however, the energy of enzyme reorganization to produce a catalytically active conformation appears to be substantial. It is possible that the collapse of the switch regions, associated with Galpha deactivation, also encounters a kinetic barrier, and is coupled to product (Pi) release or an event preceding formation of the GDP*Pi complex. Evidence for a catalytic intermediate, possibly metaphosphate, is discussed. Galpha GAPs, whether exogenous proteins or effector-linked domains, bind to a discrete locus of Galpha that is composed of Switch I and the N-terminus of Switch II. This site is immediately adjacent to, but does not substantially overlap, the Galpha effector binding site. Interactions of effectors and exogenous GAPs with Galpha proteins can be synergistic or antagonistic, mediated by allosteric interactions among the three molecules. Unlike GAPs for small GTPases, Galpha GAPs supply no catalytic residues, but rather appear to reduce the activation energy for catalytic activation of the Galpha catalytic site.

Essential helix interactions in the anion transporter domain of prestin revealed by evolutionary trace analysis

Prestin, a member of the SLC26A family of anion transporters, is a polytopic membrane protein found in outer hair cells (OHCs) of the mammalian cochlea. Prestin is an essential component of the membrane-based motor that enhances electromotility of OHCs and contributes to frequency sensitivity and selectivity in mammalian hearing. Mammalian cells expressing prestin display a nonlinear capacitance (NLC), widely accepted as the electrical signature of electromotility. The associated charge movement requires intracellular anions reflecting the membership of prestin in the SLC26A family. We used the computational approach of evolutionary trace analysis to identify candidate functional (trace) residues in prestin for mutational studies. We created a panel of mutations at each trace residue and determined membrane expression and nonlinear capacitance associated with each mutant. We observe that several residue substitutions near the conserved sulfate transporter domain of prestin either greatly reduce or eliminate NLC, and the effect is dependent on the size of the substituted residue. These data suggest that packing of helices and interactions between residues surrounding the "sulfate transporter motif" is essential for normal prestin activity.

How regulators of G protein signaling achieve selective regulation

The regulators of G protein signaling (RGS) are a family of cellular proteins that play an essential regulatory role in G protein-mediated signal transduction. There are multiple RGS subfamilies consisting of over 20 different RGS proteins. They are basically the guanosine triphosphatase (GTPase)-accelerating proteins that specifically interact with G protein alpha subunits. RGS proteins display remarkable selectivity and specificity in their regulation of receptors, ion channels, and other G protein-mediated physiological events. The molecular and cellular mechanisms underlying such selectivity are complex and cooperate at many different levels. Recent research data have provided strong evidence that the spatiotemporal-specific expression of RGS proteins and their target components, as well as the specific protein-protein recognition and interaction through their characteristic structural domains and functional motifs, are determinants for RGS selectivity and specificity. Other molecular mechanisms, such as alternative splicing and scaffold proteins, also significantly contribute to RGS selectivity. To pursue a thorough understanding of the mechanisms of RGS selective regulation will be of great significance for the advancement of our knowledge of molecular and cellular signal transduction.

Rank information: a structure-independent measure of evolutionary trace quality that improves identification of protein functional sites

Protein functional sites are key targets for drug design and protein engineering, but their large-scale experimental characterization remains difficult. The evolutionary trace (ET) is a computational approach to this problem that has been useful in a variety of case studies, but its proteomic scale application is partially hindered because automated retrieval of input sequences from databases often includes some with errors that degrade functional site identification. To recognize and purge these sequences, this study introduces a novel and structure-free measure of ET quality called rank information (RI). It is shown that RI decreases in response to errors in sequences, alignments, or functional classifications. Conversely, an automated procedure to increase RI by selectively removing sequences improves functional site identification so as to nearly match manually curated traces in kinases and in a test set of 79 diverse proteins. Thus we conclude that RI partially reflects the evolutionary consistency of sequence, structure, and function. In practice, as the size of the proteome continues to grow exponentially, it provides a novel and structure-free measure of ET quality that increases its accuracy for large-scale automated annotation of protein functional sites.

Evolutionary and structural feedback on selection of sequences for comparative analysis of proteins

It has been noted that slowly evolving protein residues have two properties: (a) they tend to cluster in the native fold, and (b) they delineate functional surfaces-parts of the surface through which the protein interacts with other proteins or small ligands. Herein, we demonstrate that the two are coupled sufficiently strongly that one effect, when observed, statistically implies the other. Detection of both can be accomplished in multiple sequence alignment related methods by the careful selection of relevant sequences. For the demonstration, we use two sets of protein families: a small set of diverse proteins with diverse functional surfaces, and a large set of homodimerizing enzymes. A practical outcome of our considerations is a simple prescriptive rule for the selection of homologous sequences for the comparative analysis of proteins: in order to optimize the detection of (potentially unknown) functional surfaces, it is sufficient to select sequences in such a way that the residues observed at any level of evolutionary divergence, as implied by the alignment, cluster on the folded protein.

Recurrent use of evolutionary importance for functional annotation of proteins based on local structural similarity

The annotation of protein function has not kept pace with the exponential growth of raw sequence and structure data. An emerging solution to this problem is to identify 3D motifs or templates in protein structures that are necessary and sufficient determinants of function. Here, we demonstrate the recurrent use of evolutionary trace information to construct such 3D templates for enzymes, search for them in other structures, and distinguish true from spurious matches. Serine protease templates built from evolutionarily important residues distinguish between proteases and other proteins nearly as well as the classic Ser-His-Asp catalytic triad. In 53 enzymes spanning 33 distinct functions, an automated pipeline identifies functionally related proteins with an average positive predictive power of 62%, including correct matches to proteins with the same function but with low sequence identity (the average identity for some templates is only 17%). Although these template building, searching, and match classification strategies are not yet optimized, their sequential implementation demonstrates a functional annotation pipeline which does not require experimental information, but only local molecular mimicry among a small number of evolutionarily important residues.

Prediction of functional specificity determinants from protein sequences using log-likelihood ratios

MOTIVATION

A number of methods have been developed to predict functional specificity determinants in protein families based on sequence information. Most of these methods rely on pre-defined functional subgroups. Manual subgroup definition is difficult because of the limited number of experimentally characterized subfamilies with differing specificity, while automatic subgroup partitioning using computational tools is a non-trivial task and does not always yield ideal results.

RESULTS

We propose a new approach SPEL (specificity positions by evolutionary likelihood) to detect positions that are likely to be functional specificity determinants. SPEL, which does not require subgroup definition, takes a multiple sequence alignment of a protein family as the only input, and assigns a P-value to every position in the alignment. Positions with low P-values are likely to be important for functional specificity. An evolutionary tree is reconstructed during the calculation, and P-value estimation is based on a random model that involves evolutionary simulations. Evolutionary log-likelihood is chosen as a measure of amino acid distribution at a position. To illustrate the performance of the method, we carried out a detailed analysis of two protein families (LacI/PurR and G protein alpha subunit), and compared our method with two existing methods (evolutionary trace and mutual information based). All three methods were also compared on a set of protein families with known ligand-bound structures.

AVAILABILITY

SPEL is freely available for non-commercial use. Its pre-compiled versions for several platforms and alignments used in this work are available at ftp://iole.swmed.edu/pub/SPEL/

Correlated evolutionary pressure at interacting transcription factors and DNA response elements can guide the rational engineering of DNA binding specificity

Understanding the molecular mechanisms of the specific interaction between transcription factor proteins and DNA is key to comprehend the regulation of gene expression and to develop technologies to engineer transcription factors. Thus far, although there have been several attempts to elucidate protein-DNA interaction through amino acid-base recognition codes, sequence based profiles, or physical models of interaction, the greatest successes in engineering DNA binding specificity remain experimental. Here we present the first systematic evidence of correlated evolutionary pressure at interacting amino acid residues and DNA base-pairs in transcription factors, and show that it can be used to rationally engineer DNA binding specificity. The correlation is between the relative evolutionary importance of protein residues and DNA bases, measured, respectively, in terms of the Evolutionary Trace (ET) rank and information entropy. The evolutionarily most important residues interact with the most conserved base-pairs within the response element while residues of least importance interact with the most variable base-pairs. The correlation averages 0.74 over 12 unrelated families of transcriptional regulators, including nuclear hormone receptors, basic helix-loop-helix, ETS- and homeo-domain family. To test the predictive power of this correlation, we targeted a mutational swap of top-ranked ET residues in a transcription factor, LRH-1. This redirects LRH-1 binding as predicted and showed that, in this case, evolutionary importance and binding specificity are coupled sufficiently strongly for the Evolutionary Trace to guide the computational design of DNA binding specificity. This establishes the existence of evolutionary importance correlation at protein-DNA interfaces, and demonstrates that it is a useful principle for the rational engineering of binding specificity.

Rod and cone opsin families differ in spectral tuning domains but not signal transducing domains as judged by saturated evolutionary trace analysis

The visual receptor of rods and cones is a covalent complex of the apoprotein, opsin, and the light-sensitive chromophore, 11-cis-retinal. This pigment must fulfill many functions including photoactivation, spectral tuning, signal transmission, inactivation, and chromophore regeneration. Rod and cone photoreceptors employ distinct families of opsins. Although it is well known that these opsin families provide unique ranges in spectral sensitivity, it is unclear whether the families have additional functional differences. In this study, we use evolutionary trace (ET) analysis of 188 vertebrate opsin sequences to identify functionally important sites in each opsin family. We demonstrate the following results. (1) The available vertebrate opsin sequences produce a definitive description of all five vertebrate opsin families. This is the first demonstration of sequence saturation prior to ET analysis, which we term saturated ET (SET). (2) The cone opsin classes have class-specific sites compared to the rod opsin class. These sites reside in the transmembrane region and tune the spectral sensitivity of each opsin class to its characteristic wavelength range. (3) The cytoplasmic loops, primarily responsible for signal transmission and inactivation, are essentially invariant in rod versus cone opsins. This indicates that the electrophysiological differences between rod and cone photoreceptors cannot be ascribed to differences in the protein interaction regions of the opsins. SET shows that chromophore binding and regeneration are the only aspects of opsin structure likely to have functionally significant differences between rods and cones, whereas excitatory and adaptational properties of the opsin families appear to be functionally invariant.

Activation of μ-Opioid Receptors Transfers Control of Gα Subunits to the Regulator of G-protein Signaling RGS9-2

In mouse periaqueductal gray matter (PAG) membranes, the mu-opioid receptor (MOR) coprecipitated the alpha-subunits of the Gi/o/z/q/11 proteins, the Gbeta1/2 subunits, and the regulator of G-protein signaling RGS9-2 and its partner protein Gbeta5. RGS7 and RGS11 present in this neural structure showed no association with MOR. In vivo intracerebroventricular injection of morphine did not alter MOR immunoreactivity, but 30 min and 3 h after administration, the coprecipitation of Galpha subunits with MORs was reduced by up to 50%. Furthermore, the association between Galpha subunits and RGS9-2 proteins was increased. Twenty-four hours after receiving intracerebroventricular morphine, the Galpha subunits left the RGS9-2 proteins and re-associated with the MORs. However, doses of the opioid able to induce tolerance promoted the stable transfer of Galpha subunits to the RGS9-2 control. This was accompanied by Ser phosphorylation of RGS9-2 proteins, which increased their co-precipitation with 14-3-3 proteins. In the PAG membranes of morphine-desensitized mice, the capacity of the opioid to stimulate G-protein-related guanosine 5'-O-(3-[35S]thiotriphosphate) binding as well as low Km GTPase activity was attenuated. The in vivo knockdown of RGS9-2 expression prevented morphine from altering the association between MORs and G-proteins, and tolerance did not develop. In PAG membranes from RGS9-2 knockdown mice, morphine showed full capacity to activate G-proteins. Thus, the tolerance that develops following an adequate dose of morphine is caused by the stabilization and retention of MOR-activated Galpha subunits by RGS9-2 proteins. This multistep process is initiated by the morphine-induced transfer of MOR-associated Galpha subunits to the RGS9-2 proteins, followed by Ser phosphorylation of the latter and their binding to 14-3-3 proteins. This regulatory mechanism probably precedes the loss of MORs from the cell membrane, which has been observed with other opioid agonists.

Evolutionary epitopes of Hsp90 and p23: implications for their interaction

The amino-terminal domain (N-domain) of Hsp90 represents the ATP binding site and is important for interaction with its cochaperone, p23. Whereas some evidence suggests that p23 may bind to this domain in an ATP-dependent manner and that this process requires the dimerization of two N-domains, the interaction sites between them and the molecular mechanism of coupling these two events to p23 binding remain unsolved. As a first step toward establishing the interaction mechanism, we used the evolutionary tracing (ET) method [Lichtarge, O., Bourne, H. R., and Cohen, F. E. (1996) J. Mol. Biol. 257, 342-358] to identify the putative functional surfaces of Hsp90 and p23, and combined with protein-protein docking techniques, to predict their binding interface. Both evolutionarily privileged surfaces of Hsp90 and p23 identified by ET appear on this putative interface. An analysis of the complex model produced using the ET results combined with available experimental data highlights a putative conformational pathway in the ATP binding domain of Hsp90, where a series of conformational changes transfer the ATP-induced N-domain dimerization signal for the binding of p23. In this pathway, the closure of "lid" may result in reorientation of the helix alpha1 and the following loop (residues 10-27 in yeast Hsp90), which will expose more hydrophobic surface, and thus triggers the dimerization of N-domain.

Accurate and scalable identification of functional sites by evolutionary tracing

A common difficulty in post genomics biology is that large-scale techniques of data collection often strip away information on the biological context of these data. The result is a massive number of disconnected observations on sequence, structure, and function from which underlying patterns and biological meaning are obscured. One solution is to build computational filters that pick out sufficiently few facts, relevant to a query, that their relationship is immediately apparent and experimentally testable. Typically, these filters rely on mathematics and statistics, and on first principles from physics and chemistry. We show here that evolution itself can be used to filter sequence and structure data in order to identify evolutionarily important amino acids. A general property of these residues is that they form clusters in native protein structures and point to regions where mutations have the greatest biological impact. The result is an accurate method of functional site annotation that is scalable for structural proteomics.

A family of evolution-entropy hybrid methods for ranking protein residues by importance

In order to identify the amino acids that determine protein structure and function it is useful to rank them by their relative importance. Previous approaches belong to two groups; those that rely on statistical inference, and those that focus on phylogenetic analysis. Here, we introduce a class of hybrid methods that combine evolutionary and entropic information from multiple sequence alignments. A detailed analysis in insulin receptor kinase domain and tests on proteins that are well-characterized experimentally show the hybrids' greater robustness with respect to the input choice of sequences, as well as improved sensitivity and specificity of prediction. This is a further step toward proteome scale analysis of protein structure and function.

Sequence, structure and energetic determinants of phosphopeptide selectivity of SH2 domains

Here, we present an approach for the prediction of binding preferences of members of a large protein family for which structural information for a number of family members bound to a substrate is available. The approach involves a number of steps. First, an accurate multiple alignment of sequences of all members of a protein family is constructed on the basis of a multiple structural superposition of family members with known structure. Second, the methods of continuum electrostatics are used to characterize the energetic contribution of each residue in a protein to the binding of its substrate. Residues that make a significant contribution are mapped onto the protein sequence and are used to define a "binding site signature" for the complex being considered. Third, sequences whose structures have not been determined are checked to see if they have binding-site signatures similar to one of the known complexes. Predictions of binding affinity to a given substrate are based on similarities in binding-site signature. An important component of the approach is the introduction of a context-specific substitution matrix suitable for comparison of binding-site residues. The methods are applied to the prediction of phosphopeptide selectivity of SH2 domains. To this end, the energetic roles of all protein residues in 17 different complexes of SH2 domains with their cognate targets are analyzed. The total number of residues that make significant contributions to binding is found to vary from nine to 19 in different complexes. These energetically important residues are found to contribute to binding through a variety of mechanisms, involving both electrostatic and hydrophobic interactions. Binding-site signatures are found to involve residues in different positions in SH2 sequences, some of them as far as 9A away from a bound peptide. Surprisingly, similarities in the signatures of different domains do not correlate with whole-domain sequence identities unless the latter is greater than 50%. An extensive comparison with the optimal binding motifs determined by peptide library experiments, as well as other experimental data indicate that the similarity in binding preferences of different SH2 domains can be deduced on the basis of their binding-site signatures. The analysis provides a rationale for the empirically derived classification of SH2 domains described by Songyang & Cantley, in that proteins in the same group are found to have similar residues at positions important for binding. Confident predictions of binding preference can be made for about 85% of SH2 domain sequences found in SWISSPROT. The approach described in this work is quite general and can, in principle, be used to analyze binding preferences of members of large protein families for which structural information for a number of family members is available. It also offers a strategy for predicting cross-reactivity of compounds designed to bind to a particular target, for example in structure-based drug design.

Evolutionary trace of G protein-coupled receptors reveals clusters of residues that determine global and class-specific functions

G protein-coupled receptor (GPCR) activation mediated by ligand-induced structural reorganization of its helices is poorly understood. To determine the universal elements of this conformational switch, we used evolutionary tracing (ET) to identify residue positions commonly important in diverse GPCRs. When mapped onto the rhodopsin structure, these trace residues cluster into a network of contacts from the retinal binding site to the G protein-coupling loops. Their roles in a generic transduction mechanism were verified by 211 of 239 published mutations that caused functional defects. When grouped according to the nature of the defects, these residues sub-divided into three striking sub-clusters: a trigger region, where mutations mostly affect ligand binding, a coupling region near the cytoplasmic interface to the G protein, where mutations affect G protein activation, and a linking core in between where mutations cause constitutive activity and other defects. Differential ET analysis of the opsin family revealed an additional set of opsin-specific residues, several of which form part of the retinal binding pocket, and are known to cause functional defects upon mutation. To test the predictive power of ET, we introduced novel mutations in bovine rhodopsin at a globally important position, Leu-79, and at an opsin-specific position, Trp-175. Both were functionally critical, causing constitutive G protein activation of the mutants and rapid loss of regeneration after photobleaching. These results define in GPCRs a canonical signal transduction mechanism where ligand binding induces conformational changes propagated through adjacent trigger, linking core, and coupling regions.

Combining inference from evolution and geometric probability in protein structure evaluation

Starting from the hypothesis that evolutionarily important residues form a spatially limited cluster in a protein's native fold, we discuss the possibility of detecting a non-native structure based on the absence of such clustering. The relevant residues are determined using the Evolutionary Trace method. We propose a quantity to measure clustering of the selected residues on the structure and show that the exact values for its average and variance over several ensembles of interest can be found. This enables us to study the behavior of the associated z-scores. Since our approach rests on an analytic result, it proves to be general, customizable, and computationally fast. We find that clustering is indeed detectable in a large representative protein set. Furthermore, we show that non-native structures tend to achieve lower residue-clustering z-scores than those attained by the native folds. The most important conclusion that we draw from this work is that consistency between structural and evolutionary information, manifested in clustering of key residues, imposes powerful constraints on the conformational space of a protein.

RGS9-1 phosphorylation and Ca2+

The duration of photoresponses in vertebrate rods and cones is controlled at the level of GTP hydrolysis by a GTPase accelerating protein (GAP) whose catalytic core is provided by RGS9-1. RGS9-1 is in turn regulated by phosphorylation on serine 475, in a reaction that is dependent on Ca2+. In living mice, the level of phosphorylation at this site is reduced by light. Thus RGS9-1 phosphorylation provides a potential mechanism by which light-regulated changes in intracellular [Ca2+] may feed back on phototransduction through effects on the lifetime of activated G protein and cGMP phosphodiesterase.

Using a neural network and spatial clustering to predict the location of active sites in enzymes

Structural genomics projects aim to provide a sharp increase in the number of structures of functionally unannotated, and largely unstudied, proteins. Algorithms and tools capable of deriving information about the nature, and location, of functional sites within a structure are increasingly useful therefore. Here, a neural network is trained to identify the catalytic residues found in enzymes, based on an analysis of the structure and sequence. The neural network output, and spatial clustering of the highly scoring residues are then used to predict the location of the active site.A comparison of the performance of differently trained neural networks is presented that shows how information from sequence and structure come together to improve the prediction accuracy of the network. Spatial clustering of the network results provides a reliable way of finding likely active sites. In over 69% of the test cases the active site is correctly predicted, and a further 25% are partially correctly predicted. The failures are generally due to the poor quality of the automatically generated sequence alignments. We also present predictions identifying the active site, and potential functional residues in five recently solved enzyme structures, not used in developing the method. The method correctly identifies the putative active site in each case. In most cases the likely functional residues are identified correctly, as well as some potentially novel functional groups.

An accurate, sensitive, and scalable method to identify functional sites in protein structures

Functional sites determine the activity and interactions of proteins and as such constitute the targets of most drugs. However, the exponential growth of sequence and structure data far exceeds the ability of experimental techniques to identify their locations and key amino acids. To fill this gap we developed a computational Evolutionary Trace method that ranks the evolutionary importance of amino acids in protein sequences. Studies show that the best-ranked residues form fewer and larger structural clusters than expected by chance and overlap with functional sites, but until now the significance of this overlap has remained qualitative. Here, we use 86 diverse protein structures, including 20 determined by the structural genomics initiative, to show that this overlap is a recurrent and statistically significant feature. An automated ET correctly identifies seven of ten functional sites by the least favorable statistical measure, and nine of ten by the most favorable one. These results quantitatively demonstrate that a large fraction of functional sites in the proteome may be accurately identified from sequence and structure. This should help focus structure-function studies, rational drug design, protein engineering, and functional annotation to the relevant regions of a protein.

Structure modeling of the chemokine receptor CCR5: implications for ligand binding and selectivity

The G-protein coupled receptor CCR5 is the main co-receptor for macrophage-tropic HIV-1 strains. I have built a structural model of the chemokine receptor CCR5 and used it to explain the binding and selectivity of the antagonist TAK779. Models of the extracellular (EC) domains of CCR5 have been constructed and used to rationalize current biological data on the binding of HIV-1 and chemokines. Residues spanning the transmembrane region of CCR5 have been modeled after rhodopsin, and their functional significance examined using the evolutionary trace method. The receptor cavity shares six residues with CC-chemokine receptors CCR1 through CCR4, while seven residues are unique to CCR5. The contribution of these residues to ligand binding and selectivity is tested by molecular docking simulations of TAK779 to CCR1, CCR2, and CCR5. TAK779 binds to CCR5 in the cavity formed by helices 1, 2, 3, and 7 with additional interactions with helices 5 and 6. TAK779 did not dock to either CCR1 or CCR2. The results are consistent with current site-directed mutagenesis data and with the observed selectivity of TAK779 for CCR5 over CCR1 and CCR2. The specific residues responsible for the observed selectivity are identified. The four EC regions of CCR5 have been modeled using constrained simulated annealing simulations. Applied dihedral angle constraints are representative of the secondary structure propensities of these regions. Tertiary interactions, in the form of distance constraints, are generated from available epitope mapping data. Analysis of the 250 simulated structures provides new insights to the design of experiments aimed at determining residue-residue contacts across the EC domains and for mapping CC-chemokines on the surface of the EC domains.

Total sequence decomposition distinguishes functional modules, "molegos" in apurinic/apyrimidinic endonucleases

BACKGROUND

Total sequence decomposition, using the web-based MASIA tool, identifies areas of conservation in aligned protein sequences. By structurally annotating these motifs, the sequence can be parsed into individual building blocks, molecular legos ("molegos"), that can eventually be related to function. Here, the approach is applied to the apurinic/apyrimidinic endonuclease (APE) DNA repair proteins, essential enzymes that have been highly conserved throughout evolution. The APEs, DNase-1 and inositol 5'-polyphosphate phosphatases (IPP) form a superfamily that catalyze metal ion based phosphorolysis, but recognize different substrates.

RESULTS

MASIA decomposition of APE yielded 12 sequence motifs, 10 of which are also structurally conserved within the family and are designated as molegos. The 12 motifs include all the residues known to be essential for DNA cleavage by APE. Five of these molegos are sequentially and structurally conserved in DNase-1 and the IPP family. Correcting the sequence alignment to match the residues at the ends of two of the molegos that are absolutely conserved in each of the three families greatly improved the local structural alignment of APEs, DNase-1 and synaptojanin. Comparing substrate/product binding of molegos common to DNase-1 showed that those distinctive for APEs are not directly involved in cleavage, but establish protein-DNA interactions 3' to the abasic site. These additional bonds enhance both specific binding to damaged DNA and the processivity of APE1.

CONCLUSION

A modular approach can improve structurally predictive alignments of homologous proteins with low sequence identity and reveal residues peripheral to the traditional "active site" that control the specificity of enzymatic activity.