A family-based approach reveals the function of residues in the nuclear receptor ligand-binding domain

Exploring Ligand Binding Domain Dynamics in the NRs Superfamily

Nuclear receptors (NRs) are transcription factors that play an important role in multiple diseases, such as cancer, inflammation, and metabolic disorders. They share a common structural organization composed of five domains, of which the ligand-binding domain (LBD) can adopt different conformations in response to substrate, agonist, and antagonist binding, leading to distinct transcription effects. A key feature of NRs is, indeed, their intrinsic dynamics that make them a challenging target in drug discovery. This work aims to provide a meaningful investigation of NR structural variability to outline a dynamic profile for each of them. To do that, we propose a methodology based on the computation and comparison of protein cavities among the crystallographic structures of NR LBDs. First, pockets were detected with the FLAPsite algorithm and then an "all against all" approach was applied by comparing each pair of pockets within the same sub-family on the basis of their similarity score. The analysis concerned all the detectable cavities in NRs, with particular attention paid to the active site pockets. This approach can guide the investigation of NR intrinsic dynamics, the selection of reference structures to be used in drug design and the easy identification of alternative binding sites.

CorNet: Assigning function to networks of co-evolving residues by automated literature mining

CorNet is a web-based tool for the analysis of co-evolving residue positions in protein super-family sequence alignments. CorNet projects external information such as mutation data extracted from literature on interactively displayed groups of co-evolving residue positions to shed light on the functions associated with these groups and the residues in them. We used CorNet to analyse six enzyme super-families and found that groups of strongly co-evolving residues tend to consist of residues involved in a same function such as activity, specificity, co-factor binding, or enantioselectivity. This finding allows to assign a function to residues for which no data is available yet in the literature. A mutant library was designed to mutate residues observed in a group of co-evolving residues predicted to be involved in enantioselectivity, but for which no literature data is available yet. The resulting set of mutations indeed showed many instances of increased enantioselectivity.

In Silico Structure and Sequence Analysis of Bacterial Porins and Specific Diffusion Channels for Hydrophilic Molecules: Conservation, Multimericity and Multifunctionality

Diffusion channels are involved in the selective uptake of nutrients and form the largest outer membrane protein (OMP) family in Gram-negative bacteria. Differences in pore size and amino acid composition contribute to the specificity. Structure-based multiple sequence alignments shed light on the structure-function relations for all eight subclasses. Entropy-variability analysis results are correlated to known structural and functional aspects, such as structural integrity, multimericity, specificity and biological niche adaptation. The high mutation rate in their surface-exposed loops is likely an important mechanism for host immune system evasion. Multiple sequence alignments for each subclass revealed conserved residue positions that are involved in substrate recognition and specificity. An analysis of monomeric protein channels revealed particular sequence patterns of amino acids that were observed in other classes at multimeric interfaces. This adds to the emerging evidence that all members of the family exist in a multimeric state. Our findings are important for understanding the role of members of this family in a wide range of bacterial processes, including bacterial food uptake, survival and adaptation mechanisms.

Development of a machine learning method to predict membrane protein-ligand binding residues using basic sequence information

Locating ligand binding sites and finding the functionally important residues from protein sequences as well as structures became one of the challenges in understanding their function. Hence a Naïve Bayes classifier has been trained to predict whether a given amino acid residue in membrane protein sequence is a ligand binding residue or not using only sequence based information. The input to the classifier consists of the features of the target residue and two sequence neighbors on each side of the target residue. The classifier is trained and evaluated on a nonredundant set of 42 sequences (chains with at least one transmembrane domain) from 31 alpha-helical membrane proteins. The classifier achieves an overall accuracy of 70.7% with 72.5% specificity and 61.1% sensitivity in identifying ligand binding residues from sequence. The classifier performs better when the sequence is encoded by psi-blast generated PSSM profiles. Assessment of the predictions in the context of three-dimensional structures of proteins reveals the effectiveness of this method in identifying ligand binding sites from sequence information. In 83.3% (35 out of 42) of the proteins, the classifier identifies the ligand binding sites by correctly recognizing more than half of the binding residues. This will be useful to protein engineers in exploiting potential residues for functional assessment.

NRfamPred: a proteome-scale two level method for prediction of nuclear receptor proteins and their sub-families

Nuclear receptor proteins (NRP) are transcription factor that regulate many vital cellular processes in animal cells. NRPs form a super-family of phylogenetically related proteins and divided into different sub-families on the basis of ligand characteristics and their functions. In the post-genomic era, when new proteins are being added to the database in a high-throughput mode, it becomes imperative to identify new NRPs using information from amino acid sequence alone. In this study we report a SVM based two level prediction systems, NRfamPred, using dipeptide composition of proteins as input. At the 1st level, NRfamPred screens whether the query protein is NRP or non-NRP; if the query protein belongs to NRP class, prediction moves to 2nd level and predicts the sub-family. Using leave-one-out cross-validation, we were able to achieve an overall accuracy of 97.88% at the 1st level and an overall accuracy of 98.11% at the 2nd level with dipeptide composition. Benchmarking on independent datasets showed that NRfamPred had comparable accuracy to other existing methods, developed on the same dataset. Our method predicted the existence of 76 NRPs in the human proteome, out of which 14 are novel NRPs. NRfamPred also predicted the sub-families of these 14 NRPs.

A series of PDB-related databanks for everyday needs

We present a series of databanks (http://swift.cmbi.ru.nl/gv/facilities/) that hold information that is computationally derived from Protein Data Bank (PDB) entries and that might augment macromolecular structure studies. These derived databanks run parallel to the PDB, i.e. they have one entry per PDB entry. Several of the well-established databanks such as HSSP, PDBREPORT and PDB_REDO have been updated and/or improved. The software that creates the DSSP databank, for example, has been rewritten to better cope with π-helices. A large number of databanks have been added to aid computational structural biology; some examples are lists of residues that make crystal contacts, lists of contacting residues using a series of contact definitions or lists of residue accessibilities. PDB files are not the optimal presentation of the underlying data for many studies. We therefore made a series of databanks that hold PDB files in an easier to use or more consistent representation. The BDB databank holds X-ray PDB files with consistently represented B-factors. We also added several visualization tools to aid the users of our databanks.

A conserved surface on the ligand binding domain of nuclear receptors for allosteric control

Nuclear receptors (NRs) form a large superfamily of transcription factors that participate in virtually every key biological process. They control development, fertility, gametogenesis and are misregulated in many cancers. Their enormous functional plasticity as transcription factors relates in part to NR-mediated interactions with hundreds of coregulatory proteins upon ligand (e.g., hormone) binding to their ligand binding domains (LBD), or following covalent modification. Some coregulator association relates to the distinct residues that shape a coactivator binding pocket termed AF-2, a surface groove that primarily determines the preference and specificity of protein-protein interactions. However, the highly conserved AF-2 pocket in the NR superfamily appears to be insufficient to account for NR subtype specificity leading to fine transcriptional modulation in certain settings. Additional protein-protein interaction surfaces, most notably on their LBD, may contribute to modulating NR function. NR coregulators and chaperones, normally much larger than the NR itself, may also bind to such interfaces. In the case of the androgen receptor (AR) LBD surface, structural and functional data highlighted the presence of another site named BF-3, which lies at a distinct but topographically adjacent surface to AF-2. AR BF-3 is a hot spot for mutations involved in prostate cancer and androgen insensitivity syndromes, and some FDA-approved drugs bind at this site. Structural studies suggested an allosteric relationship between AF-2 and BF-3, as occupancy of the latter affected coactivator recruitment to AF-2. Physiological relevant partners of AR BF-3 have not been described as yet. The newly discovered site is highly conserved among the steroid receptors subclass, but is also present in other NRs. Several missense mutations in the BF-3 regions of these human NRs are implicated in pathology and affect their function in vitro. The fact that AR BF-3 pocket is a druggable site evidences its pharmacological potential. Compounds that may affect allosterically NR function by binding to BF-3 open promising avenues to develop type-specific NR modulators.

NucleaRDB: information system for nuclear receptors

The NucleaRDB is a Molecular Class-Specific Information System that collects, combines, validates and disseminates large amounts of heterogeneous data on nuclear hormone receptors. It contains both experimental and computationally derived data. The data and knowledge present in the NucleaRDB can be accessed using a number of different interactive and programmatic methods and query systems. A nuclear hormone receptor-specific PDF reader interface is available that can integrate the contents of the NucleaRDB with full-text scientific articles. The NucleaRDB is freely available at http://www.receptors.org/nucleardb.

GPCRDB: information system for G protein-coupled receptors

The GPCRDB is a Molecular Class-Specific Information System (MCSIS) that collects, combines, validates and disseminates large amounts of heterogeneous data on G protein-coupled receptors (GPCRs). The GPCRDB contains experimental data on sequences, ligand-binding constants, mutations and oligomers, as well as many different types of computationally derived data such as multiple sequence alignments and homology models. The GPCRDB provides access to the data via a number of different access methods. It offers visualization and analysis tools, and a number of query systems. The data is updated automatically on a monthly basis. The GPCRDB can be found online at http://www.gpcr.org/7tm/.

Novel tools for extraction and validation of disease-related mutations applied to Fabry disease

Genetic disorders are often caused by nonsynonymous nucleotide changes in one or more genes associated with the disease. Specific amino acid changes, however, can lead to large variability of phenotypic expression. For many genetic disorders this results in an increasing amount of publications describing phenotype-associated mutations in disorder-related genes. Keeping up with this stream of publications is essential for molecular diagnostics and translational research purposes but often impossible due to time constraints: there are simply too many articles to read. To help solve this problem, we have created Mutator, an automated method to extract mutations from full-text articles. Extracted mutations are crossreferenced to sequence data and a scoring method is applied to distinguish false-positives. To analyze stored and new mutation data for their (potential) effect we have developed Validator, a Web-based tool specifically designed for DNA diagnostics. Fabry disease, a monogenetic gene disorder of the GLA gene, was used as a test case. A structure-based sequence alignment of the alpha-amylase superfamily was used to validate results. We have compared our data with existing Fabry mutation data sets obtained from the HGMD and Swiss-Prot databases. Compared to these data sets, Mutator extracted 30% additional mutations from the literature.

The proteolytic system of lactic acid bacteria revisited: a genomic comparison

Background

Lactic acid bacteria (LAB) are a group of gram-positive, lactic acid producing Firmicutes. They have been extensively used in food fermentations, including the production of various dairy products. The proteolytic system of LAB converts proteins to peptides and then to amino acids, which is essential for bacterial growth and also contributes significantly to flavor compounds as end-products. Recent developments in high-throughput genome sequencing and comparative genomics hybridization arrays provide us with opportunities to explore the diversity of the proteolytic system in various LAB strains.

Results

We performed a genome-wide comparative genomics analysis of proteolytic system components, including cell-wall bound proteinase, peptide transporters and peptidases, in 22 sequenced LAB strains. The peptidase families PepP/PepQ/PepM, PepD and PepI/PepR/PepL are described as examples of our in silico approach to refine the distinction of subfamilies with different enzymatic activities. Comparison of protein 3D structures of proline peptidases PepI/PepR/PepL and esterase A allowed identification of a conserved core structure, which was then used to improve phylogenetic analysis and functional annotation within this protein superfamily.

The diversity of proteolytic system components in 39 Lactococcus lactis strains was explored using pangenome comparative genome hybridization analysis. Variations were observed in the proteinase PrtP and its maturation protein PrtM, in one of the Opp transport systems and in several peptidases between strains from different Lactococcus subspecies or from different origin.

Conclusions

The improved functional annotation of the proteolytic system components provides an excellent framework for future experimental validations of predicted enzymatic activities. The genome sequence data can be coupled to other "omics" data e.g. transcriptomics and metabolomics for prediction of proteolytic and flavor-forming potential of LAB strains. Such an integrated approach can be used to tune the strain selection process in food fermentations.

Carboxylic ester hydrolases from hyperthermophiles

Carboxylic ester hydrolyzing enzymes constitute a large group of enzymes that are able to catalyze the hydrolysis, synthesis or transesterification of an ester bond. They can be found in all three domains of life, including the group of hyperthermophilic bacteria and archaea. Esterases from the latter group often exhibit a high intrinsic stability, which makes them of interest them for various biotechnological applications. In this review, we aim to give an overview of all characterized carboxylic ester hydrolases from hyperthermophilic microorganisms and provide details on their substrate specificity, kinetics, optimal catalytic conditions, and stability. Approaches for the discovery of new carboxylic ester hydrolases are described. Special attention is given to the currently characterized hyperthermophilic enzymes with respect to their biochemical properties, 3D structure, and classification.

Identification of fungal oxaloacetate hydrolyase within the isocitrate lyase/PEP mutase enzyme superfamily using a sequence marker-based method

Aspergillus niger produces oxalic acid through the hydrolysis of oxaloacetate, catalyzed by the cytoplasmic enzyme oxaloacetate acetylhydrolase (OAH). The A. niger genome encodes four additional open reading frames with strong sequence similarity to OAH yet only the oahA gene encodes OAH activity. OAH and OAH-like proteins form subclass of the isocitrate lyase/PEP mutase enzyme superfamily, which is ubiquitous present filamentous fungi. Analysis of function-specific residues using a superfamily-based approach revealed an active site serine as a possible sequence marker for OAH activity. We propose that presence of this serine in family members correlates with presence of OAH activity whereas its absence correlates with absence of OAH. This hypothesis was tested by carrying out a serine mutagenesis study with the OAH from the fungal oxalic acid producer Botrytis cinerea and the OAH active plant petal death protein as test systems.

The use of in vitro peptide binding profiles and in silico ligand-receptor interaction profiles to describe ligand-induced conformations of the retinoid X receptor alpha ligand-binding domain

It is hypothesized that different ligand-induced conformational changes can explain the different interactions of nuclear receptors with regulatory proteins, resulting in specific biological activities. Understanding the mechanism of how ligands regulate cofactor interaction facilitates drug design. To investigate these ligand-induced conformational changes at the surface of proteins, we performed a time-resolved fluorescence resonance energy transfer assay with 52 different cofactor peptides measuring the ligand-induced cofactor recruitment to the retinoid X receptor-alpha (RXRalpha) in the presence of 11 compounds. Simultaneously we analyzed the binding modes of these compounds by molecular docking. An automated method converted the complex three-dimensional data of ligand-protein interactions into two-dimensional fingerprints, the so-called ligand-receptor interaction profiles. For a subset of compounds the conformational changes at the surface, as measured by peptide recruitment, correlate well with the calculated binding modes, suggesting that clustering of ligand-receptor interaction profiles is a very useful tool to discriminate compounds that may induce different conformations and possibly different effects in a cellular environment. In addition, we successfully combined ligand-receptor interaction profiles and peptide recruitment data to reveal structural elements that are possibly involved in the ligand-induced conformations. Interestingly, we could predict a possible binding mode of LG100754, a homodimer antagonist that showed no effect on peptide recruitment. Finally, the extensive analysis of the peptide recruitment profiles provided novel insight in the potential cellular effect of the compound; for the first time, we showed that in addition to the induction of coactivator peptide binding, all well-known RXRalpha agonists also induce binding of corepressor peptides to RXRalpha.

Evolutionary identification of a subtype specific functional site in the ligand binding domain of steroid receptors

Nuclear receptors are ubiquitous eukaryotic ligand-activated transcription factors that modulate gene expression through varied interactions. However, the highly conserved functional sites known today seem insufficient to explain receptor specific recruitment of different coactivator and corepressor proteins and regulation of transcription. To search for new receptor-subtype specific functional sites, we applied difference evolutionary trace (difference ET) analysis to the ligand binding domain of steroid receptors, a subgroup of the nuclear receptor (NR) family. This computational approach identified a new functional site located on a surface opposite to currently known protein-protein interaction sites and distinct from the ligand binding pocket. Strikingly, the literature shows that in vivo variations at residues in the new site are linked to androgen resistance and leukemia, and our own targeted mutations to this site lower but do not eradicate transcriptional activation by estrogen receptor alpha (ERalpha), with reduced ligand binding affinity and SRC-1 interaction. Thus, these data demonstrate that this evolutionary important surface can function as an allosteric site that modulates some but not all receptor binding interactions. Evolutionary analysis further shows that this allosteric regulatory site is shared among all NRs from groups 2 (HNF4-like) and 4 (NGFIB-like), suggesting a role among many nuclear receptors. Its concave structure, hydrophobic composition, and residue variability among nuclear receptors further suggest that it would be amenable for specific drug design. This highlights the power of evolutionary information for the identification of new functional sites even in a protein family as well studied as NRs.

An estrogen receptor chimera senses ligands by nuclear translocation

We have developed a new mammalian cell-based assay to screen for ligands of the estrogen receptor. A fluorescently tagged chimera between the glucocorticoid and the estrogen receptors, unlike the constitutively nuclear estrogen receptor, is cytoplasmic in the absence of hormone and translocates to the nucleus in response to estradiol. The chimera maintains specificity for estrogen receptor alpha ligands and does not show cross-reactivity with other steroids, providing a clean system for drug discovery. Natural and synthetic estrogen receptor alpha agonists as well as phytoestrogens effectively translocate the receptor to the nucleus in a dose-dependent manner. Antagonists of the estrogen receptor can also transmit the structural signals that result in receptor nuclear translocation. The potency and efficacy of high-affinity ligands can be evaluated in our system by measuring the nuclear translocation of the fluorescently labeled receptor in response to increasing ligand concentrations. The chimera is transcriptionally competent on transient and replicating templates, and is inhibited by estrogen receptor antagonists. Interestingly, the nucleoplasmic mobility of the chimera, determined by FRAP analysis, is faster than that of the wild type estrogen receptor, and the chimera is resistant to ICI immobilization. The translocation properties of this chimera can be utilized in high content screens for novel estrogen receptor modulators.

Sequence variation in ligand binding sites in proteins

Background

The recent explosion in the availability of complete genome sequences has led to the cataloging of tens of thousands of new proteins and putative proteins. Many of these proteins can be structurally or functionally categorized from sequence conservation alone. In contrast, little attention has been given to the meaning of poorly-conserved sites in families of proteins, which are typically assumed to be of little structural or functional importance.

Results

Recently, using statistical free energy analysis of tetratricopeptide repeat (TPR) domains, we observed that positions in contact with peptide ligands are more variable than surface positions in general. Here we show that statistical analysis of TPRs, ankyrin repeats, Cys₂His₂ zinc fingers and PDZ domains accurately identifies specificity-determining positions by their sequence variation. Sequence variation is measured as deviation from a neutral reference state, and we present probabilistic and information theory formalisms that improve upon recently suggested methods such as statistical free energies and sequence entropies.

Conclusion

Sequence variation has been used to identify functionally-important residues in four selected protein families. With TPRs and ankyrin repeats, protein families that bind highly diverse ligands, the effect is so pronounced that sequence "hypervariation" alone can be used to predict ligand binding sites.