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

Allosteric modulation of SHP2: Quest from known to unknown

Src homology-2 domain-containing protein tyrosine phosphatase-2 (SHP2) is a key regulatory factor in the cell cycle and its activating mutations play an important role in the development of various cancers, making it an important target for antitumor drugs. Due to the highly conserved amino acid sequence and positively charged nature of the active site of SHP2, it is difficult to discover inhibitors with high affinity for the catalytic site of SHP2 and sufficient cell permeability, making it considered an "undruggable" target. However, the discovery of allosteric regulation mechanisms provides new opportunities for transforming undruggable targets into druggable ones. Given the limitations of orthosteric inhibitors, SHP2 allosteric inhibitors have become a more selective and safer research direction. In this review, we elucidate the oncogenic mechanism of SHP2 and summarize the discovery methods of SHP2 allosteric inhibitors, providing new strategies for the design and improvement of SHP2 allosteric inhibitors.

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.

Small-Molecule Inhibition of Androgen Receptor Dimerization as a Strategy against Prostate Cancer

The clinically used androgen receptor (AR) antagonists for the treatment of prostate cancer (PCa) are all targeting the AR ligand binding pocket (LBP), resulting in various drug-resistant problems. Therefore, a new strategy to combat PCa is urgently needed. Enlightened by the gain-of-function mutations of androgen insensitivity syndrome, we discovered for the first time small-molecule antagonists toward a prospective pocket on the AR dimer interface named the dimer interface pocket (DIP) via molecular dynamics (MD) simulation, structure-based virtual screening, structure-activity relationship exploration, and bioassays. The first-in-class antagonist M17-B15 targeting the DIP is capable of effectively disrupting AR self-association, thereby suppressing AR signaling. Furthermore, M17-B15 exhibits extraordinary anti-PCa efficacy in vitro and also in mouse xenograft tumor models, demonstrating that AR dimerization disruption by small molecules targeting the DIP is a novel and valid strategy against PCa.

An Evolutionary Trace method defines functionally important bases and sites common to RNA families

Functional non-coding (fnc)RNAs are nucleotide sequences of varied lengths, structures, and mechanisms that ubiquitously influence gene expression and translation, genome stability and dynamics, and human health and disease. Here, to shed light on their functional determinants, we seek to exploit the evolutionary record of variation and divergence read from sequence comparisons. The approach follows the phylogenetic Evolutionary Trace (ET) paradigm, first developed and extensively validated on proteins. We assigned a relative rank of importance to every base in a study of 1070 functional RNAs, including the ribosome, and observed evolutionary patterns strikingly similar to those seen in proteins, namely, (1) the top-ranked bases clustered in secondary and tertiary structures. (2) In turn, these clusters mapped functional regions for catalysis, binding proteins and drugs, post-transcriptional modification, and deleterious mutations. (3) Moreover, the quantitative quality of these clusters correlated with the identification of functional regions. (4) As a result of this correlation, smoother structural distributions of evolutionary important nucleotides improved functional site predictions. Thus, in practice, phylogenetic analysis can broadly identify functional determinants in RNA sequences and functional sites in RNA structures, and reveal details on the basis of RNA molecular functions. As example of application, we report several previously undocumented and potentially functional ET nucleotide clusters in the ribosome. This work is broadly relevant to studies of structure-function in ribonucleic acids. Additionally, this generalization of ET shows that evolutionary constraints among sequence, structure, and function are similar in structured RNA and proteins. RNA ET is currently available as part of the ET command-line package, and will be available as a web-server.

Traditionally, RNA has been delegated to the role of an intermediate between DNA and proteins. However, we now recognize that RNAs are broadly functional beyond their role in translation, and that a number of diverse classes exist. Because functional, non-coding RNAs are prevalent in biology and impact human health, it is important to better understand their functional determinants. However, the classical solution to this problem, targeted mutagenesis, is time-consuming and scales poorly. We propose an alternative computational approach to this problem, the Evolutionary Trace method. Previously developed and validated for proteins, Evolutionary Trace examines evolutionary history of a molecule and predicts evolutionarily important residues in the sequence. We apply Evolutionary Trace to a set of diverse RNAs, and find that the evolutionarily important nucleotides cluster on the three-dimensional structure, and that these clusters closely overlap functional sites. We also find that the clustering property can be used to refine and improve predictions. These findings are in close agreement with our observations of Evolutionary Trace in proteins, and suggest that structured functional RNAs and proteins evolve under similar constraints. In practice, the approach is to be used by RNA researches seeking insight into their molecule of interest, and the Evolutionary Trace program, along with a working example, is available at https://github.com/LichtargeLab/RNA_ET_ms.

Emerging Computational Methods for the Rational Discovery of Allosteric Drugs

Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.

Functional divergence and convergent evolution in the plastid-targeted glyceraldehyde-3-phosphate dehydrogenases of diverse eukaryotic algae

Background

Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is a key enzyme of the glycolytic pathway, reversibly catalyzing the sixth step of glycolysis and concurrently reducing the coenzyme NAD⁺ to NADH. In photosynthetic organisms a GAPDH paralog (Gap2 in Cyanobacteria, GapA in most photosynthetic eukaryotes) functions in the Calvin cycle, performing the reverse of the glycolytic reaction and using the coenzyme NADPH preferentially. In a number of photosynthetic eukaryotes that acquired their plastid by the secondary endosymbiosis of a eukaryotic red alga (Alveolates, haptophytes, cryptomonads and stramenopiles) GapA has been apparently replaced with a paralog of the host’s own cytosolic GAPDH (GapC1). Plastid GapC1 and GapA therefore represent two independent cases of functional divergence and adaptations to the Calvin cycle entailing a shift in subcellular targeting and a shift in binding preference from NAD⁺ to NADPH.

Methods

We used the programs FunDi, GroupSim, and Difference Evolutionary-Trace to detect sites involved in the functional divergence of these two groups of GAPDH sequences and to identify potential cases of convergent evolution in the Calvin-cycle adapted GapA and GapC1 families. Sites identified as being functionally divergent by all or some of these programs were then investigated with respect to their possible roles in the structure and function of both glycolytic and plastid-targeted GAPDH isoforms.

Conclusions

In this work we found substantial evidence for convergent evolution in GapA/B and GapC1. In many cases sites in GAPDHs of these groups converged on identical amino acid residues in specific positions of the protein known to play a role in the function and regulation of plastid-functioning enzymes relative to their cytosolic counterparts. In addition, we demonstrate that bioinformatic software like FunDi are important tools for the generation of meaningful biological hypotheses that can then be tested with direct experimental techniques.

Molecular adaptation and resilience of the insect's nuclear receptor USP

Background

The maintenance of biological systems requires plasticity and robustness. The function of the ecdysone receptor, a heterodimer composed of the nuclear receptors ECR (NR1H1) and USP (NR2B4), was maintained in insects despite a dramatic divergence that occurred during the emergence of Mecopterida. This receptor is therefore a good model to study the evolution of plasticity. We tested the hypothesis that selection has shaped the Ligand-Binding Domain (LBD) of USP during evolution of Mecopterida.

Results

We isolated usp and cox1 in several species of Drosophilidae, Tenebrionidae and Blattaria and estimated non-synonymous/synonymous rate ratios using maximum-likelihood methods and codon-based substitution models. Although the usp sequences were mainly under negative selection, we detected relaxation at residues located on the surface of the LBD within Mecopterida families. Using branch-site models, we also detected changes in selective constraints along three successive branches of the Mecopterida evolution. Residues located at the bottom of the ligand-binding pocket (LBP) underwent strong positive selection during the emergence of Mecopterida. This change is correlated with the acquisition of a large LBP filled by phospholipids that probably allowed the stabilisation of the new Mecopterida structure. Later, when the two subgroups of Mecopterida (Amphiesmenoptera: Lepidoptera, Trichoptera; Antliophora: Diptera, Mecoptera, Siphonaptera) diverged, the same positions became under purifying selection. Similarly, several positions of the heterodimerisation interface experienced positive selection during the emergence of Mecopterida, rapidly followed by a phase of constrained evolution. An enlargement of the heterodimerisation surface is specific for Mecopterida and was associated with a reinforcement of the obligatory partnership between ECR and USP, at the expense of homodimerisation.

Conclusions

In order to explain the episodic mode of evolution of USP, we propose a model in which the molecular adaptation of this protein is seen as a process of resilience for the maintenance of the ecdysone receptor functionality.

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.

FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level

The rapid accumulation of gene sequences, many of which are hypothetical proteins with unknown function, has stimulated the development of accurate computational tools for protein function prediction with evolution/structure-based approaches showing considerable promise. In this article, we present FINDSITE-metal, a new threading-based method designed specifically to detect metal-binding sites in modeled protein structures. Comprehensive benchmarks using different quality protein structures show that weakly homologous protein models provide sufficient structural information for quite accurate annotation by FINDSITE-metal. Combining structure/evolutionary information with machine learning results in highly accurate metal-binding annotations; for protein models constructed by TASSER, whose average Cα RMSD from the native structure is 8.9 Å, 59.5% (71.9%) of the best of top five predicted metal locations are within 4 Å (8 Å) from a bound metal in the crystal structure. For most of the targets, multiple metal-binding sites are detected with the best predicted binding site at rank 1 and within the top two ranks in 65.6% and 83.1% of the cases, respectively. Furthermore, for iron, copper, zinc, calcium, and magnesium ions, the binding metal can be predicted with high, typically 70% to 90%, accuracy. FINDSITE-metal also provides a set of confidence indexes that help assess the reliability of predictions. Finally, we describe the proteome-wide application of FINDSITE-metal that quantifies the metal-binding complement of the human proteome. FINDSITE-metal is freely available to the academic community at http://cssb.biology.gatech.edu/findsite-metal/.

Orphan nuclear receptors as targets for drug development

Orphan nuclear receptors regulate diverse biological processes. These important molecules are ligand-activated transcription factors that act as natural sensors for a wide range of steroid hormones and xenobiotic ligands. Because of their importance in regulating various novel signaling pathways, recent research has focused on identifying xenobiotics targeting these receptors for the treatment of multiple human diseases. In this review, we will highlight these receptors in several physiologic and pathophysiologic actions and demonstrate how their functions can be exploited for the successful development of newer drugs.

Evolution-guided discovery and recoding of allosteric pathway specificity determinants in psychoactive bioamine receptors

G protein-coupled receptors for dopamine and serotonin control signaling pathways targeted by many psychoactive drugs. A puzzle is how receptors with similar functions and nearly identical binding site structures, such as D2 dopamine receptors and 5-HT2A serotonin receptors, could evolve a mechanism that discriminates stringently in their cellular responses between endogenous neurotransmitters. We used the Difference Evolutionary Trace (Difference-ET) and residue-swapping to uncover two distinct sets of specificity-determining sequence positions. One at the ligand-binding pocket determines the relative affinities for these two ligands, and a distinct, surprising set of positions outside the binding site determines whether a bound ligand can trigger the conformational rearrangement leading to G protein activation. Thus one site specifies affinity while the other encodes a filter for efficacy. These findings demonstrate that allosteric pathways linking distant interactions via alternate conformational states enforce specificity independently of the ligand-binding site, such that either one may be rationally rekeyed to different ligands. The conversion of a dopamine receptor effectively into a serotonin receptor illustrates the plasticity of GPCR signaling during evolution, or in pathological states, and suggests new approaches to drug discovery, targeting both classes of sites.

Ligand unbinding from the estrogen receptor: a computational study of pathways and ligand specificity

The estrogen receptor (ER) belongs to the nuclear receptor superfamily, whose members regulate important cellular events like development and metabolism. The ER functions as a transcription regulator and can be activated on ligand binding. Consequently, ligand binding and unbinding constitute fundamental processes in the regulation of genes. Even though both biochemical and structural data of ER are available, the actual mechanism of the ligand binding/unbinding remains elusive. We have performed computational studies on the unbinding mechanism of ERalpha and ERbeta, in the presence of cofactors and with ligands ranging from agonist to a full antagonist. Our results show that agonists or selective ER modulators can dissociate from the receptor through multiple pathways with minor effect on the receptor structure, whereas an antagonist requires larger conformational changes. Furthermore, a specific receptor/ligand combination can exhibit a pathway preference depending on character and conformation of both parts. Hence, it is possible that the binding/unbinding mechanism can explain ligand subtype specificity and thus have an impact in drug discovery.

Functional rescue of beta-adrenoceptor dimerization and trafficking by pharmacological chaperones

Site-directed mutagenesis guided by evolutionary trace analysis revealed that substitution of V179 and W183 within a cluster of evolutionarily important residues on the surface of the fourth transmembrane domain of the beta(1)-adrenergic receptor (beta(1)AR) significantly reduced the propensity of the receptor to self-assemble into homodimers as assessed by bioluminescence resonance energy transfer in living cells. These results suggest that mutation of V179 and W183 result in conformational changes that reduce homodimerization either directly by interfering with the dimerization interface or indirectly by causing local misfolding that result in reduced self-assembly. However, the mutations did not cause a general misfolding of the beta(1)AR as they did not prevent heterodimerization with the beta(2)AR. The homodimerization-compromised mutants were significantly retained in the endoplasmic reticulum (ER) and could not be properly matured and trafficked to the cell surface. Lipophilic beta-adrenergic ligands acted as pharmacological chaperones by restoring both dimerization and plasma membrane trafficking of the ER-retained dimerization-compromised beta(1)AR mutants. These results clearly indicate that homodimerization occurs early in the biosynthetic process in the ER and that pharmacological chaperones can promote both dimerization and cell surface targeting, most likely by stabilizing receptor conformations compatible with the two processes.

Understanding nuclear receptors using computational methods

Nuclear receptors (NRs) are important targets for therapeutic drugs. NRs regulate transcriptional activities through binding to ligands and interacting with several regulating proteins. Computational methods can provide insights into essential ligand-receptor and protein-protein interactions. These in turn have facilitated the discovery of novel agonists and antagonists with high affinity and specificity as well as have aided in the prediction of toxic side effects of drugs by identifying possible off-target interactions. Here, we review the application of computational methods toward several clinically important NRs (with special emphasis on PXR) and discuss their use for screening and predicting the toxic side effects of xenobiotics.