Metabolic pathways in eubacteria and archaea often are encoded by operons and/or gene clusters (genome neighborhoods) that provide important clues for assignment of both enzyme functions and metabolic pathways. We describe a bioinformatic approach (genome neighborhood network; GNN) that enables large scale prediction of the in vitro enzymatic activities and in vivo physiological functions (metabolic pathways) of uncharacterized enzymes in protein families. We demonstrate the utility of the GNN approach by predicting in vitro activities and in vivo functions in the proline racemase superfamily (PRS; InterPro IPR008794). The predictions were verified by measuring in vitro activities for 51 proteins in 12 families in the PRS that represent ∼85% of the sequences; in vitro activities of pathway enzymes, carbon/nitrogen source phenotypes, and/or transcriptomic studies confirmed the predicted pathways. The synergistic use of sequence similarity networks3 and GNNs will facilitate the discovery of the components of novel, uncharacterized metabolic pathways in sequenced genomes.
DNA molecules are polymers in which four nucleotides—guanine, adenine, thymine, and cytosine—are arranged along a sugar backbone. The sequence of these four nucleotides along the DNA strand determines the genetic code of the organism, and can be deciphered using various genome sequencing techniques. Microbial genomes are particularly easy to sequence as they contain fewer than several million nucleotides, compared with the 3 billion or so nucleotides that are present in the human genome.
Reading a genome sequence is straight forward, but predicting the physiological functions of the proteins encoded by the genes in the sequence can be challenging. In a process called genome annotation, the function of protein is predicted by comparing the relevant gene to the genes of proteins with known functions. However, microbial genomes and proteins are hugely diverse and over 50% of the microbial genomes that have been sequenced have not yet been related to any physiological function. With thousands of microbial genomes waiting to be deciphered, large scale approaches are needed.
Zhao et al. take advantage of a particular characteristic of microbial genomes. DNA sequences that code for two proteins required for the same task tend to be closer to each other in the genome than two sequences that code for unrelated functions. Operons are an extreme example; an operon is a unit of DNA that contains several genes that are expressed as proteins at the same time.
Zhao et al. have developed a bioinformatic method called the genome neighbourhood network approach to work out the function of proteins based on their position relative to other proteins in the genome. When applied to the proline racemase superfamily (PRS), which contains enzymes with similar sequences that can catalyze three distinct chemical reactions, the new approach was able to assign a function to the majority of proteins in a public database of PRS enzymes, and also revealed new members of the PRS family. Experiments confirmed that the proteins behaved as predicted. The next challenge is to develop the genome neighbourhood network approach so that it can be applied to more complex systems.
sequence similarity network; genome neighborhood network; functional assignment; other
During prion diseases, a normally benign, host protein, denoted PrPC, undergoes alternative folding into the aberrant isoform, PrPSc. We used ELISA assays to identify and confirm hits in order to develop leads that reduce PrPSc in prion-infected dividing and stationary-phase mouse neuroblastoma (ScN2a-cl3) cells. We tested 52,830 diverse small molecules in dividing cells and 49,430 in stationary-phase cells. This led to 3,100 HTS and 970 single point confirmed (SPC) hits in dividing cells, 331 HTS and 55 confirmed SPC hits in stationary-phase cells as well as 36 confirmed SPC hits active in both. Fourteen chemical leads were identified from confirmed SPC hits in dividing cells and three in stationary-phase cells. From more than 682 compounds tested in concentration-effect relationships in dividing cells to determine potency (EC50), 102 had EC50 values between 1–10 µM and 50 had EC50 values of <1 µM; none affected cell viability. We observed an excellent correlation between EC50 values determined by ELISA and Western immunoblotting for 28 representative compounds in dividing cells (R2 = 0.75; p < 0.0001). Of the 55 confirmed SPC hits in stationary-phase cells, 23 were piperazine, indole, or urea leads. The potency (EC50) of one indole in stationary-phase and dividing ScN2a-cl3 cells was 7.5 and 1.6 µM, respectively. Unexpectedly, the number of hits in stationary-phase cells was ~10% of that in dividing cells. The explanation for this difference remains to be determined.
Antiprion compounds; PrPSc; dividing and stationary-phase brain cells
Reversible posttranslational modifications are emerging as critical regulators of mitochondrial proteins and metabolism. Here, we use a label-free quantitative proteomic approach to characterize the lysine succinylome in liver mitochondria and its regulation by the desuccinylase SIRT5. A total of 1190 unique sites were identified as succinylated, and 386 sites across 140 proteins representing several metabolic pathways including β-oxidation and ketogenesis were significantly hypersuccinylated in Sirt5−/− animals. Loss of SIRT5 leads to accumulation of medium- and long-chain acylcarnitines and decreased β-hydroxybutyrate production in vivo. In addition, we demonstrate that SIRT5 regulates succinylation of the rate-limiting ketogenic enzyme 3-hydroxy-3-methylglutaryl-CoA synthase 2 (HMGCS2) both in vivo and in vitro. Finally, mutation of hypersuccinylated residues K83 and K310 on HMGCS2 to glutamic acid strongly inhibits enzymatic activity. Taken together, these findings establish SIRT5 as a global regulator of lysine succinylation in mitochondria and present a mechanism for inhibition of ketogenesis through HMGCS2.
Terpenoid synthases construct the carbon skeletons of tens of thousands of natural products. To predict functions and specificity of triterpenoid synthases, a mechanism-based, multi-intermediate docking approach is proposed. In addition to enzyme function prediction, other potential applications of the current approach, such as enzyme mechanistic studies and enzyme redesign by mutagenesis, are discussed.
The rapid growth in the number of protein sequences presents challenges for enzyme function assignment. Computational methods, such as bioinformatics, homology modeling and docking, are becoming increasingly important for predicting of enzyme functions from protein sequences. Terpenoids are one of largest classes of natural products, and many drugs (e.g. taxol) consist of terpenoids or terpenoid derivatives. Understanding the biosynthesis of the terpenoids is of great interest. Terpenoid synthases catalyze the key cyclization steps of the biosynthesis of terpenoids via carbocation rearrangements, generating numerous multiple-ring carbon skeletons. Triterpenoid synthases, as an important class of terpenoid synthases, catalyze the cyclization of either squalene or oxido-squalene into cyclized products such as sterols (e.g. lanosterol). In this work, we propose a computational approach that can be used to predict product specificity of the triterpenoid synthases. Our approach provides insight into the ‘design principles’ of these fascinating enzymes, and may become a practical approach for function prediction and enzyme engineering.
Electrophilic probes that covalently modify a cysteine thiol often show enhanced pharmacological potency and selectivity. Although reversible Michael acceptors have been reported, the structural requirements for reversibility are poorly understood. Here, we report a novel class of acrylonitrile-based Michael acceptors, activated by aryl or heteroaryl electron-withdrawing groups. We demonstrate that thiol adducts of these acrylonitriles undergo β-elimination at rates that span more than three orders of magnitude. These rates correlate inversely with the computed proton affinity of the corresponding carbanions, enabling the intrinsic reversibility of the thiol-Michael reaction to be tuned in a predictable manner. We apply these principles to the design of new reversible covalent kinase inhibitors with improved properties. A co-crystal structure of one such inhibitor reveals specific noncovalent interactions between the 1,2,4-triazole activating group and the kinase. Our experimental and computational study enables the design of new Michael acceptors, expanding the palette of reversible, cysteine-targeted electrophiles.
Removal of the 5′ cap structure by Dcp2 is a major step in several 5′–3′ mRNA decay pathways. The activity of Dcp2 is enhanced by Dcp1 and bound coactivators, yet the details of how these interactions are linked to chemistry are poorly understood. Here we report three crystal structures of the catalytic Nudix hydrolase domain of Dcp2 that demonstrate binding of a catalytically essential metal ion, and enzyme kinetics are used to identify several key active site residues involved in acid/base chemistry of decapping. Using NMR and molecular dynamics, we find that a conserved metal binding loop on the catalytic domain undergoes conformational changes during the catalytic cycle. These findings describe key events during the chemical step of decapping, suggest local active site conformational changes are important for activity, and provide a framework to explain stimulation of catalysis by the regulatory domain of Dcp2 and associated coactivators.
A new coarse-grained model of the E. coli cytoplasm is developed by describing the proteins of the cytoplasm as flexible units consisting of one or more spheres that follow Brownian dynamics (BD), with hydrodynamic interactions (HI) accounted for by a mean-field approach. Extensive BD simulations were performed to calculate the diffusion coefficients of three different proteins in the cellular environment. The results are in close agreement with experimental or previously simulated values, where available. Control simulations without HI showed that use of HI is essential to obtain accurate diffusion coefficients. Anomalous diffusion inside the crowded cellular medium was investigated with Fractional Brownian motion analysis, and found to be present in this model. By running a series of control simulations in which various forces were removed systematically, it was found that repulsive interactions (volume exclusion) are the main cause for anomalous diffusion, with a secondary contribution from HI.
The stereo-specificity of D-glucarate dehydratase (GlucD) is explored by QM/MM calculations. Both the substrate binding and the chemical steps of GlucD contribute to substrate specificity. Although the identification of transition states remains computationally intensive, we suggest that QM/MM computations on ground states or intermediates can capture aspects of specificity that cannot be obtained using docking or molecular mechanics methods.
Enzyme specificity; stereochemistry; enzyme function prediction; QM/MM; MM/GBSA
Lipoxygenase (LOX) enzymes catalyze the hydroperoxidation of arachidonic acid and other polyunsaturated fatty acids to hydroxyeicosatetraenoic acids with varying positional specificity to yield important biological signaling molecules. Human epithelial 15lipoxygenase2 (15-LOX-2) is a highly specific LOX isozyme that is expressed in epithelial tissue and whose activity has been correlated with suppression of tumor growth in prostate and other epithelial derived cancers. Despite the potential utility of an inhibitor to probe the specific role of 15-LOX-2 in tumor progression, no such potent/specific 15LOX2 inhibitors have been reported to date. This study employs high throughput screening to identify two novel, specific 15LOX2 inhibitors. MLS000545091 is a mixed-type inhibitor of 15-LOX-2 with a Ki of 0.9+/−0.4 µM and has a 20-fold selectivity over 5-LOX, 12-LOX, 15-LOX-1, COX-1, and COX-2. MLS000536924 is a competitive inhibitor with a Ki of 2.5+/−0.5 µM and also possesses 20-fold selectivity toward 15-LOX-2 over the other oxygenases, listed above. Finally, neither compound possesses reductive activity towards the active-site ferrous ion.
Post-translational modification of proteins is an evolutionarily conserved mechanism for regulating activity, binding affinities and stability. Compared with established post-translational modifications such as phosphorylation or uniquitination, post-translational modification by protons within physiological pH ranges is a less recognized mechanism for regulating protein function. By changing the charge of amino acid side chains, post-translational modification by protons can drive dynamical changes in protein conformation and function. Addition and removal of a proton is rapid and reversible and in contrast to most other post-translational modifications does not require an enzyme. Signaling specificity is achieved by only a minority of sites in proteins titrating within the physiological pH range. Here, we examine the structural mechanisms and functional consequences of proton post-translational modification of pH-sensing proteins regulating different cellular processes.
pH sensor; protonation; intracellular pH; post-translational modification; coincidence detection; conformational change; ionization; charge; histidine
Cryptosporidiosis, caused by the protozoan parasite Cryptosporidium parvum, can stunt infant growth and can be lethal in immunocompromised individuals. The most widely used drugs for treating cryptosporidiosis are nitazoxanide and paromomycin, although both exhibit limited efficacy. To investigate an alternative approach to therapy, we demonstrate that the clan CA cysteine protease inhibitor N-methyl piperazine-Phe-homoPhe-vinylsulfone phenyl (K11777) inhibits C. parvum growth in mammalian cell lines in a concentration-dependent manner. Further, using the C57BL/6 gamma interferon receptor knockout (IFN-γR-KO) mouse model, which is highly susceptible to C. parvum, oral or intraperitoneal treatment with K11777 for 10 days rescued mice from otherwise lethal infections. Histologic examination of untreated mice showed intestinal inflammation, villous blunting, and abundant intracellular parasite stages. In contrast, K11777-treated mice (210 mg/kg of body weight/day) showed only minimal inflammation and no epithelial changes. Three putative protease targets (termed cryptopains 1 to 3, or CpaCATL-1, -2, and -3) were identified in the C. parvum genome, but only two are transcribed in infected mammals. A homology model predicted that K11777 would bind to cryptopain 1. Recombinant enzymatically active cryptopain 1 was successfully targeted by K11777 in a competition assay with a labeled active-site-directed probe. K11777 exhibited no toxicity in vitro and in vivo, and surviving animals remained free of parasites 3 weeks after treatment. The discovery that a cysteine protease inhibitor provides potent anticryptosporidial activity in an animal model of infection encourages the investigation and development of this biocide class as a new, and urgently needed, chemotherapy for cryptosporidiosis.
Recently, we described the aminothiazole lead (4-biphenyl-4-yl-thiazol-2-yl)-(6-methyl-pyridin-2-yl)-amine (1), which exhibits many desirable properties, including excellent stability in liver microsomes, oral bioavailability of ∼40% and high exposure in the brains of mice. Despite its good pharmacokinetic properties, compound 1 exhibited only modest potency in mouse neuroblastoma cells overexpressing the disease-causing prion protein PrPSc. Accordingly, we sought to identify analogs of 1 with improved antiprion potency in ScN2a-cl3 cells while retaining comparable or superior properties. We now report the discovery of improved lead compounds such as (6-methyl-pyridin-2-yl)-[4-(4-pyridin-3-yl-phenyl)-thiazol-2-yl]-amine (15) and cyclopropanecarboxylic acid (4-biphenyl-thiazol-2-yl)-amide (34), which exhibited brain exposure/EC50 ratios at least ten-fold greater than that of 1.
2-Aminothiazoles; neurological agents; pharmacokinetic optimization; prion disease; structure-activity relationships
The focus of CNS drug pharmacokinetics programs has recently shifted from determining the total concentrations in brain and blood to considering also unbound fractions and concentrations. Unfortunately, assessing unbound brain exposure experimentally requires demanding in vivo and in vitro studies.
We propose a physical model, based on lipid binding and pH partitioning, to predict in silico the unbound volume of distribution in the brain. The model takes into account the partition of a drug into lipids, interstitial fluid and intracellular compartments of the brain. The results are in good agreement with the experimental data, suggesting that the contributions of lipid binding and pH partitioning are important in determining drug exposure in brain. The predicted values are used, together with predictions for plasma protein binding, as corrective terms in a second model to derive the unbound brain to plasma concentration ratio starting from experimental values of total concentration ratio. The calculated values of brain free fraction and passive permeability are also used to qualitatively determine the brain to plasma equilibration time in a model that shows promising results but is limited to a very small set of compounds.
The models we propose are a step forward in understanding and predicting pharmacologically relevant exposure in brain starting from compounds’ chemical structure and neuropharmacokinetics, by using experimental total brain to plasma ratios, in silico calculated properties and simple physics-based approaches. The models can be used in central nervous system drug discovery programs for a fast and cheap assessment of unbound brain exposure. For existing compounds, the unbound ratios can be derived from experimental values of total brain to plasma ratios. For both existing and hypothetical compounds, the unbound volume of distribution due to lipid binding and pH partitioning can be calculated starting only from the chemical structure.
Brain equilibration time; Brain unbound volume of distribution; CNS exposure; Unbound brain to plasma concentration ratio
Assigning valid functions to proteins identified in genome projects is challenging, with over-prediction and database annotation errors major concerns1. We, and others2, are developing computation-guided strategies for functional discovery using “metabolite docking” to experimentally derived3 or homology-based4 three-dimensional structures. Bacterial metabolic pathways often are encoded by “genome neighborhoods” (gene clusters and/or operons), which can provide important clues for functional assignment. We recently demonstrated the synergy of docking and pathway context by “predicting” the intermediates in the glycolytic pathway in E. coli5. Metabolite docking to multiple binding proteins/enzymes in the same pathway increases the reliability of in silico predictions of substrate specificities because the pathway intermediates are structurally similar. We report that structure-guided approaches for predicting the substrate specificities of several enzymes encoded by a bacterial gene cluster allowed i) the correct prediction of the in vitro activity of a structurally characterized enzyme of unknown function (PDB 2PMQ), 2-epimerization of trans-4-hydroxy-L-proline betaine (tHyp-B) and cis-4-hydroxy-D-proline betaine (cHyp-B), and ii) the correct identification of the catabolic pathway in which Hyp-B 2-epimerase participates. The substrate-liganded pose predicted by virtual library screening (docking) was confirmed experimentally. The enzymatic activities in the predicted pathway were confirmed by in vitro assays and genetic analyses; the intermediates were identified by metabolomics; and repression of the genes encoding the pathway by high salt was established by transcriptomics, confirming the osmolyte role of tHyp-B. This study establishes the utility of structure-guide functional predictions to enable the discovery of new metabolic pathways.
To discover drugs lowering PrPSc in prion-infected cultured neuronal cells that achieve high concentrations in brain to test in mouse models of prion disease and then treat people with these fatal diseases.
We tested 2-AMT analogs for EC50 and PK after a 40 mg/kg single dose and 40–210 mg/kg/day doses for 3 days. We calculated plasma and brain AUC, ratio of AUC/EC50 after dosing. We reasoned that compounds with high AUC/EC50 ratios should be good candidates going forward.
We evaluated 27 2-AMTs in single-dose and 10 in 3-day PK studies, of which IND24 and IND81 were selected for testing in mouse models of prion disease. They had high concentrations in brain after oral dosing. Absolute bioavailability ranged from 27–40%. AUC/EC50 ratios after 3 days were >100 (total) and 48–113 (unbound). Stability in liver microsomes ranged from 30–>60 min. Ring hydroxylated metabolites were observed in microsomes. Neither was a substrate for the MDR1 transporter.
IND24 and IND81 are active in vitro and show high AUC/EC50 ratios (total and unbound) in plasma and brain. These will be evaluated in mouse models of prion disease.
antiprion drugs; drug discovery; IND24; IND81; prion disease
One of the many factors involved in determining the distribution and metabolism of a compound is the strength of its binding to human serum albumin. While experimental and QSAR approaches for determining binding to albumin exist, various factors limit their ability to provide accurate binding affinity for novel compounds. Thus, to complement the existing tools, we have developed a structure-based model of serum albumin binding. Our approach for predicting binding incorporated the inherent flexibility and promiscuity known to exist for albumin. We found that a weighted combination of the predicted logP and docking score most accurately distinguished between binders and nonbinders. This model was successfully used to predict serum albumin binding in a large test set of therapeutics that had experimental binding data.
Increased intracellular pH is sensed by FAK-His58, which facilitates FAK autophosphorylation and focal adhesion remodeling.
Intracellular pH (pHi) dynamics regulates diverse cellular processes, including remodeling of focal adhesions. We now report that focal adhesion kinase (FAK), a key regulator of focal adhesion remodeling, is a pH sensor responding to physiological changes in pH. The initial step in FAK activation is autophosphorylation of Tyr397, which increased with higher pHi. We used a genetically encoded biosensor to show increased pH at focal adhesions as they mature during cell spreading. We also show that cells with reduced pHi had attenuated FAK-pY397 as well as defective cell spreading and focal adhesions. Mutagenesis studies indicated FAK-His58 is critical for pH sensing and molecular dynamics simulations suggested a model in which His58 deprotonation drives conformational changes that may modulate accessibility of Tyr397 for autophosphorylation. Expression of FAK-H58A in fibroblasts was sufficient to restore defective autophosphorylation and cell spreading at low pHi. These data are relevant to understanding cancer metastasis, which is dependent on increased pHi and FAK activity.
In order to reach their pharmacologic targets, successful
central nervous system (CNS) drug candidates have to cross a complex
protective barrier separating brain from the blood. Being able to
predict a priori which molecules can successfully penetrate this barrier
could be of significant value in CNS drug discovery. Herein we report
a new computational approach that combines two mechanism-based models,
for passive permeation and for active efflux by P-glycoprotein, to
provide insight into the multiparameter optimization problem of designing
small molecules able to access the CNS. Our results indicate that
this approach is capable of distinguishing compounds with high/low
efflux ratios as well as CNS+/CNS– compounds and provides advantage
over estimating P-glycoprotein efflux or passive permeability alone
when trying to predict these emergent properties. We also demonstrate
that this method could be useful for rank-ordering chemically similar
compounds and that it can provide detailed mechanistic insight into
the relationship between chemical structure and efflux ratios and/or
CNS penetration, offering guidance as to how compounds could be modified
to improve their access into the brain.
P-glycoprotein; CNS drugs; blood brain barrier; BBB; efflux ratio prediction; structure-based
Through the use of genetic, enzymatic, metabolomic, and structural analyses, we have discovered the catabolic pathway for proline betaine, an osmoprotectant, in Paracoccus denitrificans and Rhodobacter sphaeroides. Genetic and enzymatic analyses showed that several of the key enzymes of the hydroxyproline betaine degradation pathway also function in proline betaine degradation. Metabolomic analyses detected each of the metabolic intermediates of the pathway. The proline betaine catabolic pathway was repressed by osmotic stress and cold stress, and a regulatory transcription factor was identified. We also report crystal structure complexes of the P. denitrificans HpbD hydroxyproline betaine epimerase/proline betaine racemase with l-proline betaine and cis-hydroxyproline betaine.
At least half of the extant protein annotations are incorrect, and the errors propagate as the number of genome sequences increases exponentially. A large-scale, multidisciplinary sequence- and structure-based strategy for functional assignment of bacterial enzymes of unknown function has demonstrated the pathway for catabolism of the osmoprotectant proline betaine.
Loop flexibility is often crucial to protein biological function in solution. We report a new Monte Carlo method for generating conformational ensembles for protein loops and cyclic peptides. The approach incorporates the triaxial loop closure method which addresses the inverse kinematic problem for generating backbone move sets that do not break the loop. Sidechains are sampled together with the backbone in a hierarchical way, making it possible to make large moves that cross energy barriers. As an initial application, we apply the method to the flexible loop in triosephosphate isomerase that caps the active site, and demonstrate that the resulting loop ensembles agree well with key observations from previous structural studies. We also demonstrate, with 3 other test cases, the ability to distinguish relatively flexible and rigid loops within the same protein.
αβ-tubulin dimers need to convert between a ‘bent’ conformation observed for free dimers in solution and a ‘straight’ conformation required for incorporation into the microtubule lattice. Here, we investigate the free energy landscape of αβ-tubulin using molecular dynamics simulations, emphasizing implications for models of assembly, and modulation of the conformational landscape by colchicine, a tubulin-binding drug that inhibits microtubule polymerization. Specifically, we performed molecular dynamics, potential-of-mean force simulations to obtain the free energy profile for unpolymerized GDP-bound tubulin as a function of the ∼12° intradimer rotation differentiating the straight and bent conformers. Our results predict that the unassembled GDP-tubulin heterodimer exists in a continuum of conformations ranging between straight and bent, but, in agreement with existing structural data, suggests that an intermediate bent state has a lower free energy (by ∼1 kcal/mol) and thus dominates in solution. In agreement with predictions of the lattice model of microtubule assembly, lateral binding of two αβ-tubulins strongly shifts the conformational equilibrium towards the straight state, which is then ∼1 kcal/mol lower in free energy than the bent state. Finally, calculations of colchicine binding to a single αβ-tubulin dimer strongly shifts the equilibrium toward the bent states, and disfavors the straight state to the extent that it is no longer thermodynamically populated.
Microtubules are composed of αβ-tubulins that play an instrumental role in regulating intracellular trafficking and formation of the mitotic spindle during mitosis and cell division. Structural studies have shown that tubulin exists in a “straight” conformation compatible with that in the microtubule lattice and a “bent” conformation thought to represent the unassembled state. There is current debate as to whether the straight-to-bent conformational change in tubulin is the cause or consequence of tubulin's assembly into the microtubule lattice. Here, we use free-energy molecular dynamics simulations to qualitatively understand the conformational landscape of tubulin in the unassembled state and upon lateral binding. We predict that soluble tubulin exists primarily in a bent conformation; our simulation results show that tubulin primarily adopts an intermediately bent conformation in agreement with structural data. We also show that lateral binding of two tubulins shifts the equilibrium in favor of the “straight” state, supporting the hypothesis that the straight-to-bent conformational change is the consequence of tubulin's incorporation into the microtubule lattice via lateral interactions. We also show that colchicine binding shifts the population of tubulin in favor of a bent state, further implicating our work in drug discovery.
Identifying novel metabolites and characterizing their biological functions are major challenges of the post-genomic era. X-ray crystallography can reveal unanticipated ligands which persist through purification and crystallization. These adventitious protein:ligand complexes provide insights into new activities, pathways and regulatory mechanisms. We describe a new metabolite, carboxy-S-adenosylmethionine (Cx-SAM), its biosynthetic pathway and its role in tRNA modification. The structure of CmoA, a member of the SAM-dependent methyltransferase superfamily, revealed a ligand in the catalytic site consistent with Cx-SAM. Mechanistic analyses demonstrated an unprecedented role for prephenate as the carboxyl donor and the involvement of a unique ylide intermediate as the carboxyl acceptor in the CmoA-mediated conversion of SAM to Cx-SAM. A second member of the SAM-dependent methyltransferase superfamily, CmoB, recognizes Cx-SAM and acts as a carboxymethyltransferase to convert 5-hydroxyuridine (ho5U) into 5-oxyacetyl uridine (cmo5U) at the wobble position of multiple tRNAs in Gram negative bacteria1, resulting in expanded codon-recognition properties2,3. CmoA and CmoB represent the first documented synthase and transferase for Cx-SAM. These findings reveal new functional diversity in the SAM-dependent methyltransferase superfamily and expand the metabolic and biological contributions of SAM-based biochemistry. These discoveries highlight the value of structural genomics approaches for identifying ligands in the context of their physiologically relevant macromolecular binding partners and for aiding in functional assignment.
A series of cyclic peptides were designed and prepared to investigate the physicochemical properties that affect oral bioavailabilty of this chemotype in rats. In particular, the ionization state of the peptide was examined by the incorporation of naturally occurring amino acid residues that are charged in differing regions of the gut. In addition, data was generated in a variety of in vitro assays and the usefulness of this data in predicting the subsequent oral bioavailability observed in the rat is discussed.
We present a thermodynamical approach to identify changes in macromolecular structure and dynamics in response to perturbations such as mutations or ligand binding, using an expansion of the Kullback-Leibler Divergence that connects local population shifts in torsion angles to changes in the free energy landscape of the protein. While the Kullback-Leibler Divergence is a known formula from information theory, the novelty and power of our implementation lies in its formal developments, connection to thermodynamics, statistical filtering, ease of visualization of results, and extendability by adding higher-order terms. We present a formal derivation of the Kullback-Leibler Divergence expansion and then apply our method at a first-order approximation to molecular dynamics simulations of four protein systems where ligand binding or pH titration is known to cause an effect at a distant site. Our results qualitatively agree with experimental measurements of local changes in structure or dynamics, such as NMR chemical shift perturbations and hydrogen-deuterium exchange mass spectrometry. The approach produces easy-to-analyze results with low background, and as such has the potential to become a routine analysis when molecular dynamics simulations in two or more conditions are available. Our method is implemented in the MutInf code package and is available on the SimTK website at https://simtk.org/home/mutinf.