Allosteric signaling in C-linker and cyclic nucleotide-binding domain of HCN2 channels.

Opening of hyperpolarization-activated cyclic nucleotide-modulated (HCN) channels is controlled by membrane hyperpolarization and binding of cyclic nucleotides to the tetrameric cyclic nucleotide-binding domain (CNBD), attached to the C-linker (CL) disk. Confocal patch-clamp fluorometry revealed pronounced cooperativity of ligand binding among protomers. However, by which pathways allosteric signal transmission occurs remained elusive. Here, we investigate how changes in the structural dynamics of the CL-CNBD of mouse HCN2 upon cAMP binding relate to inter- and intrasubunit signal transmission. Applying a rigidity-theory-based approach, we identify two intersubunit and one intrasubunit pathways that differ in allosteric coupling strength between cAMP-binding sites or toward the CL. These predictions agree with results from electrophysiological and patch-clamp fluorometry experiments. Our results map out distinct routes within the CL-CNBD that modulate different cAMP-binding responses in HCN2 channels. They signify that functionally relevant submodules may exist within and across structurally discernable subunits in HCN channels.

Biallelic mutation of human SLC6A6 encoding the taurine transporter TAUT is linked to early retinal degeneration.

We previously reported that inactivation of the transmembrane taurine transporter (TauT or solute carrier 6a6) causes early retinal degeneration in mice. Compatible with taurine's indispensability for cell volume homeostasis, protein stabilization, cytoprotection, antioxidation, and immuno- and neuromodulation, mice develop multisystemic dysfunctions (hearing loss; liver fibrosis; and behavioral, heart, and skeletal muscle abnormalities) later on. Here, by genetic, cell biologic, in vivo1H-magnetic resonance spectroscopy and molecular dynamics simulation studies, we conducted in-depth characterization of a novel disorder: human TAUT deficiency. Loss of TAUT function due to a homozygous missense mutation caused panretinal degeneration in 2 brothers. TAUTp.A78E still localized in the plasma membrane but is predicted to impact structural stabilization. 3H-taurine uptake by peripheral blood mononuclear cells was reduced by 95%, and taurine levels were severely reduced in plasma, skeletal muscle, and brain. Extraocular dysfunctions were not yet detected, but significantly increased urinary excretion of 8-oxo-7,8-dihydroguanosine indicated generally enhanced (yet clinically unapparent) oxidative stress and RNA oxidation, warranting continuous broad surveillance.-Preising, M. N., Görg, B., Friedburg, C., Qvartskhava, N., Budde, B. S., Bonus, M., Toliat, M. R., Pfleger, C., Altmüller, J., Herebian, D., Beyer, M., Zöllner, H. J., Wittsack, H.-J., Schaper, J., Klee, D., Zechner, U., Nürnberg, P., Schipper, J., Schnitzler, A., Gohlke, H., Lorenz, B., Häussinger, D., Bolz, H. J. Biallelic mutation of human SLC6A6 encoding the taurine transporter TAUT is linked to early retinal degeneration.

Basal Histamine H4 Receptor Activation: Agonist Mimicry by the Diphenylalanine Motif.

Histamine H4 receptor (H4 R) orthologues are G-protein-coupled receptors (GPCRs) that exhibit species-dependent basal activity. In contrast to the basally inactive mouse H4 R (mH4 R), human H4 R (hH4 R) shows a high degree of basal activity. We have performed long-timescale molecular dynamics simulations and rigidity analyses on wild-type hH4 R, the experimentally characterized hH4 R variants S179M, F169V, F169V+S179M, F168A, and on mH4 R to investigate the molecular nature of the differential basal activity. H4 R variant-dependent differences between essential motifs of GPCR activation and structural stabilities correlate with experimentally determined basal activities and provide a molecular explanation for the differences in basal activation. Strikingly, during the MD simulations, F16945.55 dips into the orthosteric binding pocket only in the case of hH4 R, thus adopting the role of an agonist and contributing to the stabilization of the active state. The results shed new light on the molecular mechanism of basal H4 R activation that are of importance for other GPCRs.

Converging a Knowledge-Based Scoring Function: DrugScore2018.

We present DrugScore2018, a new version of the knowledge-based scoring function DrugScore, which builds upon the same formalism used to derive DrugScore but exploits a training data set of nearly 40 000 X-ray complex structures, a highly diverse and the, by far, largest data set ever used for such an endeavor. About 2.5 times as many pair potentials than before now have a data basis required to yield smooth potentials, and pair potentials could now be derived for eight more atom types, including interactions involving halogen atoms and metal ions highly relevant for medicinal chemistry. Probing for dependence on training data set size and quality, we show that DrugScore2018 potentials are converged. We evaluated DrugScore2018 in comprehensive scoring, ranking, docking, and screening tests on the CASF-2013 data set, allowing for a comparison with >30 other scoring functions. There, DrugScore2018 showed similar or improved performance in all aspects when compared to either DrugScore, DrugScoreCSD, or DSX and was, overall, the scoring function showing the most consistently good performance in scoring, ranking, and docking tests. Applying DrugScore2018 as objective function in AutoDock3 in a large-scale docking trial, using 4056 protein-ligand complexes from PDBbind 2016, reproduced a docked pose to within 2 ŠRMSD to the crystal structure in >75% of all dockings. These results are remarkable as the DrugScore2018 potentials were derived from crystallographic information only, without any further adaptation using binding affinity or docking decoy data. DrugScore2018 should thus be a competitive scoring and objective function for structure-based ligand design purposes.

CNA web server: rigidity theory-based thermal unfolding simulations of proteins for linking structure, (thermo-)stability, and function.

The Constraint Network Analysis (CNA) web server provides a user-friendly interface to the CNA approach developed in our laboratory for linking results from rigidity analyses to biologically relevant characteristics of a biomolecular structure. The CNA web server provides a refined modeling of thermal unfolding simulations that considers the temperature dependence of hydrophobic tethers and computes a set of global and local indices for quantifying biomacromolecular stability. From the global indices, phase transition points are identified where the structure switches from a rigid to a floppy state; these phase transition points can be related to a protein's (thermo-)stability. Structural weak spots (unfolding nuclei) are automatically identified, too; this knowledge can be exploited in data-driven protein engineering. The local indices are useful in linking flexibility and function and to understand the impact of ligand binding on protein flexibility. The CNA web server robustly handles small-molecule ligands in general. To overcome issues of sensitivity with respect to the input structure, the CNA web server allows performing two ensemble-based variants of thermal unfolding simulations. The web server output is provided as raw data, plots and/or Jmol representations. The CNA web server, accessible at http://cpclab.uni-duesseldorf.de/cna or http://www.cnanalysis.de, is free and open to all users with no login requirement.

The metagenome-derived esterase PET40 is highly promiscuous and hydrolyses polyethylene terephthalate (PET).

Polyethylene terephthalate (PET) is a widely used synthetic polymer and known to contaminate marine and terrestrial ecosystems. Only few PET-active microorganisms and enzymes (PETases) are currently known, and it is debated whether degradation activity for PET originates from promiscuous enzymes with broad substrate spectra that primarily act on natural polymers or other bulky substrates, or whether microorganisms evolved their genetic makeup to accepting PET as a carbon source. Here, we present a predicted diene lactone hydrolase designated PET40, which acts on a broad spectrum of substrates, including PET. It is the first esterase with activity on PET from a GC-rich Gram-positive Amycolatopsis species belonging to the Pseudonocardiaceae (Actinobacteria). It is highly conserved within the genera Amycolatopsis and Streptomyces. PET40 was identified by sequence-based metagenome search using a PETase-specific hidden Markov model. Besides acting on PET, PET40 has a versatile substrate spectrum, hydrolyzing δ-lactones, ß-lactam antibiotics, the polyester-polyurethane Impranil® DLN, and various para-nitrophenyl ester substrates. Molecular docking suggests that the PET degradative activity is likely a result of the promiscuity of PET40, as potential binding modes were found for substrates encompassing mono(2-hydroxyethyl) terephthalate, bis(2-hydroxyethyl) terephthalate, and a PET trimer. We also solved the crystal structure of the inactive PET40 variant S178A to 1.60 Å resolution. PET40 is active throughout a wide pH (pH 4-10) and temperature range (4-65 °C) and remarkably stable in the presence of 5% SDS, making it a promising enzyme as a starting point for further investigations and optimization approaches.

Global and local indices for characterizing biomolecular flexibility and rigidity.

Understanding flexibility and rigidity characteristics of biomolecules is a prerequisite for understanding biomolecular structural stability and function. Computational methods have been implemented that directly characterize biomolecular flexibility and rigidity by constraint network analysis. For deriving maximal advantage from these analyses, their results need to be linked to biologically relevant characteristics of a structure. Such links are provided by global and local measures ("indices") of biomolecular flexibility and rigidity. To date, more than 14 indices are available with sometimes overlapping or only vague definitions. We present concise definitions of these indices, analyze the relation between, and the scope and limitations of them, and compare their informative value. For this, we probe the structural stability of the calcium binding protein α-lactalbumin as a showcase, both in the "ground state" and after perturbing the system by changing the network topology. In addition, we introduce three indices for the first time that extend the application domain of flexibility and rigidity analyses. The results allow us to provide guidelines for future studies suggesting which of these indices could best be used for analyzing, understanding, and quantifying structural features that are important for biomolecular stability and function. Finally, we make suggestions for proper index notations in future studies to prevent the misinterpretation and to facilitate the comparison of results obtained from flexibility and rigidity analyses.

Constraint Network Analysis (CNA): a Python software package for efficiently linking biomacromolecular structure, flexibility, (thermo-)stability, and function.

For deriving maximal advantage from information on biomacromolecular flexibility and rigidity, results from rigidity analyses must be linked to biologically relevant characteristics of a structure. Here, we describe the Python-based software package Constraint Network Analysis (CNA) developed for this task. CNA functions as a front- and backend to the graph-based rigidity analysis software FIRST. CNA goes beyond the mere identification of flexible and rigid regions in a biomacromolecule in that it (I) provides a refined modeling of thermal unfolding simulations that also considers the temperature-dependence of hydrophobic tethers, (II) allows performing rigidity analyses on ensembles of network topologies, either generated from structural ensembles or by using the concept of fuzzy noncovalent constraints, and (III) computes a set of global and local indices for quantifying biomacromolecular stability. This leads to more robust results from rigidity analyses and extends the application domain of rigidity analyses in that phase transition points ("melting points") and unfolding nuclei ("structural weak spots") are determined automatically. Furthermore, CNA robustly handles small-molecule ligands in general. Such advancements are important for applying rigidity analysis to data-driven protein engineering and for estimating the influence of ligand molecules on biomacromolecular stability. CNA maintains the efficiency of FIRST such that the analysis of a single protein structure takes a few seconds for systems of several hundred residues on a single core. These features make CNA an interesting tool for linking biomacromolecular structure, flexibility, (thermo-)stability, and function. CNA is available from http://cpclab.uni-duesseldorf.de/software for nonprofit organizations.

Combining structural and coevolution information to unveil allosteric sites.

Understanding allosteric regulation in biomolecules is of great interest to pharmaceutical research and computational methods emerged during the last decades to characterize allosteric coupling. However, the prediction of allosteric sites in a protein structure remains a challenging task. Here, we integrate local binding site information, coevolutionary information, and information on dynamic allostery into a structure-based three-parameter model to identify potentially hidden allosteric sites in ensembles of protein structures with orthosteric ligands. When tested on five allosteric proteins (LFA-1, p38-α, GR, MAT2A, and BCKDK), the model successfully ranked all known allosteric pockets in the top three positions. Finally, we identified a novel druggable site in MAT2A confirmed by X-ray crystallography and SPR and a hitherto unknown druggable allosteric site in BCKDK validated by biochemical and X-ray crystallography analyses. Our model can be applied in drug discovery to identify allosteric pockets.

Enzyme adaptation to habitat thermal legacy shapes the thermal plasticity of marine microbiomes.

Microbial communities respond to temperature with physiological adaptation and compositional turnover. Whether thermal selection of enzymes explains marine microbiome plasticity in response to temperature remains unresolved. By quantifying the thermal behaviour of seven functionally-independent enzyme classes (esterase, extradiol dioxygenase, phosphatase, beta-galactosidase, nuclease, transaminase, and aldo-keto reductase) in native proteomes of marine sediment microbiomes from the Irish Sea to the southern Red Sea, we record a significant effect of the mean annual temperature (MAT) on enzyme response in all cases. Activity and stability profiles of 228 esterases and 5 extradiol dioxygenases from sediment and seawater across 70 locations worldwide validate this thermal pattern. Modelling the esterase phase transition temperature as a measure of structural flexibility confirms the observed relationship with MAT. Furthermore, when considering temperature variability in sites with non-significantly different MATs, the broadest range of enzyme thermal behaviour and the highest growth plasticity of the enriched heterotrophic bacteria occur in samples with the widest annual thermal variability. These results indicate that temperature-driven enzyme selection shapes microbiome thermal plasticity and that thermal variability finely tunes such processes and should be considered alongside MAT in forecasting microbial community thermal response.

An archaeal lid-containing feruloyl esterase degrades polyethylene terephthalate.

Polyethylene terephthalate (PET) is a commodity polymer known to globally contaminate marine and terrestrial environments. Today, around 80 bacterial and fungal PET-active enzymes (PETases) are known, originating from four bacterial and two fungal phyla. In contrast, no archaeal enzyme had been identified to degrade PET. Here we report on the structural and biochemical characterization of PET46 (RLI42440.1), an archaeal promiscuous feruloyl esterase exhibiting degradation activity on semi-crystalline PET powder comparable to IsPETase and LCC (wildtypes), and higher activity on bis-, and mono-(2-hydroxyethyl) terephthalate (BHET and MHET). The enzyme, found by a sequence-based metagenome search, is derived from a non-cultivated, deep-sea Candidatus Bathyarchaeota archaeon. Biochemical characterization demonstrated that PET46 is a promiscuous, heat-adapted hydrolase. Its crystal structure was solved at a resolution of 1.71 Å. It shares the core alpha/beta-hydrolase fold with bacterial PETases, but contains a unique lid common in feruloyl esterases, which is involved in substrate binding. Thus, our study widens the currently known diversity of PET-hydrolyzing enzymes, by demonstrating PET depolymerization by a plant cell wall-degrading esterase.

Hot spots and transient pockets: predicting the determinants of small-molecule binding to a protein-protein interface.

Protein-protein interfaces are considered difficult targets for small-molecule protein-protein interaction modulators (PPIMs ). Here, we present for the first time a computational strategy that simultaneously considers aspects of energetics and plasticity in the context of PPIM binding to a protein interface. The strategy aims at identifying the determinants of small-molecule binding, hot spots, and transient pockets, in a protein-protein interface in order to make use of this knowledge for predicting binding modes of and ranking PPIMs with respect to their affinity. When applied to interleukin-2 (IL-2), the computationally inexpensive constrained geometric simulation method FRODA outperforms molecular dynamics simulations in sampling hydrophobic transient pockets. We introduce the PPIAnalyzer approach for identifying transient pockets on the basis of geometrical criteria only. A sequence of docking to identified transient pockets, starting structure selection based on hot spot information, RMSD clustering and intermolecular docking energies, and MM-PBSA calculations allows one to enrich IL-2 PPIMs from a set of decoys and to discriminate between subgroups of IL-2 PPIMs with low and high affinity. Our strategy will be applicable in a prospective manner where nothing else than a protein-protein complex structure is known; hence, it can well be the first step in a structure-based endeavor to identify PPIMs.

Physiological, Biochemical, and Structural Bioinformatic Analysis of the Multiple Inositol Dehydrogenases from Corynebacterium glutamicum.

Inositols (cyclohexanehexols) comprise nine isomeric cyclic sugar alcohols, several of which occur in all domains of life with various functions. Many bacteria can utilize inositols as carbon and energy sources via a specific pathway involving inositol dehydrogenases (IDHs) as the first step of catabolism. The microbial cell factory Corynebacterium glutamicum can grow with myo-inositol as a sole carbon source. Interestingly, this species encodes seven potential IDHs, raising the question of the reason for this multiplicity. We therefore investigated the seven IDHs to determine their function, activity, and selectivity toward the biologically most important isomers myo-, scyllo-, and d-chiro-inositol. We created an ΔIDH strain lacking all seven IDH genes, which could not grow on the three inositols. scyllo- and d-chiro-inositol were identified as novel growth substrates of C. glutamicum. Complementation experiments showed that only four of the seven IDHs (IolG, OxiB, OxiD, and OxiE) enabled growth of the ΔIDH strain on two of the three inositols. The kinetics of the four purified enzymes agreed with the complementation results. IolG and OxiD are NAD+-dependent IDHs accepting myo- and d-chiro-inositol but not scyllo-inositol. OxiB is an NAD+-dependent myo-IDH with a weak activity also for scyllo-inositol but not for d-chiro-inositol. OxiE on the other hand is an NAD+-dependent scyllo-IDH showing also good activity for myo-inositol and a very weak activity for d-chiro-inositol. Structural models, molecular docking experiments, and sequence alignments enabled the identification of the substrate binding sites of the active IDHs and of residues allowing predictions on the substrate specificity. IMPORTANCE myo-, scyllo-, and d-chiro-inositol are C6 cyclic sugar alcohols with various biological functions, which also serve as carbon sources for microbes. Inositol catabolism starts with an oxidation to keto-inositols catalyzed by inositol dehydrogenases (IDHs). The soil bacterium C. glutamicum encodes seven potential IDHs. Using a combination of microbiological, biochemical, and modeling approaches, we analyzed the function of these enzymes and identified four IDHs involved in the catabolism of inositols. They possess distinct substrate preferences for the three isomers, and modeling and sequence alignments allowed the identification of residues important for substrate specificity. Our results expand the knowledge of bacterial inositol metabolism and provide an important basis for the rational development of producer strains for these valuable inositols, which show pharmacological activities against, e.g., Alzheimer's disease, polycystic ovarian syndrome, or type II diabetes.

Uncoupling of Voltage- and Ligand-Induced Activation in HCN2 Channels by Glycine Inserts.

Hyperpolarization-activated cyclic nucleotide-modulated (HCN) channels are tetramers that generate electrical rhythmicity in special brain neurons and cardiomyocytes. The channels are activated by membrane hyperpolarization. The binding of cAMP to the four available cyclic nucleotide-binding domains (CNBD) enhances channel activation. We analyzed in the present study the mechanism of how the effect of cAMP binding is transmitted to the pore domain. Our strategy was to uncouple the C-linker (CL) from the channel core by inserting one to five glycine residues between the S6 gate and the A'-helix (constructs 1G to 5G). We quantified in full-length HCN2 channels the resulting functional effects of the inserted glycines by current activation as well as the structural dynamics and statics using molecular dynamics simulations and Constraint Network Analysis. We show functionally that already in 1G the cAMP effect on activation is lost and that with the exception of 3G and 5G the concentration-activation relationships are shifted to depolarized voltages with respect to HCN2. The strongest effect was found for 4G. Accordingly, the activation kinetics were accelerated by all constructs, again with the strongest effect in 4G. The simulations reveal that the average residue mobility of the CL and CNBD domains is increased in all constructs and that the junction between the S6 and A'-helix is turned into a flexible hinge, resulting in a destabilized gate in all constructs. Moreover, for 3G and 4G, there is a stronger downward displacement of the CL-CNBD than in HCN2 and the other constructs, resulting in an increased kink angle between S6 and A'-helix, which in turn loosens contacts between the S4-helix and the CL. This is suggested to promote a downward movement of the S4-helix, similar to the effect of hyperpolarization. In addition, exclusively in 4G, the selectivity filter in the upper pore region and parts of the S4-helix are destabilized. The results provide new insights into the intricate activation of HCN2 channels.

Pocket-space maps to identify novel binding-site conformations in proteins.

The identification of novel binding-site conformations can greatly assist the progress of structure-based ligand design projects. Diverse pocket shapes drive medicinal chemistry to explore a broader chemical space and thus present additional opportunities to overcome key drug discovery issues such as potency, selectivity, toxicity, and pharmacokinetics. We report a new automated approach to diverse pocket selection, PocketAnalyzer(PCA), which applies principal component analysis and clustering to the output of a grid-based pocket detection algorithm. Since the approach works directly with pocket shape descriptors, it is free from some of the problems hampering methods that are based on proxy shape descriptors, e.g. a set of atomic positional coordinates. The approach is technically straightforward and allows simultaneous analysis of mutants, isoforms, and protein structures derived from multiple sources with different residue numbering schemes. The PocketAnalyzer(PCA) approach is illustrated by the compilation of diverse sets of pocket shapes for aldose reductase and viral neuraminidase. In both cases this allows identification of novel computationally derived binding-site conformations that are yet to be observed crystallographically. Indeed, known inhibitors capable of exploiting these novel binding-site conformations are subsequently identified, thereby demonstrating the utility of PocketAnalyzer(PCA) for rationalizing and improving the understanding of the molecular basis of protein-ligand interaction and bioactivity. A Python program implementing the PocketAnalyzer(PCA) approach is available for download under an open-source license ( http://sourceforge.net/projects/papca/ or http://cpclab.uni-duesseldorf.de/downloads ).

Recognition motif and mechanism of ripening inhibitory peptides in plant hormone receptor ETR1.

Synthetic peptides derived from ethylene-insensitive protein 2 (EIN2), a central regulator of ethylene signalling, were recently shown to delay fruit ripening by interrupting protein-protein interactions in the ethylene signalling pathway. Here, we show that the inhibitory peptide NOP-1 binds to the GAF domain of ETR1 - the prototype of the plant ethylene receptor family. Site-directed mutagenesis and computational studies reveal the peptide interaction site and a plausible molecular mechanism for the ripening inhibition.

Ensemble- and Rigidity Theory-Based Perturbation Approach To Analyze Dynamic Allostery.

Allostery describes the functional coupling between sites in biomolecules. Recently, the role of changes in protein dynamics for allosteric communication has been highlighted. A quantitative and predictive description of allostery is fundamental for understanding biological processes. Here, we integrate an ensemble-based perturbation approach with the analysis of biomolecular rigidity and flexibility to construct a model of dynamic allostery. Our model, by definition, excludes the possibility of conformational changes, evaluates static, not dynamic, properties of molecular systems, and describes allosteric effects due to ligand binding in terms of a novel free-energy measure. We validated our model on three distinct biomolecular systems: eglin c, protein tyrosine phosphatase 1B, and the lymphocyte function-associated antigen 1 domain. In all cases, it successfully identified key residues for signal transmission in very good agreement with the experiment. It correctly and quantitatively discriminated between positively or negatively cooperative effects for one of the systems. Our model should be a promising tool for the rational discovery of novel allosteric drugs.

Efficient and robust analysis of biomacromolecular flexibility using ensembles of network topologies based on fuzzy noncovalent constraints.

We describe an approach (ENT(FNC)) for performing rigidity analyses of biomacromolecules on ensembles of network topologies (ENT) generated from a single input structure. The ENT is based on fuzzy noncovalent constraints, which considers thermal fluctuations of biomacromolecules without actually sampling conformations. Definitions for fuzzy noncovalent constraints were derived from persistency data from molecular dynamics (MD) simulations. A very good agreement between local flexibility and rigidity characteristics from ENT(FNC) and MD simulations-generated ensembles is found. Regarding global characteristics, convincing results were obtained when relative thermostabilities of citrate synthase and lipase A structures were computed. The ENT(FNC) approach significantly improves the robustness of rigidity analyses, is highly efficient, and does not require a protein-specific parameterization. Its low computational demand makes it especially valuable for the analysis of large data sets, e.g., for data-driven protein engineering.