Modulating Functional Loop Movements: The Role of Highly Conserved Residues in the Correlated Loop Motions

Targeted Mutation of a Non-catalytic Gating Residue Increases the Rate of Pseudomonas aeruginosa d-Arginine Dehydrogenase Catalytic Turnover

Commercial food and l-amino acid industries rely on bioengineered d-amino acid oxidizing enzymes to detect and remove d-amino acid contaminants. However, the bioengineering of enzymes to generate faster biological catalysts has proven difficult as a result of the failure to target specific kinetic steps that limit enzyme turnover, kcat, and the poor understanding of loop dynamics critical for catalysis. Pseudomonas aeruginosa d-arginine dehydrogenase (PaDADH) oxidizes most d-amino acids and is a good candidate for application in the l-amino acid and food industries. The side chain of the loop L2 E246 residue located at the entrance of the PaDADH active site pocket potentially favors the closed active site conformation and secures the substrate upon binding. This study used site-directed mutagenesis, steady-state, and rapid reaction kinetics to generate the glutamine, glycine, and leucine variants and investigate whether increasing the rate of product release could translate to an increased enzyme turnover rate. Upon E246 mutation to glycine, there was an increased rate of d-arginine turnover kcat from 122 to 500 s-1. Likewise, the kcat values increased 2-fold for the glutamine or leucine variants. Thus, we have engineered a faster biocatalyst for industrial applications by selectively increasing the rate of the PaDADH product release.

Mutation of a distal gating residue modulates NADH binding in NADH:Quinone oxidoreductase from Pseudomonas aeruginosa PAO1

Enzymes require flexible regions to adopt multiple conformations during catalysis. The mobile regions of enzymes include gates that modulate the passage of molecules in and out of the enzyme's active site. The enzyme PA1024 from Pseudomonas aeruginosa PA01 is a recently discovered flavin-dependent NADH:quinone oxidoreductase (NQO, EC 1.6.5.9). Q80 in loop 3 (residues 75-86) of NQO is ∼15 Å away from the flavin and creates a gate that seals the active site through a hydrogen bond with Y261 upon NADH binding. In this study, we mutated Q80 to glycine, leucine, or glutamate to investigate the mechanistic significance of distal residue Q80 in NADH binding in the active site of NQO. The UV-visible absorption spectrum reveals that the mutation of Q80 minimally affects the protein microenvironment surrounding the flavin. The anaerobic reductive half-reaction of the NQO-mutants yields a ≥25-fold increase in the Kd value for NADH compared to the WT enzyme. However, we determined that the kred value was similar in the Q80G, Q80L, and wildtype enzymes and only ∼25% smaller in the Q80E enzyme. Steady-state kinetics with NQO-mutants and NQO-WT at varying concentrations of NADH and 1,4-benzoquinone establish a ≤5-fold decrease in the kcat/KNADH value. Moreover, there is no significant difference in the kcat/KBQ (∼1 × 106 M-1s-1) and kcat (∼24 s-1) values in NQO-mutants and NQO-WT. These results are consistent with the distal residue Q80 being mechanistically essential for NADH binding to NQO with minimal effect on the quinone binding to the enzyme and hydride transfer from NADH to flavin.

Engineered control of enzyme structural dynamics and function

Enzymes undergo a range of internal motions from local, active site fluctuations to large-scale, global conformational changes. These motions are often important for enzyme function, including in ligand binding and dissociation and even preparing the active site for chemical catalysis. Protein engineering efforts have been directed towards manipulating enzyme structural dynamics and conformational changes, including targeting specific amino acid interactions and creation of chimeric enzymes with new regulatory functions. Post-translational covalent modification can provide an additional level of enzyme control. These studies have not only provided insights into the functional role of protein motions, but they offer opportunities to create stimulus-responsive enzymes. These enzymes can be engineered to respond to a number of external stimuli, including light, pH, and the presence of novel allosteric modulators. Altogether, the ability to engineer and control enzyme structural dynamics can provide new tools for biotechnology and medicine.

Bacterial Glycosyltransferases: Challenges and Opportunities of a Highly Diverse Enzyme Class Toward Tailoring Natural Products

The enzyme subclass of glycosyltransferases (GTs; EC 2.4) currently comprises 97 families as specified by CAZy classification. One of their important roles is in the biosynthesis of disaccharides, oligosaccharides, and polysaccharides by catalyzing the transfer of sugar moieties from activated donor molecules to other sugar molecules. In addition GTs also catalyze the transfer of sugar moieties onto aglycons, which is of great relevance for the synthesis of many high value natural products. Bacterial GTs show a higher sequence similarity in comparison to mammalian ones. Even when most GTs are poorly explored, state of the art technologies, such as protein engineering, domain swapping or computational analysis strongly enhance our understanding and utilization of these very promising classes of proteins. This perspective article will focus on bacterial GTs, especially on classification, screening and engineering strategies to alter substrate specificity. The future development in these fields as well as obstacles and challenges will be highlighted and discussed.

Predicting flexible loop regions that interact with ligands: the challenge of accurate scoring

Flexible loop regions play a critical role in the biological function of many proteins and have been shown to be involved in ligand binding. In the context of structure-based drug design, using or predicting an incorrect loop configuration can be detrimental to the study if the loop is capable of interacting with the ligand. Three protein systems, each with at least one flexible loop region in close proximity to the known binding site, were selected for loop prediction using the CorLps program; a six residue loop region from phosphoribosylglycinamide formyltransferase (GART), two nine residue loop regions from cytochrome P450 (CYP) 119, and an 11 residue loop region from enolase were selected for loop prediction. The results of this study indicate that the statistically based DFIRE scoring function implemented in the CorLps program did not accurately rank native-like predicted loop configurations in any protein system. In an attempt to improve the ranking of the native-like predicted loop configurations, the MM/GBSA and the optimized MM/GBSA-dsr scoring functions were used to re-rank the predicted loops with and without bound ligand. In general, single snapshot MM/GBSA scoring provided the best ranking of native-like loop configurations. Based on the scoring function analyses presented, the optimal ranking of native-like loop configurations is still a difficult challenge and the choice of the "best" scoring function appears to be system dependent.

New computational method for prediction of interacting protein loop regions

Flexible loop regions of proteins play a crucial role in many biological functions such as protein-ligand recognition, enzymatic catalysis, and protein-protein association. To date, most computational methods that predict the conformational states of loops only focus on individual loop regions. However, loop regions are often spatially in close proximity to one another and their mutual interactions stabilize their conformations. We have developed a new method, titled CorLps, capable of simultaneously predicting such interacting loop regions. First, an ensemble of individual loop conformations is generated for each loop region. The members of the individual ensembles are combined and are accepted or rejected based on a steric clash filter. After a subsequent side-chain optimization step, the resulting conformations of the interacting loops are ranked by the statistical scoring function DFIRE that originated from protein structure prediction. Our results show that predicting interacting loops with CorLps is superior to sequential prediction of the two interacting loop regions, and our method is comparable in accuracy to single loop predictions. Furthermore, improved predictive accuracy of the top-ranked solution is achieved for 12-residue length loop regions by diversifying the initial pool of individual loop conformations using a quality threshold clustering algorithm.

Comparative analysis of structural and dynamic properties of the loaded and unloaded hemophore HasA: functional implications

A heme-acquisition system present in several Gram-negative bacteria requires the secretion of hemophores. These extracellular carrier proteins capture heme and deliver it to specific outer membrane receptors. The Serratia marcescens HasA hemophore is a monodomain protein that binds heme with a very high affinity. Its alpha/beta structure, as that of its binding pocket, has no common features with other iron- or heme-binding proteins. Heme is held by two loops L1 and L2 and coordinated to iron by an unusual ligand pair, H32/Y75. Two independent regions of the hemophore beta-sheet are involved in HasA-HasR receptor interaction. Here, we report the 3-D NMR structure of apoHasA and the backbone dynamics of both loaded and unloaded hemophore. While the overall structure of HasA is very similar in the apo and holo forms, the hemophore presents a transition from an open to a closed form upon ligand binding, through a large movement, of up to 30 A, of loop L1 bearing H32. Comparison of loaded and unloaded HasA dynamics on different time scales reveals striking flexibility changes in the binding pocket. We propose a mechanism by which these structural and dynamic features provide the dual function of heme binding and release to the HasR receptor.

Hydration of a hydrophobic cavity and its functional role: a simulation study of human interleukin-1beta

Molecular dynamics simulations reveal that the hydrophobic cavity in human cytokine Interleukin-1beta is hydrated and can dynamically accommodate between one and four water molecules. These waters have residence times >> 500 ps and can give rise to detectable NOEs, in agreement with NMR observations of Ernst et al. (Science 1995; 267:1813-1817). The waters also display high positional disorder within the cavity, which explains why they have not been resolved crystallographically. The average distribution of water molecules over time within the cavity matches well the low resolution electron density extracted by Yu et al. (Proc Natl Acad Sci 1999; 96:103-108). The water molecules hydrate the hydrophobic cavity preferentially as complex clusters. These clusters result from a combination of hydrogen bonds between the waters and stabilizing interactions between the waters and aromatic rings forming the cavity. Free energy estimates suggest that it takes 4-waters to hydrate the cavity in a thermodynamically stable manner leading to a gain in free energy of transfer from bulk of approximately approximately 3.6 kcal/mol. This arises from the existence of the water clusters in multiple hydrogen bonded states. In addition, the waters are found to migrate either individually or as clusters out of the cavity through several pathways. The upper limit for one-dimensional diffusion of the waters within the protein matrix is 4 A/ps (relative to 6 A/ps for bulk). Simulations reveal pathways in addition to those identified crystallographically, with motions controlled by the rotations of sidechains. We find that only when the hydrophobic cavity is hydrated, do correlated motions couple distant sites with the sites that make contact with the receptor and this data partly offers an explanation of experimental mutagenesis data. Simulations, together with recent observations based on mutagenesis by Heidary et al. (J Mol Biol 2005; 353:1187-1198) that hydrogen bond networks couple motions across long distances in interleukin-1beta, lead us to hypothesize that the hydration of the cavity (conserved across mammals) can thermodynamically enhance hydrogen bond networks to enable coupling across long distances by acting as a plug and this in turn enables a kinetic control of the rate of transmission of signals.

Structure networks of E. coli glutaminyl-tRNA synthetase: Effects of ligand binding

It is well known that proteins undergo backbone as well as side chain conformational changes upon ligand binding, which is not necessarily confined to the active site. Both the local and the global conformational changes brought out by ligand-binding have been extensively studied earlier. However, the global changes have been reported mainly at the protein backbone level. Here we present a method that explicitly takes into account the side chain interactions, yet providing a global view of the ligand-induced conformational changes. This is achieved through the analysis of Protein Structure Networks (PSN), constructed from the noncovalent side chain interactions in the protein. Here, E. coli Glutaminyl-tRNA synthetase (GlnRS) in the ligand-free and different ligand-bound states is used as a case study to assess the effect of binding of tRNA, ATP, and the amino acid Gln to GlnRS. The PSNs are constructed on the basis of the strength of noncovalent interactions existing between the side chains of amino acids. The parameters like the size of the largest cluster, edge to node ratio, and the total number of hubs are used to quantitatively assess the structure network changes. These network parameters have effectively captured the ligand-induced structural changes at a global structure network level. Hubs, the highly connected amino acids, are also identified from these networks. Specifically, we are able to characterize different types of hubs based on the comparison of structure networks of the GlnRS system. The differences in the structure networks in both the presence and the absence of the ligands are reflected in these hubs. For instance, the characterization of hubs that are present in both the ligand-free and all the ligand-bound GlnRS (the invariant hubs) might implicate their role in structural integrity. On the other hand, identification of hubs unique to a particular ligand-bound structure (the exclusive hubs) not only highlights the structural differences mediated by ligand-binding at the structure network level, but also highlights significance of these amino acids hubs in binding to the ligand and catalyzing the biochemical function. Further, the hubs identified from this study could be ideal targets for mutational studies to ascertain the ligand-induced structure-function relationships in E. coli GlnRS. The formalism used in this study is simple and can be applied to other protein-ligands in general to understand the allosteric changes mediated by the binding of ligands.

The use of a generalized born model for the analysis of protein conformational transitions: a comparative study with explicit solvent simulations for chemotaxis Y protein (CheY)

To investigate whether implicit solvent models are appropriate for mechanistic studies of conformational transition in proteins, a recently developed generalized Born model (GBSW) was applied to a small signaling protein, chemotaxis protein Y (CheY), with different combinations of the phosphorylation state and conformation of the system; the results were compared to explicit solvent simulations using a stochastic boundary condition. The subtle but distinct conformational transitions involved in CheY activation makes the system ideally suited for comparing implicit and explicit solvent models because these conformational transitions are potentially accessible in both types of simulations. The structural and dynamical properties analyzed include not only those localized to the active site region but also throughout the protein, such as sidechain methyl group order parameters, backbone hydrogen bonding lifetime and occupancy as well as principal components of the trajectories. Overall, many properties were well reproduced by the GBSW simulations when compared with the explicit solvent calculations, although a number of observations consistently point to the suggestion that the current parameterization of the GBSW model tends to overestimate hydrogen-bonding interactions involving both charged groups and (charge-neutral) backbone atoms. This deficiency led to overstabilization of certain secondary structural motifs and more importantly, qualitatively different behaviors for the active site groups (Thr 87, Ala 88, the beta4-alpha4 loop) in response to phosphorylation, when compared with explicit solvent simulations. The current study highlights the value of carrying out both explicit and implicit solvent simulations for complementary mechanistic insights in the analysis of conformational transition in biomolecules.

A study of collective atomic fluctuations and cooperativity in the U1A-RNA complex based on molecular dynamics simulations

Cooperative interactions play an important role in recognition and binding in macromolecular systems. In this study, we find that cross-correlated atomic fluctuations can be used to identify cooperative networks in a protein-RNA system. The dynamics of the RRM-containing protein U1A-stem loop 2 RNA complex have been calculated theoretically from a 10 ns molecular dynamics (MD) simulation. The simulation was analyzed by calculating the covariance matrix of all atomic fluctuations. These matrix elements are then presented in the form of a two-dimensional grid, which displays fluctuations on a per residue basis. The results indicate the presence of strong, selective cross-correlated fluctuations throughout the RRM in U1A-RNA. The atomic fluctuations correspond well with previous biophysical studies in which a multiplicity of cooperative networks have been reported and indicate that the various networks identified in separate individual experiments are fluctuationally correlated into a hyper-network encompassing most of the RRM. The calculated results also correspond well with independent results from a statistical covariance analysis of 330 aligned RRM sequences. This method has significant implications as a predictive tool regarding cooperativity in the protein-nucleic acid recognition process.

Identification of function-associated loop motifs and application to protein function prediction

MOTIVATION

The detection of function-related local 3D-motifs in protein structures can provide insights towards protein function in absence of sequence or fold similarity. Protein loops are known to play important roles in protein function and several loop classifications have been described, but the automated identification of putative functional 3D-motifs in such classifications has not yet been addressed. This identification can be used on sequence annotations.

RESULTS

We evaluated three different scoring methods for their ability to identify known motifs from the PROSITE database in ArchDB. More than 500 new putative function-related motifs not reported in PROSITE were identified. Sequence patterns derived from these motifs were especially useful at predicting precise annotations. The number of reliable sequence annotations could be increased up to 100% with respect to standard BLAST.

CONTACT

boliva@imim.es

SUPPLEMENTARY INFORMATION

Supplementary Data are available at Bioinformatics online.

Is allostery an intrinsic property of all dynamic proteins?

Allostery involves coupling of conformational changes between two widely separated binding sites. The common view holds that allosteric proteins are symmetric oligomers, with each subunit existing in "at least" two conformational states with a different affinity for ligands. Recent observations such as the allosteric behavior of myoglobin, a classical example of a nonallosteric protein, call into question the existing allosteric dogma. Here we argue that all (nonfibrous) proteins are potentially allosteric. Allostery is a consequence of re-distributions of protein conformational ensembles. In a nonallosteric protein, the binding site shape may not show a concerted second-site change and enzyme kinetics may not reflect an allosteric transition. Nevertheless, appropriate ligands, point mutations, or external conditions may facilitate a population shift, leading a presumably nonallosteric protein to behave allosterically. In principle, practically any potential drug binding to the protein surface can alter the conformational redistribution. The question is its effectiveness in the redistribution of the ensemble, affecting the protein binding sites and its function. Here, we review experimental observations validating this view of protein allostery.

Structures and mechanisms of glycosyltransferases

Glycosyltransferases (GTs) catalyze the transfer of a sugar moiety from an activated donor sugar onto saccharide and nonsaccharide acceptors. A sequence-based classification spreads GTs in many families thus reflecting the variety of molecules that can be used as acceptors. In contrast, this enzyme family is characterized by a more conserved three-dimensional architecture. Until recently, only two different folds (GT-A and GT-B) have been identified for solved crystal structures. The recent report of a structure for a bacterial sialyltransferase allows the definition of a new fold family. Progress in the elucidation of the structures and mechanisms of GTs are discussed in this review. To accommodate the growing number of crystal structures, we created the 3D-Glycosyltransferase database to gather structural information concerning this class of enzymes.

Specificity in lipases: a computational study of transesterification of sucrose

Computational conformational searches of putative transition states of the reaction of sucrose with vinyl laurate catalyzed by lipases from Candida antarctica B and Thermomyces lanuginosus have been carried out. The dielectric of the media have been varied to understand the role of protein plasticity in modulating the observed regioselective transesterification. The binding pocket of lipase from Candida adapts to the conformational variability of the various substates of the substrates by small, local adjustments within the binding pocket. In contrast, the more constrained pocket of the lipase from Thermomyces adapts by adjusting through concerted global motions between subdomains. This leads to the identification of one large pocket in Candida that accommodates both the sucrose and the lauroyl moieties of the transition state, whereas in Thermomyces the binding pocket is smaller, leading to the localization of the two moieties in two distinct pockets; this partly rationalizes the broader specificity of the former relative to the latter. Mutations have been suggested to exploit the differences towards changing the observed selectivities.