Active-site residues move independently from the rest of the protein in a 200 ns molecular dynamics simulation of cytochrome P450 CYP119

Dynamics underlying hydroxylation selectivity of cytochrome P450cam

Structural heterogeneity and the dynamics of the complexes of enzymes with substrates can determine the selectivity of catalysis; however, fully characterizing how remains challenging as heterogeneity and dynamics can vary at the spatial level of an amino acid residue and involve rapid timescales. We demonstrate the nascent approach of site-specific two-dimensional infrared (IR) spectroscopy to investigate the archetypical cytochrome P450, P450cam, to better delineate the mechanism of the lower regioselectivity of hydroxylation of the substrate norcamphor in comparison to the native substrate camphor. Specific locations are targeted throughout the enzyme by selectively introducing cyano groups that have frequencies in a spectrally isolated region of the protein IR spectrum as local vibrational probes. Linear and two-dimensional IR spectroscopy were applied to measure the heterogeneity and dynamics at each probe and investigate how they differentiate camphor and norcamphor recognition. The IR data indicate that the norcamphor complex does not fully induce a large-scale conformational change to a closed state of the enzyme adopted in the camphor complex. Additionally, a probe directed at the bound substrate experiences rapidly interconverting states in the norcamphor complex that explain the hydroxylation product distribution. Altogether, the study reveals large- and small-scale structural heterogeneity and dynamics that could contribute to selectivity of a cytochrome P450 and illustrates the approach of site-selective IR spectroscopy to elucidate protein dynamics.

Entropic contribution to enhanced thermal stability in the thermostable P450 CYP119

The enhanced thermostability of thermophilic proteins with respect to their mesophilic counterparts is often attributed to the enthalpy effect, arising from strong interactions between protein residues. Intuitively, these strong interresidue interactions will rigidify the biomolecules. However, the present work utilizing neutron scattering and solution NMR spectroscopy measurements demonstrates a contrary example that the thermophilic cytochrome P450, CYP119, is much more flexible than its mesophilic counterpart, CYP101A1, something which is not apparent just from structural comparison of the two proteins. A mechanism to explain this apparent contradiction is that higher flexibility in the folded state of CYP119 increases its conformational entropy and thereby reduces the entropy gain during denaturation, which will increase the free energy needed for unfolding and thus stabilize the protein. This scenario is supported by thermodynamic data on the temperature dependence of unfolding free energy, which shows a significant entropic contribution to the thermostability of CYP119 and lends an added dimension to enhanced stability, previously attributed only to presence of aromatic stacking interactions and salt bridge networks. Our experimental data also support the notion that highly thermophilic P450s such as CYP119 may use a mechanism that partitions flexibility differently from mesophilic P450s between ligand binding and thermal stability.

Determinants of thermostability in the cytochrome P450 fold

Cytochromes P450 are found throughout the biosphere in a wide range of environments, serving a multitude of physiological functions. The ubiquity of the P450 fold suggests that it has been co-opted by evolution many times, and likely presents a useful compromise between structural stability and conformational flexibility. The diversity of substrates metabolized and reactions catalyzed by P450s makes them attractive starting materials for use as biocatalysts of commercially useful reactions. However, process conditions impose different requirements on enzymes to those in which they have evolved naturally. Most natural environments are relatively mild, and therefore most P450s have not been selected in Nature for the ability to withstand temperatures above ~40°C, yet industrial processes frequently require extended incubations at much higher temperatures. Thus, there has been considerable interest and effort invested in finding or engineering thermostable P450 systems. Numerous P450s have now been identified in thermophilic organisms and analysis of their structures provides information as to mechanisms by which the P450 fold can be stabilized. In addition, protein engineering, particularly by directed or artificial evolution, has revealed mutations that serve to stabilize particular mesophilic enzymes of interest. Here we review the current understanding of thermostability as it applies to the P450 fold, gleaned from the analysis of P450s characterized from thermophilic organisms and the parallel engineering of mesophilic forms for greater thermostability. We then present a perspective on how this information might be used to design stable P450 enzymes for industrial application. This article is part of a Special Issue entitled: Cytochrome P450 biodiversity and biotechnology, edited by Erika Plettner, Gianfranco Gilardi, Luet Wong, Vlada Urlacher, Jared Goldstone.

Cutoff lensing: predicting catalytic sites in enzymes

Predicting function-related amino acids in proteins with unknown function or unknown allosteric binding sites in drug-targeted proteins is a task of paramount importance in molecular biomedicine. In this paper we introduce a simple, light and computationally inexpensive structure-based method to identify catalytic sites in enzymes. Our method, termed cutoff lensing, is a general procedure consisting in letting the cutoff used to build an elastic network model increase to large values. A validation of our method against a large database of annotated enzymes shows that optimal values of the cutoff exist such that three different structure-based indicators allow one to recover a maximum of the known catalytic sites. Interestingly, we find that the larger the structures the greater the predictive power afforded by our method. Possible ways to combine the three indicators into a single figure of merit and into a specific sequential analysis are suggested and discussed with reference to the classic case of HIV-protease. Our method could be used as a complement to other sequence- and/or structure-based methods to narrow the results of large-scale screenings.

(Dis)similarity Index To Compare Correlated Motions in Molecular Simulations

Molecular dynamics (MD) simulations are widely used to complement or guide experimental studies in the characterization of protein dynamics, thanks to improvements in force-field accuracy, along with in the software and hardware to sample the conformational landscape of proteins. Among the different applications of MD simulations, the study of correlated motions is largely employed for different purposes. Several metrics have been developed to describe correlated motions in the MD ensemble, such as methods based on Pearson Correlation or Mutual Information. Cross-correlation analysis of MD trajectories is indeed appealing not only to identify residues characterized by coupled fluctuations in protein structures but also since it can be used to extrapolate motions along directions in which major conformational changes should occur, for example on longer time scales than the ones that are actually simulated. Nevertheless, most of the MD studies employ average correlation maps and mostly in a qualitative way, even when different systems or different replicates of the same system are compared. The broad application of correlation metrics in the analysis of MD simulations, especially for comparative purposes, requires a step forward toward more quantitative and accurate comparisons. We thus here employed a simple but effective index, which is based on a normalized Frobenius norm of the differences between protein correlation maps, to compare correlated motions. We applied this index for a quantitative comparison of correlated motions from MD simulations of seven proteins of different size and fold. We also employed the index to assess the robustness of correlation description when multi-replicate MD simulations of a same system are used, and we compared our index to metrics for comparison of structural ensembles such as Root Mean Square Inner Product and the Bhattacharyya Coefficient.

Analysis of cytochrome P450 CYP119 ligand-dependent conformational dynamics by two-dimensional NMR and X-ray crystallography

Defining the conformational states of cytochrome P450 active sites is critical for the design of agents that minimize drug-drug interactions, the development of isoform-specific P450 inhibitors, and the engineering of novel oxidative catalysts. We used two-dimensional (1)H,(15)N HSQC chemical shift perturbation mapping of (15)N-labeled Phe residues and x-ray crystallography to examine the ligand-dependent conformational dynamics of CYP119. Active site Phe residues were most affected by the binding of azole inhibitors and fatty acid substrates, in agreement with active site localization of the conformational changes. This was supported by crystallography, which revealed movement of the F-G loop with various azoles. Nevertheless, the NMR chemical shift perturbations caused by azoles and substrates were distinguishable. The absence of significant chemical shift perturbations with several azoles revealed binding of ligands to an open conformation similar to that of the ligand-free state. In contrast, 4-phenylimidazole caused pronounced NMR changes involving Phe-87, Phe-144, and Phe-153 that support the closed conformation found in the crystal structure. The same closed conformation is observed by NMR and crystallography with a para-fluoro substituent on the 4-phenylimidazole, but a para-chloro or bromo substituent engendered a second closed conformation. An open conformation is thus favored in solution with many azole ligands, but para-substituted phenylimidazoles give rise to two closed conformations that depend on the size of the para-substituent. The results suggest that ligands selectively stabilize discrete cytochrome P450 conformational states.

Allosteric modulation of substrate motion in cytochrome P450 3A4-mediated xylene oxidation

Many cytochrome P450 enzymes (CYPs) exhibit allosteric behavior reflecting a complex ligand-binding process involving numerous factors: conformational selection, protein-protein interactions, substrate/effector/protein structure, and multiple-ligand binding. The interplay of CYP plasticity and rigidity contributes to substrate/product selectivity and to allosterism. Detailed evidence describing how protein motion modulates product selectivity is incomplete as are descriptions of effector-induced modulation of substrate dynamics. Our intent was to discover details of allosteric behavior and CYP3A4 flexibility and rigidity by investigating substrate motion using low-molecular weight ligands. Steady state kinetics and product ratios were measured for oxidation of m-xylene-(2)H3 and p-xylene; intramolecular isotope effects were measured for m-xylene-(2)H3 oxidation as a function of m-xylene-(2)H3 and p-xylene concentration. Biphasic kinetic plots indicated homotropic cooperative behavior with xylene isomers. Selectivity for aromatic hydroxylation over benzylic hydroxylation of m-xylene-(2)H3 supports a model in which the region near the CYP3A4 active oxidizing species limits substrate dynamics. p-Xylene impedes the motion of m-xylene-(2)H3 substrates that have access to the active oxidizing species: (kH/kD)obs values for m-xylene-(2)H3 decreased with p-xylene concentration. m-Xylene-(2)H3 and p-xylene do not have simultaneous access to the active oxidizing species: deuterium-labeled and unlabeled p-xylene exhibited similar effects on the (kH/kD)obs values for m-xylene-(2)H3 oxidation. p-Xylene and m-xylene-(2)H3 bind at different sites: m-xylene-(2)H3 oxidation rates and product selectivity were consistent across the p-xylene concentration range. Overall, this study indicates that the intramolecular isotope effect experimental design provides a unique opportunity to investigate allosteric mechanisms as it provides information about substrate motion when the enzyme is primed to oxidize substrates.

Loop 7 of E2 enzymes: an ancestral conserved functional motif involved in the E2-mediated steps of the ubiquitination cascade

The ubiquitin (Ub) system controls almost every aspect of eukaryotic cell biology. Protein ubiquitination depends on the sequential action of three classes of enzymes (E1, E2 and E3). E2 Ub-conjugating enzymes have a central role in the ubiquitination pathway, interacting with both E1 and E3, and influencing the ultimate fate of the substrates. Several E2s are characterized by an extended acidic insertion in loop 7 (L7), which if mutated is known to impair the proper E2-related functions. In the present contribution, we show that acidic loop is a conserved ancestral motif in E2s, relying on the presence of alternate hydrophobic and acidic residues. Moreover, the dynamic properties of a subset of family 3 E2s, as well as their binary and ternary complexes with Ub and the cognate E3, have been investigated. Here we provide a model of L7 role in the different steps of the ubiquitination cascade of family 3 E2s. The L7 hydrophobic residues turned out to be the main determinant for the stabilization of the E2 inactive conformations by a tight network of interactions in the catalytic cleft. Moreover, phosphorylation is known from previous studies to promote E2 competent conformations for Ub charging, inducing electrostatic repulsion and acting on the L7 acidic residues. Here we show that these active conformations are stabilized by a network of hydrophobic interactions between L7 and L4, the latter being a conserved interface for E3-recruitment in several E2s. In the successive steps, L7 conserved acidic residues also provide an interaction interface for both Ub and the Rbx1 RING subdomain of the cognate E3. Our data therefore suggest a crucial role for L7 of family 3 E2s in all the E2-mediated steps of the ubiquitination cascade. Its different functions are exploited thank to its conserved hydrophobic and acidic residues in a finely orchestrate mechanism.

Equilibrium fluctuations of a single folded protein reveal a multitude of potential cryptic allosteric sites

Cryptic allosteric sites--transient pockets in a folded protein that are invisible to conventional experiments but can alter enzymatic activity via allosteric communication with the active site--are a promising opportunity for facilitating drug design by greatly expanding the repertoire of available drug targets. Unfortunately, identifying these sites is difficult, typically requiring resource-intensive screening of large libraries of small molecules. Here, we demonstrate that Markov state models built from extensive computer simulations (totaling hundreds of microseconds of dynamics) can identify prospective cryptic sites from the equilibrium fluctuations of three medically relevant proteins--β-lactamase, interleukin-2, and RNase H--even in the absence of any ligand. As in previous studies, our methods reveal a surprising variety of conformations--including bound-like configurations--that implies a role for conformational selection in ligand binding. Moreover, our analyses lead to a number of unique insights. First, direct comparison of simulations with and without the ligand reveals that there is still an important role for an induced fit during ligand binding to cryptic sites and suggests new conformations for docking. Second, correlations between amino acid sidechains can convey allosteric signals even in the absence of substantial backbone motions. Most importantly, our extensive sampling reveals a multitude of potential cryptic sites--consisting of transient pockets coupled to the active site--even in a single protein. Based on these observations, we propose that cryptic allosteric sites may be even more ubiquitous than previously thought and that our methods should be a valuable means of guiding the search for such sites.

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.