A Metallomics Study on Protein function assignment Using ProMOL

Metal ions are an integral part in both the structural and functional stability of enzymes. The function of an enzyme is exhibited through its active site region, which carries out the reaction for that particular protein. When a metal ion is present in an active site, it not only acts as a structural support for the active site but is often plays a critical role in the functional behavior of the protein. An exhaustive screening for proposed metal ion active sites was performed through online resources including Metal MACiE, Metal PDB, the Catalytic site Atlas and Metal Mine. A total of 103 motifs were generated using ProMol, which is a plugin for the molecular visualization software PyMOL. ProMOL was able to identify these motifs in homologous structures. This new library of enzyme active site motifs was then used to suggest functions for three proteins of unknown function that are found in the Protein Data Bank. An additional functionality was added to ProMOL to enable PyMOL to visualize metal ions and prosthetic groups in enzyme active sites and to calculate the interatomic distances of the active site regions

Automatic prediction of catalytic residues by modeling residue structural neighborhood

Background: Prediction of catalytic residues is a major step in characterizing the function of enzymes. In its simpler formulation, the problem can be cast into a binary classification task at the residue level, by predicting whether the residue is directly involved in the catalytic process. The task is quite hard also when structural information is available, due to the rather wide range of roles a functional residue can play and to the large imbalance between the number of catalytic and non-catalytic residues.Results: We developed an effective representation of structural information by modeling spherical regions around candidate residues, and extracting statistics on the properties of their content such as physico-chemical properties, atomic density, flexibility, presence of water molecules. We trained an SVM classifier combining our features with sequence-based information and previously developed 3D features, and compared its performance with the most recent state-of-the-art approaches on different benchmark datasets. We further analyzed the discriminant power of the information provided by the presence of heterogens in the residue neighborhood.Conclusions: Our structure-based method achieves consistent improvements on all tested datasets over both sequence-based and structure-based state-of-the-art approaches. Structural neighborhood information is shown to be responsible for such results, and predicting the presence of nearby heterogens seems to be a promising direction for further improvements.Journal ArticleResearch Support, N.I.H. Extramuralinfo:eu-repo/semantics/publishe

Deorphanizing Human Cytochrome P450 Enzymes CYP4A22 and CYP4Z1 through Mechanistic in silico Modeling

Cytochrome P450 (CYP) enzymes are monooxygenases that catalyze the oxidation of structurally diverse substrates and are present in various lifeforms, including humans. Human CYPs catalyze the metabolism of xenobiotics including drugs and are involved in the essential biosynthesis of steroids, vitamins, and lipids. CYP-catalyzed metabolism and biosynthesis has been extensively studied recently, but several CYPs remain understudied despite their potential role in key biotransformation pathways. For these so-called ‘orphaned CYPs’, physiological function and structure are yet unknown, such as for CYP4A22 and 4Z1. CYP4A22 catalyzes the ω-hydroxylation of arachidonic acid to the angiogenic 20-hydroxyeicosatetraenoic acid. CYP4Z1 is overexpressed in breast cancer and other malignancies, which is correlated with tumor progression. Hence, CYP4Z1 is considered a promising breast cancer target that was not previously addressed by small molecule inhibitors. Here, we report our efforts to deorphanize CYP4A22 and 4Z1 together with our experimental partner Prof. Bureik. We were the first to predict the structure of CYP4A22 and 4Z1 by homology modeling and overcame the challenge of low-sequence similarity templates by incorporating substrate activities. We applied substrate docking and 3D pharmacophore modeling to rationalize how the binding site structure determines structure-activity relationships (SAR) trends. The well-known structural flexibility of CYPs was partially accounted for by molecular dynamics simulations. For the first time, enzyme-substrate interactions dynamics were analyzed with our novel dynamic pharmacophore approach, which led to the prediction of key residues. For CYP4A22, a residue influencing ω-hydroxylation (Phe320) and two binding residues (Arg96 and Arg233) were predicted. For CYP4Z1, the key role of Arg487 and assisting role of Asn381 for substrate binding were predicted, which was validated by in vitro mutational studies. The thereby validated CYP4Z1 model and substrate SAR were used in a virtual screening campaign resulting in a new potent and selective CYP4Z1 inhibitor (IC50: 63 ± 19 nM). Taken together, we established an in vitro/in silico deorphanization protocol that shed light on the structure-function relationships of CYP4A22 and 4Z1. This enabled us to discover a potent inhibitor of CYP4Z1 that will allow further studies on the physiological and pathophysiological role of the enzyme and might be further improved to target CYP4Z1 in a new therapeutical approach. Similar workflows could easily be applied to study other neglected enzymes in metabolism and other biotransformation pathways.Cytochrom P450 (CYP)-Enzyme sind Monooxygenasen, die die Oxidation strukturell diverser Substrate katalysieren und in verschiedenen Lebensformen, einschließlich des Menschen, vorkommen. Menschliche CYPs katalysieren den Metabolismus von Xenobiotika einschließlich Arzneistoffen und sind an der essenziellen Biosynthese von Steroiden, Vitaminen und Lipiden beteiligt. CYP-katalysierter Metabolismus und Biosynthese wurden in der Vergangenheit intensiv untersucht, aber einige CYPs sind trotz ihrer potenziellen Rolle in wichtigen Biotransformationswegen noch wenig erforscht. Für diese so genannten „orphaned“ oder „verwaisten“ CYPs, sind physiologische Funktion und Struktur noch unbekannt, wie z.B. CYP4A22 und 4Z1. CYP4A22 katalysiert die ω-Hydroxylierung von Arachidonsäure zu der angiogenen 20-Hydroxyeicosatetraensäure. CYP4Z1 wird bei Brustkrebs und anderen malignen Erkrankungen überexprimiert, was mit der Tumorprogression korreliert ist. Daher wird CYP4Z1 als ein vielversprechendes Brustkrebs-Target angesehen, das bisher nicht durch niedermolekulare Inhibitoren adressiert wurde. Hier berichten wir über unsere Bemühungen, CYP4A22 und 4Z1 zusammen mit unserem experimentellen Partner Prof. Bureik zu deorphanisieren. Wir waren die Ersten, die die Struktur von CYP4A22 und 4Z1 durch Homologiemodellierung vorhersagten und überwanden die Herausforderung der Templates mit geringer Sequenzähnlichkeit, indem wir Substrataktivitäten mit einbezogen. Wir wendeten Substrat-Docking und 3D-Pharmakophor-Modellierung an, um zu rationalisieren, wie die Struktur der Bindungstasche die Trends der Struktur-Aktivitäts-Beziehungen (SAR) bestimmt. Die bekannte strukturelle Flexibilität von CYPs wurde partiell durch Molekulardynamik-Simulationen berücksichtigt. Zum ersten Mal wurde die Dynamik der Enzym-Substrat-Interaktionen mit unserem neuartigen dynamischen Pharmakophor-Ansatz analysiert, was zur Vorhersage von wichtigen Aminosäuren führte. Für CYP4A22 wurde eine Aminosäure, die die ω-Hydroxylierung beeinflusst (Phe320) und zwei Bindungsaminosäuren (Arg96 und Arg233) vorhergesagt. Für CYP4Z1 wurde die Schlüsselrolle von Arg487 und die unterstützende Rolle von Asn381 für die Substratbindung vorhergesagt, welche durch in vitro Mutationsstudien validiert wurde. Das dadurch validierte CYP4Z1-Modell und die Substrat-SAR wurden in einer virtuellen Screening-Kampagne verwendet, die zu einen neuen potenten und selektiven CYP4Z1-Inhibitor führte (IC50: 63 ± 19 nM). Zusammengenommen haben wir ein in vitro/in silico Deorphanisierungsprotokoll etabliert, welches die Struktur-Funktionsbeziehungen von CYP4A22 und 4Z1 beleuchtet. Dies versetzte uns in die Lage einen potenten Inhibitor von CYP4Z1 zu entdecken, der weitere Studien über die physiologische und pathophysiologische Rolle des Enzyms ermöglichen wird und möglicherweise weiter verbessert werden kann, um CYP4Z1 in einem neuen therapeutischen Ansatz zu adressieren. Ähnliche Arbeitsabläufe könnte leicht angewendet werden, um andere vernachlässigte Enzyme im Metabolismus und anderen Biotransformationswegen zu untersuchen

Access Path to the Ligand Binding Pocket May Play a Role in Xenobiotics Selection by AhR.

Understanding of multidrug binding at the atomic level would facilitate drug design and strategies to modulate drug metabolism, including drug transport, oxidation, and conjugation. Therefore we explored the mechanism of promiscuous binding of small molecules by studying the ligand binding domain, the PAS-B domain of the aryl hydrocarbon receptor (AhR). Because of the low sequence identities of PAS domains to be used for homology modeling, structural features of the widely employed HIF-2alpha and a more recent suitable template, CLOCK were compared. These structures were used to build AhR PAS-B homology models. We performed molecular dynamics simulations to characterize dynamic properties of the PAS-B domain and the generated conformational ensembles were employed in in silico docking. In order to understand structural and ligand binding features we compared the stability and dynamics of the promiscuous AhR PAS-B to other PAS domains exhibiting specific interactions or no ligand binding function. Our exhaustive in silico binding studies, in which we dock a wide spectrum of ligand molecules to the conformational ensembles, suggest that ligand specificity and selection may be determined not only by the PAS-B domain itself, but also by other parts of AhR and its protein interacting partners. We propose that ligand binding pocket and access channels leading to the pocket play equally important roles in discrimination of endogenous molecules and xenobiotics

High-resolution cryo-EM structures of plant cytochrome b6fb_{6}f at work

Plants use solar energy to power cellular metabolism. The oxidation of plastoquinol and reduction of plastocyanin by cytochrome $b_{6}f$ (Cyt $b_{6}f$) is known as one of the key steps of photosynthesis, but the catalytic mechanism in the plastoquinone oxidation site ($Q_{p}$) remains elusive. Here, we describe two high-resolution cryo-EM structures of the spinach Cyt $b_{6}f$ homodimer with endogenous plastoquinones and in complex with plastocyanin. Three plastoquinones are visible and line up one after another head to tail near $Q_{p}$ in both monomers, indicating the existence of a channel in each monomer. Therefore, quinones appear to flow through Cyt $b_{6}f$ in one direction, transiently exposing the redox-active ring of quinone during catalysis. Our work proposes an unprecedented one-way traffic model that explains efficient quinol oxidation during photosynthesis and respiration. Structures of cytochrome $b_{6}f$ with and without plastocyanin imply a one-way traffic of quinones for efficient photosynthesis

Utilizing in Silico and/or Native ESI Approaches to Provide New Insights on Haptoglobin/Globin and Haptoglobin/Receptor Interactions

Haptoglobin (Hp), an acute phase protein, binds free hemoglobin (Hb) dimers in one of the strongest non-covalent interactions known in biology. This interaction protects Hb from causing potentially severe oxidative damage and limiting nitric oxide bioavailability. Once Hb/Hp complexes are formed, they proceed to bind CD163, a cell surface receptor on macrophages leading to complex internalization and catabolism. Myoglobin, (Mb) a monomeric protein, that is normally found in the muscle but can be released into the blood in high concentrations during myocardial injury, is homologous to Hb and shares many conserved Hb/Hp interface residues. Both monomeric Hb and Mb species present potential risks, yet their interactions with Hp have not been extensively studied or are a matter of controversy, respectively. To predict possible interactions of monomeric globins with Hp, we employed a variety of cost and time effective molecular modeling approaches. Native electrospray ionization mass spectrometry (ESI MS) experiments confirm the modeling results and show that monomeric Hb and Mb bind Hp with a stoichiometry of two globin monomers per Hp tetramer. The ESI MS results also demonstrate the success of our computational approaches to Mb/Hp interactions, motivating us to model Hb/Hp/CD163 complexes. Both CD163 bound Ca2+ and specific CD163 acidic residues are known to be essential for binding specific Hp basic residues resulting in Hb/Hp/CD163 complex formation, but the structural details of Hb/Hp/CD163 interactions are unknown. We therefore constructed experimentally driven molecular models of Hb/Hp/CD163 complexes using molecular docking. In order to understand the role of Ca2+ in Hp/CD163 interactions and dynamics, all-atom molecular dynamics (MD) simulations were conducted for CD163 models in the presence and absence of Ca2+. The molecular models of Hb/Hp/CD163 suggest that Hp basic residues R252 and K262 each interact with a conserved acidic triad (E27, E28, D94) in CD163 domains 2 and 3. A calcium ion is postulated to stabilize this CD163 acidic cluster facilitating Hp recognition. Consistent with this, MD simulations on isolated CD163 domains suggest that Ca2+ bound at a specific site in CD163 preserves the arrangement of the acidic triad and protein structural stability. Our studies demonstrate how molecular modeling and molecular dynamics aided/correlated with mass spectrometry experiments can elucidate the structural basis and dynamics of interactions between Hp, globins and/or CD163. This approach may be useful for designing therapeutics that utilizes the Hb/Hp/CD263 endocytosis pathway and unraveling novel avenues for possible Hp-therapy administration for diseases or complications arising from Mb toxicity

Protein Functional Surfaces: Global Shape Matching and Local Spatial Alignments of Ligand Binding Sites

<p>Abstract</p> <p>Background</p> <p>Protein surfaces comprise only a fraction of the total residues but are the most conserved functional features of proteins. Surfaces performing identical functions are found in proteins absent of any sequence or fold similarity. While biochemical activity can be attributed to a few key residues, the broader surrounding environment plays an equally important role.</p> <p>Results</p> <p>We describe a methodology that attempts to optimize two components, global shape and local physicochemical texture, for evaluating the similarity between a pair of surfaces. Surface shape similarity is assessed using a three-dimensional object recognition algorithm and physicochemical texture similarity is assessed through a spatial alignment of conserved residues between the surfaces. The comparisons are used in tandem to efficiently search the Global Protein Surface Survey (GPSS), a library of annotated surfaces derived from structures in the PDB, for studying evolutionary relationships and uncovering novel similarities between proteins.</p> <p>Conclusion</p> <p>We provide an assessment of our method using library retrieval experiments for identifying functionally homologous surfaces binding different ligands, functionally diverse surfaces binding the same ligand, and binding surfaces of ubiquitous and conformationally flexible ligands. Results using surface similarity to predict function for proteins of unknown function are reported. Additionally, an automated analysis of the ATP binding surface landscape is presented to provide insight into the correlation between surface similarity and function for structures in the PDB and for the subset of protein kinases.</p

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.JK, MJW, JT, PJB, AB and RCG thank Unilever for funding

Modeling and Molecular Dynamics Simulations on the in situ Murine Cytochrome P450 4F System

Cytochrome P450s are major participants in the maintenance and well-being of cellular function and have important roles in the health and disease of living creatures. The ω-hydroxylation, catalyzed by CYP4 family members, has been observed to be an important metabolic pathway for the homeostasis of mammalian cells as it regulates inflammatory processes with the eicosanoid cascade of metabolites of the ω-6 polyunsaturated fatty acid, arachidonic acid. Many human CYP4F and murine Cyp4f subfamily members have recently gained interest for their usage as potential cancer biomarkers as the expression of these proteins are modified in tumor cells. 20-HETE, the ω-hydroxylated product of arachidonic acid, has gained attention for being the chief metabolic product of interest in vascular function, tumor progression and propagation. Whether or not individual Cyp4f isoforms are responsible for the production of this metabolite is of great interest to medicine as such insight could provide researchers with new avenues of study in the fight against cancer. One particular Cyp4f isozyme, Cyp4f13, has received relatively little study until only very recently and is the focus of the work presented in this thesis, as it has not fully had its role in eicosanoid metabolism understood. Using a combination of computational chemistry approaches, this study focuses on exploring the murine cytochrome P450 4f13 system and its active site using all-atomistic Molecular Dynamics Simulation of a homology model. With the embedded protein solvated and in situ environment replicated, the resting state of the substrate-free Cyp4f13 system was generated. Solvation of the active site was performed to explore the inner active cavity of the P450 system, with subsequent molecular docking and mutation of active site residues performed in order to gain insight into the interactions present in the protein-substrate complex. Protonation state changes were observed to have significant effects on both protein structure and arachidonate binding through electrostatic interactions. Leu137, Arg237, and Gly327 were modified and displayed drastic effects on predicted regiospecificity on the P450 substrate. With the insights obtained, we hope to further the understanding of murine Cyp4f13-catalyzed ω-hydroxylation of arachidonic acid