Integrated experimental/computational approaches to characterize the systems formed by vanadium with proteins and enzymes

An integrated instrumental/computational approach to characterize metallodrug–protein adducts at the molecular level is reviewed. A series of applications are described, focusing on potential vanadium drugs with a generalization to other metals.

BioMetAll: Identifying Metal-Binding Sites in Proteins from Backbone Preorganization

With a large amount of research dedicated to decoding how metallic species bind to proteins, in silico methods are interesting allies for experimental procedures. To date, computational predictors mostly work by identifying the best possible sequence or structural match of the target protein with metal-binding templates. These approaches are fundamentally focused on the first coordination sphere of the metal. Here, we present the BioMetAll predictor that is based on a different postulate: the formation of a potential metal-binding site is related to the geometric organization of the protein backbone. We first report the set of convenient geometric descriptors of the backbone needed for the algorithm and their parameterization from a statistical analysis. Then, the successful benchmark of BioMetAll on a set of more than 90 metal-binding X-ray structures is presented. Because BioMetAll allows structural predictions regardless of the exact geometry of the side chains, it appears extremely valuable for systems whose structures (either experimental or theoretical) are not optimal for metal-binding sites. We report here its application on three different challenging cases: (i) the modulation of metal-binding sites during conformational transition in human serum albumin, (ii) the identification of possible routes of metal migration in hemocyanins, and (iii) the prediction of mutations to generate convenient metal-binding sites for de novo biocatalysts. This study shows that BioMetAll offers a versatile platform for numerous fields of research at the interface between inorganic chemistry and biology and allows to highlight the role of the preorganization of the protein backbone as a marker for metal binding. BioMetAll is an open-source application available at https://github.com/insilichem/biometall.

Molecular Modeling for Artificial Metalloenzyme Design and Optimization

Artificial metalloenzymes (ArMs) are obtained by inserting homogeneous catalysts into biological scaffolds and are among the most promising strategies in the quest for new-to-nature biocatalysts. The quality of their design strongly depends on how three partners interact: the biological host, the "artificial cofactor," and the substrate. However, structural characterization of functional artificial metalloenzymes by X-ray or NMR is often partial, elusive, or absent. How the cofactor binds to the protein, how the receptor reorganizes upon the binding of the cofactor and the substrate, and which are the binding mode(s) of the substrate for the reaction to proceed are key questions that are frequently unresolved yet crucial for ArM design. Such questions may eventually be solved by molecular modeling but require a step change beyond the current state-of-the-art methodologies.Here, we summarize our efforts in the study of ArMs, presenting both the development of computational strategies and their application. We first focus on our integrative computational framework that incorporates a variety of methods such as protein-ligand docking, classical molecular dynamics (MD), and pure quantum mechanical (QM) methods, which, when properly combined, are able to depict questions that range from host-cofactor binding predictions to simulations of entire catalytic mechanisms. We also pay particular attention to the protein-ligand docking strategies that we have developed to accurately predict the binding of transition metal-containing molecules to proteins. While this aspect is fundamental to many bioinorganic fields beyond ArMs, it has been disregarded from the molecular modeling landscape until very recently.Next we describe how to apply this computational framework to particular ArMs including systems previously characterized experimentally as well as others where computation served to guide the design. We start with the prediction of the interactions between homogeneous catalysts and biological hosts. Protein-ligand docking is pivotal at that stage, but it needs to be combined with QM/MM or MD approaches when the binding of the cofactor implies significant conformational changes of the protein or involve changes of the electronic state of the metal.Then, we summarize molecular modeling studies aimed at identifying cofactor-substrate arrangements inside the ArM active pocket that are consistent with its reactivity. These calculations stand on "Theozyme"-like dockings, MD-refined or not, which provide molecular rationale of the catalytic profiles of the artificial systems.In the third section, we present case studies to decode the entire catalytic mechanism of two ArMs: (1) an iridium based asymmetric transfer hydrogenase obtained by insertion of Noyori's catalyst into streptavidin and (2) a metallohydrolase achieved by including a receptor. Transition states, second coordination sphere effects, as well as motions of the cofactors are identified as drivers of the enantiomeric profiles.Finally, we report computer-aided designs of ArMs to guide experiments toward chemical and mutational changes that improve their activity and/or enantioselective profiles and expand toward future directions.

The Effect of Cofactor Binding on the Conformational Plasticity of the Biological Receptors in Artificial Metalloenzymes: The Case Study of LmrR

The design of Artificial Metalloenzymes (ArMs), which result from the incorporation of organometallic cofactors into biological structures, has grown steadily in the last two decades and important new-to-Nature reactions have been reached. These type of exercises could greatly benefit from an understanding of the structural impact that the inclusion of organometallic moieties may have on the biological host. To date though, our understanding of this phenomenon is highly partial. This lack of knowledge is one of the elements that condition that first-generation ArMs generally display relatively poor catalytic profiles. In this work, we approach this matter by assessing the dynamics and stability of a series of ArMs resulting from the inclusion, via different anchoring strategies, of a variety of organometallic cofactors into the Lactococcal multidrug resistance regulator (LmrR) protein. To this aim, we coupled standard force field-based techniques such as Protein-Ligand Docking and Molecular Dynamics simulations with a variety of trajectory convergence analyses, capable of assessing both the stability and flexibility of the different systems under study upon the binding of cofactors. Together with the experimental evidence obtained in other studies, we provide an overview on how these changes can affect the catalytic outcomes obtained from the different ArMs. Fundamentally, our results show that the convergence analysis used in this work can assess how the inclusion of synthetic metallic cofactors in proteins can condition different structural modulations of their host. Those conformational modifications are key to the success of the desired catalytic activity and their proper identification can be wisely used to improve the quality and the rate of success of the ArMs.

Validation and Applications of Protein-Ligand Docking Approaches Improved for Metalloligands with Multiple Vacant Sites

Decoding the interaction between coordination compounds and proteins is of fundamental importance in biology, pharmacy, and medicine. In this context, protein- ligand docking represents a particularly interesting asset to predict how small compounds could interact with biomolecules, but to date, very little information is available to adapt these methodologies to metal-containing ligands. Here, we assessed the predictive capability of a metal-compatible parameter set for the docking program GOLD for metallo ligands with multiple vacant sites and different geometries. The study first presents a benchmark of 25 well-characterized X-ray metallo ligand-protein adducts. In 100% of the cases, the docking solutions are superimposable to the X-ray determination, and in 92% the value of the root-mean-square deviation between the experimental and calculated structures is lower than 1.5 Å. After the validation step, we applied these methods to five case studies for the prediction of the binding of pharmacological active metal species to proteins: (i) the anticancer copper(II) complex [Cu^II(Br)(2-hydroxy-1-naphthaldehyde benzoyl hydrazine)(indazole)] to human serum albumin (HSA); (ii) one of the active species of antidiabetic and antitumor vanadium compounds, V^IVO²⁺ ion, to carboxypeptidase; (iii) the antiarthritic species [Au^I(PEt₃)]⁺ to HSA; (iv) the antitumor oxaliplatin to ubiquitin; (v) the antitumor ruthenium(II) compound RAPTA-PentaOH to cathepsin B. The calculations suggested that the binding modes are in good agreement with the partial information retrieved from spectroscopic and spectrometric analysis and allowed us, in certain cases, to propose additional hypotheses. This method is an important update in protein-metallo ligand docking, which could have a wide field of application, from biology and inorganic biochemistry to medicinal chemistry and pharmacology.

Prediction of the interaction of metallic moieties with proteins: An update for protein-ligand docking techniques

In this article, we present a new approach to expand the range of application of protein-ligand docking methods in the prediction of the interaction of coordination complexes (i.e., metallodrugs, natural and artificial cofactors, etc.) with proteins. To do so, we assume that, from a pure computational point of view, hydrogen bond functions could be an adequate model for the coordination bonds as both share directionality and polarity aspects. In this model, docking of metalloligands can be performed without using any geometrical constraints or energy restraints. The hard work consists in generating the convenient atom types and scoring functions. To test this approach, we applied our model to 39 high-quality X-ray structures with transition and main group metal complexes bound via a unique coordination bond to a protein. This concept was implemented in the protein-ligand docking program GOLD. The results are in very good agreement with the experimental structures: the percentage for which the RMSD of the simulated pose is smaller than the X-ray spectra resolution is 92.3% and the mean value of RMSD is < 1.0 Å. Such results also show the viability of the method to predict metal complexes-proteins interactions when the X-ray structure is not available. This work could be the first step for novel applicability of docking techniques in medicinal and bioinorganic chemistry and appears generalizable enough to be implemented in most protein-ligand docking programs nowadays available. © 2017 Wiley Periodicals, Inc.

Elucidation of Binding Site and Chiral Specificity of Oxidovanadium Drugs with Lysozyme through Theoretical Calculations

This study presents an implementation of the protein-ligand docking program GOLD and a generalizable method to predict the binding site and orientation of potential vanadium drugs. Particularly, theoretical methods were applied to the study of the interaction of two V^IVO complexes with antidiabetic activity, [V^IVO(pic)₂(H₂O)] and [V^IVO(ma)₂(H₂O)], where pic is picolinate and ma is maltolate, with lysozyme (Lyz) for which electron paramagnetic resonance spectroscopy suggests the binding of the moieties VO(pic)₂ and VO(ma)₂ through a carboxylate group of an amino acid residue (Asp or Glu). The work is divided in three parts: (1) the generation of a new series of parameters in GOLD program for vanadium compounds and the validation of the method on five X-ray structures of V^IVO and V^V species bound to proteins; (2) the prediction of the binding site and enantiomeric preference of [VO(pic)₂(H₂O)] to lysozyme, for which the X-ray diffraction analysis displays the interaction of a unique isomer (i.e., OC-6-23-Δ) through Asp52 residue, and the subsequent refinement of the results with quantum mechanics/molecular mechanics methods; (3) the application of the same approach to the interaction of [VO(ma)₂(H₂O)] with lysozyme. The results show that convenient implementation of protein-ligand docking programs allows for satisfactorily reproducing X-ray structures of metal complexes that interact with only one coordination site with proteins and predicting with blind procedures relevant low-energy binding modes. The results also demonstrate that the combination of docking methods with spectroscopic data could represent a new tool to predict (metal complex)-protein interactions and have a general applicability in this field, including for paramagnetic species.

Neocarzinostatin-based hybrid biocatalysts for oxidation reactions

An anionic iron(III)-porphyrin-testosterone conjugate 1-Fe has been synthesized and fully characterized. It has been further associated with a neocarzinostatin variant, NCS-3.24, to generate a new artificial metalloenzyme following the so-called 'Trojan Horse' strategy. This new 1-Fe-NCS-3.24 biocatalyst showed an interesting catalytic activity as it was found able to catalyze the chemoselective and slightly enantioselective (ee = 13%) sulfoxidation of thioanisole by H2O2. Molecular modelling studies show that a synergy between the binding of the steroid moiety and that of the porphyrin macrocycle into the protein binding site can explain the experimental results, indicating a better affinity of 1-Fe for the NCS-3.24 variant than testosterone and testosterone-hemisuccinate themselves. They also show that the Fe-porphyrin complex is sandwiched between the two subdomains of the protein providing with good complementarities. However, the artificial cofactor entirely fills the cavity and its metal ion remains widely exposed to the solvent which explains the moderate enantioselectivity observed. Some possible improvements in the "Trojan Horse" strategy for obtaining better catalysts of selective oxidations are presented.

Unravelling novel synergies between organometallic and biological partners: a quantum mechanics/molecular mechanics study of an artificial metalloenzyme

In recent years, the design of artificial metalloenzymes obtained by the insertion of homogeneous catalysts into biological macromolecules has become a major field of research. These hybrids, and the corresponding X-ray structures of several of them, are offering opportunities to better understand the synergy between organometallic and biological subsystems. In this work, we investigate the resting state and activation process of a hybrid inspired by an oxidative haemoenzyme but presenting an unexpected reactivity and structural features. An extensive series of quantum mechanics/molecular mechanics calculations show that the resting state and the activation processes of the novel enzyme differ from naturally occurring haemoenzymes in terms of the electronic state of the metal, participation of the first coordination sphere of the metal and the dynamic process. This study presents novel insights into the sensitivity of the association between organometallic and biological partners and illustrates the molecular challenge that represents the design of efficient enzymes based on this strategy.

Assessing protein-ligand docking for the binding of organometallic compounds to proteins

Organometallic compounds are increasingly used as molecular scaffolds in drug development projects; their structural and electronic properties offering novel opportunities in protein-ligand complementarities. Interestingly, while protein-ligand dockings have long become a spearhead in computer assisted drug design, no benchmarking nor optimization have been done for their use with organometallic compounds. Pursuing our efforts to model metal mediated recognition processes, we herein present a systematic study of the capabilities of the program GOLD to predict the interactions of protein with organometallic compounds. The study focuses on inert systems for which no alteration of the first coordination sphere of the metal occurs upon binding. Several scaffolds are used as test systems with different docking schemes and scoring functions. We conclude that ChemScore is the most robust scoring function with ASP and ChemPLP providing with good results too and GoldScore slightly underperforming. This study shows that current state-of-the-art protein-ligand docking techniques are reliable for the docking of inert organometallic compounds binding to protein.

Crystal structure of two anti-porphyrin antibodies with peroxidase activity

We report the crystal structures at 2.05 and 2.45 Å resolution of two antibodies, 13G10 and 14H7, directed against an iron(III)-αααβ-carboxyphenylporphyrin, which display some peroxidase activity. Although these two antibodies differ by only one amino acid in their variable λ-light chain and display 86% sequence identity in their variable heavy chain, their complementary determining regions (CDR) CDRH1 and CDRH3 adopt very different conformations. The presence of Met or Leu residues at positions preceding residue H101 in CDRH3 in 13G10 and 14H7, respectively, yields to shallow combining sites pockets with different shapes that are mainly hydrophobic. The hapten and other carboxyphenyl-derivatized iron(III)-porphyrins have been modeled in the active sites of both antibodies using protein ligand docking with the program GOLD. The hapten is maintained in the antibody pockets of 13G10 and 14H7 by a strong network of hydrogen bonds with two or three carboxylates of the carboxyphenyl substituents of the porphyrin, respectively, as well as numerous stacking and van der Waals interactions with the very hydrophobic CDRH3. However, no amino acid residue was found to chelate the iron. Modeling also allows us to rationalize the recognition of alternative porphyrinic cofactors by the 13G10 and 14H7 antibodies and the effect of imidazole binding on the peroxidase activity of the 13G10/porphyrin complexes.

Hybrid schemes based on quantum mechanics/molecular mechanics simulations goals to success, problems, and perspectives

The development of characterization techniques, advanced synthesis methods, as well as molecular modeling has transformed the study of systems in a well-established research field. The current research challenges in biocatalysis and biotransformation evolve around enzyme discovery, design, and optimization. How can we find or create enzymes that catalyze important synthetic reactions, even reactions that may not exist in nature? What is the source of enzyme catalytic power? To answer these and other related questions, the standard strategies have evolved from trial-and-error methodologies based on chemical knowledge, accumulated experience, and common sense into a clearly multidisciplinary science that allows one to reach the molecular design of tailor-made enzyme catalysts. This is even more so when one refers to enzyme catalysts, for which the detailed structure and composition are known and can be manipulated to introduce well-defined residues which can be implicated in the chemical rearrangements taking place in the active site. The methods and techniques of theoretical and computational chemistry are becoming more and more important in both understanding the fundamental biological roles of enzymes and facilitating their utilization in biotechnology. Improvement of the catalytic function of enzymes is important from scientific and industrial viewpoints, and to put this fact in the actual perspective as well as the potentialities, we recommend the very recent report of Sanderson [Sanderson, K. (2011). Chemistry: enzyme expertise. Nature 471, 397.]. Great fundamental advances have been made toward the ab initio design of enzyme catalysts based on molecular modeling. This has been based on the molecular mechanistic knowledge of the reactions to be catalyzed, together with the development of advanced synthesis and characterization techniques. The corresponding molecular mechanism can be studied by means of powerful quantum chemical calculations. The catalytic active site can be optimized to improve the transition state analogues (TSA) and to enhance the catalytic activity, even improve the active site to favor a desired direction of some promiscuous enzymes. In this chapter, we give a brief introduction, the state of the art, and future prospects and implications of enzyme design. Current computational tools to assist experimentalists for the design and engineering of proteins with desired catalytic properties are described. The interplay between enzyme design, molecular simulations, and experiments will be presented to emphasize the interdisciplinary nature of this research field. This text highlights the recent advances and examples selected from our laboratory are shown, of how the applications of these tools are a first attempt to de novo design of protein active sites. Identification of neutral/advantageous/deleterious mutation platforms can be exploited to penetrate some of Nature's closely guarded secrets of chemical reactivity. In this chapter, we give a brief introduction, the state of the art, and future prospects and implications of enzyme design. The first part describes briefly how the molecular modeling is carried out. Then, we discuss the requirements of hybrid quantum mechanical/molecular mechanics molecular dynamics (QM/MM MD) simulations, analyzing what are the basis of these theoretical methodologies, how we can use them with a view to its application in the study of enzyme catalysis, and what are the best methodologies for assessing its catalytic potential. In the second part, we focus on some selected examples, taking as a common guide the chorismate to prephenate rearrangement, studying the corresponding molecular mechanism in vacuo, in solution and in an enzyme environment. In addition, examples involving catalytic antibodies (CAs) and promiscuous enzymes will be presented. Finally, a special emphasis is made to provide some hints about the logical evolution that can be anticipated in this research field. Moreover, it helps in understanding the open directions in this area of knowledge and highlights the importance of computational approaches in discovering specific drugs and the impact on the rational design of tailor-made enzymes.

Three dimensional models of Cu(2+)-Aβ(1-16) complexes from computational approaches

Elucidation of the coordination of metal ions to Aβ is essential to understand their role in its aggregation and to rationally design new chelators with potential therapeutic applications in Alzheimer disease. Because of that, in the last 10 years several studies have focused their attention in determining the coordination properties of Cu(2+) interacting with Aβ. However, more important than characterizing the first coordination sphere of the metal is the determination of the whole Cu(2+)-Aβ structure. In this study, we combine homology modeling (HM) techniques with quantum mechanics based approaches (QM) to determine plausible three-dimensional models for Cu(2+)-Aβ(1-16) with three histidines in their coordination sphere. We considered both ε and δ coordination of histidines 6, 13, and 14 as well as the coordination of different possible candidates containing oxygen as fourth ligand (Asp1, Glu3, Asp7, Glu11, and CO(Ala2)). Among the 32 models that enclose COO(-), the lowest energy structures correspond to [O(E3),N(δ)(H6),N(ε)(H13),N(ε)(H14)] (1), [O(E3),N(δ)(H6),N(δ)(H13),N(δ)(H14)] (2), and [O(D7),N(ε)(H6),N(δ)(H13),N(δ)(H14)] (3). The most stable model containing CO(Ala2) as fourth ligand in the Cu(2+) coordination sphere is [O(c)(A2),N(ε)(H6),N(δ)(H13),N(ε)(H14)] (4). An estimation of the relative stability between Glu3 (1) and CO(Ala2) (4) coordinated complexes seems to indicate that the preference for the latter coordination may be due to solvent effects. The present results also show the relationship between the peptidic and metallic moieties in defining the overall geometry of the complex and illustrate that the final stability of the complexes results from a balance between the metal coordination site and amyloid folding upon complexation.