Theoretical Study of Radical Inactivation, LOX Inhibition, and Iron Chelation: The Role of Ferulic Acid in Skin Protection against UVA Induced Oxidative Stress.

Ferulic acid (FA) is used in skin formulations for protection against the damaging actions of the reactive oxygen species (ROS) produced by UVA radiation. Possible underlying protective mechanisms are not fully elucidated. By considering the kinetics of proton-coupled electron transfer (PCET) and radical-radical coupling (RRC) mechanisms, it appears that direct scavenging could be operative, providing that a high local concentration of FA is present at the place of •OH generation. The resulting FA phenoxyl radical, after the scavenging of a second •OH and keto-enol tautomerization of the intermediate, produces 5-hydroxyferulic acid (5OHFA). Inhibition of the lipoxygenase (LOX) enzyme, one of the enzymes that catalyse free radical production, by FA and 5OHFA were analysed. Results of molecular docking calculations indicate favourable binding interactions of FA and 5OHFA with the LOX active site. The exergonicity of chelation reactions of the catalytic Fe2+ ion with FA and 5OHFA indicate the potency of these chelators to prevent the formation of •OH radicals via Fenton-like reactions. The inhibition of the prooxidant LOX enzyme could be more relevant mechanism of skin protection against UVA induced oxidative stress than iron chelation and assumed direct scavenging of ROS.

Simple Coordination Geometry Descriptors Allow to Accurately Predict Metal-Binding Sites in Proteins.

With more than a third of the genome encoding for metal-containing biomolecules, the in silico prediction of how metal ions bind to proteins is crucial in chemistry, biology, and medicine. To date, algorithms for metal-binding site prediction are mainly based on sequence analysis. Those methods have reached enough quality to predict the correct region of the protein and the coordinating residues involved in metal-binding, but they do not provide three-dimensional (3D) models. On the contrary, the prediction of accurate 3D models for protein-metal adducts by structural bioinformatics and molecular modeling techniques is still a challenge. Here, we present an update of our multipurpose molecular modeling suite, GaudiMM, to locate metal-binding sites in proteins. The approach is benchmarked on 105 X-ray structures with resolution lower than 2.0 Å. Results predict the correct binding site of the metal in the biological scaffold for all the entries in the data set. Generated 3D models of the protein-metal coordination complexes reach root-mean-square deviation values under 1.0 Å between calculated and experimental structures. The whole process is purely based on finding poses that satisfy metal-derived geometrical rules without needing sequence or fine electronic inputs. Additional post-optimizations, including receptor flexibility, have been tested and suggest that more extensive searches, required when the host structures present a low level of pre-organization, are also possible. With this new update, GaudiMM is now able to look for metal-binding sites in biological scaffolds and clearly shows how explicitly considering the geometric particularities of the first coordination sphere of the metal in a docking process provides excellent results.

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.

Computational insight into the interaction of oxaliplatin with insulin.

In an organism, cisplatin and its derivatives are known to interact with proteins besides their principal DNA target. These off-target interactions have major therapeutic consequences including undesired side effects, loss of bioavailability and emergence of resistance. Insulin is one of the prototypical protein targets of platinum drugs as it has been seen to be involved in bioavailability reduction and might also determine resistance in certain cancer lines. However, despite the interest in understanding the nature of the oxaliplatin-insulin adducts, no 3D models have been achieved so far. In this study, we apply our recent computational multiscale protocol optimized for bioinorganic interactions to provide structural insights into these systems. To do so, the initial structures are predicted by blind protein-metalloligand docking calculations optimized to account for a metal-containing species, and then refined using a Molecular Dynamics (MD) and Quantum Mechanics/Molecular Mechanics (QM/MM) integrated protocol. The results are consistent with experimental information obtained from fragment analysis, and also provide novel structural information like conformational changes occurring upon binding and potential effects on the biological functions of the protein. This study opens an avenue towards applying similar strategies to a wide ensemble of metallodrug-protein/peptide systems for which no structural data are available.

ESIgen: Electronic Supporting Information Generator for Computational Chemistry Publications.

Electronic supporting information (ESI) occupies a fundamental position in the way scientists report their work. It is a key element in lightening the writing of the core manuscript and makes concise communication easier for the authors. Computational chemistry, as all fields related to structural studies of molecules, tends to generate huge amounts of data that should be inserted in the ESI. ESI reports originating from computational chemistry works generally reach tens of sheets long and include 3D depictions, coordinates, energies, and other characteristics of the structures involved in the molecular process understudy. While most experienced users end up building scripts that dig throughout the output files searching for the relevant data, this is not the case for users without programming experience or time. Here we present an automated ESI generator supported by both web-based and command line interfaces. Focused on quantum mechanics calculations outputs so far, we trust that the community would find this tool useful. Source code is freely available at https://github.com/insilichem/esigen . A web app public demo can be found at http://esi.insilichem.com .

PyChimera: use UCSF Chimera modules in any Python 2.7 project.

Motivation: UCSF Chimera is a powerful visualization tool remarkably present in the computational chemistry and structural biology communities. Built on a C++ core wrapped under a Python 2.7 environment, one could expect to easily import UCSF Chimera's arsenal of resources in custom scripts or software projects. Nonetheless, this is not readily possible if the script is not executed within UCSF Chimera due to the isolation of the platform. UCSF ChimeraX, successor to the original Chimera, partially solves the problem but yet major upgrades need to be undergone so that this updated version can offer all UCSF Chimera features. Results: PyChimera has been developed to overcome these limitations and provide access to the UCSF Chimera codebase from any Python 2.7 interpreter, including interactive programming with tools like IPython and Jupyter Notebooks, making it easier to use with additional third-party software. Availability and implementation: PyChimera is LGPL-licensed and available at https://github.com/insilichem/pychimera. Contact: jaime.rodriguezguerra@uab.cat or jeandidier.marechal@uab.cat. Supplementary information: Supplementary data are available at Bioinformatics online.

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.

Elucidating the 3D structures of Al(iii)-Aß complexes: a template free strategy based on the pre-organization hypothesis.

Senile plaques are extracellular deposits found in patients with Alzheimer's Disease (AD) and are mainly formed by insoluble fibrils of ß-amyloid (Aß) peptides. The mechanistic details about how AD develops are not fully understood yet, but metals such as Cu, Zn, or Fe are proposed to have a non-innocent role. Many studies have also linked the non biological metal aluminum with AD, a species whose concentration in the environment and food has been constantly increasing since the industrial revolution. Gaining a molecular picture of how Al(iii) interacts with an Aß peptide is of fundamental interest to improve understanding of the many variables in the evolution of AD. So far, no consensus has been reached on how this metal interacts with Aß, partially due to the experimental complexity of detecting and quantifying the resulting Al(iii)-Aß complexes. Computational chemistry arises as a powerful alternative to investigate how Al(iii) can interact with Aß peptides, as suitable strategies could shed light on the metal-peptide description at the molecular level. However, the absence of any reliable template that could be used for the modeling of the metallopeptide structure makes computational insight extremely difficult. Here, we present a novel strategy to generate accurate 3D models of the Al(iii)-Aß complexes, which still circumvents first principles simulations of metal binding to peptides of Aß. The key to this approach lies in the identification of experimental structures of the isolated peptide that are favourably pre-organized for the binding of a given metal in configurations of the first coordination sphere that were previously identified as the most stable with amino acid models. This approach solves the problem of the absence of clear structural templates for novel metallopeptide constructs. The posterior refinement of the structures via QM/MM and MD calculations allows us to provide, for the first time, physically sound models for Al(iii)-Aß complexes with a 1 : 1 stoichiometry, where up to three carboxylic groups are involved in the metal binding, with a clear preference towards Glu3, Asp7, and Glu11.

GaudiMM: A modular multi-objective platform for molecular modeling.

GaudiMM (for Genetic Algorithms with Unrestricted Descriptors for Intuitive Molecular Modeling) is here presented as a modular platform for rapid 3D sketching of molecular systems. It combines a Multi-Objective Genetic Algorithm with diverse molecular descriptors to overcome the difficulty of generating candidate models for systems with scarce structural data. Its grounds consist in transforming any molecular descriptor (i.e. those generally used for analysis of data) as a guiding objective for PES explorations. The platform is written in Python with flexibility in mind: the user can choose which descriptors to use for each problem and is even encouraged to code custom ones. Illustrative cases of its potential applications are included to demonstrate the flexibility of this approach, including metal coordination of multidentate ligands, peptide folding, and protein-ligand docking. GaudiMM is available free of charge from https://github.com/insilichem/gaudi. © 2017 Wiley Periodicals, Inc.