NCIPLOT4 Guide for Biomolecules: An Analysis Tool for Noncovalent Interactions

Molecular insight into endosulfan degradation by Ese protein from Arthrobacter: Evidence-based structural bioinformatics and quantum mechanical calculations

Endosulfan is an organochlorine insecticide widely used for agricultural pest control. Many nations worldwide have restricted or completely banned it due to its extreme toxicity to fish and aquatic invertebrates. Arthrobacter sp. strain KW has the ability to degrade α, β endosulfan and its intermediate metabolite endosulfate; this degradation is associated with Ese protein, a two-component flavin-dependent monooxygenase (TC-FDM). Employing in silico tools, we obtained the 3D model of Ese protein, and our results suggest that it belongs to the Luciferase Like Monooxygenase family (LLM). Docking studies showed that the residues V59, V315, D316, and T335 interact with α-endosulfan. The residues: V59, T60, V315, D316, and T335 are implicated in the interacting site with β-endosulfan, and the residues: H17, V315, D316, T335, N364, and Q363 participate in the interaction with endosulfate. Topological analysis of the electron density by means of the Quantum Theory of Atoms in Molecules (QTAIM) and the Non-Covalent Interaction (NCI) index reveals that the Ese-ligands complexes are formed mainly by dispersive forces, where Cl atoms have a predominant role. As Ese is a monooxygenase member, we predict the homodimer formation. However, enzymatic studies must be developed to investigate the Ese protein's enzymatic and catalytic activity.

The NCIWEB Server: A Novel Implementation of the Noncovalent Interactions Index for Biomolecular Systems

It is well-known that the activity and function of proteins is strictly correlated with their secondary, tertiary, and quaternary structures. Their biological role is regulated by their conformational flexibility and global fold, which, in turn, is largely governed by complex noncovalent interaction networks. Because of the large size of proteins, the analysis of their noncovalent interaction networks is challenging, but can provide insights into the energetics of conformational changes or protein-protein and protein-ligand interactions. The noncovalent interaction (NCI) index, based on the reduced density gradient, is a well-established tool for the detection of weak contacts in biological systems. In this work, we present a web-based application to expand the use of this index to proteins, which only requires a molecular structure as input and provides a mapping of the number, type, and strength of noncovalent interactions. Structure preparation is automated and allows direct importing from the PDB database, making this server (https://nciweb.dsi.upmc.fr) accessible to scientists with limited experience in bioinformatics. A quick overview of this tool and concise instructions are presented, together with an illustrative application.

Extracting Quantitative Information at Quantum Mechanical Level from Noncovalent Interaction Index Analyses

The noncovalent interaction (NCI) index is nowadays a well-known strategy to detect NCIs in molecular systems. Even though it initially provided only qualitative descriptions, the technique has been recently extended to also extract quantitative information. To accomplish this task, integrals of powers of the electron distribution were considered, with the requirement that the overall electron density can be clearly decomposed as sum of distinct fragment contributions to enable the definition of the (noncovalent) integration region. So far, this was done by only exploiting approximate promolecular electron densities, which are given by the sum of spherically averaged atomic electron distributions and thus represent too crude approximations. Therefore, to obtain more quantum mechanically (QM) rigorous results from NCI index analyses, in this work, we propose to use electron densities obtained through the transfer of extremely localized molecular orbitals (ELMOs) or through the recently developed QM/ELMO embedding technique. Although still approximate, the electron distributions resulting from the abovementioned methods are fully QM and, above all, are again partitionable into subunit contributions, which makes them completely suitable for the NCI integral approach. Therefore, we benchmarked the integrals resulting from NCI index analyses (both those based on the promolecular densities and those based on ELMO electron distributions) against interaction energies computed at a high quantum chemical level (in particular, at the coupled cluster level). The performed test calculations have indicated that the NCI integrals based on ELMO electron densities outperform the promolecular ones. Furthermore, it was observed that the novel quantitative NCI-(QM/)ELMO approach can be also profitably exploited both to characterize and evaluate the strength of specific interactions between ligand subunits and protein residues in protein-ligand complexes and to follow the evolution of NCIs along trajectories of molecular dynamics simulations. Although further methodological improvements are still possible, the new quantitative ELMO-based technique could be already exploited in situations in which fast and reliable assessments of NCIs are crucial, such as in computational high-throughput screenings for drug discovery.

Recognition in the Domain of Molecular Chirality: From Noncovalent Interactions to Separation of Enantiomers

It is not a coincidence that both chirality and noncovalent interactions are ubiquitous in nature and synthetic molecular systems. Noncovalent interactivity between chiral molecules underlies enantioselective recognition as a fundamental phenomenon regulating life and human activities. Thus, noncovalent interactions represent the narrative thread of a fascinating story which goes across several disciplines of medical, chemical, physical, biological, and other natural sciences. This review has been conceived with the awareness that a modern attitude toward molecular chirality and its consequences needs to be founded on multidisciplinary approaches to disclose the molecular basis of essential enantioselective phenomena in the domain of chemical, physical, and life sciences. With the primary aim of discussing this topic in an integrated way, a comprehensive pool of rational and systematic multidisciplinary information is provided, which concerns the fundamentals of chirality, a description of noncovalent interactions, and their implications in enantioselective processes occurring in different contexts. A specific focus is devoted to enantioselection in chromatography and electromigration techniques because of their unique feature as "multistep" processes. A second motivation for writing this review is to make a clear statement about the state of the art, the tools we have at our disposal, and what is still missing to fully understand the mechanisms underlying enantioselective recognition.

Successes and challenges in multiscale modelling of artificial metalloenzymes: the case study of POP-Rh2 cyclopropanase

Molecular modelling applications in metalloenzyme design are still scarce due to a series of challenges. On top of that, the simulations of metal-mediated binding and the identification of catalytic competent geometries require both large conformational exploration and simulation of fine electronic properties. Here, we demonstrate how the incorporation of new tools in multiscale strategies, namely substrate diffusion exploration, allows taking a step further. As a showcase, the enantioselective profiles of the most outstanding variants of an artificial Rh₂-based cyclopropanase (GSH, HFF and RFY) developed by Lewis and co-workers (Nat. Commun., 2015, 6, 7789 and Nat. Chem., 2018, 10, 318-324) have been rationalized. DFT calculations on the free-cofactor-mediated process identify the carbene insertion and the cyclopropanoid formation as crucial events, the latter being the enantiodetermining step, which displays up to 8 competitive orientations easily altered by the protein environment. The key intermediates of the reaction were docked into the protein scaffold showing that some mutated residues have direct interaction with the cofactor and/or the co-substrate. These interactions take the form of a direct coordination of Rh in GSH and HFF and a strong hydrophobic patch with the carbene moiety in RFY. Posterior molecular dynamics sustain that the cofactor induces global re-arrangements of the protein. Finally, massive exploration of substrate diffusion, based on the GPathFinder approach, defines this event as the origin of the enantioselectivity in GSH and RFY. For HFF, fine molecular dockings suggest that it is likely related to local interactions upon diffusion. This work shows how modelling of long-range mutations on the catalytic profiles of metalloenzymes may be unavoidable and software simulating substrate diffusion should be applied.

Towards a predictive model for polymer solubility using the noncovalent interaction index: polyethylene as a case study

In this work we present the development of a novel, quantitative solubility descriptor based on the non-covalent interaction index. It is presented as a more insightful alternative to Hansen's solubility parameters and the COSMO model to assess and predict polymer solubility in different solvents. To this end, we studied the solvation behaviour as a function of the chain length of a single chain of arguably the most simple polymer, polyethylene, in anisole (solvent) and methanol (poor solvent) via molecular dynamics simulations. It was found that in anisole the solute maximized its interface with the solvent, whereas in methanol the macromolecule formed rod-like structures by folding on itself once the chain length surpassed a certain barrier. We assessed this behaviour - which can be related to solubility - quantitatively and qualitatively via well-known descriptors, namely the solvation free energy, and the solvent accessible surface area. In addition, we propose the non-covalent interaction (NCI) index as a versatile descriptor, providing information on the strength, as well as the nature, of the solute-solvent interactions, the solute's intramolecular interactions and on the solute's conformation, both qualitatively and quantitatively. Finally, as a quantitative measure for solubility, defined in this context as the solute's tendency to maximize its interactions with the solvent, we propose two new NCI-based descriptors: the relative integrated NCI density and the integrated NCI difference. The former represents the quantitative difference in solute-solvent interactions between a fully extended coil and the actual conformation during simulation and the latter the quantitative difference between the intermolecular (solute-solvent) and the intramolecular (in the solute) non-covalent interactions. The easy interpretation and calculation of these novel quantities open up the possibility of fast, reliable and insightful high-throughput screening of different (anti)solvent and solute combinations.

Unveiling the role of pyrylium frameworks on π-stacking interactions: a combined ab initio and experimental study

A multidisciplinary study is presented to shed light on how pyrylium frameworks, as π-hole donors, establish π-π interactions. The combination of CSD analysis, computational modelling (ab intitio, DFT and MD simulations) and experimental NMR spectroscopy data provides essential information on the key parameters that characterize these intereactions, opening new avenues for further applications of this versatile heterocycle.

Structural insights into SARS-CoV-2 spike protein and its natural mutants found in Mexican population

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a newly emerged coronavirus responsible for coronavirus disease 2019 (COVID-19); it become a pandemic since March 2020. To date, there have been described three lineages of SARS-CoV-2 circulating worldwide, two of them are found among Mexican population, within these, we observed three mutations of spike (S) protein located at amino acids H49Y, D614G, and T573I. To understand if these mutations could affect the structural behavior of S protein of SARS-CoV-2, as well as the binding with S protein inhibitors (cepharanthine, nelfinavir, and hydroxychloroquine), molecular dynamic simulations and molecular docking were employed. It was found that these punctual mutations affect considerably the structural behavior of the S protein compared to wild type, which also affect the binding of its inhibitors into their respective binding site. Thus, further experimental studies are needed to explore if these affectations have an impact on drug-S protein binding and its possible clinical effect.

A Step toward the Quantification of Noncovalent Interactions in Large Biological Systems: The Independent Gradient Model-Extremely Localized Molecular Orbital Approach

The independent gradient model (IGM) is a recent electron density-based computational method that enables to detect and quantify covalent and noncovalent interactions. When applied to large systems, the original version of the technique still relies on promolecular electron densities given by the sum of spherically averaged atomic electron distributions, which leads to approximate evaluations of the inter- and intramolecular interactions occurring in systems of biological interest. To overcome this drawback and perform IGM analyses based on quantum mechanically rigorous electron densities also for macromolecular systems, we coupled the IGM approach with the recently constructed libraries of extremely localized molecular orbitals (ELMOs) that allow fast and reliable reconstructions of polypeptide and protein electron densities. The validation tests performed on small polypeptides and peptide dimers have shown that the novel IGM-ELMO strategy provides results that are systematically closer to the fully quantum mechanical ones and outperforms the IGM method based on the crude promolecular approximation, but still keeping a quite low computational cost. The results of the test calculations carried out on proteins have also confirmed the trends observed for the IGM analyses conducted on small systems. This makes us envisage the future application of the novel IGM-ELMO approach to unravel complicated noncovalent interaction networks (e.g., in protein-protein contacts) or to rationally design new drugs through molecular docking calculations and virtual high-throughput screenings.

Insight into the impact of EGFR L792Y/F/H mutations on sensitivity to osimertinib: an in silico study

EGFR L792Y/F/H mutation makes it difficult for Osimertinib to recognize ATP pockets.

Atomistic fibrillar architectures of polar prion-inspired heptapeptides

This article provides the computational prediction of the atomistic architectures resulting from self-assembly of the polar heptapeptide sequences NYNYNYN, SYSYSYS and GYGYGYG. Using a combination of molecular dynamics and a newly developed tool for non-covalent interaction analysis, we uncover the properties of a new class of bionanomaterials, including hydrogen-bonded polar zippers, and the relationship between peptide composition, fibril geometry and weak interaction networks. Our results, corroborated by experimental observations, provide the basis for the rational design of prion-inspired nanomaterials.

NCIPLOT4: Fast, Robust, and Quantitative Analysis of Noncovalent Interactions

The NonCovalent Interaction index (NCI) enables identification of attractive and repulsive noncovalent interactions from promolecular densities in a fast manner. However, the approach remained up to now qualitative, only providing visual information. We present a new version of NCIPLOT, NCIPLOT4, which allows quantifying the properties of the NCI regions (volume, charge) in small and big systems in a fast manner. Examples are provided of how this new twist enables characterization and retrieval of local information in supramolecular chemistry and biosystems at the static and dynamic levels.