Combining crystallographic and quantum chemical data to understand DNA-protein π-interactions in nature

Effect of the Growth Conditions on Organic Crystals with Rare Earth Ions and 1,10-Phenanthroline

Using a simple solution growth technique, we grow crystals with phenanthroline as a ligand and various rare earth ions: thulium (Tm), ytterbium (Yb), gadolinium (Gd), lanthanum (La), neodymium (Nd), europium (Eu), and erbium (Er). We then selected the composition that forms thin plates with well-defined shapes, Er(NO3)Phen2, and explored the effects of various conditions on crystal formation and growth, including temperature regime, light illumination, and substrates where the crystals are formed and grown. The composition and local environment strongly affect the size and shape of microcrystals and substrate coverage. The use of gold substrates significantly enhances the crystal growing process. Elevated temperatures negatively affect the crystal growth.

Elucidating the biology of transcription factor-DNA interaction for accurate identification of cis-regulatory elements

Transcription factors (TFs) play a critical role in determining cell fate decisions by integrating developmental and environmental signals through binding to specific cis-regulatory modules and regulating spatio-temporal specificity of gene expression patterns. Precise identification of functional TF binding sites in time and space not only will revolutionize our understanding of regulatory networks governing cell fate decisions but is also instrumental to uncover how genetic variations cause morphological diversity or disease. In this review, we discuss recent advances in mapping TF binding sites and characterizing the various parameters underlying the complexity of binding site recognition by TFs.

Anatomy of noncovalent interactions between the nucleobases or ribose and π-containing amino acids in RNA-protein complexes

A set of >300 nonredundant high-resolution RNA–protein complexes were rigorously searched for π-contacts between an amino acid side chain (W, H, F, Y, R, E and D) and an RNA nucleobase (denoted π–π interaction) or ribose moiety (denoted sugar–π). The resulting dataset of >1500 RNA–protein π-contacts were visually inspected and classified based on the interaction type, and amino acids and RNA components involved. More than 80% of structures searched contained at least one RNA–protein π-interaction, with π–π contacts making up 59% of the identified interactions. RNA–protein π–π and sugar–π contacts exhibit a range in the RNA and protein components involved, relative monomer orientations and quantum mechanically predicted binding energies. Interestingly, π–π and sugar–π interactions occur more frequently with RNA (4.8 contacts/structure) than DNA (2.6). Moreover, the maximum stability is greater for RNA–protein contacts than DNA–protein interactions. In addition to highlighting distinct differences between RNA and DNA–protein binding, this work has generated the largest dataset of RNA–protein π-interactions to date, thereby underscoring that RNA–protein π-contacts are ubiquitous in nature, and key to the stability and function of RNA–protein complexes.

Investigating the lipid fingerprint of SLC6 neurotransmitter transporters: a comparison of dDAT, hDAT, hSERT, and GlyT2

The local lipid annulus, or "fingerprint", of four SLC6 transporters (dDAT, hDAT, hSERT, and GlyT2) embedded in a complex neuronal membrane were compared and characterised using molecular dynamics. Our analysis included the development of new tools to improve membrane leaflet detection and the analysis of leaflet-dependent properties. Overall, the lipid fingerprints of the four transporters are comprised of similar lipids when grouped by headgroup or tail saturation. The enrichment and depletion of specific lipids, including sites of cholesterol contacts, varies between transporters. The subtle differences in lipid fingerprints results in varying membrane biophysical properties near the transporter. Our results highlight that the lipid-fingerprint of SLC6 transporters in complex membranes is highly dependent on membrane composition. Our results further characterize how the presence and identity of membrane proteins affects the complex interplay of lipid-protein interactions, influencing the local lipid environment and membrane biophysical properties.

The CH-π Interaction in Protein-Carbohydrate Binding: Bioinformatics and In Vitro Quantification

The molecular recognition of carbohydrates by proteins plays a key role in many biological processes including immune response, pathogen entry into a cell, and cell-cell adhesion (e.g., in cancer metastasis). Carbohydrates interact with proteins mainly through hydrogen bonding, metal-ion-mediated interaction, and non-polar dispersion interactions. The role of dispersion-driven CH-π interactions (stacking) in protein-carbohydrate recognition has been underestimated for a long time considering the polar interactions to be the main forces for saccharide interactions. However, over the last few years it turns out that non-polar interactions are equally important. In this study, we analyzed the CH-π interactions employing bioinformatics (data mining, structural analysis), several experimental (isothermal titration calorimetry (ITC), X-ray crystallography), and computational techniques. The Protein Data Bank (PDB) has been used as a source of structural data. The PDB contains over 12 000 protein complexes with carbohydrates. Stacking interactions are very frequently present in such complexes (about 39 % of identified structures). The calculations and the ITC measurement results suggest that the CH-π stacking contribution to the overall binding energy ranges from 4 up to 8 kcal mol^-1 . All the results show that the stacking CH-π interactions in protein-carbohydrate complexes can be considered to be a driving force of the binding in such complexes.

Molecular Dynamics Simulations of DNA Adsorption on Graphene Oxide and Reduced Graphene Oxide-PEG-NH2 in the Presence of Mg2+ and Cl− ions

Graphene and its functionalised derivatives are transforming the development of biosensors that are capable of detecting nucleic acid hybridization. Using a Molecular Dynamics (MD) approach, we explored single-stranded or double-stranded deoxyribose nucleic acid (ssDNA or dsDNA) adsorption on two graphenic species: graphene oxide (GO) and reduced graphene oxide functionalized with aminated polyethylene glycol (rGO-PEG-NH2). Innovatively, we included chloride (Cl−) and magnesium (Mg2+) ions that influenced both the ssDNA and dsDNA adsorption on GO and rGO-PEG-NH2 surfaces. Unlike Cl−, divalent Mg2+ ions formed bridges between the GO surface and DNA molecules, promoting adsorption through electrostatic interactions. For rGO-PEG-NH2, the Mg2+ ions were repulsed from the graphenic surface. The subsequent ssDNA adsorption, mainly influenced by electrostatic forces and hydrogen bonds, could be supported by π–π stacking interactions that were absent in the case of dsDNA. We provide a novel insight for guiding biosensor development.

Efficient cell penetration and delivery of peptide nucleic acids by an argininocalix[4]arene

The application of Peptide Nucleic Acids (PNAs), mimics of DNA lacking the sugar-phosphate backbone, for antisense/anti-gene therapy and gene editing is limited by their low uptake by cells. Currently, no simple and efficient delivery systems and methods are available to solve this open issue. One of the most promising approach is the modification of the PNA structure through the covalent linkage of poliarginine tails, but this means that every PNA intended to be internalized must be modified. Herein we report the results relative to the delivery ability of a macrocyclic multivalent tetraargininocalix[4]arene (1) used as non-covalent vector for anti-miR-221-3p PNAs. High delivery efficiency, low cytotoxicity, maintenance of the PNA biological activity and ease preparation of the transfection formulation, simply attained by mixing PNA and calixarene, candidate this vector as universal delivery system for this class of nucleic acid analogues.

Anion-π Interactions in Computer-Aided Drug Design: Modeling the Inhibition of Malate Synthase by Phenyl-Diketo Acids

Human infection by Mycobacterium tuberculosis (Mtb) continues to be a global epidemic. Computer-aided drug design (CADD) methods are used to accelerate traditional drug discovery efforts. One noncovalent interaction that is being increasingly identified in biological systems but is neglected in CADD is the anion-π interaction. The study reported herein supports the conclusion that anion-π interactions play a central role in directing the binding of phenyl-diketo acid (PDKA) inhibitors to malate synthase (GlcB), an enzyme required for Mycobacterium tuberculosis virulence. Using density functional theory methods (M06-2X/6-31+G(d)), a GlcB active site template was developed for a predictive model through a comparative analysis of PDKA-bound GlcB crystal structures. The active site model includes the PDKA molecule and the protein determinants of the electrostatic, hydrogen-bonding, and anion-π interactions involved in binding. The predictive model accurately determines the Asp 633-PDKA structural position upon binding and precisely predicts the relative binding enthalpies of a series of 2-ortho halide-PDKAs to GlcB. A screening model was also developed to efficiently assess the propensity of each PDKA analog to participate in an anion-π interaction; this method is in good agreement with both the predictive model and the experimental binding enthalpies for the 2-ortho halide-PDKAs. With the screening and predictive models in hand, we have developed an efficient method for computationally screening and evaluating the binding enthalpy of variously substituted PDKA molecules. This study serves to illustrate the contribution of this overlooked interaction to binding affinity and demonstrates the importance of integrating anion-π interactions into structure-based CADD.

QM/MM Study of the Reaction Catalyzed by Alkyladenine DNA Glycosylase: Examination of the Substrate Specificity of a DNA Repair Enzyme

Human alkyladenine DNA glycosylase (AAG) functions as part of the base excision repair pathway to excise structurally diverse oxidized and alkylated DNA purines. Specifically, AAG uses a water molecule activated by a general base and a nonspecific active site lined with aromatic residues to cleave the N-glycosidic bond. Despite broad substrate specificity, AAG does not target the natural purines (adenine (A) and guanine (G)). Using the ONIOM(QM:MM) methodology, we provide fundamental atomic level details of AAG bound to DNA-containing a neutral substrate (hypoxanthine (Hx)), a nonsubstrate (G), or a cationic substrate (7-methylguanine (7MeG)) and probe changes in the reaction pathway that occur when AAG targets different nucleotides. We reveal that subtle differences in protein-DNA contacts upon binding different substrates within the flexible AAG active site can significantly affect the deglycosylation reaction. Notably, we predict that AAG excises Hx in a concerted mechanism that is facilitated through correct alignment of the (E125) general base due to hydrogen bonding with a neighboring aromatic amino acid (Y127). Hx departure is further stabilized by π-π interactions with aromatic amino acids and hydrogen bonds with active site water. Despite possessing a similar structure to Hx, G is not excised since the additional exocyclic amino group leads to misalignment of the general base due to disruption of the key E125-Y127 hydrogen bond, the catalytically unfavorable placement of water within the active site, and weakened π-contacts between aromatic amino acids and the nucleobase. In contrast, cationic 7MeG does not occupy the same position within the AAG active site as G due to steric clashes with the additional N7 methyl group, which results in the correct alignment of the general base and permits nucleobase excision as observed for neutral Hx. Overall, our structural data rationalizes the observed substrate specificity of AAG and contributes to our fundamental understanding of enzymes with flexible active sites and broad substrate specificities.