Investigation of α-conotoxin unbinding using umbrella sampling

Structural and Functional Analyses of Cone Snail Toxins

Cone snails are marine gastropod mollusks with one of the most powerful venoms in nature. The toxins, named conotoxins, must act quickly on the cone snails´ prey due to the fact that snails are extremely slow, reducing their hunting capability. Therefore, the characteristics of conotoxins have become the object of investigation, and as a result medicines have been developed or are in the trialing process. Conotoxins interact with transmembrane proteins, showing specificity and potency. They target ion channels and ionotropic receptors with greater regularity, and when interaction occurs, there is immediate physiological decompensation. In this review we aimed to evaluate the structural features of conotoxins and the relationship with their target types.

Snails In Silico: A Review of Computational Studies on the Conopeptides

Marine cone snails are carnivorous gastropods that use peptide toxins called conopeptides both as a defense mechanism and as a means to immobilize and kill their prey. These peptide toxins exhibit a large chemical diversity that enables exquisite specificity and potency for target receptor proteins. This diversity arises in terms of variations both in amino acid sequence and length, and in posttranslational modifications, particularly the formation of multiple disulfide linkages. Most of the functionally characterized conopeptides target ion channels of animal nervous systems, which has led to research on their therapeutic applications. Many facets of the underlying molecular mechanisms responsible for the specificity and virulence of conopeptides, however, remain poorly understood. In this review, we will explore the chemical diversity of conopeptides from a computational perspective. First, we discuss current approaches used for classifying conopeptides. Next, we review different computational strategies that have been applied to understanding and predicting their structure and function, from machine learning techniques for predictive classification to docking studies and molecular dynamics simulations for molecular-level understanding. We then review recent novel computational approaches for rapid high-throughput screening and chemical design of conopeptides for particular applications. We close with an assessment of the state of the field, emphasizing important questions for future lines of inquiry.

Determination of the μ-Conotoxin PIIIA Specificity Against Voltage-Gated Sodium Channels from Binding Energy Calculations

Voltage-gated sodium (NaV) channels generate and propagate action potentials in excitable cells, and several NaV subtypes have become important targets for pain management. The μ-conotoxins inhibit subtypes of the NaV with varied specificity but often lack of specificity to interested subtypes. Engineering the selectivity of the μ-conotoxins presents considerable complexity and challenge, as it involves the optimization of their binding affinities to multiple highly conserved NaV subtypes. In this study, a model of NaV1.4 bound with μ-conotoxin PIIIA complex was constructed using homology modeling, docking, molecular dynamic simulations and binding energy calculations. The accuracy of this model was confirmed based on the experimental mutagenesis data. The complex models of PIIIA bound with varied subtypes of NaV1.x (x = 1, 2, 3, 5, 6, 7, 8, or 9) were built using NaV1.4/PIIIA complex as a template, and refined using molecular dynamic simulations. The binding affinities of PIIIA to varied subtypes of NaV1.x (x = 1 to 9) were calculated using the Molecular Mechanics Generalized Born/Surface Area (MMGB/SA) and umbrella sampling, and were compared with the experimental values. The binding affinities calculated using MMGB/SA and umbrella sampling are correlated with the experimental values, with the former and the latter giving correlation coefficient of 0.41 (R²) and 0.68 (R²), respectively. Binding energy decomposition suggests that conserved and nonconserved residues among varied NaV subtypes have a synergistic effect on the selectivity of PIIIA.

Impact of protein-ligand solvation and desolvation on transition state thermodynamic properties of adenosine A2A ligand binding kinetics

Ligand-protein binding kinetic rates are growing in importance as parameters to consider in drug discovery and lead optimization. In this study we analysed using surface plasmon resonance (SPR) the transition state (TS) properties of a set of six adenosine A2A receptor inhibitors, belonging to both the xanthine and the triazolo-triazine scaffolds. SPR highlighted interesting differences among the ligands in the enthalpic and entropic components of the TS energy barriers for the binding and unbinding events. To better understand at a molecular level these differences, we developed suMetaD, a novel molecular dynamics (MD)-based approach combining supervised MD and metadynamics. This method allows simulation of the ligand unbinding and binding events. It also provides the system conformation corresponding to the highest energy barrier the ligand is required to overcome to reach the final state. For the six ligands evaluated in this study their TS thermodynamic properties were linked in particular to the role of water molecules in solvating/desolvating the pocket and the small molecules. suMetaD identified kinetic bottleneck conformations near the bound state position or in the vestibule area. In the first case the barrier is mainly enthalpic, requiring the breaking of strong interactions with the protein. In the vestibule TS location the kinetic bottleneck is instead mainly of entropic nature, linked to the solvent behaviour.

Homology model of the human tRNA splicing ligase RtcB

RtcB is an essential human tRNA ligase required for ligating the 2',3'-cyclic phosphate and 5'-hydroxyl termini of cleaved tRNA halves during tRNA splicing and XBP1 fragments during endoplasmic reticulum stress. Activation of XBP1 has been implicated in various human tumors including breast cancer. Here we present, for the first time, a homology model of human RtcB (hRtcB) in complex with manganese and covalently bound GMP built from the Pyrococcus horikoshii RtcB (bRtcB) crystal structure, PDB ID 4DWQA. The structure is analyzed in terms of stereochemical quality, folding reliability, secondary structure similarity with bRtcB, druggability of the active site binding pocket and its metal-binding microenvironment. In comparison with bRtcB, loss of a manganese-coordinating water and movement of Asn226 (Asn202 in 4DWQA) to form metal-ligand coordination, demonstrates the uniqueness of the hRtcB model. Rotation of GMP leads to the formation of an additional metal-ligand coordination (Mn-O). Umbrella sampling simulations of Mn binding in wild type and the catalytically inactive C122A mutant reveal a clear reduction of Mn binding ability in the mutant, thus explaining the loss of activity therein. Our results furthermore clearly show that the GTP binding site of the enzyme is a well-defined pocket that can be utilized as target site for in silico drug discovery.

Exploring the binding energy profiles of full agonists, partial agonists, and antagonists of the α7 nicotinic acetylcholine receptor

Nicotinic acetylcholine receptors (nAChRs) belong to the Cys-loop receptor family and are important drug targets for the treatment of neurological diseases. However, the precise determinants of the binding efficacies of ligands for these receptors are unclear. Therefore, in this study, the binding energy profiles of various ligands (full agonists, partial agonists, and antagonists) were quantified by docking those ligands with structural ensembles of the α7 nAChR exhibiting different degrees of C-loop closure. This approximate treatment of interactions suggested that full agonists, partial agonists, and antagonists of the α7 nAChR possess distinctive binding energy profiles. Results from docking revealed that ligand binding efficacy may be related to the capacity of the ligand to stabilize conformational states with a closed C loop.

Modelling the interactions between animal venom peptides and membrane proteins

The active components of animal venoms are mostly peptide toxins, which typically target ion channels and receptors of both the central and peripheral nervous system, interfering with action potential conduction and/or synaptic transmission. The high degree of sequence conservation of their molecular targets makes a range of these toxins active at human receptors. The high selectivity and potency displayed by some of these toxins have prompted their use as pharmacological tools as well as drugs or drug leads. Molecular modelling has played an essential role in increasing our molecular-level understanding of the activity and specificity of animal toxins, as well as engineering them for biotechnological and pharmaceutical applications. This review focuses on the biological insights gained from computational and experimental studies of animal venom toxins interacting with membranes and ion channels. A host of recent X-ray crystallography and electron-microscopy structures of the toxin targets has contributed to a dramatic increase in the accuracy of the molecular models of toxin binding modes greatly advancing this exciting field of study. This article is part of the Special Issue entitled 'Venom-derived Peptides as Pharmacological Tools.'

Estimation of kinetic and thermodynamic ligand-binding parameters using computational strategies

Kinetic and thermodynamic ligand–protein binding parameters are gaining growing importance as key information to consider in drug discovery. The determination of the molecular structures, using particularly x-ray and NMR techniques, is crucial for understanding how a ligand recognizes its target in the final binding complex. However, for a better understanding of the recognition processes, experimental studies of ligand–protein interactions are needed. Even though several techniques can be used to investigate both thermodynamic and kinetic profiles for a ligand–protein complex, these procedures are very often laborious, time consuming and expensive. In the last 10 years, computational approaches have enormous potential in providing insights into each of the above effects and in parsing their contributions to the changes in both kinetic and thermodynamic binding parameters. The main purpose of this review is to summarize the state of the art of computational strategies for estimating the kinetic and thermodynamic parameters of a ligand–protein binding.

Molecular simulation study of the unbinding of α-conotoxin [ϒ4E]GID at the α7 and α4β2 neuronal nicotinic acetylcholine receptors

The α7 and α4β2 neuronal nicotinic receptors belonging to the family of ligand-gated ion channels are most prevalent in the brain, and are implicated in various neurodegenerative disorders. α-conotoxin GID (and its analogue [ϒ4E]GID) specifically inhibits these subtypes, with more affinity towards the human α7 (hα7) subtype, and is valuable in understanding the physiological roles of these receptors. In this study, we use umbrella-sampling molecular dynamics simulations to understand the mechanism of interaction between [ϒ4E]GID and the agonist binding pockets of the α4β2 and the hα7 receptors, and to estimate their relative binding affinities (ΔG_bind). The obtained ΔG_bind values indicate stronger interaction with the hα7 receptor, in agreement with previous experimental studies. Simulations also revealed different unbinding pathways between the two receptor subtypes, enabling identification of a number of interactions at locations far from the orthosteric binding site which may explain the difference in [ϒ4E]GID potency. The pathways identified will help in the design of novel conotoxins with increased potency at α4β2, for which there is currently no known highly potent conotoxin inhibitor. Computational mutational free energy analyses also revealed a number of possible single-site mutations to GID which might enhance its selective binding to α4β2 over α7.