Computational Study of Binding of μ-Conotoxin GIIIA to Bacterial Sodium Channels NaVAb and NaVRh

Voltage-Gated Sodium Channel Inhibition by µ-Conotoxins

µ-Conotoxins are small, potent pore-blocker inhibitors of voltage-gated sodium (NaV) channels, which have been identified as pharmacological probes and putative leads for analgesic development. A limiting factor in their therapeutic development has been their promiscuity for different NaV channel subtypes, which can lead to undesirable side-effects. This review will focus on four areas of µ-conotoxin research: (1) mapping the interactions of µ-conotoxins with different NaV channel subtypes, (2) µ-conotoxin structure-activity relationship studies, (3) observed species selectivity of µ-conotoxins and (4) the effects of µ-conotoxin disulfide connectivity on activity. Our aim is to provide a clear overview of the current status of µ-conotoxin research.

Molecular Dynamics Simulations of Ion Permeation in Human Voltage-Gated Sodium Channels

The recent determination of cryo-EM structures of voltage-gated sodium (Na_v) channels has revealed many details of these proteins. However, knowledge of ionic permeation through the Na_v pore remains limited. In this work, we performed atomistic molecular dynamics (MD) simulations to study the structural features of various neuronal Na_v channels based on homology modeling of the cryo-EM structure of the human Na_v1.4 channel and, in addition, on the recently resolved configuration for Na_v1.2. In particular, single Na⁺ permeation events during standard MD runs suggest that the ion resides in the inner part of the Na_v selectivity filter (SF). On-the-fly free energy parametrization (OTFP) temperature-accelerated molecular dynamics (TAMD) was also used to calculate two-dimensional free energy surfaces (FESs) related to single/double Na⁺ translocation through the SF of the homology-based Na_v1.2 model and the cryo-EM Na_v1.2 structure, with different realizations of the DEKA filter domain. These additional simulations revealed distinct mechanisms for single and double Na⁺ permeation through the wild-type SF, which has a charged lysine in the DEKA ring. Moreover, the configurations of the ions in the SF corresponding to the metastable states of the FESs are specific for each SF motif. Overall, the description of these mechanisms gives us new insights into ion conduction in human Na_v cryo-EM-based and cryo-EM configurations that could advance understanding of these systems and how they differ from potassium and bacterial Na_v channels.

Historical Perspective of the Characterization of Conotoxins Targeting Voltage-Gated Sodium Channels

Marine toxins have potent actions on diverse sodium ion channels regulated by transmembrane voltage (voltage-gated ion channels) or by neurotransmitters (nicotinic acetylcholine receptor channels). Studies of these toxins have focused on varied aspects of venom peptides ranging from evolutionary relationships of predator and prey, biological actions on excitable tissues, potential application as pharmacological intervention in disease therapy, and as part of multiple experimental approaches towards an understanding of the atomistic characterization of ion channel structure. This review examines the historical perspective of the study of conotoxin peptides active on sodium channels gated by transmembrane voltage, which has led to recent advances in ion channel research made possible with the exploitation of the diversity of these marine toxins.

High-Throughput Prediction and Design of Novel Conopeptides for Biomedical Research and Development

Cone snail venoms have been considered a valuable treasure for international scientists and businessmen, mainly due to their pharmacological applications in development of marine drugs for treatment of various human diseases. To date, around 800 Conus species are recorded, and each of them produces over 1,000 venom peptides (termed as conopeptides or conotoxins). This reflects the high diversity and complexity of cone snails, although most of their venoms are still uncharacterized. Advanced multiomics (such as genomics, transcriptomics, and proteomics) approaches have been recently developed to mine diverse Conus venom samples, with the main aim to predict and identify potentially interesting conopeptides in an efficient way. Some bioinformatics techniques have been applied to predict and design novel conopeptide sequences, related targets, and their binding modes. This review provides an overview of current knowledge on the high diversity of conopeptides and multiomics advances in high-throughput prediction of novel conopeptide sequences, as well as molecular modeling and design of potential drugs based on the predicted or validated interactions between these toxins and their molecular targets.

Implementing and Assessing an Alchemical Method for Calculating Protein-Protein Binding Free Energy

Protein-protein binding is fundamental to most biological processes. It is important to be able to use computation to accurately estimate the change in protein-protein binding free energy due to mutations in order to answer biological questions that would be experimentally challenging, laborious, or time-consuming. Although nonrigorous free-energy methods are faster, rigorous alchemical molecular dynamics-based methods are considerably more accurate and are becoming more feasible with the advancement of computer hardware and molecular simulation software. Even with sufficient computational resources, there are still major challenges to using alchemical free-energy methods for protein-protein complexes, such as generating hybrid structures and topologies, maintaining a neutral net charge of the system when there is a charge-changing mutation, and setting up the simulation. In the current study, we have used the pmx package to generate hybrid structures and topologies, and a double-system/single-box approach to maintain the net charge of the system. To test the approach, we predicted relative binding affinities for two protein-protein complexes using a nonequilibrium alchemical method based on the Crooks fluctuation theorem and compared the results with experimental values. The method correctly identified stabilizing from destabilizing mutations for a small protein-protein complex, and a larger, more challenging antibody complex. Strong correlations were obtained between predicted and experimental relative binding affinities for both protein-protein systems.

Modulation of Lymphocyte Potassium Channel KV1.3 by Membrane-Penetrating, Joint-Targeting Immunomodulatory Plant Defensin

We describe a cysteine-rich, membrane-penetrating, joint-targeting, and remarkably stable peptide, EgK5, that modulates voltage-gated K_V1.3 potassium channels in T lymphocytes by a distinctive mechanism. EgK5 enters plasma membranes and binds to K_V1.3, causing current run-down by a phosphatidylinositol 4,5-bisphosphate-dependent mechanism. EgK5 exhibits selectivity for K_V1.3 over other channels, receptors, transporters, and enzymes. EgK5 suppresses antigen-triggered proliferation of effector memory T cells, a subset enriched among pathogenic autoreactive T cells in autoimmune disease. PET-CT imaging with ¹⁸F-labeled EgK5 shows accumulation of the peptide in large and small joints of rodents. In keeping with its arthrotropism, EgK5 treats disease in a rat model of rheumatoid arthritis. It was also effective in treating disease in a rat model of atopic dermatitis. No signs of toxicity are observed at 10-100 times the in vivo dose. EgK5 shows promise for clinical development as a therapeutic for autoimmune diseases.

Structural Determinants Mediating Tertiapin Block of Neuronal Kir3.2 Channels

Tertiapin (TPN) is a 21 amino acid venom peptide from Apis mellifera that inhibits certain members of the inward rectifier potassium (Kir) channel family at a nanomolar affinity with limited specificity. Structure-based computational simulations predict that TPN behaves as a pore blocker; however, the molecular determinants mediating block of neuronal Kir3 channels have been inconclusive and unvalidated. Here, using molecular docking and molecular dynamics (MD) simulations with 'potential of mean force' (PMF) calculations, we investigated the energetically most favored interaction of TPN with several Kir3.x channel structures. The resulting binding model for Kir3.2-TPN complexes was then tested by targeted mutagenesis of the predicted contact sites, and their impact on the functional channel block was measured electrophysiologically. Together, our findings indicate that a high-affinity TPN block of Kir3.2 channels involves a pore-inserting lysine side chain requiring (1) hydrophobic interactions at a phenylalanine ring surrounding the channel pore and (2) electrostatic interactions with two adjacent Kir3.2 turret regions. Together, these interactions collectively stabilize high-affinity toxin binding to the Kir3.2 outer vestibule, which orients the ε-amino group of TPN-K21 to occupy the outermost K⁺ binding site of the selectivity filter. The structural determinants for the TPN block described here also revealed a favored subunit arrangement for assembled Kir3.x heteromeric channels, in addition to a multimodal binding capacity of TPN variants consistent with the functional dyad model for polybasic peptide pore blockers. These novel findings will aid efforts in re-engineering the TPN pharmacophore to develop peptide variants having unique and distinct Kir channel blocking properties.

Molecular dynamics simulations of an engineered T4 lysozyme exclude helix to sheet transition, and provide insights into long distance, intra-protein switchable motion

An engineered variant of T4 lysozyme serves as a model for studying induced remote conformational changes in a full protein context. The design involves a duplicated surface helix, flanked by two loops, that switches between two different conformations spanning about 20 Å. Molecular dynamics simulations of the engineered protein, up to 1 μs, rule out α-helix to β-sheet transitions within the duplicated helix as suggested by others. These simulations highlight how the use of different force fields can lead to radical differences in the structure of the protein. In addition, Markov state modeling and transition path theory were employed to map a 6.6 μs simulation for possible early intermediate states and to provide insights into the onset of the switching motion. The putative intermediates involve the folding of one helical turn in the C-terminal loop through energy driven, sequential rearrangement of nearby salt bridges around the key residue Arg63. These results provide a first step towards understanding the energetics and dynamics of a rather complicated intra-protein motion.

ConoMode, a database for conopeptide binding modes

ConoMode is a database for complex three-dimensional (3D) structures of conopeptides binding with their target proteins. Conopeptides, a large family of peptides from the venom of marine snails of the Conus genus, have exceptionally diverse sequences, and their high specificity to block ion channels makes them crucial as drug leads and tools for physiological studies. ConoMode is a specialized archive for the collection of 3D coordinate data for the conopeptides and their binding target proteins from published literature and the Protein Data Bank. These 3D structures can be determined using experimental methods such as X-ray crystallography and electron microscopy and computational methods including docking, homology modeling and molecular dynamics simulations. The binding modes for the conopeptides determined using computational modeling must be validated based on experimental data. The 3D coordinate data from ConoMode can be searched, visualized, downloaded and uploaded. Currently, ConoMode manages 19 conopeptide sequences (from 10 Conus species), 15 protein sequences and 37 3D structures. ConoMode utilizes a modern technical framework to provide a good user experience on mobile devices with touch interaction features. Furthermore, the database is fully optimized for unstructured data and flexible data models.

Database URL: http://conomode.qnlm.ac/conomode/conomode/index

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.

Insights into the Folding of Disulfide-Rich μ-Conotoxins

The study of protein conformations using molecular dynamics (MD) simulations has been in place for decades. A major contribution to the structural stability and native conformation of a protein is made by the primary sequence and disulfide bonds formed during the folding process. Here, we investigated μ-conotoxins GIIIA, KIIIA, PIIIA, SIIIA, and SmIIIA as model peptides possessing three disulfide bonds. Their NMR structures were used for MD simulations in a novel approach studying the conformations between the folded and the unfolded states by systematically breaking the distinct disulfide bonds and monitoring the conformational stability of the peptides. As an outcome, the use of a combination of the existing knowledge and results from the simulations to classify the studied peptides within the extreme models of disulfide folding pathways, namely the bovine pancreatic trypsin inhibitor pathway and the hirudin pathway, is demonstrated. Recommendations for the design and synthesis of cysteine-rich peptides with a reduced number of disulfide bonds conclude the study.

Role of the Interaction Motif in Maintaining the Open Gate of an Open Sodium Channel

Voltage-gated sodium channels undergo transitions between open, closed, and inactivated states, enabling regulation of the translocation of sodium ions across membranes. A recently published crystal structure of the full-length prokaryotic NavMs crystal structure in the activated open conformation has revealed the presence of a novel motif consisting of an extensive network of salt bridges involving residues in the voltage sensor, S4-S5 linker, pore, and C-terminal domains. This motif has been proposed to be responsible for maintaining an open conformation that enables ion translocation through the channel. In this study, we have used long-time molecular dynamics calculations without artificial restraints to demonstrate that the interaction network of full-length NavMs indeed prevents a rapid collapse and closure of the gate, in marked difference to earlier studies of the pore-only construct in which the gate had to be restrained to remain open. Interestingly, a frequently discussed "hydrophobic gating" mechanism at nanoscopic level is also observed in our simulations, in which the discontinuous water wire close to the gate region leads to an energetic barrier for ion conduction. In addition, we demonstrate the effects of in silico mutations of several of the key residues in the motif on the open channel's stability and functioning, correlating them with existing functional studies on this channel and homologous disease-associated mutations in human sodium channels; we also examine the effects of truncating/removing the voltage sensor and C-terminal domains in maintaining an open gate.

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.

µ-Conotoxins Modulating Sodium Currents in Pain Perception and Transmission: A Therapeutic Potential

The Conus genus includes around 500 species of marine mollusks with a peculiar production of venomous peptides known as conotoxins (CTX). Each species is able to produce up to 200 different biological active peptides. Common structure of CTX is the low number of amino acids stabilized by disulfide bridges and post-translational modifications that give rise to different isoforms. µ and µO-CTX are two isoforms that specifically target voltage-gated sodium channels. These, by inducing the entrance of sodium ions in the cell, modulate the neuronal excitability by depolarizing plasma membrane and propagating the action potential. Hyperexcitability and mutations of sodium channels are responsible for perception and transmission of inflammatory and neuropathic pain states. In this review, we describe the current knowledge of µ-CTX interacting with the different sodium channels subtypes, the mechanism of action and their potential therapeutic use as analgesic compounds in the clinical management of pain conditions.

Recent Advances in Conotoxin Classification by Using Machine Learning Methods

Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer's disease, Parkinson's disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research.

Computational study of aggregation mechanism in human lysozyme[D67H]

Aggregation of proteins is an undesired phenomena that affects both human health and bioengineered products such as therapeutic proteins. Finding preventative measures could be facilitated by a molecular-level understanding of dimer formation, which is the first step in aggregation. Here we present a molecular dynamics (MD) study of dimer formation propensity in human lysozyme and its D67H variant. Because the latter protein aggregates while the former does not, they offer an ideal system for testing the feasibility of the proposed MD approach which comprises three stages: i) partially unfolded conformers involved in dimer formation are generated via high-temperature MD simulations, ii) potential dimer structures are searched using docking and refined with MD, iii) free energy calculations are performed to find the most stable dimer structure. Our results provide a detailed explanation for how a single mutation (D67H) turns human lysozyme from non-aggregating to an aggregating protein. Conversely, the proposed method can be used to identify the residues causing aggregation in a protein, which can be mutated to prevent it.