Two for the Price of One: Heterobivalent Ligand Design Targeting Two Binding Sites on Voltage-Gated Sodium Channels Slows Ligand Dissociation and Enhances Potency.

Voltage-gated sodium (NaV) channels are pore-forming transmembrane proteins that play essential roles in excitable cells, and they are key targets for antiepileptic, antiarrhythmic, and analgesic drugs. We implemented a heterobivalent design strategy to modulate the potency, selectivity, and binding kinetics of NaV channel ligands. We conjugated µ-conotoxin KIIIA, which occludes the pore of the NaV channels, to an analogue of huwentoxin-IV, a spider-venom peptide that allosterically modulates channel gating. Bioorthogonal hydrazide and copper-assisted azide-alkyne cycloaddition conjugation chemistries were employed to generate heterobivalent ligands using polyethylene glycol linkers spanning 40-120 Å. The ligand with an 80 Å linker had the most pronounced bivalent effects, with a significantly slower dissociation rate and 4-24-fold higher potency compared to those of the monovalent peptides for the human NaV1.4 channel. This study highlights the power of heterobivalent ligand design and expands the repertoire of pharmacological probes for exploring the function of NaV channels.

Computational Structural Pharmacology and Toxicology of Voltage-Gated Sodium Channels.

Voltage-gated sodium channels are targets for many toxins and medically important drugs. Despite decades of intensive studies in industry and academia, atomic mechanisms of action are still not completely understood. The major cause is a lack of high-resolution structures of eukaryotic channels and their complexes with ligands. In these circumstances a useful approach is homology modeling that employs as templates X-ray structures of potassium channels and prokaryotic sodium channels. On one hand, due to inherent limitations of this approach, results should be treated with caution. In particular, models should be tested against relevant experimental data. On the other hand, docking of drugs and toxins in homology models provides a unique possibility to integrate diverse experimental data provided by mutational analysis, electrophysiology, and studies of structure-activity relations. Here we describe how homology modeling advanced our understanding of mechanisms of several classes of ligands. These include tetrodotoxins and mu-conotoxins that block the outer pore, local anesthetics that block of the inner pore, batrachotoxin that binds in the inner pore but, paradoxically, activates the channel, pyrethroid insecticides that activate the channel by binding at lipid-exposed repeat interfaces, and scorpion alpha and beta-toxins, which bind between the pore and voltage-sensing domains and modify the channel gating. We emphasize importance of experimental data for elaborating the models.

ConoDictor: a tool for prediction of conopeptide superfamilies.

ConoDictor is a tool that enables fast and accurate classification of conopeptides into superfamilies based on their amino acid sequence. ConoDictor combines predictions from two complementary approaches-profile hidden Markov models and generalized profiles. Results appear in a browser as tables that can be downloaded in various formats. This application is particularly valuable in view of the exponentially increasing number of conopeptides that are being identified. ConoDictor was written in Perl using the common gateway interface module with a php submission page. Sequence matching is performed with hmmsearch from HMMER 3 and ps_scan.pl from the pftools 2.3 package. ConoDictor is freely accessible at http://conco.ebc.ee.

Identification and classification of conopeptides using profile Hidden Markov Models.

Conopeptides are small toxins produced by predatory marine snails of the genus Conus. They are studied with increasing intensity due to their potential in neurosciences and pharmacology. The number of existing conopeptides is estimated to be 1 million, but only about 1000 have been described to date. Thanks to new high-throughput sequencing technologies the number of known conopeptides is likely to increase exponentially in the near future. There is therefore a need for a fast and accurate computational method for identification and classification of the novel conopeptides in large data sets. 62 profile Hidden Markov Models (pHMMs) were built for prediction and classification of all described conopeptide superfamilies and families, based on the different parts of the corresponding protein sequences. These models showed very high specificity in detection of new peptides. 56 out of 62 models do not give a single false positive in a test with the entire UniProtKB/Swiss-Prot protein sequence database. Our study demonstrates the usefulness of mature peptide models for automatic classification with accuracy of 96% for the mature peptide models and 100% for the pro- and signal peptide models. Our conopeptide profile HMMs can be used for finding and annotation of new conopeptides from large datasets generated by transcriptome or genome sequencing. To our knowledge this is the first time this kind of computational method has been applied to predict all known conopeptide superfamilies and some conopeptide families.

DockoMatic: automated peptide analog creation for high throughput virtual screening.

The purpose of this manuscript is threefold: (1) to describe an update to DockoMatic that allows the user to generate cyclic peptide analog structure files based on protein database (pdb) files, (2) to test the accuracy of the peptide analog structure generation utility, and (3) to evaluate the high throughput capacity of DockoMatic. The DockoMatic graphical user interface interfaces with the software program Treepack to create user defined peptide analogs. To validate this approach, DockoMatic produced cyclic peptide analogs were tested for three-dimensional structure consistency and binding affinity against four experimentally determined peptide structure files available in the Research Collaboratory for Structural Bioinformatics database. The peptides used to evaluate this new functionality were alpha-conotoxins ImI, PnIA, and their published analogs. Peptide analogs were generated by DockoMatic and tested for their ability to bind to X-ray crystal structure models of the acetylcholine binding protein originating from Aplysia californica. The results, consisting of more than 300 simulations, demonstrate that DockoMatic predicts the binding energy of peptide structures to within 3.5 kcal mol(-1), and the orientation of bound ligand compares to within 1.8 Å root mean square deviation for ligand structures as compared to experimental data. Evaluation of high throughput virtual screening capacity demonstrated that Dockomatic can collect, evaluate, and summarize the output of 10,000 AutoDock jobs in less than 2 hours of computational time, while 100,000 jobs requires approximately 15 hours and 1,000,000 jobs is estimated to take up to a week.

Access and binding of local anesthetics in the closed sodium channel.

Local anesthetics (LAs) are known to bind Na+ channels in the closed, open, and inactivated states and reach their binding sites via extracellular and intracellular access pathways. Despite intensive studies, no atomic-scale theory is available to explain the diverse experimental data on the LA actions. Here we attempt to contribute to this theory by simulating access and binding of LAs in the KcsA-based homology model of the closed Na+ channel. We used Monte Carlo minimizations to model the channel with representative local anesthetics N-(2,6-dimethylphenylcarbamoylmethyl)triethylammonium (QX-314), cocaine, and tetracaine. We found the nucleophilic central cavity to be a common binding region for the ammonium group of LAs, whose aromatic group can extend either along the pore axis (vertical binding mode) or to the III/IV domain interface (horizontal binding mode). The vertical mode was earlier predicted for the open channel, but only the horizontal mode is consistent with mutational data on the closed-channel block. To explore hypothetical access pathways of the permanently charged QX-314, we pulled the ligand via the selectivity filter, the closed activation gate, and the III/IV domain interface. Only the last pathway, which leads to the horizontal binding mode, did not impose steric obstacles. The LA ammonium group mobility within the central cavity was more restricted in the vertical mode than in the horizontal mode. Therefore, occupation of the selectivity-filter DEKA locus by a Na+ ion destabilizes the vertical mode, thus favoring the horizontal mode. LA binding in the closed channel requires the resident Na+ ion to leave the nucleophilic central cavity through the selectivity filter, whereas the LA egress should be coupled with reoccupation of the cavity by Na+. This hypothesis on the coupled movement of Na+ and LA in the closed channel explains seemingly contradictory data on how the outer-pore mutations as well as tetrodotoxin and micro-conotoxin binding affect the ingress and egress of LAs.

Modeling P-loops domain of sodium channel: homology with potassium channels and interaction with ligands.

A large body of experimental data on Na+ channels is available, but the interpretation of these data in structural terms is difficult in the absence of a high-resolution structure. Essentially different electrophysiological and pharmacological properties of Na+ and K+ channels and poor identity of their sequences obstruct homology modeling of Na+ channels. In this work, we built the P-loops model of the Na+ channel, in which the pore helices are arranged exactly as in the MthK bacterial K+ channel. The conformation of the selectivity-filter region, which includes residues in positions -2 through +4 from the DEKA locus, was shaped around rigid molecules of saxitoxin and tetrodotoxin that are known to form multiple contacts with this region. Intensive Monte Carlo minimization that started from the MthK-like conformation produced practically identical saxitoxin- and tetrodotoxin-based models. The latter was tested to explain a wide range of experimental data that were not used at the model building stage. The docking of tetrodotoxin analogs unambiguously predicted their optimal orientation and the interaction energy that correlates with the experimental activity. The docking of mu-conotoxin produced a binding model consistent with experimentally known toxin-channel contacts. Monte Carlo-minimized energy profiles of tetramethylammonium pulled through the selectivity-filter region explain the paradoxical experimental data that this organic cation permeates via the DEAA but not the AAAA mutant of the DEKA locus. The model is also consistent with earlier proposed concepts on the Na+ channel selectivity as well as Ca2+ selectivity of the EEEE mutant of the DEKA locus. Thus, the model integrates available experimental data on the Na+ channel P-loops domain, and suggests that it is more similar to K+ channels than was believed before.