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A previously unrecognized superfamily of macro-conotoxins includes an inhibitor of the sensory neuron calcium channel Cav2.3 [1]

['Celeste M. Hackney', 'Department Of Biology', 'Linderstrøm-Lang Centre For Protein Science', 'University Of Copenhagen', 'Copenhagen', 'Paula Flórez Salcedo', 'Department Of Neurobiology', 'Anatomy', 'University Of Utah', 'Salt Lake City']

Date: 2023-08

Abstract Animal venom peptides represent valuable compounds for biomedical exploration. The venoms of marine cone snails constitute a particularly rich source of peptide toxins, known as conotoxins. Here, we identify the sequence of an unusually large conotoxin, Mu8.1, which defines a new class of conotoxins evolutionarily related to the well-known con-ikot-ikots and 2 additional conotoxin classes not previously described. The crystal structure of recombinant Mu8.1 displays a saposin-like fold and shows structural similarity with con-ikot-ikot. Functional studies demonstrate that Mu8.1 curtails calcium influx in defined classes of murine somatosensory dorsal root ganglion (DRG) neurons. When tested on a variety of recombinantly expressed voltage-gated ion channels, Mu8.1 displayed the highest potency against the R-type (Cav2.3) calcium channel. Ca2+ signals from Mu8.1-sensitive DRG neurons were also inhibited by SNX-482, a known spider peptide modulator of Cav2.3 and voltage-gated K+ (Kv4) channels. Our findings highlight the potential of Mu8.1 as a molecular tool to identify and study neuronal subclasses expressing Cav2.3. Importantly, this multidisciplinary study showcases the potential of uncovering novel structures and bioactivities within the largely unexplored group of macro-conotoxins.

Citation: Hackney CM, Flórez Salcedo P, Mueller E, Koch TL, Kjelgaard LD, Watkins M, et al. (2023) A previously unrecognized superfamily of macro-conotoxins includes an inhibitor of the sensory neuron calcium channel Cav2.3. PLoS Biol 21(8): e3002217. https://doi.org/10.1371/journal.pbio.3002217 Academic Editor: Thomas C. Südhof, Stanford University School of Medicine, UNITED STATES Received: January 17, 2023; Accepted: June 27, 2023; Published: August 3, 2023 Copyright: © 2023 Hackney et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data, unless listed below, are within the paper and its supporting information files. The atomic coordinates the structure-factor amplitudes for Mu8.1_38 and Mu8.1_59 are available with the Protein Data Bank under accession numbers 7PX1 and 7PX2, respectively. Nucleotide sequence data for Mu8.1 and Mu8.1ii have been submitted to GenBank with accession numbers ON755370 and ON755371 respectively. Funding: This work was supported by the Independent Research Fund Denmark, Technology and Production Sciences grant (#7017-00288 to LE). Research conducted at MAX IV, a Swedish national user facility, is supported by the Swedish Research Council under contract 2018-07152, the Swedish Governmental Agency for Innovation Systems under contract 2018-04969, and Formas under contract 2019-02496. CPHSAXS is funded by the Novo Nordisk Foundation (grant no. NNF19OC0055857). HS-H acknowledges a research grant from Villum Fonden (19063 to HS-H) Electrophysiological characterization was performed with support from Rebecca Cooper Foundation for Medical Research (PG2019396 to JRM). JRM and RKF-U were supported by grant funding from the National Health & Medical Research Council awarded to Prof. D.J. Adams (NHMRC Program Grant APP1072113 to DJA) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. : AEX, anion exchange; AITC, allyl isothiocyanate derived from mustard oil; CNGB, China National Genebank; APC, automated patch clamp; CD, circular dichroism spectroscopy; CGRP, calcitonin gene-related peptide; CLANS, CLuster ANalysis of Sequences; C-LTMR, C-low threshold mechanoreceptor; CNS, central nervous system; csPDI, conotoxin-specific PDI; CV, column volume; DDBJ, DNA Databank of Japan; DMEM, Dulbecco’s Modified Eagle’s Medium; DRG, dorsal root ganglia; DTT, dithiothreitol; EG, ethylene glycol; ER, endoplasmic reticulum; FDA, United States Food and Drug Administration; FOM, figure of merit; GFP, green fluorescent protein; GPCR, G protein–coupled receptor; HEK293T, human embryonic kidney cells; hPDI, human PDI; IACUC, Institutional Animal Care and Use Committee; IB4, Alexa Fluor 647 Azolectin B4; IPTG, isopropyl ß-D-1-thiogalactopyranoside; LB, lysogeny broth: MALDI-TOF, matrix-assisted laser desorption–ionization time of flight; MPC, manual patch clamp; NCBI, National Center for Biotechnology Information; ORF, open reading frame; PDB, Protein Data Bank; PDI, protein-disulfide isomerase; RMSD, root mean square deviation; ROI, region of interest; RP-HPLC, reversed-phase high pressure liquid chromatography; SapA, saposin A; SAPLIP, saposin-like proteins; SAXS, small angle X-ray scattering; SVD, singular value decomposition; SDS-PAGE, sodium dodecyl sulfate-polyacrylamide gel electrophoresis; SLC, saposin-like conotoxin; SR, seal resistant; TEV, tobacco etch virus; Ub, ubiquitin; Ub-His10, Ub containing 10 consecutive histidines; UTR, untranslated region

Introduction Animal venom peptides and proteins are employed for the incapacitation of prey or the defense against predators and competitors [1]. Venom components function by binding with high affinity and selectivity to their molecular targets. These are often specific membrane-bound proteins that control vital cellular signaling pathways and include ligand and voltage-gated ion channels, G protein–coupled receptors (GPCRs), tyrosine kinase receptors, and transporters [2]. Because of the high similarity between venom peptide targets in the prey and their orthologs in mammals, as well as the conservation of signaling pathways, venom components often show activity in mammalian systems. Animal toxins are therefore interesting for the development of molecular probes and biological tools as well as potential drug leads. Currently, 8 venom-derived drugs have been approved by the United States Food and Drug Administration (FDA) for human use, and approximately 30 other venom-derived peptides are in clinical and preclinical trials to treat a variety of diseases, such as diabetes, hypertension, chronic pain, thrombosis, cancer, and multiple sclerosis [3,4]. The venom produced by predatory marine cone snails is particularly rich in peptide toxins (known as conotoxins or conopeptides). Each of the approximately 1,000 extant cone snail species expresses a unique set of several hundred conotoxins [5], resulting in an estimated diversity of more than 200,000 conotoxins. Conotoxins often display exquisite specificity for their targets and are consequently used widely as pharmacological tools for research purposes. Moreover, the ω-MVIIA conotoxin, which inhibits the Cav2.2 calcium channel [6], is an FDA-approved drug (commercial name Prialt) for the treatment of severe chronic pain [7,8]. Conotoxins are used extensively to investigate ion channel function (and dysfunction) as illustrated by the κM-conotoxin RIIIJ from Conus radiatus that displays subtype selectivity for heteromeric voltage-gated K+ (Kv1) channels [9,10]. The selectivity of this toxin has recently been employed to identify a new subclass of peptidergic nociceptors—sensory neurons that respond to stimuli and transmit a signal to the central nervous system (CNS)—with distinct properties [11]. Somatosensory neurons comprise a heterogeneous population of neurons that can be divided into subclasses using constellation pharmacology [12]. Using this approach, individual neuronal cells in a population of mouse dorsal root ganglion (DRG) neurons are screened with a combination of calcium imaging and pharmacological compounds that each elicit a characteristic response used to differentiate neuronal cell types. Conotoxins are produced and folded in the endoplasmic reticulum (ER) of cone snail venom glandular cells. Thus, conotoxin preproproteins typically comprise a signal sequence for entry into the ER, a propeptide region of largely unknown function, and the mature peptide that is proteolytically released from the propeptide [13]. Conotoxins are classified into gene superfamilies based on N-terminal signal sequence similarity [14], with more than 50 gene superfamilies identified to date [13]. Some superfamilies comprise several subfamilies (which we term “classes” in the current work). For instance, this is the case for the C-superfamily that comprises the consomatin and contulakin-G classes [15]. In contrast to the signal sequence, the mature peptide region exhibits remarkable sequence variability except for the presence of conserved cysteines that form disulfide bonds critical for stability. In addition to disulfide bonds, conotoxins can acquire a variety of other posttranslational modifications, such as C-terminal amidation, O-glycosylation, hydroxylation, and bromination that can, in some cases, influence target binding [16–19]. The advent of new sequencing technologies and bioinformatics tools for transcriptome analysis has revealed thousands of previously unknown animal venom peptide and protein sequences in recent years [20]. Although cone snails mostly express short peptide toxins (mean length of the mature peptide: 42 residues [13]), the many new available sequences reveal that they also produce larger toxins. These more complex molecules have not been comprehensively explored, mostly because of limitations in the production of large, cysteine-rich proteins. Specifically, unlike the short peptide toxins, the larger toxins are rarely amenable to chemical synthesis and subsequent in vitro folding. Here, we coin the term “macro-conotoxin” for this group of conotoxins generally longer than 50 amino acid residues. In this study, we identify and investigate a previously uncharacterized conotoxin, Mu8.1, from the fish-hunting snail Conus mucronatus. We produce this unusually large conotoxin of 89 residues using a modified Escherichia coli expression system and uncover that it belongs to a distinct class of conotoxins evolutionarily related to the con-ikot-ikots as well as 2 hitherto undescribed conotoxin classes. We demonstrate that Mu8.1 inhibits depolarization-induced Ca2+ influx in mouse peptidergic nociceptors, likely through targeting the voltage-gated R-type (Cav2.3) Ca2+ channels. In addition to identifying a previously unrecognized conotoxin superfamily and providing structural insight at the atomic level, this study establishes the potential of Mu8.1 as a new scaffold for the investigation of the role of Cav2.3-mediated currents in sensory neurons. Our work also demonstrates that an understudied pool of macro-conotoxins, such as Mu8.1, is amenable to detailed structural and functional investigation.

Discussion The vastly increased number of conotoxin sequences obtained in recent years from transcriptome sequencing constitutes a rich source of peptides for biomedical exploration. However, the production of new peptides often represents a bottleneck for their exploration—in particular, the characterization of large, disulfide-rich venom components is lagging. In this study, we identify the macro-conotoxin Mu8.1 from C. mucronatus as the founding member of the new SLC class and use a wide range of biochemical, biophysical, structural, and electrophysiological methods to provide a comprehensive characterization of the protein. Moreover, we uncover an unexpected evolutionary relationship between the SLCs and con-ikot-ikots that extends to 2 previously unrecognized toxin classes with all 4, thus defining a single conotoxin superfamily. The relatively straightforward chemical synthesis of small peptides together with difficulties associated with the production of large venom components, in particular without a priori knowledge about the disulfide pattern, have biased functional studies towards small, disulfide-bridged conotoxin peptides shorter than 30 amino acid residues in length. The successful production of fully oxidized and correctly folded Mu8.1 in the csCyDisCo E. coli system highlights the feasibility of systematically exploring much larger multidisulfide conotoxins than was previously possible. Moreover, we recently developed the DisCoTune system based on CyDisCo to allow titration of T7 RNA polymerase repression [28]. This feature permits optimization of expression conditions to potentially further increase yields by fine-tuning the expression level of the (disulfide-rich) target protein to better match the level of the helper proteins (Erv1p and PDI). With a few notable exceptions, such as con-ikot-ikot and proteins belonging to common toxin families like conkunitzins, metalloproteases, hyaluronidases, and Phospholipase A 2 s [40–43], macro-conotoxins are generally unexplored. The large size of Mu8.1 and its modest potency against mammalian Cav2.3 channels prompts questions regarding the evolutionary advantage that producing and deploying this peptide may convey to C. mucronatus. In general, the size of macro-conotoxins likely confers specific properties not available to small toxins. For instance, larger toxins could participate in multivalent interactions with their targets, as noted for con-ikot-ikot and recently pointed out for bivalent venom peptides containing 2 homologous domains connected by an interdomain linker [44]. It is conceivable that noncovalent dimers, as seen in Mu8.1, could also allow interaction with, for instance, 2 identical subunits of a molecular target. Even in a monomeric state, large toxins may interact with different target subunits, whereas their larger binding interface may well provide higher target specificity. We found that Mu8.1 is structurally similar to con-ikot-ikot and that both display a saposin-like fold. This result raised the possibility that Mu8.1 may perform a function involving lipid interactions. Human SapA and its homologs SapB, SapC, and SapD are small, nonenzymatic proteins required to break down glycosphingolipids within the lysosome [45]. In the absence of lipid, SapA adopts a characteristic monomeric, closed conformation where α1 and α4 (held together by 2 disulfides) form the stem, and α2 and α3 (connected by 1 disulfide bond) form a hairpin region (Fig 5A and 5D). In the presence of lipids, SapA opens to expose a concave, hydrophobic surface for lipid binding. The primary areas of rearrangement are the loops between α1/α2 and α3/α4 that together operate as a hinge [46,47]. However, in contrast to SapA, the Mu8.1 structure is highly constricted by the network of disulfide bonds that crosslinks the molecule (Fig 5A). Consequently, Mu8.1 is not likely to undergo a large conformational change to adopt an open conformation and, therefore, probably does not function in lipid binding in the same manner as the SAPLIPs. Next, we sought to investigate if Mu8.1 and related sequences may have evolved from an endogenous saposin domain-containing protein. Despite using a combination of multiple BLAST algorithms and hidden Markov models of whole toxin sequences, individual exons, introns, and 5′ and 3′ UTRs against published genomes and transcriptomes from several non-venom-producing tissues, we did not identify a good candidate for an ancestral, endogenous gene. Likewise, an analysis of known Conus proteins harboring a saposin-like domain (S14 Fig) did not provide an obvious candidate for an ancestral endogenous gene. None of the endogenous proteins shared significant sequence similarities or gene structures with Mu8.1 or its related sequences. While we were unable to elucidate the evolutionary origin of Mu8.1, we find it probable that the SLCs (and the other 3 toxin classes) evolved from a common ancestral gene and then diverged to a point where their similarities are only apparent in their signal sequences, gene structures, and possibly also in their overall fold. As detailed above, Mu8.1 and con-ikot-ikot both display a saposin-like fold. Notably, AlphaFold structure predictions of Cluster 1 sequences show that these proteins are likely to also contain a saposin-like domain (S15 Fig). The Cluster 2 sequences are too short to encode a saposin-like domain. Therefore, we speculate that the ancestral gene from which the 4 classes evolved harbored a saposin-like domain, which has been retained in at least 3 of these classes throughout evolution. Alternatively, although we find this less likely, the Mu8.1 and con-ikot-ikot sequences may have emerged through convergent evolution. If so, the saposin fold could constitute a “privileged” scaffold that has been selected during evolution due to favorable properties, such as structural stability and the ability to accommodate sequence variation. Similar privileged scaffolds are found in diverse toxin peptides and proteins that are functionally unrelated, including the inhibitor cystine knot, granulin, defensin, and Kunitz folds [25,48,49]. These examples demonstrate that irrespective of the evolutionary mechanism involved, the same fold can be repurposed to interact with diverse targets. Functionally, neither the monomeric nor the dimeric states of Mu8.1 seem compatible with AMPA-receptor binding, thus rationalizing the observed lack of Mu8.1 effects on GluA2 desensitization (S8 Fig). First, the GluA2-binding surface of con-ikot-ikot corresponds to the dimer interface of Mu8.1. Second, 3 of the con-ikot-ikot residues shown to be important for GluA2 binding (Gln37, Glu48, and Ala86) are not conserved in Mu8.1 (Ala32, Asn47, and Phe49) (S16A Fig). Third, the GluA2-binding surface of con-ikot-ikot is negatively charged (S16B Fig), whereas the corresponding surface of Mu8.1 is predominantly positively charged (S16C Fig). Fourth, although the Mu8.1 dimer surface is negatively charged as in con-ikot-ikot, the 2 dimers are of unequal dimensions (S16D Fig). The voltage-gated Cav2.3 channel was identified as the highest affinity target of Mu8.1 among an extensive collection of mammalian ion channels and GPCRs. Most of the knowledge about the function of Cav2.3 has been obtained from animal knockout and cellular knockdown experiments, where the channel was linked to epilepsy, neurodegeneration, and pain [50–53]. In contrast to SNX-482, combined results from calcium imaging and electrophysiology measurements suggest that Mu8.1 modulates sensory neurons via inhibition of Cav2.3 channels without evidencing cross-actions against other neuronal conductances typically associated with somatosensory subclasses. Thus, the Mu8.1 scaffold could serve as a valuable addition to the existing molecular toolbox for investigating the physiological functions of Cav2.3, as well as its involvement in synaptic signaling and neuromodulation. Improvements guided by structure–function analysis using, for instance, mutational screening, could further enhance the potency and selectivity of Mu8.1, thereby augmenting its utility as a molecular tool. At a general level, this study illustrates that a combination of data mining and recombinant expression in E. coli can pave the way for a detailed analysis of structural and functional features of newly identified macro-conotoxins. We propose that with the advent of E. coli expression systems such as csCyDisCo and DisCoTune [25,28,54], as well as others [55,56], the time is ripe to begin the systematic exploration of a new realm of macro-conotoxins. These efforts will help provide a better understanding of the biological correlates of having large venom components, with the overarching aim of connecting the biochemical and molecular characteristics of venom components with the biology and behavior of cone snails.

Materials and methods Venom gland transcriptome analysis RNA extraction and transcriptome sequencing and assembly were performed as described previously [57,58]. Assembled transcripts were annotated using a BLASTx search [22] (E-value setting of 1 × 10−3) against a combined database derived from UniProt, Conoserver [59], and an in-house cone snail venom transcript library. The 2 toxins, SLC_Mu8.1 and SLC_Mu8.1ii, abbreviated as Mu8.1 and Mu8.1ii, were named according to [21]. Here, Mu describes the 2-letter species abbreviation (Mu for C. mucronatus), 8 describes the cysteine scaffold, and the number 1 represents the first toxin to be described from this gene family. The suffix ii is given to a sequence variant that likely represents an allelic variant. Transcriptome mining of the NCBI, DDBJ, and CNGB databases To find sequences that share similarity with Mu8.1, we mined the transcriptomes of 37 cone snail venom gland transcriptomes available in the NCBI, DDBJ, and CNGB repositories using the precursor sequence of Mu8.1 as query (accession numbers provided in S1 Table). Transcriptome assemblies were done as described previously [57,58]. Signal and propeptide sequences of the identified homologous sequences were predicted using ProP v. 1.0 [60]. Mature toxin sequences were predicted to begin after the last basic amino acid residue preceding the first cysteine in the sequence. Therefore, further trimming of sequences to meet this criterion was executed manually as needed. Multiple sequence alignments were carried out using the MAFFT version 7 multiple alignment online interface [61] and visualized in Jalview version 1.0 [62]. Plasmid generation The plasmid for bacterial expression of Mu8.1 was generated by uracil excision cloning, as described previously [63]. Polymerase chain reaction was carried out using Phusion U Hot Start polymerase (Thermo Fisher Scientific) according to the manufacturer’s instructions. Based on the transcriptome data for Mu8.1, the sequence of the mature toxin was predicted as GENSDNLTHCRLFEFRLCLLECMSLTLDHCYARCTTVITQIHGSDTNRFDCTIFKTCYYRCYVLGKTEDHCWKGTATSVTGDVGDLEFC. A codon-optimized DNA sequence for bacterial expression was generated using the CodonOpt tool. Using this codon-optimized DNA sequence as a template, the following 2 primers were designed: Mu8.1_sense: ACACGGAUCGGACACCAATCGTTTTGATTGCACAATCTTCAAGACCTGCTACTACCGGTGCTACGTTCTTGGTAAAACAGAAGACCATTGCTGGAAAGGGACGGCAACGTCAGTGACAGGTGATGTCGGAGATTTGGAATTTTGCTAAGAATTCGAGCTCCGTCGACAG;Mu8.1_antisense: ATCCGTGUATCTGGGTAATAACTGTGGTACATCTCGCATAGCAGTGGTCTAATGTAAGCGACATACACTCCAACAAACACAGCCGGAACTCGAATAATCTACAATGGGTAAGGTTGTCTGAGTTTTCGCCCTGAAAATACAGATTCTCAC. These primers were subsequently used to clone Mu8.1 into the pET39_Ub19 expression vector [64]. The resulting plasmid encoding Ub-His 10 -tagged Mu8.1 (Ub-His 10 -Mu8.1) is referred to as pLE601. The fusion protein produced from pLE601 also contains a TEV protease recognition site following the Ub–His 10 -tag. Primers were purchased from Integrated DNA Technologies, and the sequence encoding Ub-His 10 -Mu8.1 was confirmed by Eurofins. Protein expression Chemically competent E. coli BL21 (DE3) cells were transformed with pLE601 with or (as a control) without the csCyDisCo plasmid (pLE577) [25]. Cells were plated on lysogeny broth (LB) agar supplemented with kanamycin (50 μg/mL) with (when cotransforming with pLE577) or without chloramphenicol (30 μg/mL). A single colony was picked to inoculate the LB medium containing the same type and concentration of antibiotic as used on the LB agar plates. The overnight culture was incubated for approximately 16 hours at 37°C at 200 rpm in an orbital shaker. For initial small-scale expression tests (50 mL), LB medium containing appropriate antibiotics and supplemented with 0.05% glucose was inoculated with 2% overnight culture and grown at 37°C with shaking at 200 rpm until the desired OD 600 of 0.6 to 0.8 was reached. Expression was induced by adding isopropyl ß-D-1-thiogalactopyranoside (IPTG) to a final concentration of 1 mM and the cultures grown for 18 hours at 25°C with shaking at 200 rpm to allow protein expression. Large-scale expression (1 L culture volume) was performed in autoinduction media prepared as described previously [65]. Briefly, terrific broth medium containing kanamycin (100 μg/mL) and/or chloramphenicol (30 μg/mL) was supplemented with sterilized stocks of the following: 0.05% glucose, 0.2% lactose, 50 mM KH 2 PO 4 /Na 2 HPO 4 , 50 mM NH 4 Cl, 50 mM Na 2 SO 4 , 0.1 mM FeCl 3 , 2 mM MgSO 4 , 0.1 mM CaCl 2 , and 1 × metal mix (203 g/L MgCl 2 6·H 2 O, 2.1 g/L CaCl 2 2·H 2 O, 2.7 g/L FeSO 4 7·H 2 O, 20 mg/L AlCl 3 6·H 2 O, 10 mg/L CoSO 4 7·H 2 O, 2 mg/L KCr(SO 4 ) 2 12·H 2 O, 2 mg/L CuCl 2 2·H 2 O, 1 mg/L H 3 BO 4 , 20 mg/L KI, 20 mg/L MnSO 4 H 2 O, 1 mg/L NiSO 4 6·H 2 O, 4 mg/L Na 2 MoO 4 2·H 2 O, 4 mg/L ZnSO 4 7·H 2 O, 21 g/L citric acid monohydrate). Cultures were grown at 37°C at 200 rpm until OD 600 reached 0.8, at which point cells were moved to 25°C for expression performed with shaking at 200 rpm for 18 hours. Harvest and clarification of bacterial cultures Induced cultures were harvested by centrifugation at 5,000g for 20 minutes. The cell pellets were resuspended in 5 mL lysis buffer (50 mM Tris (pH 8), 300 mM NaCl, 20 mM imidazole) per gram pellet. Cell resuspensions were supplemented with approximately 12 units Benzonase Nuclease (Merck Millipore)/L culture to minimize viscosity due to the presence of nucleic acids post-lysis. Cell lysis was performed using a UP200S ultrasonic processor (Hielscher) keeping the cells on ice throughout. Cells were lysed with 8 × 30-second pulses at 90% power with 30-second rests between each pulse. Cell debris was pelleted by centrifugation at 30,000g for 45 minutes. The cleared lysates were filtered through 0.45 μm syringe filters and transferred to fresh tubes, whereas the pellets were resuspended in an equal volume lysis buffer containing 8 M urea for SDS-PAGE analysis. Protein purification Ub-His 10 -Mu8.1 was affinity purified from the clarified lysate on an ÄKTA START system equipped with a 5-mL prepacked HisTrap HP (Cytiva) column equilibrated in lysis buffer. The lysate was applied to the column and washed with approximately 20 column volumes (CVs) of lysis buffer before elution of Ub-His 10 -Mu8.1 with a gradient of 0% to 100% elution buffer (50 mM Tris (pH 8), 300 mM NaCl, 400 mM imidazole) applied over 20 CVs. Pooled fractions were dialyzed twice against 2 L anion exchange (AEX) buffer (50 mM NaH 2 PO 4 /Na 2 HPO 4 (pH 6.8), 20 mM NaCl). AEX chromatography was performed on an ÄKTA Pure system equipped with a 10/100 Tricorn column (Cytiva) packed with Source 15Q ion exchange resin (Amersham Biosciences, GE Healthcare) equilibrated in AEX buffer. Ub-His 10 -Mu8.1 was eluted using a gradient from 15% to 50% AEX elution buffer (50 mM NaH 2 PO 4 /Na 2 HPO 4 (pH 6.8), 1 M NaCl) developed over 6 CVs. The Ub-His 10 -Mu8.1 fusion protein was cleaved using His 6 -tagged TEV protease, expressed, and purified as described previously [25]. A molar ratio Ub-His 10 -Mu8.1:His 6 -TEV protease of 1:20 was used. To avoid reducing the disulfides in Mu8.1, His 6 -TEV protease—preactivated with 2 mM dithiothreitol (DTT) for 30 minutes at room temperature—was diluted to approximately 0.002 mM DTT by 3 rounds of dilution/concentration in an Amicon Ultra 15 mL 3K Centrifugal Filter (Merck Millipore). TEV protease cleavage was performed overnight at room temperature. To remove uncleaved Ub-His 10 -Mu8.1, the Ub-His 10 tag, and His 6 -TEV, the cleavage mixture was applied to a gravity flow column packed with 8 mL TALON cobalt resin (Takara) equilibrated in AEX buffer. The flow-through and the first wash fraction were collected. The presence of cleaved Mu8.1 in each fraction was investigated by analysis on 15% tricine SDS-PAGE gels, and the protein-containing fractions pooled. The cleaved Mu8.1 was subjected to size exclusion chromatography on a Superdex 75 Increase 10/300 GL column (Cytiva) equilibrated in 200 mM NH 4 HCO 3 buffer (pH 7.8). Fractions containing purified Mu8.1 were pooled and lyophilized. Analytical gel filtration To analyze the oligomeric state of Mu8.1, the protein was analyzed at 2 concentrations, 100 μm and 1 μm, on a Superdex 75 Increase 10/300 GL column (Cytiva) equilibrated in 10 mM NaPi (pH 7.8), 150 mM NaCl at a flowrate of 0.5 mL/min. An excess of the protein was loaded onto a 100 μL loop to ensure analysis of the same volume in each run. SDS-PAGE analysis Samples from bacterial expression and subsequent purification steps were separated on 15% glycine SDS-PAGE or 15% tricine SDS-PAGE gels [66]. Where indicated, reduced samples were treated with 40 mM DTT. Protein bands were visualized with Coomassie Brilliant Blue, and images were recorded with a BioRad Chemidoc Imaging System. For western blotting, proteins separated by SDS-PAGE were transferred to a PVDF membrane (Immobilon-P, Merck Millipore) in a Mini Trans-Blot (Bio-Rad) transfer system. A mouse monoclonal His-tetra (Qiagen) antibody (1:1,000 dilution) was used in combination with a horseradish peroxidase–conjugated α-mouse secondary antibody (Pierce) (1:100,000 dilution). Chemiluminescence detection was performed using ECL Select Peroxide and Luminol solutions (GE Healthcare) according to the manufacturer’s directions. Concentration determination Concentrations were determined by measuring absorbance at 280 nm and using the theoretical extinction coefficient provided by the Expasy ProtParam tool available through the Expasy bioinformatics resource web portal [67]. Concentrations used for bioassays, constellation pharmacology, and electrophysiology assumed a monomeric state of Mu8.1. Determination of molecular mass in solution by small angle X-ray scattering (SAXS) Prior to SAXS analysis, Mu8.1 was run through a Superdex 75 Increase 10/300 GL column to ensure a monodisperse sample. The protein was subsequently dialyzed into a solution containing 150 mM NaCl and 10 mM NaPi (pH 7.8), and the dialysis buffer was reserved for measurement of background scattering. Immediately preceding SAXS measurements any precipitates were removed from the sample by centrifugation at 20,000g for 15 minutes at 4°C. Six dilutions were prepared ranging from 0.5 mg/mL to 12.4 mg/mL (49 μM to 1.2 mM) in dilution buffer. SAXS data were collected by the beamline staff at CPHSAXS using a Xenocs BioXolver L equipped with a liquid gallium X-ray source (λ = 1.34 Å). A sample-to-detector distance of 632.5 mm was used, corresponding to a Q-range of 0.013 to 0.5 Å−1. Samples and buffer were measured at room temperature, and automatic loading was performed robotically from a 96-well plate. Data were collected as a minimum of 10 frames with 120-second exposure per frame. The longer exposure time was to account for the lower concentration and the expected presence of multimeric species. Data reduction and primary analysis were performed using RAW [68], singular value decomposition (SVD), and oligomer analysis performed with OLIGOMER (ATSAS program package) [69], and scattering curves plotted in Matlab R2020b. X-ray crystallography Freeze-dried Mu8.1 was dissolved in Milli-Q water to a concentration of 5 mg/mL. Crystallization screening experiments were performed with the Structure screen II (Molecular Dimensions, MD1-02) and the Index screen (Hampton Research, HR2-144) by the hanging drop vapor diffusion method. The crystal drops were mixed using 1 μL of protein and 1 μL precipitant solution as hanging drops on siliconized glass cover slides and equilibrated against 500 μL of precipitant solution in a 24-well plate setup. Wells were sealed with immersion oil (Sigma-Aldrich) and incubated at 21°C. Initial crystals of Mu8.1 appeared in several conditions in a few days to weeks. Diffracting crystals were obtained from the Structure screen II condition 38 (0.1 M NaOAc (pH 4.6), 0.1 M CdCl hemi(pentahydrate), 30% v/v PEG400), abbreviated as Mu8.1_38, and from the Index screen condition 59 (0.1 M HEPES (pH 7.5), 0.02 M magnesium chloride hexahydrate, 22% w/v polyacrylic acid sodium salt 5,100), abbreviated as Mu8.1_59. Crystals were harvested using mounted CryoLoops (Hampton Research) and flash-cooled in liquid nitrogen. Cryoprotection was performed by quickly dipping the crystal in approximately 17% ethylene glycol (EG) prepared by mixing 1 μL 50% EG with 2 μL of the reservoir condition specific to each crystal condition. Both native crystals and iodide-soaked crystals were prepared from all 3 conditions. Single iodide crystals (Sigma-Aldrich) were added to the abovementioned cryo condition for each of the crystal conditions. Crystals from each condition were transferred to the iodide-containing cryo conditions and left to soak 5 to 10 seconds before harvesting them. Data collection Flash-cooled crystals were shipped to the beamline for remote data collection. Data were collected at 100K on a PILATUS detector at BioMax (MAX-IV, Lund, Sweden). A full sweep of 360° data was collected with an oscillation degree of 0.1°, with 0.050-second exposure at 12,700 eV and 7,000 eV. Complete data set was processed from 360° (3,600 images) with the X-ray beam reduced to 5% intensity. Data processing Native data were collected for Mu8.1_59 at 12,700 eV, and only Mu8.1_38 showed an anomalous signal from the data collected at 7,000 eV. All data were processed with xia2 using the 3dii pipeline [70,71] (Table 1). PPT PowerPoint slide

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TIFF original image Download: Table 1. Data collection and refinement statistics. https://doi.org/10.1371/journal.pbio.3002217.t001 The phases for Mu8.1_38 were experimentally determined using autosol in the PHENIX package [72] with 13 iodide sites identified and an initial figure of merit (FOM) of 0.4. Density resembling helical structures were visible in the electron density map. The following AutoBuild wizard within the PHENIX package [72] was able to build a preliminary structure with the main helices in place. This structure was used as initial search model for molecular replacement and performed with the program Phaser [73] against the highest-resolution dataset Mu8.1_59. All structures were manually refined using phenix.refine [72], and final model building was performed in Coot [74]. Data collection and refinement statistics are summarized in Table 1. Molecular graphics were presented with the PyMOL Molecular Graphics System, Version 2.2r7pre, Schrödinger, LLC. Electrostatic potentials were modelled using the Adaptive Poisson-Boltzmann Solver (APBS) plugin in PyMOL2.3 [75]. Structure search and topological comparisons Structures similar to Mu8.1 were identified using PDBeFold at the European Bioinformatics Institute (https://www.ebi.ac.uk/msd-srv/ssm/) [76,77]. Monomeric Mu8.1 was used as query molecule to identify similar structures with a minimum acceptable match set to 60% or higher from the entire PDB database. Structural overlays were generated with the CLICK structural alignment tool (http://cospi.iiserpune.ac.in/click/) selecting “CA” as representative atoms and visualized using the PyMOL Molecular Graphics System [78] or executed manually in PyMol. Constellation pharmacology Primary cell cultures were dissociated from CGRP-GFP mice, STOCK Tg(Calca-EGFP)FG104Gsat/Mmucd, ages 34 to 38 days old as described previously [11]. In brief, lumbar DRG from vertebrae L1 to L6 were dissected, trimmed, and treated with 0.25% trypsin for 20 minutes. Following trypsinization, the DRGs were mechanically triturated using fired polished pipettes and plated in poly-l-lysine–coated plates. All plated cells were kept overnight at 37°C in a minimal essential medium supplemented with 10% fetal bovine serum, 1X penicillin/streptomycin, 10 mM HEPES, and 0.4% (w/v) glucose. One hour before the experiment, the dissociated cells were loaded with 4 μM Fura-2-AM dye (Sigma-Aldrich) and kept at 37°C. During each experiment, all dissociated cells were exposed to different pharmacological agents utilizing an automatic perfusion system and were imaged at 340/380 nm at 2 frames per second. In brief, cells were incubated with the pharmacological agents for 15 seconds followed by 6 consecutive washes and a 5-minute incubation period with extracellular solution for controls or Mu8.1. Five different pharmacological agents were used for cell classification: mustard oil (AITC) at 100 μM, menthol at 400 μM, capsaicin at 300 nM, K+ at 25 and 40 mM, and conotoxin kM-RIIIJ at 1 μM as described previously [11]. SNX-482 (Alomone Labs) was used at 100 nM. At the end of each experiment, all cells were incubated for 7 minutes with a 2.5-μg/mL Alexa Fluor 647 Azolectin B4 (IB4). All data acquisition was performed using the Nikon NIS-Elements platform. CellProfiler [79] was used for region of interest (ROI) selection, and a custom-built script in python and R was used for further data analysis and visualization. The package used to visualize, analyze, and deploy models for constellation pharmacology experiments is available at https://github.com/leeleavitt/procPharm. All procedures were approved by the University of Utah institutional animal care and use committee (IUCAC) Protocol number: 17–05017. Statistical analysis of cellular calcium imaging We utilized a min-max normalization (f(x)) to assess the effects of Mu8.1 on the peak height of the calcium signal induced by high concentrations of potassium using the formula: where K+ test is the peak height after the incubation with Mu8.1, and K+ control is the peak height before the incubation with Mu8.1 [11]. If f(x) = 0, this suggests that the conotoxin did not affect the Ca2+ concentration in the cytosol induced by the high concentration of potassium. An f(x) > 0 indicates that the conotoxin increased the cytosolic calcium concentration after a high potassium concentration, resulting in amplification. Finally, if f(x) < 0, the conotoxin decreased calcium concentration in the cytosol resulting in a calcium block. After calculating the f(x) for all sensory neuron cell types, we performed a two-tailed t test to assess if the f(x) values calculated for every cell type were significantly different from 0. Electrophysiology APC recordings were performed in a Patchliner Octo (Nanion Technologies GmbH, Munich, Germany) equipped with 2 EPC-10 quadro patch clamp amplifiers (HEKA Electronics). PatchControlHT (Nanion) was used for cell capture, seal formation, and establishment of the whole-cell configuration, while voltage was controlled, and currents sampled with PatchMaster (HEKA Electronik). Recordings were performed under the whole-cell configuration using single-hole planar NPC-16 chips (resistance of approximately 2.5 MΩ) at room temperature. Stably transfected cell lines (D.J. Adams collection, IHMRI-UOW) were cultured according to the manufacturer’s instructions and detached using TrypLE. Cells were resuspended in cold external recording solution and kept in suspension by automatic pipetting at 4°C. The extracellular solution used for Kv1, hERG, and Nav1 recordings contained (in mM): 140 NaCl, 5 KCl, 2 CaCl 2 , 2 MgCl 2 , 10 glucose, and 10 HEPES (pH 7.4 with NaOH, 298 mOsmol/kg). Kv1 and hERG intracellular solution (in mM): 60 KF, 70 KCl, 10 EGTA, 10 glucose, and 10 HEPES (pH 7.2 with KOH, 285 mOsmol/kg). Peak I K currents for Kv1.1–3: 500 ms test pulse to 20 mV (Vh = −120 mV; 0.1 Hz). hERG I K : 1-second prepulse to +40 mV was followed by 200 ms test pulse to −40 mV (Vh = −80 mV; 0.1 Hz). Nav1 intracellular solution (mM): 60 CsF, 60 CsCl, 10 NaCl, 10 EGTA, and 10 HEPES (pH 7.2 with CsOH, 285 mOsmol/kg). Nav currents were elicited by 10 ms test pulses to −10 mV with a 1-second prepulse to −120 mV (Vh = −90 mV; 0.1 Hz). APC of Cav1 and Cav2: extracellular solution (in mM): 135 NaCl, 4 KCl, 10 BaCl 2 , 1 MgCl 2 , 5 glucose, and 10 HEPES (pH 7.4 with NaOH, 298 mOsmol/kg) and intracellular solution (in mM): 90 CsSO 4 , 10 CsCl, 10 NaCl, 10 EGTA, and 10 HEPES (pH 7.2 with CsOH, 285 mOsmol/kg). Peak calcium currents were measured upon 50 ms step depolarization to +10 mV (Vh = −80 mV; 0.1 Hz). Recordings where seal resistance (SR) was >500 MΩ and access resistance was <3xSR were considered acceptable. Chip and whole-cell capacitance were fully compensated, and series resistance compensation (70%) was applied via Auto Rs Comp function. Recordings were acquired with PatchMaster (HEKA Elektronik, Lambrecht/Pfalz, Germany) and stored on a computer running PatchControlHT software (Nanion Technologies GmbH, Munich, Germany). Manual patch clamp (MPC) was performed on human embryonic kidney (HEK293T) cells containing the SV40 Large T-antigen cultured and transiently transfected by calcium phosphate method as reported previously [80]. In brief, cells were cultured at 37°C, 5% CO 2 in Dulbecco’s Modified Eagle’s Medium (DMEM, Invitrogen Life Technologies, Victoria, Australia), supplemented with 10% fetal bovine serum (Bovigen, Victoria, Australia), 1% GlutaMAX and penicillin–streptomycin (Invitrogen). The human orthologues of Cav3.1, Cav3.2, and Cav3.3 channels were cotransfected with GFP for identification of positive transfectants. cDNAs encoding hCav3.1 (kindly provided by G. Zamponi, University of Calgary), hCav3.2 (a1Ha-pcDNA3, Addgene #45809), and hCav3.3 (a1Ic-HE3-pcDNA3, Addgene #45810) were a kind gift from E. Perez-Reyes (University of Virginia). MPC experiments employed a MultiClamp 700B amplifier, digitalized with a DigiData1440, and controlled using Clampex11.1 software (Molecular Devices, California, USA). Recordings of I Ca through hCav3.1–3 were performed using an extracellular solution containing (in mM): 110 NaCl, 10 CaCl 2 , 1 MgCl 2 , 5 CsCl, 30 TEA-Cl, 10 D-Glucose, and 10 HEPES (pH 7.35 with TEA-OH, 305 mOsmol/kg). Pipettes were pulled from borosilicate glass capillaries (GC150F-15, Harvard Apparatus, Massachusetts, USA), fire polished to a final resistance of 1 to 3 MΩ, and filled with intracellular solution (in mM): 140 KGluconate, 5 NaCl, 2 MgCl 2 , 5 EGTA, and 10 HEPES (pH 7.2 with KOH, 295 mOsmol/kg). Peak currents were measured upon stimulation using 50 ms test pulses to −20 mV from a holding potential (Vh) of −90 mV and pulsed at 0.2 Hz. Whole-cell currents were sampled at 100 kHz and filtered to 10 kHz, with leak and capacitive currents subtracted using a P/4 protocol, and 60% to 80% series resistance compensation. Data analysis of electrophysiology experiments APC analysis was performed using Igor Pro-6.37 (WaveMetrics). Cav2.3 peak currents measured in the presence of increasing Mu8.1 concentrations (I Mu8.1 ) were divided by the current in control conditions (I Ctr ) to generate a concentration–response curve that was fit with a Hill equation of the form: where IC 50 is the half-maximal inhibitory concentration, and h is the Hill coefficient (nH). For ease of comparison, IC 50 values were calculated from fractional inhibition for the other voltage-gated channels that were screened at a single Mu8.1 concentration according to the following equation:

Acknowledgments We acknowledge the MAX IV Laboratory for time on Beamline Biomax under Proposal 20190334, and the University of Copenhagen, Small Angle X-ray facility, CPHSAXS (https://drug.ku.dk/core-facilities/cphsaxs/). We thank Dr. Uwe Müller for assistance during the data collection and Cecilie L. Søltoft for expert technical assistance. Radioligand binding and GPCR binding assays were generously provided by the National Institute of Mental Health’s Psychoactive Drug Screening Program, Contract # HHSN-271-2018-00023-C (NIMH PDSP). The NIMH PDSP is Directed by Dr. Bryan L. Roth at the University of North Carolina at Chapel Hill and Project Officer Jamie Driscoll at NIMH, Bethesda, Maryland, USA.

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