DOI: 10.5061/dryad.m63xsj49v
title: Data from: Towards high-resolution modeling of small molecule - ion channel interactions

description: Ion channels are critical drug targets for a range of pathologies, such as
 epilepsy, pain, itch, autoimmunity, and cardiac arrhythmias. To develop
 effective and safe therapeutics, it is necessary to design small molecules
 with high potency and selectivity for specific ion channel subtypes. There
 has been increasing implementation of structure-guided drug design for the
 development of small molecules targeting ion channels. We evaluated the
 performance of two Rosetta ligand docking methods, RosettaLigand and
 GALigandDock, on structures of known ligand - cation channel complexes.
 Ligands were docked to voltage-gated sodium (NaV), voltage-gated calcium
 (CaV), and transient receptor potential vanilloid (TRPV) channel families.
 For each test case, RosettaLigand and GALigandDock methods were able to
 frequently sample a ligand binding pose within 1-2 Å root mean square
 deviation (RMSD) relative to the experimental ligand coordinates. However,
 RosettaLigand and GALigandDock scoring functions cannot consistently
 identify experimental ligand coordinates as top-scoring models. Our study
 reveals that the proper scoring criteria for RosettaLigand and
 GALigandDock modeling of ligand - ion channel complexes should be assessed
 on a case-by-case basis using sufficient ligand and receptor interface
 sampling, knowledge about state specific interactions of the ion channel
 and inherent receptor site flexibility that could influence ligand
 binding.
description: <strong>Ligand
 generation</strong> Ligands were extracted as
 Structure Data Files (.sdf) from PubChem (Kim et al, 2023). Using the
 Avogadro software (Hanwell et al, 2012), each ligand structure underwent
 bond-correction, protonation at pH 7.4, and energy minimization using the
 Merck Molecular Force Field (Halgren, 1996a; b; c; Halgren and Nachbar,
 1999; Halgren, 1999a; b). The resulting models were saved as Tripos Mol2
 (.mol2) files. The protonation and bond order of saxitoxin and
 tetrodotoxin was matched to experimentally reported work (Hinman and
 DuBois, 2003; Thomas-Tran and Du Bois, 2016). Both experimentally resolved
 structures of verapamil docked to rabbit Ca<sub>V</sub>1.1
 were tested (Zhao et al, 2019). Next, using Amber
 Tools’ Antechamber protocol, the partial atomic charge, atom, and bond
 type assignments for each ligand were AM1-BCC corrected (Case et al, 2021;
 Salomon-Ferrer, Case, and Walker, 2012; <strong>Appendix
 S1</strong>). AM1-BCC correction is commonly used in Rosetta-based
 ligand docking protocols (Smith and Meiler, 2020; Park et al, 2021) and
 has demonstrated a similar performance correlation with other
 RosettaLigand input preparation protocols (Smith and Meiler,
 2020). The AM1-BCC corrected ligands were used for
 subsequent steps specific to each method. For RosettaLigand, an in-house
 script (<strong>Appendix S2</strong>) using the OpenEye Omega
 toolkit (Hawkins et al, 2010) was used to generate the conformer library,
 followed by Rosetta to generate the associated ligand parameters file. For
 GALigandDock, the input conformer was generated using the RosettaGenFF
 crystal structure prediction protocol (Park et al, 2021;
 <strong>Appendix S3</strong>), taking the lowest energy
 packing arrangement as input. <strong>Ion channel
 preparation</strong> Ion channel structures were
 downloaded from the Protein Data Bank (Berman et al, 2000). Prior to
 RosettaLigand docking, structures were relaxed with backbone constraints
 using the RosettaRelax protocol (Nivón, Moretti, and Baker, 2013). This
 protocol allows the repacking of protein sidechains and minimization of
 the structure into the Rosetta score function for comparison between
 poses. The lowest energy pose from 100 relaxed poses was used for
 docking. <strong>RosettaLigand
 docking</strong> RosettaLigand docking was
 performed using previously described RosettaScripts protocols (Davis and
 Baker, 2009; DeLuca, Khar, and Meiler, 2015; <strong>Appendix
 S4-S7</strong>). Briefly, the initial placement of all ligands into
 their respective ion channels was performed by superimposing the initial
 ligand poses onto the experimental ligand coordinates.
 RosettaLigand uses grid-based sampling to score the ion channel –
 ligand interface, with a low-resolution and high-resolution sampling
 phase. For an unbiased sampling of the local space, an initial
 transformation during the low-resolution sampling phase is performed on
 the initial ligand position using Rosetta’s Transform mover, The
 low-resolution Transform mover is a grid-based Monte Carlo simulation,
 where the ligand is translated up to 0.2 Å and rotated up to 20 degrees
 per iteration, for a total of 500 iterations. A box size of 7-8 Å
 restrains the ligand center, while all ligand atoms are constrained to a
 grid with a user defined width to prevent ligand scoring outside the
 target site. This scoring grid width was calculated uniquely for each
 ligand to ensure all ligand conformers would not automatically fail the
 Transform step. As used previously, the scoring grid width was calculated
 as the maximum conformer atom-atom distance plus twice the box size value
 used in the Transform mover (Moretti, Bender, Allison, and Meiler, 2016;
 <strong>Appendix S4</strong>). The pose with the lowest score
 is then used as the starting pose for high-resolution docking. For
 high-resolution docking using Rosetta’s HighResDocker mover, six docking
 cycles of rotamer sampling are performed with repacking of sidechains
 every third iteration. Lastly, the ion channel – ligand complex is
 minimized using Rosetta’s FinalMinimizer mover, and the interface scores
 are reported using the InterfaceScoreCalculator. For
 all RosettaLigand docking runs, the ligand_soft_rep and hard_rep scoring
 functions were reweighted based on previous work assessing Rosetta score
 functions with the Comparative Assessment of Score Function 2016
 (CASF-2016) dataset (Su et al., 2019; Smith and Meiler, 2020).
 Specifically, reweights of Coulombic electrostatic potential (fa_elec),
 Lennard-Jones repulsive energy between atoms in the same residue
 (fa_intra_rep), sidechain-backbone hydrogen bond energy (hbond_bb_sc),
 sidechain-sidechain hydrogen bond energy (hbond_sc), and Ramachandran
 preferences (rama) were applied for the soft-repulsive and hard-repulsive
 docking phases (<strong>Appendix S6</strong>).
 For each docking run, either 20,000 poses or 100,000 poses were
 generated to assess if there are statistically significant differences in
 the lowest recorded pose RMSD (RMSD<sub>Min</sub>) to
 experimental ligand coordinates. In RosettaLigand, a ligand interface is
 defined either by a representative ligand atom (a “neighbor atom”, defined
 as the geometric center of mass by default), or all ligand atoms, relative
 to all ion channel C<sub>β</sub> atoms within a specified
 radius from the ligand (commonly default to 6 or 7 Å). For RosettaLigand,
 two mutually exclusive ligand area interface modes are available for
 scoring the pose interface: the ligand neighbor atom cutoffs mode
 (add_nbr_radius=”True”), and the all ligand atom cutoffs mode
 (all_atom_mode=”True”). In previous work, both modes were used (Moretti,
 Bender, Allison, and Meiler, 2016; Smith and Meiler, 2020), thus, both
 modes were evaluated for any differences in performance. Four individual
 RosettaLigand docking sets were performed for each PDB structure, by
 combining different pose totals and ligand area interface modes, for
 performance comparison. <strong>GALigandDock
 docking</strong> As described in the original
 study, docking was performed using the RosettaScripts’ GALigandDock mover
 in DockFlex mode (Park et al, 2021; <strong>Appendix
 S8-S10</strong>). Replicate runs of GALigandDock were performed in
 parallel for each structure evaluated. Each run consisted of 20
 generations with a pool of 100 poses, where each generation updates the
 pool by total energy. By default, GALigandDock outputs the top 20
 structures from the final generation, however, for this study, the entire
 pool of poses were used. For each docking run, 20,000 poses (1,000 runs)
 or 100,000 poses (5,000 runs) were output with a padding value of 2 Å, 4
 Å, or 7 Å to test for statistical differences in the
 RMSD<sub>Min</sub>. In total, six individual GALigandDock
 docking sets were performed for each PDB structure, by combining different
 pose totals and padding sizes, for performance comparison.
 <strong>P<sub>Near</sub>
 calculation</strong> P<sub>Near</sub>
 is a quantitative metric to evaluate an energy funnel’s quality from a
 population of poses, with the lowest-scoring pose as the converged minima,
 or reference state. P<sub>Near</sub> was calculated as
 previously defined (Bhardwaj, Mulligan, Bahl, et al., 2016), and applied
 as previously described for ligand docking (Smith and Meiler, 2020), with
 the reference state being the experimental ligand coordinates.
 P<sub>Near</sub> ranges from 0 (protein will not converge to
 reference state) to 1 (protein will always converge to the reference
 state). <br>The numerator is the Boltzmann probability of an
 individual pose being near the reference state, governed by the “likeness”
 parameter (λ), and the thermal energy, the product of the Boltzmann
 constant and absolute temperature (k<sub>B</sub>T). The
 denominator is the partition function of a canonical ensemble. For small
 molecule docking, the root mean square deviation (RMSD) of the pose’s
 ligand coordinates relative to experimental ligand coordinates when the
 protein pose is superimposed to the entire protein structure, or the
 receptor site is used. The energy scoring metric
 (E<sub>i</sub>) is the interface energy: the energy solely
 composed of protein-ligand interactions. A previous
 Rosetta small-molecule docking study (Park et al., 2021) categorized
 reference-like poses at 1 Å or 2 Å RMSD without calculating
 P<sub>Near</sub>, while another study evaluating RosettaLigand
 performance calculated P<sub>Near</sub> using λ =1.5 Å, and
 k<sub>B</sub>T =0.62 (Meiler and Smith, 2020). Therefore, we
 calculated P<sub>Near</sub> with reference-like poses defined
 by λ=2.0 Å and k<sub>B</sub>T =0.62 Rosetta energy units
 (REU), however we calculated P<sub>Near </sub>using all
 previously reported values for the parameters λ and
 k<sub>B</sub>T (<strong>Tables
 S4.1-4.10</strong>). A specific P<sub>Near</sub> cutoff
 indicative for drug discovery pipelines has not been established, hence we
 will refer to P<sub>Near</sub> ≥ 0.5 as a ‘first pass’ cutoff
 for this study when evaluating energy funnel convergence to the
 experimental ligand coordinates.
 <strong>Statistical tests</strong>
 Tests for normality, heteroscedasticity, and Pearson’s
 correlation between covariates were performed in Python using NumPy
 (Harris et al., 2020), SciPy (Virtanen et al., 2020), and Pingouin
 (Vallat, 2018) with a significance level (α) of 0.05. Population data
 consisted of the RMSD<sub>Min</sub> for each docking set. Not
 all RMSD<sub>Min </sub>data for each docking set fit a normal
 distribution. Since RMSD<sub>Min </sub>data should skew
 towards zero Å, a lognormal base 10 transformation was applied to all
 RMSD<sub>Min</sub> population data when comparing across
 docking sets. Shapiro Wilk tests for normality and Levene
 heteroscedasticity tests were performed to ensure transformed data were
 normal and of equal variance, respectively. Covariates
 for both methods were the number of rotatable bonds of the ligand, the
 number of ligand heavy atoms, and the resolution of the structure.
 Statistical tests for RosettaLigand also included the number of conformers
 generated, transformed with a lognormal base 10, as a covariate. The
 ligand molecular weight was initially included as a covariate but was
 discarded after identifying strong Pearson’s correlation with the number
 of ligand heavy atoms (<strong>Table S3, Figure
 S1.</strong>). To assess
 RMSD<sub>Min</sub> across all docking sets for a given method,
 a repeated measures factorial analysis of variance (ANOVA) with covariates
 were performed using IBM SPSS (version 29). For RosettaLigand, the two
 factors were sample size and ligand area interface mode, while for
 GALigandDock the two factors were sample size and padding value. Mauchly’s
 test for sphericity was performed for levels of padding (p =
 0.63).
description: # Data from: Towards high-resolution modeling of small molecule - ion
 channel interactions This README file was generated on 2024-05-02 by
 Brandon Harris and Vladimir Yarov-Yarovoy. **GENERAL INFORMATION** **1.
 Title of Dataset:** Towards high-resolution modeling of small molecule –
 ion channel interactions  **2. Author Information**             A.
 Principal Investigator Contact Information              Name: Vladimir
 Yarov-Yarovoy                       Institution: University of California,
 Davis              Address: 1275 Med Science Drive, Davis, CA 95616-5271
 USA              Email: [yarovoy@ucdavis.edu](mailto:yarovoy@ucdavis.edu)
 **3. Date of data collection** (single date, range, approximate date):
 2023-2024  **4. Geographic location of data collection**: 1275 Med Science
 Drive, Davis, CA 95616-5271 USA **5. Information about funding sources
 that supported the collection of the data**: National Institutes of
 Health, Award: R61NS127285 National Institutes of Health, Award:
 R01HL128537 National Institutes of Health, Award: R01HL159304 National
 Institutes of Health, Award: R01GM132110 National Institutes of Health,
 Award: R56NS9706 National Institutes of Health, Award: R01NS128180
 National Institutes of Health, Award: F31NS124337 National Institutes of
 Health, Award: T32GM007377 SHARING/ACCESS INFORMATION **1.
 Licenses/restrictions placed on the data**: CC0 1.0 Universal (CC0 1.0)
 Public Domain **2. Links to publications that cite or use the data**:
 Brandon J Harris, Phuong T. Nguyen, Guangfeng Zhou, Heike Wulff, Frank
 DiMaio, Vladimir Yarov-Yarovoy (2024). Towards high-resolution modeling of
 small molecule – ion channel interactions. *bioRxiv* 2024.04.02.587818;
 doi:
 [https://doi.org/10.1101/2024.04.02.587818](https://doi.org/10.1101/2024.04.02.587818) **3. Links to other publicly accessible locations of the data**: None **4. Links/relationships to ancillary data sets**: None **5. Was data derived from another source**? No             A. If yes, list source(s): NA **6. Recommended citation for this dataset**: Brandon J Harris, Phuong T. Nguyen, Guangfeng Zhou, Heike Wulff, Frank DiMaio, Vladimir Yarov-Yarovoy (2024). Towards high-resolution modeling of small molecule – ion channel interactions. *bioRxiv* 2024.04.02.587818; [https://doi.org/10.5061/dryad.m63xsj49v](https://doi.org/10.5061/dryad.m63xsj49v) **DATA & FILE OVERVIEW** 1\. File List: A)    5ek0.zip B)    6j8g.zip C)    6j8i.zip D)    6jp5.zip E)    6jp8.zip F)     6jpaV1.zip G)    6jpaV2.zip H)    6jpb.zip I)      6kzp.zip J)     6u88.zip K)    6uz0.zip 2\. Relationship between files, if important: None 3\. Additional related data collected that was not included in the current data package: The entire pose population for each test case ran was not provided, as that would result in approximately 6.6 million poses. Instead, for each test case, the input files for RosettaLigand and GALigandDock, and all score file data are provided. All models used to generate the main and supplemental figures are provided. Together, these are the minimal components needed for replication of the data presented in the study. 4\. Are there multiple versions of the dataset? No             A. If yes, name of file(s) that was updated: NA                         i. Why was the file updated? NA                         ii. When was the file updated? NA \######################################################################### DATA-SPECIFIC INFORMATION FOR: 5ek0.zip 1\. Data for PDB ID: 5ek0 contains RosettaLigand inputs, GALigandDock inputs, text-readable score files from all test conditions, and models of the top ten RMSD and interface score poses from each test condition (in Protein Data Bank .pdb format).: 2\. Each .pdb file should be opened using UCSF ChimeraX software: [https://www.cgl.ucsf.edu/chimerax/](https://www.cgl.ucsf.edu/chimerax/) 3\. Missing data codes: None 4\. Specialized formats or other abbreviations used: A .slurm file is a Slurm Workload Manager-executable file. A .sc file is a score file in text-readable format. \######################################################################### DATA-SPECIFIC INFORMATION FOR: 6j8g.zip 1\. Data for PDB ID: 6j8g contains RosettaLigand inputs, GALigandDock inputs, text-readable score files from all test conditions, and models of the top ten RMSD and interface score poses from each test condition (in Protein Data Bank .pdb format).: 2\. Each .pdb file should be opened using UCSF ChimeraX software: [https://www.cgl.ucsf.edu/chimerax/](https://www.cgl.ucsf.edu/chimerax/) 3\. Missing data codes: None 4\. Specialized formats or other abbreviations used: A .slurm file is a Slurm Workload Manager-executable file. A .sc file is a score file in text-readable format. \######################################################################### DATA-SPECIFIC INFORMATION FOR: 6j8i.zip 1\. Data for PDB ID: 6j8i contains RosettaLigand inputs, GALigandDock inputs, text-readable score files from all test conditions, and models of the top ten RMSD and interface score poses from each test condition (in Protein Data Bank .pdb format).: 2\. Each .pdb file should be opened using UCSF ChimeraX software: [https://www.cgl.ucsf.edu/chimerax/](https://www.cgl.ucsf.edu/chimerax/) 3\. Missing data codes: None 4\. Specialized formats or other abbreviations used: A .slurm file is a Slurm Workload Manager-executable file. A .sc file is a score file in text-readable format. \######################################################################### DATA-SPECIFIC INFORMATION FOR: 6jp5.zip 1\. Data for PDB ID: 6jp5 contains RosettaLigand inputs, GALigandDock inputs, text-readable score files from all test conditions, and models of the top ten RMSD and interface score poses from each test condition (in Protein Data Bank .pdb format).: 2\. Each .pdb file should be opened using UCSF ChimeraX software: [https://www.cgl.ucsf.edu/chimerax/](https://www.cgl.ucsf.edu/chimerax/) 3\. Missing data codes: None 4\. Specialized formats or other abbreviations used: A .slurm file is a Slurm Workload Manager-executable file. A .sc file is a score file in text-readable format. \######################################################################### DATA-SPECIFIC INFORMATION FOR: 6jp8.zip 1\. Data for PDB ID: 6jp8 contains RosettaLigand inputs, GALigandDock inputs, text-readable score files from all test conditions, and models of the top ten RMSD and interface score poses from each test condition (in Protein Data Bank .pdb format).: 2\. Each .pdb file should be opened using UCSF ChimeraX software: [https://www.cgl.ucsf.edu/chimerax/](https://www.cgl.ucsf.edu/chimerax/) 3\. Missing data codes: None 4\. Specialized formats or other abbreviations used: A .slurm file is a Slurm Workload Manager-executable file. A .sc file is a score file in text-readable format. \######################################################################### DATA-SPECIFIC INFORMATION FOR: 6jpaV1.zip 1\. Data for PDB ID: 6jpaV1 contains RosettaLigand inputs, GALigandDock inputs, text-readable score files from all test conditions, and models of the top ten RMSD and interface score poses from each test condition (in Protein Data Bank .pdb format).: 2\. Each .pdb file should be opened using UCSF ChimeraX software: [https://www.cgl.ucsf.edu/chimerax/](https://www.cgl.ucsf.edu/chimerax/) 3\. Missing data codes: None 4\. Specialized formats or other abbreviations used: A .slurm file is a Slurm Workload Manager-executable file. A .sc file is a score file in text-readable format. \######################################################################### DATA-SPECIFIC INFORMATION FOR: 6jpaV2.zip 1\. Data for PDB ID: 6jpaV2 contains RosettaLigand inputs, GALigandDock inputs, text-readable score files from all test conditions, and models of the top ten RMSD and interface score poses from each test condition (in Protein Data Bank .pdb format).: 2\. Each .pdb file should be opened using UCSF ChimeraX software: [https://www.cgl.ucsf.edu/chimerax/](https://www.cgl.ucsf.edu/chimerax/) 3\. Missing data codes: None 4\. Specialized formats or other abbreviations used: A .slurm file is a Slurm Workload Manager-executable file. A .sc file is a score file in text-readable format. \######################################################################### DATA-SPECIFIC INFORMATION FOR: 6jpb.zip 1\. Data for PDB ID: 6jpb contains RosettaLigand inputs, GALigandDock inputs, text-readable score files from all test conditions, and models of the top ten RMSD and interface score poses from each test condition (in Protein Data Bank .pdb format).: 2\. Each .pdb file should be opened using UCSF ChimeraX software: [https://www.cgl.ucsf.edu/chimerax/](https://www.cgl.ucsf.edu/chimerax/) 3\. Missing data codes: None 4\. Specialized formats or other abbreviations used: A .slurm file is a Slurm Workload Manager-executable file. A .sc file is a score file in text-readable format. \######################################################################### DATA-SPECIFIC INFORMATION FOR: 6kzp.zip 1\. Data for PDB ID: 6kzp contains RosettaLigand inputs, GALigandDock inputs, text-readable score files from all test conditions, and models of the top ten RMSD and interface score poses from each test condition (in Protein Data Bank .pdb format).: 2\. Each .pdb file should be opened using UCSF ChimeraX software: [https://www.cgl.ucsf.edu/chimerax/](https://www.cgl.ucsf.edu/chimerax/) 3\. Missing data codes: None 4\. Specialized formats or other abbreviations used: A .slurm file is a Slurm Workload Manager-executable file. A .sc file is a score file in text-readable format. \######################################################################### DATA-SPECIFIC INFORMATION FOR: 6u88.zip 1\. Data for PDB ID: 6u88 contains RosettaLigand inputs, GALigandDock inputs, text-readable score files from all test conditions, and models of the top ten RMSD and interface score poses from each test condition (in Protein Data Bank .pdb format).: 2\. Each .pdb file should be opened using UCSF ChimeraX software: [https://www.cgl.ucsf.edu/chimerax/](https://www.cgl.ucsf.edu/chimerax/) 3\. Missing data codes: None 4\. Specialized formats or other abbreviations used: A .slurm file is a Slurm Workload Manager-executable file. A .sc file is a score file in text-readable format. \######################################################################### DATA-SPECIFIC INFORMATION FOR: 6uz0.zip 1\. Data for PDB ID: 6uz0 contains RosettaLigand inputs, GALigandDock inputs, text-readable score files from all test conditions, and models of the top ten RMSD and interface score poses from each test condition (in Protein Data Bank .pdb format).: 2\. Each .pdb file should be opened using UCSF ChimeraX software: [https://www.cgl.ucsf.edu/chimerax/](https://www.cgl.ucsf.edu/chimerax/) 3\. Missing data codes: None 4\. Specialized formats or other abbreviations used: A .slurm file is a Slurm Workload Manager-executable file. A .sc file is a score file in text-readable format. \#########################################################################