DOI: 10.5061/dryad.pvmcvdnsz
title: Control over Conformational Landscapes of Polypeptoids by Monomer Sequence Patterning

description: This dataset accompanies the article "Control Over Conformational
 Landscapes of Polypeptoids by Monomer Sequence Patterning" by Audra
 J. DeStefano, Shawn D. Mengel, Morgan W. Bates, Sally Jiao, M. Scott
 Shell, Songi Han, and Rachel A. Segalman in Macromolecules in 2024. The
 article demonstrates how patterning of hydrophobic residues along a
 polymer backbone can tune the distribution of end-to-end distances and
 that these effects can be predicted by a simple bead-spring simulation.
 This dataset contains the necessary experimental data to reproduce the
 main text and supporting figures. High performance liquid chromatographs,
 mass spectra, double electron-electron resonance time domain signals, and
 simulated end-to-end distance distributions are included.
description: Please refer to the methods section of the published main text
 for details on dataset collection and processing.
description: This README.txt file was generated on 20230601 by Audra DeStefano and
 Shawn Mengel ### GENERAL INFORMATION 1. Title of Dataset: Control Over
 Conformational Landscapes of Polypeptoids by Monomer Sequence Patterning
 2. Author Information A. Principal Investigator Contact Information Name:
 Rachel A. Segalman Institution: University of California Santa Barbara
 Address: Department of Chemical Engineering, University of California,
 Santa Barbara, 93106 Email: B. Associate or
 Co-investigator Contact Information Name: Audra J. DeStefano Institution:
 University of California Santa Barbara Address: Department of Chemical
 Engineering, University of California, Santa Barbara, 93106 Email:
 C. Associate or Co-investigator Contact
 Information Name: Shawn D. Mengel Institution: University of California
 Santa Barbara Address: Department of Chemical Engineering, University of
 California, Santa Barbara, 93106 Email: D.
 Associate or Co-investigator Contact Information Name: Morgan W. Bates
 Institution: University of California Santa Barbara Address: California
 NanoSystems Institute, University of California, Santa Barbara, 93106
 Email: E. Associate or Co-investigator
 Contact Information Name: Sally Jiao Institution: University of California
 Santa Barbara Address: Department of Chemical Engineering, University of
 California, Santa Barbara, 93106 Email: F.
 Associate or Co-investigator Contact Information Name: M. Scott Shell
 Institution: University of California Santa Barbara Address: Department of
 Chemical Engineering, University of California, Santa Barbara, 93106
 Email: G. Associate or Co-investigator Contact
 Information Name: Songi Han Institution: University of California Santa
 Barbara Address: Department of Chemical Engineering, University of
 California, Santa Barbara, 93106 Email: 3.
 Date of data collection (single date, range, approximate date): 2022-03-01
 to 2023-05-25 4. Geographic location of data collection: Santa Barbara, CA
 5. Information about funding sources that supported the collection of the
 data: The polymer synthesis and characterization were supported by the
 National Science Foundation under Grant No. 2203179 (SDM, AJD, RAS)
 leveraging facilities and expertise from the BioPACIFIC Materials
 Innovation Platform of the National Science Foundation under Award No.
 DMR-1933487 (MWB). Development of the DEER technique and computational
 model was supported by the Center for Materials for Water and Energy
 Systems (M-WET), an Energy Frontier Research Center funded by the U.S.
 Department of Energy, Office of Science, Basic Energy Sciences under Award
 #DE-SC0019272. SDM and SJ acknowledge support from the National Science
 Foundation Graduate Research Fellowship (DGE 2139319, DGE 1650114). AJD
 acknowledges support from the Department of Defense through the National
 Defense Science & Engineering Graduate (NDSEG) Fellowship Program. ###
 SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data:
 N/A 2. Links to publications that cite or use the data:
 3. Links to other publicly accessible
 locations of the data: N/A 4. Links/relationships to ancillary data sets:
 N/A 5. Was data derived from another source? no 6. Recommended citation
 for this dataset: DeStefano, Audra et al. (2023), Control Over
 Conformational Landscapes of Short Polypeptoids by Monomer Sequence
 Patterning, Dryad, Dataset, ### DATA &
 FILE OVERVIEW 1. File List: Files are orgranized into folders based on
 figure/table number from the corresponding manuscript. Files are named as
 "Figure#_Technqiue_SampleID.csv". A. Figure2: Model time domain
 data and end-to-end distance distribution to demonstrate DEER data
 processing. a. Figure2_DEER_TimeDomain.csv 1\. Variables: time
 (units=microseconds), background corrected data (units=arbitrary),
 calculated fit (units=arbitrary), offset (units=arbitrary), raw data
 (units=arbitrary) 2\. Column names: time, Data, Fit, Offset, Raw b.
 Figure2_DEER_PofR.csv 1\. Variables: end-to-end distance (units=angstrom),
 end-to-end distance distribution (units=none), lower standard deviation of
 distribution (units=none), upper standard deviation of distribution
 (units=none) 2\. Column names: Ree_nm, P(Ree), Lower STDEV, Upper STDEV B.
 Figure4: End-to-end distance distrubutions and average end-to-end
 distances for polypeptoids with increasing hydrophobic content. Files a-g
 share the same file structure. a. Figure4_DEER_PofR_0H.csv 1\. Variables:
 end-to-end distance (units=angstrom), end-to-end distance distribution
 (units=none), lower standard deviation of distribution (units=none), upper
 standard deviation of distribution (units=none) 2\. Column names: Ree_nm,
 P(Ree), Lower STDEV, Upper STDEV b. Figure4_DEER_PofR_1H.csv c.
 Figure4_DEER_PofR_2H.csv d. Figure4_DEER_PofR_3H.csv e.
 Figure4_DEER_PofR_4H.csv f. Figure4_DEER_PofR_0H_38.csv g.
 Figure4_DEER_PofR_Ends_38.csv h. Figure4_DEER_Ree.csv 1\. Variables:
 sequence (units=none), hydrophobic fraction at chain ends
 (units=dimensionless), root-mean-squared end-to-end distance
 (units=angstrom) 2\. Column names: sequence, hydrophobic_fraction, Ree C.
 Figure5: End-to-end distance distrubutions and average end-to-end
 distances for polypeptoids with varying hydrophobe patterning. Files a-g
 share the same file structure. a. Figure5_DEER_PofR_Ends_38.csv 1\.
 Variables: end-to-end distance (units=angstrom), end-to-end distance
 distribution (units=none), lower standard deviation of distribution
 (units=none), upper standard deviation of distribution (units=none) 2\.
 Column names: Ree_nm, P(Ree), Lower STDEV, Upper STDEV b.
 Figure5_DEER_PofR_Mid_38.csv c. Figure5_DEER_PofR_2B_38.csv d.
 Figure5_DEER_PofR_3B_38.csv e. Figure5_DEER_PofR_6B_38.csv f.
 Figure5_DEER_PofR_12B_38.csv g. Figure5_DEER_PofR_1p5_38.csv h.
 Figure5_DEER_Ree.csv 1\. Variables: sequence (units=none), sequence chain
 length (units=residues), root-mean-squared average end-to-end distance
 (units=angstrom) 2\. Column names: sequence, length, Ree D. Figure6:
 Comparison between root-mean-squared end-to-end distance distributions
 from DEER and bead-spring simulations. a.
 Figure6_Simulation_Experiment_Ree.csv 1\. Variables: Sequence
 (units=none), simulated average end-to-end distance (units=sigma),
 experimental average end-to-end distance (units=angstrom) 2\. Column
 names: sequence, sim_Ree, exp_Ree E. Figure7: Statistical moments of
 polypeptoid 38mers. a. Figure7_38mer_moments.csv 1\. Variables: sequence
 (units=none), root-mean-squared average end-to-end distance
 (units=angstrom), variance divided by mean squared (units=dimensionless),
 skewness (units=dimensionless) 2\. Column names: sequence, Ree,
 normalized_variance, skewness F. FigureS1-19 - HPLC-MS chromatograms and
 mass spectrometry files for peaks of interest. Files a-s have same
 structure outlined for Figure_S2_HPLC_0H.csv. Files t-al have same
 structure outlined for Figure_S2_MS_0H.csv. a. FigureS1_HPLC_0H.txt 1\.
 Variables: retention time (units=minutes), intensity at 214nm
 (units=arbitrary) 2\. Column names: R.Time, Intensity b.
 FigureS2_HPLC_1H.txt c. FigureS3_HPLC_2H.txt d. FigureS4_HPLC_3H.txt e.
 FigureS5_HPLC_4H.txt f. FigureS6_HPLC_Mid.txt g. FigureS7_HPLC_AB.txt h.
 FigureS8_HPLC_2B.txt i. FigureS9_HPLC_3B.txt j. FigureS10_HPLC_6B.txt k.
 FigureS11_HPLC_1p5.txt l. FigureS12_HPLC_0H_38.txt m.
 FigureS13_HPLC_Mid_38.txt n. FigureS14_HPLC_Ends_38.txt o.
 FigureS15_HPLC_2B_38.txt p. FigureS16_HPLC_3B_38.txt q.
 FigureS17_HPLC_6B_38.txt r. FigureS18_HPLC_12B_38.txt s.
 FigureS19_HPLC_1p5_38.txt t. FigureS1_MS_0H.txt 1\. Variables: mass to
 charge ratio (units=atomic mass unit per ion charge), intensity
 (units=arbitrary) 2\. Column names: mz, intensity u. FigureS2_MS_1H.txt v.
 FigureS3_MS_2H.txt w. FigureS4_MS_3H.txt x. FigureS5_MS_4H.txt y.
 FigureS6_MS_Mid.txt z. FigureS7_MS_AB.txt aa. FigureS8_MS_2B.txt ab.
 FigureS9_MS_3B.txt ac. FigureS10_MS_6B.txt ad. FigureS11_MS_1p5.txt ae.
 FigureS12_MS_0H_38.txt af. FigureS13_MS_Mid_38.txt ag.
 FigureS14_MS_Ends_38.txt ah. FigureS15_MS_2B_38.txt ai.
 FigureS16_MS_3B_38.txt aj. FigureS17_MS_6B_38.txt ak.
 FigureS18_MS_12B_38.txt al. FigureS19_MS_1p5_38.txt G. FigureS20-38 - Time
 domain DEER. Raw, background corrected, and calculated traces are
 included. All files have the same structure outlined for
 FigureS20_DEER_TimeDomain_OH.csv a. FigureS20_DEER_TimeDomain_0H.csv 1\.
 Variables: time (units=microseconds), background corrected data
 (units=arbitrary), calculated fit (units=arbitrary), offset
 (units=arbitrary), raw data (units=arbitrary) 2\. Column names: time,
 Data, Fit, Offset, Raw b. FigureS21_DEER_TimeDomain_1H.csv c.
 FigureS22_DEER_TimeDomain_2H.csv d. FigureS23_DEER_TimeDomain_3H.csv e.
 FigureS24_DEER_TimeDomain_4H.csv f. FigureS25_DEER_TimeDomain_Mid.csv g.
 FigureS26_DEER_TimeDomain_AB.csv h. FigureS27_DEER_TimeDomain_2B.csv i.
 FigureS28_DEER_TimeDomain_3B.csv j. FigureS29_DEER_TimeDomain_6B.csv k.
 FigureS30_DEER_TimeDomain_1p5.csv l. FigureS31_DEER_TimeDomain_0H_38.csv
 m. FigureS32_DEER_TimeDomain_Mid_38.csv n.
 FigureS33_DEER_TimeDomain_Ends_38.csv o.
 FigureS34_DEER_TimeDomain_2B_38.csv p. FigureS35_DEER_TimeDomain_3B_38.csv
 q. FigureS36_DEER_TimeDomain_6B_38.csv r.
 FigureS37_DEER_TimeDomain_12B_38.csv s.
 FigureS38_DEER_TimeDomain_1p5_38.csv H. FigureS39: Bead-spring molecular
 dynamics simulation of end-to-end distance distributions at
 T=0.75epsilon/k_B. All files have same structure outlined for
 FigureS39_simulation_Mid_20mer.txt a. FigureS39_simulation_Mid_20mer.txt
 1\. Variables: end-to-end distance (units=sigma), probability distribution
 of the end-to-end distance (units=1/sigma), 2.5% confidence boundary for
 P(Ree) (units=1/sigma), 97.5% confidence boundary for P(Ree)
 (units=1/sigma) 2\. Column names: Ree, y, error_low, error_high b.
 FigureS39_simulation_0H_38mer.txt c. FigureS39_simulation_1p5_38mer.txt d.
 FigureS39_simulation_2B_38mer.txt e. FigureS39_simulation_3B_38mer.txt f.
 FigureS39_simulation_6B_38mer.txt g. FigureS39_simulation_12B_38mer.txt h.
 FigureS39_simulation_Ends_38mer.txt i. FigureS39_simulation_Mid_38mer.txt
 j. FigureS39_simulation_0H_20mer.txt k. FigureS39_simulation_1H_20mer.txt
 l. FigureS39_simulation_1p5_20mer.txt m. FigureS39_simulation_2B_20mer.txt
 n. FigureS39_simulation_2H_20mer.txt o. FigureS39_simulation_3B_20mer.txt
 p. FigureS39_simulation_3H_20mer.txt q. FigureS39_simulation_4H_20mer.txt
 r. FigureS39_simulation_6B_20mer.txt s. FigureS39_simulation_AB_20mer.txt
 I. FigureS40: Comparison of simulated and experimental average end-to-end
 distances at multiple simulation temperatures. a.
 FigureS40_Simulation_Experiment_Ree.csv 1\. Variables: Sequence
 (units=none), Length of spin-labeled sequence (units=residues),
 Temperature (units=epsilon/k_B), simulated average end-to-end distance
 (units=sigma), experimental average end-to-end distance (units=angstrom)
 2\. Column names: sequence, length, temperature, sim_Ree, exp_Ree J.
 FigureS41: Model distributions to demonstrate high/low variance and
 skewness. a. FigureS41_IdealDistributions_Variance.csv 1\. Variables:
 x-axis (units=none), Normal probability distribution function with low
 variance (units=none), Normal probability distribution function with high
 variance (units=none) 2\. Column names: x, PDF_low_variance,
 PDF_high_variance b. FigureS41_IdealDistributions_Skewness.csv 1\.
 Variables: x-axis (units=none), Lognormal probability distribution
 function with low positive skewness (units=none), Lognormal probability
 distribution function with high positive skewness (units=none) 2\. Column
 names: x, PDF_low_skew, PDF_high_skew K. FigureS42: Statistical moments of
 simulated end-to-end distance distributions. a.
 FigureS42_simulation_moments.csv 1\. Variables: sequence (units=none),
 root-mean-squared average end-to-end distance (units=sigma), variance
 divided by mean squared (units=none), skewness (units=none) 2\. Column
 names: sequence, average_Ree, normalized_variance, skewness L. FigureS43:
 Average end-to-end distances for polypeptoids with varying hydrophobe
 patterning. a. FigureS43_DEER_Ree.csv 1\. Variables: sequence
 (units=none), sequence chain length (units=residues), root-mean-squared
 average end-to-end distance (units=angstrom) 2\. Column names: sequence,
 length, Ree M. FigureS44: Cw-EPR spectra for spin label and polypeptoid.
 a. FigureS44_EPR_Spinlabel 1\. Variables: index (units=none), magnetic
 field (units=Gauss), signal intensity (units=a.u.) 2\. Column names:
 index, magnetic field, signal intensity a. FigureS44_EPR_3H 1\. Variables:
 index (units=none), magnetic field (units=Gauss), signal intensity
 (units=a.u.) 2\. Column names: index, magnetic field, signal intensity N.
 TableS2: Sample masses obtained by HPLC-MS. a. TableS2_HPLC-MS.csv 1\.
 Variables: Label (units=none), chain length (units=number of monomers),
 sequence (units=none), theoretical molecular weight (grams/mole),
 experimental mass divided by charge number of ion (units=none), ion type
 (units=none) 2\. Column names: Label, Chain Length, Sequence, Theoretical
 MW (g/mol), Exp. m/z, Ion type O. TableS3: Correlation and intercept of
 the comparison between bead-spring model and DEER measurements. a.
 TableS3_correlation_Ree.txt 1\. Variables: Simulation temperature
 (units=epsilon/k_B), Pearson correlation coefficient (units=none),
 x-intercept (units=angstrom) 2\. Column names: temperature, correlation,
 x_intercept 2. Relationship between files, if important: N/A 3. Additional
 related data collected that was not included in the current data package:
 N/A 4. Are there multiple versions of the dataset? no 5. Experimental
 context of each technique: 1. High-performance liquid chromatography mass
 spectrometry (HPLC-MS) is used to quantify the purity and molecular
 weights of the synthesized polypeptoids. 2. Double electron-electron
 resonance (DEER) spectroscopy is an advanced electron paramagnetic
 resonance technique able to measure ensemble distributions between two
 nitroxide spin probes. We use this technique to characterize the
 end-to-end distance ensembles of polypeptoids with patterned hydrophobic
 sequences. 3. Bead-spring model simulations are used to develop
 computational models that accurately predict the sequence effects measured
 experimentally. ### METHODOLOGICAL INFORMATION 1. Description of methods
 used for collection/generation of data: &lt;&lt;&lt;&lt;&gt;&gt;&gt;&gt; The mass and purity of the final polypeptoid
 products was confirmed by analytical HPLC mass spectrometry. Analytical
 HPLC-MS was performed using a C18 column (XBridge BEH C18, 300 angstroms,
 5 micrometers, 4.6 millimeters by 150 millimeters) with a 5-95% solvent A
 gradient over 30 minutes, where solvent A was water with 0.1% v/v
 trifluoroacetic acid and solvent B was acetonitrile with 0.1% v/v
 trifluoroacetic acid. End-to-end distance distributions were measured for
 each polypeptoid sequence using double electron-electron resonanance
 (DEER). Samples containing approximately 100 µM of polypeptoid were
 dissolved in 50/50 v/v D2O/d-THF to ensure full solubility across a range
 of hydrophobic contents. Solutions were cryo-protected with 30% deuterated
 glycerol, by volume. Then, approximately 40 µL of solution was loaded into
 a 3 mm OD, 2 mm ID quartz tube. Samples were flash frozen in liquid
 nitrogen immediately prior to performing DEER. A Bruker/ColdEdge FlexLine
 Cryostat (Model ER 4118HV-CF100) maintained the sample temperature at 60
 K. All DEER spectra were obtained using a Bruker QT-II resonator with a
 pulsed Q-band Bruker E580 Elexsys spectrometer with an Applied Systems
 Engineering, Model 177 Ka 300 W TWT amplifier. The following four pulse
 DEER sequence was applied to all samples: πobs/2- τ1-πobs-(t-πpump)-
 (τ2-t)-πobs- τ2-echo. Nutation experiments were used to determine optimal
 observer pulses (approximately 10 ns for 90 pulses and 20 ns for 180
 pulses). The linear chirp πpump frequency width was set at 80 MHz and its
 duration at 100 ns, while πobs was 90 MHz above the center of the pump
 frequency range. τ1 was set at 126 ns and τ2 was set at 6 µs. All DEER
 experiments were signal averaged over at least 10 averages.
 Continuous-wave electron paramagnetic resonance spectra are measured at a
 concentration of 150 μM. A quartz round capillary tube of 0.60 mm inner
 diameter and 0.84 mm outer diameter is loaded with 3.5 μL of solution and
 sealed at one end with beeswax and at the other with Critoseal. The
 dispersive electron paramagnetic resonance (EPR) spectrum is obtained with
 a fixed frequency (9.8 GHz) at 25 dB, while the magnetic field is swept
 with a modulation frequency of 140.0 kHz and a modulation amplitude of
 0.70 G.  2. Methods for processing the data: Time-domain DEER signals were
 background corrected using the LongDistances software
 (). Distance distributions were extracted from time-domain DEER signals using “model-free” Tikhonov regularization, as implemented by the LongDistances software. 3. Instrument- or software-specific information needed to interpret the data: N/A 4. Standards and calibration information, if appropriate: N/A 5. Environmental/experimental conditions: N/A 6. Describe any quality-assurance procedures performed on the data: N/A 7. People involved with sample collection, processing, analysis and/or submission: Audra DeStefano, Shawn Mengel, Sally Jiao