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Data from: Ensemble prediction enables computational rescue of Tribolium castaneum cysteine peptidase denaturation

Published by Agricultural Research Service | Department of Agriculture | Catalog Last Checked: August 01, 2026 at 04:50 AM | Dataset Last Updated: May 22, 2026
This dataset presents modeled and simulated structures of a cysteine peptidase enzyme from Tribolium castaneum (red flour beetle), which participates in gluten digestion. The molecular structures were computationally generated using prediction tools (e.g., RoseTTAFold2) and molecular dynamics simulation software (i.e., AMBER24). This dataset provides a worked example of how multiple sequence alignment (MSA) subsampling can predict protein conformational ensembles towards protein engineering goals at the molecular level. MSA subsampling was used to develop a hypothesis for protein denaturation; determine a collective variable for measuring extent of denaturation; and then be used as a data propagator with molecular simulation to aid the in silico design of anti-aggregation mutations. Overall, this work highlights how predicted ensembles of proteins can (1) be made more interpretable and (2) be augmented with conventional molecular biophysics workflows.Metadata files that describe the simulation sampling schemes; model settings; molecular system; directory architectures used; data processing workflows; and motivation for presented simulations and analyses, are prepared. This work serves as a proof of concept for computational engineering of insect enzymes to potentially modify agricultural commodities for improved food safety. Beyond its application to food safety, the denaturation mechanism proposed for the red flour beetle cysteine peptidase in this study has potential to drive related cysteine peptidase protein engineering campaigns for additional agricultural research.This is a reduced version of the dataset to be used for reproducing publication results. This version is lacking certain PDB and XTC trajectory files. Full access to the entire data repository (~410 GB) from SCINet resources is available upon request.This research used resources provided by the SCINet project and/or the AI Center of Excellence of the USDA Agricultural Research Service, ARS project numbers 0201-88888-003-000D and 0201-88888-002-000D.DIRECTORY TREE- FinalFigureFiles/ # Final figure files used in the manuscript submission- MainText_Fig1/ # Predicted native state for mature T. castaneum cathepsin- MainText_Fig2/ # TC011001-A116 MSA subsampling conformational ensemble- MainText_Fig3/ # Thermal denaturation simulations- MainText_Fig4/ # Workflow for rationalizing acid-stable TC011001-A116 variants- MainText_Fig5/ # Protein-peptide docking and cofolding- MainText_Fig6/ # MSA subsampling as a computational rescue assay- MainText_Table1/ # Melting simulations for in silico validation of anti-aggregation variants- Movies/ # Movies of MD trajectories, morphs between metastable states, and spin movies of docked structures- SuppMat_Fig1/ # MSA subsampling pilot study- SuppMat_Fig2/ # Independent component analysis of conformational ensemble- SuppMat_Fig4/ # Representative structures from MSA subsampling, ReFOLD simulations- SuppMat_Fig5/ # Active site integrity versus fraction helicity (Hf)- SuppMat_Fig6/ # Hydration site water shell density- SuppMat_Fig7/ # Normal mode analysis on metastable states from denaturation landscape- SuppMat_Table2/ # Variant fasta sequencesFILE TYPES+ Molecular Models: .pdb, .cif+ Simulation Topology: .prmtop+ Simulation Coordinates: .rst, .ncrst+ Simulation Trajectories: .dcd, .xtc+ Codes: .sh (bash), .py (python), .cxc (Chimera file), .in (input script for compiled/executable software), .ipynb (jupyter notebook)+ Data files: .npy (numpy array), .pkl (pickle file), .dat (dat file), .csv (comma-separated files), .xlsx (Excel Spreadsheet)+ Text/notes: .md (README), .txt (plain text files)SAMPLING SCHEME OVERVIEW+ All simulations contained herein are either protein structure predictions or classical molecular dynamics.REQUIRED SOFTWARE+ Molecular Dynamics Simulations:- AMBER24 https://ambermd.org/- GROMACS 2024 https://www.gromacs.org/+ Protein Structure Prediction, Structural Analysis, and Visualization:- ColabFold https://github.com/sokrypton/colabfold- RoseTTAFold2 https://github.com/uw-ipd/RoseTTAFold2-- For issues with installation, see: https://github.com/uw-ipd/RoseTTAFold2/issues/25- TM-Align https://aideepmed.com/TM-align/- Phenix https://www.phenix-online.org/- chai1 https://github.com/chaidiscovery/chai-lab/tree/main- PatchMAN https://github.com/Furman-Lab/PatchMAN- Rosetta https://rosettacommons.org/- ChimeraX https://www.cgl.ucsf.edu/chimerax/- Molecular Operating Environment (MOE) https://www.chemcomp.com/en/Products.htm- Visual Molecular Dynamics (VMD) https://github.com/Furman-Lab/PatchMAN+ Multiple Sequence Alignment and Sequence Database Curation:- hmmer http://hmmer.org/- seqkit https://github.com/shenwei356/seqkit- FAMSA https://github.com/refresh-bio/FAMSA- T-COFFEE https://tcoffee.crg.eu/- trimAl https://vicfero.github.io/trimal/- AF_Cluster https://github.com/HWaymentSteele/AF_Cluster- Promals3D http://prodata.swmed.edu/promals3d/promals3d.php- SSBondPre https://github.com/gao666999/SSBONDPredict- YOSSHI https://biokinet.belozersky.msu.ru/yosshi+ Analysis Software for Informing Mutation Design:- DSSP https://ssbio.readthedocs.io/en/latest/instructions/dssp.html- ENCoM https://github.com/NRGlab/ENCoM- I-Mutant https://folding.biofold.org/i-mutant/i-mutant2.0.html- PyRosetta https://www.pyrosetta.org/- PLIP https://github.com/pharmai/plip?tab=readme-ov-fileYAML/CONDA ENV INFOA yml file `repo_env_py39.yml` is provided to create a python 3.9 environment that can perform all calculations except I-Mutant ddG calculations (this requires python=2.7). The environment contents were also listed into `repo_env_py39.txt`. For line by commands on creating the conda environment, see `cathepsin_conda_environment_instructions.txt`. NOTE that the conda environment provided cannot perform ColabFold, RoseTTAFold2, or chai1 protein structure prediction

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Complete Metadata
@type dcat:Dataset
accessLevel public
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contactPoint 
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description <p dir="ltr">This dataset presents modeled and simulated structures of a cysteine peptidase enzyme from Tribolium castaneum (red flour beetle), which participates in gluten digestion. The molecular structures were computationally generated using prediction tools (e.g., RoseTTAFold2) and molecular dynamics simulation software (i.e., AMBER24). This dataset provides a worked example of how multiple sequence alignment (MSA) subsampling can predict protein conformational ensembles towards protein engineering goals at the molecular level. MSA subsampling was used to develop a hypothesis for protein denaturation; determine a collective variable for measuring extent of denaturation; and then be used as a data propagator with molecular simulation to aid the in silico design of anti-aggregation mutations. Overall, this work highlights how predicted ensembles of proteins can (1) be made more interpretable and (2) be augmented with conventional molecular biophysics workflows.</p><p dir="ltr">Metadata files that describe the simulation sampling schemes; model settings; molecular system; directory architectures used; data processing workflows; and motivation for presented simulations and analyses, are prepared. This work serves as a proof of concept for computational engineering of insect enzymes to potentially modify agricultural commodities for improved food safety. Beyond its application to food safety, the denaturation mechanism proposed for the red flour beetle cysteine peptidase in this study has potential to drive related cysteine peptidase protein engineering campaigns for additional agricultural research.</p><p dir="ltr">This is a reduced version of the dataset to be used for reproducing publication results. This version is lacking certain PDB and XTC trajectory files. Full access to the entire data repository (~410 GB) from SCINet resources is available upon request.</p><p dir="ltr">This research used resources provided by the SCINet project and/or the AI Center of Excellence of the USDA Agricultural Research Service, ARS project numbers 0201-88888-003-000D and 0201-88888-002-000D.</p><p dir="ltr">DIRECTORY TREE</p><p dir="ltr">- FinalFigureFiles/ # Final figure files used in the manuscript submission</p><p dir="ltr">- MainText_Fig1/ # Predicted native state for mature T. castaneum cathepsin</p><p dir="ltr">- MainText_Fig2/ # TC011001-A116 MSA subsampling conformational ensemble</p><p dir="ltr">- MainText_Fig3/ # Thermal denaturation simulations</p><p dir="ltr">- MainText_Fig4/ # Workflow for rationalizing acid-stable TC011001-A116 variants</p><p dir="ltr">- MainText_Fig5/ # Protein-peptide docking and cofolding</p><p dir="ltr">- MainText_Fig6/ # MSA subsampling as a computational rescue assay</p><p dir="ltr">- MainText_Table1/ # Melting simulations for in silico validation of anti-aggregation variants</p><p dir="ltr">- Movies/ # Movies of MD trajectories, morphs between metastable states, and spin movies of docked structures</p><p dir="ltr">- SuppMat_Fig1/ # MSA subsampling pilot study</p><p dir="ltr">- SuppMat_Fig2/ # Independent component analysis of conformational ensemble</p><p dir="ltr">- SuppMat_Fig4/ # Representative structures from MSA subsampling, ReFOLD simulations</p><p dir="ltr">- SuppMat_Fig5/ # Active site integrity versus fraction helicity (Hf)</p><p dir="ltr">- SuppMat_Fig6/ # Hydration site water shell density</p><p dir="ltr">- SuppMat_Fig7/ # Normal mode analysis on metastable states from denaturation landscape</p><p dir="ltr">- SuppMat_Table2/ # Variant fasta sequences</p><p dir="ltr">FILE TYPES</p><p dir="ltr">+ Molecular Models: .pdb, .cif</p><p dir="ltr">+ Simulation Topology: .prmtop</p><p dir="ltr">+ Simulation Coordinates: .rst, .ncrst</p><p dir="ltr">+ Simulation Trajectories: .dcd, .xtc</p><p dir="ltr">+ Codes: .sh (bash), .py (python), .cxc (Chimera file), .in (input script for compiled/executable software), .ipynb (jupyter notebook)</p><p dir="ltr">+ Data files: .npy (numpy array), .pkl (pickle file), .dat (dat file), .csv (comma-separated files), .xlsx (Excel Spreadsheet)</p><p dir="ltr">+ Text/notes: .md (README), .txt (plain text files)</p><p dir="ltr">SAMPLING SCHEME OVERVIEW</p><p dir="ltr">+ All simulations contained herein are either protein structure predictions or classical molecular dynamics.</p><p dir="ltr">REQUIRED SOFTWARE</p><p dir="ltr">+ Molecular Dynamics Simulations:</p><p dir="ltr">- AMBER24 https://ambermd.org/</p><p dir="ltr">- GROMACS 2024 https://www.gromacs.org/</p><p dir="ltr">+ Protein Structure Prediction, Structural Analysis, and Visualization:</p><p dir="ltr">- ColabFold https://github.com/sokrypton/colabfold</p><p dir="ltr">- RoseTTAFold2 https://github.com/uw-ipd/RoseTTAFold2</p><p dir="ltr">-- For issues with installation, see: https://github.com/uw-ipd/RoseTTAFold2/issues/25</p><p dir="ltr">- TM-Align https://aideepmed.com/TM-align/</p><p dir="ltr">- Phenix https://www.phenix-online.org/</p><p dir="ltr">- chai1 https://github.com/chaidiscovery/chai-lab/tree/main</p><p dir="ltr">- PatchMAN https://github.com/Furman-Lab/PatchMAN</p><p dir="ltr">- Rosetta https://rosettacommons.org/</p><p dir="ltr">- ChimeraX https://www.cgl.ucsf.edu/chimerax/</p><p dir="ltr">- Molecular Operating Environment (MOE) https://www.chemcomp.com/en/Products.htm</p><p dir="ltr">- Visual Molecular Dynamics (VMD) https://github.com/Furman-Lab/PatchMAN</p><p dir="ltr">+ Multiple Sequence Alignment and Sequence Database Curation:</p><p dir="ltr">- hmmer http://hmmer.org/</p><p dir="ltr">- seqkit https://github.com/shenwei356/seqkit</p><p dir="ltr">- FAMSA https://github.com/refresh-bio/FAMSA</p><p dir="ltr">- T-COFFEE https://tcoffee.crg.eu/</p><p dir="ltr">- trimAl https://vicfero.github.io/trimal/</p><p dir="ltr">- AF_Cluster https://github.com/HWaymentSteele/AF_Cluster</p><p dir="ltr">- Promals3D http://prodata.swmed.edu/promals3d/promals3d.php</p><p dir="ltr">- SSBondPre https://github.com/gao666999/SSBONDPredict</p><p dir="ltr">- YOSSHI https://biokinet.belozersky.msu.ru/yosshi</p><p dir="ltr">+ Analysis Software for Informing Mutation Design:</p><p dir="ltr">- DSSP https://ssbio.readthedocs.io/en/latest/instructions/dssp.html</p><p dir="ltr">- ENCoM https://github.com/NRGlab/ENCoM</p><p dir="ltr">- I-Mutant https://folding.biofold.org/i-mutant/i-mutant2.0.html</p><p dir="ltr">- PyRosetta https://www.pyrosetta.org/</p><p dir="ltr">- PLIP https://github.com/pharmai/plip?tab=readme-ov-file</p><p dir="ltr">YAML/CONDA ENV INFO</p><p dir="ltr">A yml file `repo_env_py39.yml` is provided to create a python 3.9 environment that can perform all calculations except I-Mutant ddG calculations (this requires python=2.7). The environment contents were also listed into `repo_env_py39.txt`. For line by commands on creating the conda environment, see `cathepsin_conda_environment_instructions.txt`. NOTE that the conda environment provided cannot perform ColabFold, RoseTTAFold2, or chai1 protein structure prediction</p><p dir="ltr"><br></p>
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identifier 10.15482/USDA.ADC/31964613.v1
keyword 
[
  "cysteine peptidase family",
  "food allergy",
  "molecular dynamics simulation",
  "molten globule",
  "multiple sequence alignment subsampling",
  "papain",
  "protein surface hydrophobicity",
  "rational design",
  "source code"
]
license https://creativecommons.org/publicdomain/zero/1.0/
modified 2026-05-22
programCode 
[
  "005:040"
]
publisher 
{
  "name": "Agricultural Research Service",
  "@type": "org:Organization"
}
temporal 2024-03-01/2026-04-08
title Data from: Ensemble prediction enables computational rescue of Tribolium castaneum cysteine peptidase denaturation

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