Mechanistic basis for enhanced strigolactone sensitivity in KAI2 triple mutant
Data files
May 08, 2025 version files 1.02 GB
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Mutant_Adaptive_Sampling.zip
12.75 MB
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Mutant_Exploration.zip
503.19 MB
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pyemma.yml
4.89 KB
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README.md
6.06 KB
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WT_Adaptive_Sampling.zip
11.54 MB
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WT_Exploration.zip
493.93 MB
Abstract
Striga hermonthica is a parasitic weed that destroys billions of dollars’ worth of staple crops every year. Its rapid proliferation stems from an enhanced ability to metabolize strigolactones (SLs), plant hormones that direct root branching and shoot growth. Striga’s SL receptor, ShHTL7, bears more similarity to the staple crop karrikin receptor KAI2 than to SL receptor D14, though KAI2 variants in plants like Arabidopsis thaliana show minimal SL sensitivity. Recently, studies have indicated that a small number of point mutations to HTL7 residues can confer SL sensitivity to AtKAI2. Here, we analyze both wild-type AtKAI2 and SL-sensitive mutant Var64 through all-atom, long-timescale molecular dynamics simulations to determine the effects of these mutations on receptor function at a molecular level. We demonstrate that the mutations stabilize SL binding by about 2 kcal/mol. They also result in a doubling of the average pocket volume and eliminate the dependence of binding on certain pocket conformational arrangements. While the probability of certain non-binding SL-receptor interactions increases in the mutant compared with the wild-type, the rate of binding also increases by a factor of ten. All these changes account for the increased SL sensitivity in mutant KAI2 and suggest mechanisms for increasing functionality of host crop SL receptors.
Dataset DOI: 10.5061/dryad.wh70rxx0j
Description of the data and file structure
The repository contains all of the features data (.npy files), featurization scripts (.py) and scripts used to plot the figures in the manuscript: Mechanistic Basis for Enhanced Strigolactone Sensitivity in KAI2 Triple Mutant.
Files and variables
File: WT_Adaptive_Sampling.zip
Description: This zip folder contains the information for rounds 1-3 using adaptive sampling with K-means clustering to explore the landscape for WT KAI2. Within this zip folder is 3 folders:
Featurescontains the*.tar.gzfeatures files for Mutant and Ligand Features in.npyform.- Mutant features contain 615
.npyfiles of Residue pair 153-190, 157-190, and 153-157 distances. - Ligand features are 1025
.npyfiles of all pairs of Residues 2345, 2406, 2490, A-ring, D-ring, and GR24 distances.
- Mutant features contain 615
Analysiscontains all.pyfiles used to generate the.npyfiles inFeatures.Plottingcontains all.pyfiles used to generate the Figures for the manuscript and supporting information.
File: WT_Exploration.zip
Description: This zip folder contains the information for the 4th extensive round of simulations for WT KAI2. Within this zip folder is 3 folders:
Featurescontains the*.tar.gzfeatures files for Mutant and Ligand Features in.npyform.- CatTriad features are 4,269
.npyfiles on catalytic triad contact distances. - DLoopLig features are 8,538
.npyfiles on D-loop vs. Ligand contact distances. - Ligand features are 5,692
.npyfiles of Ligand distances from the binding pocket features. - Mutations features are 4,269
.npyfiles on contact distances between key mutations in the protein. - T1T2 features are 4,269
.npyfiles on T1 vs T2 helix contact distances. - DLoop features are 8,538
.npyfiles on inter D-loop contact distances. - F26 features are 4,269
.npyfiles on contact distances between many key residues and the F26 residue. - Ligand2 features are 4,269
.npyfiles on additional relevant contacts, primarily between the T1T2 helices and the ligand GR24. - Pocket features are 2,846
.npyfiles on contact distances within the protein binding pocket. - T1T2Lig features are 17,076
.npyfiles on contact distances between the ligand and the T1 and T2 helices of the protein.
- CatTriad features are 4,269
Analysiscontains all.pyfiles used to generate the.npyfiles inFeatures.Plottingcontains all.pyfiles used to generate the Figures for the manuscript and supporting information.
File: Mutant_Adaptive_Sampling.zip
Description: This zip folder contains the information for rounds 1-3 using adaptive sampling with K-means clustering to explore the landscape for Mutant KAI2. Within this zip folder is 3 folders:
Featurescontains the*.tar.gzfeatures files for Mutant and Ligand Features in.npyform.- Mutant features contain 615
.npyfiles of Residue pair 153-190, 157-190, and 153-157 distances. - Ligand features are 1025
.npyfiles of all pairs of Residues 95, 2345, 2401, 2891, A-ring, D-ring, and GR24 distances.
- Mutant features contain 615
Analysiscontains all.pyfiles used to generate the.npyfiles inFeatures.Plottingcontains all.pyfiles used to generate the Figures for the manuscript and supporting information.
File: Mutant_Exploration.zip
Description: This zip folder contains the information for the 4th extensive round of simulations for Mutant KAI2. Within this zip folder is 3 folders:
Featurescontains the*.tar.gzfeatures files for Mutant and Ligand Features in.npyform.- CatTriad features are 4,377
.npyfiles on catalytic triad contact distances. - DLoopLig features are 8,754
.npyfiles on D-loop vs. Ligand contact distances. - Ligand features are 5,8636
.npyfiles of Ligand distances from the binding pocket features. - Mutations features are 4,377
.npyfiles on contact distances between key mutations in the protein. - T1T2 features are 4,377
.npyfiles on T1 vs T2 helix contact distances. - DLoop features are 8,754
.npyfiles on inter D-loop contact distances. - F26 features are 4,377
.npyfiles on contact distances between many key residues and the F26 residue. - Ligand2 features are 4,377
.npyfiles on additional relevant contacts, primarily between the T1T2 helices and the ligand GR24. - Pocket features are 2,918
.npyfiles on contact distances within the protein binding pocket. - T1T2Lig features are 17,508
.npyfiles on contact distances between the ligand and the T1 and T2 helices of the protein.
- CatTriad features are 4,377
Analysiscontains all.pyfiles used to generate the.npyfiles inFeatures.Plottingcontains all.pyfiles used to generate the Figures for the manuscript and supporting information.
File: Pyemma.yml
Description: This .yml file creates a conda environment with the necessary package versions to execute all .py files provided within the zip folders. Assuming the user has conda, to generate the environment please execute: conda env create -f pyemma.yml -n my_custom_env_name where my_custom_environ_name is the name you would like for the conda environment.
Code/software
All code provided can be executed via the terminal using python {code}.py. For the exact software required to execute the code, a pyemma.yml file of the conda environment used has been provided with the dataset.
To extract the data (and print to screen) from a numpy .npy file, an example code execution in python would be:
import numpy as np
data = np.load (‘path/to/numpy_file.npy’, allow_pickle=True)
print(data)
Access information
Other publicly accessible locations of the data:
- All stripped trajectories for this manuscript are available on Box.
