DOI: 10.5061/dryad.jsxksn0jm
title: Data for: Energetics of substrate transport in proton-dependent oligopeptide transporters

description: The PepTSo transporter mediates the transport of peptides across
 biological membranes. Despite advancements in structural biology,
 including cryogenic electron microscopy structures resolving PepTSo in
 different states, the molecular basis of peptide recognition and transport
 by PepTSo is not fully elucidated. In this study, we used molecular
 dynamics simulations, Markov State Models (MSMs), and Transition Path
 Theory (TPT) to investigate the transport mechanism of an alanine-alanine
 peptide (Ala-Ala) through the PepTSo transporter. Our simulations revealed
 conformational changes and key intermediate states involved in peptide
 translocation. We observed that the presence of the Ala-Ala peptide
 substrate lowers the free energy barriers associated with transition to
 the inward-facing state. We also show a proton transport model and
 analyzed the pharmacophore features of intermediate states, providing
 insights for rational drug design. These findings highlight the
 significance of substrate binding in modulating the conformational
 dynamics of PepTSo and identify critical residues that facilitate
 transport.
description: README: Energetics of substrate transport in proton-dependent oligopeptide
 transporters This dataset includes data from Markov State Model (MSM) and
 Transition Path Theory (TPT) analyses conducted on molecular dynamics
 simulations for this study. The code for generating figures using the
 provided numeric files (NumPy and pickle formats) can be found on GitHub
 at the following link:
 [https://github.com/ShuklaGroup/Selvam_et_al_Communications_Chemistry_2024/tree/main](https://github.com/ShuklaGroup/Selvam_et_al_Communications_Chemistry_2024/tree/main). Descriptions Fig1_Data.zip 1.apo_count_mat.dat: * ``` Count matrix for the apo (without substrate) system. ``` * ``` Represents transition counts between states, used for constructing the MSM. ``` * This data can be viewed using the following commands: * ``` import numpy as np ``` * ``` np.loadtxt("apo_count_mat.dat") ``` 2.apo_data_x.npy: * ``` Array of extracellular distances for the apo (without substrate) system. ``` * ``` Serves as one axis in the free energy landscape. ``` * This data can be viewed using the following commands: * ``` import numpy as np ``` * ``` np.load("apo_data_x.npy", allow_pickle=True) ``` 3.apo_data_y.npy: * ``` Array of intracellular distances for the apo (without substrate) system. ``` * ``` Used as the second axis in the free energy landscape. ``` * This data can be viewed using the following commands: * ``` import numpy as np ``` * ``` np.load("apo_data_y.npy", allow_pickle=True) ``` 4.apo_msm_eq_pop.dat: * ``` Equilibrium populations of MSM states for the apo (without substrate) system. ``` * This data can be viewed using the following commands: * ``` import numpy as np ``` * ``` np.loadtxt("apo_msm_eq_pop.dat") ``` 5.hologates_count_mat.dat: * ``` Count matrix for the holo (with substrate) system. ``` * This data can be viewed using the following commands: * ``` import numpy as np ``` * ``` np.loadtxt("hologates_count_mat.dat") ``` 6.hologates_data_x.npy: * ``` Extracellular distances for the holo (with substrate) system. ``` * ``` Free energy landscape's x-axis data. ``` * This data can be viewed using the following commands: * ``` import numpy as np ``` * ``` np.load("hologates_data_x.npy", allow_pickle=True) ``` 7.hologates_data_y.npy: * ``` Intracellular distances for the holo (with substrate) system. ``` * ``` Free energy landscape's y-axis data. ``` * This data can be viewed using the following commands: * ``` import numpy as np ``` * ``` np.load("hologates_data_y.npy", allow_pickle=True) ``` 8.msm_eq_pop.dat: * ``` Equilibrium populations for the holo (with substrate) system. ``` * This data can be viewed using the following commands: * ``` import numpy as np ``` * ``` np.loadtxt("msm_eq_pop.dat") ``` Fig2_Data.zip 1.holotic_count_mat.dat: * ``` Count matrix from the Markov State Model (MSM) analysis of the holo system of PepTso. ``` * ``` Represents transition counts between states, used for constructing the MSM. ``` * This data can be viewed using the following commands: * ``` import numpy as np ``` * ``` np.loadtxt("holotic_count_mat.dat") ``` 2.holotic_data_x.npy: * ``` Time-independent component 1 (TIC1) for the holo system. ``` * ``` Serves as the x-axis for the projected free energy landscape. ``` * This data can be viewed using the following commands: * ``` import numpy as np ``` * ``` np.load("holotic_data_x.npy", allow_pickle=True) ``` 3.holotic_data_y.npy: * ``` Time-independent component 2 (TIC2) for the holo system. ``` * ``` Serves as the y-axis for the projected free energy landscape. ``` * This data can be viewed using the following commands: * ``` import numpy as np ``` * ``` np.load("holotic_data_y.npy", allow_pickle=True) ``` 4.msm_eq_pop.dat: * ``` Equilibrium populations from the MSM analysis of the holo system. ``` * This data can be viewed using the following commands: * ``` import numpy as np ``` * ``` np.loadtxt("msm_eq_pop.dat") ``` Fig3_Data.zip 1.holo-PepTSo-14dist-4tics-500cls-py.pkl: * Contains discretized trajectories (dtrajs) from the clustering of the holo system of PepTso. * 14dist: Refers to the 14 distance-based features used for tICA analysis. * 4tics: The data was projected onto four Time-Independent Components (TICs). * 500cls: Specifies that the data was clustered into 500 discrete states. The dtrajs represent the mapping of simulation frames to these clusters, used as input for constructing the MSM. * This data can be viewed using the following commands: * ``` import pickle ``` * ``` pickle.load(open("holo-PepTSo-14dist-4tics-500cls-py.pkl",'rb')) ``` 2.MSM-holo-PepTSo-14dist-4tics-500cls-py.pkl: * ``` Stores the Markov State Model (MSM) object constructed for the holo system of PepTso. ``` * ``` 14dist: Refers to the same 14 distance-based features. ``` * ``` 4tics: MSM was built using the data projected onto four TICs. ``` * ``` 500cls: The MSM is based on the 500 clusters defined in the corresponding clustering file. ``` * This data can be viewed using the following commands: * ``` import pickle ``` * ``` pickle.load(open("MSM-holo-PepTSo-14dist-4tics-500cls-py.pkl", 'rb')) ```