Find research datasets worth reusing

opencc-zeroNov 2023View details →

Data from: The role of mutation bias in adaptive molecular evolution: insights from convergent changes in protein function

An underexplored question in evolutionary genetics concerns the extent to which mutational bias in the production of genetic variation influences outcomes and pathways of adaptive molecular evolution. In the genomes of at least some vertebrate taxa, an important form of mutation bias involves changes at CpG dinucleotides: If the DNA nucleotide cytosine (C) is immediately 5' to guanine (G) on the same coding strand, and if the C is methylated, then C→T and G→A mutations occur at an elevated rate relative to mutations at non-CpG sites. Here we examine experimental data from case studies in which it has been possible to identify the causative substitutions that are responsible for adaptive changes in the functional properties of vertebrate hemoglobin (Hb). Specifically, we examine the molecular basis of convergent increases in Hb-O2 affinity in high-altitude birds. Using a data set of experimentally verified, affinity-enhancing mutations in the Hbs of highland avian taxa, we tested whether causative changes are enriched for mutations at CpG dinucleotides relative to the frequency of CpG mutations among all possible missense mutations. The tests revealed that a disproportionate number of causative amino acid replacements were attributable to CpG mutations, demonstrating that mutation bias can influence outcomes of molecular adaptation.

zenodo40/100

Estimates of molecular convergence reveal genes with intermediate pleiotropy underlying adaptive variation across teleost fish

This dataset comprises raw sequence data, output of analyses, code used to reproduce the study, figures, and supplementary materials. Code and input files are in Datasets.zip Use the README to navigate this folder.  Data to reproduce the CSUBST analysis are in the .tar.gz folder.   For more information please check:  https://github.com/agneeshbarua/Teleost_convergence

opencc-by-4.0Jul 2024View details →

opencc-zeroMay 2023View details →

Scripts and sample information for: Molecular mechanisms of Eda-mediated adaptation to freshwater in threespine stickleback

A main goal of evolutionary biology is to understand the genetic basis of adaptive evolution. Although the genes that underlie some adaptive phenotypes are now known, the molecular pathways and regulatory mechanisms mediating the phenotypic effects of those genes often remain a black box. Unveiling this black box is necessary to fully understand the genetic basis of adaptive phenotypes, and to understand why particular genes might be used during phenotypic evolution. Here, we investigated which genes and regulatory mechanisms are mediating the phenotypic effects of the Eda haplotype, a locus responsible for the loss of lateral plates and changes in the sensory lateral line of freshwater threespine stickleback (Gasterosteus aculeatus) populations. Using a combination of RNAseq and a cross design that isolated the Eda haplotype on a fixed genomic background, we found that the Eda haplotype affects both gene expression and alternative splicing of genes related to bone development, neuronal development and immunity. These include genes in conserved pathways, like the BMP, netrin and bradykinin signalling pathways, known to play a role in these biological processes. Furthermore, we found that differentially expressed and differentially spliced genes had different levels of connectivity and expression, suggesting that these factors might influence which regulatory mechanisms are used during phenotypic evolution. Taken together, these results provide a better understanding of the mechanisms mediating the effects of an important adaptive locus in stickleback and suggest that alternative splicing could be an important regulatory mechanism mediating adaptive phenotypes.

publicNov 2023View details →

Data from: The role of mutation bias in adaptive molecular evolution: insights from convergent changes in protein function

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publicMay 2023View details →

Scripts and sample information for: Molecular mechanisms of Eda-mediated adaptation to freshwater in threespine stickleback

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publicNov 2024View details →

Data from: High-resolution chromosome-level genome of Scylla paramamosain provides molecular insights into adaptive evolution in crab

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03_HTMD_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes.  # The forders are organized as: Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, **run_adaptiveMD.py** : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...

06_HTMD_Tunnels: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes.  # The folders are organized as: Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, run_adaptiveMD.py : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...

Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

00_Caver.tar.gz - Contains CAVER (https://caver.cz/fil/download/manual/caver_userguide.pdf) input and output files used for identification of transport pathways in LinB86(PDB ID: 5LKA).  final_clustering ├── tunnel_custers # contains caver output files for individual tunnels clusters  │   ├── ... ├── analysis # contains .csv output files for botttlenecks and tunnels charecteristics of individual tunnels clusters  │   ├── ... 01_CaverDock_Tunnels_Profile.tar.gz - Contains tunnel clusters consisting of the top 100 tunnels and the CaverDock analysis files obtained.  # Each folder (tun_cluster_p1a, tun_cluster_p1b, tun_cluster_p2, tun_cluster_p3) contains input raw files used for CaverDock calculations for individual snapshots of the respective tunnel clusters named as stripped_system*. The details of those files are: - calculations/*/caverdock.conf :  The config file input for caverdock calculation.  - calculations/*/DBE.pdbqt : Input file for the substrate DBE. - calculations/*/stripped_system*.pdbqt : Input file for the Protein/Receptor - calculations/*/stripped_system*.dsd : Tunnel discretization file. Notes:  - calculations/*/stripped_system*.pdb : PDB file for the tunnel.  02_Minimization_and_Equilibration.tar.gz -  Contains input and output files used for AMBER minimization and equilibration of the molecular systems and seed conformations used for adaptive sampling simulations. 03_HTMD_Bulk - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes.  04_HTMD_Cavity - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes. 05_HTMD_Cavity_Bulk - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes.  06_HTMD_Tunnels - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes.  07_MD-Analysis.tar.gz - Contains MD analysis files obtained from 45 micro-seconds adaptive sampling simulations at 310K.  # Each folder contains input raw files used to calculate epochs convergence, distances, RMSD, RMSF, kinetics, percentage, sample proportions and tunnel lengths. The details of those files are: - Epoch_Convergence/epochs_dist_counts.csv :  Contains the counts of DBE distances from the active-site (0-5 Å), tunnel (5-19 Å), and bulk (>19 Å) for the 30 epochs.  -  Distances/*/dist_s_r*.csv : Contains .csv file for the  frames wise for all studied schemes. The analysis were performed using the cpptraj program (https://amber-md.github.io/cpptraj/CPPTRAJ.xhtml). The following columns are present: D107_OD1_DBE_C1    D107_OD2_DBE_C1    D107_OD1_DBE_C2   D107_OD2_DBE_C2    N37_ND2_DBE_Br1   N37_ND2_DBE_Br2   W108_NE1_DBE_Br1   W108_NE1_DBE_Br2  D107_COM_DBE_COM    W108_COM_DBE_COM     N37_COM_DBE_COM    catal_COM_p1aCOM     catal_COM_p1bCOM    catal_COM_p2COM     catal_COM_p3COM     p1aCOM_DBE_COM     p1bCOM_DBE_COM     p2COM_DBE_COM     p3COM_DBE_COM   catal_COM_DBE_COM     p1aCOM_p1bCOM    p1aCOM_p2COM   p1aCOM_p3COM   p1bCOM_p2COM  p1bCOM_p3COM  p2COM_p3COM  - RMSD_RMSF/*/*.csv : Contains .csv files with RMSD and RMSF from the protein residues. For RMSF 1st row are residue number (1-295) and 2nd row are RMSF. For RMSD, 1st column are frame no. (0.1ns) and 2nd column are RMSD values respectively. The calcualtion were performed using pytraj (https://amber-md.github.io/pytraj/latest/index.html) program.             Example input: pytraj.rmsd(traj, mask='1-295@CA')             Example input: pytraj.rmsf(traj, mask=':1-295', options='byres') - COM_RMSF/.xlsx : Contains the center of mass (COM) distances calculated using the bottleneck residues for catalytic residues (N38, D108, W109), p1a (D147, F151, and V173), p1b (D147, W177, and L248), p2 (L211 and L248), and p3 (L143, F151, and I213) - Kinetics/kinetics.txt : Contains .csv file with kinetic information from studied scheme: Cavity, Cavity&Bulk and Tunnels for all the three replicates and calculated average kon, koff, koff/kon rates. - Percentages/.csv : Contains csv files for the percentages of DBE localization and distances from the active-site (0-5 Å), tunnel (5-19 Å), and bulk (>19 Å). - Tunnels_lengths/.csv : Contains tunnel_lengths.csv, Summary of tunnel lengths.xlsx files with lengths of top 100 tunnels snapshots for tunnel clusters p1a, p1b, p2 and p3 in tunnel_lengths.csv and summary of respective tunnel clusters generated from Caver output (for more details please check https://www.caver.cz/fil/download/manual/caver_userguide.pdf with keywork "summary.txt") in the Summary of tunnel lengths.xlsx file.  - Sample_proportions/.csv : Contains sample_proportions.csv file with proportions or fraction individual metastable states while performing the transition pathway analysis. For more details please check https://software.acellera.com/htmd/htmd.kinetics.html  or http://www.emma-project.org/v1.2.1/api/generated/pyemma.msm.flux.pathways.html?highlight=transition%20path  - *.py : Python scripts used to build and analysis Markov state models with use case and distances of ligand. - Generated_models/ : Contains models_rep[].dat files representating the matric data used to build MSM for respective schemes and replicates. The dirs are arranged as below: ├── Cavity │   ├── model_rep1.dat │   ├── model_rep2.dat │   └── model_rep3.dat ├── Cavity_Bulk │   ├── model_rep1.dat │   ├── model_rep2.dat │   └── model_rep3.dat └── Tunnels     ├── model_rep1.dat     ├── model_rep2.dat     └── model_rep3.dat  08_TransportTools.tar.gz - Contains TransportTools (TT) analysis output, log and summary files for Cavity, Cavity&Bulk and Tunnels schemes. For more details on the workflow of TT, please visit https://github.com/labbit-eu/transport_tools results-*_rep0 # results for replicate 1 for given schemes for example cavity, cavity&bulk or tunnels. ├── data │   ├── super_clusters ├──  _internal │   ├── ... ├── statistics │   ├── ... results-*_rep1 # results for replicate 2 for given schemes for example cavity, cavity&bulk or tunnels. ├── data │   ├── super_clusters ├──  _internal │   ├── ... ├── statistics │   ├── ... results-*_rep2 # results for replicate 3 for given schemes for example cavity, cavity&bulk or tunnels. ├── data │   ├── super_clusters ├──  _internal │   ├── ... ├── statistics │   ├── ... - event.csv file contains the aggregated summary of events inferred from the 4-filtered_events_statistics.txt files of each results of respective schemes 09_MSM_states.tar.gz - Contains the Markov state models (MSM) output files for Cavity, Cavity&Bulk and Tunnels schemes and three replicates. The MSMs were generated using the pyEMMA program and HTMD framework, for further details please follow https://software.acellera.com/htmd/documentation.html. The directories looks as below:  ├── Bulk │   ├── rep1 # MSM states for replicate 1 │   ├── rep2 # MSM states for replicate 2 │   ├── rep3 # MSM states for replicate 3 ├── Cavity │   ├── rep1  │   ├── rep2  │   ├── rep3  ├── Cavity&Bulk │   ├── rep1  │   ├── rep2 │   ├── rep3 ├── Tunnels │   ├── rep1  │   ├── rep2 │   ├── rep3 10_MSM_fingerprints.tar.gz - Contains the Markov state models (MSMs) distances generated from repository dir 09_MSM_states consisting the model*.pdb files. The distances were calculated using the cpptraj program of AMBER18 package. - MSM_Distances/*/rep*/*.csv : Contains .csv file for the generated MSM models (0,1,2..). The following columns (calculated distances) are present in the .csv files: D107_OD1_DBE_C1    D107_OD2_DBE_C1    D107_OD1_DBE_C2   D107_OD2_DBE_C2    N37_ND2_DBE_Br1   N37_ND2_DBE_Br2   W108_NE1_DBE_Br1   W108_NE1_DBE_Br2  D107_COM_DBE_COM    W108_COM_DBE_COM     N37_COM_DBE_COM    catal_COM_p1aCOM     catal_COM_p1bCOM    catal_COM_p2COM     catal_COM_p3COM     p1aCOM_DBE_COM     p1bCOM_DBE_COM     p2COM_DBE_COM     p3COM_DBE_COM   catal_COM_DBE_COM     p1aCOM_p1bCOM    p1aCOM_p2COM   p1aCOM_p3COM   p1bCOM_p2COM  p1bCOM_p3COM  p2COM_p3COM  11_ULS_clustering_and_transition_assignments.tar.gz - Contains files for analysis of utilization of the substrate DBE. Each folder contains two types of .csv files: 1. for the transition detection of DBE and the classification in  Bulk (out_), Bottleneck (bt_), Unknown bottleneck (bt_unknown), Inside (in_) and  2. the second type as the charecterization on the tunnels utilization: Tunnel (p1a, p1b, p2, and p3), Mixed and Unknnown. # the details of the file arangements are as below for the studied schemes Bulk, Cavity, Cavity&Bulk and Tunnels: ├── average_tunnel_utilization_per_scheme.png ├── average_tunnel_utilization.png ├── Bulk │   ├── Bulk_run_htmd_0_combined_df.csv │   ├── Bulk_run_htmd_0_transitions_counts.csv │   ├── Bulk_run_htmd_1_combined_df.csv │   ├── Bulk_run_htmd_1_transitions_counts.csv │   ├── Bulk_run_htmd_2_combined_df.csv │   └── Bulk_run_htmd_2_transitions_counts.csv ├── Bulk&Cavity │   ├── Cavity&Bulk_run_htmd_0_combined_df.csv │   ├── Cavity&Bulk_run_htmd_0_transitions_counts.csv │   ├── Cavity&Bulk_run_htmd_1_combined_df.csv │   ├── Cavity&Bulk_run_htmd_1_transitions_counts.csv │   ├── Cavity&Bulk_run_htmd_2_combined_df.csv │   └── Cavity&Bulk_run_htmd_2_transitions_counts.csv ├── Cavity │   ├── Cavity_run_htmd_0_combined_df.csv │   ├── Cavity_run_htmd_0_transitions_counts.csv │   ├── Cavity_run_htmd_1_combined_df.csv │   ├── Cavity_run_htmd_1_transitions_counts.csv │   ├── Cavity_run_htmd_2_combined_df.csv │   └── Cavity_run_htmd_2_transitions_counts.csv ├── parse_distances_msm.py ├── schemes_comparison_piechart_per_scheme.png └── Tunnels     ├── Tunnels_run_htmd_0_combined_df.csv     ├── Tunnels_run_htmd_0_transitions_counts.csv     ├── Tunnels_run_htmd_1_combined_df.csv     ├── Tunnels_run_htmd_1_transitions_counts.csv     ├── Tunnels_run_htmd_2_combined_df.csv     └── Tunnels_run_htmd_2_transitions_counts.csv

05_HTMD_Cavity_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output, and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes.  # The folders are organized as: Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, *run_adaptiveMD.py* : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...

04_HTMD_Cavity: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes.  # The forders are organized as: Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, **run_adaptiveMD.py** : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...

opencc-zeroAug 2022View details →

dryad36/100

Data supporting: Molecular, behavioural and morphological comparisons of sperm adaptations in a fish with alternative mating tactics

In species with alternative reproductive tactics, there is much empirical support that sneaker males have larger testes size and greater sperm numbers. However, support for higher sperm performance by sneakers is inconsistent. We used the sand goby (Pomatoschistus minutus) to test whether sperm performance differed between breeding-coloured males (small testes, but large mucus-filled sperm-duct glands; builds a nest lined with a sperm-containing mucus, provides care) and sneaker-morph males (no breeding colouration, large testes, rudimentary sperm-duct glands; no nest, no care). We compared motility (proportion motile sperm), velocity and longevity of sperm, between the two morphs. Furthermore, we compared gene expression of testes, and tested if the sperm-duct gland contents affected sperm performance, and if sperm morphometrics differed between male morphs. We found a clear difference in gene expression of testes between the male morphs with 109 transcripts differentially expressed between the morphs. Notably, several mucin genes were upregulated in breeding-coloured males and two ATP-related genes were upregulated in sneaker-morph males. There was some evidence of higher sperm velocity in sneaker-morph males, but no difference in sperm motility. Presence of the sperm-duct gland contents significantly increased sperm velocity, but equally so for the two morphs. The same was true for sperm motility although the difference was not significant. The sand goby has remarkably long-lived sperm, with only small or no decline in motility and velocity over time (5 min vs. 22 hours), but again, this was equally true for both morphs. Sperm length (head, tail, total) did not differ between morphs, and did not correlate with sperm velocity for either morph. Thus, other than a clear difference in testes gene expression, we found only modest differences between the two male morphs, confirming previous findings that high sperm performance as an adaptation to sperm competition is not a primary target of evolution.

opencc-by-4.0Mar 2024View details →

Data from: Diversity and molecular evolution of non-visual opsin genes across environmental, developmental, and morphological adaptations in frogs

Dataset for the article Diversity and molecular evolution of non-visual opsin genes across environmental, developmental, and morphological adaptations in frogs. Includes non-visual opsin coding sequences from frogs, sequence alingments, phylogenetics trees, and raw PAML results files.

dryad36/100

Molecular evolution and depth-related adaptations of rhodopsin in the adaptive radiation of cichlid fishes in Lake Tanganyika

The visual sensory system is essential for animals to perceive their environment and is thus under strong selection. In aquatic environments, light intensity and spectrum differ primarily along a depth gradient. Rhodopsin (RH1) is the only opsin responsible for dim-light vision in vertebrates and has been shown to evolve in response to the respective light conditions, including along a water depth gradient in fishes. In this study, we examined the diversity and sequence evolution of RH1 in the virtually entire adaptive radiation of cichlid fishes in Lake Tanganyika, focusing on adaptations to the achromatic environment with respect to depth. We show that Tanganyikan cichlid genomes contain a single copy of RH1. The 76 variable amino acid sites detected in RH1 across the radiation were not uniformly distributed along the protein sequence, and 31 of these variable sites show signals of positive selection. Moreover, the amino acid substitutions at 15 positively selected sites appeared to be depth-related, including three key tuning sites that directly mediate shifts in the peak spectral sensitivity, one site involved in protein stability, and 11 sites that may be functionally important on the basis of their physicochemical properties. Among the strongest candidate sites for deep-water adaptations are two known key tuning sites (positions 292 and 299) and three newly identified variable sites (37, 104 and 290). Our study, which is th first compralehensive analysis of RH1 evolution in a massive adaptive radiation of cichlid fishes, provides novel insights into the evolution of RH1 in a freshwater environment.

opencc-zeroApr 2022View details →

dryad36/100

TE invasion fuels molecular adaptation in laboratory populations of Drosophila melanogaster

Transposable elements are mobile genetic parasites that frequently invade new host genomes through horizontal transfer. Invading TEs often exhibit a burst of transposition, followed by reduced transposition rates as repression evolves in the host. We recreated the horizontal transfer of P-element DNA transposons into a D. melanogaster host and followed the expansion of TE copies and evolution of host repression in replicate laboratory populations reared at different temperatures. We observed that while populations maintained at high temperatures rapidly go extinct after TE invasion, those maintained at lower temperatures persist, allowing for TE spread and the evolution of host repression. We also surprisingly discovered that invaded populations experienced recurrent insertion of P-elements into a specific long non-coding RNA, lncRNA:CR43651, and that these insertion alleles are segregating at unusually high frequency in experimental populations, indicative of positive selection. We propose that, in addition to driving the evolution of repression, transpositional bursts of invading TEs can drive molecular adaptation.

opencc-zeroJan 2023View details →

opencc-by-4.0Feb 2023View details →

Main text figure data and scripts for "Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach"

(as README.txt): Main text figure data and scripts for “Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach”, by Tarun Gera, Lipeng Chen, Alex Eisfeld, Jeffrey R. Reimers, Elliot J. Taffet and Doran I. G. B. Raccah. Each directory is dedicated to a particular figure published in the paper. In each directory there are sub-directories which contains the data plotted in each panel. Each data file is a 2-D list in the format of (x,y) for each plot. There are python scripts (Fig_X.py) in each directory to plot the data. Table of contents: Figure_2:     - 4_site_edge_contri.npy: Calculated edge sites contribution to the total absorption spectrum for a 4-site chain system v/s energy.      - 4_site_inner_contri.npy: Calculated inner sites contribution to the total absorption spectrum for a 4-site chain system v/s energy.      - 4_site_total_spectra.npy: Calculated total absorption spectrum for a 4-site chain system v/s energy.   Figure_3: Panel A:          - Mean_Error_Edge.npy: Mean error for the edge case v/s number of trajectories.     - Mean_Error_Inner.npy: Mean error for the inner case v/s number of trajectories.     - Mean_Error_SS.npy: Mean error for a single site initial condition v/s number of trajectories.     - Mean_Error_GD.npy: Mean error for a 4-site chain system with Gaussian distributed site energies v/s number of trajectories. Panel B:      - Scaled_error_SS.npy:  Mean error for a single site initial condition normalized by the square-root of one v/s number of trajectories.     - Scaled_error_PS.npy:  Mean error for a pair site initial condition normalized by the square-root of two v/s number of trajectories.     - Scaled_error_AS.npy:  Mean error for an all site initial condition normalized by the square-root of four v/s number of trajectories. Figure_4:  Panel_A:     - List_Error.npy: Calculated mean error for a 4-site chain for a set of auxiliary error bounds. Panel_B:     - Cw_4S_HOPS.npy: Absorption spectrum for a 4-site chain calculated using dyadic HOPS v/s energy.     - Cw_4S_DadHOPS.npy: Absorption spectrum for a 4-site chain calculated using DadHOPS v/s energy. Panel_C:      - Cw_12S_DadHOPS.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS without including state adaptivity v/s energy.     - Cw_12S_DadHOPS_SA.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS with state adaptivity v/s energy. Panel_D:     - Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-pigment system.     - Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-pigment system.     - N_states_DadHOPS.npy: Number of site states required to run a DadHOPS calculation for each N-pigment system.     - N_states_HOPS.npy: Number of site states required to run a dyadic HOPS calculation for each N-pigment system.      Figure_5:      Panel_C:      - PSI_Cw_HEOM.npy: PSI absorption spectrum calculated using HEOM v/s energy.     - PSI_Cw_HOPS.npy: PSI absorption spectrum calculated using dyadic HOPS v/s energy. Panel_D:     - PSI_Error_Random.npy: Calculated mean error, where clusters of 4 were assigned randomly v/s number of trajectories.     - PSI_Error_Coupling.npy: Calculated mean error, where clusters of 4 were assigned based on electronic coupling values v/s number of trajectories. Figure_6: Panel_A:     - PBI_Exp_data_dil.npy: Experimental data for a dilute solution of PBI v/s energy.     - PBI_Cw_DadHOPS_300.npy: Calculated spectrum for a PBI monomer with the spread in static disorder of value 300 cm^{-1} v/s energy.     - PBI_Cw_DadHOPS_400.npy:: Calculated spectrum for a PBI monomer with the spread in static disorder of value 400 cm^{-1} v/s energy. Panel_B:     - PBI_Exp_data_conc.npy: Experimental data for a concentrated solution of PBI v/s energy.     - PBI_trimer_Cw_DadHOPS.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy. Panel_C:      - Cw_PBI_monomer.npy: Calculated spectrum for a PBI monomer using DadHOPS v/s energy.     - Cw_PBI_dimer.npy: Calculated spectrum for a PBI dimer using DadHOPS v/s energy.     - Cw_PBI_trimer.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.     - Cw_PBI_heptamer.npy: Calculated spectrum for a PBI heptamer using DadHOPS v/s energy.     - Cw_PBI_1000mer.npy: Calculated spectrum for a PBI 1000mer using DadHOPS v/s energy. Panel_D:     - peak_00_position.npy: relative position of the 00 peak for different number of pigments.     - peak_00_position_1000.npy: relative position of the 0,0 peak for a system with 1000 pigments. (Single value file)     - peak_I_ratio.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for different number of pigments.     - peak_I_ratio_1000.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for a system with 1000 pigments. (Single value file) Figure_7:     - PBI_N_states_DadHOPS.npy: Number of states required to run a DadHOPS calculation for each N-PBI molecules system.       - PBI_Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-PBI molecules system.       - PBI_Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-PBI molecules system.   The packaged scripts may be run with Python 3.10 and the associated versions of the os, numpy, and matplotlib packages.

opencc-by-4.0Mar 2023View details →

Data for manuscript "Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting"

The tar file contains the input files for RADICAL augmented MDFF implementation (R-MDFF) for two protein systems, Adenylate Kinase (ADK) and Carbon Monoxide Dehydrogenase (CODH). These examples demonstrate the implementation of R-MDFF using RADICAL-Cybertools to flexibly fit biomolecules in cryo-EM density maps with on-the-fly decision making. All molecular simulations were performed using CUDA enabled NAMD 2.14 installed on OLCF Summit HPC resource. The CHARMM36 force field parameters were used for the proteins. Synthetic density maps were prepared at 1.8, 3 and 5 Å for ADK and 1.8 and 3 Å for CODH using VMD 1.9.3 software installed on OLCF Summit HPC resource. During the analysis stage, the cross correlation coefficients between density maps and atomic model were computed using VMD 1.9.3 on Summit HPC as part of the R-MDFF workflow. The source code is publicly available on GitHub: <a href="https://github.com/radical-collaboration/MDFF-EnTK">https://github.com/radical-collaboration/MDFF-EnTK </a> The preprint of this research is submitted on bioRxiv, doi: <a href="https://doi.org/10.1101/2021.12.07.471672">https://doi.org/10.1101/2021.12.07.471672 </a> To obtain maximum compression of the data, the tar command used to generate this tarball was: <pre><code class="language-bash">GZIP=-9 tar --exclude='last.pdb' --exclude='*last_from_prev_iter.pdb' --exclude='*old' --exclude='*log' --exclude='*coor' --exclude='*vel' --exclude='*xsc' --exclude='*dcd' --exclude='lastframepdbs_fix' --exclude='*out' --exclude='*sl' --exclude='*rs' --exclude='*prof' --exclude='*err' --exclude='*dx' --exclude='*grid.pdb' --exclude='*txt' -cvzf rmdffv2.tar.gz rmdff-zenodo/</code></pre>