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88 results for “dynamic targets”
Small molecules targeting the structural dynamics of AR-V7 partially disordered protein using deep learning and physics based models.
<p>Partially disordered proteins can contain both stable and unstable secondary structure segments and are involved in various (mis)functions in the cell. The extensive conformational dynamics of partially disordered proteins scaling with extent of disorder and length of the protein hampers the efficiency of traditional experimental and in-silico structure-based drug discovery approaches. Therefore new efficient paradigms in drug discovery taking into account conformational ensembles of proteins need to emerge. In this study, using as a test case the AR-V7 transcription factor splicing variant related to prostate cancer, we present an automated methodology that can accelerate the screening of small molecule binders targeting partially disordered proteins. By swiftly identifying the conformational ensemble of AR-V7, and reducing the dimension of binding-sites by a factor of 90 by applying appropriate physicochemical filters, we combine physics based molecular docking and multi-objective classification machine learning models that speed up the screening of thousands of compounds targeting AR-V7 multiple binding sites. Our method not only identifies previously known binding sites of AR-V7, but also discovers new ones, as well as increases the multi-binding site hit-rate of small molecules by a factor of 17 compared to naive physics-based molecular docking. </p>
Dynamic robotic tracking of underwater targets using reinforcement learning
To realize the potential of autonomous underwater robots that scale up our observational capacity in the ocean, new approaches and techniques are needed. Fleets of autonomous robots could be used to study complex marine systems and animals with either new imaging configurations or by tracking tagged animals to study their behavior. These activities can then inform and create new policies for community conservation. The role of animal connectivity via active movement of animals represents a major knowledge gap related to the distribution of deep ocean populations. Tracking underwater targets represents a major challenge for observing biological processes in situ, and methods to robustly respond to a changing environment during monitoring missions are needed. Analytical techniques for optimal sensor placement and path planning to locate underwater targets are not straightforward in such cases. The aim of this study is to investigate the use of deep reinforcement learning as a tool for range-only underwater target tracking optimization, whose promising capabilities have been demonstrated in terrestrial scenarios. To evaluate its usefulness, a reinforcement learning method was implemented as a path planning system for an autonomous surface vehicle while tracking an underwater mobile target. A complete description of an open-source model, performance metrics in simulated environments, and evaluated algorithms based on more than 15 hours of at-sea field experiments are presented. These efforts demonstrate that deep reinforcement learning is a powerful approach that enhances the abilities of autonomous robots in the ocean and encourages the deployment of algorithms like these for monitoring marine biological systems in the future.
Dynamic robotic tracking of underwater targets using reinforcement learning
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'CellTrajectory' for cellular automata modelling of leukaemic stem cell dynamics in acute myeloid leukaemia: insights into predictive outcomes and targeted therapies
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'Biosim' for cellular automata modelling of leukaemic stem cell dynamics in acute myeloid leukaemia: insights into predictive outcomes and targeted therapies
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Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of melanoma
<p>Processed data:</p><p>inferCNV_output_WM4237.zip, inferCNV_output_WM4007.zip - InferCNV-derived CNV profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>ad_all_human_clustered_cnv_WM4237.h5ad, ad_all_human_clustered_cnv_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of the inferCNV-derived CNV profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>ad_all_human_clustered_im_st_WM4237.h5ad, ad_all_human_clustered_im_st_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of imaging and nuclear morphometric features (output of STQ pipeline) for 12 Visium samples of model WM4237 or WM4007. "_im" for imaging, "_st" for Visium.</p><p>ad_all_human_clustered_im_ad_WM4237_m.h5ad, ad_all_human_clustered_im_ad_WM4007_m.h5ad - AnnData object containing integration, dimensionality reduction and clustering of imaging and nuclear morphometric features (output of STQ pipeline) for additional H&E slides (non-Visium) of model WM4237 or WM4007. "_im" for imaging, "_m" for Macenko normalization of the H&E slides with STQ.</p><p>ids_WM4237_AD.txt, ids_WM4007_AD.txt, ids_WM4237_ST.txt, ids_WM4237_ST.txt - list of identifiers of all samples (tissue sections).</p><p>ad_all_human_clustered_st_WM4237.h5ad, ad_all_human_clustered_st_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of RNA profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>CNV_burden_WM4237.csv, CNV_burden_WM4007.csv - Per-spot values of InferCNV-derived CNV burden for samples of models WM4237 or WM4007.</p><p>ad_all_scaled_filtered_st_WM4237.h5ad, ad_all_scaled_filtered_st_WM4007.h5ad - AnnData objects containing concatenated and pre-processed samples of WM4237 or WM4007.</p><p>WM4237_3_AD_m-Imaging-STQ.zip, WM4007_3_AD_m-Imaging-STQ.zip - Imaging portion of STQ pipeline output derived from additional (non-Visium) H&E-stained tissue sections of model WM4237 or WM4007.</p><p>WM4237_ST-Imaging-STQ.tar.gz, WM4007_ST-Imaging-STQ.tar.gz - Imaging portion of STQ pipeline output derived from Visium H&E-stained tissue sections of model WM4237 or WM4007.</p><p>WM4237-ST-downstream-output.tar.gz, WM4007-ST-downstream-output.tar.gz - ST-downstream-processing pipeline output for Visium samples of model WM4237 or WM4007.</p><p>WM4237-STQ-sequencing.tar.gz, WM4007-STQ-sequencing.tar.gz - RNA sequencing portion of STQ pipeline output derived from Visium samples of model WM4237 or WM4007.</p><p>rna-pseudotime-ordered.zip - Pseudotime ordering of RNA profiles of spots for each time point (T0, T1, T2, T3, T4, TC) for samples of models WM4237 and WM4007.</p>
Dynamic targeting enables domain-general inhibitory control over action and thought by the prefrontal cortex (data & code)
<p><strong>Data and code for:</strong></p> <p>Apšvalka, D., Ferreira, C. S., Schmitz, T. W., Rowe, J. B., & Anderson, M. C. (2022). Dynamic targeting enables domain-general inhibitory control over action and thought by the prefrontal cortex. <em>Nature Communications, </em> <strong>13, </strong>274<em>.</em> <a href="https://doi.org/10.1038/s41467-021-27926-w"> https://doi.org/10.1038/s41467-021-27926-w</a></p> <blockquote> <p>Over the last two decades, inhibitory control has featured prominently in accounts of how humans and other organisms regulate their behaviour and thought. Previous work on how the brain stops actions and thoughts, however, has emphasised distinct prefrontal regions supporting these functions, suggesting domain-specific mechanisms. Here we show that stopping actions and thoughts recruits common regions in the right dorsolateral and ventrolateral prefrontal cortex to suppress diverse content, via dynamic targeting. Within each region, classifiers trained to distinguish action-stopping from action-execution also identify when people are suppressing their thoughts (and vice versa). Effective connectivity analysis reveals that both prefrontal regions contribute to action and thought stopping by targeting the motor cortex or the hippocampus, depending on the goal, to suppress their task-specific activity. These findings support the existence of a domain-general system that underlies inhibitory control and establish Dynamic Targeting as a mechanism enabling this ability.</p> </blockquote>
Molcular Dynamics Data for Therapeutic High Affinity T Cell Receptor Targeting a KRAS G12D Cancer Neoantigen
<p>This folder contains the starting structures and input scripts required to simulate the wild-type and G12D KRAS peptide bound TCR-pHLA complexes, as performed in this study.</p> <p><br> Starting_Structures - This folder contains the amber parameter/topology files used to simulate each system (.prmtop) and the coordinates of the starting structure both as amber coordinate file (.rst) and PDB file (.pdb).<br> MD_Inputs - This folder contains the amber MD inputs used to run the md simulations. <br> MMPBSA_inputs - This folder contains the input files for running MMPBSA with the MMPBSA.py script in amber. The mmpbsa.in script was used for calculating overall binding energy whereas the mmpbsa_decomp.in script was used for calculating the per-residue contribution to binding energy. </p>
Molecular Dynamics Simulation and Docking Studies Reveals Inhibition of NF-kB signaling as a Promising Therapeutic Drug Target for reduction in Cytokines Storms
<p><span>The complexes of the top identified molecules with NF-kB-kB site, as well as all the designed molecules used in the screening process. </span></p>
Mechanisms Governing Target Search and Binding Dynamics of Hypoxia-Inducible Factors
<p>Raw fast SPT data for Hypoxia-Inducible Factors:</p> <p><strong>HIF2a_HIF1b_KIN_Clones.zip </strong>corresponds to Figure 2A-C and Figure 2-Figure supplement 2A-B, contains fast SPT data for Halo-HIF-2α KIN clone A31 and clone B50, Halo-HIF-1β KIN clone A21 and clone B89.</p> <p><strong>HIF1b_with_HIF2a_overexpression.zip </strong>corresponds to Figure 2D-E and Figure 2-Figure supplement 2C, contains fast SPT data for: Halo-HIF-1β KIN clone A21, Halo-HIF-1β KIN clone A21 with HIF-2α stably over-expressed, and Halo-HIF-1β KIN clone A21 with a dimerization mutant form of HIF-2α (R171A/V192D) stably over-expressed.</p> <p><strong>Belzutifan_dosage.zip </strong>corresponds to Figure 3-Figure supplement 1, contains fast SPT data for: Halo-HIF-2α KIN clone A31, treated with 0.0002 uM, 0.002 uM, 0.005 uM, 0.01 uM, 0.02 uM, and 0.2 uM of Belzutifan.</p> <p><strong>HIF1b_with_HIFa_chimera_overexpression_and_Belzutifan.zip </strong>corresponds to Figure 3 and Figure 3-Figure supplement 2, contains fast SPT data for: Parental Halo-HIF-1β KIN clone A21 cells and cells stably over-expressing either a certain form of HIF-α (WT or domain swap), treated with either Belzutifan or DMSO. </p> <p><strong>HIFa_domain_swap.zip </strong>corresponds to Figure 4 and Figure 4-Figure supplement 2, contains fast SPT data for: Halo-HIF-2α KIN clone A31, WT 786-O cells stably express V5-Halo-HIF-2α with an L30 promoter, WT 786-O cells stably express V5-Halo-HIF-2α/1α with an L30 promoter, WT 786-O cells stably express V5-Halo-HIF-1α with an L30 promoter, and WT 786-O cells stably express V5-Halo-HIF-1α/2α with an L30 promoter. </p> <p><strong>DBD_mutant.zip </strong>corresponds to Figure 5A-C, contains fast SPT data for: WT 786-O cells stably express V5-Halo-HIF-2α with an L30 promoter, WT 786-O cells stably express a DBD mutant form of V5-Halo-HIF-2α (R27E) with an L30 promoter, WT 786-O cells stably express V5-Halo-HIF-1α with an L30 promoter, and WT 786-O cells stably express a DBD mutant form of V5-Halo-HIF-1α (R30E) with an L30 promoter.</p> <p><strong>Dimerization_mutant.zip </strong>corresponds to Figure 5D-F, contains fast SPT data for: WT 786-O cells stably express V5-Halo-HIF-2α with an L30 promoter, WT 786-O cells stably express a dimerization mutant form of V5-Halo-HIF-2α (R171A/V192D) with an L30 promoter, WT 786-O cells stably express V5-Halo-HIF-1α with an L30 promoter, and WT 786-O cells stably express a dimerization mutant form of V5-Halo-HIF-1α (R170A/V191D) with an L30 promoter.<br> </p>
Target cell-specific synaptic dynamics of excitatory to inhibitory neuron connections in supragranular layers of human neocortex
<p>Rodent studies have demonstrated that synaptic dynamics from excitatory to inhibitory neuron types are often dependent on the target cell type. However, these target cell-specific properties have not been well investigated in human cortex, where there are major technical challenges in reliably obtaining healthy tissue, conducting multiple patch-clamp recordings on inhibitory cell types, and identifying those cell types. Here, we take advantage of newly developed methods for human neurosurgical tissue analysis with multiple patch-clamp recordings, <em>post-hoc</em> fluorescent <em>in situ</em> hybridization (FISH), machine learning-based cell type classification, and prospective GABAergic AAV-based labeling to investigate synaptic properties between pyramidal neurons and PVALB- vs. SST-positive interneurons. We find that there are robust molecular differences in synapse-associated genes between these neuron types, and that individual presynaptic pyramidal neurons evoke postsynaptic responses with heterogeneous synaptic dynamics in different postsynaptic cell types. Using molecular identification with FISH and classifiers based on transcriptomically identified PVALB neurons analyzed by Patch-seq, we find that PVALB neurons typically show depressing synaptic characteristics, whereas other interneuron types including SST-positive neurons show facilitating characteristics. Together, these data support the existence of target cell-specific synaptic properties in human cortex that are similar to rodents, thereby indicating evolutionary conservation of local circuit connectivity motifs from excitatory to inhibitory neurons and their synaptic dynamics.</p>
Ablation Targets of Scar-related Ventricular Tachycardia Identified by Dynamic Functional Substrate Mapping
ClinicalTrials.gov study NCT05086510. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Target cell-specific synaptic dynamics of excitatory to inhibitory neuron connections in supragranular layers of human neocortex
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Trade-offs between biodiversity and agriculture are moving targets in dynamic landscapes
<ol> <li>Understanding how biodiversity responds to intensifying agriculture is critical to mitigating the trade-offs between them. These trade-offs are particularly strong in tropical and subtropical deforestation frontiers, yet it remains unclear how changing landscape context in such frontiers alters agriculture-biodiversity trade-offs.</li> <li>We focus on the Argentinean Chaco, a global deforestation hotspot, to explore how landscape context shapes trade-off curves between agricultural intensity and avian biodiversity. We use a space-for-time approach and integrate a large field dataset of bird communities (197 species, 234 survey plots), three agricultural intensity metrics (meat yield, energy yield and profit), and a range of environmental covariates in a hierarchical Bayesian occupancy framework.</li> <li>Woodland extent in the landscape consistently determines how individual bird species, and the bird community as a whole, respond to agricultural intensity. Many species switch in their fundamental response, from decreasing occupancy with increased agricultural intensity when woodland extent in the landscape is low (loser species), to increasing occupancy with increased agricultural intensity when woodland extent is high (winner species).</li> <li>This suggests that landscape context strongly mediates who wins and loses along agricultural intensity gradients. Likewise, where landscapes change, such as in deforestation frontiers, the very nature of the agriculture-biodiversity trade-offs can change as landscapes transformation progresses.</li> <li> <i>Synthesis and applications</i>. Schemes to mitigate agriculture-biodiversity trade-offs, such as land sparing or sharing, must consider landscape context. Strategies that are identified based on a snapshot of data risk failure in dynamic landscapes, particularly where agricultural expansion continues to reduce natural habitats. Rather than a single, fixed strategy, adaptive management of agriculture-biodiversity trade-offs is needed in such situations. Here we provide a toolset for considering changing landscape contexts when exploring such trade-offs. This can help to better align agriculture and biodiversity in tropical and subtropical deforestation <a>frontiers.</a> </li> </ol>
Molecular dynamics trajectory videos for high-affinity short peptides targeting BamA protein of Candidatus liberibacter asiaticus
<p>Molecular dynamics trajectory videos and trajectory analysis reports for high-affinity short peptides targeting BamA protein of <em>Candidatus</em> liberibacter asiaticus</p>
Targeted micro-fiber arrays for measuring and manipulating localized multi-scale neural dynamics over large, deep brain volumes during behavior: resources and main figure data
<p><strong>Summary:</strong></p> <p>Neural population dynamics relevant for behavior vary over multiple spatial and temporal scales across 3-dimensional volumes. Current optical approaches lack the spatial coverage and resolution necessary to measure and manipulate naturally occurring patterns of large-scale, distributed dynamics within and across deep brain regions such as the striatum. We designed a new micro-fiber array approach capable of chronically measuring and optogenetically manipulating local dynamics across over 100 targeted locations simultaneously in head-fixed and freely moving mice. We developed a semi-automated micro-CT based strategy to precisely localize positions of each optical fiber. This highly-customizable approach enables investigation of multi-scale spatial and temporal patterns of cell-type and neurotransmitter specific signals over arbitrary 3-D volumes at a spatial resolution and coverage previously inaccessible. We applied this method to resolve rapid dopamine release dynamics across the striatum volume which revealed distinct, modality specific spatiotemporal patterns in response to salient sensory stimuli extending over millimeters of tissue. Targeted optogenetics through our fiber arrays enabled flexible control of neural signaling on multiple spatial scales, better matching endogenous signaling patterns, and spatial localization of behavioral function across large circuits. </p> <p><strong>Files included: </strong></p> <ul> <li>Vu, M-A. et al. (2023) - Key Resources Table.xlsx -- key resources used in this study</li> <li>README_figure_data_and_analysis.txt --a description of the organization of the files within this repository</li> <li>README_preprocessing_code.txt -- details about the preprocessing pipeline (including registration and localization pipelines) and annotation of the preprocessed data structure</li> <li>preprocessing_code.zip -- contains all the preprocessing code</li> <li>Fig01.zip -- preprocessed data and analysis code for main Figure 1</li> <li>Fig03.zip -- preprocessed data and analysis code for main Figure 3</li> <li>Fig04.zip -- preprocessed data and analysis code for main Figure 4</li> <li>Fig05.zip -- preprocessed data and analysis code for main Figure 5</li> <li>Fig06.zip -- preprocessed data and analysis code for main Figure 6</li> <li>Fig07.zip -- preprocessed data and analysis code for main Figure 7</li> <li>SuppFig01.zip -- preprocessed data and analysis code for supplemental Figure 1</li> <li>SuppFig02.zip -- preprocessed data and analysis code for supplemental Figure 2</li> <li>SuppFig03.zip -- preprocessed data and analysis code for supplemental Figure 3</li> <li>SuppFig04.zip -- preprocessed data and analysis code for supplemental Figure 4</li> <li>SuppFig05.zip -- preprocessed data and analysis code for supplemental Figure 5</li> <li>SuppFig06.zip -- preprocessed data and analysis code for supplemental Figure 6</li> <li>SuppFig07.zip -- preprocessed data and analysis code for supplemental Figure 7</li> </ul> <p>Each .zip file contains a folder corresponding to a figure. Within each figure folder will be a folder corresponding to the letter (e.g., A, B, C) of the panel within the figure. In addition to the panel folders, when applicable, there may also be a folder containing general-use functions or scripts, or interim results .mat files, that are called by the scripts within the subfolders. In each subfolder there will be 3 things:</p> <ol> <li>a folder of preprocessed data (see README_preprocessing_code.txt)</li> <li>a folder of the analysis scripts that analyzed the preprocessed data and generated the figure</li> <li>a README text file detailing the contents of both folders and the analysis workflow.</li> </ol> <p> </p>
Data from: Molecular docking and dynamics studies to identify novel active compounds targeting potential breast cancer receptor proteins from an indigenous herb Euphorbia thymifolia Linn
<p>Breast cancer has become most prevalent disease and their incidence has doubled in Indian scenario. Targeted therapy with the novel compounds derived from plants could be the promising approach for the development of drugs. <em>Euphorbia thymifolia</em> L is a widely growing tropical herb which has been reported for its various ethnopharmacological properties, including anticancer properties. The aim of the present study was to identify the active phytocompounds present in the methanolic extract using an <em>I</em><em>n-silico</em> approach. The methanolic extract of <em>E. thymifolia</em> (ME.ET) was subjected to GC-MS analysis and the identified compounds were docked with potential protein targets implicated in breast cancer such as ERK1, AKT, EGFR/HER2, ER, MELK, PLK1, PTK6. Compounds with good docking score were further subjected to dynamics study to understand the protein ligand binding stability, ligand pathway calculation, molecular mechanics energies combined with Poisson-Boltzmann (MM/PBSA) calculation using Schrodinger suite. Out of 219 unique phytocompounds subjected to docking, two compounds namely, 3,6,9,12-tetraoxatetradecane-1,14-diyl dibenzoate (TTDB) and succinic acid, 2-(dimethylamino)ethyl 4-isopropylphenyl ester (SADPE) showed good docking score. Molecular dynamics study showed high affinity and low binding energy for TTDB with HER2, ERK1 and SADPE with ER. Hence this is the first study to identify and report active compounds from <em>E.thymifolia</em> linn. Further <em>invitro</em> and <em>invivo</em> anticancer studies can be performed to confirm these results and understand the molecular mechanism by which TTDB and SADPE exhibit anticancer activity against breast cancer.</p>
All-atom molecular dynamics simulations for Targeting Human Prostaglandin Reductase 1 with Licochalcone A: Insights from Molecular Dynamics and Covalent Docking Studies
<p>The dataset comprises simulations of the PTGR1 protein under four different conditions: in its apo (unbound) form, bound to the cofactor NADH, and in complex with both covalently and non-covalently bound licochalcone A. Each simulation was conducted using the ff19SB force field and the OPC water model, with water molecules excluded from the trajectories.</p> <p>For the apo form, NADH-bound, and non-covalently bound licochalcone A conditions, each trajectory consists of 5000 snapshots, representing a total of 500 nanoseconds of simulation. However, the trajectory for the no covalently bound licochalcone A condition includes only 1000 frames, corresponding to 150 nanoseconds. This discrepancy in frame count and simulation length across conditions is important to consider when comparing dynamics and structural behavior within the dataset.</p> <p><strong>PTGR1-NADPH.tar.xz</strong> - PTGR1 dimer in complex with NADPH<br><strong>PTGR1-apo.tar.xz</strong> - PTGR1 apo dimer<br><strong>PTGR1-monomer_NADPH.tar.xz</strong> - PTGR1 monomer in complex with NADPH<br><strong>PTGR1-LicA_covalent.tar.xz </strong>- PTGR1 dimer with covalently bound licochalcone A<br><strong>PTGR1-LicA_NO_covalent.tar.xz</strong> - PTGR1 dimer with covalently bound licochalcone A</p> <p> </p> <p>All folders contain:</p> <p><em>*.parm7</em> - dry topology in amber format</p> <p><em>*.nc</em> - dry trajectories in netcdf format</p> <p> </p> <p><strong>Plain molecular dynamics simulations.</strong> Two structures of human PTGR1 have been deposited in the PDB: one bound to NADPH and the raloxifene inhibitor (PDB ID 2Y05, 2.2 Å resolution), and another in apo form (PDB ID 1ZSV, 2.3 Å resolution). In the first structure, it is reported as a monomer, whereas in the second as a dimer. However, the protomers exhibit highly similar conformations in both structures (backbone RMSD ~0.5 Å). Experimental evidence, akin to PTGR1 orthologs and many other MDR enzymes, indicates that the functional form of human PTGR1 is a homodimer (Mesa et al., 2015). Thus, to construct the dimeric form of the coenzyme complex, we duplicated the NADPH-bound protomer from 2Y05 and aligned the two subunits with the dimer from 1ZSV, deleting the raloxifene molecule. In the resulting structure, no steric clashes between the protomers were observed. This structure was used as the starting point for the simulations. Additionally, the apo dimer was generated by removing the NADPH from both subunits, and the monomeric form in complex with NADPH was derived from the initial structure. </p> <p>The pmemd.cuda module of AMBER 22 was used to perform the MD simulations, employing the force field FF19SB and the OPC water model (Case et al., 2022; Izadi, Anandakrishnan, & Onufriev, 2014; Salomon-Ferrer, Götz, Poole Duncan and Le Grand, & Walker, 2013; Tian et al., 2020). NADPH parameters were taken from (Cummins, Ramnarayan, Singh, & Gready, 1991). The system was protonated at pH 7.4 with PDBfixer (Eastman et al., 2017a) and placed in a truncated octahedral box, initially spanning 12 Å further from the solute in each direction using the AMBER tLeap module. The overall charge of the system was neutralized by the addition of four sodium ions. ParmEd (Eastman et al., 2017b) was used to implement the hydrogen mass repartitioning scheme (Hopkins, Le Grand, Walker, & Roitberg, 2015). Local clashes and solvent orientation were corrected using the steepest descent algorithm for 5,000 cycles. During the initial NVT equilibration, the velocities gradually increased through five steps of 200 ps each. The temperature progression started at 150 K and was raised to 200 K, 250 K, 300 K, and finally, 310 K. Position restraints were applied to heavy atoms of the protein, with the restraining forces progressively decreasing at each step. The spring constants were set at 4, 5, 3, and 1 kcal/mol Å2, respectively, to allow for the gradual relaxation of the protein. The system was further equilibrated for 1 ns in the NPT ensemble with no restraints. For treating long-range electrostatic interactions, periodic boundary conditions and Ewald sums were used with a 9 Å cutoff for direct interactions (Darden, York, & Pedersen, 1993; Simmonett & Brooks, 2021). The same cutoff was used for Lennard-Jones interactions. The Langevin thermostat (Sindhikara, Kim, Voter, & Roitberg, 2009) with a collision frequency of 4 ps-1 and the Monte Carlo barostat(Åqvist, Wennerström, Nervall, Bjelic, & Brandsdal, 2004) with a pressure relaxation time of 2 ps were used to control temperatures and pressures, respectively. The SHAKE algorithm was used to fix any bond involving hydrogen atoms (Ryckaert, Ciccotti, & Berendsen, 1977), and a 4-fs time step integration was used. This protocol was taken from (Cofas-Vargas et al., 2022; Medrano‐Cerano et al., 2024) Unless otherwise stated, no other constraints were used. Five replicas of 500 ns each per system were produced.</p> <p>The topology and parameter files for a LicA molecule and for this inhibitor covalently bound to the sulfur atom of a cysteine residue were generated with Antechamber suite (J. Wang, Wang, Kollman, & Case, 2006), using the general Amber force field (GAFF2) for organic molecules (He, Man, Yang, Lee, & Wang, 2020). Atomic charges were derived using the AM1-BB method (Jakalian, Jack, & Bayly, 2002). The parameters are documented in Supplementary Tables SI-1 and SI-2. Trajectories for PTGR1 covalently and noncovalently bound to LicA were run using the same conditions as described above. All molecular structure representations were created using UCSF ChimeraX v1.8 (Meng et al., 2023; Pettersen et al., 2021).</p> <p> </p> <p><strong>Solvent-site identification and guided docking.</strong> Determination of solvent sites (SS) for ethanol and water molecules was conducted by employing the MDmix method. After removing both NADPH molecules from the enzyme dimer, the system was protonated at pH 7.4 with PDBfixer (Eastman et al., 2017a) and placed in a truncated octahedral box of water/ethanol 80/20% v/v, extending12 Å beyond the solute in each direction using the AMBER tLeap module. Five 20 ns replicas were run, using the same conditions described above, but applying Cartesian restrictions of 0.01 kcal/mol A2 over all heavy atoms. After the alignment of trajectories, density maps for probe atoms were generated by constructing a static mesh with cubic grids (0.5 Å edge length) over the entire simulation box. The occurrence of probe atoms within each grid were tracked across the trajectories. These density distributions were then converted into binding free energy using the Boltzmann relationship, comparing observed probe atom distributions against the expected bulk solvent distribution at 1.0 M. Solvent sites were then filtered by applying an energy threshold of 1 kcal/mol, as previously described (Alvarez-Garcia & Barril, 2014; Avila-Barrientos et al., 2022).</p> <p><strong>LicA docking. </strong>For covalent docking, LicA, bound through its Cb atom to the sulfur atom of C239, was docked onto the NADPH-binding site of human PTGR1 employing the covalent docking module of AutoDock4 v4.2.6 (Bianco, Forli, Goodsell, & Olson, 2016; Morris et al., 2009). The flexible side-chain methodology was used. In a subsequent non-covalent docking, solvent sites previously identified for ethanol and water were used as pharmacophoric element for rDock (Ruiz-Carmona et al., 2014). This docking involved defining the receptor system and generating a binding cavity using the NADPH as a reference molecule. During the non-covalent docking, a penalty score proportional to the square of the distance from each ligand conformation to a solvent site (SS) was applied when the separation exceeded 2 Å. The docking run included 100 simulations, generating a set of potential binding modes for LicA within the NADPH site. </p>
FIG. 3 in Breeding Population Dynamics of Threatened Crawfish Frogs Inform Targets for Habitat Management
FIG. 3. Correlation between tadpole survival and the instantaneous population growth rate (k) for 1,000 points drawn randomly from 1,000 simulations of stochastic population simulations of Nate's Pond with (left panel) and without (right panel) immigration included in the simulation model. The white line is a third order polynomial function fitted to the data, and the gray band represents the 95% confidence interval. The horizontal dashed line indicates a k of 1. The distributions of tadpole survival rates and k values are provided as marginal plots.
FIG. 2 in Breeding Population Dynamics of Threatened Crawfish Frogs Inform Targets for Habitat Management
FIG. 2. Percent change in the instantaneous population growth rate (k) in response to a percent change in female Crawfish Frog tadpole, juvenile, or adult survival rates for a population located in southwest Indiana, USA, between 2009–2013. Percent change in each vital rate represents the general range of that vital rate observed in this study. Change was estimated by varying the focal rate while holding all other rates constant.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.