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15 results for “Antibody Dynamics”
Molecular dynamics simulation trajectories of HIV protein gp120 in complex with antibody VRC01 and 30 of its Ala mutants
<p>This data set accompanies the publication by S. Conti, E. Lau, and V. Ovchinnikov entitled "On the rapid calculation of binding affinities for antigen and antibody design and affinity maturation simulations", to be published in the MDPI journal Antibodies. It contains molecular dynamics simulation trajectories of HIV protein gp120 in complex with antibody VRC01 and 30 of its Ala mutants, as described in the paper. The format of the trajectory files is CHARMM-compatible dcd. The files can be visualized with the program Visual Molecular Dynamics (VMD) (see paper by Humphrey et al. 1996, J. Molec. Graphics). The accompanying file "view" is a tcl-based script for VMD that can be executed in the Linux environment using: "vmd -e view", which will display the trajectory of the mutant specified by editing the first noncomment line of the script.<br> </p>
Data from: SARS-CoV-2 antibody dynamics in blood donors and COVID-19 epidemiology in eight Brazilian state capitals
<p class="MsoNormal"><span>The COVID-19 situation in Brazil is complex due to large differences in the shape and size of regional epidemics. Here we tested monthly blood donation samples for IgG antibodies from March 2020 to March 2021 in eight of Brazil's most populous cities. The inferred attack rate of SARS-CoV-2 adjusted for seroreversion in December 2020, before the Gamma VOC was dominant, ranged from 19.3% (95% CrI 17.5% - 21.2%) in Curitiba to 75.0% (95% CrI 70.8% - 80.3%) in Manaus. Seroprevalence was consistently smaller in women and donors older than 55 years. The age-specific infection fatality rate (IFR) differed between cities and consistently increased with age. The infection hospitalisation rate (IHR) increased significantly during the Gamma-dominated second wave in Manaus, suggesting increased morbidity of the Gamma VOC compared to previous variants circulating in Manaus. The higher disease penetrance associated with the health system's collapse increased the overall IFR by a minimum factor of 2.91 (95% CrI 2.43 – 3.53). These results highlight the utility of blood donor serosurveillance to track epidemic maturity and demonstrate demographic and spatial heterogeneity in SARS-CoV-2 spread.</span></p>
Somatic hypermutation-mediated paratope flexibility improves the cross-reactivity of human malaria antibodies -- Molecular Dynamics dataset
<p>4493 Manuscript Data<br>====================</p> <p>author: Anton Hanke<br>size of uncompressed folder: ~19Gb.<br>DOI: 10.5281/zenodo.11470585</p> <p># Standard MD simulation data</p> <p>Standard Simulations were generated with gromacs 2021.5 using the charmm36m forcefield Juli 2021 release tarball (https://mackerell.umaryland.edu/download.php?filename=CHARMM_ff_params_files/charmm36-jul2021.ff.tgz).<br>Post processed (PBC) simulations are structured as follows:<br>Mature generally refers to the wildtype 4493 antibody.</p> <p>- simulations/standardMD<br> |<br> |- prod.mdp example production mdp file used to run all production simulations.<br> |<br> |- mature Mature simulation set. (folder and file naming the same in all simulation directories)<br> | |- {peptide}_{replicate}_prod.gro {peptide} = peptide; {replicate} = standard MD replicate<br> | |- {peptide}_{replicate}_prod.tpr<br> | `- {peptide}_{replicate}_prod_align_noPBC.xtc (10Frames/ns)<br> |<br> |- mature_rerun Additional set of replicates with the wildtype 4493.<br> |- matureCapped Set of simulations with termini capped peptides<br> |- mature_nanpv2 Set of simulations with NPDP similar positioning of NANP<br> |- wo_pep Set of simulations without peptides for germline and mature<br> `- germline Set of germline simulations.</p> <p><br># RAMD simulation data</p> <p>RAMD simulations were generated with gromacs_2020.5 patched with RAMDv2 modified to account for the connected multiple ligand groups.<br>(Source code provided as tar file ./sw/gromacs_ramd_patchv2.tar.gz)</p> <p>Not all trajectories contain an unbinding event (gromacs CUDA bug.). <br>These trajectories were not considered in the analysis of simulations. </p> <p>- simulations/ramd<br> |<br> |- prod.mdp Exemplary production mdp file with RAMD settings, these were used in all trajectories w/<br> | differing RAMD random seed.<br> |<br> |- mature_2.625kcalmolA_4.0_3.0<br> | |- {peptide}_{replicate}_prod_{startFrame}.gro {peptide} = peptide; {replicate} = standard MD replicate; {startFrame} = Frame in standard MD used to start simulation.<br> | |- {peptide}_{replicate}_prod_{startFrame}.tpr<br> | |- {peptide}_{replicate}_prod_{startFrame}.ndx<br> | |- {peptide}_{replicate}_prod_{startFrame}_align_noPBC.xtc (100Frames/ns) Files omited due to size -- available on request.<br> | `- {peptide}_{replicate}_prod_{startFrame}_lastframe.pdb Last frame of the processed RAMD trajectory.<br> `- gl_2.625kcalmolA_4.0_3.0</p> <p><br># Analysis</p> <p>- analysis<br> |<br> |- entropie Quasi harmonic entropy estimation.<br> | |- inp Concatenated & Bootstrapped, coarse-grained and aligned trajectories of all systems<br> | |- out CPPTRAJ runs to calculate QHE on the bootstrapped trajectories<br> | |- run_complex.sh Script running analysis.<br> | |- ana.py Script to calculte average and std of QHE for each system. (generates *.out *.tsv *.png)<br> | |- cg.py Script used to bootstrap, coarse-grain align and build average structure with.<br> | `- delta_entropies.ods Excel file used to calculate Tab 1. in Main text of paper from entropies.out.<br> |<br> |- mmpbsa MMPBSA calculations (MM + SolvEnergy) with gmx_MMPBSA<br> | |- inp Input trajectories and topologies processed for MMPBSA<br> | |- out/gmx_mmpbsa Output directories in which gmx_MMPBSA was run.<br> | | ` *.dat Output files containing calculated energy terms from gmx_MMPBSA.<br> | |- mmpbsa.in MMPBSA input file used to run analysis.<br> | |- plot_results.py Python script to plot correlation of MMPBSA output with experimental data<br> | |- pca_eig_extr.py Script to reduce simulations to regions of high probability density within trajectory (not used in the present analysis)<br> | |- slurm-91315023.out Log file of the analysis run<br> | `- run_mmpbsa.sh Shell script to run the MMPBSA analysis (generates input and output file trees).<br> |<br> `- ramd RAMD analysation.<br> |- run.sh Shell script to run the analysis<br> |- run_ramd_ana.py Python script called by `run.sh` to run the analysis using `ramdAnalysis.py`<br> |- contact_clusters.py Python script to generate plots based on output of the analysis.<br> |- ramdAnalysis.py Python module containing analysis classes called/used within `run_ramd_ana.py`<br> | Based on tauRAMD & Fingerprint analysis by Dr. Daria Khokh (https://doi.org/10.1021%2Facs.jctc.8b00230; https://doi.org/10.1063%2F5.0019088)<br> |- abrun.* Log files from the present run<br> |- *.svg; *.png Analysis output files.<br> |- tramd_patchv2/ Output PDB structures from the analysis (excluded due to size, available on request)<br> `- representatives.pse Pymol session of cluster representatives along unbinding for germline and wildtype with contact probabilities within the<br> cluster mapped as b-factor.</p> <p># Figures</p> <p>- figure_pdbs PDB files (and pymol sessions) used to generate figures in the papers main text.</p>
Project files provided as supporting information to the manuscript "How Communication Pathways Bridge Local and Global Conformations in an IgG4 Antibody: a Molecular Dynamics Study"
<p>June 23, 2021</p> <p>Thomas Tarenzi, Marta Rigoli and Raffaello Potestio</p> <p>==================================</p> <p>The dataset contains the following folders:</p> <p>- contact_area_binding_site: files with the computed surface area, used for the calculation of the contact area between PD-1 and the antibody Fab (Fig. S41).</p> <p>- hbonds_ab-pd1: number of hydrogen bonds between the antigen and the antibody, for each holo cluster (Fig. S41).</p> <p>- mutual_information: matrices with the computed mutual information, for each pair of residues (Fig. S34, S35, S46). The folder contains also the generalized correlation coefficients (Fig. S43) and the correlation scores (Fig. 4, S44, S45), computed from the mutual informations.</p> <p>- networks: communities - for each cluster, each residue is assigned to a community within the interaction network (Fig. S32, S33). betweenness - the values of edge betweenness for each cluster (S30, S31).</p> <p>- output_clustering: each frame of the apo and holo simulations is assigned a cluster index, on the basis of the structural similarity (Fig. S4).</p> <p>- PAD: per-residue values of PAD parameter, for apo and holo systems (Fig. 3, S39).</p> <p>- PCA: principal component analysis for each conformational cluster (Section S2.1).</p> <p>- representative_structures: representative structures for each conformational cluster (Fig. 2).</p> <p>- r_gyr_antibody: radii of gyration of the antibody, for each cluster (Fig. 2).</p> <p>- r_gyr_hinge: radii of gyration of the sole hinge segment, for each cluster (Fig. S36).</p> <p>- RMSD_antibody: distributions of the root-mean-square deviation of the antibody, for each cluster (Fig. S5). </p> <p>- RMSD_antigen: root-mean-square deviation of the antigen PD-1, for each cluster (Fig. S42).</p> <p>- RMSD_binding_site: distributions of the root-mean-square deviation of the residues belonging to the paratope, for each cluster (Fig. S40, S47).</p> <p>- RMSD_matrix: root-mean-square deviation between structures belonging to different pairs of clusters (Fig. S9).</p> <p>- rmsf_antigen: root-mean-square fluctuation of the antigen PD-1, for each cluster (Fig. S42).</p> <p>- rmsf_hinge: difference between the total root-mean-square fluctuations of the two hinge segments, for each cluster (Fig. S38).</p> <p>- salt_bridge: distribution of distances between residues R979 and D1377 (Fig. 4).</p> <p>- sasa_domains: contact area between Fab and Fc antibody domains, for each cluster (Fig. S8).</p> <p>- sasa_hinge: solvent accessible surface area of each hinge segment, for each cluster (Fig. S37).</p>
Data from: SARS-CoV-2 antibody dynamics in blood donors and COVID-19 epidemiology in eight Brazilian state capitals
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Antibody Molecular Dynamics
<p>Antibody Molecular Dynamics. By Victor Padilla Sanchez, PhD Email: drvictorpadilla@aol.com Website: https://www.drvictorpadillasanchez.com</p>
Longitudinal Dynamics of AT1R Antibodies in Kidney Transplant Recipients
ClinicalTrials.gov study NCT07374484. IPD Sharing: NO. Countries: 1. Publications: 4.
Dynamics of heavy chain junctional length biases in antibody repertoires
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Data from: Maternal antibodies contribute to sex based difference in hantavirus transmission dynamics
Individuals often differ in their ability to transmit disease and identifying key individuals for transmission is a major issue in epidemiology. Male hosts are often thought to be more important than females for parasite transmission and persistence. However, the role of infectious females, particularly the transient immunity provided to offspring through maternal antibodies (MatAbs), has been neglected in discussions about sex-biased infection transmission. We examined the effect of host sex upon infection dynamics of zoonotic Puumala hantavirus (PUUV) in semi-natural, experimental populations of bank vole (Myodes glareolus). Populations were founded with either females or males that were infected with PUUV while the other sex was immunized against PUUV infection. The likelihood of the next generation being infected was lower when the infected founders were females, underlying the putative importance of adult males in PUUV transmission and persistence in host populations. However, we show that this effect likely results from transient immunity that infected females provide to their offspring, rather than any sex-biased transmission efficiency per se. Our study proposes a potential contrasting nature of female and male hosts in the transmission dynamics of hantaviruses.
AlphaFold2 Modeling and Molecular Dynamics Simulations of the Conformational Ensembles for the SARS-CoV-2 Spike Omicron JN.1, KP.2 and KP.3 Variants : Mutational Profiling of Binding Energetics Reveals Epistatic Drivers of the ACE2 Affinity and Escape Hotspots of Antibody Resistance
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Data from: Maternal antibodies contribute to sex based difference in hantavirus transmission dynamics
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Single-cell analysis reveals dynamics of human B cell differentiation and identifies novel B and antibody-secreting cell intermediates
GEO Series GSE214265. Homo sapiens. 384 samples. Type: Expression profiling by high throughput sequencing.
Dynamics of the Anti-factor VIII Antibody Signature During Treatment With Emicizumab
ClinicalTrials.gov study NCT05802836. IPD Sharing: NO. Countries: 1. Publications: 0.
Study on Dynamic Changes of the Maternal Anti-EV71 and Anti-CVA16 Antibody Levels in Infants and Young Children
ClinicalTrials.gov study NCT01255124. IPD Sharing: Not stated. Countries: 1. Publications: 0.
High throughput analysis of B cell dynamics and neutralizing antibody development during immunization with a novel clade C HIV-1 envelope
GEO Series GSE244293. Macaca mulatta. 89 samples. Type: Expression profiling by high throughput sequencing; Other.
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