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9 results for “protein biophysics”
Combining Solid-State NMR with Structural and Biophysical Techniques to Design Challenging Protein-Drug Conjugates
<p>Solid-state NMR spectra (DARR and NCA) of rehydrated freeze-dried free TTR and TTR in the presence of Tafamidis and Taf-PTX</p> <p>Reference citation: Combining Solid-State NMR with Structural and Biophysical Techniques to Design Challenging Protein-Drug Conjugates. Angew Chem Int Ed Engl. 2023 Jun 5:e202303202. doi: 10.1002/anie.202303202. PMID: 37276329.</p>
Data from: A multiscale biophysical model for the recruitment of actin nucleating proteins at the membrane interface
<p>The dynamics and organization of the actin cytoskeleton are crucial to many cellular events such as motility, polarization, cell shaping, and cell division. The intracellular and extracellular signaling associated with this cytoskeletal network is communicated through cell membranes. Hence the organization of membrane macromolecules and actin filament assembly are highly interdependent. Although the actin-membrane linkage is known to happen through many routes, the major class of interactions is through the direct interaction of actin-binding proteins with the lipid class containing poly-phosphatidylinositols (PPIs). Among the PPIs, phosphatidylinositol bisphosphate (PI(4,5)P<sub>2</sub>) acts as a significant factor controlling actin polymerization in the proximity of the membrane by binding to actin-associated proteins. The molecular interactions between these actin-binding proteins and the membrane lipids remain elusive. Here, using molecular modeling, analytical theory, and experimental methods, we investigate the binding of three different actin-binding proteins, mDia2, NWASP, and gelsolin, to membranes containing PI(4,5)P<sub>2</sub> lipids. We perform molecular dynamics simulations on the protein-bilayer system and analyze the membrane binding in the form of hydrogen bonds and salt bridges at various PI(4,5)P<sub>2</sub> and cholesterol concentrations. Our experimental study with PI(4,5)P<sub>2</sub>-containing large unilamellar vesicles mimics the computational experiments. Using the multivalencies of the proteins obtained in molecular simulations and the cooperative binding mechanisms of the proteins, we also propose a multivalent binding model that predicts the actin filament distributions at various PI(4,5)P<sub>2 </sub>and protein concentrations.</p>
Data from: A multiscale biophysical model for the recruitment of actin nucleating proteins at the membrane interface
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Data from: Biophysically inspired model for functionalized nanocarrier adhesion to cell surface: roles of protein expression and mechanical factors
In order to achieve selective targeting of affinity–ligand coated nanoparticles to the target tissue, it is essential to understand the key mechanisms that govern their capture by the target cell. Next-generation pharmacokinetic (PK) models that systematically account for proteomic and mechanical factors can accelerate the design, validation and translation of targeted nanocarriers (NCs) in the clinic. Towards this objective, we have developed a computational model to delineate the roles played by target protein expression and mechanical factors of the target cell membrane in determining the avidity of functionalized NCs to live cells. Model results show quantitative agreement with in vivo experiments when specific and non-specific contributions to NC binding are taken into account. The specific contributions are accounted for through extensive simulations of multivalent receptor–ligand interactions, membrane mechanics and entropic factors such as membrane undulations and receptor translation. The computed NC avidity is strongly dependent on ligand density, receptor expression, bending mechanics of the target cell membrane, as well as entropic factors associated with the membrane and the receptor motion. Our computational model can predict the in vivo targeting levels of the intracellular adhesion molecule-1 (ICAM1)-coated NCs targeted to the lung, heart, kidney, liver and spleen of mouse, when the contributions due to endothelial capture are accounted for. The effect of other cells (such as monocytes, etc.) do not improve the model predictions at steady state. We demonstrate the predictive utility of our model by predicting partitioning coefficients of functionalized NCs in mice and human tissues and report the statistical accuracy of our model predictions under different scenarios.
Exploring Protein Conformational Changes Using a Large-scale Biophysical Sampling Augmented Deep Learning Strategy
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Data from: Biophysically inspired model for functionalized nanocarrier adhesion to cell surface: roles of protein expression and mechanical factors
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Predicting biophysical characteristics of proteins from their amino acid sequence
<p>As part of the Galaxy Training Material, this is the tutorial workflow's input file in FASTA format. It contains the amino acid sequence of protein P04156 ("PRIO_HUMAN OS=Homo sapiens OX=9606 GN=PRNP PE=1 SV=1").</p>
Discovery of small molecules as HPV16 E6 protein inhibitors against cervical cancer: An in silico and biophysical approach
<p>All the input files used for molecular docking and molecular dynamics simulations performed with HPV16 E6 protein to find potential inhibitors.</p> <p>Considering the critical role of high-risk HPV E6 protein in malignant tumorigenesis, it is widely recognized as a therapeutic target to develop novel and effective treatments for CC. In this work, we explored computational methods to discover new E6 inhibitors with potential interest in CC therapy. Using the E6 protein structure published in the Protein Data Bank (4XR8), molecular docking studies and molecular dynamics simulations were performed to predict the binding energy and orientation of synthetic compounds at the E6-p53 binding site. For experimental studies, the recombinant MBP-E6 protein was expressed from the <em>Escherichia coli </em>host, and affinity chromatography was applied to obtain highly pure protein. Circular dichroism confirmed that the target protein adopts an α-helical, random coil, and β-sheet structure. Finally, a thermal shift assay was used to access potential protein-ligand interactions.</p> <p> </p>
Data from: Biophysical models reveal the relative importance of transporter proteins and impermeant anions in chloride homeostasis
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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.