Most mitochondrial proteins are produced in the cytosol and imported through the translocase of the outer mitochondrial membrane (TOM) to reach their final destination. Although this protein entry gat...

Amylose and cellulose are important biopolymers with diverse applications in biotechnology and materials science. Understanding their structural, dynamic, and solvation properties at the molecular level is critical for harnessing their potential. This study investigates the electronic and structural properties of single-chain cellulose and single- and double-chain amylose in aqueous solution using molecular dynamics simulations with both nonpolarizable (CHARMM) and polarizable (Drude) force fields. CHARMM simulations show stable hydrogen bonding between amylose and water, higher glucose ring dipole moments, increased rigidity, adoption of chair conformations, and less variation in dihedral angles. In contrast, Drude simulations captured dynamic electronic polarization, enhanced conformational flexibility, and resulted in heterogeneous inter- and intramolecular hydrogen bonds. For cellulose, structural and solvation behaviors were largely similar between CHARMM and Drude. These findings highlight molecular interactions and solvation dynamics of amylose and cellulose, with potential relevance in materials science and biotechnology.

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Sterol-rich microdomain accumulation at the MCV is crucial for Mm-induced damage and infection in D. discoideum and BV-2 cells.

This is the official web page for the James Fraser Lab at UCSF.

Membrane proteins play a vital role in numerous cellular processes, including ion transport, intercellular communication, and antibiotic resistance. Ensuring their accurate orientation within lipid bi...

Here, the authors show that Mycobacterium tuberculosis manipulates lipid metabolism to overcome host restriction, by remodelling its lipidome and utilising host lipids as an alternative phosphate sour...

Author summary Here, we provide a roadmap for users to leverage the Protein Data Bank’s vast collection of protein structural models into reliable and valuable insights. It lays out 10 clear rules that help readers quality control their data, choose fair comparison sets, and judge model quality so results aren’t led astray by noise, bias, or overconfidence. The guide also shows how to connect structures to other databases. By highlighting best practices, such as utilizing re-refined models and being aware of common pitfalls, we guide users to leverage this rich data for enhanced biological insights. These guidelines will enable stronger, more reproducible structural analyses that accelerate drug discovery, illuminate disease mechanisms, and make open data broadly useful across the life sciences.

Coarse-grained molecular dynamics simulations, such as those performed with the recently parametrized Martini 3 force field, simplify molecular models and enable the study of larger systems over longer time scales. With this new implementation, Martini 3 allows more bead types and sizes, becoming more amenable to studying dynamical phenomena involving small molecules such as protein–ligand interactions and membrane permeation. However, while solutions existed to automatically model small molecules using the previous iteration of the Martini force field, there is no simple way to generate such molecules for Martini 3 yet. Here, we introduce Auto-MartiniM3, an advanced and updated version of the Auto-Martini program designed to automate the coarse-graining of small molecules to be used with the Martini 3 force field. We validated our approach by modeling 81 simple molecules from the Martini Database and comparing their structural and thermodynamic properties with those obtained from models designed by Martini experts. Additionally, we assessed the behavior of Auto-MartiniM3-generated models by calculating solute translocation and free energy across lipid bilayers. We also evaluated more complex molecules such as caffeine by testing its binding to the adenosine A2A receptor. Finally, our results from deploying Auto-MartiniM3 on a large data set of molecular fragments demonstrate that this program can become a tool of choice for fast, high-throughput creation of coarse-grained models of small molecules, offering a good balance between automation and accuracy. Auto-MartiniM3 source code is freely available at https://github.com/Martini-Force-Field-Initiative/Automartini_M3.