Impact of membrane partitioning on the spatial structure of an S-type cobra cytotoxin
Abstract
Cobra cytotoxins (CTs) belong to the three-fingered protein family. They are classified into S- and P-types, the latter exhibiting higher membrane-perturbing capacity. In this work, we investigated the interaction of CTs with phospholipid bilayers, using coarse-grained (CG) and full-atom (FA) molecular dynamics (MD). The object of this work is a CT of an S-type, cytotoxin I (CT1) from N.oxiana venom. Its spatial structure in aqueous solution and in the micelles of dodecylphosphocholine (DPC) were determined by 1H-NMR spectroscopy. Then, via CG- and FA MD-computations, we evaluated partitioning of CT1 molecule into palmitoyloleoylphosphatidylcholine (POPC) membrane, using the toxin spatial models, obtained either in aqueous solution, or detergent micelle. The latter model exhibits minimal structural changes upon partitioning into the membrane, while the former deviates from the starting conformation, loosing the tightly bound water molecule in the loop-2. These data show that the structural changes elicited by CT1 molecule upon incorporation into DPC micelle take place likely in the lipid membrane, although the mode of the interaction of this toxin with DPC micelle (with the tips of the all three loops) is different from its mode in POPC membrane (primarily with the tip of the loop-1 and both the tips of the loop-1 and loop-2).
Read the paper
Related studies
- In silico toxicology protocols
- QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
- Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
- The computational power of the human brain
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.