dorsal/arxiv
View SchemaA network model to investigate structural and electrical properties of proteins
| Authors | E. Alfinito, C. Pennetta, L. Reggiani |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0703065 |
| URL | https://arxiv.org/abs/q-bio/0703065 |
| DOI | 10.1088/0957-4484/19/6/065202 |
| Journal | Nanotechnology, 19, 065202 (2008) |
Abstract
One of the main trend in to date research and development is the miniaturization of electronic devices. In this perspective, integrated nanodevices based on proteins or biomolecules are attracting a major interest. In fact, it has been shown that proteins like bacteriorhodopsin and azurin, manifest electrical properties which are promising for the development of active components in the field of molecular electronics. Here we focus on two relevant kinds of proteins: The bovine rhodopsin, prototype of GPCR protein, and the enzyme acetylcholinesterase (AChE), whose inhibition is one of the most qualified treatments of Alzheimer disease. Both these proteins exert their functioning starting with a conformational change of their native structure. Our guess is that such a change should be accompanied with a detectable variation of their electrical properties. To investigate this conjecture, we present an impedance network model of proteins, able to estimate the different electrical response associated with the different configurations. The model resolution of the electrical response is found able to monitor the structure and the conformational change of the given protein. In this respect, rhodopsin exhibits a better differential response than AChE. This result gives room to different interpretations of the degree of conformational change and in particular supports a recent hypothesis on the existence of a mixed state already in the native configuration of the protein.
{
"annotation_id": "dec10b62-d51b-46c5-ae8d-1c83c3794758",
"date_created": "2026-03-02T18:01:34.823000Z",
"date_modified": "2026-03-02T18:01:34.823000Z",
"file_hash": "d6638fdc7fbd0b77fb5c3743c5c2b897e4505b6c7143f6512d03e7c14af73d48",
"private": false,
"record": {
"abstract": "One of the main trend in to date research and development is the\nminiaturization of electronic devices. In this perspective, integrated\nnanodevices based on proteins or biomolecules are attracting a major interest.\nIn fact, it has been shown that proteins like bacteriorhodopsin and azurin,\nmanifest electrical properties which are promising for the development of\nactive components in the field of molecular electronics. Here we focus on two\nrelevant kinds of proteins: The bovine rhodopsin, prototype of GPCR protein,\nand the enzyme acetylcholinesterase (AChE), whose inhibition is one of the most\nqualified treatments of Alzheimer disease. Both these proteins exert their\nfunctioning starting with a conformational change of their native structure.\nOur guess is that such a change should be accompanied with a detectable\nvariation of their electrical properties. To investigate this conjecture, we\npresent an impedance network model of proteins, able to estimate the different\nelectrical response associated with the different configurations. The model\nresolution of the electrical response is found able to monitor the structure\nand the conformational change of the given protein. In this respect, rhodopsin\nexhibits a better differential response than AChE. This result gives room to\ndifferent interpretations of the degree of conformational change and in\nparticular supports a recent hypothesis on the existence of a mixed state\nalready in the native configuration of the protein.",
"arxiv_id": "q-bio/0703065",
"authors": [
"E. Alfinito",
"C. Pennetta",
"L. Reggiani"
],
"categories": [
"q-bio.QM",
"cond-mat.soft",
"physics.bio-ph"
],
"doi": "10.1088/0957-4484/19/6/065202",
"journal_ref": "Nanotechnology, 19, 065202 (2008)",
"title": "A network model to investigate structural and electrical properties of proteins",
"url": "https://arxiv.org/abs/q-bio/0703065"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "2856489a-aa97-4262-9604-67725ae5746c",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}