Search arXiv⌕ Search

arXiv · q-bio/0505022

Modelization of Thermal Fluctuations in G Protein-Coupled Receptors

Abstract

We simulate the electrical properties of a device realized by a G protein coupled receptor (GPCR), embedded in its membrane and in contact with two metallic electrodes through which an external voltage is applied. To this purpose, recently, we have proposed a model based on a coarse graining description, which describes the protein as a network of elementary impedances. The network is built from the knowledge of the positions of the C-alpha atoms of the amino acids, which represent the nodes of the network. Since the elementary impedances are taken depending of the inter-nodes distance, the conformational change of the receptor induced by the capture of the ligand results in a variation of the network impedance. On the other hand, the fluctuations of the atomic positions due to thermal motion imply an impedance noise, whose level is crucial to the purpose of an electrical detection of the ligand capture by the GPCR. Here, in particular, we address this issue by presenting a computational study of the impedance noise due to thermal fluctuations of the atomic positions within a rhodopsin molecule. In our model, the C-alpha atoms are treated as independent, isotropic, harmonic oscillators, with amplitude depending on the temperature and on the position within the protein (alpha-helix or loop). The relative fluctuation of the impedance is then calculated for different temperatures.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

C. Pennetta, V. Akimov, E. Alfinito, L. Reggiani, G. Gomila, G. Ferrari, L. Fumagalli, M. Sampietro. 2005-06-30. Modelization of Thermal Fluctuations in G Protein-Coupled Receptors. https://doi.org/10.1063/1.2036827

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

scTREAT: Latent Diffusion for Single-Cell Drug-Response Prediction

Generating drug-induced transcriptional responses requires a model to account for cellular background together with molecular identity and dose. We present scTREAT, the first diffusion framework to factorize state and drug guidance for single-cell drug-response prediction. A shared latent expression space connects baseline and response, while Guided Decomposable Attention (GD-Attn) injects a baseline representation and a dose-conditioned molecular embedding. Independent channel dropout exposes four conditioning states during training; inference combines three conditional predictions with separately specified state and drug coefficients, and dose modulates drug-guidance strength. On condition-level pseudobulk profiles derived from the Tahoe-100M single-cell atlas, scTREAT achieves the best value on 10 of 12 metrics for unseen cell line-drug combinations (UC) and 11 of 12 for unseen drugs (UD). Ablations examine condition injection, molecular representation, and guidance design. On UD, the relative gains over the second-best model reach 36.11% and 34.21% for DEG logFC-Spearman and logFC-Pearson. These results demonstrate the effectiveness of combining shared latent expression modeling with explicit state and drug conditioning for response prediction.

q-bio.QM↗

Sensitivity Analysis of Cryo-Electron Microscopy Reconstructions via the Log-Likelihood Hessian

Cryo-electron microscopy (cryo-EM) is a technique in structural biology which is used to produce high-resolution 3D reconstructions of molecules. One common step in the cryo-EM pipeline involves ab-initio single-particle reconstruction (SPR). This is a type of inverse-problem which takes as input a collection of 2D-images -- each a projection of a molecule from an unknown viewing-angle -- and attempts to reconstruct the 3D-volume representing the underlying molecular density. Most methods for solving SPR search for a solution which optimizes a posterior likelihood of generating the observed image-data, given the reconstructed volume. Within this framework, it is natural to study the Hessian of the log- likelihood: the eigenvectors and eigenvalues of the Hessian determine how the likelihood changes with respect to perturbations in the solution, which can give insight into the sensitivity of the solution to aspects of the input, such as the orientation distribution of image viewing-angles. In this paper, we describe a simple strategy for estimating the smallest eigenvalues and eigenvectors (i.e., the 'softest modes') of the Hessian of the log-likelihood for the ab-initio single-particle reconstruction problem. This strategy involves rewriting the log-likelihood as a 3D-integral. Once we have estimated the softest modes, we can use them to determine which parts of the reconstructed volume are trustworthy, and which are unreliable, and how this unreliability might depend on the data-set and the imaging parameters. We believe that this kind of analysis can be used alongside more traditional strategies for sensitivity analysis, as well as in other applications, such as free-energy estimation.

q-bio.QM↗

Agentic Literature Extraction Augments Targeted Protein Degradation Databases

Predictive models in medicinal chemistry depend on structured assay data locked in primary publications. This bottleneck is acute in targeted protein degradation (TPD), where each assay record must combine compound identity, degradation target, recruiter, assay context, and endpoint values reported across sections, tables, and supplementary files. Existing TPD databases are manually curated and often lack assay context. We present an expert-in-the-loop large language model workflow in which a cross-validated prompt-refinement module adapts extraction instructions from scarce expert annotations. With only seven annotated molecular glue publications, the workflow achieved record-level $F_1 = 0.98$ and transferred to proteolysis-targeting chimeras (PROTACs) by terminology substitution alone ($F_1 \geq 0.93$). The resulting database, AutoTPD, contained 81% and 92% more molecular glue and PROTAC records than standardized reference datasets; expert audits found 92/100 and 33/40 new records to be correct. Applying the workflow to eligible PubMed Central open-access literature added 4,483 records from 598 publications, yielding AutoTPD+, a public, versioned database of 7,622 TPD records, including degradation assay measurements, designed for monthly updates. In a controlled surrogate model comparison, PROTAC degradation activity models trained on AutoTPD+ saw a 8.9-18.2% error reduction relative to models trained on the original PROTAC-DB data.

q-bio.QM↗