ReviewProteomics2012
Latest developments in experimental and computational approaches to characterize protein-lipid interactions.
Review in Proteomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Artificial Intelligence in Computer-Aided Drug Design (CADD) Tools for the Finding of Potent Biologically Active Small Molecules: Traditional to Modern Approach.Combinatorial chemistry & high throughput screening · 2026Review
- Membrane Composition Modulates Vp54 Binding: A Combined Experimental and Computational Study.Pathogens (Basel, Switzerland) · 2025Article
- Binding equations for the lipid composition dependence of peripheral membrane-binding proteins.Biophysical journal · 2024Article
- A machine learning approach to estimation of phase diagrams for three-component lipid mixtures.Biochimica et biophysica acta. Biomembranes · 2020Article
- Unlabeled lysophosphatidic acid receptor binding in free solution as determined by a compensated interferometric reader.Journal of lipid research · 2020Article
- Sensitivity of peripheral membrane proteins to the membrane context: A case study of phosphatidylserine and the TIM proteins.Biochimica et biophysica acta. Biomembranes · 2018Review
- Detecting Protein-Glycolipid Interactions Using Glycomicelles and CaR-ESI-MS.Journal of the American Society for Mass Spectrometry · 2016Article
- A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces.International journal of molecular sciences · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Understanding the functional roles of all the molecules in cells is an ultimate goal of modern biology. An important facet is to understand the functional contributions from intermolecular interactions, both within a class of molecules (e.g. protein-protein) or between classes (e.g. protein-DNA). While the technologies for analyzing protein-protein and protein-DNA interactions are well established, the field of protein-lipid interactions is still relatively nascent. Here, we review the current status of the experimental and computational approaches for detecting and analyzing protein-lipid interactions. Experimental technologies fall into two principal categories, namely solution-based and array-based methods. Computational methods include large-scale data-driven analyses and predictions/dynamic simulations based on prior knowledge of experimentally identified interactions. Advances in the experimental technologies have led to improved computational analyses and vice versa, thereby furthering our understanding of protein-lipid interactions and their importance in biological systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.