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Related Concept Videos

Proteomics01:33

Proteomics

9.9K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

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Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Extracellular Protein Microarray Technology for High Throughput Detection of Low Affinity Receptor-Ligand Interactions
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ProtRAP-LM: Efficient Protein Relative Accessibility Prediction and Proteome-wide Membrane Protein Screening.

Lei Wang1,2, Kai Kang1,2, Chen Song1,2

  • 1Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.

Genomics, Proteomics & Bioinformatics
|February 15, 2026
PubMed
Summary
This summary is machine-generated.

A new model, ProtRAP-LM, uses protein language models to quickly predict membrane protein properties. This tool accelerates the analysis of membrane proteins across entire proteomes, aiding future research into their structure and function.

Keywords:
Deep learningLanguage modelMembrane proteinProteome-scale screeningRelative accessibility

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Structural Biology

Background:

  • Membrane proteins are crucial for cellular functions and are key therapeutic targets.
  • Accurate prediction of membrane protein properties is essential for drug discovery and biological research.
  • Previous methods relied on multiple sequence alignments (MSAs), limiting prediction speed.

Purpose of the Study:

  • To develop a rapid and accurate method for predicting membrane protein properties.
  • To leverage protein language models (pLMs) for enhanced prediction capabilities.
  • To overcome the speed limitations of MSA-based prediction models.

Main Methods:

  • Introduced ProtRAP-LM, a transformer-based model utilizing pLM embeddings.
  • Applied the model to predict membrane contact probability (MCP) and residue relative accessibility.
  • Evaluated performance on a 184-protein test set.

Main Results:

  • ProtRAP-LM achieved superior performance compared to previous MSA-based models.
  • Demonstrated a speed-up of over 300 times, enabling proteome-wide predictions within hours.
  • Provided comprehensive annotations for challenging membrane protein types, including single-pass transmembrane and beta-sheet proteins.

Conclusions:

  • ProtRAP-LM offers a significant advancement in the rapid and accurate prediction of membrane protein properties.
  • Facilitates large-scale proteome annotations, providing a valuable resource for biological research.
  • Enables deeper investigation into the structure and function of essential membrane biomolecules.