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Related Concept Videos

Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
Conserved Binding Sites01:49

Conserved Binding Sites

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 analyses the...
Nuclear Protein Sorting01:34

Nuclear Protein Sorting

Nuclear protein sorting is the selective trafficking of histones, polymerases, gene regulatory proteins into the nucleus and exporting RNAs and ribosomes to the cytosol. It is a tightly controlled process that regulates gene expression within a cell.
Proteins targeted to the nucleus carry nuclear localization signals or NLS recognized by import receptors in the cytosol. Similarly, proteins with nuclear export signals are recognized by export receptors. Import and export receptors are...
Nuclear Localization Signals and Import01:46

Nuclear Localization Signals and Import

Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of  2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...
Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...

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Related Experiment Video

Updated: Jul 8, 2026

An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

A comprehensive benchmark of sequence-based subcellular localization predictors for human proteins.

Zoe Wefers1,2, Ankit Gupta3, Noorsher Ahmed2

  • 1Computer Science Department, Stanford University, Stanford, CA, USA.

Nature Methods
|July 6, 2026
PubMed
Summary

Predicting protein localization is key to understanding human biology. Current computational methods struggle with complex protein behavior, highlighting the need for better models and datasets for accurate subcellular localization prediction.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Computational protein localization prediction aids in understanding protein function and interactions, crucial for human biology and disease research.
  • Existing evaluation methods are limited by small datasets, broad cellular compartment labels, and single-label classification, despite many proteins localizing to multiple compartments.

Purpose of the Study:

  • To construct a larger, validated benchmark dataset for human proteins.
  • To systematically evaluate existing sequence-based predictors and compare different modeling approaches.
  • To identify limitations in current prediction models for fine-grained localization and multilocalizing proteins.

Main Methods:

  • Integrated annotations from major protein databases to create a benchmark test set of 3,814 human proteins.
  • Systematically evaluated existing sequence-based predictors.
  • Compared combinations of protein language models and aggregation strategies.

Main Results:

  • Current computational models underperform on predicting localization to fine-grained cellular compartments.
  • Models show limitations in predicting the localization of multilocalizing proteins and pathogenic variants.
  • The developed benchmark dataset is twice the size of previous ones and highly validated.

Conclusions:

  • Existing sequence-based protein localization predictors have fundamental limitations.
  • There is a critical need for improved computational models for subcellular localization prediction.
  • Standardized benchmark datasets and more rigorous evaluation protocols are essential for advancing the field.