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Related Concept Videos

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...
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...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

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Related Experiment Video

Updated: May 28, 2026

SUMO-Binding Entities (SUBEs) as Tools for the Enrichment, Isolation, Identification, and Characterization of the SUMO Proteome in Liver Cancer
08:29

SUMO-Binding Entities (SUBEs) as Tools for the Enrichment, Isolation, Identification, and Characterization of the SUMO Proteome in Liver Cancer

Published on: November 1, 2019

Predicting protein sumoylation sites from sequence features.

Shaolei Teng1, Hong Luo, Liangjiang Wang

  • 1Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA.

Amino Acids
|October 12, 2011
PubMed
Summary
This summary is machine-generated.

Predicting protein sumoylation sites is crucial for understanding cellular processes and human genetic disorders. A new machine learning approach using Random Forests accurately identifies these sites from protein sequences.

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SUMO-Binding Entities (SUBEs) as Tools for the Enrichment, Isolation, Identification, and Characterization of the SUMO Proteome in Liver Cancer
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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology
  • Genetics

Background:

  • Protein sumoylation, a key post-translational modification, regulates numerous cellular processes.
  • Dysregulation of sumoylation is linked to various human genetic disorders.
  • Accurate identification of sumoylation sites aids experimental design and mechanistic understanding.

Purpose of the Study:

  • To develop a novel machine learning approach for predicting protein sumoylation sites.
  • To enhance the accuracy of sumoylation site prediction using sequence information.
  • To provide a practical tool for researchers studying sumoylation.

Main Methods:

  • Utilized Random Forests (RFs) and Support Vector Machines (SVMs) for classification.
  • Employed domain-specific biological features for input vector encoding.
  • Analyzed the impact of sequence context, identifying a core motif (ΨKXE) as informative.

Main Results:

  • RF classifiers demonstrated superior performance compared to SVM models.
  • Sequence context, particularly the ΨKXE motif within 20 residues, significantly influenced prediction accuracy.
  • The developed machine learning approach achieved higher accuracy than existing methods.

Conclusions:

  • Machine learning, specifically RFs, offers a highly accurate method for predicting protein sumoylation sites from sequence data.
  • The findings facilitate a deeper understanding of sumoylation mechanisms and related diseases.
  • A web server, seeSUMO, has been developed for accessible, sequence-based sumoylation site prediction.