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Updated: Jun 15, 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

Identification of functionally diverse lipocalin proteins from sequence information using support vector machine.

Ganesan Pugalenthi1, Krishna Kumar Kandaswamy, P N Suganthan

  • 1School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore.

Amino Acids
|February 27, 2010
PubMed
Summary
This summary is machine-generated.

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A new method, LipoPred, uses support vector machines to accurately identify lipocalin proteins from their amino acid sequences. This tool aids in discovering novel lipocalins, even those without known sequence homologs.

Area of Science:

  • Proteomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Lipocalins are a diverse protein family with crucial biological roles, including ligand transport and immune functions.
  • Identifying lipocalins from primary sequences is challenging due to low sequence identity, hindering discovery.
  • Existing methods lack specificity for identifying lipocalins based solely on sequence data.

Purpose of the Study:

  • To develop a computational method for accurate lipocalin prediction from protein sequences.
  • To overcome the limitations of sequence homology-based identification methods for lipocalins.
  • To provide a tool for identifying and annotating novel lipocalin family members.

Main Methods:

  • A support vector machine (SVM) approach named LipoPred was developed.

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Enrichment of Bacterial Lipoproteins and Preparation of N-terminal Lipopeptides for Structural Determination by Mass Spectrometry
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Enrichment of Bacterial Lipoproteins and Preparation of N-terminal Lipopeptides for Structural Determination by Mass Spectrometry

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Last Updated: Jun 15, 2026

An Integrated Approach for Microprotein Identification and Sequence Analysis
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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Enrichment of Bacterial Lipoproteins and Preparation of N-terminal Lipopeptides for Structural Determination by Mass Spectrometry
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Enrichment of Bacterial Lipoproteins and Preparation of N-terminal Lipopeptides for Structural Determination by Mass Spectrometry

Published on: May 21, 2018

  • The SVM model was trained on a dataset of 325 lipocalin and 325 non-lipocalin proteins.
  • Performance was evaluated using an independent test set and compared against PSI-BLAST, HMM, and SVM-Prot.

    • Main Results:

    • LipoPred achieved high accuracy (88.61%) and sensitivity (89.26%) in initial training.
    • On the test dataset, LipoPred demonstrated 84.25% accuracy with 88.57% sensitivity.
    • The method successfully identified lipocalins lacking sequence homologs and predicted novel lipocalin proteins.

    Conclusions:

    • LipoPred offers a robust and accurate method for identifying lipocalins from primary sequences.
    • The tool is valuable for discovering and annotating new lipocalins, especially those with no known homologs.
    • LipoPred outperforms existing sequence-based identification methods for the lipocalin family.