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From a Natural Product to Its Biosynthetic Gene Cluster: A Demonstration Using Polyketomycin from Streptomyces diastatochromogenes Tü6028

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Computational tools for exploring sequence databases as a resource for antimicrobial peptides.

W F Porto¹, A S Pires², O L Franco³

¹Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia Universidade Católica de Brasília, Brasília, DF, Brazil; Porto Reports, Brasília, DF, Brazil.

Biotechnology Advances

|February 21, 2017

Summary

This summary is machine-generated.

This review explores data mining tools for identifying antimicrobial peptides (AMPs) in biological databases. It highlights methods for discovering novel AMPs to combat resistant bacteria and advance structural genomics.

Keywords:

Antimicrobial activity prediction Data mining Local alignments Molecular modelling Profile-HMM Regular expression Structural genomics

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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Area of Science:

Bioinformatics
Genomics
Drug Discovery

Background:

The post-genomic era yields vast sequence data, making databases crucial for novel biological insights.
Identifying antimicrobial peptides (AMPs) in these databases is gaining traction for understanding their evolution and potential therapeutic applications.

Purpose of the Study:

To review existing tools and techniques for mining antimicrobial peptides (AMPs) from biological databases.
To discuss the limitations of current methods and suggest advancements for identifying unannotated proteins.

Main Methods:

Data mining using local alignments and regular expressions for AMP identification.
Employing advanced techniques like antimicrobial activity prediction and molecular modeling for distant homologous sequences.

Main Results:

Current data mining primarily relies on local alignments and regular expressions.
Advanced methods are necessary for identifying distantly related AMP sequences.
The review consolidates information on tools, techniques, and their limitations for AMP discovery.

Conclusions:

Effective data mining strategies are essential for discovering novel AMPs from large biological databases.
These discoveries can aid in developing new antimicrobial agents to combat resistant bacteria.
The methodologies discussed have broader implications for structural genomics and understanding unannotated proteins.