LAMP: A Database Linking Antimicrobial Peptides

PLoS One. 2013 Jun 18;8(6):e66557. doi: 10.1371/journal.pone.0066557. Print 2013.

Abstract

The frequent emergence of drug-resistant bacteria has created an urgent demand for new antimicrobial agents. Traditional methods of novel antibiotic development are almost obsolete. Antimicrobial peptides (AMPs) are now regarded as a potential solution to revive the traditional methods of antibiotic development, although, until now, many AMPs have failed in clinical trials. A comprehensive database of AMPs with information about their antimicrobial activity and cytotoxicity will help promote the process of finding novel AMPs with improved antimicrobial activity and reduced cytotoxicity and eventually accelerate the speed of translating the discovery of new AMPs into clinical or preclinical trials. LAMP, a database linking AMPs, serves as a tool to aid the discovery and design of AMPs as new antimicrobial agents. The current version of LAMP has 5,547 entries, comprising 3,904 natural AMPs and 1,643 synthetic peptides. The database can be queried using either simply keywords or combinatorial conditions searches. Equipped with the detailed antimicrobial activity and cytotoxicity data, the cross-linking and top similar AMPs functions implemented in LAMP will help enhance our current understanding of AMPs and this may speed up the development of new AMPs for medical applications. LAMP is freely available at: http://biotechlab.fudan.edu.cn/database/lamp.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antimicrobial Cationic Peptides / chemistry*
  • Databases, Protein*
  • Humans
  • Internet
  • User-Computer Interface

Substances

  • Antimicrobial Cationic Peptides

Grants and funding

This work was supported in part by the Shanghai Science and Technology Innovation Action Plan (2012) of China (Grant 12DE1940500) and the 'Yangtze River Delta' joint scientific and technological project of China (Grant 10495810600). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.