Literature DB >> 15892624

Improving on nature's defenses: optimization & high throughput screening of antimicrobial peptides.

D Raventós1, O Taboureau, P H Mygind, J D Nielsen, C P Sonksen, H-H Kristensen.   

Abstract

Antimicrobial peptides (AMPs) are ubiquitous in nature where they play important roles in host defense and microbial control. Despite their natural origin, antimicrobial spectrum and potency, the lead peptide candidates that so far have entered pharmaceutical development have all been further optimized by rational or semi-rational approaches. In recent years, several high throughput screening (HTS) systems have been developed to specifically address optimization of AMPs. These include a range of computational in silico systems and cell-based in vivo systems. The in silico-based screening systems comprise several computational methods such as Quantitative Structure/Activity Relationships (QSAR) as well as simulation methods mimicking peptide/membrane interactions. The in vivo-based systems can be divided in cis-acting and trans-acting screening systems. The cis-acting pre-screens, where the AMP exerts its antimicrobial effect on the producing cell, allow screening of millions or even billions of lead candidates for their basic antimicrobial or membrane-perturbating activity. The trans-acting screens, where the AMP is secreted or actively liberated from the producing cell and interacts with cells different from the producing cell, allow for screening under more complex and application-relevant conditions. This review describes the application of HTS systems employed for AMPs and lists advantages as well as limitations of these systems.

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Year:  2005        PMID: 15892624     DOI: 10.2174/1386207053764549

Source DB:  PubMed          Journal:  Comb Chem High Throughput Screen        ISSN: 1386-2073            Impact factor:   1.339


  9 in total

1.  High cerebrospinal fluid (CSF) penetration and potent bactericidal activity in CSF of NZ2114, a novel plectasin variant, during experimental pneumococcal meningitis.

Authors:  Christian Ostergaard; Dorthe Sandvang; Niels Frimodt-Møller; Hans-Henrik Kristensen
Journal:  Antimicrob Agents Chemother       Date:  2009-02-02       Impact factor: 5.191

2.  An FPGA implementation to detect selective cationic antibacterial peptides.

Authors:  Carlos Polanco González; Marco Aurelio Nuño Maganda; Miguel Arias-Estrada; Gabriel del Rio
Journal:  PLoS One       Date:  2011-06-28       Impact factor: 3.240

3.  Bacterial membrane activity of α-peptide/β-peptoid chimeras: influence of amino acid composition and chain length on the activity against different bacterial strains.

Authors:  Line Hein-Kristensen; Kolja M Knapp; Henrik Franzyk; Lone Gram
Journal:  BMC Microbiol       Date:  2011-06-22       Impact factor: 3.605

4.  Exploring the pharmacological potential of promiscuous host-defense peptides: from natural screenings to biotechnological applications.

Authors:  Osmar N Silva; Kelly C L Mulder; Aulus E A D Barbosa; Anselmo J Otero-Gonzalez; Carlos Lopez-Abarrategui; Taia M B Rezende; Simoni C Dias; Octávio L Franco
Journal:  Front Microbiol       Date:  2011-11-22       Impact factor: 5.640

5.  A Rapid and Quantitative Flow Cytometry Method for the Analysis of Membrane Disruptive Antimicrobial Activity.

Authors:  Neil M O'Brien-Simpson; Namfon Pantarat; Troy J Attard; Katrina A Walsh; Eric C Reynolds
Journal:  PLoS One       Date:  2016-03-17       Impact factor: 3.240

Review 6.  Antimicrobial Peptides as Anti-Infective Agents in Pre-Post-Antibiotic Era?

Authors:  Tomislav Rončević; Jasna Puizina; Alessandro Tossi
Journal:  Int J Mol Sci       Date:  2019-11-14       Impact factor: 5.923

7.  A Fungal Defensin Inhibiting Bacterial Cell-Wall Biosynthesis with Non-Hemolysis and Serum Stability.

Authors:  Sudong Qi; Bin Gao; Shunyi Zhu
Journal:  J Fungi (Basel)       Date:  2022-02-10

8.  Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach.

Authors:  Jesus A Beltran; Longendri Aguilera-Mendoza; Carlos A Brizuela
Journal:  BMC Genomics       Date:  2018-09-24       Impact factor: 3.969

9.  Evolution-Based Protein Engineering for Antifungal Peptide Improvement.

Authors:  Jing Gu; Noriyoshi Isozumi; Shouli Yuan; Ling Jin; Bin Gao; Shinya Ohki; Shunyi Zhu
Journal:  Mol Biol Evol       Date:  2021-10-27       Impact factor: 16.240

  9 in total

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