This work is part of the wide field of research on cationic antimicrobial peptides (CAMPs), molecules of great therapeutical potential for their antibacterial action, directed also against multi-drug resistant strains. By using a panel of bioinformatic, experimental and computational techniques, three novel tools were developed: (1) a scoring function, which allows the identification of putative CAMPs inside protein sequences and permits to perform strain-specific researches; (2) a fusion construct for the expression of recombinant CAMPs, which allows to prepare high purity peptides of variable length in high yields; (3) methods for the modelling of CAMPs by means of the Monte Carlo strategy and implicit solvation energy functions.

Identification, production and structural modelling of cationic antimicrobial peptides (CAMPs)

2014

Abstract

This work is part of the wide field of research on cationic antimicrobial peptides (CAMPs), molecules of great therapeutical potential for their antibacterial action, directed also against multi-drug resistant strains. By using a panel of bioinformatic, experimental and computational techniques, three novel tools were developed: (1) a scoring function, which allows the identification of putative CAMPs inside protein sequences and permits to perform strain-specific researches; (2) a fusion construct for the expression of recombinant CAMPs, which allows to prepare high purity peptides of variable length in high yields; (3) methods for the modelling of CAMPs by means of the Monte Carlo strategy and implicit solvation energy functions.
2014
it
File in questo prodotto:
File Dimensione Formato  
durante_lorenzo_26.pdf

accesso solo da BNCF e BNCR

Tipologia: Altro materiale allegato
Licenza: Tutti i diritti riservati
Dimensione 8.21 MB
Formato Adobe PDF
8.21 MB Adobe PDF

I documenti in UNITESI sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/341644
Il codice NBN di questa tesi è URN:NBN:IT:BNCF-341644