Abstract
As an advanced approach to identify suitable targeting molecules required for various diagnostic and therapeutic interventions, we developed a procedure to devise peptides with customizable features by an iterative computer-assisted optimization strategy. An evolutionary algorithm was utilized to breed peptides in silico and the “fitness” of peptides was determined in an appropriate laboratory in vitro assay. The influence of different evolutional parameters and mechanisms such as mutation rate, crossover probability, gaussian variation and fitness value scaling on the course of this artificial evolutional process was investigated. As a proof of concept peptidic ligands for a model target molecule, the cell surface glycolipid ganglioside GM1, were identified. Consensus sequences describing local fitness optima were reached from diverse sets of L- and proteolytically stable D lead peptides. Ten rounds of evolutional optimization encompassing a total of just 4400 peptides lead to an increase in affinity of the peptides towards fluorescently labeled ganglioside GM1 by a factor of 100 for L- and 400 for D-peptides.
Highlights
In the field of bioactive substances, peptides are drawing increasing attention as they close the gap between small molecules and proteins, combining the compact size and synthetic accessibility of the former with the high specificity in molecular recognition processes of the latter
The use of peptides in therapy and diagnostics may be hampered by their proteolytic lability or limited cell penetration, too, these obstacles can be overcome by building up proteolytically stable peptide isomers from D-amino acid residues or by coupling the peptides to membrane shuttles [7,8]
The methodology smartly combines in silico evolution with in vitro testing to quickly obtain promising peptide ligand candidates with desired properties
Summary
In the field of bioactive substances, peptides are drawing increasing attention as they close the gap between small molecules and proteins, combining the compact size and synthetic accessibility of the former with the high specificity in molecular recognition processes of the latter. If the 3D molecular structure of the target is available it can be used in docking approaches for the design of peptide ligands for these targets using mere in silico procedures [9,10] Another way to optimize peptide sequences for desired applications is the use of structural scaffolds [10] in molecular dynamics simulations. Both approaches work best with rigid proteinacious target molecules. In protein design, directed evolution strategies which aim to improve candidates by iterative rounds of mutations and functional screenings constitute another way to optimize biomolecules [11].
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