Molecular Evolution of Peptide Ligands with Custom-Tailored Characteristics for Targeting of Glycostructures

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.

Recommendations on the Implementation of Genetic Algorithms for the Directed Evolution of Enzymes for Industrial Purposes

“…The “standard GA parameters” : The “standard GA parameters” are the initial settings for the GA, which we aim to improve in this work. Although the selected values are to some extent arbitrary they are based on previous work reported in the literature . Figure S4 is a schematic showing the design of a typical GA.…”

“…Alternative methods use evolutionarily based algorithms. For example, a GA has been used to improve the experimentally based evolution of trypsin‐inhibiting hexapeptides, and an algorithm similar to a GA has facilitated the experimentally based evolution of peptide ligands (containing 16 amino acids) to improve binding to a model target glycostructure (ganglioside G M1 ) …”

Increasing the activity of cell adherent cyclic NGR peptides by optimizing the peptide length and amino acid character

“…The variation of peptides becomes enormous as the peptides length increases because peptides consist of a combination of 20 natural amino acids (e.g., 1.0 × 10 13 sequences for 10-mer L-peptides). 14 Amino peptidase N (APN or CD13) is a Zn 2+ -dependent metalloprotease that has been implicated in various cell functions, such as proliferation, invasion, and angiogenesis. 14,15 CD13 is highly expressed in various tumor and angiogenic cells.…”

“…14 Amino peptidase N (APN or CD13) is a Zn 2+ -dependent metalloprotease that has been implicated in various cell functions, such as proliferation, invasion, and angiogenesis. 14,15 CD13 is highly expressed in various tumor and angiogenic cells. 16 It has also been shown that CD13 is expressed in vascular endothelial cells, such as human umbilical vein endothelial cells (HUVECs), and plays multiple roles in angiogenesis.…”

Dynamics and binding interactions of peptide inhibitors of dengue virus entry

“…Their molecular weight ranges between a typical molecular weight of a small molecule to a protein. Peptide-based drugs also have a high specificity, potency, and low toxicity/limited side effects in the biomolecular recognition processes [15,16]. Furthermore, peptides are part of the components of the defense system found in protozoans, invertebrate, plants, and vertebrate as well as mammals which fight against invaded pathogens such as viruses, bacteria, and fungi by disrupting the orientation of their membrane [17].…”