Computer-aided design of amino acid-based therapeutics : a review

During the last two decades, the pharmaceutical industry has progressed from detecting small molecules to designing biologic-based therapeutics. Amino acid-based drugs are a group of biologic-based therapeutics that can effectively combat the diseases caused by drug resistance or molecular deficiency. Computational techniques play a key role to design and develop the amino acid-based therapeutics such as proteins, peptides and peptidomimetics. In this study, it was attempted to discuss the various elements for computational design of amino acid-based therapeutics. Protein design seeks to identify the properties of amino acid sequences that fold to predetermined structures with desirable structural and functional characteristics. Peptide drugs occupy a middle space between proteins and small molecules and it is hoped that they can target "undruggable" intracellular protein-protein interactions. Peptidomimetics, the compounds that mimic the biologic characteristics of peptides, present refined pharmacokinetic properties compared to the original peptides. Here, the elaborated techniques that are developed to characterize the amino acid sequences consistent with a specific structure and allow protein design are discussed. Moreover, the key principles and recent advances in currently introduced computational techniques for rational peptide design are spotlighted. The most advanced computational techniques developed to design novel peptidomimetics are also summarized.

Medienart:

E-Artikel

Erscheinungsjahr:

2018

Erschienen:

2018

Enthalten in:

Zur Gesamtaufnahme - volume:12

Enthalten in:

Drug design, development and therapy - 12(2018) vom: 31., Seite 1239-1254

Sprache:

Englisch

Beteiligte Personen:

Farhadi, Tayebeh [VerfasserIn]
Hashemian, Seyed MohammadReza [VerfasserIn]

Links:

Volltext

Themen:

Amino Acids
In silico designing
Journal Article
Peptide
Peptides
Peptidomimetics
Protein
Protein-based drugs
Review

Anmerkungen:

Date Completed 29.10.2018

Date Revised 12.11.2023

published: Electronic-eCollection

Citation Status MEDLINE

doi:

10.2147/DDDT.S159767

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM284416517