275
Views
3
CrossRef citations to date
0
Altmetric
Articles

Affinity enhancement of HER2-binding Z(HER2:342) affibody via rational design approach: a molecular dynamics study

, , , &
Pages 1919-1928 | Received 02 Jun 2013, Accepted 04 Sep 2013, Published online: 15 Oct 2013
 

Abstract

Human epidermal growth factor receptor 2 (HER2) contributes to the development of breast cancers and malignancies. On the other hand, engineered affibody Z(HER2:342) that binds to HER2 can be successfully used for both diagnostic purposes and specific ablation of malignant HER2-positive cell lines. In the current study, electrostatics-based prediction was applied for improving Z(HER2:342) binding affinity using computational design. The affibody Z(HER2:342) alone and in complex with HER2 was energetically minimized, solvated in explicit water, and neutralized. After heating and equilibration steps, the system was studied by isothermal-isobaric (NPT) MD simulation. According to trajectories, Z(HER2:342) specifically binds to HER2 through hydrogen bonds and salt bridges. Based on the electrostatic binding contributions, two affinity-matured variants namely V1 (Tyr35Arg) and V2 (Asn6Asp and Met9Glu) were rationally designed. More investigations through MD simulation show that V1 interacts with HER2 receptor more strongly, compared to Z(HER2:342) and V2.

Acknowledgment

The authors are grateful to School of Computer Science, Institute for Research in Fundamental Science (IPM), Tehran, Iran, for professional technical assistance.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 1,074.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.