Header

UZH-Logo

Maintenance Infos

Computational Modeling of Designed Ankyrin Repeat Protein Complexes with Their Targets


Radom, Filip; Paci, Emanuele; Plückthun, Andreas (2019). Computational Modeling of Designed Ankyrin Repeat Protein Complexes with Their Targets. Journal of Molecular Biology, 431(15):2852-2868.

Abstract

Recombinant therapeutic proteins are playing an ever-increasing role in the clinic. High-affinity binding candidates can be produced in a high-throughput manner through the process of selection and evolution from large libraries, but the structures of the complexes with target protein can only be determined for a small number of them in a costly, low-throughput manner, typically by x-ray crystallography. Reliable modeling of complexes would greatly help to understand their mode of action and improve them by further engineering, for example, by designing bi-paratopic binders. Designed ankyrin repeat proteins (DARPins) are one such class of antibody mimetics that have proven useful in the clinic, in diagnostics and research. Here we have developed a standardized procedure to model DARPin-target complexes that can be used to predict the structures of unknown complexes. It requires only the sequence of a DARPin and a structure of the unbound target. The procedure includes homology modeling of the DARPin, modeling of the flexible parts of a target, rigid body docking to ensembles of the target and docking with a partially flexible backbone. For a set of diverse DARPin-target complexes tested it generated a single model of the complex that well approximates the native state of the complex. We provide a protocol that can be used in a semi-automated way and with tools that are freely available. The presented concepts should help to accelerate the development of novel bio-therapeutics for scaffolds with similar properties.

Abstract

Recombinant therapeutic proteins are playing an ever-increasing role in the clinic. High-affinity binding candidates can be produced in a high-throughput manner through the process of selection and evolution from large libraries, but the structures of the complexes with target protein can only be determined for a small number of them in a costly, low-throughput manner, typically by x-ray crystallography. Reliable modeling of complexes would greatly help to understand their mode of action and improve them by further engineering, for example, by designing bi-paratopic binders. Designed ankyrin repeat proteins (DARPins) are one such class of antibody mimetics that have proven useful in the clinic, in diagnostics and research. Here we have developed a standardized procedure to model DARPin-target complexes that can be used to predict the structures of unknown complexes. It requires only the sequence of a DARPin and a structure of the unbound target. The procedure includes homology modeling of the DARPin, modeling of the flexible parts of a target, rigid body docking to ensembles of the target and docking with a partially flexible backbone. For a set of diverse DARPin-target complexes tested it generated a single model of the complex that well approximates the native state of the complex. We provide a protocol that can be used in a semi-automated way and with tools that are freely available. The presented concepts should help to accelerate the development of novel bio-therapeutics for scaffolds with similar properties.

Statistics

Citations

Dimensions.ai Metrics
5 citations in Web of Science®
4 citations in Scopus®
Google Scholar™

Altmetrics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Department of Biochemistry
07 Faculty of Science > Department of Biochemistry
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Scopus Subject Areas:Life Sciences > Structural Biology
Life Sciences > Molecular Biology
Language:English
Date:12 July 2019
Deposited On:26 Sep 2019 09:10
Last Modified:22 Sep 2023 01:46
Publisher:Elsevier
ISSN:0022-2836
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.jmb.2019.05.005
PubMed ID:31082438
Full text not available from this repository.