Opendata, web and dolomites


Automated computational design of site-targeted repertoires of camelid antibodies

Total Cost €


EC-Contrib. €






Project "AutoCAb" data sheet

The following table provides information about the project.


Organization address
address: HERZL STREET 234
postcode: 7610001

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Israel [IL]
 Total cost 2˙337˙500 €
 EC max contribution 2˙337˙500 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-COG
 Funding Scheme ERC-COG
 Starting year 2019
 Duration (year-month-day) from 2019-01-01   to  2023-12-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    WEIZMANN INSTITUTE OF SCIENCE IL (REHOVOT) coordinator 2˙337˙500.00


 Project objective

We propose to develop the first high-throughput strategy to design, synthesize, and screen repertoires comprising millions of single-domain camelid antibodies (VHH) that target desired protein surfaces. Each VHH will be individually designed for high stability and target-site affinity. We will leverage recent methods developed by our lab for designing stable, specific, and accurate backbones at interfaces, the advent of massive and affordable custom-DNA oligo synthesis, and machine learning methods to accomplish the following aims: Aim 1: Establish a completely automated computational pipeline that uses Rosetta to design millions of VHHs targeting desired protein surfaces. The variable regions in each design will be encoded in DNA oligo pools, which will be assembled to generate the entire site-targeted repertoire. We will then use high-throughput binding screens followed by deep sequencing to characterize the designs’ target-site affinity and isolate high-affinity binders. Aim 2: Develop an epitope-focusing strategy that designs several variants of a target antigen, each of which encodes dozens of radical surface mutations outside the target site to disrupt potential off-target site binding. The designs will be used to isolate site-targeting binders from repertoires of Aim 1. Each high-throughput screen will provide unprecedented experimental data on target-site affinity in millions of individually designed VHHs. Aim 3: Use machine learning methods to infer combinations of molecular features that distinguish high-affinity binders from non binders. These will be encoded in subsequent designed repertoires, leading to a continuous “learning loop” of methods for high-affinity, site-targeted binding. AutoCAb’s interdisciplinary strategy will thus lead to deeper understanding of and new general methods for designing stable, high-affinity, site-targeted antibodies, potentially revolutionizing binder and inhibitor discovery in basic and applied biomedical research.

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "AUTOCAB" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email ( and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

The information about "AUTOCAB" are provided by the European Opendata Portal: CORDIS opendata.

More projects from the same programme (H2020-EU.1.1.)

MEMO (2020)

The Memory of Solitons

Read More  


Dynamic Modeling of Labor Market Mobility and Human Capital Accumulation

Read More  

VITAE (2018)

VIrTual BrAin PErfusion: Assessing cerebrovascular function by High Performance Computing from 3D brain vessel network data for vascular-targeted drug development in neurodegenerative diseases.

Read More