Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

In Silico Driven Prediction of MAPK14 Off-Targets Reveals Unrelated Proteins with High Accuracy

View ORCID ProfileFlorian Kaiser, Maximilian G. Plach, View ORCID ProfileChristoph Leberecht, Thomas Schubert, View ORCID ProfileV. Joachim Haupt
doi: https://doi.org/10.1101/2020.07.24.219071
Florian Kaiser
1PharmAI GmbH, Dresden, Germany /
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Florian Kaiser
Maximilian G. Plach
22bind GmbH, Regensburg, Germany /
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Christoph Leberecht
1PharmAI GmbH, Dresden, Germany /
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Christoph Leberecht
Thomas Schubert
22bind GmbH, Regensburg, Germany /
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: schubert@2bind.com haupt@pharm.ai
V. Joachim Haupt
1PharmAI GmbH, Dresden, Germany /
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for V. Joachim Haupt
  • For correspondence: schubert@2bind.com haupt@pharm.ai
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

During the discovery and development of new drugs, candidates with undesired and potentially harmful side-effects can arise at all stages, which poses significant scientific and economic risks. Most of such phenotypic side-effects can be attributed to binding of the drug candidate to unintended proteins, so-called off-targets. The early identification of potential off-targets is therefore of utmost importance to mitigate any downstream risks. We showcase how the combination of knowledge-based in silico off-target screening and state-of-the-art biophysics can be applied to rapidly identify off-targets for a MAPK14 inhibitor. Out of 13 predicted off-targets, six proteins were confirmed to interact with the inhibitor in vitro, which translates to an exceptional hit rate of 46%. For two proteins, affinities in the lower micromolar range were obtained: The kinase IRE1 and the Hematopoietic Prostaglandin D Synthase, which is entirely unrelated to MAPK14 and is involved in different cell-regulatory processes. The whole off-target identification/validation pipeline can be completed as fast as within two months, excluding delivery times of proteins. These results emphasize how computational off-target screening in combination with MicroScale Thermophoresis can effectively reduce downstream development risks in a very competitive time frame and at low cost.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted July 24, 2020.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
In Silico Driven Prediction of MAPK14 Off-Targets Reveals Unrelated Proteins with High Accuracy
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
In Silico Driven Prediction of MAPK14 Off-Targets Reveals Unrelated Proteins with High Accuracy
Florian Kaiser, Maximilian G. Plach, Christoph Leberecht, Thomas Schubert, V. Joachim Haupt
bioRxiv 2020.07.24.219071; doi: https://doi.org/10.1101/2020.07.24.219071
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
In Silico Driven Prediction of MAPK14 Off-Targets Reveals Unrelated Proteins with High Accuracy
Florian Kaiser, Maximilian G. Plach, Christoph Leberecht, Thomas Schubert, V. Joachim Haupt
bioRxiv 2020.07.24.219071; doi: https://doi.org/10.1101/2020.07.24.219071

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4230)
  • Biochemistry (9123)
  • Bioengineering (6766)
  • Bioinformatics (23968)
  • Biophysics (12109)
  • Cancer Biology (9510)
  • Cell Biology (13753)
  • Clinical Trials (138)
  • Developmental Biology (7623)
  • Ecology (11674)
  • Epidemiology (2066)
  • Evolutionary Biology (15492)
  • Genetics (10631)
  • Genomics (14310)
  • Immunology (9473)
  • Microbiology (22822)
  • Molecular Biology (9086)
  • Neuroscience (48919)
  • Paleontology (355)
  • Pathology (1480)
  • Pharmacology and Toxicology (2566)
  • Physiology (3840)
  • Plant Biology (8322)
  • Scientific Communication and Education (1468)
  • Synthetic Biology (2295)
  • Systems Biology (6180)
  • Zoology (1299)