Evidence map›Paper›PMID 41900397›Full record

ArticleMicroorganisms2026

Unintended Creation or Insertion of Antisense Promoter Motifs During Codon Optimization: A Cyber-Biosecurity Risk.

Elad Carmi, Roni Glikman, Yuval Dorfan

Abstract read
In one paragraph

Article in Microorganisms, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Elad CarmiFaculty of Electrical Engineering, H.I.T.-Holon Institute of Technology, Holon 5810201, Israel.
Roni GlikmanFaculty of Electrical Engineering, H.I.T.-Holon Institute of Technology, Holon 5810201, Israel.ORCID 0009-0006-0399-3492
Yuval DorfanFaculty of Electrical Engineering, H.I.T.-Holon Institute of Technology, Holon 5810201, Israel.ORCID 0000-0003-3236-0572

Funding

Ariel University Joint grant with HITHolon Institute of Technology InternalIsrael Science Foundation 1142/24Ministry of Innovation, Science and Technology 0007618
6 · The paper itself

Abstract

Codon optimization is a cornerstone technique in synthetic biology and biotechnological production, aimed at enhancing heterologous protein expression through synonymous codon substitutions. While optimization traditionally focuses on forward-strand translation efficiency, its impact on the complementary DNA strand is not always carefully examined. In this study, we investigate whether codon optimization inadvertently introduces antisense motifs, specifically bacterial antisense promoter (e.g., "TATAAT"), and whether such motifs can be silently inserted into coding sequences on purpose without altering protein output. We developed a computational pipeline that (i) scans optimized sequences for antisense motifs. These could be either natural or synthetic unintended motifs; (ii) implements a silent insertion algorithm that preserves amino acid sequence; and (iii) evaluates insertion feasibility across a large genomic dataset. These components can also lead to useful scanning of synthetic sequences, before they are synthesized or ordered. It has the potential to save a great deal of time and money that might be spent in wet labs that are using the wrong sequences. Their experiments often fail due to predictable reasons, while these failures can be avoided using the software (SW) we developed, which is published here as an open source for academic and industrial usage. In a dataset of 484,741 protein-coding sequences, only 4.8% naturally contained the motif, yet 77.28% of motif-free sequences permitted silent insertions. We extend these findings with codon bias analysis, derive analytical bounds for insertion complexity, and propose computational defense strategies. These results uncover a novel cyber-biosecurity vulnerability in DNA design pipelines, emphasizing the need for bi-directional screening in codon optimization tools.

Indexed as

antisense promotercodon biascodon optimizationcyber-biosecurityDNA designE. colimotif insertionsynthetic biologytranslation efficiency

Identifiers

PMID41900397
PMCPMC13029128

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.