Biology subjects

Alexanian, T.

Publications and source records attributed to Alexanian, T..

3 recordsLinked to original sources

bioRxiv2026

The Limits of Sequence-Based Biosecurity Screening Tools in the Age of AI-Assisted Protein Design

Rapid advancements in AI have enabled significant progress in protein and nucleic acid design, but they also pose biosecurity challenges. We examine the vulnerabilities of biosecurity screening software (BSS) to AI-reformulated synthetic homologs of proteins of concern (POCs) that have been fragmented into smaller segments. We evaluate four BSS tools that were recently patched to enhance their AI resiliency. Without any further modification, we found that two of the four tools were capable of robustly detecting fragments as short as 50 nucleotides, demonstrating screening capabilities that exceed those requested in the U.S. Framework for Nucleic Acid Synthesis. Upgraded versions of the other two tools improved performance. Although our findings confirm the effectiveness of the tested BSS tools, at the same time, they emphasize the urgency of developing alternate BSS approaches to counter evolving AI-enabled biosecurity risks.

synthetic biology↗

bioRxiv2025

Inter-tool analysis of a NIST dataset for assessing baseline nucleic acid sequence screening

Nucleic acid synthesis is a dual-use technology that can benefit fields such as biology, medicine, and information storage. However, synthetic nucleic acids could also potentially be used negligently and ultimately cause harm, or be used with malicious intent to cause harm. Thus, this technology needs to be appropriately safeguarded. Sequence screening is one component of a biosecurity protocol for preventing such harm and consists of differentiating Sequences of Concern (SOCs) from benign sequences that are not associated with pathogenicity or toxicity. There exist many fit-for-purpose tools that have been developed for DNA synthesis sequence screening. However, questions remain regarding their performance with respect to consistency of screening. To aid in determining if screening tools are harmonized in regard to baseline sequence screening, NIST constructed a test dataset based on current screening recommendations. NIST then sent blinded datasets to sequence screening tool developers for testing. Overall, there was a general agreement between the tools and NIST assignments of the sequences and all tools had a baseline performance of greater than 95% sensitivity and 97% accuracy. Disagreement on specific sequences largely arose from single tools and could be traced to differences in defining a SOC and/or methodological differences in screening algorithms.

bioinformatics↗

bioRxiv2024

Toward AI-Resilient Screening of Nucleic Acid Synthesis Orders: Process, Results, and Recommendations

Fast-moving advances in AI-assisted protein engineering are enabling breakthroughs in the life sciences that promise numerous beneficial applications. At the same time, these new capabilities are creating potential biosecurity challenges by providing new pathways to intentional or accidental synthesis of genes that encode hazardous proteins. The synthesis of nucleic acids is a key choke point in the AI-assisted protein engineering pipeline as it is where digital designs are transformed into physical instructions that can produce potentially harmful proteins. Thus, one focus for efforts to enhance biosecurity in the face of new AI-enabled capabilities is on bolstering the screening of orders by nucleic acid synthesis providers. We describe a multistakeholder, cross-sector effort to address biosecurity challenges with uses of AI-powered biological design tools to reformulate naturally occurring proteins of concern to create synthetic homologs that have low sequence identity to the wild-type proteins. We evaluated the abilities of traditional nucleic acid biosecurity screening tools to detect these synthetic homologs and found that, of tools tested, not all could previously detect such AI-redesigned sequences reliably. However, as we report, patches were built and deployed to improve detection rates over the course of the project, resulting in a final mean detection rate over tools of 97% of the synthetic homologs that were determined, using in-silico metrics, to be more likely to retain wild-type-like function. Finally, we make recommendations on approaches for studying and addressing the rising risk of adversarial AI-assisted protein engineering attacks like the one we identified and worked to mitigate.

synthetic biology↗