- Vast high-throughput screening study used to train unique machine learning algorithm to design synthetic activators -
- Rational engineering produced activators that induce the most durable and mitotically stable gene activation reported to date -
SOUTH SAN FRANCISCO, Calif., Sept. 14, 2023 (GLOBE NEWSWIRE) -- Epic Bio, a biotechnology company developing therapies to modulate gene expression using compact, non-cutting dCas proteins, today announced data supporting the breakthrough potential of its Gene Expression Modulation System (GEMS) platform for epigenetic engineering. In two preprint studies posted on bioRxiv, the company reported the discovery of exceptionally durable, hypercompact gene activators, and the training of a machine learning model to generate additional synthetic activators. Epic Bio, a biotechnology company developing therapies to modulate gene expression using compact, non-cutting dCas proteins, today announced data supporting the breakthrough potential of its Gene Expression Modulation System (GEMS) platform for epigenetic engineering. In two preprint studies posted on bioRxiv, the company reported the discovery of exceptionally durable, hypercompact gene activators, and the training of a machine learning model to generate additional synthetic activators.
Novel Activators from High-Throughput Screen are Optimized
In the first paper, “Discovery and engineering of hypercompact epigenetic modulators for durable gene activation,” Epic Bio’s team reports the outcomes of the widest-known survey of naturally occurring protein sequences to identify novel activators, and subsequent engineering to overcome the primary barriers to therapeutic use.
Epic scientists designed a high-throughput screen to systematically integrate the transcriptional effects of peptide sequences from human, viral and archaeal species. The peptide sequences were incorporated into a GEMS construct and assayed for their ability to activate a synthetic genetic locus.
Resulting activators were then subjected to protein engineering to overcome the three main barriers to therapeutic use: Activator potency (strength of activation), robustness (activity against multiple different target types), and durability (persistence/heritability of the gene activation after transient delivery of the activator).
Ultimately, Epic’s team created activators that induce the most durable and mitotically stable gene activation reported to date. These display a novel ability to maintain target activation through dozens of cell divisions after a single transient delivery, despite occupying ~12-20% of the cargo size of the currently most commonly used activators.
Machine Learning Improves Success in Discovering Novel Activators
In the second paper, “Improving few-shot learning-based protein engineering with evolutionary sampling,” Epic reports on the development of a novel machine-learning approach, trained on the activators discovered in its prior work, to design entirely new synthetic activators. Improving few-shot learning-based protein engineering with evolutionary sampling,” Epic reports on the development of a novel machine-learning approach, trained on the activators discovered in its prior work, to design entirely new synthetic activators.
To address the challenges of limited training data and the rarity of positive hits in this setting, Doctors Zaki Jawaid, Robin Yeo, and Timothy Daley created a novel Evolutionary Monte Carlo algorithm, called Evolutionary Monte Carlo Search, to efficiently sample the fitness landscape and propose novel, potent gene activators. Proposed activator sequences were experimentally validated for their ability to activate a synthetic genetic locus.
Researchers found that Evolutionary Monte Carlo Search was not only capable of improving the sequence diversity and novelty of designed sequences, but that it dramatically improved the hit rate of finding functional gene activators, both compared to more traditional machine-learning approaches as well as compared to the outputs of high-throughput screening.
This approach therefore holds promise for a number of diverse protein engineering challenges, and has the potential to accelerate the design of novel and active proteins for a variety of purposes including therapeutics.
About Epic Bio
Epic Bio is a leading epigenetic editing company, leveraging the power of CRISPR without cutting DNA. The company’s proprietary Gene Expression Modulation System (GEMS) includes the smallest Cas protein known to work in human cells, enabling in vivo delivery via a single AAV vector. Epic’s lead program, EPI-321, is in IND-enabling studies for treatment of facioscapulohumeral muscular dystrophy (FSHD); additional programs seek to address alpha-1 antitrypsin deficiency (A1AD), heterozygous familial hypercholesterolemia (HeFH), and other indications. Visit www.epic-bio.com for more information or follow us on Twitter and LinkedIn.
Investor Contact
Shawn M. Cox
Epic Bio
Manager, Investor Relations and Corporate Communications
shawn.cox@epic-bio.com
Media Contact
Lisa Raffensperger
Ten Bridge Communications
lisa@tenbridgecommunications.com
(617) 903-8783
