Engineering topics

Nisonoff, Hunter

Publications and source records attributed to Nisonoff, Hunter.

3 records

DOE OSTI2024

GENTANGLE: integrated computational design of gene entanglements

The design of two overlapping genes in a microbial genome is an emerging technique for adding more reliable control mechanisms in engineered organisms for increased stability. The design of functional overlapping gene pairs is a challenging procedure, and computational design tools are used to improve the efficiency to deploy successful designs in genetically engineered systems. GENTANGLE (Gene Tuples ArraNGed in overLapping Elements) is a high-performance containerized pipeline for the computational design of two overlapping genes translated in different reading frames of the genome. This new software package can be used to design and test gene entanglements for microbial engineering projects using arbitrary sets of user-specified gene pairs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Eukaryotic RNA-guided endonucleases evolved from a unique clade of bacterial enzymes

Abstract RNA-guided endonucleases form the crux of diverse biological processes and technologies, including adaptive immunity, transposition, and genome editing. Some of these enzymes are components of insertion sequences (IS) in the IS200/IS605 and IS607 transposon families. Both IS families encode a TnpA transposase and a TnpB nuclease, an RNA-guided enzyme ancestral to CRISPR-Cas12s. In eukaryotes, TnpB homologs occur as two distinct types, Fanzor1s and Fanzor2s. We analyzed the evolutionary relationships between prokaryotic TnpBs and eukaryotic Fanzors, which revealed that both Fanzor1s and Fanzor2s stem from a single lineage of IS607 TnpBs with unusual active site arrangement. The widespread nature of Fanzors implies that the properties of this particular lineage of IS607 TnpBs were particularly suited to adaptation in eukaryotes. Biochemical analysis of an IS607 TnpB and Fanzor1s revealed common strategies employed by TnpBs and Fanzors to co-evolve with their cognate transposases. Collectively, our results provide a new model of sequential evolution from IS607 TnpBs to Fanzor2s, and Fanzor2s to Fanzor1s that details how genes of prokaryotic origin evolve to give rise to new protein families in eukaryotes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions

Abstract Despite recent advances in high-throughput combinatorial mutagenesis assays, the number of labeled sequences available to predict molecular functions has remained small for the vastness of the sequence space combined with the ruggedness of many fitness functions. While deep neural networks (DNNs) can capture high-order epistatic interactions among the mutational sites, they tend to overfit to the small number of labeled sequences available for training. Here, we developed Epistatic Net (EN), a method for spectral regularization of DNNs that exploits evidence that epistatic interactions in many fitness functions are sparse. We built a scalable extension of EN, usable for larger sequences, which enables spectral regularization using fast sparse recovery algorithms informed by coding theory. Results on several biological landscapes show that EN consistently improves the prediction accuracy of DNNs and enables them to outperform competing models which assume other priors. EN estimates the higher-order epistatic interactions of DNNs trained on massive sequence spaces-a computational problem that otherwise takes years to solve.

Aghazadeh, Amirali (ORCID:0000000302230873)↗