Engineering topics

Egbert, Robert G. (ORCID:0000000294707124)

Publications and source records attributed to Egbert, Robert G. (ORCID:0000000294707124).

3 records

DOE OSTI2026

Beyond Component Optimization: Systems Level Biodesign for Lanthanide Recovery

Global demand for lanthanides (Ln) is projected to rise sharply over the next decade, while geographically concentrated supply chains and the low concentrations and matrix complexity of secondary feedstocks limit the reach of conventional hydro- and pyrometallurgical separation. Engineered biological systems offer a selective, low-energy alternative, and component-level advances in Ln-binding proteins, AI-designed selective scaffolds, and cell-surface display platforms now rival synthetic chelators in affinity and selectivity. These components, however, remain functionally isolated. Currently, there are no engineered chassis coupling recognition, intracellular trafficking, accumulation, and controlled release into an end-to-end pipeline. Here, we outline how new biodesign strategies and chassis selection must move beyond bioleaching to encompass the full recovery pathway. Achieving this requires integrating AI/ML-guided design, genome-scale build tools, high-throughput phenotyping, and biophysical transport modeling within a Design–Build–Test–Learn cycle tuned to recognition, trafficking, accumulation, and release.

Biodesign↗

DOE OSTI2025

A Rhodopseudomonas strain with a substantially smaller genome retains the core metabolic versatility of its genus

ABSTRACT Rhodopseudomonas are a group of phototrophic microbes with a marked metabolic versatility and flexibility that underpins their potential use in the production of value-added products, bioremediation, and plant growth promotion. Members of this group have an average genome size of about 5.5 Mb, but two closely related strains have genome sizes of about 4.0 Mb. To identify the types of genes missing in a reduced genome strain, we compared strain DSM127 with other Rhodopseudomonas isolates at the genomic and phenotypic levels. We found that DSM127 can grow as well as other members of the Rhodopseudomonas genus and retains most of their metabolic versatility, but it has many fewer genes associated with high-affinity transport of nutrients, iron uptake, nitrogen metabolism, and biodegradation of aromatic compounds. This analysis indicates genes that can be deleted in genome reduction campaigns and suggests that DSM127 could be a favorable choice for biotechnology applications using Rhodopseudomonas or as a strain that can be engineered further to reside in a specialized natural environment. IMPORTANCE Rhodopseudomonas are a cohort of phototrophic bacteria with broad metabolic versatility. Members of this group are present in diverse soil and water environments, and some strains are found associated with plants and have plant growth-promoting activity. Motivated by the idea that it may be possible to design bacteria with reduced genomes that can survive well only in a specific environment or that may be more metabolically efficient, we compared Rhodopseudomonas strains with typical genome sizes of about 5.5 Mb to a strain with a reduced genome size of 4.0 Mb. From this, we concluded that metabolic versatility is part of the identity of the Rhodopseudomonas group, but high-affinity transport genes and genes of apparent redundant function can be dispensed with.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding

Abstract Motivation The vast expansion of sequence data generated from single organisms and microbiomes has precipitated the need for faster and more sensitive methods to assess evolutionary and functional relationships between proteins. Representing proteins as sets of short peptide sequences (kmers) has been used for rapid, accurate classification of proteins into functional categories; however, this approach employs an exact-match methodology and thus may be limited in terms of sensitivity and coverage. We have previously used similarity groupings, based on the chemical properties of amino acids, to form reduced character sets and recode proteins. This amino acid recoding (AAR) approach simplifies the construction of protein representations in the form of kmer vectors, which can link sequences with distant sequence similarity and provide accurate classification of problematic protein families. Results Here, we describe Snekmer, a software tool for recoding proteins into AAR kmer vectors and performing either (i) construction of supervised classification models trained on input protein families or (ii) clustering for de novo determination of protein families. We provide examples of the operation of the tool against a set of nitrogen cycling families originally collected using both standard hidden Markov models and a larger set of proteins from Uniprot and demonstrate that our method accurately differentiates these sequences in both operation modes. Availability and implementation Snekmer is written in Python using Snakemake. Code and data used in this article, along with tutorial notebooks, are available at http://github.com/PNNL-CompBio/Snekmer under an open-source BSD-3 license. Supplementary information Supplementary data are available at Bioinformatics Advances online.

59 BASIC BIOLOGICAL SCIENCES↗