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All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

WEBINAR, May 6: New Discoveries Using GeneLab

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics data and collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 220 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab sample processing lab. The GLDS contains rich metadata about each experiment and has recently integrated radiation dosimetry data from experiments flown on the Space Shuttle. GeneLab has also recently implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretation of the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 120 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. Discoveries made using GeneLab have begun and will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

Sylvain V. Costes↗

NASA NTRSDate not supplied

Maximizing Spaceflight Biological Data with Omics Analytics: The NASA GeneLab Database

NASA’s GeneLab includes an open-access repository of some 250+ omics datasets generated by biological experiments relevant to spaceflight including simulated cosmic radiation and microgravity. In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics background, GeneLab has become a knowledgebase platform converting raw genetic and proteomic signatures found in flight samples into biological and physiological meanings. A large community of more than 100 scientists has rallied behind GeneLab and organized into four Analysis Working Groups (AWGs: Animal, Plant, Microbe, and Multi-Omics). Together, the AWGs have gained scientific recognition worldwide by establishing a consortium in charge of adopting new complex standards for data analysis workflows and omics sample processing in a rapidly evolving field. We will demonstrate the usage of the repository with smart search capability, an online controlled-access toolshed "Galaxy" to process user data with vetted standard workflows, a workspace for data sharing and a data submission portal with ontology control for better metadata curation. The GeneLab visualization portal will also be demonstrated, showing how anyone without formal training in bioinformatics can now browse the space biology omics data to discover new biology and potential solutions to improve life in space.

Sylvain Vincent Costes↗

NASA NTRSDate not supplied

GeneLab: The NASA System Biology Platform for Space Omics Repository, Analysis and Visualization

GeneLab↗

NASA NTRSDate not supplied

Spaceflight Biospecimen and Data Sharing in Support of Science Discovery and Exploration

For decades, NASA and international partners have conducted biological experiments in space to understand effects of spaceflight and address potential hazards. To enable spaceflight back to the Moon, and then to Mars and beyond, it is imperative to further understand basic science and health risks associated with spaceflight, along with developing countermeasures. The sending of experiments and organisms into space is a costly endeavor. To maximize scientific return, sharing with the scientific community both space-flown biospecimens and data from completed experiments is essential. New fundamental, applied, and bioinformatic science insights can be gained from specimen and data sharing efforts. Data reuse enables spaceflight health risk modeling, analyzing adverse outcomes across spaceflight hazards, and deep space autonomous support for the flight medical officer. Space-flown biospecimens not required by mission Principal Investigators are regularly archived and made available for scientific request. The largest biorepository of these samples are found within NASA’s Institutional Scientific Collection at Ames Research Center (ISC-ARC), which stores over 32,000 specimens mostly from Shuttle and International Space Station (ISS) missions, but also some ground-based analog samples. The Ames Life Sciences Data Archive manages the ISC-ARC. Tissues are predominantly from mice and rats, though samples are also available from bacteria and quail. Only a handful of other similar collections exist worldwide. Rodent biospecimens exposed to simulated space radiation at Brookhaven National Laboratory are archived under the purview of NASA HRP Space Radiation Element. Microbial collection and analyses from 20 years of routine environmental monitoring of air, surfaces, and water systems of the ISS were performed to ensure a safe environment for astronauts. Samples from the ISC-ARC, space radiation and microbial collections are searchable and requestable through the NASA Life Sciences Data Archive (LSDA). Decades of planetary protection microbial isolates derived from spacecraft bioburden are archived in JPL’s microbial collection. Rodent biospecimens from spaceflight investigations conducted by the Japan Aerospace Exploration Agency (JAXA) are archived and available at the JAXA Biorepository at Tsukuba Space Center. The Russian Institute of Biomedical Problems also has a collection of animal, microbial, cellular, and fungi available for research from ground analog experiments. Several data repositories exist for scientists to utilize. The LSDA is the primary NASA source of life sciences research data and information. It contains decades of spaceflight and ground-analog research involving human, microbial, cellular, plant, and animal subjects. Data is collected from NASA-funded investigations through the Human Research Program and the Space Biology Program. The NASA Lifetime Surveillance of Astronaut Health collects and grants access to clinical and occupational health monitoring data from astronauts, with a list and description of data collected available for request through the LSDA. NASA GeneLab at ARC collects genomic, transcriptomic, proteomic, and metabolomic data from any species. It is a repository and platform for collaborative open-science bioinformatic approaches. JAXA is establishing an ‘omics-based repository in collaboration with the Tohoku Medical Megabank (ToMMo), called the JAXA-ToMMo Integrated Biobank for Space Life Science. Overall, the sharing of these biospecimen and data resources can assist researchers worldwide in understanding spaceflight effects on biology, along with enabling next generation data science applications for space exploration platforms. Websites: https://lsda.jsc.nasa.gov/ ; https://www.nasa.gov/ames/research/space-biosciences/isc-bsp ; https://www.nasa.gov/ames/research/space-biosciences/alsda

Ryan T. Scott↗

NASA NTRSDate not supplied

NASA GeneLab: Open Science for Life in Space

NASA’s GeneLab helps scientists understand how the fundamental building blocks of life – DNA, RNA, proteins, and metabolites – change from exposure to the space environment including microgravity and cosmic radiation exposure. GeneLab does so by providing fully coordinated epigenomics, genomics, transcriptomics, proteomics, and metabolomics data (collectively known as omics data) alongside essential metadata describing each spaceflight and space-relevant experiment. The open-access GeneLab repository currently consists of over 300 omics datasets generated by biological experiments, involving various model organisms, that are relevant to spaceflight. In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics knowledge, GeneLab has started processing and analyzing these datasets to generate differential gene expression data and identify biological and physiological pathways that are dysregulated as a result of spaceflight. To aide GeneLab’s efforts to harmonize and democratize space-relevant omics data, over 130 scientists have joined one of four GeneLab Analysis Working Groups (Animal AWG, Plant AWG, Microbe AWG, Multi-Omics AWG) and together helped develop and adopted standard data analysis workflows for all data types available in GeneLab. Currently, the GeneLab Data System includes a data repository with federated search capability, an online controlled-access toolshed powered by "Galaxy" for users to process data with vetted standard workflows, a workspace for data sharing, a data submission portal, and the ability to browse and visualize transcriptomics processed data. The user interface was designed to be accessible to a broad variety of users, including high school and college students who can use it to learn about omics data analysis and space biology. The visualization portal enhances GeneLab’s ability to democratize omics data by removing the need for bioinformatics expertise to interpret transcriptomics data hosted on GeneLab. This presentation will provide an over-view of NASA’s GeneLab including how to navigate the GeneLab Data System and will conclude by providing resources for opportunities to work with GeneLab and NASA at large.

Amanda M Saravia-Butler↗

NASA NTRSDate not supplied

Culture-Independent Fungal Profiling for the International Space Station using Nanopore Sequencing: Method Development

Microbial monitoring of the International Space Station (ISS) environment is a crew health requirement that encompasses both bacterial and fungal identification. To achieve this currently, culture-based methods are used for sample collection, and these samples must be returned to the laboratory for analysis. The use of culture and the need for sample return to Earth results in a bias toward culturable organisms and causes a significant delay between sample collection and delivery of final data (weeks to months), respectively. Recently, advancements in molecular technology have aided a broad range of applications, including medical, industrial, and basic sciences. Additionally, increases in portability and ease-of-use of molecular platforms have provided point-of-use capabilities demonstrated by the miniPCR thermal cycler (miniPCR bio) and the MinION sequencer (Oxford Nanopore Technologies). Together, these devices have been applied to, and validated for, the identification of bacteria onboard the ISS. Building on this work, we have developed a spaceflight-compatible fungal workflow. Molecular-based fungal analysis is complicated by low biomass, difficult-to-lyse spores, debate regarding the region for taxonomic assignment, and the lack of bioinformatic pipelines and reference databases. To overcome these difficulties, primers yielding an ~ 2 Kb amplicon were validated against a wide range of ISS fungal isolates. The current spaceflight library preparation was substantially optimized, a bioinformatic pipeline was created, and refinements to the UNITE database were implemented. To compare this optimized method to the current culture-based standard, 30 sample sets (60 total swabs, two swabs held in tandem) were evaluated. Parallel fungal profiles were obtained between the two methods, with the culture-independent method revealing increased diversity. The addition of this method to the already established bacterial process fulfills the crew health identification requirement. Moreover, the implementation of this method onboard ISS will enhance our understanding of its unique fungal microbiome.

Hang N. Nguyen↗

DOE OSTI2015

Order-to-Disorder Transitions in BECN1 Regulate Autophagy

Autophagy, an essential conserved, cell survival pathway in eukaryotes is responsible for degradation and recycling of unwanted, damaged or harmful cytoplasmic components. Our pathway-wide bioinformatics analysis shows that intrinsically disordered regions (IDRs) are the most common structural feature of autophagy proteins and these IDRs may be responsible for many regulatory interactions. BECN1 is a critical component of the autophagy nucleation complex. Aberrant or reduced autophagy, due to BECN1 defects or low levels is implicated in many diseases. Over 20 cellular or viral proteins appear to interact with BECN1 to modulate autophagy. We are also using a multi-faceted experimental approach encompassing bioinformatics, computational, structural, biophysical, biochemical, cell biology and molecular biology methods to understand the mechanism by which BECN1 mediates autophagy, and how partners bind to BECN1 to modulate autophagy. This work reveals a structural and functional dichotomy within the 450-residue human BECN1: residues 42-132 constitute a poorly conserved IDR that includes a BECN1 BH3 domain (BH3D) responsible for binding Bcl-2 proteins, while the highly-conserved C-terminal residues 133-450 comprise three conformationally-flexible domains: the helical domain (HD), the coiled-coil domain and the b-a-repeat in autophagy domain. Finally, our structural and biophysical results show that the BH3D and HD undergo binding-associated disorder-to-structure transitions and have identified residues important for regulating autophagy.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Lake Arrowhead Microbial Genomics Conference

The Lake Arrowhead Microbial Genomics Conference (formerly the E. coli and Small Genomes Conference, and the Microbial Genomes Conference) was held September 11-15, 2022, at the UCLA Conference Center at Lake Arrowhead, California. This conference is part of a yearly meeting initiated in 1991 to bring together genome sequencers, bioinformatics specialists, biologists, and geneticists, to forge interactions that would result in meaningful functional genomics. The goal of the meeting was to translate the influx of new genome sequencing information into useful biological studies. The Lake Arrowhead 2022 meeting had a major focus on microbial communities, the human microbiome, pathogens, phage therapy, bioinformatic methods of analysis, bioenergenics, and environmental microbiology. The field of genomics has reached the point where deriving the sequence of an organism’s entire genome is now seen as a beginning rather than an endpoint. Moreover, understanding the sequences of whole communities, and particularly those that make up different microbiomes, is now a central point of the field. The 2022 meeting covered microorganisms for which extensive analyses exist, and those for which new biological and technical strategies are being developed. The focus on biodiversity, the human microbiome, pathogenic organisms and methods of countering them, and bioenergetics added special significance to this meeting. This meeting was designed to have a mix of 36 invited presentations and poster sessions with more than 80 posters, and had 147 participants. This meeting has become established as a major annual microbial genomics conference during the last 29 years and has assured status and quality. It offers a major opportunity for young investigators and students to attend, present posters, and give talks. In fact, 50% of the invited speakers were early career stage scientists. An important impact of the conference was the forging of new collaborations between scientists and particularly young scientists with the array of multidisciplinary researchers at the meeting.

Miller, Jeffrey H.↗

DOE OSTI2026

The need for standardization and improved open (meta)data practices in metaproteomics

Metaproteomics enables functional insight into microbial communities by identifying and quantifying proteins in complex samples. Yet, heterogeneous analytical workflows and the lack of standardization across experimental and bioinformatics stages hinder reproducibility and comparability, limiting integration with other omics data. We here present a community-developed reporting checklist tailored to the specific needs of metaproteomics. We also outline current efforts to enable structured and interoperable metadata capture, drawing on standards from proteomics and microbiome research wherever possible. By promoting transparent reporting and advancing metadata practices, our recommendations aim to align metaproteomics more closely with FAIR principles and support reproducible and interoperable research practices.

Armengaud, Jean [Universite Paris-Saclay, France]↗

DOE OSTI2016

Unification of [FeFe]-hydrogenases into three structural and functional groups

[FeFe]-hydrogenases (Hyd) are structurally diverse enzymes that catalyze the reversible oxidation of hydrogen (H 2 ). Recent biochemical data demonstrate new functional roles for these enzymes, including those that function in electron bifurcation where an exergonic reaction is coupled with an endergonic reaction to drive the reversible oxidation/production of H 2 . To identify the structural determinants that underpin differences in enzyme functionality, a total of 714 homologous sequences of the catalytic subunit, HydA, were compiled. Bioinformatics approaches informed by biochemical data were then used to characterize differences in inferred quaternary structure, HydA active site protein environment, accessory iron-sulfur clusters in HydA, and regulatory proteins encoded in HydA gene neighborhoods. HydA homologs were clustered into one of three classification groups, Group 1 (G1), Group 2 (G2), and Group 3 (G3). G1 enzymes were predicted to be monomeric while those in G2 and G3 were predicted to be multimeric and include HydB, HydC (G2/G3) and HydD (G3) subunits. Variation in the HydA active site and accessory iron-sulfur clusters did not vary by group type. Group-specific regulatory genes were identified in the gene neighborhoods of both G2 and G3 Hyd. Analyses of purified G2 and G3 enzymes by mass spectrometry strongly suggests that they are post-translationally modified by phosphorylation. In conclusion, these results suggest that bifurcation capability is dictated primarily by the presence of both HydB and HydC in Hyd complexes, rather than by variation in HydA.

09 BIOMASS FUELS↗

DOE OSTI2018

Whole-genome sequencing and single nucleotide polymorphisms in multidrug-resistant clinical isolates of Mycobacterium tuberculosis from the Philippines

Thousands of cases of multidrug-resistant tuberculosis (TB) have been observed in the Philippines, but studies on the Mycobacterium tuberculosis (MTB) genotypes that underlie the observed drug resistance profiles are lacking. This study aimed to analyse the whole genomes of clinical MTB isolates representing various resistance profiles to identify single nucleotide polymorphisms (SNPs) in resistance-associated genes. The genomes of ten MTB isolates cultured from banked sputum sources were sequenced. Bioinformatics analysis consisted of assembly, annotation and SNP identification in genes reported to be associated with resistance to isoniazid (INH), rifampicin (RIF), ethambutol (ETH), streptomycin, pyrazinamide (PZA) and fluoroquinolones (FQs). The draft assemblies covered an average of 97.08% of the expected genome size. Seven of the ten isolates belonged to the Indo-Oceanic lineage/EA12-Manila clade. Two isolates were classified into the Euro-American lineage, whilst the pre-XDR (pre-extensively drug-resistant) isolate was classified under the East Asian/Beijing clade. The SNPs katG Ser315Thr, rpoB Ser450Leu and embB Met306Val were found in INH- (4/7), RIF- (3/6) and ETH-resistant (2/6) isolates, respectively, but not in susceptible isolates. Mutations in the inhA promoter and in the pncA and gyrA genes known to be involved in resistance to INH, PZA and FQs, respectively, were also identified. This study represents the first effort to investigate the whole genomes of Philippine clinical strains of MTB exhibiting various multidrug resistance profiles. Whole-genome data can provide valuable insights to the mechanistic and epidemiological qualities of TB in a high-burden setting such as the Philippines.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

rcsb-api : Python Toolkit for Streamlining Access to RCSB Protein Data Bank APIs

The Protein Data Bank (PDB) was founded in 1971 as the first open-access digital data resource in biology to serve as the single global archive for three-dimensional (3D) macromolecular structure data. Current PDB holdings exceed 230,000 experimentally determined structures of proteins, nucleic acids, viruses, and macromolecular machines. The RCSB Protein Data Bank RCSB.org research-focused web portal facilitates search, analyses, and visualization of every PDB structure along with more than one million Computed Structure Models from AlphaFold DB and the ModelArchive. It is powered by a set of publicly available Application Programming Interfaces (APIs) that both support RCSB.org users and provide programmatic access to PDB data. Given the breadth and levels of granularity encompassed in this rich data collection, efficiently accessing the information programmatically may be challenging for new users. RCSB PDB has developed a Python software package, rcsb-api , that facilitates easy and efficient use of RCSB PDB APIs within a Python environment. This software tool is designed to streamline access to the extensive corpus of data housed within the PDB, enabling researchers to search, retrieve, and analyze 3D biostructure data seamlessly. Its use will accelerate research in structural biology, molecular biology and biochemistry, drug discovery, and bioinformatics by providing more efficient tools for data integration and analysis. The new toolkit is available on GitHub (github.com/rcsb/py-rcsb-api) and published to the public Python package repository (PyPI) to foster wider usage and support basic and applied research in fundamental biology, biomedicine, and the energy sciences.

FAIR principles↗

DOE OSTI2015

Large Scale Comparative Visualisation of Regulatory Networks with TRNDiff

The advent of Next Generation Sequencing (NGS) technologies has seen explosive growth in genomic datasets, and dense coverage of related organisms, supporting study of subtle, strain-specific variations as a determinant of function. Such data collections present fresh and complex challenges for bioinformatics, those of comparing models of complex relationships across hundreds and even thousands of sequences. Transcriptional Regulatory Network (TRN) structures document the influence of regulatory proteins called Transcription Factors (TFs) on associated Target Genes (TGs). TRNs are routinely inferred from model systems or iterative search, and analysis at these scales requires simultaneous displays of multiple networks well beyond those of existing network visualisation tools [1]. In this paper we describe TRNDiff, an open source system supporting the comparative analysis and visualization of TRNs (and similarly structured data) from many genomes, allowing rapid identification of functional variations within species. The approach is demonstrated through a small scale multiple TRN analysis of the Fur iron-uptake system of Yersinia, suggesting a number of candidate virulence factors; and through a larger study exploiting integration with the RegPrecise database (http://regprecise.lbl.gov; [2]) - a collection of hundreds of manually curated and predicted transcription factor regulons drawn from across the entire spectrum of prokaryotic organisms.

Bioinformatics, Visualisation, Transcription, Regu↗

DOE OSTI2023

A landing pad system for multicopy gene integration in Issatchenkia orientalis

The robust nature of the non-conventional yeast Issatchenkia orientalis allows it to grow under highly acidic conditions and therefore, has gained increasing interest in producing organic acids using a variety of carbon sources. Recently, the development of a genetic toolbox for I. orientalis, including an episomal plasmid, characterization of multiple promoters and terminators, and CRISPR-Cas9 tools, has eased the metabolic engineering efforts in I. orientalis. However, multiplex engineering is still hampered by the lack of efficient multicopy integration tools. To facilitate the construction of large, complex metabolic pathways by multiplex CRISPR-Cas9-mediated genome editing, we developed a bioinformatics pipeline to identify and prioritize genome-wide intergenic loci and characterized 47 gRNAs located in 21 intergenic regions. These loci are screened for guide RNA cutting efficiency, integration efficiency of a gene cassette, the resulting cellular fitness, and GFP expression level. We further developed a landing pad system using components from these well-characterized loci, which can aid in the integration of multiple genes using single guide RNA and multiple repair templates of the user’s choice. We have demonstrated the use of the landing pad for simultaneous integrations of 2, 3, 4, or 5 genes to the target loci with efficiencies greater than 80%. As a proof of concept, we showed how the production of 5-aminolevulinic acid can be improved by integrating five copies of genes at multiple sites in one step. We have further demonstrated the efficiency of this tool by constructing a metabolic pathway for succinic acid production by integrating five gene expression cassettes using a single guide RNA along with five different repair templates, leading to the production of 9 g/L of succinic acid in batch fermentations. Furthermore, this study demonstrates the effectiveness of a single gRNA-mediated CRISPR platform to build complex metabolic pathways in a non-conventional yeast. This landing pad system will be a valuable tool for the metabolic engineering of I. orientalis.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Structure and assembly of the diiron cofactor in the heme-oxygenase–like domain of the N-nitrosourea–producing enzyme SznF

In biosynthesis of the pancreatic cancer drug streptozotocin, the tridomain nonheme-iron oxygenase SznF hydroxylates N δ and N ω ' of N ω -methyl- L -arginine before oxidatively rearranging the triply modified guanidine to the N-methyl-N-nitrosourea pharmacophore. A previously published structure visualized the monoiron cofactor in the enzyme’s C-terminal cupin domain, which promotes the final rearrangement, but exhibited disorder and minimal metal occupancy in the site of the proposed diiron cofactor in the N-hydroxylating heme-oxygenase–like (HO-like) central domain. We leveraged our recent observation that the N-oxygenating µ-peroxodiiron(III/III) intermediate can form in the HO-like domain after the apo protein self-assembles its diiron(II/II) cofactor to solve structures of SznF with both of its iron cofactors bound. These structures of a biochemically validated member of the emerging heme-oxygenase–like diiron oxidase and oxygenase (HDO) superfamily with intact diiron cofactor reveal both the large-scale conformational change required to assemble the O 2 -reactive Fe 2 (II/II) complex and the structural basis for cofactor instability—a trait shared by the other validated HDOs. During cofactor (dis)assembly, a ligand-harboring core helix dynamically (un)folds. The diiron cofactor also coordinates an unanticipated Glu ligand contributed by an auxiliary helix implicated in substrate binding by docking and molecular dynamics simulations. The additional carboxylate ligand is conserved in another N-oxygenating HDO but not in two HDOs that cleave carbon–hydrogen and carbon–carbon bonds to install olefins. Among ~9,600 sequences identified bioinformatically as members of the emerging HDO superfamily, ~25% conserve this additional carboxylate residue and are thus tentatively assigned as N-oxygenases.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Rewiring the specificity of extracytoplasmic function sigma factors

Significance Bacterial phenotypes require the concerted expression of multiple genes, usually coordinated by a transcriptional regulator. Although the functions of many genes in sequenced bacterial genomes can be inferred, the regulatory networks that coordinate their expression are only known in a few model systems. Using a bioinformatic and experimental approach, we solve the DNA-specificity code of extracytoplasmic function sigma factors (ECF σs), a major class of bacterial regulators. We develop and use a high-stringency pipeline to predict the genes regulated by 67% of ECF σs in >10,000 species, providing a comprehensive look at the role of a broadly distributed family of gene regulatory proteins. This conceptual and computational framework is potentially applicable to other bacterial regulators.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Enhancing subtilisin thermostability through a modified normalized B-factor analysis and loop-grafting strategy

Rational design–guided improvement of protein thermostability typically requires identification of residues or regions contributing to instability and introduction of mutations into these residues or regions. One popular method, B-FIT, utilizes B-factors to identify unstable residues or regions and combines them with other strategies, such as directed evolution. Here, we performed structure-based engineering to improve the thermostability of the subtilisin E-S7 (SES7) peptidase. The B-value of each residue was redefined in a normalized B-factor calculation, which was implemented with a refined bioinformatics analysis strategy to identify the critical area (loop 158–162) related to flexibility and to screen for suitable thermostable motif sequences in the Protein Data Bank that can act as transplant loops. In total, we analyzed 445 structures and identified 29 thermostable motifs as candidates. Using these motifs as a starting point, we performed iterative homologous modeling to obtain a desirable chimera loop and introduced five different mutations into this loop to construct thermostable SES7 proteins. Differential scanning fluorimetry revealed increases of 7.3 °C in the melting temperature of an SES7 variant designated M5 compared with the WT. The X-ray crystallographic structure of this variant was resolved at 1.96 Å resolution. The crystal structure disclosed that M5 forms more hydrogen bonds than the WT protein, consistent with design and molecular dynamics simulation results. In summary, the modified B-FIT strategy reported here has yielded a subtilisin variant with improved thermostability and promising industrial applications, supporting the notion that this modified method is a powerful tool for protein engineering.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Emerging protein sequencing technologies: proteomics without mass spectrometry?

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been a leading method for proteomics for 30 years. Advantages provided by LC-MS/MS are offset by significant disadvantages, including cost. Recently, several non-mass spectrometric methods have emerged, but little information is available about their capacity to analyze the complex mixtures routine for mass spectrometry. Areas Covered: We review recent non-mass-spectrometric methods for sequencing proteins and peptides, including those using nanopores, sequencing by degradation, reverse translation, and short-epitope mapping, with comments on bioinformatics challenges, fundamental limitations, and areas where new technologies will be more or less competitive with LC-MS/MS. In addition to conventional literature searches, instrument vendor websites, patents, webinars, and preprints were also consulted to give a more up-to-date picture. Expert Opinion: Many new technologies are promising. However, demonstrations that they outperform mass spectrometry in terms of peptides and proteins identified have not yet been published, and astute observers note important disadvantages, especially relating to the dynamic range of single-molecule measurements of complex mixtures. Still, even if the performance of emerging methods proves inferior to LC-MS/MS, their low cost could create a different kind of revolution: a dramatic increase in the number of biology laboratories engaging in new forms of proteomics research.

59 BASIC BIOLOGICAL SCIENCES↗