Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “protein sequence”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Site specific N- and O-glycosylation mapping of the spike proteins of SARS-CoV-2 variants of concern

Abstract The glycosylation on the spike (S) protein of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, modulates the viral infection by altering conformational dynamics, receptor interaction and host immune responses. Several variants of concern (VOCs) of SARS-CoV-2 have evolved during the pandemic, and crucial mutations on the S protein of the virus have led to increased transmissibility and immune escape. In this study, we compare the site-specific glycosylation and overall glycomic profiles of the wild type Wuhan-Hu-1 strain (WT) S protein and five VOCs of SARS-CoV-2: Alpha, Beta, Gamma, Delta and Omicron. Interestingly, both N- and O-glycosylation sites on the S protein are highly conserved among the spike mutant variants, particularly at the sites on the receptor-binding domain (RBD). The conservation of glycosylation sites is noteworthy, as over 2 million SARS-CoV-2 S protein sequences have been reported with various amino acid mutations. Our detailed profiling of the glycosylation at each of the individual sites of the S protein across the variants revealed intriguing possible association of glycosylation pattern on the variants and their previously reported infectivity. While the sites are conserved, we observed changes in the N- and O-glycosylation profile across the variants. The newly emerged variants, which showed higher resistance to neutralizing antibodies and vaccines, displayed a decrease in the overall abundance of complex-type glycans with both fucosylation and sialylation and an increase in the oligomannose-type glycans across the sites. Among the variants, the glycosylation sites with significant changes in glycan profile were observed at both the N -terminal domain and RBD of S protein, with Omicron showing the highest deviation. The increase in oligomannose-type happens sequentially from Alpha through Delta. Interestingly, Omicron does not contain more oligomannose-type glycans compared to Delta but does contain more compared to the WT and other VOCs. O-glycosylation at the RBD showed lower occupancy in the VOCs in comparison to the WT. Our study on the sites and pattern of glycosylation on the SARS-CoV-2 S proteins across the VOCs may help to understand how the virus evolved to trick the host immune system. Our study also highlights how the SARS-CoV-2 virus has conserved both N - and O - glycosylation sites on the S protein of the most successful variants even after undergoing extensive mutations, suggesting a correlation between infectivity/ transmissibility and glycosylation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Strangers in a foreign land: ‘Yeastizing’ plant enzymes

Abstract Expressing plant metabolic pathways in microbial platforms is an efficient, cost‐effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here, we first summarize the current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labour‐intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost‐effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data‐driven machine learning framework designed to extract ‘yeastizing’ rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design‐build‐test cycle.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Bacterial hemophilin homologs and their specific type eleven secretor proteins have conserved roles in heme capture and are diversifying as a family

Cellular life relies on enzymes that require metals, which must be acquired from extracellular sources. Bacteria utilize surface and secreted proteins to acquire such valuable nutrients from their environment. These include the cargo proteins of the type eleven secretion system (T11SS), which have been connected to host specificity, metal homeostasis, and nutritional immunity evasion. This Sec-dependent, Gram-negative secretion system is encoded by organisms throughout the phylum Proteobacteria, including human pathogens Neisseria meningitidis, Proteus mirabilis, Acinetobacter baumannii, and Haemophilus influenzae. Experimentally verified T11SS-dependent cargo include transferrin-binding protein B (TbpB), the hemophilin homologs heme receptor protein C (HrpC), hemophilin A (HphA), the immune evasion protein factor-H binding protein (fHbp), and the host symbiosis factor nematode intestinal localization protein C (NilC). Here, we examined the specificity of T11SS systems for their cognate cargo proteins using taxonomically distributed homolog pairs of T11SS and hemophilin cargo and explored the ligand binding ability of those hemophilin cargo homologs. In vivo expression in Escherichia coli of hemophilin homologs revealed that each is secreted in a specific manner by its cognate T11SS protein. Sequence analysis and structural modeling suggest that all hemophilin homologs share an N-terminal ligand-binding domain with the same topology as the ligand-binding domains of the Haemophilus haemolyticus heme binding protein (Hpl) and HphA. We term this signature feature of this group of proteins the hemophilin ligand-binding domain. Network analysis of hemophilin homologs revealed five subclusters and representatives from four of these showed variable heme-binding activities, which, combined with sequence-structure variation, suggests that hemophilins are diversifying in function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network

Abstract Background Estimation of the accuracy (quality) of protein structural models is important for both prediction and use of protein structural models. Deep learning methods have been used to integrate protein structure features to predict the quality of protein models. Inter-residue distances are key information for predicting protein’s tertiary structures and therefore have good potentials to predict the quality of protein structural models. However, few methods have been developed to fully take advantage of predicted inter-residue distance maps to estimate the accuracy of a single protein structural model. Result We developed an attentive 2D convolutional neural network (CNN) with channel-wise attention to take only a raw difference map between the inter-residue distance map calculated from a single protein model and the distance map predicted from the protein sequence as input to predict the quality of the model. The network comprises multiple convolutional layers, batch normalization layers, dense layers, and Squeeze-and-Excitation blocks with attention to automatically extract features relevant to protein model quality from the raw input without using any expert-curated features. We evaluated DISTEMA’s capability of selecting the best models for CASP13 targets in terms of ranking loss of GDT-TS score. The ranking loss of DISTEMA is 0.079, lower than several state-of-the-art single-model quality assessment methods. Conclusion This work demonstrates that using raw inter-residue distance information with deep learning can predict the quality of protein structural models reasonably well. DISTEMA is freely at https://github.com/jianlin-cheng/DISTEMA

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Genome sequence, phylogenetic analysis, and structure-based annotation reveal metabolic potential of Chlorella sp. SLA-04

Algae are a broad class of photosynthetic eukaryotes that are phylogenetically and physiologically diverse. Most of the phylogenetic diversity has been inferred from 18S rDNA sequencing since there are only a few complete genomes available in public databases. Here we use ultra-long-read Nanopore sequencing to determine a gapless, telomere-to-telomere complete genome sequence of Chlorella sp. SLA-04, previously described as Chlorella sorokiniana SLA-04. Chlorella sp. SLA-04 is a green alga that grows to high cell density in a wide variety of environments - high and neutral pH, high and low alkalinity, and high and low salinity. SLA-04's ability to grow in high pH and high alkalinity media without external CO 2 supply is favorable for large-scale algal biomass production. Phylogenetic analysis performed using ribosomal DNA and conserved protein sequences consistently reveal that Chlorella sp. SLA-04 forms a distinct lineage from other strains of Chlorella sorokiniana. We complement traditional genome annotation methods with high throughput structural predictions and demonstrate that this approach expands functional prediction of the SLA-04 proteome. Genomic analysis of the SLA-04 genome identifies the genes capable of utilizing TCA cycle intermediates to replenish cytosolic acetyl-CoA pools for lipid production. We also identify a complete metabolic pathway for sphingolipid anabolism that may allow SLA-04 to readily adapt to changing environmental conditions and facilitate robust cultivation in mass production systems. Altogether, this work clarifies the phylogeny of Chlorella sp. SLA-04 within Trebouxiophyceae and demonstrates how structural predictions can be used to improve annotation beyond sequencebased methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Structural models and functional annotations for the Sphagnum divinum proteome

This dataset contains the structural models for the primary transcripts of the Sphagnum divinum proteome. Additionally, for a subset of these proteins, sequence and structural alignment results are provided. This dataset represents the most thorough structural study of a Sphagnum species, also known as peat mosses, by providing three-dimensional atomic resolution structures of the majority of the encoded proteins as well as structural alignment results used in the application of annotating the proteome. References (DOI) AlphaFold v2 Monomer: https://doi.org/10.1038/s41586-021-03819-2. References (DOI) US-align2: https://doi.org/10.1038/s41592-022-01585-1

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Towards understanding the formation of internal fragments generated by collisionally activated dissociation for top-down mass spectrometry

Top-down mass spectrometry (TD-MS) generates fragment ions that returns information on the polypeptide amino acid sequence. In addition to terminal fragments, internal fragments that result from multiple cleavage events can also be formed. Traditionally, internal fragments are largely ignored due to a lack of available software to reliably assign them, mainly caused by a poor understanding of their formation mechanism. To accurately assign internal fragments, their formation process needs to be better understood. Here we applied a statistical method to compare fragmentation patterns of internal and terminal fragments of peptides and proteins generated by collisionally activated dissociation (CAD). Internal fragments share similar fragmentation propensities with terminal fragments (e.g., enhanced cleavages N-terminal to proline and C-terminal to acidic residues), suggesting that their formation follows conventional CAD pathways. Internal fragments should be generated by subsequent cleavages of terminal fragments and their formation can be explained by the well-known mobile proton model. Additionally, internal fragments can be coupled with terminal fragments to form complementary product ions that span the entire protein sequence. These enhance our understanding of internal fragment formation and can help improve sequencing algorithms to accurately assign internal fragments, which will ultimately lead to more efficient and comprehensive TD-MS analysis of proteins and proteoforms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Exposing structural variations in SARS-CoV-2 evolution

The mutation of SARS-CoV-2 influences viral function as residue replacements affect both physiochemical properties and folding conformations. Although a large amount of data on SARS-CoV-2 is available, the investigation of how viral functions change in response to mutations is hampered by a lack of effective structural analysis. Here, we exploit the advances of protein structure fingerprint technology to study the folding conformational changes induced by mutations. With integration of both protein sequences and folding conformations, the structures are aligned for SARS-CoV to SARS-CoV-2, including Alpha variant (lineage B.1.1.7) and Delta variant (lineage B.1.617.2). The results showed that the virus evolution with change in mutational positions and physicochemical properties increased the affinity between spike protein and ACE2, which plays a critical role in coronavirus entry into human cells. Additionally, these structural variations impact vaccine effectiveness and drug function over the course of SARS-CoV-2 evolution. The analysis of structural variations revealed how the coronavirus has gradually evolved in both structure and function and how the SARS-CoV-2 variants have contributed to more severe acute disease worldwide.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Decoding the protein–ligand interactions using parallel graph neural networks

Abstract Protein–ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: $$\hbox {GNN}_{\mathrm{F}}$$ GNN F is the base implementation that employs distinct featurization to enhance domain-awareness, while $$\hbox {GNN}_{\mathrm{P}}$$ GNN P is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein’s 3D structure with 0.979 test accuracy for $$\hbox {GNN}_{\mathrm{F}}$$ GNN F and 0.958 for $$\hbox {GNN}_{\mathrm{P}}$$ GNN P for predicting activity of a protein–ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and $$\hbox {pIC}_{\mathrm{50}}$$ pIC 50 crucial for compound’s potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on $$\hbox {pIC}_{\mathrm{50}}$$ pIC 50 with $$\hbox {GNN}_{\mathrm{F}}$$ GNN F and $$\hbox {GNN}_{\mathrm{P}}$$ GNN P , respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of $$\hbox {GNN}_{\mathrm{P}}$$ GNN P on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Diversity of genomic adaptations to the post‐fire environment in Pezizales fungi points to crosstalk between charcoal tolerance and sexual development

Summary Wildfires drastically impact the soil environment, altering the soil organic matter, forming pyrolyzed compounds, and markedly reducing the diversity of microorganisms. Pyrophilous fungi, especially the species from the orders Pezizales and Agaricales, are fire‐responsive fungal colonizers of post‐fire soil that have historically been found fruiting on burned soil and thus may encode mechanisms of processing these compounds in their genomes. Pyrophilous fungi are diverse. In this work, we explored this diversity and sequenced six new genomes of pyrophilous Pezizales fungi isolated after the 2013 Rim Fire near Yosemite Park in California, USA: Pyronema domesticum , Pyronema omphalodes , Tricharina praecox , Geopyxis carbonaria , Morchella snyderi , and Peziza echinospora . A comparative genomics analysis revealed the enrichment of gene families involved in responses to stress and the degradation of pyrolyzed organic matter. In addition, we found that both protein sequence lengths and G + C content in the third base of codons (GC3) in pyrophilous fungi fall between those in mesophilic/nonpyrophilous and thermophilic fungi. A comparative transcriptome analysis of P. domesticum under two conditions – growing on charcoal, and during sexual development – identified modules of genes that are co‐expressed in the charcoal and light‐induced sexual development conditions. In addition, environmental sensors such as transcription factors STE12, LreA, LreB, VosA, and EsdC were upregulated in the charcoal condition. Taken together, these results highlight genomic adaptations of pyrophilous fungi and indicate a potential connection between charcoal tolerance and fruiting body formation in P. domesticum .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Data for Characterization of the Ghd8 Flowering Time Gene in a Mini-Core Collection of Miscanthus sinensis

The optimal flowering time for bioenergy crop miscanthus is essential for environmental adaptability and biomass accumulation. However, little is known about how genes controlling flowering in other grasses contribute to flowering regulation in miscanthus. Here, we report on the sequence characterization and gene expression of Miscanthus sinensisGhd8 , a transcription factor encoding a HAP3/NF-YB DNA-binding domain, which has been identified as a major quantitative trait locus in rice, with pleiotropic effects on grain yield, heading date and plant height. In M. sinensis , we identified two homoeologous loci, MsiGhd8A located on chromosome 13 and MsiGhd8B on chromosome 7, with one on each of this paleo-allotetraploid species’ subgenomes. A total of 46 alleles and 28 predicted protein sequence types were identified in 12 wild-collected accessions. Several variants of MsiGhd8 showed a geographic and latitudinal distribution. Quantitative real-time PCR revealed that MsiGhd8 expressed under both long days and short days, and MsiGhd8B showed a significantly higher expression than MsiGhd8A . The comparison between flowering time and gene expression indicated that MsiGhd8B affected flowering time in response to day length for some accessions. This study provides insight into the conserved function of Ghd8 in the Poaceae, and is an important initial step in elucidating the flowering regulatory network of Miscanthus.

Feedstock Production↗
DOE OSTI2021

Characterization of the Ghd8 Flowering Time Gene in a Mini-Core Collection of Miscanthus sinensis

The optimal flowering time for bioenergy crop Miscanthus is essential for environmental adaptability and biomass accumulation. However, little is known about how genes controlling flowering in other grasses contribute to flowering regulation in Miscanthus. Here, we report on the sequence characterization and gene expression of Miscanthus sinensisGhd8, a transcription factor encoding a HAP3/NF-YB DNA-binding domain, which has been identified as a major quantitative trait locus in rice, with pleiotropic effects on grain yield, heading date and plant height. In M. sinensis, we identified two homoeologous loci, MsiGhd8A located on chromosome 13 and MsiGhd8B on chromosome 7, with one on each of this paleo-allotetraploid species’ subgenomes. A total of 46 alleles and 28 predicted protein sequence types were identified in 12 wild-collected accessions. Several variants of MsiGhd8 showed a geographic and latitudinal distribution. Quantitative real-time PCR revealed that MsiGhd8 expressed under both long days and short days, and MsiGhd8B showed a significantly higher expression than MsiGhd8A. The comparison between flowering time and gene expression indicated that MsiGhd8B affected flowering time in response to day length for some accessions. This study provides insight into the conserved function of Ghd8 in the Poaceae, and is an important initial step in elucidating the flowering regulatory network of Miscanthus.

geographic distribution↗
DOE OSTI2022

Orthogonal glycolytic pathway enables directed evolution of noncanonical cofactor oxidase

Abstract Noncanonical cofactor biomimetics (NCBs) such as nicotinamide mononucleotide (NMN + ) provide enhanced scalability for biomanufacturing. However, engineering enzymes to accept NCBs is difficult. Here, we establish a growth selection platform to evolve enzymes to utilize NMN + -based reducing power. This is based on an orthogonal, NMN + -dependent glycolytic pathway in Escherichia coli which can be coupled to any reciprocal enzyme to recycle the ensuing reduced NMN + . With a throughput of >10 6 variants per iteration, the growth selection discovers a Lactobacillus pentosus NADH oxidase variant with ~10-fold increase in NMNH catalytic efficiency and enhanced activity for other NCBs. Molecular modeling and experimental validation suggest that instead of directly contacting NCBs, the mutations optimize the enzyme’s global conformational dynamics to resemble the WT with the native cofactor bound. Restoring the enzyme’s access to catalytically competent conformation states via deep navigation of protein sequence space with high-throughput evolution provides a universal route to engineer NCB-dependent enzymes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Mycobacterium tuberculosis Phe-tRNA synthetase: structural insights into tRNA recognition and aminoacylation

Abstract Tuberculosis, caused by Mycobacterium tuberculosis, responsible for ∼1.5 million fatalities in 2018, is the deadliest infectious disease. Global spread of multidrug resistant strains is a public health threat, requiring new treatments. Aminoacyl-tRNA synthetases are plausible candidates as potential drug targets, because they play an essential role in translating the DNA code into protein sequence by attaching a specific amino acid to their cognate tRNAs. We report structures of M. tuberculosis Phe-tRNA synthetase complexed with an unmodified tRNAPhe transcript and either L-Phe or a nonhydrolyzable phenylalanine adenylate analog. High-resolution models reveal details of two modes of tRNA interaction with the enzyme: an initial recognition via indirect readout of anticodon stem-loop and aminoacylation ready state involving interactions of the 3′ end of tRNAPhe with the adenylate site. For the first time, we observe the protein gate controlling access to the active site and detailed geometry of the acyl donor and tRNA acceptor consistent with accepted mechanism. We biochemically validated the inhibitory potency of the adenylate analog and provide the most complete view of the Phe-tRNA synthetase/tRNAPhe system to date. The presented topography of amino adenylate-binding and editing sites at different stages of tRNA binding to the enzyme provide insights for the rational design of anti-tuberculosis drugs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

SPARC: Structural properties associated with residue constraints

SPARC facilitates the generation of plausible hypotheses regarding underlying biochemical mechanisms by structurally characterizing protein sequence constraints. Such constraints appear as residues co-conserved in functionally related subgroups, as subtle pairwise correlations (i.e., direct couplings), and as correlations among these sequence features or with structural features. SPARC performs three types of analyses. First, based on pairwise sequence correlations, it estimates the biological relevance of alternative conformations and of homomeric contacts, as illustrated here for death domains. Second, it estimates the statistical significance of the correspondence between directly coupled residue pairs and interactions at heterodimeric interfaces. Third, given molecular dynamics simulated structures, it characterizes interactions among constrained residues or between such residues and ligands that: (a) are stably maintained during the simulation; (b) undergo correlated formation and/or disruption of interactions with other constrained residues; or (c) switch between alternative interactions. We illustrate this for two homohexameric complexes: the bacterial enhancer binding protein (bEBP) NtrC1, which activates transcription by remodeling RNA polymerase (RNAP) containing σ 54 , and for DnaB helicase, which opens DNA at the bacterial replication fork. Based on the NtrC1 analysis, we hypothesize possible mechanisms for inhibiting ATP hydrolysis until ADP is released from an adjacent subunit and for coupling ATP hydrolysis to restructuring of σ 54 binding loops. Based on the DnaB analysis, we hypothesize that DnaB ‘grabs’ ssDNA by flipping every fourth base and inserting it into cavities between subunits and that flipping of a DnaB-specific glutamine residue triggers ATP hydrolysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Phaseolus vulgaris SUT1.1 is a high affinity sucrose–proton co–transporter

Plant sucrose transporters are required for phloem loading, and therefore are essential for plant growth and development. In common beans (Phaseolus vulgaris) there are only two sucrose transporters functionally characterized. Through a previous RNA-seq study, we identified a putative sucrose transporter in common bean, which we hypothesize to function in import of sucrose into plant cells. In silico analysis revealed that PvSUT1.1 is a putative sucrose-proton co-transporter distinct from other characterized sucrose transporters in common bean indicating that this is a previously undescribed transporter protein in beans. Further analysis revealed that PvSUT1.1 shares high protein sequence homology to the phloem loader Arabidopsis SUC2; both have 12 transmembrane domains, a typical characteristic of plant sucrose transporters. Heterologous expression in yeast further showed PvSUT1.1 to be functional and it imported sucrose into yeast cells with a K m of 0.7 mM sucrose. Import of sucrose through PvSUT1.1 is also pH-dependent with highest uptake at pH 4.0, and activity is lost in the presence of the uncoupler carbonyl cyanide 3-chlorophenylhydrazone. Consistent with identification of PvSUT1.1 as a Type I transporter, PvSUT1.1 also transports esculin. Finally, PvSUT1.1 showed expression in multiple tissues and the protein was localized to the plasma membrane. The results show that PvSUT1.1 is a sucrose transporter that is probably involved in the uptake of sucrose into source and sink cells. The potential role of PvSUT1.1 in leaf phloem loading of sucrose in common beans and its importance in heat tolerance of reproductive tissues are further discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

The MHC Associated Peptide Proteomics assay is a useful tool for the non-clinical assessment of immunogenicity

The propensity of therapeutic proteins to elicit an immune response, poses a significant challenge in clinical development and safety of the patients. Assessment of immunogenicity is crucial to predict potential adverse events and design safer biologics. In this study, we employed MHC Associated Peptide Proteomics (MAPPS) to comprehensively evaluate the immunogenic potential of re-engineered variants of immunogenic FVIIa analog (Vatreptacog Alfa). Our finding revealed the correlation between the protein sequence affinity for MHCII and the number of peptides identified in a MAPPS assay and this further correlates with the reduced T-cell responses. Moreover, MAPPS enable the identification of “relevant” T cell epitopes and may contribute to the development of biologics with lower immunogenic potential.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

ATCUN-like Copper Site in βB2-Crystallin Plays a Protective Role in Cataract-Associated Aggregation

Cataract is the leading cause of blindness worldwide, and it is caused by crystallin damage and aggregation. Senile cataractous lenses have relatively high levels of metals, while some metal ions can directly induce the aggregation of human γ-crystallins. Here, for this work, we evaluated the impact of divalent metal ions in the aggregation of human βB2-crystallin, one of the most abundant crystallins in the lens. Turbidity assays showed that Pb 2+ , Hg 2+ , Cu 2+ , and Zn 2+ ions induce the aggregation of βB2-crystallin. Metal-induced aggregation is partially reverted by a chelating agent, indicating the formation of metal-bridged species. Our study focused on the mechanism of copper-induced aggregation of βB2-crystallin, finding that it involves metal-bridging, disulfide-bridging, and loss of protein stability. Circular dichroism and electron paramagnetic resonance (EPR) revealed the presence of at least three Cu 2+ binding sites in βB2-crystallin, one of them with spectroscopic features typical for Cu 2+ bound to an amino-terminal copper and nickel (ATCUN) binding motif, which is found in Cu transport proteins. The ATCUN-like Cu binding site is located at the unstructured N-terminus of βB2-crystallin, and it could be modeled by a peptide with the first six residues in the protein sequence (NH 2 -ASDHQF-). Isothermal titration calorimetry indicates a nanomolar Cu 2+ binding affinity for the ATCUN-like site. An N-truncated form of βB2-crystallin is more susceptible to Cu-induced aggregation and is less thermally stable, indicating a protective role for the ATCUN-like site. EPR and X-ray absorption spectroscopy studies reveal the presence of a copper redox active site in βB2-crystallin that is associated with metal-induced aggregation and formation of disulfide-bridged oligomers. Our study demonstrates metal-induced aggregation of βB2-crystallin and the presence of putative copper binding sites in the protein. Whether the copper-transport ATCUN-like site in βB2-crystallin plays a functional/protective role or constitutes a vestige from its evolution as a lens structural protein remains to be elucidated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗