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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

iPNHOT: a knowledge-based approach for identifying protein-nucleic acid interaction hot spots

The interaction between proteins and nucleic acids plays pivotal roles in various biological processes such as transcription, translation, and gene regulation. Hot spots are a small set of residues that contribute most to the binding affinity of a protein-nucleic acid interaction. Compared to the extensive studies of the hot spots on protein-protein interfaces, the hot spot residues within protein-nucleic acids interfaces remain less well-studied, in part because mutagenesis data for protein-nucleic acids interaction are not as abundant as that for protein-protein interactions. In this study, we built a new computational model, iPNHOT, to effectively predict hot spot residues on protein-nucleic acids interfaces. One training data set and an independent test set were collected from dbAMEPNI and some recent literature, respectively. To build our model, we generated 97 different sequential and structural features and used a two-step strategy to select the relevant features. The final model was built based only on 7 features using a support vector machine (SVM). The features include two unique features such as ΔSASsa 1/2 and esp3, which are newly proposed in this study. Based on the cross validation results, our model gave F1 score and AUROC as 0.725 and 0.807 on the subset collected from ProNIT, respectively, compared to 0.407 and 0.670 of mCSM-NA, a state-of-the art model to predict the thermodynamic effects of protein-nucleic acid interaction. The iPNHOT model was further tested on the independent test set, which showed that our model outperformed other methods. Here, by collecting data from a recently published database dbAMEPNI, we proposed a new model, iPNHOT, to predict hotspots on both protein-DNA and protein-RNA interfaces. The results show that our model outperforms the existing state-of-art models. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPNHOT/.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The Landscape of RNA-Protein Interactions in Plants: Approaches and Current Status

RNAs transmit information from DNA to encode proteins that perform all cellular processes and regulate gene expression in multiple ways. From the time of synthesis to degradation, RNA molecules are associated with proteins called RNA-binding proteins (RBPs). The RBPs play diverse roles in many aspects of gene expression including pre-mRNA processing and post-transcriptional and translational regulation. In the last decade, the application of modern techniques to identify RNA–protein interactions with individual proteins, RNAs, and the whole transcriptome has led to the discovery of a hidden landscape of these interactions in plants. Global approaches such as RNA interactome capture (RIC) to identify proteins that bind protein-coding transcripts have led to the identification of close to 2000 putative RBPs in plants. Interestingly, many of these were found to be metabolic enzymes with no known canonical RNA-binding domains. Here, we review the methods used to analyze RNA–protein interactions in plants thus far and highlight the understanding of plant RNA–protein interactions these techniques have provided us. We also review some recent protein-centric, RNA-centric, and global approaches developed with non-plant systems and discuss their potential application to plants. We also provide an overview of results from classical studies of RNA–protein interaction in plants and discuss the significance of the increasingly evident ubiquity of RNA–protein interactions for the study of gene regulation and RNA biology in plants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Distinct Conformations of SARS-CoV-2 Omicron Spike Protein and Its Interaction with ACE2 and Antibody

Since November 2021, Omicron has been the dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant that causes the coronavirus disease 2019 (COVID-19) and has continuously impacted human health. Omicron sublineages are still increasing and cause increased transmission and infection rates. The additional 15 mutations on the receptor binding domain (RBD) of Omicron spike proteins change the protein conformation, enabling the Omicron variant to evade neutralizing antibodies. For this reason, many efforts have been made to design new antigenic variants to induce effective antibodies in SARS-CoV-2 vaccine development. However, understanding the different states of Omicron spike proteins with and without external molecules has not yet been addressed. In this review, we analyze the structures of the spike protein in the presence and absence of angiotensin-converting enzyme 2 (ACE2) and antibodies. Compared to previously determined structures for the wildtype spike protein and other variants such as alpha, beta, delta, and gamma, the Omicron spike protein adopts a partially open form. The open-form spike protein with one RBD up is dominant, followed by the open-form spike protein with two RBD up, and the closed-form spike protein with the RBD down. It is suggested that the competition between antibodies and ACE2 induces interactions between adjacent RBDs of the spike protein, which lead to a partially open form of the Omicron spike protein. The comprehensive structural information of Omicron spike proteins could be helpful for the efficient design of vaccines against the Omicron variant.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

The temporal onset of associations of cortical proteins with cognitive resilience vary during late life

Background: Cortical proteins associated with cognitive resilience have been identified but their temporal onset in older adults is unknown. We present a multistage approach to first identify cortical proteins associated with cognitive resilience and then examine their associated temporal onset. Methods: We used data from a subset of 1088 decedents from two cohort-studies who had selected reaction monitoring proteomics from the dorsolateral prefrontal cortex, and at least 3 cognitive assessments. Cognition was assessed using a composite derived from 19 tests. We first used linear mixed-effects models to identify cortical proteins associated with cognitive resilience. We then used functional mixed-effects models to examine non-linear associations between proteins and cognitive resilience to identify their temporal onset. Results: Mean age at death was 90 years (SD = 6.4); 69 % were female. On average, cognition started to decline at around 15 years before death, with accelerated decline in the last 7 years. We identified 40 proteins associated with cognitive resilience, of which 17 proteins also showed non-linear associations. Non-linear associations indicated that higher levels of 10 proteins were associated with slower cognitive decline between 23 and 4 years before death. In contrast, higher levels of 7 proteins were associated with faster decline only within the last 7 years before death. Conclusions: Cognitive resilience proteins are differentially related to late-life cognitive aging; the onset of proteins that maintain cognition may begin many years before the onset of proteins that hasten cognitive decline. The temporal onset of cognitive resilience proteins may be crucial for timing efficacious interventions.

Zammit, Andrea↗
DOE OSTI2026

Exploring Local Vibrational Structure in Protein-Bound Chlorophyll a: Isotope-Enrichment Experiments and Electrostatic Analysis

Local chlorophyll (Chl) vibrations play an essential role in biological photosynthesis by facilitating rapid energy transfer between pigments. In research studies, they also provide a useful spectroscopic probe of the local protein environment that surrounds each pigment. However, measuring the complete vibrational absorption spectrum of a protein-bound Chl molecule is much more difficult than, for example, Chl in neat solvent due to overlap with protein vibrations that typically drown out Chl vibrational features. Resonance Raman and fluorescence spectroscopies provide a way around this problem for Franck–Condon active vibrations, but these often rely on cryogenic measurement conditions and fail to capture vibrational signatures from, e.g., ester-group vibrations that lack coupling to a convenient electronic transition. In the present contribution, we use 13 C-enrichment of the protein backbone to shift protein background signals to lower frequency, providing a largely clean spectral window in which to study local Chl a C═O stretch modes. A room-temperature absorption spectrum for Chl a in the water-soluble chlorophyll protein (WSCP) of Lepidium virginicum is thus extracted as a difference between protein-plus-pigment and protein-only vibrational spectra. Excellent agreement in the molecular fingerprint region with the vibrational spectrum of Chl a in organic solvents confirms that the resulting spectrum represents the response of the protein-bound Chl a molecule. Furthermore, the ester group resonance is observed to shift in response to the S53P mutation that eliminates a 17 3 ester group hydrogen bond from the protein environment. Finally, we analyze these experimental results using MD-based electrostatic analysis, finding that electric-field mapping at the C atom of the ester group provides a satisfactory explanation for the observed frequency shifts between organic solvent and protein environment. MD analysis further suggests that a red-shifted ester peak observed experimentally for the S53P mutant results from solvation of the 17 3 Chl ester group due increase water penetration into the Chl-binding pocket relative to wild-type WSCP. Here, we anticipate that these results will prove useful both for benchmarking future simulation work and as a reference for interpreting Chl vibrational spectra as a probe of pigment–protein interactions.

14 SOLAR ENERGY↗
DOE OSTI2023

De novo design of protein structure and function with RFdiffusion

Abstract There has been considerable recent progress in designing new proteins using deep-learning methods 1–9 . Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models 10,11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence–structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.

Science & Technology - Other Topics↗
DOE OSTI2023

Metal-induced oxidative stress and human plasma protein oxidation after SARS-CoV-2 infection

Pathogenesis of COVID-19 by SARS-CoV-2 resulted in a global pandemic and public health emergency in 2020. Viral infection can induce oxidative stress through reactive oxygen species (ROS). Inflammation and environmental stress are major sources of oxidative stress after infection. Micronutrients such as iron, copper, zinc, and manganese play various roles in human tissues and their imbalance in blood can impact immune responses against pathogens including SARS CoV-2. We hypothesized that alteration of free metal ions during infection and metal-catalyzed oxidation plays a critical role towards pathogenesis after infection. We analyzed convalescent and hospitalized COVID-19 patient plasma using orthogonal analytical techniques to determine redox active metal concentrations, overall protein oxidation, oxidative modifications, and protein levels via proteomics to understand the consequences of metal-induced oxidative stress in COVID-19 plasma proteins. Metal analysis using ICP-MS showed significantly greater concentrations of copper in COVID-19 plasma compared to healthy controls. We demonstrate significantly greater total protein carbonylation, other oxidative modifications, and deamidation of plasma proteins in COVID-19 plasma compared to healthy controls. Proteomics analysis showed that levels of redox active proteins including hemoglobulin were elevated in COVID-19 plasma. Molecular modeling concurred with potential interactions between iron binding proteins and SARS CoV-2 surface proteins. Overall, increased levels of redox active metals and protein oxidation indicate that oxidative stress-induced protein oxidation in COVID-19 may be a consequence of the interactions of SARS-CoV-2 proteins with host cell metal binding proteins resulting in altered cellular homeostasis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects

Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. In this study, we developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

First comprehensive identification of cardiac proteins with putative increased O-GlcNAc levels during pressure overload hypertrophy

Protein posttranslational modifications (PTMs) by O-GlcNAc globally rise during pressure-overload hypertrophy (POH). However, a major knowledge gap exists on the specific proteins undergoing changes in O-GlcNAc levels during POH primarily because this PTM is low abundance and easily lost during standard mass spectrometry (MS) conditions used for protein identification. Methodologies have emerged to enrich samples for O-GlcNAcylated proteins prior to MS analysis. Accordingly, our goal was to identify the specific proteins undergoing changes in O-GlcNAc levels during POH. We used C57/Bl6 mice subjected to Sham or transverse aortic constriction (TAC) to create POH. From the hearts, we labelled the O-GlcNAc moiety with tetramethylrhodamine azide (TAMRA) before sample enrichment by TAMRA immunoprecipitation (IP). We used LC-MS/MS to identify and quantify the captured putative O-GlcNAcylated proteins. We identified a total of 700 putative O-GlcNAcylated proteins in Sham and POH. Two hundred thirty-three of these proteins had significantly increased enrichment in POH over Sham suggesting higher O-GlcNAc levels whereas no proteins were significantly decreased by POH. We examined two MS identified metabolic enzymes, CPT1B and the PDH complex, to validate by immunoprecipitation. We corroborated increased O-GlcNAc levels during POH for CPT1B and the PDH complex. Enzyme activity assays suggests higher O-GlcNAcylation increases CPT1 activity and decreases PDH activity during POH. In summary, we generated the first comprehensive list of proteins with putative changes in O-GlcNAc levels during POH. Our results demonstrate the large number of potential proteins and cellular processes affected by O-GlcNAc and serve as a guide for testing specific O-GlcNAc-regulated mechanisms during POH.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Improving Protein–Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

Elucidating protein–ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein–ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein–ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein–ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein–ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein–ligand interactions on cryo-EM data using prior structural information.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Designing protein–material interfaces

This article addresses recent advances in using de novo protein design to create coherent interfaces between proteins and inorganic materials, either through protein self-assembly on crystal lattices or through directed nucleation and growth of crystals by protein scaffolds. Inspired by natural protein-crystal interfaces, we focus on a class of designed helical repeat proteins that present a repeating pattern of charged amino acid residues. We describe the use of in situ imaging and spectroscopic methods to investigate both the assembly of these proteins and their ability to direct crystal nucleation and growth. Furthermore, the findings reveal the importance of surface charge, facet-specific binding, solvent organization, and, more generally, the balance of protein-substrate-solvent interactions in determining how organized protein-materials interfaces emerge. Moreover, the results demonstrate the vast potential of protein design in materials science and elucidate the mechanisms by which interactions between biomolecules and inorganic surfaces lead to unique materials and morphologies.

Biomaterials-Proteins↗
DOE OSTI2022

Chapter 10: Advances in Protein Engineering and its Application in Synthetic Biology

Protein engineering has been used successfully in fields ranging from medicine to food science to biofuels. Applications of protein engineering include developing antiviral peptides or other protein therapeutics, antibody engineering, designing protein-based logic circuits, engineering enzymes to be more specific or to function under industrially relevant conditions such as at higher temperatures or high/low pH, modifying cell signaling or regulatory functions, and so on. Advances in recombinant DNA, "omics," and CRISPR-Cas (clustered regularly interspaced short palindromic repeats and its associated proteins) technologies, combined with high-throughput screening facilities, will lead to improved methods for protein engineering, enabling easy modification of more proteins/enzymes for new specific applications. New methods for rational design, directed evolution, and computer-aided protein design will further accelerate the speed of protein evolution and expand the scope for protein engineering. In this chapter we discuss general protein engineering strategies and advances in engineering proteins with desired functions, focusing on the "design" and "build" part of the design-build-test-learn cycle.

BIOMASS FUELS↗
DOE OSTI2024

Effect of polyphenols on the rheology, microstructure and in vitro digestion of pea protein gels at various pH

Polyphenols exist widely in plants and interact with plant proteins distinctly depending on the environmental pH. This could potentially affect the gelling property of plant proteins and their digestion. In the present study, pea protein suspensions containing 0 %, 0.5 % and 1 % green tea polyphenols (GTP) were heated to form gels at pH5, pH7, and pH8.5. A strain amplitude sweep showed that the storage modulus (G’) and critical strain of pea protein gels decreased with increased GTP concentration at all examined pH. Gelation dynamic showed gelling of pea protein-GTP was delayed at pH7 and pH8.5 compared to the control. Differential scanning calorimetry showed that pea proteins became less heat resistant in the presence of GTP at pH5 and pH7, but not at pH8.5. Ultra-small and small-angle X-ray scattering showed that the radius of gyration of small- and medium-sized aggregates in pea protein gels was increased ~10–16 % and 22–30 % at pH7 and pH8.5, respectively when 1 % GTP was present. Finally, under such pH conditions, the proportion of structure with a radius of ~10–200 nm was increased in pea protein-GTP gels based on the volume size distribution. In vitro digestion found the soluble protein content of digesta of pea protein-GTP gels had a 5–8.5 % decrement with the presence of larger peptides when compared to pea protein gels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Internal Fragments Generated by Electron Ionization Dissociation Enhance Protein Top-Down Mass Spectrometry

Top-down proteomics by mass spectrometry (MS) involves the mass measurement of an intact protein followed by subsequent activation of the protein to generate product ions. Electron-based fragmentation methods like electron capture dissociation and electron transfer dissociation are widely used for these types of analyses. Recently, electron ionization dissociation (EID), which utilizes higher energy electrons (>20 eV) has been suggested to be more efficient for top-down protein fragmentation compared to other electron-based dissociation methods. In this paper, we demonstrate that the use of EID enhances protein fragmentation and subsequent detection of protein fragments. Protein product ions can form by either single cleavage events, resulting in terminal fragments containing the C-terminus or N-terminus of the protein, or by multiple cleavage events to give rise to internal fragments that include neither the C-terminus nor the N-terminus of the protein. Conventionally, internal fragments have been disregarded, as reliable assignments of these fragments were limited. Here, we demonstrate that internal fragments generated by EID can account for ~20–40% of the mass spectral signals detected by top-down EID-MS experiments. By including internal fragments, the extent of the protein sequence that can be explained from a single tandem mass spectrum increases from ~50 to ~99% for 29 kDa carbonic anhydrase II and 8.6 kDa ubiquitin. When searching for internal fragments during data analysis, previously unassigned peaks can be readily and accurately assigned to confirm a given protein sequence and to enhance the utility of top-down protein sequencing experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

AF2Complex predicts direct physical interactions in multimeric proteins with deep learning

Abstract Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Remarkably accurate atomic structures have been recently computed for individual proteins by AlphaFold2 (AF2). Here, we demonstrate that the same neural network models from AF2 developed for single protein sequences can be adapted to predict the structures of multimeric protein complexes without retraining. In contrast to common approaches, our method, AF2Complex, does not require paired multiple sequence alignments. It achieves higher accuracy than some complex protein-protein docking strategies and provides a significant improvement over AF-Multimer, a development of AlphaFold for multimeric proteins. Moreover, we introduce metrics for predicting direct protein-protein interactions between arbitrary protein pairs and validate AF2Complex on some challenging benchmark sets and the E. coli proteome. Lastly, using the cytochrome c biogenesis system I as an example, we present high-confidence models of three sought-after assemblies formed by eight members of this system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Properties of protein unfolded states suggest broad selection for expanded conformational ensembles

Much attention is being paid to conformational biases in the ensembles of intrinsically disordered proteins. However, it is currently unknown whether or how conformational biases within the disordered ensembles of foldable proteins affect function in vivo. Recently, we demonstrated that water can be a good solvent for unfolded polypeptide chains, even those with a hydrophobic and charged sequence composition typical of folded proteins. These results run counter to the generally accepted model that protein folding begins with hydrophobicity-driven chain collapse. Here we investigate what other features, beyond amino acid composition, govern chain collapse. We found that local clustering of hydrophobic and/or charged residues leads to significant collapse of the unfolded ensemble of pertactin, a secreted autotransporter virulence protein from Bordetella pertussis , as measured by small angle X-ray scattering (SAXS). Sequence patterns that lead to collapse also correlate with increased intermolecular polypeptide chain association and aggregation. Crucially, sequence patterns that support an expanded conformational ensemble enhance pertactin secretion to the bacterial cell surface. Similar sequence pattern features are enriched across the large and diverse family of autotransporter virulence proteins, suggesting sequence patterns that favor an expanded conformational ensemble are under selection for efficient autotransporter protein secretion, a necessary prerequisite for virulence. More broadly, we found that sequence patterns that lead to more expanded conformational ensembles are enriched across water-soluble proteins in general, suggesting protein sequences are under selection to regulate collapse and minimize protein aggregation, in addition to their roles in stabilizing folded protein structures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Targeted protein degradation: from small molecules to complex organelles—a Keystone Symposia report

Targeted protein degradation is critical for proper cellular function and development. Protein degradation pathways, such as the ubiquitin proteasomes system, autophagy, and endosome–lysosome pathway, must be tightly regulated to ensure proper elimination of misfolded and aggregated proteins and regulate changing protein levels during cellular differentiation, while ensuring that normal proteins remain unscathed. Protein degradation pathways have also garnered interest as a means to selectively eliminate target proteins that may be difficult to inhibit via other mechanisms. Additionally, on June 7 and 8, 2021, several experts in protein degradation pathways met virtually for the Keystone eSymposium “Targeting protein degradation: from small molecules to complex organelles.” The event brought together researchers working in different protein degradation pathways in an effort to begin to develop a holistic, integrated vision of protein degradation that incorporates all the major pathways to understand how changes in them can lead to disease pathology and, alternatively, how they can be leveraged for novel therapeutics.

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↗