Search NASA⌕ Search

SEARCH · Search NASA

Results for “Protein”

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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

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

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

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Graphic contrastive learning analyses of discontinuous molecular dynamics simulations: Study of protein folding upon adsorption

A comprehensive understanding of the interfacial behaviors of biomolecules holds great significance in the development of biomaterials and biosensing technologies. In this work, we used discontinuous molecular dynamics (DMD) simulations and graphic contrastive learning analysis to study the adsorption of ubiquitin protein on a graphene surface. Our high-throughput DMD simulations can explore the whole protein adsorption process including the protein structural evolution with sufficient accuracy. Contrastive learning was employed to train a protein contact map feature extractor aiming at generating contact map feature vectors. Subsequently, these features were grouped using the k-means clustering algorithm to identify the protein structural transition stages throughout the adsorption process. The machine learning analysis can illustrate the dynamics of protein structural changes, including the pathway and the rate-limiting step. Our study indicated that the protein–graphene surface hydrophobic interactions and the π–π stacking were crucial to the seven-stage adsorption process. Upon adsorption, the secondary structure and tertiary structure of ubiquitin disintegrated. The unfolding stages obtained by contrastive learning-based algorithm were not only consistent with the detailed analyses of protein structures but also provided more hidden information about the transition states and pathway of protein adsorption process and structural dynamics. Our combination of efficient DMD simulations and machine learning analysis could be a valuable approach to studying the interfacial behaviors of biomolecules.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

SARS-CoV-2 spike protein variant binding affinity to an angiotensin-converting enzyme 2 fusion glycoproteins

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the causative agent of the Coronavirus disease 2019 (Covid-19) pandemic, continues to evolve and circulate globally. Current prophylactic and therapeutic countermeasures against Covid-19 infection include vaccines, small molecule drugs, and neutralizing monoclonal antibodies. SARS-CoV-2 infection is mainly mediated by the viral spike glycoprotein binding to angiotensin converting enzyme 2 (ACE2) on host cells for viral entry. As emerging mutations in the spike protein evade efficacy of spike-targeted countermeasures, a potential strategy to counter SARS-CoV-2 infection is to competitively block the spike protein from binding to the host ACE2 using a soluble recombinant fusion protein that contains a human ACE2 and an IgG1-Fc domain (ACE2-Fc). Here, we have established Chinese Hamster Ovary (CHO) cell lines that stably express ACE2-Fc proteins in which the ACE2 domain either has or has no catalytic activity. The fusion proteins were produced and purified to partially characterize physicochemical properties and spike protein binding. Our results demonstrate the ACE2-Fc fusion proteins are heavily N-glycosylated, sensitive to thermal stress, and actively bind to five spike protein variants (parental, alpha, beta, delta, and omicron) with different affinity. Our data demonstrates a proof-of-concept production strategy for ACE2-Fc fusion glycoproteins that can bind to different spike protein variants to support the manufacture of potential alternative countermeasures for emerging SARS-CoV-2 variants.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

3D Visualization of Proteins within Metal–Organic Frameworks via Ferritin‐Enabled Electron Microscopy

Abstract Electron tomography holds great promise as a tool for investigating the 3D morphologies and internal structures of metal‐organic framework‐based protein biocomposites (protein@MOFs). Understanding the 3D spatial arrangement of proteins within protein@MOFs is paramount for developing synthetic methods to control their spatial localization and distribution patterns within the biocomposite crystals. In this study, the naturally occurring iron oxide mineral core of the protein horse spleen ferritin (Fn) is leveraged as a contrast agent to directly observe individual proteins once encapsulated into MOFs by electron microscopy techniques. This methodology couples scanning electron microscopy, transmission electron microscopy, and electron tomography to garner detailed 2D and 3D structural interpretations of where proteins spatially lie in Fn@MOF crystals, addressing the significant gaps in understanding how synthetic conditions relate to overall protein spatial localization and aggregation. These findings collectively reveal that adjusting the ligand‐to‐metal ratios, protein concentration, and the use of denaturing agents alters how proteins are arranged, localized, and aggregated within MOF crystals.

Chemistry↗
DOE OSTI2020

Constructing protein polyhedra via orthogonal chemical interactions

Many proteins exist naturally as symmetrical homooligomers or homopolymers. The emergent structural and functional properties of such protein assemblies have inspired extensive efforts in biomolecular design2–5. As synthesized by ribosomes, proteins are inherently asymmetric. Therefore, they must acquire multiple surface patches that selectively associate to generate the different symmetry elements needed to form higher-order architectures—a daunting task for protein design. Here we address this problem using an inorganic chemical approach, whereby multiple modes of protein–protein interactions and symmetry are simultaneously achieved by selective, ‘one-pot’ coordination of soft and hard metal ions. We show that a monomeric protein (protomer) appropriately modified with biologically inspired hydroxamate groups and zinc-binding motifs assembles through concurrent Fe 3+ and Zn 2+ coordination into discrete dodecameric and hexameric cages. Our cages closely resemble natural polyhedral protein architectures and are, to our knowledge, unique among designed systems in that they possess tightly packed shells devoid of large apertures. At the same time, they can assemble and disassemble in response to diverse stimuli, owing to their heterobimetallic construction on minimal interprotein-bonding footprints. With stoichiometries ranging from [2 Fe:9 Zn:6 protomers] to [8 Fe:21 Zn:12 protomers], these protein cages represent some of the compositionally most complex protein assemblies—or inorganic coordination complexes—obtained by design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Engineering an efficient and bright split Corynactis californica green fluorescent protein

Split green fluorescent protein (GFP) has been used in a panoply of cellular biology applications to study protein translocation, monitor protein solubility and aggregation, detect protein–protein interactions, enhance protein crystallization, and even map neuron contacts. Recent work shows the utility of split fluorescent proteins for large scale labeling of proteins in cells using CRISPR, but sets of efficient split fluorescent proteins that do not cross-react are needed for multiplexing experiments. We present a new monomeric split green fluorescent protein (ccGFP) engineered from a tetrameric GFP found in Corynactis californica, a bright red colonial anthozoan similar to sea anemones and scleractinian stony corals. Split ccGFP from C. californica complements up to threefold faster compared to the original Aequorea victoria split GFP and enable multiplexed labeling with existing A. victoria split YFP and CFP.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Assessing the potential of deep learning for protein–ligand docking

The effects of ligand binding on protein structures and their in vivo functions carry numerous implications for modern biomedical research and biotechnology development efforts such as drug discovery. Although several deep learning (DL) methods and benchmarks designed for protein–ligand docking have recently been introduced, so far no previous works have systematically studied the behaviour of the latest docking and structure prediction methods within the broadly applicable context of: (1) using predicted (apo) protein structures for docking (for example, for applicability to new proteins); (2) binding multiple (cofactor) ligands concurrently to a given target protein (for example, for enzyme design); and (3) having no previous knowledge of binding pockets (for example, for generalization to unknown pockets). To enable a deeper understanding of the real-world utility of docking methods, we introduce PoseBench, a comprehensive benchmark for broadly applicable protein–ligand docking. PoseBench enables researchers to rigorously and systematically evaluate DL methods for apo-to-holo protein–ligand docking and protein–ligand structure prediction using both primary ligand and multiligand benchmark datasets, the latter of which we introduce to the DL community. Empirically, using PoseBench, we find that: (1) DL cofolding methods generally outperform comparable conventional and DL docking baseline algorithms, but popular methods such as AlphaFold 3 are still challenged by prediction targets with new protein–ligand binding poses; (2) certain DL cofolding methods are highly sensitive to their input multiple sequence alignments, whereas others are not; and (3) DL methods struggle to strike a balance between structural accuracy and chemical specificity when predicting new or multiligand protein targets.

Morehead, Alex [Lawrence Berkeley National Laborat↗
DOE OSTI2023

3D-equivariant graph neural networks for protein model quality assessment

Quality assessment (QA) of predicted protein tertiary structure models plays an important role in ranking and using them. With the recent development of deep learning end-to-end protein structure prediction techniques for generating highly confident tertiary structures for most proteins, it is important to explore corresponding QA strategies to evaluate and select the structural models predicted by them since these models have better quality and different properties than the models predicted by traditional tertiary structure prediction methods. We develop EnQA, a novel graph-based 3D-equivariant neural network method that is equivariant to rotation and translation of 3D objects to estimate the accuracy of protein structural models by leveraging the structural features acquired from the state-of-the-art tertiary structure prediction method—AlphaFold2. We train and test the method on both traditional model datasets (e.g. the datasets of the Critical Assessment of Techniques for Protein Structure Prediction) and a new dataset of high-quality structural models predicted only by AlphaFold2 for the proteins whose experimental structures were released recently. Our approach achieves state-of-the-art performance on protein structural models predicted by both traditional protein structure prediction methods and the latest end-to-end deep learning method—AlphaFold2. It performs even better than the model QA scores provided by AlphaFold2 itself. The results illustrate that the 3D-equivariant graph neural network is a promising approach to the evaluation of protein structural models. Integrating AlphaFold2 features with other complementary sequence and structural features is important for improving protein model QA.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

Large scale crystallization of protein pharmaceuticals in microgravity via temperature change

The major objective of this research effort is the temperature driven growth of protein crystals in large batches in the microgravity environment of space. Pharmaceutical houses are developing protein products for patient care, for example, human insulin, human growth hormone, interferons, and tissue plasminogen activator or TPA, the clot buster for heart attack victims. Except for insulin, these are very high value products; they are extremely potent in small quantities and have a great value per gram of material. It is feasible that microgravity crystallization can be a cost recoverable, economically sound final processing step in their manufacture. Large scale protein crystal growth in microgravity has significant advantages from the basic science and the applied science standpoints. Crystal growth can proceed unhindered due to lack of surface effects. Dynamic control is possible and relatively easy. The method has the potential to yield large quantities of pure crystalline product. Crystallization is a time honored procedure for purifying organic materials and microgravity crystallization could be the final step to remove trace impurities from high value protein pharmaceuticals. In addition, microgravity grown crystals could be the final formulation for those medicines that need to be administered in a timed release fashion. Long lasting insulin, insulin lente, is such a product. Also crystalline protein pharmaceuticals are more stable for long-term storage. Temperature, as the initiation step, has certain advantages. Again, dynamic control of the crystallization process is possible and easy. A temperature step is non-invasive and is the most subtle way to control protein solubility and therefore crystallization. Seeding is not necessary. Changes in protein and precipitant concentrations and pH are not necessary. Finally, this method represents a new way to crystallize proteins in space that takes advantage of the unique microgravity environment. The results from two flights showed that the hardware performed perfectly, many crystals were produced, and they were much larger than their ground grown controls. Morphometric analysis was done on over 4,000 crystals to establish crystal size, size distribution, and relative size. Space grown crystals were remarkably larger than their earth grown counterparts and crystal size was a function of PCF volume. That size distribution for the space grown crystals was a function of PCF volume may indicate that ultimate size was a function of temperature gradient. Since the insulin protein concentration was very low, 0.4 mg/ml, the size distribution could also be following the total amount of protein in each of the PCF's. X-ray analysis showed that the bigger space grown insulin crystals diffracted to higher resolution than their ground grown controls. When the data were normalized for size, they still indicated that the space crystals were better than the ground crystals.

Long, Marianna M.↗
NASA NTRS1993

Protein crystal growth

Proteins account for 50% or more of the dry weight of most living systems and play a crucial role in virtually all biological processes. Since the specific functions of essentially all biological molecules are determined by their three-dimensional structures, it is obvious that a detailed understanding of the structural makeup of a protein is essential to any systematic research pertaining to it. At the present time, protein crystallography has no substitute, it is the only technique available for elucidating the atomic arrangements within complicated biological molecules. Most macromolecules are extremely difficult to crystallize, and many otherwise exciting and promising projects have terminated at the crystal growth stage. There is a pressing need to better understand protein crystal growth, and to develop new techniques that can be used to enhance the size and quality of protein crystals. There are several aspects of microgravity that might be exploited to enhance protein crystal growth. The major factor that might be expected to alter crystal growth processes in space is the elimination of density-driven convective flow. Another factor that can be readily controlled in the absence of gravity is the sedimentation of growing crystal in a gravitational field. Another potential advantage of microgravity for protein crystal growth is the option of doing containerless crystal growth. One can readily understand why the microgravity environment established by Earth-orbiting vehicles is perceived to offer unique opportunities for the protein crystallographer. The near term objectives of the Protein Crystal Growth in a Microgravity Environment (PCG/ME) project is to continue to improve the techniques, procedures, and hardware systems used to grow protein crystals in Earth orbit.

Bugg, Charles E.↗
NASA NTRS1998

The Effect of Protein Impurities on Lysozyme Crystal Growth

While bulk crystallization from impure solutions is used industrially as a purification step for a wide variety of materials, it is a technique that has rarely been used for proteins. Proteins have a reputation for being difficult to crystallize and high purity of the initial crystallization solution is considered paramount for success in the crystallization. Although little is written on the purifying capability of protein crystallization or of the effect of impurities on the various aspects of the crystallization process, recent published reports show that crystallization shows promise and feasibility as a purification technique for proteins. In order to further examine the issue of purity in macromolecule crystallization this study investigates the effect of the protein impurities, avidin, ovalbumin and conalbumin, at concentrations up to 50%, on the solubility, crystal face growth rates and crystal purity, of the protein lysozyme. Solubility was measured in batch experiments while a computer controlled video microscope system was used to measure the f {101} and {101} lysozyme crystal face growth rates. While little effect was observed on solubility and high crystal purity was obtained (>99.99%), the effect of the impurities on the face growth rates varied from no effect to a significant face specific effect leading to growth cessation, a phenomenon that is frequently observed in protein crystal growth. The results shed interesting light on the effect of protein impurities on protein crystal growth and strengthen the feasibility of using crystallization as a unit operation for protein purification.

Judge, Russell A.↗
NASA NTRS1998

The Development of an Edible Peanut Protein Film

The peanut is one of the crops chosen for use in NASA's Advanced Life Support Program (ALS). The peanut is a source of both oil and protein. After oil is extracted from the peanut, a protein rich flour remains. An edible peanut protein film is one use for this flour. Two types of film are developed for this study, one set of film contains 10% fat while the other set contains no fat. For film without fat the defatting of the peanut by the Soxhlet method is the first step in the manufacturing process of the film. Secondly, the protein is precipitated at its isoelectric point (pH 4.5) and centrifuged to separate the protein from the non-protein. After freeze-drying the protein it is milled in a ceramic ball mill to decrease particle size and sifted through a series of sieves to determine particle size distribution. Those particles retained on the 100 mesh sieves are utilized for film formation. Larger particles are re-ground and sifted. Five grams of protein is mixed with 50 mL of distilled water, 70 mL of 80% ethanol, 15 mL of 6N ammonium hydroxide and a plasticizer. This mixture is heated for 30 minutes until the temperature reaches 70 C. The mixture is then poured onto a level Teflon coated glass surface. After allowing the film to form overnight under a ventilation hood, it is manually removed from the plate. The processes and methods adopted have created flexible films of uniform thickness that are free of air bubbles. Thickness of films made from defatted peanut protein and partially defatted peanut protein were 0.10 Lm and 0.13 Lm respectively. Films with natural peanut fat are approximately three times as flexible and almost four times as strong as the films made without fat. Further research will be performed to evaluate its mechanical properties. This paper will greatly contribute to food preservation and waste management. Potential applications of this film are edible/biodegradable containers, wrapping for food preservation (against water, oxygen and oil), storage packets for seasonings or other ingredients and encapsulation for pharmaceutical use.

Patrick, N.↗
DOE OSTI2022

Putting Humpty Dumpty Back Together Again: What Does Protein Quantification Mean in Bottom-Up Proteomics?

Bottom-up proteomics provides peptide measurements and has been invaluable for moving proteomics into large-scale analyses. Commonly, a single quantitative value is reported for each protein-coding gene by aggregating peptide quantities into protein groups following protein inference or parsimony. However, given the complexity of both RNA splicing and post-translational protein modification, it is overly simplistic to assume that all peptides that map to a singular protein-coding gene will demonstrate the same quantitative response. Here, by assuming that all peptides from a protein-coding sequence are representative of the same protein, we may miss the discovery of important biological differences. To capture the contributions of existing proteoforms, we need to reconsider the practice of aggregating protein values to a single quantity per protein-coding gene.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

evSeq: Cost-Effective Amplicon Sequencing of Every Variant in a Protein Library

Widespread availability of protein sequence-fitness data would revolutionize both our biochemical understanding of proteins and our ability to engineer them. Unfortunately, even though thousands of protein variants are generated and evaluated for fitness during a typical protein engineering campaign, most are never sequenced, leaving a wealth of potential sequence-fitness information untapped. Primarily, this is because sequencing is unnecessary for many protein engineering strategies; the added cost and effort of sequencing is thus unjustified. It also results from the fact that, even though many lower cost sequencing strategies have been developed, they often require at least some sequencing or computational resources, both of which can be barriers to access. In this work, we present every variant sequencing (evSeq), a method and collection of tools/standardized components for sequencing a variable region within every variant gene produced during a protein engineering campaign at a cost of cents per variant. evSeq was designed to democratize low-cost sequencing for protein engineers and, indeed, anyone interested in engineering biological systems. Execution of its wet-lab component is simple, requires no sequencing experience to perform, relies only on resources and services typically available to biology labs, and slots neatly into existing protein engineering workflows. Analysis of evSeq data is likewise made simple by its accompanying software (found at github.com/fhalab/evSeq, documentation at fhalab.github.io/evSeq), which can be run on a personal laptop and was designed to be accessible to users with no computational experience. Here, low-cost and easy to use, evSeq makes collection of extensive protein variant sequence-fitness data practical.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

De novo design of knotted tandem repeat proteins

De novo protein design methods can create proteins with folds not yet seen in nature. These methods largely focus on optimizing the compatibility between the designed sequence and the intended conformation, without explicit consideration of protein folding pathways. Deeply knotted proteins, whose topologies may introduce substantial barriers to folding, thus represent an interesting test case for protein design. Here we report our attempts to design proteins with trefoil (3 1 ) and pentafoil (5 1 ) knotted topologies. We extended previously described algorithms for tandem repeat protein design in order to construct deeply knotted backbones and matching designed repeat sequences (N = 3 repeats for the trefoil and N = 5 for the pentafoil). We confirmed the intended conformation for the trefoil design by X ray crystallography, and we report here on this protein’s structure, stability, and folding behaviour. The pentafoil design misfolded into an asymmetric structure (despite a 5-fold symmetric sequence); two of the four repeat-repeat units matched the designed backbone while the other two diverged to form local contacts, leading to a trefoil rather than pentafoil knotted topology. Our results also provide insights into the folding of knotted proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A General Framework to Learn Tertiary Structure for Protein Sequence Characterization

During the past five years, deep-learning algorithms have enabled ground-breaking progress towards the prediction of tertiary structure from a protein sequence. Very recently, we developed SAdLSA, a new computational algorithm for protein sequence comparison via deep-learning of protein structural alignments. SAdLSA shows significant improvement over established sequence alignment methods. In this contribution, we show that SAdLSA provides a general machine-learning framework for structurally characterizing protein sequences. By aligning a protein sequence against itself, SAdLSA generates a fold distogram for the input sequence, including challenging cases whose structural folds were not present in the training set. About 70% of the predicted distograms are statistically significant. Although at present the accuracy of the intra-sequence distogram predicted by SAdLSA self-alignment is not as good as deep-learning algorithms specifically trained for distogram prediction, it is remarkable that the prediction of single protein structures is encoded by an algorithm that learns ensembles of pairwise structural comparisons, without being explicitly trained to recognize individual structural folds. As such, SAdLSA can not only predict protein folds for individual sequences, but also detects subtle, yet significant, structural relationships between multiple protein sequences using the same deep-learning neural network. The former reduces to a special case in this general framework for protein sequence annotation.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2003

Method for printing functional protein microarrays

Piezoelectric dispensing of proteins from borosilicate glass capillaries is a popular method of protein biochip fabrication that offers the advantages of sample recovery and noncontact with the printing substrate. However, little regard has been given to the quantitative aspects of dispensing minute volumes (1 nL or less) at the low protein concentrations (20 micrograms/mL or less) typically used in microprinting. Specifically, loss of protein sample due to nonspecific adsorption to the glass surface of the dispensing capillaries can limit the amount of protein delivered to the substrate. We demonstrate the benefits of a low ionic strength buffer containing the carrier protein BSA that effectively minimizes the ionic strength-dependent phenomenon of nonspecific protein adsorption to borosilicate glass. Over the concentration range of 20-2.5 micrograms/mL, the dispensing of a reference IgG in 10 mM PBS including 0.1% BSA resulted in the deposition of 3.6- to 44-fold more IgG compared to the deposition of IgG in standard 150 mM PBS in the absence of BSA. Furthermore, when the IgG was dispensed with carrier protein, the resulting spots exhibited a more uniform morphology. In a direct immunoassay for cholera toxin, capture antibody spots dispensed in 10 mM PBS containing 0.1% BSA produced fluorescent signals that were 2.8- to 4.3-fold more intense than antibody spots that were dispensed in 150 mM PBS without BSA. Interestingly, no differences were observed in the specific activities of the capture antibodies as a result of printing in the different buffers. The implications of these results on the future development of protein biochips are discussed.

Evaluation Studies↗
NASA NTRS1999

Collagenase-3 binds to a specific receptor and requires the low density lipoprotein receptor-related protein for internalization

We have previously identified a specific receptor for collagenase-3 that mediates the binding, internalization, and degradation of this ligand in UMR 106-01 rat osteoblastic osteosarcoma cells. In the present study, we show that collagenase-3 binding is calcium-dependent and occurs in a variety of cell types, including osteoblastic and fibroblastic cells. We also present evidence supporting a two-step mechanism of collagenase-3 binding and internalization involving both a specific collagenase-3 receptor and the low density lipoprotein receptor-related protein. Ligand blot analysis shows that (125)I-collagenase-3 binds specifically to two proteins ( approximately 170 kDa and approximately 600 kDa) present in UMR 106-01 cells. Western blotting identified the 600-kDa protein as the low density lipoprotein receptor-related protein. Our data suggest that the 170-kDa protein is a specific collagenase-3 receptor. Low density lipoprotein receptor-related protein-null mouse embryo fibroblasts bind but fail to internalize collagenase-3, whereas UMR 106-01 and wild-type mouse embryo fibroblasts bind and internalize collagenase-3. Internalization, but not binding, is inhibited by the 39-kDa receptor-associated protein. We conclude that the internalization of collagenase-3 requires the participation of the low density lipoprotein receptor-related protein and propose a model in which the cell surface interaction of this ligand requires a sequential contribution from two receptors, with the collagenase-3 receptor acting as a high affinity primary binding site and the low density lipoprotein receptor-related protein mediating internalization.

NASA Discipline Musculoskeletal↗