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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.

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240 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

PRELP (proline/arginine-rich end leucine-rich repeat protein) promotes osteoblastic differentiation of preosteoblastic MC3T3-E1 cells by regulating the β-catenin pathway

Proline/arginine-rich end leucine-rich repeat protein (PRELP) is a collagen-binding proteoglycan highly expressed in the developing bones. Recent studies indicated that PRELP could inhibit osteoclastogenesis as a NF-κB inhibitor. However, its role during osteoblast differentiation is still unclear. In this study, we confirmed that the expression of PRELP increased with the osteogenesis induction of preosteoblastic MC3T3-E1 cells. Down-regulation of PRELP expression by shRNA reduced ALP activity, mineralization and expression of osteogenic marker gene Runx2. Our microarray analysis data suggested that β-catenin may act as a hub gene in the PRELP-mediated gene network. We validated furtherly that PRELP knockdown could inhibit the level of connexin43, a key regulator of osteoblast differentiation by affecting β-catenin protein expression, and its nuclear translocation in MC3T3-E1 preosteoblasts. Therefore, this study established a new role of PRELP in modulating β-catenin/connexin43 pathway and osteoblast differentiation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Leveraging structure-informed machine learning for fast steric zipper propensity prediction across whole proteomes

Predicting the amyloid fold and the propensity of peptide segments to adopt amyloid-like structures remain a challenge. However, recent progress has facilitated structure-based prediction of steric zipper propensity and the use of machine learning to accelerate the calculation of predictive models across many scientific areas. Leveraging these advances, we have developed a new approach for rapid proteome-wide assessment of zipper profiles that is informed by four million steric zipper predictions collected over ten years. This collection is used to build a machine learning model capable of rapidly predicting steric zipper propensity, and allowing for the assessment of zippers at both the protein and proteome level. Our predictions show enrichment for zipper forming segments in proteins involved in cell wall reorganization in yeast, highlighting a potential category of interest for experimental characterization. Overall, our predictive model allows for the exploration of amyloid formation across the tree of life and provides a tool for assessment of both novel and designed sequences for zipper density.

Biochemistry & Molecular Biology↗
DOE OSTI2014

Inter-Species Cross-Seeding: Stability and Assembly of Rat–Human Amylin Aggregates

Diseases such as type 2 diabetes, Alzheimer’s and Parkinson’s share as common feature the accumulation of mis-folded disease-specific protein aggregates into fibrillar structures, or plaques. These fibrils may either be toxic by themselves, or act as reservoirs for smaller cytotoxic oligomers. This suggests to investigate molecules as potential therapeutics that either reduce fibril formation or increase fibril stability. One example is rat amylin, which can inhibit aggregation of human amylin, a hallmark of type 2 diabetes. In the present paper, we use molecular dynamics to compare the stability of various preformed aggregates, built out of either human amylin, rat amylin, or mixtures of both. We considered two types of fibril-like oligomers: a single-layer in-register conformation, and a double-layer conformation in which the first U-shaped layer consists of rat amylin and the second layer of human amylin. Our results explain the weak amyloid-inhibiting properties of rat amylin and suggest that membrane leakage due to pore formation is responsible for the toxicity of rat amylin observed in a recent experiment. Together, our results put in question the use of rat amylin or the similar FDA approved drug pramlintide as an inhibitor of human amylin aggregation. They also point to mixed human-rat amylin fibril-like oligomers as possible model-systems for studies of amyloid formation that involve cross-species transmission.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Secondary structure of rat and human amylin across force fields

The aggregation of human amylin has been strongly implicated in the progression of Type II diabetes. This 37-residue peptide forms a variety of secondary structures, including random coils, α-helices, and β-hairpins. The balance between these structures depends on the chemical environment, making amylin an ideal candidate to examine inherent biases in force fields. Rat amylin differs from human amylin by only 6 residues; however, it does not form fibrils. Therefore it provides a useful complement to human amylin in studies of the key events along the aggregation pathway. In this work, the free energy of rat and human amylin was determined as a function of α-helix and β-hairpin content for the Gromos96 53a6, OPLS-AA/L, CHARMM22/CMAP, CHARMM22*, Amberff99sb*-ILDN, and Amberff03w force fields using advanced sampling techniques, specifically bias exchange metadynamics. This work represents a first systematic attempt to evaluate the conformations and the corresponding free energy of a large, clinically relevant disordered peptide in solution across force fields. The NMR chemical shifts of rIAPP were calculated for each of the force fields using their respective free energy maps, allowing us to quantitatively assess their predictions. We show that the predicted distribution of secondary structures is sensitive to the choice of force-field: Gromos53a6 is biased towards β-hairpins, while CHARMM22/CMAP predicts structures that are overly α-helical. OPLS-AA/L favors disordered structures. Amberff99sb*-ILDN, AmberFF03w and CHARMM22* provide the balance between secondary structures that is most consistent with available experimental data. In contrast to previous reports, our findings suggest that the equilibrium conformations of human and rat amylin are remarkably similar, but that subtle differences arise in transient alpha-helical and beta-strand containing structures that the human peptide can more readily adopt. We hypothesize that these transient states enable dynamic pathways that facilitate the formation of aggregates and, eventually, amyloid fibrils.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Assessment of software methods for estimating protein-protein relative binding affinities

A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < -0.5 kcal/mol) with high (83–98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.

59 BASIC BIOLOGICAL SCIENCES↗