Search NASA⌕ Search

SEARCH · Search NASA

Results for “Protein function predictions”

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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Mechanism of Nitrogenase H 2 Formation by Metal-Hydride Protonation Probed by Mediated Electrocatalysis and H/D Isotope Effects

Nitrogenase catalyzes the reduction of dinitrogen (N2) to ammonia (NH3) with obligatory reduction of protons (H+) to dihydrogen (H2) through a mechanism involving reductive elimination of two [Fe-H-Fe] bridging hydrides at its active site FeMo-cofactor. The overall rate-limiting step is associated with ATP-driven electron delivery from Fe protein, precluding isotope effect measurements on substrate reduction steps. Here, we use mediated bioelectrocatalysis to drive electron delivery to MoFe protein without Fe protein and ATP hydrolysis, thereby eliminating the normal rate-limiting step. The ratio of catalytic current in mixtures of H2O and D2O, the proton inventory, changes linearly with the D2O/H2O ratio, revealing that a single H/D is involved in the rate limiting step. Kinetic models, along with measurements that vary the electron/proton delivery rate and use different substrates, reveal that the rate-limiting step under these conditions is the H2 formation reaction. Altering the chemical environment around the active site FeMo-cofactor in the MoFe protein either by substituting nearby amino acids or transferring the isolated FeMo-cofactor into a different peptide matrix, changes the net isotope effect, but the proton inventory plot remains linear, consistent with an unchanging rate-limiting step. Density functional theory predicts a transition state for H2 formation where the proton from S-H+ moves to the hydride in Fe-H-, predicting the number and magnitude of the observed H/D isotope effect. This study not only reveals the mechanism of H2 formation, but also illustrates a strategy for mechanistic study that can be applied to other enzymes and to biomimetic complexes.

BINDING↗
DOE OSTI2025

PNNL-Predictive-Phenomics/ProteoMeter

ProteoMeter is a Python package that assists in the statistical analysis of global proteomics, protein post-translation modification (PTM), and limited proteolysis (LiP) data. It contains batch correction, normalization, and statistical testing methods, as well as functions that "roll up" peptide-level data to the single-site level. It has a robust user configuration system, allowing it to flexibly integrate different types of experiment designs. For basic usage, a simple configuration file provides the essential functionality. Advanced users have access to the entire statistical pipeline for fine-tuning analyses. Processed data is easily exported to many common spreadsheet and data-frame formats.

Rozum, Jordan [Pacific Northwest National Lab]↗
DOE OSTI2025

Genome-wide identification of novel flagellar motility genes in Pseudomonas syringae pv. tomato DC3000

Pseudomonas syringaepv.tomatoDC3000 (PstDC3000) is a plant pathogenic bacterium that possesses complicated motility regulation pathways including a typical chemotaxis system. A significant portion of our understanding about the genes functioning inPstDC3000 motility is based on comparison to other bacteria. This leaves uncertainty about whether gene functions are conserved, especially since specific regulatory modules can have opposite functions in sets ofPseudomonas. In this study, we used a competitive selection to enrich for mutants with altered swimming motility and used random barcode transposon-site sequencing (RB-TnSeq) to identify genes with significant roles in swimming motility. Besides many of the known or predicted chemotaxis and motility genes, our method identified PSPTO_0406 (dipA), PSPTO_1042 (chrR) and PSPTO_4229 (hypothetical protein) as novel motility regulators. PSPTO_0406 is a homolog ofdipA, a known cyclic di-GMP degrading enzyme inP. aeruginosa. PSPTO_1042 is part of an extracytoplasmic sensing system that controls gene expression in response to reactive oxygen species, suggesting that PSPTO_1042 may function as part of a mechanism that enablesPstDC3000 to alter motility when encountering oxidative stressors. PSPTO_4229 encodes a protein containing an HD-related output domain (HDOD), but with no previously identified functions. We found that deletion and overexpression of PSPTO_4229 both reduce swimming motility, suggesting that its function is sensitive to expression level. We used the overexpression phenotype to screen for nonsense and missense mutants of PSPTO_4229 that no longer reduce swimming motility and found a pair of conserved arginine residues that are necessary for motility suppression. Together these results provide a global perspective on regulatory and structural genes controlling flagellar motility inPstDC3000.

Microbiology↗
DOE OSTI2023

A multiplexed bacterial two-hybrid for rapid characterization of protein–protein interactions and iterative protein design

Protein-protein interactions (PPIs) are crucial for biological functions and have applications ranging from drug design to synthetic cell circuits. Coiled-coils have been used as a model to study the sequence determinants of specificity. However, building well-behaved sets of orthogonal pairs of coiled-coils remains challenging due to inaccurate predictions of orthogonality and difficulties in testing at scale. To address this, we develop the next-generation bacterial two-hybrid (NGB2H) method, which allows for the rapid exploration of interactions of programmed protein libraries in a quantitative and scalable way using next-generation sequencing readout. We design, build, and test large sets of orthogonal synthetic coiled-coils, assayed over 8,000 PPIs, and used the dataset to train a more accurate coiled-coil scoring algorithm (iCipa). After characterizing nearly 18,000 new PPIs, we identify to the best of our knowledge the largest set of orthogonal coiled-coils to date, with fifteen on-target interactions. Our approach provides a powerful tool for the design of orthogonal PPIs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Design of a PROTAC that antagonizes and destroys the cancer-forming X-protein of the hepatitis B virus

Highlights: • A novel proteolysis targeting chimeric molecule (PROTAC) to treat hepatitis B. • The PROTAC antagonizes and destroys the X-protein of the hepatitis B virus. • The PROTAC is a fusion of the X-protein oligomerization and instability domains. • The oligomerization domain is a dominant-negative inhibitor of X-protein function. • X-protein-targeting PROTACs have potential to prevent hepatocellular carcinoma. - Abstract: The X-protein of the hepatitis B virus (HBV) is essential for virus infection and contributes to the development of HBV-induced hepatocellular carcinoma (HCC), a disease which causes more than one million deaths each year. Here we describe the design of a novel PROTAC (proteolysis targeting chimeric molecule) capable of simultaneously inducing the degradation of the X-protein, and antagonizing its function. The PROTAC was constructed by fusing the N-terminal oligomerization and C-terminal instability domains of the X-protein to each other, and rendering them cell-permeable by the inclusion of a polyarginine cell-penetrating peptide (CPP). It was predicted that the oligomerization domain would bind the X-protein, and that the instability domain would cause the X-protein to be targeted for proteasomal degradation. Addition of the PROTAC to HepG2 liver cancer cells, engineered to express full-length and C-terminally truncated forms of the X-protein, resulted in the degradation of both forms of the X-protein. A cell-permeable stand-alone form of the oligomerization domain was taken up by HepG2 cells, and acted as a dominant-negative inhibitor, causing inhibition of X-protein-induced apoptosis. In summary, the PROTAC described here induces the degradation of the X-protein, and antagonizes its function, and warrants investigation in a preclinical study for its ability to prevent or treat HBV infection and/or the development of HCC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Pushing the size limit of de novo structure ensemble prediction guided by sparse SDSL-EPR restraints to 200 residues: The monomeric and homodimeric forms of BAX

Structure determination remains a challenge for many biologically important proteins. In particular, proteins that adopt multiple conformations often evade crystallization in all biologically relevant states. Although computational de novo protein folding approaches often sample biologically relevant conformations, the selection of the most accurate model for different functional states remains a formidable challenge, in particular, for proteins with more than about 150 residues. Electron paramagnetic resonance (EPR) spectroscopy can obtain limited structural information for proteins in well-defined biological states and thereby assist in selecting biologically relevant conformations. The present study demonstrates that de novo folding methods are able to accurately sample the folds of 192-residue long soluble monomeric Bcl-2-associated X protein (BAX). The tertiary structures of the monomeric and homodimeric forms of BAX were predicted using the primary structure as well as 25 and 11 EPR distance restraints, respectively. The predicted models were subsequently compared to respective NMR/X-ray structures of BAX. EPR restraints improve the protein-size normalized root-mean-square-deviation (RMSD100) of the most accurate models with respect to the NMR/crystal structure from 5.9 Å to 3.9 Å and from 5.7 Å to 3.3 Å, respectively. Additionally, the model discrimination is improved, which is demonstrated by an improvement of the enrichment from 5% to 15% and from 13% to 21%, respectively.

Biochemistry & Molecular Biology↗
DOE OSTI2016

Distinct temporal changes in host cell lncRNA expression during the course of an adenovirus infection

The deregulation of cellular long non-coding RNA (lncRNA) expression during a human adenovirus infection was studied by deep sequencing. Expression of lncRNAs increased substantially following the progression of the infection. Among 645 significantly expressed lncRNAs, the expression of 398 was changed more than 2-fold. More than 80% of them were up-regulated and 80% of them were detected during the late phase. Based on the genomic locations of the deregulated lncRNAs in relation to known mRNAs and miRNAs, they were predicted to be involved in growth, structure, apoptosis and wound healing in the early phase, cell proliferation in the intermediate phase and protein synthesis, modification and transport in the late phase. The most significant functions of cellular RNA-binding proteins, previously shown to interact with the deregulated lncRNAs identified here, are involved in RNA splicing, nuclear export and translation events. We hypothesize that adenoviruses exploit the lncRNA network to optimize their reproduction. - Highlights: • The expression of 398 lncRNAs showed a distinct temporal pattern during Ad2 infection. • 80% of the deregulated lncRNAs were up-regulated during the late phase of infection. • The deregulated lncRNAs potentiallyinteract with 33 cellular RNA binding proteins. • These RBPs are involved in RNA splicing, nuclear export and translation. • Adenovirus exploits the cellular lncRNA network to optimize its replication.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations

The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Molecular architecture and functional dynamics of the pre-incision complex in nucleotide excision repair

Nucleotide excision repair (NER) is vital for genome integrity. Yet, our understanding of the complex NER protein machinery remains incomplete. Combining cryo-EM and XL-MS data with AlphaFold2 predictions, we build an integrative model of the NER pre-incision complex(PInC). Here TFIIH serves as a molecular ruler, defining the DNA bubble size and precisely positioning the XPG and XPF nucleases for incision. Using simulations and graph theoretical analyses, we unveil PInC’s assembly, global motions, and partitioning into dynamic communities. Remarkably, XPG caps XPD’s DNA-binding groove and bridges both junctions of the DNA bubble, suggesting a novel coordination mechanism of PInC’s dual incision. XPA rigging interlaces XPF/ERCC1 with RPA, XPD, XPB, and 5' ssDNA, exposing XPA’s crucial role in licensing the XPF/ERCC1 incision. Mapping disease mutations onto our models reveals clustering into distinct mechanistic classes, elucidating xeroderma pigmentosum and Cockayne syndrome disease etiology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Structural Basis and Sequence Rules for Substrate Recognition by Tankyrase Explain the Basis for Cherubism Disease

The poly(ADP-ribose)polymerases Tankyrase 1/2 (TNKS/TNKS2) catalyze the covalent linkage of ADP-ribose polymer chains onto target proteins, regulating their ubiquitylation, stability, and function. Dysregulation of substrate recognition by Tankyrases underlies the human disease cherubism. Tankyrases recruit specific motifs (often called RxxPDG hexapeptides) in their substrates via an N-terminal region of ankyrin repeats. These ankyrin repeats form five domains termed ankyrin repeat clusters (ARCs), each predicted to bind substrate. Here we report crystal structures of a representative ARC of TNKS2 bound to targeting peptides from six substrates. Using a solution-based peptide library screen, we derive a rule-based consensus for Tankyrase substrates common to four functionally conserved ARCs. This 8-residue consensus allows us to rationalize all known Tankyrase substrates and explains the basis for cherubism-causing mutations in the Tankyrase substrate 3BP2. Structural and sequence information allows us to also predict and validate other Tankyrase targets, including Disc1, Striatin, Fat4, RAD54, BCR, and MERIT40.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Characterization of Metalloproteins by High-throughput X-ray Absorption Spectroscopy

High-throughput X-ray absorption spectroscopy was used to measure transition metal content based on quantitative detection of X-ray fluorescence signals for 3879 purified proteins from several hundred different protein families generated by the New York SGX Research Center for Structural Genomics. Approximately 9% of the proteins analyzed showed the presence of transition metal atoms (Zn, Cu, Ni, Co, Fe, or Mn) in stoichiometric amounts. The method is highly automated and highly reliable based on comparison of the results to crystal structure data derived from the same protein set. To leverage the experimental metalloprotein annotations, we used a sequence-based de novo prediction method, MetalDetector, to identify Cys and His residues that bind to transition metals for the redundancy reduced subset of 2411 sequences sharing <70% sequence identity and having at least one His or Cys. As the HT-XAS identifies metal type and protein binding, while the bioinformatics analysis identifies metal-binding residues, the results were combined to identify putative metal-binding sites in the proteins and their associated families. We explored the combination of this data with homology models to generate detailed structure models of metal-binding sites for representative proteins. Finally, we used extended X-ray absorption fine structure data from two of the purified Zn metalloproteins to validate predicted metalloprotein binding site structures. This combination of experimental and bioinformatics approaches provides comprehensive active site analysis on the genome scale for metalloproteins as a class, revealing new insights into metalloprotein structure and function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

PERCEPTIVE: an R shiny $\underline{p}$ipelin$\underline{e}$ for the p$\underline{r}$edi$\underline{c}$tion of $\underline{ep}$igenetic modula$\underline{t}$ors $\underline{i}$n no$\underline{v}$el sp$\underline{e}$cies

Epigenetic processes are central to regulating gene expression, genome stability, and metabolic function across the tree of life; yet, their roles remain underexplored in microalgae, especially as new species continue to be identified and characterized. This is likely due to the cumbersome nature and species-dependent attributes of epigenetic wet-lab methodologies, which preclude the rapid identification of epigenetic modifications and modulators. However, there is high conservation of epigenetic processes from budding yeast to humans; in many cases, one may infer how behavior and function are epigenetically regulated in novel species by identifying epigenetic modulators, or the proteins responsible for conferring epigenetic modifications. Here, to this end, we have developed a graphical software package, titled PERCEPTIVE (pipeline for the prediction of epigenetic modulators in novel species). This platform solely uses the genomic sequence of an algal species, and preexisting information from other model organisms, to predict the epigenetic modulators and associated modifications in algae. Predictions are presented to the user in a graphical interface, which provides literature-based interpretation of results, enabling users to quickly understand potential epigenetic processes in their algal species of interest and plan follow-up experiments. To test PERCEPTIVE, we predicted epigenetic modulators in several feedstock candidate algae species. To validate these predictions, wet-lab studies were performed, including mass spectrometry; these results underscore the high accuracy of PERCEPTIVE predictions. Overall, PERCEPTIVE represents a powerful in silico tool for the research and manipulation of algal species, which does not require a priori knowledge of epigenetics and is accessible to a broad set of investigators.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Quantification of the transferability of a designed protein specificity switch reveals extensive epistasis in molecular recognition

Re-engineering protein–protein recognition is an important route to dissecting and controlling complex interaction networks. Experimental approaches have used the strategy of “second-site suppressors,” where a functional interaction is inferred between two proteins if a mutation in one protein can be compensated by a mutation in the second. Mimicking this strategy, computational design has been applied successfully to change protein recognition specificity by predicting such sets of compensatory mutations in protein–protein interfaces. To extend this approach, it would be advantageous to be able to “transplant” existing engineered and experimentally validated specificity changes to other homologous protein–protein complexes. Here, we test this strategy by designing a pair of mutations that modulates peptide recognition specificity in the Syntrophin PDZ domain, confirming the designed interaction biochemically and structurally, and then transplanting the mutations into the context of five related PDZ domain–peptide complexes. We find a wide range of energetic effects of identical mutations in structurally similar positions, revealing a dramatic context dependence (epistasis) of designed mutations in homologous protein–protein interactions. To better understand the structural basis of this context dependence, we apply a structure-based computational model that recapitulates these energetic effects and we use this model to make and validate forward predictions. The context dependence of these mutations is captured by computational predictions, our results both highlight the considerable difficulties in designing protein–protein interactions and provide challenging benchmark cases for the development of improved protein modeling and design methods that accurately account for the context.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge

Understanding the interactions and regulatory relationships among biomolecules is essential for deciphering complex biological systems and elucidating the mechanisms behind diverse biological functions. Traditionally, the collection of such molecular interaction data has relied on expert curation, a process that is both time-consuming and labor-intensive. To address these limitations, this study explores the use of large language models (LLMs) to automate the genome-scale extraction of molecular interaction knowledge. Here, we evaluate the performance of various LLMs on key biological tasks, including the identification of protein-protein interactions, detection of genes associated with pathways influenced by low-dose radiation, and inference of gene regulatory relationships. Our findings demonstrate that larger LLMs tend to perform better, particularly in extracting intricate gene and protein interactions. Despite their strengths, these models face challenges in recognizing functionally diverse gene groups and highly correlated regulatory relationships. Through a comprehensive analysis using established molecular interaction and pathway databases, we show that LLMs possess the potential to identify relevant biomolecules and predict their interactions, offering valuable insights and marking a significant step toward AI-driven biological knowledge discovery.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2012

LCLS-II New Instruments Workshops Report

The LCLS-II New Instruments workshops chaired by Phil Heimann and Jerry Hastings were held on March 19-22, 2012 at the SLAC National Accelerator Laboratory. The goal of the workshops was to identify the most exciting science and corresponding parameters which will help define the LCLS-II instrumentation. This report gives a synopsis of the proposed investigations and an account of the workshop. Scientists from around the world have provided short descriptions of the scientific opportunities they envision at LCLS-II. The workshops focused on four broadly defined science areas: biology, materials sciences, chemistry and atomic, molecular and optical physics (AMO). Below we summarize the identified science opportunities in the four areas. The frontiers of structural biology lie in solving the structures of large macromolecular biological systems. Most large protein assemblies are inherently difficult to crystallize due to their numerous degrees of freedom. Serial femtosecond protein nanocrystallography, using the 'diffraction-before-destruction' approach to outrun radiation damage has been very successfully pioneered at LCLS and diffraction patterns were obtained from some of the smallest protein crystals ever. The combination of femtosecond x-ray pulses of high intensity and nanosized protein crystals avoids the radiation damage encountered by conventional x-ray crystallography with focused beams and opens the door for atomic structure determinations of the previously largely inaccessible class of membrane proteins that are notoriously difficult to crystallize. The obtained structures will allow the identification of key protein functions and help in understanding the origin and control of diseases. Three dimensional coherent x-ray imaging at somewhat lower resolution may be used for larger objects such as viruses. The chemistry research areas of primary focus are the predictive understanding of catalytic mechanisms, with particular emphasis on photo- and heterogeneous catalysis. Of particular interest is the efficient conversion of light to electrical or chemical energy, which requires understanding the non-adiabatic dynamics of electronic excited states. Ultrafast x-ray scattering presents an excellent opportunity to investigate structural dynamics of molecular systems with atomic resolution, and x-ray scattering and spectroscopy present an excellent opportunity to investigating the dynamics of the electronic charge distribution. Harnessing solar energy to generate fuels, either indirectly with photovoltaics and electrochemical catalysis or directly with photocatalysts, presents a critical technological challenge that will require the use of forefront scientific tools such as ultrafast x-rays. At the center of this technical challenge is the rational design of efficient and cost effective catalysts. Important materials science opportunities relate to information technology applications, in particular the transport and storage of information on increasingly smaller length- and faster time-scales. Of interest are the understanding of the intrinsic size limits associated with the storage of information bits and the speed limits of information or bit processing. Key questions revolve about how electronic charges and spins of materials can be manipulated by electric and magnetic fields. This requires the exploration of speed limits subject to the fundamental conservation laws of energy and linear and angular momentum and the different coupling of polar electric and axial magnetic fields to charge and spin. Of interest are novel composite materials, including molecular systems combining multi electric and magnetic functionality. Ultrafast x-rays offer the required probing speed, can probe either the charge or spin properties through polarization control and through scattering and spectroscopy cover the entire energy-time-momentum-distance phase space. In the field of atomic and molecular science, LCLS II promises to elucidate the fundamental interactions among electrons and between electrons and nuclei, and to explore the frontiers of light-matter interactions. The high x-ray intensities unique to LCLS II will allow for highly localized electron excitation and probing at different positions inside a molecule, thereby interrogating the fundamental electron correlations. At the ultimate intensities, a breakdown of our established model of x-ray - matter interaction is predicted, leading to possibly new regimes of plasma physics.

14 SOLAR ENERGY↗
DOE OSTI2025

Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions

Predicting the binding affinity of ligands to protein pockets is key in the drug design pipeline. The flexibility of ligand-pocket motifs arises from a range of attractive and repulsive electronic interactions during binding. Accurately accounting for all interactions requires robust quantum-mechanical (QM) benchmarks, which are scarce for ligand-pocket systems. Additionally, disagreement between “gold standard” Coupled Cluster (CC) and Quantum Monte Carlo (QMC) methods casts doubt on many benchmarks for larger non-covalent systems. We introduce the “QUantum Interacting Dimer” (QUID) benchmark framework containing 170 non-covalent (non-)equilibrium systems modeling chemically and structurally diverse ligand-pocket motifs. Symmetry-adapted perturbation theory shows that QUID broadly covers non-covalent binding motifs and energetic contributions. Robust binding energies are obtained using complementary CC and QMC methods, achieving agreement of 0.5 kcal/mol. The benchmark data analysis reveals that several dispersion-inclusive density functional approximations provide accurate energy predictions, though their atomic van der Waals forces differ in magnitude and orientation. Contrarily, semiempirical methods and empirical force fields require improvements in capturing non-covalent interactions (NCIs) for out-of-equilibrium geometries. The wide span of NCIs, highly accurate interaction energies, and analysis of molecular properties take QUID beyond the “gold standard” for QM benchmarks of ligand-protein systems.

Puleva, Mirela [University of Luxembourg, Luxembou↗
DOE OSTI2016

Evidence for Osteocalcin Binding and Activation of GPRC6A in β-Cells

The possibility that G protein-coupled receptor family C member A (GPRC6A) is the osteocalcin (Ocn)-sensing G protein-coupled receptor that directly regulates pancreatic β-cell functions is controversial. In the current study, we found that Ocn and an Ocn-derived C-terminal hexapeptide directly activate GPRC6A-dependent ERK signaling in vitro. Computational models probe the structural basis of Ocn binding to GPRC6A and predict that the C-terminal hexapeptide docks to the extracellular side of the transmembrane domain of GPRC6A. Consistent with the modeling, mutations in the computationally identified binding pocket of GPRC6A reduced Ocn and C-terminal hexapeptide activation of this receptor. In addition, selective deletion of Gprc6a in β-cells (Gprc6a β-cell-cko ) by crossing Gprc6a flox/flox mice with Ins2-Cre mice resulted in reduced pancreatic weight, islet number, insulin protein content, and insulin message expression. Both islet size and β-cell proliferation were reduced in Gprc6a β-cell-cko compared with control mice. Gprc6a β-cell-cko exhibited abnormal glucose tolerance, but normal insulin sensitivity. Islets isolated from Gprc6a β-cell-cko mice showed reduced insulin simulation index in response to Ocn. Here, these data establish the structural basis for Ocn direct activation of GPRC6A and confirm a role for GPRC6A in regulating β-cell proliferation and insulin secretion.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Predicting peak spectral sensitivities of vertebrate cone visual pigments using atomistic molecular simulations

Vision is the dominant sensory modality in many organisms for foraging, predator avoidance, and social behaviors including mate selection. Vertebrate visual perception is initiated when light strikes rod and cone photoreceptors within the neural retina of the eye. Sensitivity to individual colors, i.e., peak spectral sensitivities (λ max ) of visual pigments, are a function of the type of chromophore and the amino acid sequence of the associated opsin protein in the photoreceptors. Large differences in peak spectral sensitivities can result from minor differences in amino acid sequence of cone opsins. To determine how minor sequence differences could result in large spectral shifts we selected a spectrally-diverse group of 14 teleost Rh2 cone opsins for which sequences and λ max are experimentally known. Classical molecular dynamics simulations were carried out after embedding chromophore-associated homology structures within explicit bilayers and water. These simulations revealed structural features of visual pigments, particularly within the chromophore, that contributed to diverged spectral sensitivities. Statistical tests performed on all the observed structural parameters associated with the chromophore revealed that a two-term, first-order regression model was sufficient to accurately predict λ max over a range of 452–528 nm. The approach was accurate, efficient and simple in that site-by-site molecular modifications or complex quantum mechanics models were not required to predict λ max . These studies identify structural features associated with the chromophore that may explain diverged spectral sensitivities, and provide a platform for future, functionally predictive opsin modeling.

59 BASIC BIOLOGICAL SCIENCES↗