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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Protein changes associated with reprotonation of the Schiff base in the photocycle of Asp96-->Asn bacteriorhodopsin. The MN intermediate with unprotonated Schiff base but N-like protein structure

The difference Fourier transform infrared spectrum for the N intermediate in the photoreaction of the light-adapted form of bacteriorhodopsin can be recorded at pH 10 at 274 K (Pfefferle, J.-M., Maeda, A., Sasaki, J., and Yoshizawa, T. (1991) Biochemistry 30, 6548-6556). Under these conditions, Asp96-->Asn bacteriorhodopsin gives a photoproduct which shows changes in protein structure similar to those observed in N of wild-type bacteriorhodopsin. However, decreased intensity of the chromophore bands and the single absorbance maximum at about 400 nm indicate that the Schiff base is unprotonated, as in the M intermediate. This photoproduct was named MN. At pH 7, where the supply of proton is not as restricted as at pH 10, Asp96-->Asn bacteriorhodopsin yields N with a protonated Schiff base. The Asn96 residue, which cannot deprotonate as Asp96 in wild-type bacteriorhodopsin, is perturbed upon formation of both MN at pH 10 and N at pH 7. We suggest that the reprotonation of the Schiff base is preceded by a large change in the protein structure including perturbation of the residue at position 96.

Non-NASA Center↗
DOE OSTI2021

CryoFold: Determining protein structures and data-guided ensembles from cryo-EM density maps

Cryoelectron microscopy requires molecular modeling for refinement of structures. Ensemble models arrive at low free-energy molecular structures, but are computationally expensive and limited to resolving only small proteins. Here, we introduce CryoFold, a pipeline of molecular dynamics simulations that determines ensembles of protein structures by integrating density data of varying sparsity at 3–5 Å resolution with sequence information and coarse-grained topological knowledge of the protein folds. We present six examples, folding proteins between 72 and 2,000 residues, including large membrane and multi-domain systems, and results from two Electron Microscopy Data Bank (EMDB) competitions. Driven by data from a single state, CryoFold discovers ensembles of common low-energy models together with rare low-probability structures that capture the equilibrium distribution of proteins constrained by the density maps. Many of these conformations are experimentally validated and functionally relevant. We arrive at a set of best practices for data-guided protein folding that are controlled using a Python graphical user interface (GUI).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

DIPS-Plus: The enhanced database of interacting protein structures for interface prediction

Abstract In this work, we expand on a dataset recently introduced for protein interface prediction (PIP), the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for machine learning of protein interfaces. While the original DIPS dataset contains only the Cartesian coordinates for atoms contained in the protein complex along with their types, DIPS-Plus contains multiple residue-level features including surface proximities, half-sphere amino acid compositions, and new profile hidden Markov model (HMM)-based sequence features for each amino acid, providing researchers a curated feature bank for training protein interface prediction methods. We demonstrate through rigorous benchmarks that training an existing state-of-the-art (SOTA) model for PIP on DIPS-Plus yields new SOTA results, surpassing the performance of some of the latest models trained on residue-level and atom-level encodings of protein complexes to date.

59 BASIC BIOLOGICAL SCIENCES↗
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 OSTI2024

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures

The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. Cryo2StructData is larger than existing, publicly available datasets for training AI methods to build atomic protein structures from cryo-EM density maps. We trained and tested deep learning models on Cryo2StructData to validate its quality showing that it is ready for being used to train and test AI methods for building atomic models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Ligand-Binding-Site Refinement to Generate Reliable Holo Protein Structure Conformations from Apo Structures

The first important step in a structure-based virtual screening is the judicious selection of a receptor protein. In cases where the holo protein receptor structure is unavailable, significant reduction in virtual screening performance has been reported. In this work, we present a robust method to generate reliable holo protein structure conformations from apo structures using molecular dynamics (MD) simulation with restraints derived from holo structure binding-site templates. We perform benchmark tests on two different datasets: 40 structures from a directory of useful decoy-enhanced (DUD-E) and 84 structures from the Gunasekaran dataset. Our results show successful refinement of apo binding-site structures toward holo conformations in 82% of the test cases. In addition, virtual screening performance of 40 DUD-E structures is significantly improved using our MD-refined structures as receptors with an average enrichment factor (EF), an EF1% value of 6.2 compared to apo structures with 3.5. Docking of native ligands to the refined structures shows an average ligand root mean square deviation (RMSD) of 1.97 Å (DUD-E dataset and Gunasekaran dataset) relative to ligands in the holo crystal structures, which is comparable to the self-docking (i.e., docking of the native ligand back to its crystal structure receptor) average, 1.34 Å (DUD-E dataset) and 1.36 Å (Gunasekaran dataset). On the other hand, docking to the apo structures yields an average ligand RMSD of 3.65 Å (DUD-E) and 2.90 Å (Gunasekaran). These results indicate that our method is robust and can be useful to improve virtual screening performance of apo structures.

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

Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting

Recent advances in cryo-electron microscopy (cryo-EM) have enabled modeling macromolecular complexes that are essential components of the cellular machinery. The density maps derived from cryo-EM experiments are often integrated with manual, knowledge or artificial intelligence driven, and physics-guided computational methods to build, fit, and refine molecular structures. Going beyond a single stationary- structure determination scheme, it is becoming more common to interpret the experimental data with an ensemble of models, which contributes to an average observation. Hence, there is a need to decide on the quality of an ensemble of protein structures on-the-fly, while refining them against the density maps. Here, we introduce such an adaptive decision making scheme during the molecular dynamics flexible fitting (MDFF) of biomolecules. Using RADICAL-Cybertools, and the new RADICAL augmented MDFF implementation (R-MDFF) is examined in high-performance computing environments for refinement of two protein systems, Adenylate Kinase and Carbon Monoxide Dehydrogenase. For the test cases, use of multiple replicas in flexible fitting with adaptive decision making in R-MDFF improves the overall correlation to the density by 40% relative to the refinements of the brute-force MDFF. The improvements are particularly significant at high, 2 - 3 Å, map resolutions. More importantly, the ensemble model captures key features of biologically relevant molecular dynamics that is inaccessible to a single-model interpretation. Finally, the pipeline is applicable to systems of growing sizes, which is demonstrated using ensemble refinement of capsid proteins from Chimpanzee adenovirus. The overhead for decision making remaining low and robust to computing environments. The software is publicly available on GitHub and includes a short user guide to install the R-MDFF on different computing environments, from local Linux based workstations to High Performance Computing (HPC) environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1982

Protein folding, protein structure and the origin of life: Theoretical methods and solutions of dynamical problems

Theoretical methods and solutions of the dynamics of protein folding, protein aggregation, protein structure, and the origin of life are discussed. The elements of a dynamic model representing the initial stages of protein folding are presented. The calculation and experimental determination of the model parameters are discussed. The use of computer simulation for modeling protein folding is considered.

Weaver, D. L.↗
DOE OSTI2021

High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function

Computational biology is one of many scientific disciplines ripe for innovation and acceleration with the advent of high-performance computing (HPC). In recent years, the field of machine learning has also seen significant benefits from adopting HPC practices. In this work, we present a novel HPC pipeline that incorporates various machine-learning approaches for structure-based functional annotation of proteins on the scale of whole genomes. Our pipeline makes extensive use of deep learning and provides computational insights into best practices for training advanced deep-learning models for high-throughput data such as proteomics data. We showcase methodologies our pipeline currently supports and detail future tasks for our pipeline to envelop, including large-scale sequence comparison using SAdLSA and prediction of protein tertiary structures using AlphaFold2.

Gao, Mu↗
DOE OSTI2021

Effect of the abolition of intersubunit salt bridges on allosteric protein structural dynamics

A salt bridge, one of the representative structural factors established by non-covalent interactions, plays a crucial role in stabilizing the structure and regulating the protein function, but its role in dynamic processes has been elusive. Here, to scrutinize the structural and functional roles of the salt bridge in the process of performing the protein function, we investigated the effects of salt bridges on the allosteric structural transition of homodimeric hemoglobin (HbI) by applying time-resolved X-ray solution scattering (TRXSS) to the K30D mutant, in which the interfacial salt bridges of the wild type (WT) are abolished. The TRXSS data of K30D are consistent with the kinetic model that requires one monomer intermediate in addition to three structurally distinct dimer intermediates (I 1 , I 2 , and I 3 ) observed in WT and other mutants. The kinetic and structural analyses show that K30D has an accelerated biphasic transition from I 2 to I 3 by more than nine times compared to WT and lacks significant structural changes in the transition from R-like I 2 to T-like I 3 observed in WT, unveiling that the loss of the salt bridges interrupts the R–T allosteric transition of HbI. Besides, the correlation between the bimolecular CO recombination rates in K30D, WT, and other mutants reveals that the bimolecular CO recombination is abnormally decelerated in K30D, indicating that the salt bridges also affect the cooperative ligand binding in HbI. These comparisons of the structural dynamics and kinetics of K30D and WT show that the interfacial salt bridges not only assist the physical connection of two subunits but also play a critical role in the global structural signal transduction of one subunit to the other subunit via a series of well-organized structural transitions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2003

Website on Protein Interaction and Protein Structure Related Work

In today's world, three seemingly diverse fields - computer information technology, nanotechnology and biotechnology are joining forces to enlarge our scientific knowledge and solve complex technological problems. Our group is dedicated to conduct theoretical research exploring the challenges in this area. The major areas of research include: 1) Yeast Protein Interactions; 2) Protein Structures; and 3) Current Transport through Small Molecules.

Samanta, Manoj↗
DOE OSTI2024

Protein Structure Inspired Discovery of a Novel Inducer of Anoikis in Human Melanoma

Drug discovery historically starts with an established function, either that of compounds or proteins. This can hamper discovery of novel therapeutics. As structure determines function, we hypothesized that unique 3D protein structures constitute primary data that can inform novel discovery. Using a computationally intensive physics-based analytical platform operating at supercomputing speeds, we probed a high-resolution protein X-ray crystallographic library developed by us. For each of the eight identified novel 3D structures, we analyzed binding of sixty million compounds. Top-ranking compounds were acquired and screened for efficacy against breast, prostate, colon, or lung cancer, and for toxicity on normal human bone marrow stem cells, both using eight-day colony formation assays. Effective and non-toxic compounds segregated to two pockets. One compound, Dxr2-017, exhibited selective anti-melanoma activity in the NCI-60 cell line screen. In eight-day assays, Dxr2-017 had an IC50 of 12 nM against melanoma cells, while concentrations over 2100-fold higher had minimal stem cell toxicity. Dxr2-017 induced anoikis, a unique form of programmed cell death in need of targeted therapeutics. Our findings demonstrate proof-of-concept that protein structures represent high-value primary data to support the discovery of novel acting therapeutics. This approach is widely applicable.

Oncology↗
DOE OSTI2020

Escherichia coli amino acid auxotrophic expression host strains for investigating protein structure–function relationships

Abstract A set of C43(DE3) and BL21(DE3) Escherichia coli host strains that are auxotrophic for various amino acids is briefly reviewed. These strains require the addition of a defined set of one or more amino acids in the growth medium, and have been specifically designed for overproduction of membrane or water-soluble proteins selectively labelled with stable isotopes, such as 2H, 13C and 15N. The strains described here are available for use and have been deposited into public strain banks. Although they cannot fully eliminate the possibility of isotope dilution and mixing, metabolic scrambling of the different amino acid types can be minimized through a careful consideration of the bacterial metabolic pathways. The use of a suitable auxotrophic expression host strain with an appropriately isotopically labelled growth medium ensures high levels of isotope labelling efficiency as well as selectivity for providing deeper insight into protein structure–function relationships.

Biochemistry & Molecular Biology↗
DOE OSTI2022

Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation

While deep learning models have seen increasing applications in protein science, few have been implemented for protein backbone generation—an important task in structure-based problems such as active site and interface design. We present a new approach to building class-specific backbones, using a variational auto-encoder to directly generate the 3D coordinates of immunoglobulins. Our model is torsion- and distance-aware, learns a high-resolution embedding of the dataset, and generates novel, high-quality structures compatible with existing design tools. We show that the Ig-VAE can be used with Rosetta to create a computational model of a SARS-CoV2-RBD binder via latent space sampling. We further demonstrate that the model’s generative prior is a powerful tool for guiding computational protein design, motivating a new paradigm under which backbone design is solved as constrained optimization problem in the latent space of a generative model.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

Evidence for zinc binding by two structural proteins of Plodia interpunctella granulosis virus

Workers in our laboratory previously reported the possibility of cation involvement in the in vitro dissociation of the Plodia interpunctella granulosis virus nucleocapsids (K. A. Tweeten, L. A. Bulla, Jr., and R. A. Consigli, J. Virol. 33:866-876, 1980; M. E. Wilson and R. A. Consigli, Virology 143:516-525, 1985). The current study found zinc associated with both granulosis virus nucleocapsids and granulin by atomic absorption analysis. A blotting assay with 65Zn2+ specifically identified the radioactive cation as binding to two viral structural proteins, granulin and VP12. These findings indicate that zinc may have a critical role in maintaining virus stability.

NASA Discipline Cell Biology↗
DOE OSTI2023

Overall protein structure quality assessment using hydrogen-bonding parameters

Atomic model refinement at low resolution is often a challenging task. This is mostly because the experimental data are not sufficiently detailed to be described by atomic models. To make refinement practical and ensure that a refined atomic model is geometrically meaningful, additional information needs to be used such as restraints on Ramachandran plot distributions or residue side-chain rotameric states. However, using Ramachandran plots or rotameric states as refinement targets diminishes the validating power of these tools. Therefore, finding additional model-validation criteria that are not used or are difficult to use as refinement goals is desirable. Hydrogen bonds are one of the important noncovalent interactions that shape and maintain protein structure. These interactions can be characterized by a specific geometry of hydrogen donor and acceptor atoms. Systematic analysis of these geometries performed for quality-filtered high-resolution models of proteins from the Protein Data Bank shows that they have a distinct and a conserved distribution. Here, it is demonstrated how this information can be used for atomic model validation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Electrochemically modulated single-molecule localization microscopy for in vitro imaging cytoskeletal protein structures

A new concept of electrochemically modulated single-molecule localization super-resolution imaging is developed. Applications of single-molecule localization super-resolution microscopy have been limited due to insufficient availability of qualified fluorophores with favorable low duty cycles. The key for the new concept is that the “On” state of a redox-active fluorophore with unfavorable high duty cycle could be driven to “Off” state by electrochemical potential modulation and thus become available for single-molecule localization imaging. The new concept was carried out using redox-active cresyl violet with unfavorable high duty cycle as a model fluorophore by synchronizing electrochemical potential scanning with a single-molecule localization microscope. The two cytoskeletal protein structures, the microtubules from porcine brain and the actins from rabbit muscle, were selected as the model target structures for the conceptual imaging in vitro. The super-resolution images of microtubules and actins were obtained from precise single-molecule localizations determined by modulating the On/Off states of single fluorophore molecules on the cytoskeletal proteins via electrochemical potential scanning. Importantly, this method could allow more fluorophores even with unfavorable photophysical properties to become available for a wider and more extensive application of single-molecule localization microscopy.

electrochemical modulation↗