Search NASA⌕ Search

SEARCH · Search NASA

Results for “sequence design”

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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

CRISpy-Pop: A Web Tool for Designing CRISPR/Cas9-Driven Genetic Modifications in Diverse Populations

CRISPR/Cas9 is a powerful tool for editing genomes, but design decisions are generally made with respect to a single reference genome. With population genomic data becoming available for an increasing number of model organisms, researchers are interested in manipulating multiple strains and lines. CRISpy-pop is a web application that generates and filters guide RNA sequences for CRISPR/Cas9 genome editing for diverse yeast and bacterial strains. The current implementation designs and predicts the activity of guide RNAs against more than 1000 Saccharomyces cerevisiae genomes, including 167 strains frequently used in bioenergy research. Zymomonas mobilis, an increasingly popular bacterial bioenergy research model, is also supported. CRISpy-pop is available as a web application (https://CRISpy-pop.glbrc.org/) with an intuitive graphical user interface. CRISpy-pop also cross-references the human genome to allow users to avoid the selection of sgRNAs with potential biosafety concerns. Additionally, CRISpy-pop predicts the strain coverage of each guide RNA within the supported strain sets, which aids in functional population genetic studies. Lastly, we validate how CRISpy-pop can accurately predict the activity of guide RNAs across strains using population genomic data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Toward controlled functional sequencing and hierarchical structuring in imidazolium ionenes

This focused mini-review highlights recent advances related to the design and applications of imidazolium ionenes and forward-looking perspectives. Creative strategies which employ established synthetic approaches and utilize the modularity of the imidazole moiety unlock great potential for tailoring both functional and structural features arising from precise spacing of ionic groups within the polymer backbone. Here, as imidazolium ionenes draw more interest, increased sophistication of chemical modifications and material processing is occurring, with a corresponding shift toward more demanding applications. Synthetic routes for placement and sequencing of ionic content are discussed, in addition to recent approaches to control structure and self-assembly or tune behaviors of imidazolium ionenes and composites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Deep learning with plasma plume image sequences for anomaly detection and prediction of growth kinetics during pulsed laser deposition

Abstract Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the need arises to develop machine learning (ML) techniques that are capable of extracting information from in situ diagnostics. Here, we demonstrate that intensified-CCD image sequences of the plasma plume generated during PLD can be used for anomaly detection and the prediction of thin film growth kinetics. We develop multi-output (2 + 1)D convolutional neural network regression models that extract deep features from plume dynamics that not only correlate with the measured chamber pressure and incident laser energy, but more importantly, predict parameters of an auto-catalytic film growth model derived from in situ laser reflectivity experiments. Our results demonstrate how ML with in situ plume diagnostics data in PLD can be utilized to maintain deposition conditions in an optimal regime. Further, the predictive capabilities of plume dynamics on the kinetics of film growth or other film properties prior to deposition provides a means for rapid pre-screening of growth conditions for the non-expert, which promises to accelerate materials optimization with PLD.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Structural and biochemical analyses of selectivity determinants in chimeric Streptococcus Class A sortase enzymes

Abstract Sequence variation in related proteins is an important characteristic that modulates activity and selectivity. An example of a protein family with a large degree of sequence variation is that of bacterial sortases, which are cysteine transpeptidases on the surface of gram‐positive bacteria. Class A sortases are responsible for attachment of diverse proteins to the cell wall to facilitate environmental adaption and interaction. These enzymes are also used in protein engineering applications for sortase‐mediated ligations (SML) or sortagging of protein targets. We previously investigated SrtA from Streptococcus pneumoniae , identifying a number of putative β7–β8 loop‐mediated interactions that affected in vitro enzyme function. We identified residues that contributed to the ability of S. pneumoniae SrtA to recognize several amino acids at the P1′ position of the substrate motif, underlined in LPXT G , in contrast to the strict P1′ Gly recognition of SrtA from Staphylococcus aureus . However, motivated by the lack of a structural model for the active, monomeric form of S. pneumoniae SrtA, here, we expanded our studies to other Streptococcus SrtA proteins. We solved the first monomeric structure of S. agalactiae SrtA which includes the C‐terminus, and three others of β7–β8 loop chimeras from S. pyogenes and S. agalactiae SrtA. These structures and accompanying biochemical data support our previously identified β7–β8 loop‐mediated interactions and provide additional insight into their role in Class A sortase substrate selectivity. A greater understanding of individual SrtA sequence and structural determinants of target selectivity may also facilitate the design or discovery of improved sortagging tools.

Gao, Melody↗
DOE OSTI2024

Powdery mildew effectors AVR A1 and BEC1016 target the ER J‐domain protein Hv ERdj3B required for immunity in barley

Abstract The barley powdery mildew fungus, Blumeria hordei (Bh), secretes hundreds of candidate secreted effector proteins (CSEPs) to facilitate pathogen infection and colonization. One of these, CSEP0008, is directly recognized by the barley nucleotide‐binding leucine‐rich‐repeat (NLR) receptor MLA1 and therefore is designated AVR A1 . Here, we show that AVR A1 and the sequence‐unrelated Bh effector BEC1016 (CSEP0491) suppress immunity in barley. We used yeast two‐hybrid next‐generation interaction screens (Y2H‐NGIS), followed by binary Y2H and in planta protein–protein interactions studies, and identified a common barley target of AVR A1 and BEC1016, the endoplasmic reticulum (ER)‐localized J‐domain protein Hv ERdj3B. Silencing of this ER quality control (ERQC) protein increased Bh penetration. Hv ERdj3B is ER luminal, and we showed using split GFP that AVR A1 and BEC1016 translocate into the ER signal peptide‐independently. Overexpression of the two effectors impeded trafficking of a vacuolar marker through the ER; silencing of Hv ERdj3B also exhibited this same cellular phenotype, coinciding with the effectors targeting this ERQC component. Together, these results suggest that the barley innate immunity, preventing Bh entry into epidermal cells, requires ERQC. Here, the J‐domain protein Hv ERdj3B appears to be essential and can be regulated by AVR A1 and BEC1016. Plant disease resistance often occurs upon direct or indirect recognition of pathogen effectors by host NLR receptors. Previous work has shown that AVR A1 is directly recognized in the cytosol by the immune receptor MLA1. We speculate that the AVR A1 J‐domain target being inside the ER, where it is inapproachable by NLRs, has forced the plant to evolve this challenging direct recognition.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Rapid Patterning and Advanced Device Structures for Low Cost Manufacturable Crystalline Si IBC Cells

Recent record efficiency Si cells have had an interdigitated back contact (IBC) design where both positive and negative contacts are fabricated on the back side of the Si wafer. Many industry groups have investigated IBC cells but used photolithography to pattern the rear contacts although it is considered impractical for low cost, high volume manufacturing. Alternative patterning methods using lasers and mechanical masking have been utilized in fabrication of patterned regions in IBC Si solar cells to replace photolithography. Another exciting advance in Si solar cell technology has been replacing the high temperature diffusion of doped regions inside the crystalline Si wafer with low temperature deposition of very thin layers of doped amorphous hydrogenated Si (a-Si:H) on the Si wafer. This is called a Si heterojunction (HJ) device. The a-Si:H provides excellent passivation of surface defects and has produced the highest open circuit voltages (VOC) of any Si solar cell device structure. The Institute of Energy Conversion (IEC) demonstrated the first IBC-HJ solar cell combining these two strategies in 2007 and has fabricated IBC-HJ cells with 20% efficiency using three photolithography steps. Despite the demonstrated efficiency potential of this device by industry groups (>26%) there is no commercial production because of the challenges of patterning and processing the structure in an industrial environment. Our work sought to address that challenge. The objective of this 3 year project was to develop the processing for the IBC-HJ Si solar cell using lasers for patterning and contact formation instead of photolithography. Laser patterning enables rapid, contactless manufacturing of patterned regions. We intensively studied laser fired contacts, laser patterning of the a-Si multi-layer stacks and metal layers and the use of plasma shadow masks. After an exhaustive focus in the first year on the laser fired emitter (LFE) and contact (LFC), we were unable to achieve VOC greater than 660 mV, compared to the 720 mV required to meet our milestones. We discovered that an additional issue with our original IBC structure was the presence of an inversion layer at the back surface connecting the p and n regions which reduced the VOC and fill factor (FF). We developed innovative methods for characterizing the inversion layer. These two limitations in the original design lead to development of a new IBC-HJ process sequence and device structure which we called Plasma Masked Laser Processed (PMLP). It retained the original high efficiency features including manufacturability. The LFC was replaced with a standard n-type a-Si HJ contact. The inversion layer was eliminated by replacing the previous p-type stack in the gap with an n-type or SiN stack. The PMLP used laser ablation of a multi-layer a-Si stack followed by chemical etching to open the n-contact. It required deposition of a patterned stack through a mask in the plasma deposition chamber. This turned out to be a source of significant problems due to inevitable unwanted deposition ‘leakage’ under the mask. This formed a blocking contact on the emitter which significantly reduced Voc and FF. Several iterations in PMLP device structure and chemical etching steps resulted in a large improvements, e.g. the efficiency increasing from 3 to 15% and Voc from 450 to 660 mV but they were unable to completely eliminate it. The best IBC-HJ device we fabricated had only 15% efficiency. For perspective, our standard FHJ devices had 20% efficiency.

14 SOLAR ENERGY↗
DOE OSTI2026

A Generic and Multifunctional Electromagnetic Transient Model for Grid-Following Inverters

This article presents a generic and multi-functional electromagnetic transient (EMT) dynamic model of grid following (GFL) inverter-based resource (IBR) using the PSCAD software platform. The features of the developed model includes the flexibility in selecting various types and combinations of DC sources covering PV modules, battery modules, ideal DC source module, as well as flexibility in selecting either switched or averaged model of inverter. This model also covers exhaustive lists of controller logic covering open-loop/closed-loop PQ dispatch control, DC voltage and AC terminal voltage control along with the conventional current control designed in dq-domain, ate- domain and positive-negative sequence domain. Moreover, this model is equipped with the flexibility in selecting various types of current limiting schemes that includes saturation-based as well as latching-based current limiter, anti-windup protection. Moreover, the EMT model is agnostic to the MVA rating and is suitable for interfacing transmission systems by being complaint with the IEEE Std. 2800. The generality in the power circuits and the multi-functional options in operation and control of the developed EMT model makes it suitable for both academia and industry to study various power system aspects not limited to but such as fault behavior of GFL IBR and impacts on protection system, transient stability of a system interfaced with large number of GFL IBRs etc.

14 SOLAR ENERGY↗
DOE OSTI2024

A Generic and Multi-Functional Electromagnetic Transient Model for Grid-Following Inverter: Preprint

This article presents a generic and multi-functional electromagnetic transient (EMT) dynamic model of grid following (GFL) inverter-based resource (IBR) using the PSCAD TM software platform. The features of the developed model includes the flexibility in selecting various types and combinations of DC sources covering PV modules, battery modules, ideal DC source module, as well as flexibility in selecting either switched or averaged model of inverter. This model also covers exhaustive lists of controller logic covering open-loop/closed-loop PQ dispatch control, DC voltage and AC terminal voltage control along with the conventional current control designed in dq-domain, aB- domain and positive-negative sequence domain. Moreover, this model is equipped with the flexibility in selecting various types of current limiting schemes that includes saturation-based as well as latching-based current limiter, anti-windup protection. Moreover, the EMT model is agnostic to the MVA rating and is suitable for interfacing transmission systems by being complaint with the IEEE Std. 2800. The generality in the power circuits and the multi-functional options in operation and control of the developed EMT model makes it suitable for both academia and industry to study various power system aspects not limited to but such as fault behavior of GFL IBR and impacts on protection system, transient stability of a system interfaced with large number of GFL IBRs etc.

grid following inverter↗
DOE OSTI2021

Mesh independency in topology optimization

The topology optimization community has regularly employed optimization algorithms from the operations research community. However, these algorithms are implemented in the sequence space l2 instead of the proper function space where the design variable resides. In this software, we benchmark the mesh independent implementation of pyMMAopt with three common problems in topology optimization. We show how the volume fraction variable discretization on non-uniform meshes affects the convergence of l2 based optimization algorithms and how pyMMAopt successfully achieves mesh independent convergence.

Salazar De Troya, Miguel↗
DOE OSTI2023

Large-scale computational polymer solubility predictions and applications to dissolution-based plastic recycling

Dissolution-based plastic recycling is a promising approach to separate and recover high quality pure polymer resins from multicomponent plastic waste by exploiting differences in polymer solubility. The design of a dissolution-based polymer recycling process requires the selection of appropriate solvent systems and operating temperatures to dissolve only target polymers. Determining these parameters experimentally is challenging due to the wide range of solvents and temperatures possible for a given set of target polymers. In this work, we report a computational scheme that employs molecular dynamics simulations and the Conductor-like Screening Model for Realistic Solvents to predict polymer solubilities. Using this scheme, we established a computational solubility database for 8 common polymers and 1007 solvents at multiple temperatures and measured selected solubilities experimentally to validate computational predictions. Analysis of functional groups within this large database then provides chemical heuristics relating the molecular structures of good and non-solvents for selected polymers. We further developed a tool that automates the selection of solvents for all possible sequences in which target polymers can be selectively dissolved to guide the design of dissolution-based plastic recycling processes. Here, we demonstrate the application of these methods via multiple experimental case studies of representative dissolution-based polymer recycling processes in which pure polymer resins were successfully recovered from physical mixtures of polymers.

42 ENGINEERING↗
DOE OSTI2025

NEAR: Neural Embeddings for Amino acid Relationships

Protein language models (PLMs) have recently demonstrated potential to supplant classical protein database search methods based on sequence alignment, but are slower than common alignment-based tools and appear to be prone to a high rate of false labeling. Here, we present NEAR, a method based on neural representation learning that is designed to improve both speed and accuracy of search for likely homologs in a large protein sequence database. NEAR’s ResNet embedding model is trained using contrastive learning guided by trusted sequence alignments. It computes per-residue embeddings for target and query protein sequences, and identifies alignment candidates with a pipeline consisting of residue-level k-NN search and a simple neighbor aggregation scheme. Tests on a benchmark consisting of trusted remote homologs and randomly shuffled decoy sequences reveal that NEAR substantially improves accuracy relative to state-of-the-art PLMs, with lower memory requirements and faster embedding and search speed. While these results suggest that the NEAR model may be useful for standalone homology detection with increased sensitivity over standard alignment-based methods, in this manuscript we focus on a more straightforward analysis of the model’s value as a high-speed pre-filter for sensitive annotation. In that context, NEAR is at least 5x faster than the pre-filter currently used in the widely-used profile hidden Markov model (pHMM) search tool HMMER3, and also outperforms the pre-filter used in our fast pHMM tool, nail.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation

The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.

ChemBERTa↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
DOE OSTI2022

AbBERT: Learning Antibody Humanness via Masked Language Modeling

Understanding the degree of humanness of antibody sequences is critical to the therapeutic antibody development process to reduce the risk of failure modes like immunogenicity or poor manufacturability. We introduce AbBERT, a transformer-based language model trained on up to 20 million unpaired heavy/light chain sequences from the Observed Antibody Space database. We first validate AbBERT using a novel “multi-mask” scoring procedure to demonstrate high accuracy in predicting complementary determining regions—including the challenging hypervariable H3 region. We then demonstrate several uses of AbBERT at various points along the antibody design process. AbBERT enhances in silico antibody optimization via deep reinforcement learning by utilizing its learned embeddings as additional observations during optimization. Within a larger computational antibody design platform, AbBERT has been successfully applied as an additional design objective, where it displays strong correlations with computational tools predicting antibody structural stability. Finally, mutant antibody sequences that have been scored as unfavorable by AbBERT have shown corresponding low yields when expressed in cells. These use cases demonstrate the power of language modeling within computational antibody design.

Bioinformatics↗
DOE OSTI2020

CP-DSSS: An OFDM Compatible Variable Rate Modulation for 5G and Beyond

Cyclic prefix direct sequence spread spectrum (CPDSSS) is a recently proposed waveform that has been designed for coexistence with OFDM (orthogonal frequency division multiplexing) in the same or a pair of parallel networks. The first contribution of this paper is to present equations that reveal similarities and differences of CP-DSSS and OFDM. Furthermore, we show that the channel model of CP-DSSS reduces to that of a cyclic prefixed block-wise single carrier modulation (CP-SCM). However, unlike CP-SCM whose symbol rate is always equal to the transmission bandwidth, CP-DSSS can be adopted to any symbol rate equal to or smaller than the transmission bandwidth. This reduction in symbol rate allows CP-DSSS power spectral density to drop to an arbitrarily low level, hence, facilitates it coexistence as a secondary waveform in a network of primary users. The peak-to-average power ratio (PAPR) of CP-DSSS is also explored and a method for reducing it is proposed.

5G↗
DOE OSTI2023

TransPolymer: a Transformer-based language model for polymer property predictions

Accurate and efficient prediction of polymer properties is of great significance in polymer design. Conventionally, expensive and time-consuming experiments or simulations are required to evaluate polymer functions. Recently, Transformer models, equipped with self-attention mechanisms, have exhibited superior performance in natural language processing. However, such methods have not been investigated in polymer sciences. Herein, we report TransPolymer, a Transformer-based language model for polymer property prediction. Our proposed polymer tokenizer with chemical awareness enables learning representations from polymer sequences. Rigorous experiments on ten polymer property prediction benchmarks demonstrate the superior performance of TransPolymer. Moreover, we show that TransPolymer benefits from pretraining on large unlabeled dataset via Masked Language Modeling. Experimental results further manifest the important role of self-attention in modeling polymer sequences. We highlight this model as a promising computational tool for promoting rational polymer design and understanding structure-property relationships from a data science view.

36 MATERIALS SCIENCE↗
DOE OSTI2022

RCSB Protein Data Bank 1D3D module: displaying positional features on macromolecular assemblies

Mapping positional features from one-dimensional (1D) sequences onto three-dimensional (3D) structures of biological macromolecules is a powerful tool to show geometric patterns of biochemical annotations and provide a better understanding of the mechanisms underpinning protein and nucleic acid function at the atomic level. We present a new library designed to display fully customizable interactive views between 1D positional features of protein and/or nucleic acid sequences and their 3D structures as isolated chains or components of macromolecular assemblies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Stress-Drop and Source Scaling of the 2019 Ridgecrest, California, Earthquake Sequence

Stress drop, while difficult to measure reliably and at scale, is a key source parameter for understanding the earthquake rupture process and its relationship to strong ground motion. Here, we use a P-wave spectral decomposition approach, designed for large and densely sampled datasets, to measure earthquake stress drop in the region surrounding the 2019 Ridgecrest, California, earthquake sequence. With more than 11,000 measurements of earthquake stress drop in the 20-yr time period from 2000 through 2019, this dataset provides an opportunity to understand how coseismic stress changes and how other geophysical factors relate to the distribution of stress drop and its evolution in space and time. We observe a mild but persistent deviation from self-similar scaling, with larger events having systematically higher stress drops, though this trend depends on the assumption of an omega-square source spectral model. Earthquake stress drop increases with hypocentral depth in this study region, and the Ridgecrest aftershocks tend to have higher stress drops than the pre-event seismicity. This is in part due to their deeper hypocenters. Coherent spatial patterns of stress drop in the aftershock sequence correlate with the slip distribution of the M 7.1 mainshock, whose northwest rupture tip terminated in a long-lived zone of enervated stress drop. Although physical interpretation of these results is complicated by the trade-offs between the timing, depth, and location of these earthquakes, the observations provide new insight into the physics of the earthquake source in an area of renewed seismic activity in southern California.

58 GEOSCIENCES↗