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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Identification of hot water end-use process of electric water heaters from energy measurements

This paper presents an algorithm for the identification of parameters for a stochastic hot water end-use process that drives a homogeneous population of thermostatically controlled electric water heaters (EWH). Usually, only metered interval consumption data (kWh) is collected and the hot water end-use process is unobservable to utility and aggregators. However, the availability of EWHs for demand response (DR) is closely coupled with the hot water end-use process. In this context, the hot water end-use process is modeled as a two-state Markov chain (Use / No use), which causes the thermostatic ON-OFF switching process to behave as a Markov renewal process (MRP). A set of first passage-time problems is developed to obtain the moments of the transition probability densities of the MRP. These problems are addressed by establishing a system of coupled partial differential equations characterizing the temperature evolution of the EWH population. A key quantity in the methodology for estimating the parameters is the total time an EWH is ON within a period of interest. It is referred to as the total busy time. Total busy time in this approach is a random variable for which analytical expressions of the moments are developed as a function of the metered window length. The latter expressions become the basis of a hot water demand model identification algorithm which is validated using agent-based simulations of EWHs.

42 ENGINEERING↗
DOE OSTI2013

Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of charged peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Increasing Confidence of LC-MS Identifications by Utilizing Ion Mobility Spectrometry

Ion mobility spectrometry in conjunction with liquid chromatography separations and mass spectrometry offers a range of new possibilities for analyzing complex biological samples. To fully utilize the information obtained from these three measurement dimensions, informatics tools based on the accurate mass and time tag methodology were modified to incorporate ion mobility spectrometry drift times for peptides observed in human serum. A reference human serum database was created using 12,139 peptides, tracking the monoisotopic mass, liquid chromatography normalized elution time, and ion mobility spectrometry drift time(s) for each peptide. We demonstrate that the use of three dimensions for peak matching during the peptide identification process resulted in increased numbers of identifications and lower false discovery rates relative to the use of only the mass and normalized elution time dimensions.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2013

Automated Patient Identification and Localization Error Detection Using 2-Dimensional to 3-Dimensional Registration of Kilovoltage X-Ray Setup Images

Purpose: To determine whether kilovoltage x-ray projection radiation therapy setup images could be used to perform patient identification and detect gross errors in patient setup using a computer algorithm. Methods and Materials: Three patient cohorts treated using a commercially available image guided radiation therapy (IGRT) system that uses 2-dimensional to 3-dimensional (2D-3D) image registration were retrospectively analyzed: a group of 100 cranial radiation therapy patients, a group of 100 prostate cancer patients, and a group of 83 patients treated for spinal lesions. The setup images were acquired using fixed in-room kilovoltage imaging systems. In the prostate and cranial patient groups, localizations using image registration were performed between computed tomography (CT) simulation images from radiation therapy planning and setup x-ray images corresponding both to the same patient and to different patients. For the spinal patients, localizations were performed to the correct vertebral body, and to an adjacent vertebral body, using planning CTs and setup x-ray images from the same patient. An image similarity measure used by the IGRT system image registration algorithm was extracted from the IGRT system log files and evaluated as a discriminant for error detection. Results: A threshold value of the similarity measure could be chosen to separate correct and incorrect patient matches and correct and incorrect vertebral body localizations with excellent accuracy for these patient cohorts. A 10-fold cross-validation using linear discriminant analysis yielded misclassification probabilities of 0.000, 0.0045, and 0.014 for the cranial, prostate, and spinal cases, respectively. Conclusions: An automated measure of the image similarity between x-ray setup images and corresponding planning CT images could be used to perform automated patient identification and detection of localization errors in radiation therapy treatments.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Stability boundary and optimal operating parameter identification in milling using Bayesian learning

Here, this paper describes a novel Bayesian learning approach for stability boundary and optimal parameter identification in milling without the knowledge of the underlying tool dynamics or material cutting force coefficients. The paper is divided into two parts. First, a Bayesian learning method for stability lobe identification using test results is described. Each axial depth and spindle speed combination is characterized by a probability of stability which is updated using Bayes’ rule when a test result (stable or unstable) is made available. A novel likelihood function is defined which incorporates knowledge of the stability behavior. Numerical results show convergence to the analytical stability lobe diagram. Second, an adaptive experimental strategy to identify optimal operating parameters that maximize material removal rate is described. Numerical evaluation shows convergence to the optimal operating point with error less than 15 % within ten tests on average. The approach is validated using experimental results. Results show that the proposed method is an efficient and robust learning method to identify the stability lobe diagram and optimal operating parameters with a limited number of tests/data points.

Bayesian machine learning↗
DOE OSTI2013

A new way of phase identification, of AgGaGeS{sub 4}∙nGeS{sub 2} crystals

The phase identification of AgGaGeS{sub 4}·nGeS{sub 2} (n=0–4) crystals grown by vertical Bridgman–Stockbarger technique was carried out to find the boundary value n between a homogeneous solid solution and its mixture with GeS{sub 2}. To obtain reliable results, the conventional methods of X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy (EDX) were completed by less common vapor pressure measurement in a closed volume and precise density measurements, which are very sensitive to the detection of small amounts of crystalline and glassy GeS{sub 2} and heterogeneous state of the crystals. The boundary value n=1.5 at 1045 K and the coexistence of the solid solution AgGaGeS{sub 4}·1.5GeS{sub 2} with the β-GeS{sub 2} phase for n>1.5 was found. Glassy GeS{sub 2} (∼2 mol%) was revealed by vapor pressure measurement and XRD studies in all the crystals. This is discussed in terms of the supersaturated solid solution decomposition upon temperature decreasing and the microphase separation of overcooled melt near the melting point under non-equilibrium crystallization. For the first time, the p–T dependence for glassy GeS{sub 2} was measured by the vapor pressure measurements. - Graphical abstract: lg p–1/T dependences of as-grown AgGaGeS{sub 4}·nGeS{sub 2} crystals. - Highlights: • Vapor pressure measurement as a powerful tool of phase identification. • Thermal characteristics of glassy GeS{sub 2}. • The homogeneity range of AgGaGeS{sub 4} from the GeS{sub 2} side.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Non-destructive identification of unknown minor phases in polycrystalline bulk alloys using three-dimensional X-ray diffraction

Minor phases make considerable contributions to the mechanical and physical properties of metals and alloys. Unfortunately, it is difficult to identify unknown minor phases in a bulk polycrystalline material using conventional metallographic methods. Here, a non-destructive method based on three-dimensional X-ray diffraction (3DXRD) is developed to solve this problem. Simulation results demonstrate that this method is simultaneously able to identify minor phase grains and reveal their positions, orientations and sizes within bulk alloys. According to systematic simulations, the 3DXRD method is practicable for an extensive sample set, including polycrystalline alloys with hexagonal, orthorhombic and cubic minor phases. Experiments were also conducted to confirm the simulation results. The results for a bulk sample of aluminum alloy AA6061 show that the crystal grains of an unexpected γ-Fe (austenite) phase can be identified, three-dimensionally and nondestructively. Therefore, we conclude that the 3DXRD method is a powerful tool for the identification of unknown minor phases in bulk alloys belonging to a variety of crystal systems. This method also has the potential to be used for in situ observations of the effects of minor phases on the crystallographic behaviors of alloys. - Highlights: •A method based on 3DXRD is developed for identification of unknown minor phase. •Grain position, orientation and size, is simultaneously acquired. •A systematic simulation demonstrated the applicability of the proposed method. •Experimental results on a AA6061 sample confirmed the practicability of the method.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Comparison of EBSD, DIC, AFM, and ECCI for active slip system identification in deformed Ti-7Al

The use of SEM-DIC, AFM, ECCI, and HR-EBSD to characterize slip-system activity was assessed on the same material volume of Ti-7Al. This study presents a robust comparison of the various methods for the first time, including an assessment of their advantages and disadvantages, and how they can be used effectively in a complementary fashion. The analysis of the different approaches was carried out in a blind, round-robin manner at three different universities. A Ti-7Al specimen was deformed in uniaxial tension to approximately 3% axial strain, and the active slip systems were independently identified using (i) trace analysis; (ii) in-SEM digital image correlation, (iii) observations of residual dislocations from ECCI, and (iv) long-range rotation gradients through HR-EBSD, with consistent trace identification in all cases. Displacement data from AFM was used to augment SEM-DIC displacement data by providing complementary out-of-plane displacement information. Furthermore, short-range dislocation gradients (measured by DIC) provided insight into the residual geometrically necessary dislocation (GND) content, and was consistent with the GND content extracted from EBSD data and ECCI images, confirming the presence of residual GNDs on the dominant slip systems resulting in visible slip bands. Furthermore, these approaches can be used in tandem to provide multi-modal information on slip band identification, strain and orientation gradients, out-of-plane displacements, and the presence of GNDs and SSDs, all of which can be used to inform and validate the development of dislocation-based crystal plasticity and strain gradient models.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Protein markers for identification of Yersinia pestis and their variation related to culture

The detection of high consequence pathogens, such as Yersinia pestis, is well established in biodefense laboratories for bioterror situations. Laboratory protocols are well established using specified culture media and a growth temperature of 37 °C for expression of specific antigens. Direct detection of Y. pestis protein markers, without prior culture, depends on their expression. Unfortunately protein expression can be impacted by the culture medium which cannot be predicted ahead of time. Furthermore, higher biomass yields are obtained at the optimal growth temperature (i.e. 28 °C–30 °C) and therefore are more likely to be used for bulk production. Analysis of Y. pestis grown on several types of media at 30 °C showed that several protein markers were found to be differentially detected in different media. Analysis of the identified proteins against a comprehensive database provided an additional level of organism identification. Peptides corresponding to variable regions of some proteins could separate large groups of strains and aid in organism identification. This work illustrates the need to understand variability of protein expression for detection targets. The potential for relating expression changes of known proteins to specific media factors, even in nutrient rich and chemically complex culture medium, may provide the opportunity to draw forensic information from protein profiles.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Carbonyl Identification and Quantification Uncertainties for Oxidative Polymer Degradation

The most revealing indicator for oxidative processes or state of degraded plastics is usually carbonyl formation, a key step in materials degradation as part of the carbon cycle for man-made materials. Hence, the identification and quantification of carbonyl species with infrared spectroscopy have been the method of choice for generations, thanks to their strong absorbance and being an essential intermediate in carbon oxidation pathways. Despite their importance, precise identification and quantification can be challenging and rigorous fully traceable data are surprisingly rare in the existing literature. An overview of the complexity of carbonyl quantification is presented by the screening of reference compounds in solution with transmission and polymer films with ATR IR spectroscopy, and systematic data analyses. Significant variances in existing data and their past use have been recognized. Guidance is offered how better measurements and data reporting could be accomplished. Experimental variances depend on the combination of uncertainty in exact carbonyl species, extinction coefficient, contributions from neighboring convoluting peaks, matrix interaction phenomena and instrumental variations in primary IR spectral acquisition (refractive index and penetration depth for ATR measurements). In addition, diverging sources for relevant extinction coefficients may exist, based on original spectral acquisition. For common polymer degradation challenges, a relative comparison of carbonyl yields for a material is easily accessible, but quantification for other purposes, such as degradation rates and spatially dependent interpretation, requires thorough experimental validation. All variables featured in this overview demonstrate the significant error margins in carbonyl quantification, with exact carbonyl species and extinction coefficients already being major contributors on their own.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Identification of human ferritin, heavy polypeptide 1 (FTH1) and yeast RGI1 (YER067W) as pro-survival sequences that counteract the effects of Bax and copper in Saccharomyces cerevisiae

Ferritin is a sub-family of iron binding proteins that form multi-subunit nanotype iron storage structures and prevent oxidative stress induced apoptosis. Here we describe the identification and characterization of human ferritin, heavy polypeptide 1 (FTH1) as a suppressor of the pro-apoptotic murine Bax sequence in yeast. In addition we demonstrate that FTH1 is a general pro-survival sequence since it also prevents the cell death inducing effects of copper when heterologously expressed in yeast. Although ferritins are phylogenetically widely distributed and are present in most species of Bacteria, Archaea and Eukarya, ferritin is conspicuously absent in most fungal species including Saccharomyces cerevisiae. An in silico analysis of the yeast proteome lead to the identification of the 161 residue RGI1 (YER067W) encoded protein as a candidate for being a yeast ferritin. In addition to sharing 20% sequence identity with the 183 residue FTH1, RGI1 also has similar pro-survival properties as ferritin when overexpressed in yeast. Analysis of recombinant protein by SDS-PAGE and by electron microscopy revealed the expected formation of higher-order structures for FTH1 that was not observed with Rgi1p. Further analysis revealed that cells overexpressing RGI1 do not show increased resistance to iron toxicity and do not have enhanced capacity to store iron. In contrast, cells lacking RGI1 were found to be hypersensitive to the toxic effects of iron. Overall, our results suggest that Rgi1p is a novel pro-survival protein whose function is not related to ferritin but nevertheless it may have a role in regulating yeast sensitivity to iron stress. - Highlights: • Human ferritin, heavy polypeptide 1 (FTH1) was identified as a suppressor of the pro-apoptotic Bax in yeast. • Based on its similarity to ferritin we examined Rgi1p/YER067W for potential ferritin like functions. • Like human H-ferritin, RGI1 confers increased resistance to apoptotic inducing stresses in yeast. • Rgi1p is a pro-survival protein that is not a ferritin but that may play a role in iron metabolism of yeast.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Identification and characterization of distinct cell cycle stages in cardiomyocytes using the FUCCI transgenic system

Understanding the regulatory mechanism by which cardiomyocyte proliferation transitions to endoreplication and cell cycle arrest during the neonatal period is crucial for identifying proproliferative factors and developing regenerative therapies. }We used a transgenic mouse model based on the fluorescent ubiquitination-based cell cycle indicator (FUCCI) system to isolate and characterize cycling cardiomyocytes at different cell cycle stages at a single-cell resolution. Single-cell transcriptome analysis of cycling and noncycling cardiomyocytes was performed at postnatal days 0 (P0) and 7 (P7). }The FUCCI system proved to be efficient for the identification of cycling cardiomyocytes with the highest mitotic activity at birth, followed by a gradual decline in the number of cycling and mitotic cardiomyocytes during the neonatal period. Cardiomyocytes showed premature cell cycle exit at G1/S shortly after birth and delayed G1/S progression during endoreplication at P7. Single-cell RNA-seq confirmed previously described signaling pathways involved in cardiomyocyte proliferation (Erbb2 and Hippo/YAP), and maturation-related transcriptional changes during postnatal development, including the metabolic switch from glycolysis to fatty acid oxidation in cardiomyocytes. Importantly, we generated transcriptional profiles specific to cell division and endoreplication in cardiomyocytes at different developmental stages that may facilitate the identification of genes important for adult cardiomyocyte proliferation and heart regeneration. }In conclusion, the FUCCI mouse provides a valuable system to study cardiomyocyte cell cycle activity at single cell resolution that can help to decipher the switch from cardiomyocyte proliferation to endoreplication, and to revert this process to facilitate endogenous repair.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling

An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on the fly, the experiments described herein combine this technology with the bioinformatic resources of XCMS and METLIN. We can analyze large profiling datasets and simultaneously obtain structural identifications, as a result of this unique integration. Furthermore, validation of the workflow on bacterial samples allowed the profiling on the order of a thousand metabolite features with simultaneous tandem mass spectra data acquisition. The tandem mass spectrometry data acquisition enabled automatic search and matching against the METLIN tandem mass spectrometry database, shortening the current workflow from days to hours. Overall, the autonomous approach to untargeted metabolomics provides an efficient means of metabolomic profiling, and will ultimately allow the more rapid integration of comparative analyses, metabolite identification, and data analysis at a systems biology level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

CFM-ID 4.0: More Accurate ESI MS/MS Spectral Prediction and Compound Identification

In the field of metabolomics, mass spectrometry (MS) is the method most commonly used for identifying and annotating metabolites. As this typically involves matching a given MS spectrum against an experimentally acquired reference spectral library, this approach is limited by the coverage and size of such libraries (which typically number in the thousands). These experimental libraries can be greatly extended by predicting the MS spectra of known chemical structures (which number in the millions) to create computational reference spectral libraries. To facilitate the generation of predicted spectral reference libraries we developed CFM-ID, a computer program that can accurately predict ESI-MS/MS spectrum for a given compound structure. CFM-ID is one of the best-performing methods for compound-to-mass-spectrum prediction, and also one of the top tools for in silico mass-spectrum-to-compound identification. This work improves CFM-ID’s ability to predict ESI-MS/MS spectra from compounds by: (1) learning parameters from features based on the molecular topology, (2) adding a new approach to ring cleavage that models such cleavage as a sequence of simple chemical bond dissociations and (3) expanding its hand-written rule-based predictor to cover more chemical classes, including acylcarnitines, acylcholines, flavonols, flavones, flavanones, and flavonoid glycosides. We demonstrate that this new version of CFM-ID (version 4.0) is significantly more accurate than previous CFM-ID versions, in terms of both EI-MS/MS spectral prediction and compound identification. CFM-ID 4.0 is available at http://cfmid4.wishartlab.com/ as a webservice and docker images can be downloaded at https://hub.docker.com/r/wishartlab/cfmid

Wang, Fei↗
DOE OSTI2023

Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances

The market for illicit drugs has been reshaped by the emergence of more than 1100 new psychoactive substances (NPS) over the past decade, posing a major challenge to the forensic and toxicological laboratories tasked with detecting and identifying them. Tandem mass spectrometry (MS/MS) is the primary method used to screen for NPS within seized materials or biological samples. The most contemporary workflows necessitate labor-intensive and expensive MS/MS reference standards, which may not be available for recently emerged NPS on the illicit market. Here, we present NPS-MS, a deep learning method capable of accurately predicting the MS/MS spectra of known and hypothesized NPS from their chemical structures alone. NPS-MS is trained by transfer learning from a generic MS/MS prediction model on a large data set of MS/MS spectra. We show that this approach enables a more accurate identification of NPS from experimentally acquired MS/MS spectra than any existing method. We demonstrate the application of NPS-MS to identify a novel derivative of phencyclidine (PCP) within an unknown powder seized in Denmark without the use of any reference standards. We anticipate that NPS-MS will allow forensic laboratories to identify more rapidly both known and newly emerging NPS. NPS-MS is available as a web server at https://nps-ms.ca/, which provides MS/MS spectra prediction capabilities for given NPS compounds. Additionally, it offers MS/MS spectra identification against a vast database comprising approximately 8.7 million predicted NPS compounds from DarkNPS and 24.5 million predicted ESI-QToF-MS/MS spectra for these compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X-ray Diffraction

Powder X-ray diffraction (XRD) is a foundational technique for characterizing crystalline materials. However, the reliable interpretation of XRD patterns, particularly in multiphase systems, remains a manual and expertise-demanding task. As a characterization method that only provides structural information, multiple reference phases can often be fit to a single pattern, leading to potential misinterpretation when alternative solutions are overlooked. To ease humans’ efforts and address the challenge, we introduce Dara (data-driven automated Rietveld analysis), a framework designed to automate the robust identification and refinement of multiple phases from powder XRD data. Dara performs an exhaustive tree search over all plausible phase combinations within a given chemical space and validates each hypothesis using the BGMN Rietveld refinement routine. Key features include structural database filtering, automatic clustering of isostructural phases during tree expansion, and peak-matching-based scoring to identify promising phases for refinement. When ambiguity exists, Dara generates multiple hypothesis which can then be decided between by human experts or with further characterization tools. By enhancing the reliability and accuracy of phase identification, Dara enables scalable analysis of realistic complex XRD patterns and provides a foundation for integration into multimodal characterization workflows, moving toward fully self-driving materials discovery.

Biological databases↗
DOE OSTI2025

Constraining the Low-Temperature Oxidation Mechanism of n -Hexanol through the Detection and Identification of C 6 Elusive Intermediates

Alcohol-based fuels are currently considered to be viable energy carriers for the transportation sector. Consequently, a comprehensive mechanistic understanding of the low-temperature oxidation of alcohols is essential for application in advanced low-temperature compression engines. Here, in this work, a multidimensional approach involving experimental investigations, kinetic modeling, and theoretical calculations was used to provide new insights into the low-temperature oxidation mechanism of a C 6 alcohol, n -hexanol (CH 3 (CH 2 ) 5 OH), through the detection and identification of elusive C 6 intermediates. The oxidation of n-hexanol was investigated in a jet-stirred reactor under stoichiometric conditions (ϕ = 1.0), an initial fuel concentration of 2%, a residence time of 2 s, a temperature range between 500 and 660 K, and a pressure of 700 Torr. The reactants, intermediates, and final products were detected and identified by means of molecular-beam mass spectrometry coupled with single-photon ionization employing tunable synchrotron-generated vacuum ultraviolet radiation. Chemical kinetic simulations were performed using a previously published kinetic model (Togbé et al., Energy Fuels 2010, 11, 5859−5875) to predict the reactivity of n-hexanol and elucidate the predominant formation pathways of the observed low-temperature species. Experimental photoionization efficiency curves in conjunction with ab initio calculations, enabled the identification of important low-temperature species, such as C 6 unsaturated alcohols, C 6 olefinic hydroperoxides, C 6 cyclic ethers, C 6 diones, and C 6 ketohydroperoxides. The results of this study provide valuable insight into the mechanism of the low-temperature oxidation chemistry of n -hexanol, contributing to the development of kinetic models for the low-temperature oxidation of n-hexanol and other long-chain linear alcohols.

alcohols↗
DOE OSTI2018

Identification of Thiourea-Based Inhibitors of the B-Cell Lymphoma 6 BTB Domain via NMR-Based Fragment Screening and Computer-Aided Drug Design

Protein–protein interactions (PPI) between the transcriptional repressor B-cell lymphoma 6 (BCL6) BTB domain (BCL6 BTB ) and its corepressors have emerged as a promising target for anticancer therapeutics. However, identification of potent, drug-like inhibitors of BCL6 BTB has remained challenging. Using NMR-based screening of a library of fragment-like small molecules, we have identified a thiourea compound (7CC5) that binds to BCL6 BTB . From this hit, the application of computer-aided drug design (CADD), medicinal chemistry, NMR spectroscopy, and X-ray crystallography has yielded an inhibitor, 15f, that demonstrated over 100-fold improved potency for BCL6 BTB . This gain in potency was achieved by a unique binding mode that mimics the binding mode of the corepressor SMRT in the aromatic and the HDCH sites. Overall, the structure–activity relationship based on these new inhibitors will have a significant impact on the rational design of novel BCL6 inhibitors, facilitating the identification of therapeutics for the treatment of BCL6-dependent tumors.

60 APPLIED LIFE SCIENCES↗