PCM-based uncertainty reduction in support of model validation for depletion calculations
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42 ENGINEERING↗
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Validating the Use of Gaussian Process Regression for Adaptive Mapping of Residual Stress Fields
Probing the stress state using a high density of measurement points is time intensive and presents a limitation for what is experimentally feasible. Alternatively, individual strain fields used for determining stresses can be reconstructed from a subset of points using a Gaussian process regression (GPR). Results presented in this paper evidence that determining stresses from reconstructed strain fields is a viable approach for reducing the number of measurements needed to fully sample a component’s stress state. The approach was demonstrated by reconstructing the stress fields in wire-arc additively manufactured walls fabricated using either a mild steel or low-temperature transition feedstock. Effects of errors in individual GP reconstructed strain maps and how these errors propagate to the final stress maps were assessed. Implications of the initial sampling approach and how localized strains affect convergence are explored to give guidance on how best to implement a dynamic sampling experiment.
36 MATERIALS SCIENCE↗
{sup 18}F-FLT uptake kinetics in head and neck squamous cell carcinoma: A PET imaging study
Purpose: To analyze the kinetics of 3{sup ′}-deoxy-3{sup ′}-[F-18]-fluorothymidine (18F-FLT) uptake by head and neck squamous cell carcinomas and involved nodes imaged using positron emission tomography (PET). Methods: Two- and three-tissue compartment models were fitted to 12 tumor time-activity-curves (TACs) obtained for 6 structures (tumors or involved nodes) imaged in ten dynamic PET studies of 1 h duration, carried out for five patients. The ability of the models to describe the data was assessed using a runs test, the Akaike information criterion (AIC) and leave-one-out cross-validation. To generate parametric maps the models were also fitted to TACs of individual voxels. Correlations between maps of different parameters were characterized using Pearson'sr coefficient; in particular the phosphorylation rate-constants k{sub 3-2tiss} and k{sub 5} of the two- and three-tissue models were studied alongside the flux parameters K{sub FLT-2tiss} and K{sub FLT} of these models, and standardized uptake values (SUV). A methodology based on expectation-maximization clustering and the Bayesian information criterion (“EM-BIC clustering”) was used to distil the information from noisy parametric images. Results: Fits of two-tissue models 2C3K and 2C4K and three-tissue models 3C5K and 3C6K comprising three, four, five, and six rate-constants, respectively, pass the runs test for 4, 8, 10, and 11 of 12 tumor TACs. The three-tissue models have lower AIC and cross-validation scores for nine of the 12 tumors. Overall the 3C6K model has the lowest AIC and cross-validation scores and its fitted parameter values are of the same orders of magnitude as literature estimates. Maps ofK{sub FLT} and K{sub FLT-2tiss} are strongly correlated (r = 0.85) and also correlate closely with SUV maps (r = 0.72 for K{sub FLT-2tiss}, 0.64 for K{sub FLT}). Phosphorylation rate-constant maps are moderately correlated with flux maps (r = 0.48 for k{sub 3-2tiss} vs K{sub FLT-2tiss} and r = 0.68 for k{sub 5} vs K{sub FLT}); however, neither phosphorylation rate-constant correlates significantly with SUV. EM-BIC clustering reduces the parametric maps to a small number of levels—on average 5.8, 3.5, 3.4, and 1.4 for K{sub FLT-2tiss}, K{sub FLT}, k{sub 3-2tiss}, and k{sub 5.} This large simplification is potentially useful for radiotherapy dose-painting, but demonstrates the high noise in some maps. Statistical simulations show that voxel level noise degrades TACs generated from the 3C6K model sufficiently that the average AIC score, parameter bias, and total uncertainty of 2C4K model fits are similar to those of 3C6K fits, whereas at the whole tumor level the scores are lower for 3C6K fits. Conclusions: For the patients studied here, whole tumor FLT uptake time-courses are represented better overall by a three-tissue than by a two-tissue model. EM-BIC clustering simplifies noisy parametric maps, providing the best description of the underlying information they contain and is potentially useful for radiotherapy dose-painting. However, the clustering highlights the large degree of noise present in maps of the phosphorylation rate-constantsk{sub 5} and k{sub 3-2tiss}, which are conceptually tightly linked to cellular proliferation. Methods must be found to make these maps more robust—either by constraining other model parameters or modifying dynamic imaging protocols.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Learning Latent Representations to Bridge Coarse-Grained and Atomistic Resolutions in Polymer Simulations
We present a machine-learning-based framework for learning reduced-order representations of polymer chain conformations across coarse-grained (CG) and united-atom (UA) fidelities. By employing linear singular value decomposition and nonlinear autoencoders, we compress high-dimensional polymer configurations into latent spaces with minimal loss of structural accuracy. Crucially, we demonstrate a near-perfect linear mapping between CG and UA latent spaces, enabling an efficient super-resolution back-mapping procedure that reconstructs high-fidelity UA configurations from CG simulations. While minor structural inaccuracies occur, they are effectively corrected through a brief molecular dynamics relaxation, forming a practical hybrid machine learning−physics scheme. This approach establishes the key structural prerequisites for accelerated polymer dynamics simulations: a compact and accurate latent encoding of polymer chain conformations and a validated multi-fidelity mapping that permits reconstruction of UA structures from CG configurations. The extension of this framework to explicit time evolution within the latent space, enabling dynamics to be propagated at CG fidelity and decoded to UA resolution only when required, represents a natural and well-motivated direction for future work.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem
As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (≤ 5 m) from very-high-resolution (VHR) data. Here we assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severity was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal — pre- and post-fire event — WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). Lastly, this work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the < 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.
54 ENVIRONMENTAL SCIENCES↗
Low Temperature Geothermal Play Fairway Analysis For The Appalachian Basin: Phase 1 Revised Report November 18, 2016
Geothermal energy is an attractive sustainable energy source. Yet project developers need confirmation of the resource base to warrant their time and financial resources. The Geothermal Play Fairway Analysis of the Appalachian Basin evaluated risk metrics that communicate the favorability of potential low-temperature geothermal energy resources in reservoirs more than 1000 m below the surface. This analysis is focused on the direct use of the heat, rather than on electricity production. Four risk factors of concern for direct-use geothermal plays in the Appalachian Basin portions of New York, Pennsylvania, and West Virginia are examined individually, and then in combination: 1) thermal resource quality, 2) natural reservoir quality, 3) induced seismicity, and 4) utilization opportunities. Uncertainty in the risk estimation is quantified. Based on these metrics, geothermal plays in the Appalachian Basin were identified as potentially viable for a variety of direct-use-heat applications. The methodologies developed in this project may be applied in other sedimentary basins as a foundation for low temperature (50-150 °C), direct use geothermal resource, risk, and uncertainty assessment. Three methods with which to combine the four risk factors were used. Among these, the averaging of the individual risk factors indicates the most favorable counties within the study area are the West Virginia counties of Monongalia, Harrison, Lewis (dubbed the Morgantown–Clarksburg play fairway), Putnam, and Kanawha (Charleston play fairway), the New York counties of Chemung and Steuben plus adjacent Bradford county in Pennsylvania (Corning–Ithaca play fairway), and the Pennsylvania counties of Mercer, Crawford, Erie, and Warren, and adjacent Chautauqua county in New York (together, the Meadville–Jamestown play fairway). These higher priority regions are surrounded by broader medium priority zones. Also worthy of additional exploration is a broad region near Pittsburgh Pennsylvania, for which the available geological data are insufficient to fully analyze the geological risks but yet the population is high. First, to assess the spatial variation in the depth to which one would need to drill to obtain geothermal temperatures that are useful to a future project, the project used bottom-hole temperature data from Appalachian Basin oil and gas exploration. These bottom hole temperature data are abundant but of low quality. Second, the project examined the potential for sufficient water flow rates through rocks to harvest heat from a geothermal well field, considering only natural reservoirs. This analysis provides a very incomplete picture of spatial variability of natural reservoirs because the oil and gas reservoir data lack key properties and are spatially biased toward those locations with profitable amounts of hydrocarbons in the rock pore spaces. Third, in light of the fact that earthquake activity has been induced in several states by subsurface work related to the oil and gas industry, this project examined the potential for similar activity in the Appalachian Basin. Acknowledging that data for such a task are insufficient, we utilized what was available: records of seismic activity, regional estimates of the orientations of stress in the rocks, and locations and orientations of zones of lateral change in rock properties at depths down to several kilometers below Earth’s surface. With these data, we created a first approximation of spatially variable risks for induced earthquakes. Because no data existed with which to test the reliability of these methods, the results have a high degree of uncertainty. Fourth, we examined the spatial variability of the above-the-ground factors that contribute to the economical viability of projects to tap low-temperature geothermal resources for direct-use. We worked principally with population density as a regionally known variable that would impact the cost of district heating. The resulting maps omit the costs of producing the hot water from the ground, because the below-ground costs are directly coupled to the thermal resource risk factor and natural reservoir risk factor – later analyses of those costs will be needed. The result of the district heating analysis is highly skewed: few census locations yielded a low estimated surface cost. The team also identified more than 165 prospects for high value direct-use geothermal energy opportunities such as industrial sites, university campuses, and federal facilities, among others. At the closure of this regional analysis, the most significant technical uncertainties are 1) reservoir distribution and capacities; 2) validity of thermal resource maps, and 3) the holistic estimation of Levelized Cost of Heat for favorable geological situations.
15 GEOTHERMAL ENERGY↗
Joint analysis of Dark Energy Survey Year 3 data and CMB lensing from SPT and Planck . I. Construction of CMB lensing maps and modeling choices
Joint analyses of cross-correlations between measurements of galaxy positions, galaxy lensing, and lensing of the cosmic microwave background (CMB) offer powerful constraints on the large-scale structure of the Universe. In a forthcoming analysis, we will present cosmological constraints from the analysis of such cross-correlations measured using Year 3 data from the Dark Energy Survey (DES), and CMB data from the South Pole Telescope (SPT) and Planck. Here we present two key ingredients of this analysis: (1) an improved CMB lensing map in the SPT-SZ survey footprint and (2) the analysis methodology that will be used to extract cosmological information from the cross-correlation measurements. Relative to previous lensing maps made from the same CMB observations, we have implemented techniques to remove contamination from the thermal Sunyaev Zel’dovich effect, enabling the extraction of cosmological information from smaller angular scales of the cross-correlation measurements than in previous analyses with DES Year 1 data. We describe our model for the cross-correlations between these maps and DES data, and validate our modeling choices to demonstrate the robustness of our analysis. We then forecast the expected cosmological constraints from the galaxy survey-CMB lensing auto and cross-correlations. We find that the galaxy-CMB lensing and galaxy shear-CMB lensing correlations will on their own provide a constraint on S 8 =σ 8 $\sqrt{Ωm/0.3}$ at the few percent level, providing a powerful consistency check for the DES-only constraints. Finally, we explore scenarios where external priors on shear calibration are removed, finding that the joint analysis of CMB lensing cross-correlations can provide constraints on the shear calibration amplitude at the 5% to 10% level.
79 ASTRONOMY AND ASTROPHYSICS↗
SU-F-J-91: Sparing Lung Function in Treatment Planning Using Dual Energy Tomography
Purpose: To propose an alternate treatment plan that minimizes the dose to the functional lung tissues. In clinical situation, the evaluation of the lung functionality is typically derived from perfusion scintigraphy. However, such technique has spatial and temporal resolutions generally inferior to those of a CT scan. Alternatively, it is possible to evaluate pulmonary function by analysing the iodine concentration determined via contrast-enhanced dual energy CT (DECT) scan. Methods: Five lung cancer patients underwent a scintigraphy and a contrast-enhanced DECT scan (SOMATOM Definition Flash, Siemens). The iodine concentration was evaluated using the two-material decomposition method to produce a functional map of the lung. The validation of the approach is realized by comparison between the differential function computed by DECT and scintigraphy. The functional map is then used to redefine the V5 (volume of the organ that received more than 5 Gy during a radiotherapy treatment) to a novel functional parameter, the V5f. The V5f, that uses a volume weighted by its function level, can assist in evaluating optimal beam entry points for a specific treatment plan. Results: The results show that the differential functions obtained by scintigraphy and DECT are in good agreement with a mean difference of 6%. In specific cases, we are able to visually correlate low iodine concentration with abnormal pulmonary lung or cancerous tumors. The comparison between V5f and V5 has shown that some entry points can be better exploited and that new ones are now accessible, 2.34 times more in average, without increasing the V5f - thus allowing easier optimization of other planning objectives. Conclusion: In addition to the high-resolution DECT images, the iodine map provides local information used to detect potential functional heterogeneities in the 3D space. We propose that this information be used to calculate new functional dose parameters such as the V5f. The presenting author, Andreanne Lapointe, received a canadian scholarship from MITACS. Part of the funding is from the compagny Siemens.
60 APPLIED LIFE SCIENCES↗
Foaming prediction in pure liquids from dimensionless numbers inspired by the theory of fluid behavior for drops
Foaming prediction is critical for selecting materials and designing processes in industries such as bioprocessing and gas processing. Existing models lack the generality needed for a wide range of materials and overlook the foaming behavior in pure liquids. Here, this work presents a novel method for predicting foaming in pure liquids based on their density, surface tension, and viscosity, using Reynolds ( Re ) and Ohnesorge ( Oh ) numbers. A foaming prediction map, leveraging the theory of fluid drop behavior, was developed by plotting these numbers. This map delineates distinct non-foaming and foaming regions, functioning as a binary classifier for foaming predictions. The map was fitted and validated through shake test experiments on 46 liquids, demonstrating reliable predictions, except for a specific region characterized by small Oh and large Re numbers. This region corresponded to relatively low foam stability and high turbulence, making foaming predictions challenging for liquids in this category.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A cost-benefit analysis to select the most effective method for positional cloning: genotyping by sequencing versus allele-specific PCR
Next-generation sequencing technologies have enabled the rapid generation of high-resolution genetic maps to enable the identification of genomic regions associated with traits of interest. In certain cases these regions of interest need to be narrowed down via fine mapping to increase resolution to the level of the individual gene. In those cases, there is a choice of genotyping options, whereby cost, time, and information content need to be considered. We developed detailed, customizable models to compare the cost of genotyping by sequencing and allele-specific PCR for fine-mapping genomic regions of interest. The models were validated experimentally with data from a fine-mapping experiment designed to identify candidate genes within a 3.2-Mb disease resistance locus in the cereal crop sorghum (Sorghum bicolor (L.). Moench), which has a sequenced genome. The size of the mapping population, genetic diversity of the parental lines, choice of reagents, and labor cost were shown to influence the overall cost of the two methods, and, consequently, which method would be most cost-effective for a given experiment. Here, the model can be easily customized to reflect experiments with different species, genetic populations, experimental and personnel costs to determine the most cost-effective procedure for a new positional cloning project.
Cost analysis↗
Evolution of standardization and dissemination of cryo-EM structures and data jointly by the community, PDB, and EMDB
Cryogenic electron microscopy (cryo-EM) methods began to be used in the mid-1970s to study thin and periodic arrays of proteins. Following a half-century of development in cryo-specimen preparation, instrumentation, data collection, data processing and modeling software, cryo-EM has become a routine method for solving structures from large biological assemblies to small biomolecules at near to true atomic resolution. This review explores the critical roles played by the Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB) in partnership with the community to develop the necessary infrastructure to archive cryo-EM maps and associated models. Public access to cryo-EM structure data has in turn facilitated better understanding of structure-function relationships and advancement of image processing and modeling tool development. The partnership between the global cryo-EM community and PDB and EMDB leadership has synergistically shaped the standards for metadata, one-stop deposition of maps and models, and validation metrics to assess the quality of cryo-EM structures. The advent of cryo-electron tomography (cryo-ET) for in situ molecular cell structures at a broad resolution range and their correlations with other imaging data introduces new data archival challenges in terms of data size and complexity in the years to come.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Design of an alumina forming coating for Nb-base refractory alloys
Refractory multi-principal element alloys (RMPEAs) promise to significantly enhance gas turbine engine efficiency, but their poor oxidation performance inhibits their implementation. Alumina-forming bond coat alloys can provide oxidation protection, but discovering suitable chemistries remains a challenge. We employed a design methodology that screens for alumina-formation capability using Al activity and phase constitution predictions from CalPhaD (Thermo-Calc). Alloy down-selection from approximately 7,800 alloys in the Nb-Si-Ti-Al-Hf system was conducted via analysis of calculated thermodynamic properties with number-density topology style maps. This approach is validated by creating and testing the composition Nb 12 Si 23 Ti 24 Al 36 Hf 5 , which forms protective alumina scales up to 1400 °C and resists pesting at 800 °C. Further, the alloy has an average coefficient of thermal expansion of ~10.1 ppm/K, making it well matched to Nb-based refractory alloys. The methodology will be useful for the design of coatings for RMPEAs, enabling their implementation and significant efficiency benefits sooner.
36 MATERIALS SCIENCE↗
Data initiatives for ocean-driven melt of Antarctic ice shelves
Ocean-driven melt of Antarctic ice shelves is an important control on mass loss from the ice sheet, but is complex to study due to significant variability in melt rates both spatially and temporally. Here we assess the strengths and weakness of satellite and field-based observations as tools for testing models of ice-shelf melt. We discuss how the complementary use of field, satellite and model data can be a powerful but underutilised tool for studying melt processes. Finally, we identify some community initiatives working to collate and publish coordinated melt rate datasets, which can be used in future for validating satellite-derived maps of melt and evaluating processes in numerical simulations.
54 ENVIRONMENTAL SCIENCES↗
Mediation of plant–mycorrhizal interaction by a lectin receptor-like kinase
The molecular mechanisms underlying mycorrhizal symbioses, the most ubiquitous and impactful mutualistic plant–microbial interaction in nature, are largely unknown. Through genetic mapping, resequencing and molecular validation, we demonstrate that a G-type lectin receptor-like kinase (lecRLK) mediates the symbiotic interaction between Populus and the ectomycorrhizal fungus Laccaria bicolor . Furthermore, this finding uncovers an important molecular step in the establishment of symbiotic plant–fungal associations and provides a molecular target for engineering beneficial mycorrhizal relationships.
59 BASIC BIOLOGICAL SCIENCES↗
An informatics guided classification of miscible and immiscible binary alloy systems
The classification of miscible and immiscible systems of binary alloys plays a critical role in the design of multicomponent alloys. By mining data from hundreds of experimental phase diagrams, and thousands of thermodynamic data sets from experiments and high-throughput first-principles (HTFP) calculations, we have obtained a comprehensive classification of alloying behavior for 813 binary alloy systems consisting of transition and lanthanide metals. Among several physics-based descriptors, the slightly modified Pettifor chemical scale provides a unique two-dimensional map that divides the miscible and immiscible systems into distinctly clustered regions. Based on an artificial neural network algorithm and elemental similarity, the miscibility of the unknown systems is further predicted and a complete miscibility map is thus obtained. Impressively, the classification by the miscibility map yields a robust validation on the capability of the well-known Miedema’s theory (95% agreement) and shows good agreement with the HTFP method (90% agreement). Our results demonstrate that a state-of-the-art physics-guided data mining can provide an efficient pathway for knowledge discovery in the next generation of materials design.
36 MATERIALS SCIENCE↗
SECARB Commercial Scale CO 2 Injection and Optimization of Storage Capacity in the Southeastern United States
The Commercial Scale Project is a US DOE/NETL funded initiative aimed at enhancing the knowledge-base and industry’s ability to geologically store vast quantities of anthropogenic carbon. In support of this goal, a large-scale, stacked reservoir geologic model was developed for Gulf Coast sediments centered on the Citronelle Dome in southwest Alabama, the site of the SECARB Phase III Anthropogenic Test. Characterization of regional geology to construct the model consists of an assessment of the entire stratigraphic continuum at Citronelle Dome, from surface to the depth of the Donovan oil-bearing formation. This project utilizes all available geologic data available, which includes: modern geophysical well logs from three new wells drilled for SECARB’s Anthropogenic Test; vintage logs from the Citronelle oilfield wells; porosity and permeability data from whole core and sidewall cores obtained from the injection and observation wells drilled for the Anthropogenic Test; core data obtained from the SECARB Phase II saline aquifer injection test; regional core data for relevant formations from the Geological Survey of Alabama archives. Cross sections, isopach maps, and structure maps were developed to validate the geometry and architecture of the Citronelle Dome for building the model, and assuring that no major structural defects exist in the area. A synthetic neural network approach was used to predict porosity using the available SP and resistivity log data for the storage reservoir formations. These data are validated and applied to extrapolate porosity data over the study area wells, and to interpolate permeability amongst these data points. Geostatistical assessments were conducted over the study area. In addition to geologic characterization of the region, a suite of core analyses was conducted to construct a depositional model and constrain caprock integrity. Petrographic assessment of core was conducted by OSU and analyzed to build a depositional framework for the region and provide modern day analogues. Stability of the caprock over several test parameters was conducted by UAB to yield comprehensive measurements on long term stability of caprocks. The detailed geologic model of the full earth volume from surface thru the Donovan oil reservoir is incorporated into a state-of-the-art reservoir simulation conducted by the University of Alabama at Birmingham (UAB) to explore optimization of CO 2 injection and storage under different characterizations of reservoir flow properties. The application of a scaled up geologic modeling and reservoir simulation provides a proof of concept for the large scale volumetric modeling of CO 2 injection and storage the subsurface.
58 GEOSCIENCES↗
Inference of Unresolved Point Sources at High Galactic Latitudes Using Probabilistic Catalogs
The detection of point sources in images is a fundamental operation in astrophysics, and is crucial for constraining population models of the underlying point sources or characterizing the background emission. Standard techniques fall short in the crowded-field limit, losing sensitivity to faint sources and failing to track their covariance with close neighbors. We construct a Bayesian framework to perform inference of faint or overlapping point sources. The method involves probabilistic cataloging, where samples are taken from the posterior probability distribution of catalogs consistent with an observed photon count map. In order to validate our method, we sample random catalogs of the gamma-ray sky in the direction of the North Galactic Pole (NGP) by binning the data in energy and point-spread function classes. Using three energy bins spanning 0.3–1, 1–3, and 3–10 GeV, we identify 270{sub −10}{sup +30} point sources inside a 40{sup ∘}×40{sup ∘} region around the NGP above our point-source inclusion limit of 3×10{sup −11} cm{sup −2} s{sup −1} sr{sup −1} GeV{sup −1} at the 1–3 GeV energy bin. Modeling the flux distribution as a power law, we infer the slope to be −1.92{sub −0.05}{sup +0.07} and estimate the contribution of point sources to the total emission as 18{sub −2}{sup +2}%. These uncertainties in the flux distribution are fully marginalized over the number as well as the spatial and spectral properties of the unresolved point sources. This marginalization allows a robust test of whether the apparently isotropic emission in an image is due to unresolved point sources or of truly diffuse origin.
79 ASTRONOMY AND ASTROPHYSICS↗
Allosteric prediction via convolutional neural networks and protein structural and dynamical features
Allostery is the phenomenon whereby a binding event or covalent modification at one site in a protein modulates function at a distal site, thus changing a protein’s functional state. As such, it is a ubiquitous aspect of protein functional regulation. Computationally predicting allosteric states is important as part of the broader challenge of functional annotation, but it also has practical implications for drug development, as targeting an allosteric site often affords greater specificity compared with targeting an orthosteric site. This study introduces a machine learning approach to predict the allosteric functional state using the small G-protein KRas as the model system, due to its implication in many types of cancer and being well studied as a result with many x-ray crystallographic structures of KRas available with different mutations and ligands bound. Using structural and dynamical features that can be cast as images, namely interatomic distances, contact maps, covariance, and mutual information, supervised learning was performed using convolutional neural networks. Two pretrained convolutional neural network architectures, GoogLeNet and ResNet18, were fine-tuned to classify KRas into active or inactive states based on these features. Across training regimes, atomic contact maps emerged as the most effective structural feature, whereas linearized mutual information outperformed covariance in capturing dynamical correlations relevant to allostery. Models achieved significant validation accuracy, with atomic contact maps yielding up to 90% accuracy. In conclusion, the findings suggest that integrating global structural rearrangements and correlated motion patterns with deep learning can reliably predict protein allosteric states, offering a promising framework for understanding allosteric regulation and developing targeted therapeutics.
Rajeshwar T., Rajitha [Oak Ridge National Laborato↗