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Stanley, Christopher

Publications and source records attributed to Stanley, Christopher.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Structural characterization of an intrinsically disordered protein complex using integrated small-angle neutron scattering and computing

Characterizing structural ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins is essential for studying structure–function relationships. Due to the different neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small-angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, experimental timescales typically capture measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a complex formed by two IDRs. We use data from both full contrast and contrast matching with residue-specific deuterium labeling SANS experiments, microsecond all-atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder-based deep learning (DL) algorithm. From our combined approach, we show that selective deuteration provides additional information that helps characterize structural ensembles. We find that among the four force fields, a99SB-disp and CHARMM36m show the strongest agreement with SANS and NMR experiments. In addition, our DL algorithm not only complements conventional structural analysis methods but also successfully differentiates NMR and MD structures which are indistinguishable on the free energy surface. Finally, we present an ensemble that describes experimental SANS and NMR data better than MD ensembles generated by one single force field and reveal three clusters of distinct conformations. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge

In a secure collaborative environment, tera-bytes of data generated from powerful scientific instruments are used to train secure machine learning (ML) models on exascale computing systems, which are then securely shared with internal or external collaborators as cloud-based services. Devising such a secure platform is necessary for seamless scientific knowledge sharing without compromising individual, or institute-level, intellectual property and privacy details. By enabling new computing opportunities with sensitive data, we envision a secure collaborative environment that will play a significant role in accelerating scientific discovery. Several recent technological advancements have made it possible to realize these capabilities. In this paper, we present our efforts at ORNL toward developing a secure computation platform. We present a use case where scientific data generated from complex instruments, like those at the Spallation Neutron Source (SNS), are used to train a differential privacy enabled deep learning (DL) network on Summit, which is then hosted as a secure multi-party computation (MPC) service on ORNL’s Compute and Data Environment for Science (CADES) cloud computing platform for third-party inference. In this feasibility study, we discuss the challenges involved, elaborate on leveraged technologies, analyze relevant performance results and present the future vision of our work to establish secure collaboration capabilities within and outside of ORNL.

Yoginath, Srikanth↗
DOE OSTI2022

How Distinct Structural Flexibility within SARS-CoV-2 Spike Protein Reveals Potential Therapeutic Targets

The emergence and rapid worldwide spread of the novel coronavirus disease, COVID-19, has prompted concerted efforts to find successful treatments. The causative virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), uses its spike (S) protein to gain entry into host cells. Therefore, the S protein presents a viable target to develop a directed therapy. Here, we deployed an integrated artificial intelligence with all-atom molecular dynamics simulation approach to provide new details of the S protein structure. Based on a comprehensive structural analysis of S proteins from SARS-CoV-2 and previous human coronaviruses, we found that the protomer state of S proteins is structurally flexible. Without the presence of a stabilizing beta sheet from another protomer chain, two regions in the S2 domain and the hinge connecting the S1 and S2 subunits lose their secondary structures. Interestingly, the region in the S2 domain was previously identified as an immunodominant site in the SARS-CoV-1 S protein. We anticipate that the molecular details elucidated here will assist in effective therapeutic development for COVID-19.

Chen, Serena↗
DOE OSTI2021

Optimal Balance of Privacy and Utility with Differential Privacy Deep Learning Frameworks

As the number of online services has increased, the amount of sensitive data being recorded is rising. Simultaneously, the decision-making process has improved by using the vast amounts of data, where machine learning has transformed entire industries. This paper addresses the development of optimal private deep neural networks and discusses the challenges associated with this task. We focus on differential privacy implementations and finding the optimal balance between accuracy and privacy, benefits and limitations of existing libraries, and challenges of applying private machine learning models in practical applications. Our analysis shows that learning rate, and privacy budget are the key factors that impact the results, and we discuss options for these settings.

Kotevska, Olivera↗
DOE OSTI2021

Generalizable coordination of large multiscale workflows: challenges and learnings at scale

The advancement of machine learning techniques and the heterogeneous architectures of most current supercomputers are propelling the demand for large multiscale simulations that can automatically and autonomously couple diverse components and map them to relevant resources to solve complex problems at multiple scales. Nevertheless, despite the recent progress in workflow technologies, current capabilities are limited to coupling two scales. In the first-ever demonstration of using three scales of resolution, we present a scalable and generalizable framework that couples pairs of models using machine learning and in situ feedback. We expand upon the massively parallel Multiscale Machine-Learned Modeling Infrastructure (MuMMI), a recent, award-winning workflow, and generalize the framework beyond its original design. We discuss the challenges and learnings in executing a massive multiscale simulation campaign that utilized over 600,000 node hours on Summit and achieved more than 98% GPU occupancy for more than 83% of the time. We present innovations to enable several orders of magnitude scaling, including simultaneously coordinating 24,000 jobs, and managing several TBs of new data per day and over a billion files in total. Finally, we describe the generalizability of our framework and, with an upcoming open-source release, discuss how the presented framework may be used for new applications.

Bhatia, Harsh↗
DOE OSTI2021

Privacy-Preserving Knowledge Transfer with Bootstrap Aggregation of Teacher Ensembles

There is a need to transfer knowledge among institutions and organizations to save effort in annotation and labeling or in enhancing task performance. However, knowledge transfer is difficult because of restrictions that are in place to ensure data security and privacy. Institutions are not allowed to exchange data or perform any activity that may expose personal information. With the leverage of a differential privacy algorithm in a high-performance computing environment, we propose a new training protocol, Bootstrap Aggregation of Teacher Ensembles (BATE), which is applicable to various types of machine learning models. The BATE algorithm is based on and provides enhancements to the PATE algorithm, maintaining competitive task performance scores on complex datasets with underrepresented class labels.We conducted a proof-of-the-concept study of the information extraction from cancer pathology report data from four cancer registries and performed comparisons between four scenarios: no collaboration, no privacy-preserving collaboration, the PATE algorithm, and the proposed BATE algorithm. The results showed that the BATE algorithm maintained competitive macro-averaged F1 scores, demonstrating that the suggested algorithm is an effective yet privacy-preserving method for machine learning and deep learning solutions.

Yoon, Hong-Jun↗
DOE OSTI2021

Using GANs with adaptive training data to search for new molecules

The process of drug discovery involves a search over the space of all possible chemical compounds. Generative Adversarial Networks (GANs) provide a valuable tool towards exploring chemical space and optimizing known compounds for a desired functionality. Standard approaches to training GANs, however, can result in mode collapse, in which the generator primarily produces samples closely related to a small subset of the training data. In contrast, the search for novel compounds necessitates exploration beyond the original data. In this work, we present an approach to training GANs that promotes incremental exploration and limits the impacts of mode collapse using concepts from Genetic Algorithms. In our approach, valid samples from the generator are used to replace samples from the training data. We consider both random and guided selection along with recombination during replacement. By tracking the number of novel compounds produced during training, we show that updates to the training data drastically outperform the traditional approach, increasing potential applications for GANs in drug discovery.

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

Computer-aided Abnormality Detection in Chest Radiographs in a Clinical Setting via Domain-adaptation

Deep learning (DL) models are being deployed at medical centers to aid radiologists for diagnosis of lung conditions from chest radiographs. Such models are often trained on a large volume of publicly available labeled radiographs. These pre-trained DL models’ ability to generalize in clinical settings is poor because of the changes in data distributions between publicly available and privately held radiographs. In chest radiographs, the heterogeneity in distributions arises from the diverse conditions in X-ray equipment and their configurations used for generating the images. In the machine learning community, the challenges posed by the heterogeneity in the data generation source is known as domain shift, which is a mode shift in the generative model. In this work, we introduce a domain-shift detection and removal method to overcome this problem. Our experimental results show the proposed method’s effectiveness in deploying a pre-trained DL model for abnormality detection in chest radiographs in a clinical setting.

Dubey, Abhishek↗
DOE OSTI2020

First Experiments: New Science Opportunities at the Spallation Neutron Source Second Target Station (abridged)

This brochure is an abridged version of a more comprehensive companion report. DOE proposes to build a Second Target Station (STS) at SNS to produce beams of cold neutrons with world-leading peak brightness at a repetition rate of 15 Hz, providing broad energy/wavelength ranges that can be used simultaneously. Construction of the STS will provide transformative capabilities that allow thousands of users from national laboratories, universities, and industry to address grand scientific challenges [Hemminger 2015], advance energy research, and accelerate industrial innovations through the combination of cold (long-wavelength) neutrons of unprecedented peak brightness and short neutron pulses with broad ranges of usable wavelengths or energy. The STS Project, now in progress at ORNL, will design, construct, install, and commission the facilities and equipment needed to create a world-leading source of cold neutrons of unprecedented peak brightness at SNS. Key aspects of the STS design, including preliminary descriptions of an illustrative suite of eight instruments, are presented in the section “The STS at SNS.” The capabilities offered by the STS instruments (22 when fully built out) will complement those of the FTS and HFIR, providing the United States with unparalleled resources for neutron scattering at the world’s leading high–peak brightness cold neutron source.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

First Experiments: New Science Opportunities at the Spallation Neutron Source Second Target Station

Leadership in materials science underpins future technologies in energy, security, and other applications that drive this nation’s economy. Neutron scattering is among the crucial characterization techniques necessary to ensure a world-leading position in materials science for the United States. The Second Target Station (STS) at the Spallation Neutron Source (SNS) will provide transformative new capabilities for the study of a broad range of materials using neutron scattering and will support users in many fields of research—materials science, physics, chemistry, geology, biology, and engineering, among others—and from academia, government laboratories, and industry.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

A nucleotide-dependent oligomerization of the Escherichia coli replication initiator DnaA requires residue His136 for remodeling of the chromosomal origin

Escherichia coli replication initiator protein DnaA binds ATP with high affinity but the amount of ATP required to initiate replication greatly exceeds the amount required for binding. Previously, we showed that ATP-DnaA, not ADP-DnaA, undergoes a conformational change at the higher nucleotide concentration, which allows DnaA oligomerization at the replication origin but the association state remains unclear. Here, we used Small Angle X-ray Scattering (SAXS) to investigate oligomerization of DnaA in solution. Whereas ADP-DnaA was predominantly monomeric, AMP–PNP–DnaA (a non-hydrolysable ATP-analog bound-DnaA) was oligomeric, primarily dimeric. Functional studies using DnaA mutants revealed that DnaA(H136Q) is defective in initiating replication in vivo. The mutant retains high-affinity ATP binding, but was defective in producing replication-competent initiation complexes. Docking of ATP on a structure of E. coli DnaA, modeled upon the crystallographic structure of Aquifex aeolicus DnaA, predicts a hydrogen bond between ATP and imidazole ring of His136, which is disrupted when Gln is present at position 136. SAXS performed on AMP–PNP–DnaA (H136Q) indicates that the protein has lost its ability to form oligomers. These results show the importance of high ATP in DnaA oligomerization and its dependence on the His136 residue.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Neutron scattering in the biological sciences: progress and prospects

The scattering of neutrons can be used to provide information on the structure and dynamics of biological systems on multiple length and time scales. Pursuant to a National Science Foundation-funded workshop in February 2018, recent developments in this field are reviewed here, as well as future prospects that can be expected given recent advances in sources, instrumentation and computational power and methods. Crystallography, solution scattering, dynamics, membranes, labeling and imaging are examined. For the extraction of maximum information, the incorporation of judicious specific deuterium labeling, the integration of several types of experiment, and interpretation using high-performance computer simulation models are often found to be particularly powerful.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

The suite of small-angle neutron scattering instruments at Oak Ridge National Laboratory

Oak Ridge National Laboratory is home to the High Flux Isotope Reactor (HFIR), a high-flux research reactor, and the Spallation Neutron Source (SNS), the world's most intense source of pulsed neutron beams. The unique co-localization of these two sources provided an opportunity to develop a suite of complementary small-angle neutron scattering instruments for studies of large-scale structures: the GP-SANS and Bio-SANS instruments at the HFIR and the EQ-SANS and TOF-USANS instruments at the SNS. This article provides an overview of the capabilities of the suite of instruments, with specific emphasis on how they complement each other. As a result, a description of the plans for future developments including greater integration of the suite into a single point of entry for neutron scattering studies of large-scale structures is also provided.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Small Angle Neutron Scattering Studies of R67 Dihydrofolate Reductase, a Tetrameric Protein with Intrinsically Disordered N-Termini

R67 dihydrofolate reductase (DHFR) is a homotetramer with a single active site pore and no sequence or structural homology with chromosomal DHFRs. The R67 enzyme provides resistance to trimethoprim, an active site-directed inhibitor of Escherichia coli DHFR. Sixteen to twenty N-terminal amino acids are intrinsically disordered in the R67 dimer crystal structure. Chymotrypsin cleavage of 16 N-terminal residues results in an active enzyme with a decreased stability. We investigated the space sampled by the disordered N-termini of R67 DHFR using small angle neutron scattering. From a combined analysis using molecular dynamics and the program SASSIE (http://www.smallangles.net/sassie/SASSIE_HOME.html), the apoenzyme displays a radius of gyration (Rg) of 21.46 ± 0.50 Å. Addition of glycine betaine, an osmolyte, does not result in folding of the termini as the Rg increases slightly to 22.78 ± 0.87 Å. SASSIE fits of the latter SANS data indicate that the disordered N-termini sample larger regions of space and remain disordered, suggesting they might function as entropic bristles. Pressure perturbation calorimetry also indicated that the volume of R67 DHFR increases upon addition of 10% betaine and decreased at 20% betaine because of the dehydration of the protein. Studies of the hydration of full-length R67 DHFR in the presence of the osmolytes betaine and dimethyl sulfoxide find around 1250 water molecules hydrating the protein. Similar studies with truncated R67 DHFR yield around 400 water molecules hydrating the protein in the presence of betaine. The difference of ~900 waters indicates the N-termini are well-hydrated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗