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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Development of Martini 2.2 parameters for N -glycans: a case study of the HIV-1 Env glycoprotein dynamics

N-linked glycans are ubiquitous in nature and play key roles in biology. For example, glycosylation of pathogenic proteins is a common immune evasive mechanism, hampering the development of successful vaccines. Due to their chemical variability and complex dynamics, an accurate molecular understanding of glycans is still limited by the lack of effective resolution of current experimental approaches. Here, we have developed and implemented a reductive model based on the popular Martini 2.2 coarse-grained force field for the computational study of N-glycosylation. We used the HIV-1 Env as a direct applied example of a highly glycosylated protein. Finally, our results indicate that the model not only reproduces many observables in very good agreement with a fully atomistic force field but also can be extended to study large amount of glycosylation variants, a fundamental property that can aid in the development of drugs and vaccines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

The 1.3 Å resolution structure of the truncated group Ia type IV pilin from Pseudomonas aeruginosa strain P1

The type IV pilus is a diverse molecular machine capable of conferring a variety of functions and is produced by a wide range of bacterial species. The ability of the pilus to perform host-cell adherence makes it a viable target for the development of vaccines against infection by human pathogens such as Pseudomonas aeruginosa . Here, the 1.3 Å resolution crystal structure of the N-terminally truncated type IV pilin from P. aeruginosa strain P1 (ΔP1) is reported, the first structure of its phylogenetically linked group (group I) to be discussed in the literature. The structure was solved from X-ray diffraction data that were collected 20 years ago with a molecular-replacement search model generated using AlphaFold ; the effectiveness of other search models was analyzed. Examination of the high-resolution ΔP1 structure revealed a solvent network that aids in maintaining the fold of the protein. On comparing the sequence and structure of P1 with a variety of type IV pilins, it was observed that there are cases of higher structural similarities between the phylogenetic groups of P. aeruginosa than there are between the same phylogenetic group, indicating that a structural grouping of pilins may be necessary in developing antivirulence drugs and vaccines. These analyses also identified the α–β loop as the most structurally diverse domain of the pilins, which could allow it to serve a role in pilus recognition. Studies of ΔP1 in vitro polymerization demonstrate that the optimal hydrophobic catalyst for the oligomerization of the pilus from strain K122 is not conducive for pilus formation of ΔP1; a model of a three-start helical assembly using the ΔP1 structure indicates that the α–β loop and the D-loop prevent in vitro polymerization.

Bragagnolo, Nicholas↗
DOE OSTI2020

Emergence of SARS-CoV-2 through recombination and strong purifying selection

COVID-19 has become a global pandemic caused by the novel coronavirus SARS-CoV-2. Understanding the origins of SARS-CoV-2 is critical for deterring future zoonosis, discovering new drugs, and developing a vaccine. We show evidence of strong purifying selection around the receptor binding motif (RBM) in the spike and other genes among bat, pangolin, and human coronaviruses, suggesting similar evolutionary constraints in different host species. We also demonstrate that SARS-CoV-2’s entire RBM was introduced through recombination with coronaviruses from pangolins, possibly a critical step in the evolution of SARS-CoV-2’s ability to infect humans. Similar purifying selection in different host species, together with frequent recombination among coronaviruses, suggests a common evolutionary mechanism that could lead to new emerging human coronaviruses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Super-relaxed myosins contribute to respiratory muscle hibernation in mechanically ventilated patients

Patients receiving mechanical ventilation in the intensive care unit (ICU) frequently develop contractile weakness of the diaphragm. Consequently, they may experience difficulty weaning from mechanical ventilation, which increases mortality and poses a high economic burden. Because of a lack of knowledge regarding the molecular changes in the diaphragm, no treatment is currently available to improve diaphragm contractility. We compared diaphragm biopsies from ventilated ICU patients (N= 54) to those of non-ICU patients undergoing thoracic surgery (N= 27). By integrating data from myofiber force measurements, x-ray diffraction experiments, and biochemical assays with clinical data, we found that in myofibers isolated from the diaphragm of ventilated ICU patients, myosin is trapped in an energy-sparing, super-relaxed state, which impairs the binding of myosin to actin during diaphragm contraction. Studies on quadriceps biopsies of ICU patients and on the diaphragm of previously healthy mechanically ventilated rats suggested that the super-relaxed myosins are specific to the diaphragm and not a result of critical illness. Exposing slow- and fast-twitch myofibers isolated from the diaphragm biopsies to small-molecule compounds activating troponin restored contractile force in vitro. These findings support the continued development of drugs that target sarcomere proteins to increase the calcium sensitivity of myofibers for the treatment of ICU-acquired diaphragm weakness.

Cell Biology↗
DOE OSTI2020

Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models

Abstract Background A mechanistic understanding of the spread of SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented opportunity to analyze evolutionary adaptation in novel ways. Addition of high-resolution structural information can reveal the functional basis of these processes at the molecular level. Integrated systems biology-directed analyses of these data layers afford valuable insights to build a global understanding of the COVID-19 pandemic. Results Here we identify globally distributed haplotypes from 15,789 SARS-CoV-2 genomes and model their success based on their duration, dispersal, and frequency in the host population. Our models identify mutations that are likely compensatory adaptive changes that allowed for rapid expansion of the virus. Functional predictions from structural analyses indicate that, contrary to previous reports, the Asp 614 Gly mutation in the spike glycoprotein (S) likely reduced transmission and the subsequent Pro 323 Leu mutation in the RNA-dependent RNA polymerase led to the precipitous spread of the virus. Our model also suggests that two mutations in the nsp13 helicase allowed for the adaptation of the virus to the Pacific Northwest of the USA. Finally, our explainable artificial intelligence algorithm identified a mutational hotspot in the sequence of S that also displays a signature of positive selection and may have implications for tissue or cell-specific expression of the virus. Conclusions These results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The progeria research foundation 10 th international scientific workshop; researching possibilities, ExTENding lives – webinar version scientific summary

Progeria is an ultra-rare (prevalence 1 in 20 million), fatal, pediatric autosomal dominant premature aging disease caused by a mutation in the LMNA gene. This mutation results in accumulation of a high level of an aberrant form of the nuclear membrane protein, Lamin A. This aberrant protein, termed progerin, accumulates in many tissues and is responsible for the diverse array of disease phenotypes. Children die predominantly from premature atherosclerotic cardiovascular disease. The Progeria Research Foundation’s 10th International Scientific Workshop took place via webinar on November 2 and 3, 2020. Participants from 30 countries joined in this new, virtual meeting format. Patient family presentations led the program, followed by updates on Progeria’s first-ever application for FDA drug approval as well as initial results from the only current Progeria clinical trial. This was followed by presentations of unpublished preclinical data on drugs in development targeting the disease-causing DNA mutation, the aberrant mRNA, progerin protein, and its downstream effector proteins. Tying bench to bedside, clinicians presented new discoveries on the natural history of disease to inform future clinical trial development and new Progeria aortic valve replacement procedures. The program engaged the Progeria research community as a single unit with a common goal – to treat and cure children with Progeria worldwide.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2008

A Modified LC/MS/MS Method with Enhanced Sensitivity for the Determination of Scopolamine in Human Plasma

Intranasal scopolamine is a choice drug for the treatment of motion sickness during space flight because of its quick onset of action, short half-life and favorable sideeffects profile. The dose administered usually ranges between 0.1 and 0.4 mg. Such small doses make it difficult to detect concentrations of scopolamine in biological fluids using existing sensitive LC/MS/MS method, especially when the biological sample volumes are limited. To measure scopolamine in human plasma to facilitate pharmacokinetic evaluation of the drug, we developed a sensitive LC/MS/MS method using 96 well micro elution plates for solid phase extraction (SPE) of scopolamine in human plasma. Human plasma (100-250 micro L) were loaded onto Waters Oasis HLB 96 well micro elution plate and eluted with 50 L of organic solvent without evaporation and reconstitution. HPLC separation of the eluted sample was performed using an Agilent Zorbax SB-CN column (50 x 2.1 mm) at a flow rate of 0.2 mL/min for 3 minutes. The mobile phase for separation was 80:20 (v/v) methanol: ammonium acetate (30 mM) in water. Concentrations of scopolamine were determined using a Micromass Quattro Micro(TM) mass spectrometer with electrospray ionization (ESI). ESI mass spectra were acquired in positive ion mode with multiple reaction monitoring for the determination of scopolamine m/z = 304.2 right arrow 138.1 and internal standard hyoscyamine m/z = 290.2 right arrow 124.1. The method is rapid, reproducible, specific and has the following parameters: scopolamine and the IS are eluted at about 1.1 and 1.7 min respectively. The linear range is 25-10000 pg/mL for scopolamine in human plasma with correlation coefficients greater than 0.99 and CV less than 0.5%. The intra-day and inter-day CVs are less than 15% for quality control samples with concentrations of 75,300, and 750 pg/mL of scopolamine in human plasma. SPE using 96 well micro elution plates allows rapid sample preparation and enhanced sensitivity for the LC/MS/MS determination of scopolamine in a small volume of biological samples. The new method is also cost effective since it uses a small volume of organic solvents compared to the methods using SPE cartridges or regular 96 well SPE plates. This method can be successfully used for bioavailability and pharmacokinetic evaluations of scopolamine, especially when volumes of biological samples are limited. Further investigation to use automated SPE system with 96 well micro elution plates is planned.

Wang, Zuwei↗
DOE OSTI2022

Deep generative molecular design reshapes drug discovery

Recent advances and accomplishments of artificial intelligence (AI) and deep generative models have established their usefulness in medicinal applications, especially in drug discovery and development. To correctly apply AI, the developer and user face questions such as which protocols to consider, which factors to scrutinize, and how the deep generative models can integrate the relevant disciplines. This review summarizes classical and newly developed AI approaches, providing an updated and accessible guide to the broad computational drug discovery and development community. We introduce deep generative models from different standpoints and describe the theoretical frameworks for representing chemical and biological structures and their applications. We discuss the data and technical challenges and highlight future directions of multimodal deep generative models for accelerating drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Drug-Inducible Gene Therapy Effectively Reduces Spontaneous Seizures in Kindled Rats but Creates Off-Target Side Effects in Inhibitory Neurons

Over a third of patients with temporal lobe epilepsy (TLE) are not effectively treated with current anti-seizure drugs, spurring the development of gene therapies. The injection of adeno-associated viral vectors (AAV) into the brain has been shown to be a safe and viable approach. However, to date, AAV expression of therapeutic genes has not been regulated. Moreover, a common property of antiepileptic drugs is a narrow therapeutic window between seizure control and side effects. Therefore, a long-term goal is to develop drug-inducible gene therapies that can be regulated by clinically relevant drugs. In this study, a first-generation doxycycline-regulated gene therapy that delivered an engineered version of the leak potassium channel Kcnk2 (TREK-M) was injected into the hippocampus of male rats. Rats were electrically stimulated until kindled. EEG was monitored 24/7. Electrical kindling revealed an important side effect, as even low expression of TREK M in the absence of doxycycline was sufficient to cause rats to develop spontaneous recurring seizures. Treating the epileptic rats with doxycycline successfully reduced spontaneous seizures. Localization studies of infected neurons suggest seizures were caused by expression in GABAergic inhibitory neurons. In contrast, doxycycline increased the expression of TREK-M in excitatory neurons, thereby reducing seizures through net inhibition of firing. These studies demonstrate that drug-inducible gene therapies are effective in reducing spontaneous seizures and highlight the importance of testing for side effects with pro-epileptic stressors such as electrical kindling. These studies also show the importance of evaluating the location and spread of AAV-based gene therapies in preclinical studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure --- this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

Wu, Yulun↗
DOE OSTI2022

SPATIAL GRAPH ATTENTION AND CURIOSITY-DRIVEN POLICY FOR ANTIVIRAL DRUG DISCOVERY

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure - this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

Wu, Y↗
DOE OSTI2022

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Networks (DGAPNs), a curiosity-driven reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention Network (sGAT) that leverages self-attention over both node and edge attributes as well as encoding spatial structure -- this capability is of considerable interest in areas such as molecular and synthetic biology and drug discovery. An attentional policy network is then introduced to learn decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with enhanced stability. Exploration is efficiently encouraged by incorporating innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while increasing the diversity of proposed molecules and reducing the complexity of paths to chemical synthesis.

Wu, Yulun↗
DOE OSTI2021

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Networks (DGAPNs), a curiosity-driven reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. Here, the framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention Network (sGAT) that leverages self-attention over both node and edge attributes as well as encoding spatial structure -- this capability is of considerable interest in areas such as molecular and synthetic biology and drug discovery. An attentional policy network is then introduced to learn decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with enhanced stability. Exploration is efficiently encouraged by incorporating innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while increasing the diversity of proposed molecules and reducing the complexity of paths to chemical synthesis.

Wu, Yulun↗
DOE OSTI2020

A Comprehensive Scientific Survey of Excipients Used in Currently Marketed, Therapeutic Biological Drug Products

The steady development of biotechnology-derived therapeutic biologics over the last few decades has generated drugs that are now standard medical treatments for a range of indications. While the development of protein products has surged in recent years, the formulation and delivery of these complex molecules have relied on drug-specific studies and, in some instances, data from non-proteinaceous drug products. The commonalities, trends, and gaps in excipient technologies used to support the development of therapeutic proteins largely remain unexplored due to the drug-specific nature of many formulations. Using a comprehensive and relational database approach, we aimed to provide a scientific survey of all approved or licensed biotechnology-derived drug products with the goal of providing evidence-based information on common attributes and trending features in protein product excipients. We examined 665 formulations, and 395 unique formulations based on having unique excipients within them, that supported 211 therapeutic proteins as of June 2020. We report the prevalence of each excipient class and excipient chemical used in eight different drug types including monoclonal antibodies, antibody conjugates, cytokines and growth factors, enzymes, polypeptide hormones, pulmonary surfactants, recombinant fusion proteins, and toxins. We also report the prevalence by excipient type among all therapeutic proteins, in the context of each drug’s recommended pH range, concentration ranges for excipients, and route of administration. The results of our analyses indicate certain excipients common to monoclonal antibodies, cytokines, and polypeptide hormones. We also report on excipients unique to protein drug products, such as amino acids, solubilizers, and lyoprotectants. Overall, our report summarizes the current landscape of excipients used in marketed biotechnology-derived therapeutic biologic products.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A cross-study analysis of drug response prediction in cancer cell lines

Abstract To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: National Cancer Institute 60, ancer Therapeutics Response Portal (CTRP), Genomics of Drug Sensitivity in Cancer, Cancer Cell Line Encyclopedia and Genentech Cell Line Screening Initiative (gCSI). Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Identification of a pharmacological approach to reduce ACE2 expression and development of an in vitro COVID-19 viral entry model

Because of rapid emergence and circulation of the SARS-CoV-2 variants, especially Omicron which shows increased transmissibility and resistant to antibodies, there is an urgent need to develop novel therapeutic drugs to treat COVID-19. In this study we developed an in vitro cellular model to explore the regulation of ACE2 expression and its correlation with ACE2-mediated viral entry. We examined ACE2 expression in a variety of human cell lines, some of which are commonly used to study SARS-CoV-2. Using the developed model, we identified a number of inhibitors which reduced ACE2 protein expression. The greatest reduction of ACE2 expression was observed when CK869, an inhibitor of the actin-related protein 2/3 (ARP2/3) complex, was combined with 5-(N-ethyl-N-isopropyl)-amiloride (EIPA), an inhibitor of sodium-hydrogen exchangers (NHEs), after treatment for 24 h. Using pseudotyped lentivirus expressing the SARS-CoV-2 full-length spike protein, we found that ACE2-dependent viral entry was inhibited in CK869 + EIPA-treated Calu-3 and MDA-MB-468 cells. This study provides an in vitro model that can be used for the screening of novel therapeutic candidates that may be warranted for further pre-clinical and clinical studies on COVID-19 countermeasures.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1991

Cooperative research and development opportunities with the National Cancer Institute

The Office of Technology Development (OTD) of the National Cancer Institute (NCI) is responsible for negotiating Cooperative Research and Development Agreements (CRADAs), whereby the knowledge resulting from NCI investigators' government-sponsored research is developed in collaboration with universities and/or industry into new products of importance for the diagnosis and treatment of cancer and acquired immunodeficiency syndrome (AIDS). The NCI has recently executed a unique 'clinical trials' CRADA and is developing a model agreement based upon it for the development and commercialization of products for the diagnosis and treatment of cancer and AIDS. NCI drug screening, preclinical testing, clinical trials, and AIDS program capabilities form the basis for this new technology development/technology transfer vehicle. NCI's extensive drug screening program and 'designer foods' program serve as potential sources of investigational new drugs (INDs) and cancer preventatives. Collaborations between NCI and pharmaceutical companies having the facilities, experience, and expertise necessary to develop INDs into approved drugs available to the public are being encouraged where the companies have proprietary rights to INDs, or where NCI has proprietary rights to INDs and invites companies to respond to a collaborator announcement published in the Federal Register. The joint efforts of the NCI and the chosen collaborator are designed to generate the data necessary to obtain pharmaceutic regulatory approval from the Food and Drug Administration (FDA) to market the drugs developed, and thereby make them available to health care providers for the diagnosis and treatment of cancer and AIDS.

Sybert, Kathleen↗