Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 11 de 11
Filter
1.
Non-conventional in English | [Unspecified Source], Grey literature | ID: grc-750347

ABSTRACT

Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic.

2.
Mil Med ; 2021 Oct 20.
Article in English | MEDLINE | ID: covidwho-1475819

ABSTRACT

OBJECTIVES: We explored factors related to testing positive for severe acute respiratory coronavirus 2 (SARS-CoV-2) to identify populations most at risk for this airborne pathogen. METHODS: Data were abstracted from the medical record database of the U.S. Department of Veterans Affairs and from public sources. Veterans testing positive were matched in a 1:4 ratio to those at a similar timepoint and local disease burden who remained negative between March 1, 2020, and December 31, 2020. Multivariable logistic regression was used to calculate odds ratios for the association of each potential risk factor with a positive test result. RESULTS: A total of 24,843 veterans who tested positive for SARS-CoV-2 were matched with 99,324 controls. Cases and controls were similar in age, sex, ethnicity, and rurality, but cases were more likely to be Black, reside in low-income counties, and suffer from dementia. Multivariable analysis demonstrated highest risk for Black veterans, those with dementia or diabetes, and those living in nursing homes or high-poverty areas. Veterans living in counties likely to be more adherent to public health guidelines were at the lowest risk. CONCLUSIONS: Our results are similar to those from studies of other populations and add to that work by accounting for several important proxies for risk. In particular, this work has implications for the value of infection control measures at the population level in helping to stem widespread outbreaks of this type.

3.
Mil Med ; 2021 Oct 06.
Article in English | MEDLINE | ID: covidwho-1455331

ABSTRACT

INTRODUCTION: Early identification of patients with coronavirus disease 2019 (COVID-19) who are at risk for hospitalization may help to mitigate disease burden by allowing healthcare systems to conduct sufficient resource and logistical planning in the event of case surges. We sought to develop and validate a clinical risk score that uses readily accessible information at testing to predict individualized 30-day hospitalization risk following COVID-19 diagnosis. METHODS: We assembled a retrospective cohort of U.S. Veterans Health Administration patients (age ≥ 18 years) diagnosed with COVID-19 between March 1, 2020, and December 31, 2020. We screened patient characteristics using Least Absolute Shrinkage and Selection Operator logistic regression and constructed the risk score using characteristics identified as most predictive for hospitalization. Patients diagnosed before November 1, 2020, comprised the development cohort, while those diagnosed on or after November 1, 2020, comprised the validation cohort. We assessed risk score discrimination by calculating the area under the receiver operating characteristic (AUROC) curve and calibration using the Hosmer-Lemeshow (HL) goodness-of-fit test. This study was approved by the Veteran's Institutional Review Board of Northern New England at the White River Junction Veterans Affairs Medical Center (Reference no.:1473972-1). RESULTS: The development and validation cohorts comprised 11,473 and 12,970 patients, of whom 4,465 (38.9%) and 3,669 (28.3%) were hospitalized, respectively. The independent predictors for hospitalization included in the risk score were increasing age, male sex, non-white race, Hispanic ethnicity, homelessness, nursing home/long-term care residence, unemployed or retired status, fever, fatigue, diarrhea, nausea, cough, diabetes, chronic kidney disease, hypertension, and chronic obstructive pulmonary disease. Model discrimination and calibration was good for the development (AUROC = 0.80; HL P-value = .05) and validation (AUROC = 0.80; HL P-value = .31) cohorts. CONCLUSIONS: The prediction tool developed in this study demonstrated that it could identify patients with COVID-19 who are at risk for hospitalization. This could potentially inform clinicians and policymakers of patients who may benefit most from early treatment interventions and help healthcare systems anticipate capacity surges.

4.
JAMA Netw Open ; 4(10): e2128391, 2021 10 01.
Article in English | MEDLINE | ID: covidwho-1453501

ABSTRACT

Importance: Effectiveness of mRNA vaccinations in a diverse older population with high comorbidity is unknown. Objectives: To describe the scope of the COVID-19 vaccination rollout among US veterans, and to estimate mRNA COVID-19 vaccine effectiveness (VE) as measured by rates of SARS-CoV-2 infection. Design, Setting, and Participants: This matched test-negative case-control study was conducted using SARS-CoV-2 test results at Veterans Health Administration sites from December 14, 2020, to March 14, 2021. Vaccine coverage was estimated for all veterans. VE against SARS-CoV-2 infection and COVID-19-related hospitalization and death were estimated using electronic health records from veterans who routinely sought care at a VHA facility and had a test result positive for SARS-CoV-2 (cases) or negative for SARS-CoV-2 (controls). Cases and controls were matched on time of test and geographic region. Data were analyzed from May to July 2021. Exposures: Vaccination status, defined as unvaccinated, partially vaccinated (≥14 days after first dose until second dose), or fully vaccinated (≥14 days after second dose), at time of test. Main Outcomes and Measures: The main outcome of interest was a positive result for SARS-CoV-2 on a polymerase chain reaction or antigen test. Secondary outcomes included COVID-19-related hospitalization and death, defined by discharge data and proximity of event to positive test result. VE was estimated from odds ratios for SARS-CoV-2 infection with 95% CIs. Results: Among 6 647 733 veterans included (3 350 373 veterans [50%] aged ≥65 years; 6 014 798 [90%] men and 632 935 [10%] women; 461 645 Hispanic veterans of any race [7%], 1 102 471 non-Hispanic Black veterans [17%], and 4 361 621 non-Hispanic White veterans [66%]), 1 363 180 (21%) received at least 1 COVID-19 vaccination by March 7, 2021. In this period, during which the share of SARS-CoV-2 variants Alpha, Epsilon, and Iota had started to increase in the US, estimates of COVID-19 VE against infection, regardless of symptoms, was 95% (95% CI, 93%-96%) for full vaccination and 64% (95% CI, 59%-68%) for partial vaccination. Estimated VE against COVID-19-related hospitalization for full vaccination was 91% (95% CI 83%-95%); there were no deaths among veterans who were fully vaccinated. VE against infection was similar across subpopulations (non-Hispanic Black, 94% [95% CI, 88%-97%]; Hispanic [any race], 83% [95% CI, 45%-95%]; non-Hispanic White, 92% [95% CI 88%-94%]; rural, 94% [95% CI, 89%-96%]; urban, 93% 95% CI, 89%-95%]). Conclusions and Relevance: For veterans of all racial and ethnic subgroups living in urban or rural areas, mRNA vaccination was associated with substantially decreased risk of COVID-19 infection and hospitalization, with no deaths among fully vaccinated veterans.


Subject(s)
COVID-19 Vaccines , COVID-19/prevention & control , RNA, Messenger , Vaccination Coverage , Veterans , African Americans , Aged , Aged, 80 and over , Case-Control Studies , European Continental Ancestry Group , Female , Hispanic Americans , Hospitalization , Humans , Male , Odds Ratio , Pandemics , SARS-CoV-2 , Treatment Outcome , United States , United States Department of Veterans Affairs
5.
J Phys Chem Lett ; 12(17): 4195-4202, 2021 May 06.
Article in English | MEDLINE | ID: covidwho-1387119

ABSTRACT

The catalytic reaction in SARS-CoV-2 main protease is activated by a proton transfer (PT) from Cys145 to His41. The same PT is likely also required for the covalent binding of some inhibitors. Here we use a multiscale computational approach to investigate the PT thermodynamics in the apo enzyme and in complex with two potent inhibitors, N3 and the α-ketoamide 13b. We show that with the inhibitors the free energy cost to reach the charge-separated state of the active-site dyad is lower, with N3 inducing the most significant reduction. We also show that a few key sites (including specific water molecules) significantly enhance or reduce the thermodynamic feasibility of the PT reaction, with selective desolvation of the active site playing a crucial role. The approach presented is a cost-effective procedure to identify the enzyme regions that control the activation of the catalytic reaction and is thus also useful to guide the design of inhibitors.


Subject(s)
Drug Design , Protease Inhibitors/chemistry , SARS-CoV-2/enzymology , Viral Matrix Proteins/antagonists & inhibitors , Antiviral Agents/chemistry , Antiviral Agents/metabolism , Biocatalysis , COVID-19/pathology , COVID-19/virology , Catalytic Domain , Humans , Molecular Dynamics Simulation , Protease Inhibitors/metabolism , Protons , Quantum Theory , SARS-CoV-2/isolation & purification , Thermodynamics , Viral Matrix Proteins/metabolism
7.
Proteins ; 89(2): 163-173, 2021 02.
Article in English | MEDLINE | ID: covidwho-745464

ABSTRACT

Human interleukin-6 (hIL-6) is a multifunctional cytokine that regulates immune and inflammatory responses in addition to metabolic and regenerative processes and cancer. hIL-6 binding to the IL-6 receptor (IL-6Rα) induces homodimerization and recruitment of the glycoprotein (gp130) to form a hexameric signaling complex. Anti-IL-6 and IL-6R antibodies are clinically approved inhibitors of IL-6 signaling pathway for treating rheumatoid arthritis and Castleman's disease, respectively. There is a potential to develop novel small molecule IL-6 antagonists derived from understanding the structural basis for IL-6/IL-6Rα interactions. Here, we combine homology modeling with extensive molecular dynamics (MD) simulations to examine the association of hIL-6 with IL-6Rα. A comparison with MD of apo hIL-6 reveals that the binding of hIL-6 to IL-6Rα induces structural and dynamic rearrangements in the AB loop region of hIL-6, disrupting intraprotein contacts and increasing the flexibility of residues 48 to 58 of the AB loop. In contrast, due to the involvement of residues 59 to 78 in forming contacts with the receptor, these residues of the AB loop are observed to rigidify in the presence of the receptor. The binary complex is primarily stabilized by two pairs of salt bridges, Arg181 (hIL-6)- Glu182 (IL-6Rα) and Arg184 (hIL-6)- Glu183 (IL-6Rα) as well as hydrophobic and aromatic stacking interactions mediated essentially by Phe residues in both proteins. An interplay of electrostatic, hydrophobic, hydrogen bonding, and aromatic stacking interactions facilitates the formation of the hIL-6/IL-6Rα complex.


Subject(s)
Apoproteins/chemistry , Interleukin-6/chemistry , Molecular Dynamics Simulation , Receptors, Interleukin-6/chemistry , Apoproteins/metabolism , Binding Sites , Crystallography, X-Ray , Humans , Hydrogen Bonding , Hydrophobic and Hydrophilic Interactions , Interleukin-6/metabolism , Protein Binding , Protein Conformation, alpha-Helical , Protein Conformation, beta-Strand , Protein Interaction Domains and Motifs , Receptors, Interleukin-6/metabolism , Static Electricity , Structural Homology, Protein , Thermodynamics
8.
Preprint | ChemRxiv | ID: ppcovidwho-242

ABSTRACT

The novel Wuhan coronavirus (SARS-CoV-2) has been sequenced, and the virus shares substantial similarity with SARS-CoV. Here, using a computational model of the spike protein (S-protein) of SARS-CoV-2 interacting with the human ACE2 receptor, we make use of the world's most powerful supercomputer, SUMMIT, to enact an ensemble docking virtual high-throughput screening campaign and identify small-molecules which bind to either the isolated Viral S-protein at its host receptor region or to the S protein-human ACE2 interface. We hypothesize the identified small-molecules may be repurposed to limit viral recognition of host cells and/or disrupt host-virus interactions. A ranked list of compounds is given that can be tested experimentally. br

9.
Non-conventional in English | WHO COVID | ID: covidwho-664398

ABSTRACT

Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic.

SELECTION OF CITATIONS
SEARCH DETAIL
...