Browsing by Author "Baenziger, Peter H."

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    BioDrugScreen: a computational drug design resource for ranking molecules docked to the human proteome
    (Oxford University Press, 2009-11-18) Li, Liwei; Bum-Erdene, Khuchtumur; Baenziger, Peter H.; Rosen, Joshua J.; Hemmert, Jamison R.; Nellis, Joy A.; Pierce, Marlon E.; Meroueh, Samy O.; Biochemistry and Molecular Biology, School of Medicine
    BioDrugScreen is a resource for ranking molecules docked against a large number of targets in the human proteome. Nearly 1600 molecules from the freely available NCI diversity set were docked onto 1926 cavities identified on 1589 human targets resulting in >3 million receptor–ligand complexes requiring >200 000 cpu-hours on the TeraGrid. The targets in BioDrugScreen originated from Human Cancer Protein Interaction Network, which we have updated, as well as the Human Druggable Proteome, which we have created for the purpose of this effort. This makes the BioDrugScreen resource highly valuable in drug discovery. The receptor–ligand complexes within the database can be ranked using standard and well-established scoring functions like AutoDock, DockScore, ChemScore, X-Score, GoldScore, DFIRE and PMF. In addition, we have scored the complexes with more intensive GBSA and PBSA approaches requiring an additional 120 000 cpu-hours on the TeraGrid. We constructed a simple interface to enable users to view top-ranking molecules and access purchasing and other information for further experimental exploration.
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    Indiana Medical Resident’s Knowledge of Surrogate Decision Making Laws
    (Sage, 2022-07) Bartlett, Stephanie; Fettig, Lyle P.; Baenziger, Peter H.; DiOrio, Eliana N.; Herget, Kayla M.; D'Cruz, Lynn; Coughlin, Johanna R.; Lake, Mikaela; Truong, Amy; Comer, Amber R.; Health Sciences, School of Health and Human Sciences
    Introduction During the care of incapacitated patients, physicians, and medical residents discuss treatment options and gain consent to treat through healthcare surrogates. The purpose of this study is to ascertain medical residents’ knowledge of healthcare consent laws, application during clinical practice, and appraise the education residents received regarding surrogate decision making laws. Methods Beginning in February of 2018, 35 of 113 medical residents working with patients within Indiana completed a survey. The survey explored medical residents’ knowledge of health care surrogate consent laws utilized in Indiana hospitals and Veterans Affairs (VA) hospitals via clinical vignettes. Results Only 22.9% of medical residents knew the default state law in Indiana did not have a hierarchy for settling disputes among surrogates. Medical residents correctly identified which family members could participate in medical decisions 86% of the time. Under the Veterans Affairs surrogate law, medical residents correctly identified appropriate family members or friends 50% of the time and incorrectly acknowledged the chief decision makers during a dispute 30% of the time. All medical residents report only having little or some knowledge of surrogate decision making laws with only 43% having remembered receiving surrogate decision making training during their residency. Conclusions These findings demonstrate that medical residents lack understanding of surrogate decision making laws. In order to ensure medical decisions are made by the appropriate surrogates and patient autonomy is upheld, an educational intervention is required to train medical residents about surrogate decision making laws and how they are used in clinical practice.
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    MutDB: update on development of tools for the biochemical analysis of genetic variation
    (Oxford University Press, 2007-09-07) Singh, Arti; Olowoyeye, Adebayo; Baenziger, Peter H.; Dantzer, Jessica; Kann, Maricel G.; Radivojac, Predrag; Heiland, Randy; Mooney, Sean D.; Medical and Molecular Genetics, School of Medicine
    Understanding how genetic variation affects the molecular function of gene products is an emergent area of bioinformatic research. Here, we present updates to MutDB ( http://www.mutdb.org ), a tool aiming to aid bioinformatic studies by integrating publicly available databases of human genetic variation with molecular features and clinical phenotype data. MutDB, first developed in 2002, integrates annotated SNPs in dbSNP and amino acid substitutions in Swiss-Prot with protein structural information, links to scores that predict functional disruption and other useful annotations. Though these functional annotations are mainly focused on nonsynonymous SNPs, some information on other SNP types included in dbSNP is also provided. Additionally, we have developed a new functionality that facilitates KEGG pathway visualization of genes containing SNPs and a SNP query tool for visualizing and exporting sets of SNPs that share selected features based on certain filters.