“Systems Level Causal Discovery in Heterogeneous TOPMed Data”
Takis Benos PhD, Frank Sciurba MD, and Panos Chrysanthis PhD
The team will investigate the mechanism, pathology and pathophysiology of chronic obstructive pulmonary disease (COPD) facilitation and progression by analyzing existing TOPMed clinical and omics datasets. COPD is the third leading cause of death and a major cause of disability and health care costs in the US. The tools and methods developed through this grant will be made publicly available using cloud services.
“REAL-TIME DISCOVERY OF INHIBITORS AMONG BILLION COMPOUNDS FOR PREVIEW AND
Innovations in computational techniques have enabled us to advance interactive virtual screening
platforms to speed up the identification of small molecules to disrupt protein function. As a step towards addressing polypharmacology issues, we plan to apply our state-of-the-art methodologies to build efficient tools to perform compound-and-target centric virtual screening of human proteins to discover pre-clinical compounds for hard to treat diseases.
CO-I on the grant is Alexander Dömling (U. of Groningen)
From Hi-C maps to Chromosome Dynamics and Cross-Correlations between Gene Loci
This is the first structure-based study of chromosomal dynamics using elastic network models, between the Bahar and Kingsford labs.
In this study, we constructed Gaussian Network Models (GNM) for chromosome structures based on Hi-C maps. The GNM analysis permits us to predict chromatin mobility profile, identify hierarchical structural domains, and discover cross-correlated distal domains (CCDDs). These biological findings were found consistent with various types of experimental data, including chromatin accessibility (ATAC-seq and DNase-seq), interacting pairs of regulatory elements and gene loci (ChIA-PET), and gene co-expression.
Sauerwald,N.*, Zhang,S.*, Kingsford,C., Bahar, I. (2017) Chromosomal dynamics predicted by an elastic network model explains genome-wide accessibility and long-range couplings. Nucleic Acids Research.
Making a Difference by Daring to be Different: Read about the life and career of Ivet Bahar
- Ivet’s beginning in Turkey, her home country, where she attained her degree and started her academic career
- Her move to the US, where she founded our department and our Ph.D. program
- Her pioneering work on the Gaussian Network Model, which has become an established tool to compute protein dynamics
- Her continued scholarly success both at the University and nationally
- And her advice to young researcher. Here is an excerpt:
Ivet has had several female doctoral students even though women are underrepresented in her area of research. Ivet’s message to young women aspiring to have scientific careers is to be brave and to take risks. Women (as well as men) can do whatever they want but she says that they should not ‘wait’ and hope for the best time, as there may never be one, but just seize the day and do it.
See the Biophysical Society’s Tweet here: https://twitter.com/BiophysicalSoc/status/839181537715322880
Find the article here: http://www.biophysics.org/Portals/1/PDFs/Biophysics%20Week/Profiles/IBahar.pdf
Dr. Koes’s grant, “Methods, Tools and Resources for Interactive Online Virtual Screening and Lead Optimization,” was scored in the top 1% of proposals and selected for renewal by the NIGMS.
The proposed work will accelerate the pace of drug discovery by developing, validating, and testing new methods, tools, and resources for structure-based drug design. Two fundamental challenges of structure-based drug design are the accurate scoring and ranking of protein-ligand structures, which identifies active compounds, and the ability to efficiently search a large number of ligands, which ensures that active compounds are sampled. This proposal will address these challenges by developing a novel approach for protein-ligand scoring and expanding the size of the chemical space that can be efficiently searched during lead optimization. The methods will be validated by their prospective application toward the discovery of new anti-cancer molecules and will be made readily accessible through online resources and open-source tools.