Jeffrey Skolnick, Ph.D.
Computational Systems Biology
Georgia Institute of Technology
Using computers for modeling is much faster than experimenting in the lab. And Jeffrey Skolnick has made such modeling exponentially faster.
Scientists rely on computer-based analysis to observe biological processes on a massive scale. But when they run their computations using high-resolution models taken from experimental findings, computer modeling still takes a lot of computing power – and time. That slows the pace of discovery.
Skolnick discovered a way to make it faster and simpler. He wrote an algorithm that predicts the general structure of a protein molecule, based on the amino acids it contains. The result is a low-resolution model that allows for far more rapid screening of potential medicines, yet is just as effective at predicting how the molecule will behave.
That’s important because evaluating protein structures reveals where they may be able to bind to other molecules, such as those contained in drugs. With Skolnick’s system, scientists can screen thousands of drug candidates and identify those most effective at binding to the target site. Those best performers then can take the next step into clinical experimentation.
- Systems biology
- Computational biology and bioinformatics
- Cancer metabolomics
- Prediction of protein tertiary and quaternary structure and folding pathways in proteomes
- Prediction of membrane protein tertiary structure
- Prediction of DNA-binding proteins
- Protein evolution
- Protein function prediction
- Prediction of small molecule ligands for drug discovery
- Prediction of druggable protein targets
- Drug design
- Automatic assignment of enzymes to metabolic pathways
- Simulation of virtual cells
The academic reputation of Georgia Tech and the quality of life in Atlanta convinced Skolnick to come to Georgia, along with his team of 19 research scientists.