Computer-aided drug discovery

The MacKerell lab has developed a computer-aided drug discovery method called Site Identification by Ligand Competitive Saturation (SILCS), which uses convential molecular dynamics simulations of a target protein solvated by a saturated solution of water and many small molecule fargments. The simulation inherently gives the free energy profile of each ligand across the complete surface of the protein as a dynamic, rather than static object. A key breakthrough to this technology was the use of an enhanced type of molecular simulation technique, oscillating-chemical potential Grand Canonical Monte Carlo simulations, to enhance the sampling of small molecule fragments within hard-to-reach binding pockets. This approach is somewhat computationally-analogous to the experimental method of Fragment-based screening using crystallography or NMR, but is much faster than both and improves on the lack of protein flexibility in crystallography and the spatial resolution of NMR.

Disclaimer: Ths GCMC-MD simulation of many ligand fragments saturating a solution around a biomolecule target is the basis of a private company, SilcsBio. The technique was co-invested by my postdoctoral advisor Alex MacKerell, Ph.D. (currently CSO) and his former postdoc Olgun Guvench, Ph.D. (currently Managing Partner). I am working for Alex as part of the University of Maryland Balitmore and the NIH, not SilcsBio; however, we are all invested in essentially the same goals: target proteins as moving, dynamic objects using molecular simulations as a starting point to ultimately alleviate or cure diseases. I am working on using these GCMC-MD simulations on large, transmembrane proteins like the BK channel, which have enormous health implications in many diseases including heart disease, epilepsy and other neurological disorders, and cancer.

You can read more about the method of GCMC-MD at the MacKerell lab's Research page, or by reading this paper. There is a fantastic-looking and easy-to-use Interactive demo on the SilcsBio (R) website for a hands-on and visual guide of the method and how it identifies binding sites.