Computational and physical modeling challenges in structural molecular biology and proteomics

摘要

Some of the most challenging and influential opportunities for Physical Geometric Algorithms (PGA) arise in developing and applying information technology to understand the molecular machinery of the cell. Our recent work (and work by others) shows that many PGA techniques may be fruitfully applied to the challenges of computational molecular biology. PGA research may lead to computer systems and algorithms that are useful in structural molecular biology, proteomics, and rational drug design.Concomitantly, a wealth of interesting computational and physical modeling problems arise in proposed methods for discovering new pharmaceuticals. In this talk, I'll discuss some recent results from my lab, including new algorithms for interpreting X-ray crystallography and NMR (nuclear magnetic resonance) data, disease classification using mass spectrometry of human serum, and protein redesign. Our algorithms have recently been used, respectively, to reveal the enzymatic architecture of organisms high on theCDC bioterrorism watch-list, for probabilistic cancer classification from human peripheral blood, and to redesign an antibiotic-producing enzyme to bind a novel substrate. I'll overview these projects, and survey some of the algorithmic, modeling, and computational challenges.