Algorithmic Challenges in Structural Molecular Biology and Proteomics

Abstract

This paper reviews our research in computational biology and chemistry. 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 (e.g., [1-20]) 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 problems arise in proposed methods for discovering new pharmaceuticals. I’ll briefly discuss some recent results from my lab, including new algorithms for interpreting X-ray crystallography [14, 17, 16] and NMR (nuclear magnetic resonance) data [3, 9, 6, 19, 10, 5, 7, 18, 4], disease classification using mass spectrometry of human serum [12], and protein redesign [13]. Our algorithms have recently been used, respectively, to reveal the enzymatic architecture of organisms high on the CDC bioterrorism watch-list [17, 16], for probabilistic cancer classification from human peripheral blood [12], and to redesign an antibioticproducing enzyme to bind a novel substrate [13]. I’ll overview these projects, and highlight some of the algorithmic and computational challenges.

Author’s address: 6211 Sudiko. Laboratory, Dartmouth Computer Science Department, Hanover, NH 03755, USA. Email: brd@cs.dartmouth.edu.