In the post-genomic era, key problems in molecular biology center on the determination and exploitation of three-dimensional protein structure and function. For example, modern drug design techniques use protein structure to understand how a drug can bind to an enzyme and inhibit its function. Structural proteomics will require high-throughput experimental techniques, coupled with sophisticated computer algorithms for data analysis and experiment planning.

We report a new algorithm, called Nuclear Vector Replacement (NVR) for high-throughput Nuclear Magnetic Resonance (NMR) structural biology. NVR correlates experimental NMR data to a priori protein structural information using a probabilistic geometric framework. The algorithm is combinatorially efficient (both in theory and in practice), and operates on NMR data that can be recorded in a fraction of the time required by traditional NMR methods. I will introduce NVR, analyze its computational complexity, and demonstrate it on two different applications in structural biology, 1) NMR resonance assignment and 2) 3D structural homology detection for proteins with remote amino acid sequences. NVR could play a role in structural genomics, whose ultimate goal is to determine the three-dimensional structures of all proteins in nature.