10.2. Predicting the Structures of Proteins

Ideally, what we'd like to do in analyzing proteins is take the sequence of a protein, which is cheap to obtain experimentally, and predict the structure of the protein, which is expensive and sometimes impossible to determine experimentally. It would also be interesting to be able to accurately predict function from sequence, identify functional sites in uncharacterized 3D structures, and eventually, build designed proteins—molecular machines that do whatever we need them to do. But without an understanding of how sequence determines structure, these other goals can't reliably be achieved.

There are two approaches in computational modeling of protein structure. The first is knowledge-based modeling. Knowledge-based methods employ parameters extracted from the database of existing structures to evaluate and optimize structures or predict structure from sequence (the protein structure prediction problem). The second approach is based on simulation of physical forces and molecular dynamics. Physicochemical simulations are often used to attempt to model how a protein folds into its compact, functional, native form from a nonfunctional, not-as-compact, denatured form (the protein folding problem). In this chapter we focus on knowledge-based protein structure prediction and analysis methods in which bioinformatics plays an important role.