De Novo Protein Structure Prediction - Background

Background

Currently, the gap between known protein sequences and confirmed protein structures is immense. At the beginning of 2008, only about 1% of the sequences listed in the UniProtKB database corresponded to structures in the Protein Data Bank (PDB), leaving a gap between sequence and structure of approximately five million. Experimental techniques for determining tertiary structure have faced serious bottlenecks in their ability to determine structures for particular proteins. For example, whereas X-ray crystallography has been successful in crystallizing approximately 80,000 cytosolic proteins, it has been far less successful in crystallizing membrane proteins – approximately 280. In light of experimental limitations, devising efficient computer programs to close the gap between known sequence and structure is believed to be the only feasible option.

De novo protein structure prediction methods attempt to predict tertiary structures from sequences based on general principles that govern protein folding energetics and/or statistical tendencies of conformational features that native structures acquire, without the use of explicit templates. Research into de novo structure prediction has been primarily focused into three areas: alternate lower-resolution representations of proteins, accurate energy functions, and efficient sampling methods.

A general paradigm for de novo prediction involves sampling conformation space, guided by scoring functions and other sequence-dependent biases such that a large set of candidate (“decoy") structures are generated. Native-like conformations are then selected from these decoys using scoring functions as well as conformer clustering. High-resolution refinement is sometimes used as a final step to fine-tune native-like structures. There are two major classes of scoring functions. Physics-based functions are based on mathematical models describing aspects of the known physics of molecular interaction. Knowledge-based functions are formed with statistical models capturing aspects of the properties of native protein conformations.