Protein structure prediction

Protein structure prediction is one of the most significant technologies pursued by computational structural biology and theoretical chemistry. It has the aim of determining the three-dimensional structure of proteins from their amino acid sequences. In more formal terms, this is expressed as the prediction of protein tertiary structure from primary structure. Given the usefulness of known protein structures in such valuable tasks as rational drug design, this is a highly active field of research.

Every two years, the performance of current methods is assessed in the CASP experiment.

Overview

The practical role of protein structure prediction is now more important than ever. Massive amounts of protein sequence data may be derived from modern large-scale DNA sequencing efforts such as the Human Genome Project. Despite community-wide efforts in structural genomics, the output of experimentally determined protein structures - typically by time-consuming and relatively expensive X-ray crystallography or NMR spectroscopy - is lagging far behind the output of protein sequences.

A number of factors exist that make protein structure prediction a very difficult task, including:

• The number of possible structures that proteins may possess is extremely large, as highlighted by the Levinthal paradox.
• The physical basis of protein structural stability is not fully understood.
• The primary sequence may not fully specify the tertiary structure. For example, proteins known as chaperones have the ability to induce proteins to fold in specific ways.
• A particular sequence may be able to assume multiple conformations depending on its environment, and the biologically active conformation may not be the most thermodynamically favorable.
• Direct simulation of protein folding via methods such as molecular dynamics is not generally tractable for both practical and theoretical reasons. However, the distributed computing projects Folding@home are tackling such simulation difficulties.

Despite the above hindrances, much progress is being made by the many research groups that are interested in the task. Prediction of structures for small proteins is now a perfectly realistic goal. A wide range of approaches are routinely applied for such predictions. These approaches may be classified into two broad classes; ab initio modelling and comparative modelling.

Distributed computing projects that attempt to solve the protein prediction problem include Rosetta@home and Predictor@home

Ab initio protein modelling

Ab initio- or de novo- protein modelling methods seek to build three-dimensional protein models "from scratch", i.e., based on physical principles rather than (directly) on previously solved structures. There are many possible procedures that either attempt to mimic protein folding or apply some stochastic method to search possible solutions (i.e. global optimization of a suitable energy function). These procedures tend to require vast computational resources, and have thus only been carried out for tiny proteins. To attempt to predict protein structure de novo for larger proteins, we will need better algorithms and larger computational resources like those afforded by either powerful supercomputers (such as Blue Gene) or distributed computing (see Human Proteome Folding Project). Although these computational barriers are vast, the potential benefits of structural genomics (by predicted or experimental methods) make ab initio structure prediction an active research field.

Comparative protein modelling

Comparative protein modelling uses previously solved structures as starting points, or templates. This is effective because it appears that although the number of actual proteins is vast, there is a limited set of tertiary structural motifs to which most proteins belong. It has been suggested that there are only around 2000 distinct protein folds in nature, though there are many millions of different proteins.

These methods may also be split into two groups:

• Homology modelling is based on the reasonable assumption that two homologous proteins will share very similar structures. Because a protein's fold is more evolutionarily conserved than its amino acid sequence, a target sequence can be modeled with reasonable accuracy on a very distantly related template, provided that the relationship between target and template can be discerned through sequence alignment. It has been suggested that the primary bottleneck in comparative modelling arises from difficulties in alignment rather than from errors in structure prediction given a known-good alignment. Unsurprisingly, homology modelling is most accurate when the target and template have similar sequences.

• Protein threading scans the amino acid sequence of an unknown structure against a database of solved structures. In each case, a scoring function is used to assess the compatibility of the sequence to the structure, thus yielding possible three-dimensional models. This type of method is also known as 3D-1D fold recognition due to its compatibility analysis between three-dimensional structures and linear protein sequences. This method has also given rise to methods performing an inverse folding search by evaluating the compatibility of a given structure with a large database of sequences, thus predicting which sequences have the potential to produce a given fold.

Side chain geometry prediction

Even structure prediction methods that are reasonably accurate for the peptide backbone often get the orientation and packing of the amino acid side chains wrong. Methods that specifically address the problem of predicting side chain geometry include dead-end elimination and the self-consistent mean field method. Both discretize the continuously varying dihedral angles that determine a side chain's orientation relative to the backbone into a set of rotamers with fixed dihedral angles. The methods then attempt to identify the set of rotamers that minimize the model's overall energy. Such methods are most useful for analyzing the protein's hydrophobic core, where side chains are more closely packed; they have more difficulty addressing the looser constraints and higher flexibility of surface residues.

Software

MODELLER is a popular software tool for producing homology models using methodology derived from NMR spectroscopy data processing. SwissModel provides an automated web server for basic homology modeling. A common software tool for protein threading is 3D-PSSM. The basic algorithm for threading is described in and is fairly straightforward to implement.

A very recent review of currently popular software for structure prediction can be found at . A partial list of web servers and available tools is maintained here.

Protein-Protein Complexes

In the case of complexes of two or more proteins, where the structures of the proteins are known or can be predicted with high accuracy, protein-protein docking methods can be used to predict the structure of the complex. Information of the effect of mutations at specific sites on the affinity of the complex helps to understand the complex structure and to guide docking methods.

See also

• Pharmaceutical company

References

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Bonneau R, Baliga NS, Deutsch EW, Shannon P, Hood L. (2004) Comprehensive de novo structure prediction in a systems-biology context for the archaea Halobacterium sp. NRC-1.Genome Biology. 5(8):R52-68