The practical role of protein structure prediction is now more important than ever. Massive amounts of protein sequence data are produced by 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. The two main problems are that the number of possible protein structures is extremely large, and that the physical basis of protein structural stability is not fully understood. As a result, any protein structure prediction method needs a way to explore the space of possible structures efficiently (a search strategy), and a way to identify the most plausible structure (an energy function).
In comparative structure prediction, the search space is pruned by the assumption that the protein in question adopts a structure that is reasonably close to the structure of at least one known protein. In de novo or ab initio structure prediction, no such assumption is made, which results in a much harder search problem. In both cases, an energy function is needed to recognize the native structure, and to guide the search for the native structure. Unfortunately, the construction of such an energy function is to a great extent an open problem.
Direct simulation of protein folding in atomic detail, via methods such as molecular dynamics with a suitable energy function, is typically not tractable due to the high computational cost, despite the efforts of distributed computing projects such as Folding@home. Therefore, most de novo structure prediction methods rely on simplified representations of the atomic structure of proteins.
The above mentioned issues apply to all proteins, including well-behaving, small, monomeric proteins. In addition, for specific proteins (such as for example multimeric proteins and disordered proteins), the following issues also arise:
Due to the increase in computer power, and especially new algorithms, much progress is being made to overcome these problems. However, routine de novo prediction of protein structures, even for small proteins, is still not achieved.
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 predict protein structure de novo for larger proteins will require better algorithms and larger computational resources like those afforded by either powerful supercomputers (such as Blue Gene or MDGRAPE-3) or distributed computing (such as Folding@home, the Human Proteome Folding Project and Rosetta@Home). 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.
As an intermediate step towards predicted protein structures, contact map predictions have been proposed.
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:
TIP is a knowledgebase of STRUCTFAST models and precomputed similarity relationships between sequences, structures, and binding sites.
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
Several distributed computing projects concerning protein structure prediction have also been implemented, such as the Folding@home, Rosetta@home, Human Proteome Folding Project, Predictor@home and TANPAKU.
The Foldit program seeks to investigate the pattern-recognition and puzzle-solving abilities inherent to the human mind in order to create more successful computer protein structure prediction software.
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.
Application of nonnegative matrix factorization to improve profile-profile alignment features for fold recognition and remote homolog detection.(Research article)(Report)
Jul 01, 2008; Authors: Inkyung Jung ; Jaehyung Lee ; Soo-Young Lee ; Dongsup Kim (corresponding author) [1,2]BackgroundNonnegative...
A discriminative method for protein remote homology detection and fold recognition combining Top- n -grams and latent semantic analysis.(Research article)
Dec 01, 2008; Authors: Bin Liu (corresponding author) ; Xiaolong Wang [1,2]; Lei Lin ; Qiwen Dong ; Xuan Wang BackgroundProtein...