Near-native Models Research Articles

Homology modelling of protein-protein complexes: a simple method and its possibilities and limitations.

BackgroundStructure-based computational methods are needed to help identify and characterize protein-protein complexes and their function. For individual proteins, the most successful technique is homology modelling. We investigate a simple extension of this technique to protein-protein complexes. We consider a large set of complexes of known structures, involving pairs of single-domain proteins. The complexes are compared with each other to establish their sequence and structural similarities and the relation between the two. Compared to earlier studies, a simpler dataset, a simpler structural alignment procedure, and an additional energy criterion are used. Next, we compare the Xray structures to models obtained by threading the native sequence onto other, homologous complexes. An elementary requirement for a successful energy function is to rank the native structure above any threaded structure. We use the DFIREβ energy function, whose quality and complexity are typical of the models used today. Finally, we compare near-native models to distinctly non-native models.ResultsIf weakly stable complexes are excluded (defined by a binding energy cutoff), as well as a few unusual complexes, a simple homology principle holds: complexes that share more than 35% sequence identity share similar structures and interaction modes; this principle was less clearcut in earlier studies. The energy function was then tested for its ability to identify experimental structures among sets of decoys, produced by a simple threading procedure. On average, the experimental structure is ranked above 92% of the alternate structures. Thus, discrimination of the native structure is good but not perfect. The discrimination of near-native structures is fair. Typically, a single, alternate, non-native binding mode exists that has a native-like energy. Some of the associated failures may correspond to genuine, alternate binding modes and/or native complexes that are artefacts of the crystal environment. In other cases, additional model filtering with more sophisticated tools is needed.ConclusionThe results suggest that the simple modelling procedure applied here could help identify and characterize protein-protein complexes. The next step is to apply it on a genomic scale.

Development of a scoring method for predicting protein complex structures

The information about protein-protein interactions increases much more rapidly than the increase of the number of the tertiary structures of those protein complexes. Therefore, precise prediction of protein complex structures by protein-protein docking simulations is required. When the protein complex is rebuilt from its component protomers which derive from experimentally determined complex structure (native structure) by docking, the complex models with rmsd < 10 A from the native structure (near-native model) could be obtained, along with a great number of false positives (decoy). The separation of near-native models from many decoys is therefore needed in the prediction of complex structures by docking. In this study, we developed the method for scoring docking models so that the near-native models were higher in rank than decoys, based on the assumption that the interfaces of near-native models are more complementary in terms of surface properties and shapes compared to those of decoys.

Development of a Scoring Method for Predicting Protein Complex Structures

The information about protein-protein interactions increases much more rapidly than the increase of the number of the tertiary structures of those protein complexes. Therefore, precise prediction of protein complex structures by protein-protein docking simulations is required. When the protein complex is rebuilt from its component protomers which derive from experimentally determined complex structure (native structure) by docking, the complex models with rmsd < 10 A from the native structure (near-native model) could be obtained, along with a great number of false positives (decoy). The separation of near-native models from many decoys is therefore needed in the prediction of complex structures by docking. In this study, we developed the method for scoring docking models so that the near-native models were higher in rank than decoys, based on the assumption that the interfaces of near-native models are more complementary in terms of surface properties and shapes compared to those of decoys.

Making optimal use of empirical energy functions: force-field parameterization in crystal space.

Today's energy functions are not able yet to distinguish reliably between correct and almost correct protein models. Improving these near-native models is currently a major bottle-neck in homology modeling or experimental structure determination at low resolution. Increasingly accurate energy functions are required to complete the "last mile of the protein folding problem," for example during a molecular dynamics simulation. We present a new approach to reach this goal. For 50 high resolution X-ray structures, the complete unit cell was reconstructed, including disordered water molecules, counter ions, and hydrogen atoms. Simulations were then run at the pH at which the crystal was solved, while force-field parameters were iteratively adjusted so that the damage done to the structures was minimal. Starting with initial parameters from the AMBER force field, the optimization procedure converged at a new force field called YAMBER (Yet Another Model Building and Energy Refinement force field), which is shown to do significantly less damage to X-ray structures, often move homology models in the right direction, and occasionally make them look like experimental structures. Application of YAMBER during the CASP5 structure prediction experiment yielded a model for target 176 that was ranked first among 150 submissions. Due to its compatibility with the well-established AMBER format, YAMBER can be used by almost any molecular dynamics program. The parameters are freely available from www.yasara.org/yamber.