Application of information theory to feature selection in protein docking

A new scoring function for protein-protein docking that identifies native structures with unprecedented accuracy

Protein-protein (P-P) 3D structures are fundamental to structural biology and drug discovery. However, most of them have never been determined. Many docking algorithms were developed for that purpose, but they have a very limited accuracy in generating native-like structures and identifying the most correct one, in particular when a single answer is asked for. With such a low success rate it is difficult to point out one docked structure as being native-like. Here we present a new, high accuracy, scoring method to identify the 3D structure of P-P complexes among a set of trial poses. It incorporates alanine scanning mutagenesis experimental data that need to be obtained a priori. The scoring scheme works by matching the computational and the experimental alanine scanning mutagenesis results. The size of the trial P-P interface area is also taken into account. We show that the method ranks the trial structures and identifies the native-like structures with unprecedented accuracy (∼94%), providing the correct P-P 3D structures that biochemists and molecular biologists need to pursue their studies. With such a success rate, the bottleneck of protein-protein docking moves from the scoring to searching algorithms.

Scoring docking conformations using predicted protein interfaces

Background

Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP).

Results

First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between T-PioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations.

Conclusion

Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations.

An information-theoretic classification of amino acids for the assessment of interfaces in protein-protein docking

Docking represents a versatile and powerful method to predict the geometry of protein-protein complexes. However, despite significant methodical advances, the identification of good docking solutions among a large number of false solutions still remains a difficult task. We have previously demonstrated that the formalism of mutual information (MI) from information theory can be adapted to protein docking, and we have now extended this approach to enhance its robustness and applicability. A large dataset consisting of 22,934 docking decoys derived from 203 different protein-protein complexes was used for an MI-based optimization of reduced amino acid alphabets representing the protein-protein interfaces. This optimization relied on a clustering analysis that allows one to estimate the mutual information of whole amino acid alphabets by considering all structural features simultaneously, rather than by treating them individually. This clustering approach is fast and can be applied in a similar fashion to the generation of reduced alphabets for other biological problems like fold recognition, sequence data mining, or secondary structure prediction. The reduced alphabets derived from the present work were converted into a scoring function for the evaluation of docking solutions, which is available for public use via the web service score-MI: http://score-MI.biochem.uni-erlangen.de.