Abstract
The structures of protein complexes are increasingly predicted via protein-protein docking (PPD) using ambiguous interaction data to help guide the docking. These data often are incomplete and contain errors and therefore could lead to incorrect docking predictions. In this study, we performed a series of PPD simulations to examine the effects of incompletely and incorrectly assigned interface residues on the success rate of PPD predictions. The results for a widely used PPD benchmark dataset obtained using a new interface information-driven PPD (IPPD) method developed in this work showed that the success rate for an acceptable top-ranked model varied, depending on the information content used, from as high as 95% when contact relationships (though not contact distances) were known for all residues to 78% when only the interface/non-interface state of the residues was known. However, the success rates decreased rapidly to ∼40% when the interface/non-interface state of 20% of the residues was assigned incorrectly, and to less than 5% for a 40% incorrect assignment. Comparisons with results obtained by re-ranking a global search and with those reported for other data-guided PPD methods showed that, in general, IPPD performed better than re-ranking when the information used was more complete and more accurate, but worse when it was not, and that when using bioinformatics-predicted information on interface residues, IPPD and other data-guided PPD methods performed poorly, at a level similar to simulations with a 40% incorrect assignment. These results provide guidelines for using information about interface residues to improve PPD predictions and reveal a bottleneck for such improvement imposed by the low accuracy of current bioinformatic interface residue predictions.
Highlights
Proteins work in close association with other proteins to mediate the intricate functions of a cell
Information-guided Docking Predictions state of these residues but not their pairing relationship, for one or both of the two interacting proteins. To determine how these different levels of residue information content can help Protein–protein docking (PPD) predictions and the extent to which the use of incorrectly assigned residues degrades prediction success rates, we have developed a new interface informationdriven PPD method (IPPD) and carried out a series of PPD simulations on a well-tested benchmark dataset
The results showed that when the information content was rich, excellent predictions could be made via IPPD or by re-ranking a global search’s solutions using the same interface information, and that, encouragingly, the success of predictions remained respectable when the content was poor
Summary
Because experimentally determined bound complex structures were used to derive the matrix, the information used to constrain/re-rank the docking was without errors, but it was still ambiguous because the residue–residue pairing relationships (for contact vectors) and the specific distances between residues in the contact matrix were unknown. Degradation was largely a consequence of docking not accounting for binding-induced conformational changes, but it in small part, was a result of constraining the unbound structures to data derived from bound complexes [11]. The main difference between IPPD and ZDOCK re-ranking was that ZDOCK re-ranking had a somewhat worse performance
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