Recovering Protein-Protein and Domain-Domain Interactions from Aggregation of IP-MS Proteomics of Coregulator Complexes

Coregulator proteins (CoRegs) are part of multi-protein complexes that transiently assemble with transcription factors and chromatin modifiers to regulate gene expression. In this study we analyzed data from 3,290 immuno-precipitations (IP) followed by mass spectrometry (MS) applied to human cell lines aimed at identifying CoRegs complexes. Using the semi-quantitative spectral counts, we scored binary protein-protein and domain-domain associations with several equations. Unlike previous applications, our methods scored prey-prey protein-protein interactions regardless of the baits used. We also predicted domain-domain interactions underlying predicted protein-protein interactions. The quality of predicted protein-protein and domain-domain interactions was evaluated using known binary interactions from the literature, whereas one protein-protein interaction, between STRN and CTTNBP2NL, was validated experimentally; and one domain-domain interaction, between the HEAT domain of PPP2R1A and the Pkinase domain of STK25, was validated using molecular docking simulations. The scoring schemes presented here recovered known, and predicted many new, complexes, protein-protein, and domain-domain interactions. The networks that resulted from the predictions are provided as a web-based interactive application at http://maayanlab.net/HT-IP-MS-2-PPI-DDI/

Can the Energy Gap in the Protein-Ligand Binding Energy Landscape Be Used as a Descriptor in Virtual Ligand Screening?

<div><p>The ranking of scores of individual chemicals within a large screening library is a crucial step in virtual screening (VS) for drug discovery. Previous studies showed that the quality of protein-ligand recognition can be improved using spectrum properties and the shape of the binding energy landscape. Here, we investigate whether the energy gap, defined as the difference between the lowest energy pose generated by a docking experiment and the average energy of all other generated poses and inferred to be a measure of the binding energy landscape sharpness, can improve the separation power between true binders and decoys with respect to the use of the best docking score. We performed retrospective single- and multiple-receptor conformation VS experiments in a diverse benchmark of 40 domains from 38 therapeutically relevant protein targets. Also, we tested the performance of the energy gap on 36 protein targets from the Directory of Useful Decoys (DUD). The results indicate that the energy gap outperforms the best docking score in its ability to discriminate between true binders and decoys, and true binders tend to have larger energy gaps than decoys. Furthermore, we used the energy gap as a descriptor to measure the height of the native binding phase and obtained a significant increase in the success rate of near native binding pose identification when the ligand binding conformations within the boundaries of the native binding phase were considered. The performance of the energy gap was also evaluated on an independent test case of VS-identified PKR-like ER-localized eIF2α kinase (PERK) inhibitors. We found that the energy gap was superior to the best docking score in its ability to more highly rank active compounds from inactive ones. These results suggest that the energy gap of the protein-ligand binding energy landscape is a valuable descriptor for use in VS.</p> </div

Distributions of AUC values obtained from multiple-receptor conformation VS.

<p>(A) for 40 protein domains included in the Pocketome benchmark and (B) for 40 protein domains split in 40 holo and 36 apo domains. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046532#pone.0046532.s004" target="_blank">Tables S4</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046532#pone.0046532.s005" target="_blank">S5</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046532#pone.0046532.s006" target="_blank">S6</a> for details.</p

Histogram of the difference in the ranking of PERK inhibitors by the energy gap and the best docking score.

<p>“0” indicates no change in ranking by the energy gap as compared to the best docking score: for example, “0” means that ligand X was the 11^th ranked compound in the list by the energy gap and also the 11^th ranked compound in the list by the best docking score. Negative numbers mean that the energy gap ranking is higher than the best docking score ranking: e.g. if the compound is ranked 5^th in the list by the energy gap and 7^th in the list by the best docking score the above score would be −2. The histogram shows many more compounds with negative difference scores showing that the energy gap results in a higher true positive yield upon experimental testing of the top N compounds in this case of VS against protein homology models that was independent of the set in this study.</p