CRF-based models of protein surfaces improve protein-protein interaction site predictions

Abstract

BackgroundThe identification of protein-protein interaction sites is a computationally challenging task and important for understanding the biology of protein complexes. There is a rich literature in this field. A broad class of approaches assign to each candidate residue a real-valued score that measures how likely it is that the residue belongs to the interface. The prediction is obtained by thresholding this score.Some probabilistic models classify the residues on the basis of the posterior probabilities. In this paper, we introduce pairwise conditional random fields (pCRFs) in which edges are not restricted to the backbone as in the case of linear-chain CRFs utilized by Li et al. (2007). In fact, any 3D-neighborhood relation can be modeled. On grounds of a generalized Viterbi inference algorithm and a piecewise training process for pCRFs, we demonstrate how to utilize pCRFs to enhance a given residue-wise score-based protein-protein interface predictor on the surface of the protein under study. The features of the pCRF are solely based on the interface predictions scores of the predictor the performance of which shall be improved.ResultsWe performed three sets of experiments with synthetic scores assigned to the surface residues of proteins taken from the data set PlaneDimers compiled by Zellner et al. (2011), from the list published by Keskin et al. (2004) and from the very recent data set due to Cukuroglu et al. (2014). That way we demonstrated that our pCRF-based enhancer is effective given the interface residue score distribution and the non-interface residue score are unimodal.Moreover, the pCRF-based enhancer is also successfully applicable, if the distributions are only unimodal over a certain sub-domain. The improvement is then restricted to that domain. Thus we were able to improve the prediction of the PresCont server devised by Zellner et al. (2011) on PlaneDimers.ConclusionsOur results strongly suggest that pCRFs form a methodological framework to improve residue-wise score-based protein-protein interface predictors given the scores are appropriately distributed. A prototypical implementation of our method is accessible at http://ppicrf.informatik.uni-goettingen.de/index.html.

IDP–CRF: Intrinsically Disordered Protein/Region Identification Based on Conditional Random Fields

“…Conditional random fields (CRFs) were proposed by Lafferty et al [ 19 ], and compose a probabilistic model for labeling sequence data. Due to their advantages, CRFs have been widely applied to solve a number of prediction tasks in the field of bioinformatics and computational biology, including protein-protein interaction prediction [ 79 , 80 ], phosphorylation site prediction [ 81 ], transcription factor binding site prediction [ 82 ], and protein-RNA residue-based contact prediction [ 83 ].…”

Combining features in a graphical model to predict protein binding sites

“…As a postprocessing step to the inference algorithm, following, residues are filtered as nonbinding sites if the corresponding RASA values are ≤5%. For further comparison, we included the results of Dong et al . (page 12, Table ), which are indicated as points in Figure and are called Enhancer CRF.…”

“…Dong et al . recently have shown that a conditional random field (CRF) can be used to improve upon methods like SVMs that produce a scoring function for each residue . They “enhanced” such a score‐based residue‐wise prediction by considering the spacial neighborhood in the CRF and thus improved the classification accuracy of PresCont using only the score output of PresCont and the residue neighborhoods.…”

A Two-Layer SVM Ensemble-Classifier to Predict Interface Residue Pairs of Protein Trimers

“…Ovchinnikov predicted residue–residue interactions across protein interfaces using evolutionary information [ 6 ]. There are many other methods that are not described here [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”