Prediction of Short Linear Protein Binding Regions

Abstract

Publication informationJournal of Molecular Biology, 415 (1) AbstractShort linear motifs in proteins, typically of 3-12 residues in length, play key roles in protein-protein interactions, frequently binding specifically to peptide-binding domains within interacting proteins. Their tendency to be found in disordered segments of proteins has meant that they have often been overlooked. Here we present SLiMPred (Short Linear Motif Predictor), the first general de novo method to computationally predict such regions in protein primary sequences independent of experimentally defined homologs and interactors. The method applies machine learning techniques to predict new motifs based on annotated instances from the Eukaryotic Linear Motif database as well as structural, biophysical and biochemical features derived from the protein primary sequence. We have integrated these data sources and benchmarked the predictive accuracy of the method finding that it performs equivalently to a predictor of protein binding regions in disordered regions in addition to having predictive power for other classes of motifs sites such as polyproline II helix motifs and short linear motifs lying in ordered regions. It will be useful to predict peptides involved in potential protein associations and aid in the functional characterisation of proteins, especially in proteins lacking experimental knowledge of structure and interactions. We conclude that, despite the diversity of motif sequences and structures, SLiMPred is a valuable tool to prioritise potential interaction motifs in proteins.

Evolution of domain–peptide interactions to coadapt specificity and affinity to functional diversity

“…Whereas other methods for finding motifs are designed to search for known motifs (e.g., Eukaryotic Linear Motif database) (29)(30)(31)(32), or motifs with properties previously observed in linear binding sites (e.g., 3D structures, intrinsic disorder, sequence conservation, and solvent accessibility) (33)(34)(35)(36)(37)(38), or motifs that are over-represented according to a statistical model (e.g., hidden Markov model, Gibbs sampling, and Nested sampling) (39)(40)(41)(42), our method exhaustively enumerates all possible motifs, covering the entire space of peptide variations. Here, we determine the specificity of all of the enumerated motifs for a target peptidebinding domain.…”

Research Resource: EPSLiM: Ensemble Predictor for Short Linear Motifs in Nuclear Hormone Receptors

“…The individual methods tested for building the EPSLiM model are ANCHOR (23), MoRFpred (24), SLiMPrints (25), SLiMPred (26), and a modified ANCHOR method that incorporates simulated mutations that we present in the following section. Predictions from each method were made using publically accessible executable programs or web services.…”

“…Among the aforementioned individual predictors, SLiMPrints and SLiMPred are fully or partially based on sequence conservation (25,26). MoRFpred also considers sequence similarity, although it is a minor factor considered in the model (24).…”

“…The second and third examples of verified EPSLiM predictions involve two separate 5-amino acid short peptides that are specific to AR and mediate the interactions with coregulatory proteins and also the N/C intramolecular interaction of AR. The motif, FXXLF (AR [23][24][25][26][27], interacts with the AR LBD and forms a head-tail structure that regulates gene expression. In addition, by interaction with this motif, cyclin D1 can inhibit this head-tail structure to repress AR function (45).…”

“…AN-CHOR and MoRFpred use physicochemical features of proteins to predict binding sites in IDRs (23,24). SLiMPrints evaluates the sequence-level evolutionary conservation for motif prediction (25), whereas SLiMPred uses a neural network-based method to consider multiple structural context features, including relevant solvent accessibility and predicted secondary structures (26). These algorithms perform well, but there are discrepancies between the predictions.…”