Prediction of protein-protein interaction types using association rule based classification

This article has been made available through the Brunel Open Access Publishing Fund - Copyright @ 2009 Park et alBackground: Protein-protein interactions (PPI) can be classified according to their characteristics into, for example obligate or transient interactions. The identification and characterization of these PPI types may help in the functional annotation of new protein complexes and in the prediction of protein interaction partners by knowledge driven approaches. Results: This work addresses pattern discovery of the interaction sites for four different interaction types to characterize and uses them for the prediction of PPI types employing Association Rule Based Classification (ARBC) which includes association rule generation and posterior classification. We incorporated domain information from protein complexes in SCOP proteins and identified 354 domain-interaction sites. 14 interface properties were calculated from amino acid and secondary structure composition and then used to generate a set of association rules characterizing these domain-interaction sites employing the APRIORI algorithm. Our results regarding the classification of PPI types based on a set of discovered association rules shows that the discriminative ability of association rules can significantly impact on the prediction power of classification models. We also showed that the accuracy of the classification can be improved through the use of structural domain information and also the use of secondary structure content. Conclusion: The advantage of our approach is that we can extract biologically significant information from the interpretation of the discovered association rules in terms of understandability and interpretability of rules. A web application based on our method can be found at http://bioinfo.ssu.ac.kr/~shpark/picasso/SHP was supported by the Korea Research Foundation Grant funded by the Korean Government(KRF-2005-214-E00050). JAR has been supported by the Programme Alβan, the European Union Programme of High level Scholarships for Latin America, scholarship E04D034854CL. SK was supported by Soongsil University Research Fund

Photoreactive Stapled BH3 Peptides to Dissect the BCL-2 Family Interactome

SummaryDefining protein interactions forms the basis for discovery of biological pathways, disease mechanisms, and opportunities for therapeutic intervention. To harness the robust binding affinity and selectivity of structured peptides for interactome discovery, we engineered photoreactive stapled BH3 peptide helices that covalently capture their physiologic BCL-2 family targets. The crosslinking α helices covalently trap both static and dynamic protein interactors, and enable rapid identification of interaction sites, providing a critical link between interactome discovery and targeted drug design

Prediction of Protein-Protein Interaction Sites in Sequences and 3D Structures by Random Forests

Identifying interaction sites in proteins provides important clues to the function of a protein, and is becoming increasingly relevant in topics like systems biology and drug discovery. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using i) a combination of sequence and structure-derived parameters ; and ii) sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and the F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with the F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modelling the Ras-Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information

Binding-Site Assessment by Virtual Fragment Screening

The accurate prediction of protein druggability (propensity to bind high-affinity drug-like small molecules) would greatly benefit the fields of chemical genomics and drug discovery. We have developed a novel approach to quantitatively assess protein druggability by computationally screening a fragment-like compound library. In analogy to NMR-based fragment screening, we dock ∼11000 fragments against a given binding site and compute a computational hit rate based on the fraction of molecules that exceed an empirically chosen score cutoff. We perform a large-scale evaluation of the approach on four datasets, totaling 152 binding sites. We demonstrate that computed hit rates correlate with hit rates measured experimentally in a previously published NMR-based screening method. Secondly, we show that the in silico fragment screening method can be used to distinguish known druggable and non-druggable targets, including both enzymes and protein-protein interaction sites. Finally, we explore the sensitivity of the results to different receptor conformations, including flexible protein-protein interaction sites. Besides its original aim to assess druggability of different protein targets, this method could be used to identifying druggable conformations of flexible binding site for lead discovery, and suggesting strategies for growing or joining initial fragment hits to obtain more potent inhibitors

Binding Site Prediction for Protein-Protein Interactions and Novel Motif Discovery using Re-occurring Polypeptide Sequences

Background: While there are many methods for predicting protein-protein interaction, very few can determine the specific site of interaction on each protein. Characterization of the specific sequence regions mediating interaction (binding sites) is crucial for an understanding of cellular pathways. Experimental methods often report false binding sites due to experimental limitations, while computational methods tend to require data which is not available at the proteome-scale. Here we present PIPE-Sites, a novel method of protein specific binding site prediction based on pairs of re-occurring polypeptide sequences, which have been previously shown to accurately predict proteinprotein interactions. PIPE-Sites operates at high specificity and requires only the sequences of query proteins and a database of known binary interactions with no binding site data, making it applicable to binding site prediction at the proteome-scale. Results: PIPE-Sites was evaluated using a dataset of 265 yeast and 423 human interacting proteins pairs with experimentally-determined binding sites. We found that PIPE-Sites predictions were closer to the confirmed binding site than those of two existing binding site prediction methods based on domain-domain interactions, when applied to the same dataset. Finally, we applied PIPE-Sites to two datasets of 2347 yeast and 14,438 human novel interacting protein pairs predicted to interact with high confidence. An analysis of the predicted interaction sites revealed a number of protein subsequences which are highly re-occurring in binding sites and which may represent novel binding motifs. Conclusions: PIPE-Sites is an accurate method for predicting protein binding sites and is applicable to the proteome-scale. Thus, PIPE-Sites could be useful for exhaustive analysis of protein binding patterns in whole proteomes as well as discovery of novel binding motifs. PIPE-Sites is available online a

Regulation of Translesion Synthesis DNA Polymerase η by Monoubiquitination

DNA polymerase eta is a Y family polymerase involved in translesion synthesis (TLS). Its action is initiated by simultaneous interaction between the PIP box in pol eta and PCNA and between the UBZ in pol eta and monoubiquitin attached to PCNA. Whereas monoubiquitination of PCNA is required for its interaction with pol eta during TLS, we now show that monoubiquitination of pol eta inhibits this interaction, preventing its functions in undamaged cells. Identification of monoubiquitination sites within pol eta nuclear localization signal (NLS) led to the discovery that pol eta NLS directly contacts PCNA, forming an extended pol eta-PCNA interaction surface. We name this the PCNA-interacting region (PIR) and show that its monoubiquitination is downregulated by various DNA-damaging agents. We propose that this mechanism ensures optimal availability of nonubiquitinated, TLS-competent pol eta after DNA damage. Our work shows how monoubiquitination can either positively or negatively regulate the assembly of a protein complex, depending on which substrates are targeted by ubiquitin

COMPUTATIONAL ANALYSIS OF NARINGENIN AS AN ANTIDEPRESSANT

Objective: This works aims at analyzing the potential of Naringenin (NAR) as an antidepressant drug by computational methods. Methods: The database Protein Data Bank and PubChem were used to retrieve the three-dimensional structures of the protein and the compound. The software Discovery Studio was used to study the interactions between the protein and the ligand. Results: NAR, one of the flavanone, which has strong anti-inflammatory and antioxidant activities, is studied for its antidepressant and neuroprotective effects through in silico approach. Interaction study and pharmacophore analysis using Discovery Studio show that the molecule NAR interacts with the protein at different sites. The interaction has a maximum dock score at position B. Conclusion: The compound NAR shows to have antidepressant quality from the computational study. The docking studies show promising result that NAR can be explored further as a potential drug

A moonlighting metabolic protein influences repair at DNA double-stranded breaks.

Catalytically active proteins with divergent dual functions are often described as 'moonlighting'. In this work we characterize a new, chromatin-based function of Lys20, a moonlighting protein that is well known for its role in metabolism. Lys20 was initially described as homocitrate synthase (HCS), the first enzyme in the lysine biosynthetic pathway in yeast. Its nuclear localization led to the discovery of a key role for Lys20 in DNA damage repair through its interaction with the MYST family histone acetyltransferase Esa1. Overexpression of Lys20 promotes suppression of DNA damage sensitivity of esa1 mutants. In this work, by taking advantage of LYS20 mutants that are active in repair but not in lysine biosynthesis, the mechanism of suppression of esa1 was characterized. First we analyzed the chromatin landscape of esa1 cells, finding impaired histone acetylation and eviction. Lys20 was recruited to sites of DNA damage, and its overexpression promoted enhanced recruitment of the INO80 remodeling complex to restore normal histone eviction at the damage sites. This study improves understanding of the evolutionary, structural and biological relevance of independent activities in a moonlighting protein and links metabolism to DNA damage repair

Motif Discovery in Protein Sequences

Biology has become a data‐intensive research field. Coping with the flood of data from the new genome sequencing technologies is a major area of research. The exponential increase in the size of the datasets produced by “next‐generation sequencing” (NGS) poses unique computational challenges. In this context, motif discovery tools are widely used to identify important patterns in the sequences produced. Biological sequence motifs are defined as short, usually fixed length, sequence patterns that may represent important structural or functional features in nucleic acid and protein sequences such as transcription binding sites, splice junctions, active sites, or interaction interfaces. They can occur in an exact or approximate form within a family or a subfamily of sequences. Motif discovery is therefore an important field in bioinformatics, and numerous methods have been developed for the identification of motifs shared by a set of functionally related sequences. This chapter will review the existing motif discovery methods for protein sequences and their ability to discover biologically important features as well as their limitations for the discovery of new motifs. Finally, we will propose new horizons for motif discovery in order to address the short comings of the existent methods

Small-Molecule Binding Sites to Explore New Targets in the Cancer Proteome

The Cancer Genome Atlas (TCGA) offers an unprecedented opportunity to identify small-molecule binding sites on proteins with overexpressed mRNA levels that correlate with poor survival. Here, we analyze RNA-seq and clinical data for 10 tumor types to identify genes that are both overexpressed and correlate with patient survival. Protein products of these genes were scanned for binding sites that possess shape and physicochemical properties that can accommodate small-molecule probes or therapeutic agents (druggable). These binding sites were classified as enzyme active sites (ENZ), protein-protein interaction sites (PPI), or other sites whose function is unknown (OTH). Interestingly, the overwhelming majority of binding sites were classified as OTH. We find that ENZ, PPI, and OTH binding sites often occurred on the same structure suggesting that many of these OTH cavities can be used for allosteric modulation of enzyme activity or protein-protein interactions with small molecules. We discovered several ENZ (PYCR1, QPRT, and HSPA6) and PPI (CASC5, ZBTB32, and CSAD) binding sites on proteins that have been seldom explored in cancer. We also found proteins that have been extensively studied in cancer that have not been previously explored with small molecules that harbor ENZ (PKMYT1, STEAP3, and NNMT) and PPI (HNF4A, MEF2B, and CBX2) binding sites. All binding sites were classified by the signaling pathways to which the protein that harbors them belongs using KEGG. In addition, binding sites were mapped onto structural protein-protein interaction networks to identify promising sites for drug discovery. Finally, we identify pockets that harbor missense mutations previously identified from analysis of the TCGA data. The occurrence of mutations in these binding sites provides new opportunities to develop small-molecule probes to explore their function in cancer.