Types Of Protein-protein Interactions Research Articles

Profiling protein-protein interactions to predict the efficacy of B-cell-lymphoma-2-homology-3 mimetics for acute myeloid leukaemia.

B-cell-lymphoma-2 (BCL2) homology-3 (BH3) mimetics are inhibitors of protein-protein interactions (PPIs) that saturate anti-apoptotic proteins in the BCL2 family to induce apoptosis in cancer cells. Despite the success of the BH3-mimetic ABT-199 for the treatment of haematological malignancies, only a fraction of patients respond to the drug and most patients eventually develop resistance to it. Here we show that the efficacy of ABT-199 can be predicted by profiling the rewired status of the PPI network of the BCL2 family via single-molecule pull-down and co-immunoprecipitation to quantify more than 20 types of PPI from a total of only 1.2 × 106 cells per sample. By comparing the obtained multidimensional data with BH3-mimetic efficacies determined ex vivo, we constructed a model for predicting the efficacy of ABT-199 that designates two complexes of the BCL2 protein family as the primary mediators of drug effectiveness and resistance, and applied it to prospectively assist therapeutic decision-making for patients with acute myeloid leukaemia. The characterization of PPI complexes in clinical specimens opens up opportunities for individualized protein-complex-targeting therapies.

Visualization of the Association of Dimeric Protein Complexes on Specific Enhancers in the Salivary Gland Nuclei of Drosophila Larva.

Transcription factors (TFs) regulate gene expression by recognizing specific target enhancers in the genome. The DNA-binding and regulatory activity of TFs depend on the presence of additional protein partners, leading to the formation of versatile and dynamic multimeric protein complexes. Visualizing these protein-protein interactions (PPIs) in the nucleus is key for decrypting the molecular cues underlying TF specificity in vivo. Over the last few years, Bimolecular Fluorescence Complementation (BiFC) has been developed in several model systems and applied in the analysis of different types of PPIs. In particular, BiFC has been applied when analyzing PPIs with hundreds of TFs in the nucleus of live Drosophila embryos. However, the visualization of PPIs at the level of specific target enhancers or genomic regions of interest awaits the advent of DNA-labelling methods that can be coupled with BiFC. Here, we present a novel experimental strategy that we have called BiFOR and that is based on the coupling of BiFC with the bacterial ANCHOR DNA-labelling system. We demonstrate that BiFOR enables the precise quantification of the enrichment of specific dimeric protein complexes on target enhancers in Drosophila salivary gland nuclei. Given its versatility and sensitivity, BiFOR could be applied more widely to other tissues during Drosophila development. Our work sets up the experimental basis for future applications of this strategy.

Microscale Thermophoresis as a Tool to Study Protein Interactions and Their Implication in Human Diseases.

The review highlights how protein–protein interactions (PPIs) have determining roles in most life processes and how interactions between protein partners are involved in various human diseases. The study of PPIs and binding interactions as well as their understanding, quantification and pharmacological regulation are crucial for therapeutic purposes. Diverse computational and analytical methods, combined with high-throughput screening (HTS), have been extensively used to characterize multiple types of PPIs, but these procedures are generally laborious, long and expensive. Rapid, robust and efficient alternative methods are proposed, including the use of Microscale Thermophoresis (MST), which has emerged as the technology of choice in drug discovery programs in recent years. This review summarizes selected case studies pertaining to the use of MST to detect therapeutically pertinent proteins and highlights the biological importance of binding interactions, implicated in various human diseases. The benefits and limitations of MST to study PPIs and to identify regulators are discussed.

Computational studies evidenced the potential of steroidal lactone to disrupt surface interaction of SARS-CoV-2 spike protein and hACE2

The critical event in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pathogenesis is recognition of host cells by the virus, which is facilitated by protein-protein interaction (PPI) of viral Spike-Receptor Binding Domain (S-RBD) and Human Angiotensin Converting Enzyme 2-Receptor (hACE2-R). Thus, disrupting the interaction between S-RBD and hACE2-R is widely accepted as a primary strategy for managing COVID-19. The purpose of this study is to assess the ability of three steroidal lactones (SL) (4-Dehydrowithaferin A, Withaferin A, and Withalongolide A) derived from plants to disrupt the PPI of S-RBD and hACE2-R under two conditions (CON–I and CON-II) using in-silico methods. Under CON–I, 4-Dehydrowithaferin A destabilizing the interactions between S-RBD and hACE2-R, as indicated by an increase in binding energy (BE) from −1028.5 kJ/mol (control) to −896.12 kJ/mol 4-Dehydrowithaferin A exhibited a strong interaction with S-RBD GLY496 with a hydrogen bond occupancy (HBO) of 37.33%. Under CON-II, Withalongolide A was capable of disrupting all types of PPI, as evidenced by an increased BE from −913 kJ/mol (control) to −133.69 kJ/mol and an increased distance (>3.55 nm) between selected AAR combinations of S-RBD and hACE2-R. Withalongolide A formed a hydrogen bond with TYR453 (97%, HBO) of S-RBD, which is required for interaction with hACE2-R's HIS34. Our studies demonstrated that SL molecules have the potential to disrupt the S-RBD and hACE2-R interaction, thereby preventing SARS-CoV-2 from recognizing host cells. The SL molecules can be considered for additional in-vitro and in-vivo studies with this research evidence.

Methods for the Discovery and Identification of Small Molecules Targeting Oxidative Stress-Related Protein-Protein Interactions: An Update.

The oxidative stress response pathway is one of the hotspots of current pharmaceutical research. Many proteins involved in these pathways work through protein–protein interactions (PPIs). Hence, targeting PPI to develop drugs for an oxidative stress response is a promising strategy. In recent years, small molecules targeting protein–protein interactions (PPIs), which provide efficient methods for drug discovery, are being investigated by an increasing number of studies. However, unlike the enzyme–ligand binding mode, PPIs usually exhibit large and dynamic binding interfaces, which raise additional challenges for the discovery and optimization of small molecules and for the biochemical techniques used to screen compounds and study structure–activity relationships (SARs). Currently, multiple types of PPIs have been clustered into different classes, which make it difficult to design stationary methods for small molecules. Deficient experimental methods are plaguing medicinal chemists and are becoming a major challenge in the discovery of PPI inhibitors. In this review, we present current methods that are specifically used in the discovery and identification of small molecules that target oxidative stress-related PPIs, including proximity-based, affinity-based, competition-based, structure-guided, and function-based methods. Our aim is to introduce feasible methods and their characteristics that are implemented in the discovery of small molecules for different types of PPIs. For each of these methods, we highlight successful examples of PPI inhibitors associated with oxidative stress to illustrate the strategies and provide insights for further design.

Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter

BackgroundIdentification of protein-protein interactions (PPIs) is crucial for understanding biological processes and investigating the cellular functions of genes. Self-interacting proteins (SIPs) are those in which more than two identical proteins can interact with each other and they are the specific type of PPIs. More and more researchers draw attention to the SIPs detection, and several prediction model have been proposed, but there are still some problems. Hence, there is an urgent need to explore a efficient computational model for SIPs prediction.ResultsIn this study, we developed an effective model to predict SIPs, called RP-FIRF, which merges the Random Projection (RP) classifier and Finite Impulse Response Filter (FIRF) together. More specifically, each protein sequence was firstly transformed into the Position Specific Scoring Matrix (PSSM) by exploiting Position Specific Iterated BLAST (PSI-BLAST). Then, to effectively extract the discriminary SIPs feature to improve the performance of SIPs prediction, a FIRF method was used on PSSM. The R’classifier was proposed to execute the classification and predict novel SIPs. We evaluated the performance of the proposed RP-FIRF model and compared it with the state-of-the-art support vector machine (SVM) on human and yeast datasets, respectively. The proposed model can achieve high average accuracies of 97.89 and 97.35% using five-fold cross-validation. To further evaluate the high performance of the proposed method, we also compared it with other six exiting methods, the experimental results demonstrated that the capacity of our model surpass that of the other previous approaches.ConclusionExperimental results show that self-interacting proteins are accurately well-predicted by the proposed model on human and yeast datasets, respectively. It fully show that the proposed model can predict the SIPs effectively and sufficiently. Thus, RP-FIRF model is an automatic decision support method which should provide useful insights into the recognition of SIPs.

Making the Right Choice: Critical Parameters of the Y2H Systems.

Two-hybrid methods remain among the most preferred choices for detecting protein-protein interactions (PPIs) and much of the PPI data in databases have been produced using yeast two-hybrid (Y2H) screens. The Y2H methods are extensively used to detect PPIs because of their scalability and accessibility. Several variants of Y2H methods have been developed and used by different research groups, increasing the accessibility of these methods and their applications in detecting different types of PPIs. However, the availability of variations on the same core methodology emphasizes the need to have a systematic comparison of available Y2H methods in the context of their applicability, coverage and efficiency. In this chapter, we discuss the key parameters of Y2H methods, namely proteins of interest, vectors, libraries, screening strategies, data analysis, and provide a flowchart that should help to decide which Y2H strategy is most appropriate for a protein interaction screen.

Interacting partners of FEN1 and its role in the development of anticancer therapeutics.

Protein-protein interaction (PPI) plays a key role in cellular communication, Protein-protein interaction connected with each other with hubs and nods involved in signaling pathways. These interactions used to develop network based biomarkers for early diagnosis of cancer. FEN1(Flap endonuclease 1) is a central component in cellular metabolism, over expression and decrease of FEN1 levels may cause cancer, these regulation changes of Flap endonuclease 1reported in many cancer cells, to consider this data may needs to develop a network based biomarker. The current review focused on types of PPI, based on nature, detection methods and its role in cancer. Interacting partners of Flap endonuclease 1 role in DNA replication repair and development of anticancer therapeutics based on Protein-protein interaction data.

A functional feature analysis on diverse protein-protein interactions: application for the prediction of binding affinity.

Protein-protein interactions (PPIs) play crucial roles in diverse cellular processes. There are different types of PPIs based on the composition, affinity and whether the association is permanent or transient. Analyzing the diversity of PPIs at the atomic level is crucial for uncovering the key features governing the interactions involved in PPI. A systematic physico-chemical and conformational studies were implemented on interfaces involved in different PPIs, including crystal packing, weak transient heterodimers, weak transient homodimers, strong transient heterodimers and homodimers. The comparative analysis shows that the interfaces tend to be larger, less planar, and more tightly packed with the increase of the interaction strength. Meanwhile the strong interactions undergo greater conformational changes than the weak ones involving main chains as well as side chains. Finally, using 18 features derived from our analysis, we developed a support vector regression model to predict the binding affinity with a promising result, which further demonstrate the reliability of our studies. We believe this study will provide great help in more thorough understanding the mechanism of diverse PPIs.

The role of electrostatic energy in prediction of obligate protein-protein interactions.

BackgroundPrediction and analysis of protein-protein interactions (PPI) and specifically types of PPIs is an important problem in life science research because of the fundamental roles of PPIs in many biological processes in living cells. In addition, electrostatic interactions are important in understanding inter-molecular interactions, since they are long-range, and because of their influence in charged molecules. This is the main motivation for using electrostatic energy for prediction of PPI types.ResultsWe propose a prediction model to analyze protein interaction types, namely obligate and non-obligate, using electrostatic energy values as properties. The prediction approach uses electrostatic energy values for pairs of atoms and amino acids present in interfaces where the interaction occurs. The main features of the complexes are found and then the prediction is performed via several state-of-the-art classification techniques, including linear dimensionality reduction (LDR), support vector machine (SVM), naive Bayes (NB) and k-nearest neighbor (k-NN). For an in-depth analysis of classification results, some other experiments were performed by varying the distance cutoffs between atom pairs of interacting chains, ranging from 5Å to 13Å. Moreover, several feature selection algorithms including gain ratio (GR), information gain (IG), chi-square (Chi2) and minimum redundancy maximum relevance (mRMR) are applied on the available datasets to obtain more discriminative pairs of atom types and amino acid types as features for prediction.ConclusionsOur results on two well-known datasets of obligate and non-obligate complexes confirm that electrostatic energy is an important property to predict obligate and non-obligate protein interaction types on the basis of all the experimental results, achieving accuracies of over 98%. Furthermore, a comparison performed by changing the distance cutoff demonstrates that the best values for prediction of PPI types using electrostatic energy range from 9Å to 12Å, which show that electrostatic interactions are long-range and cover a broader area in the interface. In addition, the results on using feature selection before prediction confirm that (a) a few pairs of atoms and amino acids are appropriate for prediction, and (b) prediction performance can be improved by eliminating irrelevant and noisy features and selecting the most discriminative ones.

Prediction of protein–protein interaction types using the decision templates based on multiple classier fusion

Protein–protein interactions (PPIs) play a key role in many cellular processes, such as the regulation of enzymes, signal transduction or mediating the adhesion of cells. Knowing about the multitude of PPIs that allow the cell to function can help the biological scientist understand the molecular machinery of the cell. Unfortunately, it is both time-consuming and expensive to do so solely based on experiments due to the nature of the problem whose complexity is obviously overwhelming, just like the fact that “life is complicated”. Therefore, developing computational approaches for predicting PPIs, binding sites and interaction types would be of significant value in this regard. In this paper, we propose a novel method for predicting the types of PPIs based on decision templates. First, we introduce the concept of the tensor product to construct three kinds of feature vectors which are the amino acid composition tensor product, the residue multi-scale conservation energy tensor product and the secondary structure content tensor product, then the correlation-based feature selection method was also introduced to reduce the dimensionality of these feature vectors. So, the protein pair can be represented by our three new kinds of feature vectors and Zhu’s six kinds of feature vectors. The nine kinds of feature vectors are further taken as the inputs of individual support vector machine classifier respectively, and the outputs of these classifiers are aggregated with decision templates. The overall success rate obtained by jackknife cross-validation was 90.95%, indicating our method is very promising for predicting PPI types, might become a useful vehicle for studying the network biology in the post-genomic era.

Exploiting likely-positive and unlabeled data to improve the identification of protein-protein interaction articles

BackgroundExperimentally verified protein-protein interactions (PPI) cannot be easily retrieved by researchers unless they are stored in PPI databases. The curation of such databases can be made faster by ranking newly-published articles' relevance to PPI, a task which we approach here by designing a machine-learning-based PPI classifier. All classifiers require labeled data, and the more labeled data available, the more reliable they become. Although many PPI databases with large numbers of labeled articles are available, incorporating these databases into the base training data may actually reduce classification performance since the supplementary databases may not annotate exactly the same PPI types as the base training data. Our first goal in this paper is to find a method of selecting likely positive data from such supplementary databases. Only extracting likely positive data, however, will bias the classification model unless sufficient negative data is also added. Unfortunately, negative data is very hard to obtain because there are no resources that compile such information. Therefore, our second aim is to select such negative data from unlabeled PubMed data. Thirdly, we explore how to exploit these likely positive and negative data. And lastly, we look at the somewhat unrelated question of which term-weighting scheme is most effective for identifying PPI-related articles.ResultsTo evaluate the performance of our PPI text classifier, we conducted experiments based on the BioCreAtIvE-II IAS dataset. Our results show that adding likely-labeled data generally increases AUC by 3~6%, indicating better ranking ability. Our experiments also show that our newly-proposed term-weighting scheme has the highest AUC among all common weighting schemes. Our final model achieves an F-measure and AUC 2.9% and 5.0% higher than those of the top-ranking system in the IAS challenge.ConclusionOur experiments demonstrate the effectiveness of integrating unlabeled and likely labeled data to augment a PPI text classification system. Our mixed model is suitable for ranking purposes whereas our hierarchical model is better for filtering. In addition, our results indicate that supervised weighting schemes outperform unsupervised ones. Our newly-proposed weighting scheme, TFBRF, which considers documents that do not contain the target word, avoids some of the biases found in traditional weighting schemes. Our experiment results show TFBRF to be the most effective among several other top weighting schemes.

Diversity of protein-protein interactions.

In this review, we discuss the structural and functional diversity of protein-protein interactions (PPIs) based primarily on protein families for which three-dimensional structural data are available. PPIs play diverse roles in biology and differ based on the composition, affinity and whether the association is permanent or transient. In vivo, the protomer's localization, concentration and local environment can affect the interaction between protomers and are vital to control the composition and oligomeric state of protein complexes. Since a change in quaternary state is often coupled with biological function or activity, transient PPIs are important biological regulators. Structural characteristics of different types of PPIs are discussed and related to their physiological function, specificity and evolution.