Promising Protein-protein Interaction Research Articles

Design, synthesis, and biological evaluation of novel 8-substituted quercetin derivatives targeting the β‑catenin/B-cell lymphoma 9 interaction

The β-catenin/B-cell lymphoma 9 (BCL9) protein–protein interaction (PPI) is a potential target for aberrantly active Wnt/β-catenin signaling which actively participates in initiating and progressing of many cancers. Herein, we discovered novel 8-substituted quercetin derivatives with potential inhibitory activities targeting β-catenin/BCL9 PPI. Among all the derivatives, compound B4 displayed the most promising PPI inhibitory activity with an IC50 value of 2.25 μM in a competitive fluorescence polarization assay and a KD value of 1.44 μM for the β-catenin protein. Furthermore, B4 selectively inhibited the growth of colorectal cancer (CRC) cells, suppressed the transactivation of Wnt signaling, and downregulated the expression of oncogenic Wnt target gene. Especially, B4 showed potent anti-CRC activity in vivo with the tumor growth inhibition (TGI) of 75.99 % and regulated the tumor immune microenvironment.

Inhibition of Autophagy by a Small Molecule through Covalent Modification of the LC3 Protein

AbstractThe autophagic ubiquitin‐like protein LC3 functions through interactions with LC3‐interaction regions (LIRs) of other autophagy proteins, including autophagy receptors, which stands out as a promising protein–protein interaction (PPI) target for the intervention of autophagy. Post‐translational modifications like acetylation of Lys49 on the LIR‐interacting surface could disrupt the interaction, offering an opportunity to design covalent small molecules interfering with the interface. Through screening covalent compounds, we discovered a small molecule modulator of LC3A/B that covalently modifies LC3A/B protein at Lys49. Activity‐based protein profiling (ABPP) based evaluations reveal that a derivative molecule DC‐LC3in‐D5 exhibits a potent covalent reactivity and selectivity to LC3A/B in HeLa cells. DC‐LC3in‐D5 compromises LC3B lipidation in vitro and in HeLa cells, leading to deficiency in the formation of autophagic structures and autophagic substrate degradation. DC‐LC3in‐D5 could serve as a powerful tool for autophagy research as well as for therapeutic interventions.

Inhibition of Autophagy by a Small Molecule through Covalent Modification of the LC3 Protein.

The autophagic ubiquitin-like protein LC3 functions through interactions with LC3-interaction regions (LIRs) of other autophagy proteins, including autophagy receptors, which stands out as a promising protein-protein interaction (PPI) target for the intervention of autophagy. Post-translational modifications like acetylation of Lys49 on the LIR-interacting surface could disrupt the interaction, offering an opportunity to design covalent small molecules interfering with the interface. Through screening covalent compounds, we discovered a small molecule modulator of LC3A/B that covalently modifies LC3A/B protein at Lys49. Activity-based protein profiling (ABPP) based evaluations reveal that a derivative molecule DC-LC3in-D5 exhibits a potent covalent reactivity and selectivity to LC3A/B in HeLa cells. DC-LC3in-D5 compromises LC3B lipidation in vitro and in HeLa cells, leading to deficiency in the formation of autophagic structures and autophagic substrate degradation. DC-LC3in-D5 could serve as a powerful tool for autophagy research as well as for therapeutic interventions.

Exploring the binding mechanism between human profilin (PFN1) and polyproline-10 through binding mode screening.

The large magnitude of protein-protein interaction (PPI) pairs within the human interactome necessitates the development of predictive models and screening tools to better understand this fundamental molecular communication. However, despite enormous efforts from various groups to develop predictive techniques in the last decade, PPI complex structures are in general still very challenging to predict due to the large number of degrees of freedom. In this study, we use the binding complex of human profilin (PFN1) and polyproline-10 (P10) as a model system to examine various approaches, with the aim of going beyond normal protein docking for PPI prediction and evaluation. The potential of mean force (PMF) was first obtained from the time-consuming umbrella sampling, which confirmed that the most stable binding structure identified by the maximal PMF difference is indeed the crystallographic binding structure. Moreover, crucial residues previously identified in experimental studies, W3, H133, and S137 of PFN1, were found to form favorable hydrogen bonds with P10, suggesting a zipping process during the binding between PFN1 and P10. We then explored both regular molecular dynamics (MD) and steered molecular dynamics (SMD) simulations, seeking for better criteria of ranking the PPI prediction. Despite valuable information obtained from conventional MD simulations, neither the commonly used interaction energy between the two binding parties nor the long-term root mean square displacement correlates well with the PMF results. On the other hand, with a sizable collection of trajectories, we demonstrated that the average and minimal rupture works calculated from SMD simulations correlate fairly well with the PMFs (R 2 = 0.67), making this a promising PPI screening method.

Prediction of cassava protein interactome based on interolog method

Cassava is a starchy root crop whose role in food security becomes more significant nowadays. Together with the industrial uses for versatile purposes, demand for cassava starch is continuously growing. However, in-depth study to uncover the mystery of cellular regulation, especially the interaction between proteins, is lacking. To reduce the knowledge gap in protein-protein interaction (PPI), genome-scale PPI network of cassava was constructed using interolog-based method (MePPI-In, available at http://bml.sbi.kmutt.ac.th/ppi). The network was constructed from the information of seven template plants. The MePPI-In included 90,173 interactions from 7,209 proteins. At least, 39 percent of the total predictions were found with supports from gene/protein expression data, while further co-expression analysis yielded 16 highly promising PPIs. In addition, domain-domain interaction information was employed to increase reliability of the network and guide the search for more groups of promising PPIs. Moreover, the topology and functional content of MePPI-In was similar to the networks of Arabidopsis and rice. The potential contribution of MePPI-In for various applications, such as protein-complex formation and prediction of protein function, was discussed and exemplified. The insights provided by our MePPI-In would hopefully enable us to pursue precise trait improvement in cassava.

Application of the fragment molecular orbital method analysis to fragment-based drug discovery of BET (bromodomain and extra-terminal proteins) inhibitors

The molecular interactions of inhibitors of bromodomains (BRDs) were investigated. BRDs are protein interaction modules that recognizing ε-N-acetyl-lysine (εAc-Lys) motifs found in histone tails and are promising protein–protein interaction (PPI) targets. First, we analyzed a peptide ligand containing εAc-Lys to evaluate native PPIs. We then analyzed tetrahydroquinazoline-6-yl-benzensulfonamide derivatives found by fragment-based drug design (FBDD) and examined their interactions with the protein compared with the peptide ligand in terms of the inter-fragment interaction energy. In addition, we analyzed benzodiazepine derivatives that are high-affinity ligands for BRDs and examined differences in the CH/π interactions of the amino acid residues. We further surveyed changes in the charges of the amino acid residues among individual ligands, performed pair interaction energy decomposition analysis and estimated the water profile within the ligand binding site. Thus, useful insights for drug design were provided. Through these analyses and considerations, we show that the FMO method is a useful drug design tool to evaluate the process of FBDD and to explore PPI inhibitors.

Core and specific network markers of carcinogenesis from multiple cancer samples

Cancer is the leading cause of death worldwide and is generally caused by mutations in multiple proteins or the dysregulation of pathways. Understanding the causes and the underlying carcinogenic mechanisms can help fight this disease. In this study, a systems biology approach was used to construct the protein–protein interaction (PPI) networks of four cancers and the non-cancers by their corresponding microarray data, PPI modeling and database-mining. By comparing PPI networks between cancer and non-cancer samples to find significant proteins with large PPI changes during carcinogenesis process, core and specific network markers were identified by the intersection and difference of significant proteins, respectively, with carcinogenesis relevance values (CRVs) for each cancer. A total of 28 significant proteins were identified as core network markers in the carcinogenesis of four types of cancer, two of which are novel cancer-related proteins (e.g., UBC and PSMA3). Moreover, seven crucial common pathways were found among these cancers based on their core network markers, and some specific pathways were particularly prominent based on the specific network markers of different cancers (e.g., the RIG-I-like receptor pathway in bladder cancer, the proteasome pathway and TCR pathway in liver cancer, and the HR pathway in lung cancer). Additional validation of these network markers using the literature and new tested datasets could strengthen our findings and confirm the proposed method. From these core and specific network markers, we could not only gain an insight into crucial common and specific pathways in the carcinogenesis, but also obtain a high promising PPI target for cancer therapy.

Can simple codon pair usage predict protein–protein interaction?

Deciphering functional interactions between proteins is one of the great challenges in biology. Sequence-based homology-free encoding schemes have been increasingly applied to develop promising protein-protein interaction (PPI) predictors by means of statistical or machine learning methods. Here we analyze the relationship between codon pair usage and PPIs in yeast. We show that codon pair usage of interacting protein pairs differs significantly from randomly expected. This motivates the development of a novel approach for predicting PPIs, with codon pair frequency difference as input to a Support Vector Machine predictor, termed as CCPPI. 10-fold cross-validation tests based on yeast PPI datasets with balanced positive-to-negative ratios indicate that CCPPI performs better than other sequence-based encoding schemes. Moreover, it ranks the best when tested on an unbalanced large-scale dataset. Although CCPPI is subjected to high false positive rates like many PPI predictors, statistical analyses of the predicted true positives confirm that the success of CCPPI is partly ascribed to its capability to capture proteomic co-expression and functional similarities between interacting protein pairs. Our findings suggest that codon pairs of interacting protein pairs evolve in a coordinated manner and consequently they provide additional information beyond amino acids-based encoding schemes. CCPPI has been made freely available at: http://protein.cau.edu.cn/ccppi.