BackgroundStudy of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner.Methods/Principal FindingsTo realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively.Conclusion/SignificanceOur results indicate that the network prediction system thus established is quite promising and encouraging.
The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein's metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein's metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: “short”, “medium”, “long”, and “extra-long” half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age.
Ubiquitination, one of the most important post-translational modifications of proteins, occurs when ubiquitin (a small 76-amino acid protein) is attached to lysine on a target protein. It often commits the labeled protein to degradation and plays important roles in regulating many cellular processes implicated in a variety of diseases. Since ubiquitination is rapid and reversible, it is time-consuming and labor-intensive to identify ubiquitination sites using conventional experimental approaches. To efficiently discover lysine-ubiquitination sites, a sequence-based predictor of ubiquitination site was developed based on nearest neighbor algorithm. We used the maximum relevance and minimum redundancy principle to identify the key features and the incremental feature selection procedure to optimize the prediction engine. PSSM conservation scores, amino acid factors and disorder scores of the surrounding sequence formed the optimized 456 features. The Mathew's correlation coefficient (MCC) of our ubiquitination site predictor achieved 0.142 by jackknife cross-validation test on a large benchmark dataset. In independent test, the MCC of our method was 0.139, higher than the existing ubiquitination site predictor UbiPred and UbPred. The MCCs of UbiPred and UbPred on the same test set were 0.135 and 0.117, respectively. Our analysis shows that the conservation of amino acids at and around lysine plays an important role in ubiquitination site prediction. What's more, disorder and ubiquitination have a strong relevance. These findings might provide useful insights for studying the mechanisms of ubiquitination and modulating the ubiquitination pathway, potentially leading to potential therapeutic strategies in the future.
BackgroundWith the huge amount of uncharacterized protein sequences generated in the post-genomic age, it is highly desirable to develop effective computational methods for quickly and accurately predicting their functions. The information thus obtained would be very useful for both basic research and drug development in a timely manner.Methodology/Principal FindingsAlthough many efforts have been made in this regard, most of them were based on either sequence similarity or protein-protein interaction (PPI) information. However, the former often fails to work if a query protein has no or very little sequence similarity to any function-known proteins, while the latter had similar problem if the relevant PPI information is not available. In view of this, a new approach is proposed by hybridizing the PPI information and the biochemical/physicochemical features of protein sequences. The overall first-order success rates by the new predictor for the functions of mouse proteins on training set and test set were 69.1% and 70.2%, respectively, and the success rate covered by the results of the top-4 order from a total of 24 orders was 65.2%.Conclusions/SignificanceThe results indicate that the new approach is quite promising that may open a new avenue or direction for addressing the difficult and complicated problem.
Background: Heat-shock transcription factor 4 (HSF4) mutations are associated with autosomal dominant lamellar cataract and Marner cataract. Disruptions of the Hsf4 gene cause lens defects in mice, indicating a requirement for HSF4 in fiber cell differentiation during lens development. However, neither the relationship between HSF4 and crystallins nor the detailed mechanism of maintenance of lens transparency by HSF4 is fully understood.
Although much is known about the molecular players in insulin signaling, there is scant information about transcriptional regulation of its key components. We now find that NUCKS is a transcriptional regulator of the insulin signaling components, including the insulin receptor (IR). Knockdown of NUCKS leads to impaired insulin signaling in endocrine cells. NUCKS knockout mice exhibit decreased insulin signaling and increased body weight/fat mass along with impaired glucose tolerance and reduced insulin sensitivity, all of which are further exacerbated by a high-fat diet (HFD). Genome-wide ChIP-seq identifies metabolism and insulin signaling as NUCKS targets. Importantly, NUCKS is downregulated in individuals with a high body mass index and in HFD-fed mice, and conversely, its levels increase upon starvation. Altogether, NUCKS is a physiological regulator of energy homeostasis and glucose metabolism that works by regulating chromatin accessibility and RNA polymerase II recruitment to the promoters of IR and other insulin pathway modulators.
Aims/hypothesis While chronic low-grade inflammation is associated with obesity, acute inflammation reduces food intake and leads to negative energy balance. Although both types of inflammation activate nuclear factor κB (NF-κB) signalling, it remains unclear how NF-κB activation results in opposite physiological responses in the two types of inflammation. The goal of this study was to address this question, and to understand the link between inflammation and leptin signalling.Methods We studied the ability of NF-κB to modulate Pomc transcription, and how it impinges on signal transducer and activator of transcription 3 (STAT3)-mediated leptin signalling by using a combination of animal models, biochemical assays and molecular biology. Results We report that suppression of food intake and physical movement with acute inflammation is not dependent on STAT3 activation in pro-opiomelanocortin (POMC) neurons. Under these conditions, activated NF-κB independently leads to increased Pomc transcription. Electrophoretic mobility shift assay and chromatin immunoprecipitation (ChIP) experiments reveal that NF-κB v-rel reticuloendotheliosis viral oncogene homologue A (avian) (RELA [also known as p65]) binds to the Pomc promoter region between −138 and −88 bp, which also harbours the trans-acting transcription factor 1 (SP1) binding site. We found significant changes in the methylation pattern at this region and reduced Pomc activation under chronic inflammation induced by a high-fat diet. Furthermore, RELA is unable to bind and activate transcription when the Pomc promoter is methylated. Finally, RELA binds to STAT3 and inhibits STAT3-mediated promoter activity, suggesting that RELA, possibly together with forkhead box-containing protein 1 (FOXO1), may prevent STAT3-mediated leptin activation of the Pomc promoter. Conclusions/interpretation Our study provides a mechanism for the involvement of RELA in the divergent regulation of energy homeostasis in acute and chronic inflammation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.