Protein Interaction Information Research Articles

Deep learning methods for protein function prediction.

Predicting protein function from protein sequence, structure, interaction, and other relevant information is important for generating hypotheses for biological experiments and studying biological systems, and therefore has been a major challenge in protein bioinformatics. Numerous computational methods had been developed to advance protein function prediction gradually in the last two decades. Particularly, in the recent years, leveraging the revolutionary advances in artificial intelligence (AI), more and more deep learning methods have been developed to improve protein function prediction at a faster pace. Here, we provide an in-depth review of the recent developments of deep learning methods for protein function prediction. We summarize the significant advances in the field, identify several remaining major challenges to be tackled, and suggest some potential directions to explore. The data sources and evaluation metrics widely used in protein function prediction are also discussed to assist the machine learning, AI, and bioinformatics communities to develop more cutting-edge methods to advance protein function prediction.

Method for B Cell Receptor Enrichment in Malignant B Cells.

B cells are central to the adaptive immune response and provide long-lasting immunity after infection. B cell activation is mediated by the surface membrane-bound B cell receptor (BCR) following recognition of a specific antigen. The BCR has been challenging to analyse using mass spectrometry (MS) due to the difficulty of isolating and enriching this membrane-bound protein complex. There are approximately 120,000 BCRs on the B cell surface; however, depending on the B cell activation state, there may be hundreds-of-millions to billions of proteins in a B cell. Consequently, advanced proteomic techniques such as MS workflows that use purified proteins to yield structural and protein-interaction information have not been published for the BCR complex. This paper describes a method for enriching the BCR complex that is MS-compatible. The method involves a Protein G pull down on agarose beads using an intermediary antibody to each of the BCR complex subcomponents (CD79a, CD79b, and membrane immunoglobulin). The enrichment process is shown to pull down the entire BCR complex and has the advantage of being readily compatible with further proteomic study including MS analysis. Using intermediary antibodies has the potential to enrich all isotypes of the BCR, unlike previous methods described in the literature that use protein G-coated beads to directly pull down the membrane IgG (mIgG) but cannot be used for other mIg isotypes.

Exploring the Potential Lipid-Lowering and Weight-Reducing Mechanisms of FH06 Fermented Beverages Based on Non-Targeted Metabolomics and Network Pharmacology

Investigating the intricate pathways through which FH06 fermentation broth exerts lipid-lowering and weight-loss effects is pivotal for advancing our comprehension of metabolic regulation and therapeutic interventions. Ultrahigh-performance liquid chromatography quadrupole electrostatic field orbit trap mass spectrometry (UHPLC-QE-MS) detection and the ChEMBL database were used to determine the effective compounds in the FH06 fermentation broth and predict their targets. The TTD database and DisGeNET database were used to query obesity-related targets. The STRING database was used to construct protein interaction information. The Gene Ontology (GO) database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database were used to perform biological function annotation (GO) and KEGG pathway enrichment analyses of the targets. Results: A total of 85 effective compounds were screened from the fermentation broth of FH06; these compounds may act on TP53, PPARG, TNF, and other targets through 10 signaling pathways, such as the chemical carcinogenesis-receptor activation and lipid and atherosclerosis pathways, and exert pharmacological effects, such as hypoglycemic effects and weight loss. They also have anti-inflammatory, antioxidant, antitumor, and immunoregulatory effects. These findings reveal the active ingredients of FH06 fermentation broth and its multi-target and multi-channel characteristics in lipid lowering and weight loss. This study has positive implications for the clinical treatment of obesity using FH06, providing a theoretical and scientific basis for further developing of FH06-assisted lipid-lowering products.

AAAKB: A manually curated database for tracking and predicting genes of Abdominal aortic aneurysm (AAA).

Abdominal aortic aneurysm (AAA), an extremely dangerous vascular disease with high mortality, causes massive internal bleeding due to aneurysm rupture. To boost the research on AAA, efforts should be taken to organize and link the information about AAA-related genes and their functions. Currently, most researchers screen through genetic databases manually, which is cumbersome and time-consuming. Here, we developed "AAAKB" a manually curated knowledgebase containing genes, SNPs and pathways associated with AAA. In order to facilitate researchers to further explore the mechanism network of AAA, AAAKB provides predicted genes that are potentially associated with AAA. The prediction is based on the protein interaction information of genes collected in the database, and the random forest algorithm (RF) is used to build the prediction model. Some of these predicted genes are differentially expressed in patients with AAA, and some have been reported to play a role in other cardiovascular diseases, illustrating the utility of the knowledgebase in predicting novel genes. Also, AAAKB integrates a protein interaction visualization tool to quickly determine the shortest paths between target proteins. As the first knowledgebase to provide a comprehensive catalog of AAA-related genes, AAAKB will be an ideal research platform for AAA. Database URL: http://www.lqlgroup.cn:3838/AAAKB/.

Predictive role of E2F6 in cancer prognosis and responses of immunotherapy

BackgroundE2F6 is a member of the E2F transcription factor family. Numerous studies have demonstrated that E2F6 is critical to cancer development and progression, but its role in cancer immunotherapy remains unclear. MethodsGenotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases were used to obtain RNA-seq data for cancer and normal tissues, and we utilized the cBioPortal to analyze E2F6 genomic alterations in pan-cancer. The protein localization of E2F6 was obtained using the Human Protein Atlas (HPA), and the upregulation of E2F6 expression in clinical glioblastoma multiforme (GBM) tissues was detected by Western blot analysis. The ComPPI website was used to analyze the protein interaction information of E2F6. To evaluate the role of E2F6 in pan-cancer prognosis, we used univariate Cox regression and Kaplan-Meier methods, and gene set enrichment analysis (GSEA) was utilized to identify markers associated with E2F6 expression in tumors. TIMER 2.0 was used to study E2F6-related immune cell infiltration in tumor tissues, and the correlation of E2F6 with immunotherapy biomarkers was investigated using Spearman correlation analysis. The role of E2F6 in the cell cycle was analyzed by flow cytometry, and the Cell Counting Kit-8 (CCK-8) and colony formation assays were utilized to determine the proliferative ability of cells. ResultsIn most tumor types, E2F6 was highly expressed and was a good predictor of prognosis. E2F6 was significantly related to markers of immune activation, tumor immune cell infiltration, and immune regulators. Furthermore, E2F6 knockdown significantly attenuated the proliferative ability of glioma cells. Finally, E2F6 effectively predicted anti-programmed cell death 1 (PD1) treatment response. ConclusionE2F6 is an effective biomarker that predicts the prognosis of cancer patients treated with anti-immune checkpoint therapy.

Characterizing the circadian connectome of Ocimum tenuiflorum using an integrated network theoretic framework

Across the three domains of life, circadian clock is known to regulate vital physiological processes, like, growth, development, defence etc. by anticipating environmental cues. In this work, we report an integrated network theoretic methodology comprising of random walk with restart and graphlet degree vectors to characterize genome wide core circadian clock and clock associated raw candidate proteins in a plant for which protein interaction information is available. As a case study, we have implemented this framework in Ocimum tenuiflorum (Tulsi); one of the most valuable medicinal plants that has been utilized since ancient times in the management of a large number of diseases. For that, 24 core clock (CC) proteins were mined in 56 template plant genomes to build their hidden Markov models (HMMs). These HMMs were then used to identify 24 core clock proteins in O. tenuiflorum. The local topology of the interologous Tulsi protein interaction network was explored to predict the CC associated raw candidate proteins. Statistical and biological significance of the raw candidates was determined using permutation and enrichment tests. A total of 66 putative CC associated proteins were identified and their functional annotation was performed.

Network Pharmacology to Investigate the Mechanism of Ginseng and Atractylodis Macrocephalae to Prevent Diarrhea

To investigate the molecular mechanism of Ginseng and Atractylodes macrocephala in the prevention and treatment of diarrhea based on network pharmacology and molecular docking. The active ingredients and the related targets of Ginseng and Atractylodes were searched and collected through TCMSP database, PubChem database and Swiss TargetPrediction. With the help of GeneCards database, OMIM database and DisGeNET database, we searched for disease related targets with "Diarrhea" as the keyword, and the obtained targets of drug ingredients were intersected with the disease targets to obtain the results. The predicted targets of Ginseng and Atractylodes for diarrhea were obtained by intersection. Cytoscape 3.9.1 software was used to construct the "drug-active ingredient-target network" diagram. Protein interaction information was obtained by using String database, and the target network was further analyzed by CytoNCA plug-in to screen the core targets. The core targets were enriched by KEGG pathway analysis using the microbiology platform, and the results were based on the enrichment results. The KEGG enrichment analysis showed that the core targets acted on PI3K-Akt signaling pathwa, Human T-cell leukemia virus 1 ntection, Kaposi sarcoma- associated herpesvirus intection, Measles, Proteoglyeans in cancer, and 10 other pathways. Through the network pharmacology study, Ginseng and Atractylodesan regulated diarrhea involving 127 targets and 10 pathways, indicating that Ginseng and Atractylodesan can exert its effects on diarrhea prevention and treatment through multiple components, targets and pathways.

GePI: large-scale text mining, customized retrieval and flexible filtering of gene/protein interactions.

We present GePI, a novel Web server for large-scale text mining of molecular interactions from the scientific biomedical literature. GePI leverages natural language processing techniques to identify genes and related entities, interactions between those entities and biomolecular events involving them. GePI supports rapid retrieval of interactions based on powerful search options to contextualize queries targeting (lists of) genes of interest. Contextualization is enabled by full-text filters constraining the search for interactions to either sentences or paragraphs, with or without pre-defined gene lists. Our knowledge graph is updated several times a week ensuring the most recent information to be available at all times. The result page provides an overview of the outcome of a search, with accompanying interaction statistics and visualizations. A table (downloadable in Excel format) gives direct access to the retrieved interaction pairs, together with information about the molecular entities, the factual certainty of the interactions (as verbatim expressed by the authors), and a text snippet from the original document that verbalizes each interaction. In summary, our Web application offers free, easy-to-use, and up-to-date monitoring of gene and protein interaction information, in company with flexible query formulation and filtering options. GePI is available at https://gepi.coling.uni-jena.de/.

SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy

Drug combinations can be the prime strategy for increasing the initial treatment options in cancer therapy. However, identifying the combinations through experimental approaches is very laborious and costly. Notably, in vitro and/or in vivo examination of all the possible combinations might not be plausible. This study presented a novel computational approach to predicting synergistic drug combinations. Specifically, the deep neural network-based binary classification was utilized to develop the model. Various physicochemical, genomic, protein–protein interaction and protein-metabolite interaction information were used to predict the synergy effects of the combinations of different drugs. The performance of the constructed model was compared with shallow neural network (SNN), k-nearest neighbors (KNN), random forest (RF), support vector machines (SVMs), and gradient boosting classifiers (GBC). Based on our findings, the proposed deep neural network model was found to be capable of predicting synergistic drug combinations with high accuracy. The prediction accuracy and AUC metrics for this model were 92.21% and 97.32% in tenfold cross-validation. According to the results, the integration of different types of physicochemical and genomics features leads to more accurate prediction of synergy in cancer drugs.

Clinical features and shared mechanisms of chronic gastritis and osteoporosis

Chronic gastritis (CG) and osteoporosis (OP) are common and occult diseases in the elderly and the relationship of these two diseases have been increasingly exposed. We aimed to explore the clinical characteristics and shared mechanisms of CG patients combined with OP. In the cross-sectional study, all participants were selected from BEYOND study. The CG patients were included and classified into two groups, namely OP group and non-OP group. Univariable and multivariable logistic regression methods were used to evaluate the influencing factors. Furthermore, CG and OP-related genes were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the GEO2R tool and the Venny platform. Protein–protein interaction information was obtained by inputting the intersection targets into the STRING database. The PPI network was constructed by Cytoscape v3.6.0 software again, and the key genes were screened out according to the degree value. Gene function enrichment of DEGs was performed by Webgestalt online tool. One hundred and thirty CG patients were finally included in this study. Univariate correlation analysis showed that age, gender, BMI and coffee were the potential influencing factors for the comorbidity (P < 0.05). Multivariate Logistic regression model found that smoking history, serum PTH and serum β-CTX were positively correlated with OP in CG patients, while serum P1NP and eating fruit had an negative relationship with OP in CG patients. In studies of the shared mechanisms, a total of 76 intersection genes were identified between CG and OP, including CD163, CD14, CCR1, CYBB, CXCL10, SIGLEC1, LILRB2, IGSF6, MS4A6A and CCL8 as the core genes. The biological processes closely related to the occurrence and development of CG and OP mainly involved Ferroptosis, Toll-like receptor signaling pathway, Legionellosis and Chemokine signaling pathway. Our study firstly identified the possible associated factors with OP in the patients with CG, and mined the core genes and related pathways that could be used as biomarkers or potential therapeutic targets to reveal the shared mechanisms.

Analysis and prediction of protein stability based on interaction network, gene ontology, and KEGG pathway enrichment scores

Metabolic stability of proteins plays a vital role in various dedicated cellular processes. Traditional methods of measuring the metabolic stability are time-consuming and expensive. Therefore, we developed a more efficient computational approach to understand the protein dynamic action mechanisms in biological process networks. In this study, we collected 341 short-lived proteins and 824 non-short-lived proteins from U2OS; 342 short-lived proteins and 821 non-short-lived proteins from HEK293T; 424 short-lived proteins and 1153 non-short-lived proteins from HCT116; and 384 short-lived proteins and 992 non-short-lived proteins from RPE1. The proteins were encoded by GO and KEGG enrichment scores based on the genes and their neighbors in STRING, resulting in 20,681 GO term features and 297 KEGG pathway features. We also incorporated the protein interaction information from STRING into the features and obtained 19,247 node features. Boruta and mRMR methods were used for feature filtering, and IFS method was used to obtain the best feature subsets and create the models with the highest performance. The present study identified 42 features that did not appear in previous studies and classified them into eight groups according to their functional annotation. By reviewing the literature, we found that the following three functional groups were critical in determining the stability of proteins: synaptic transmission, post-translational modifications, and cell fate determination. These findings may serve as a valuable reference for developing drugs that target protein stability.

Effects of 6-Hydroxykaempferol: A Potential Natural Product for Amelioration of Tendon Impairment.

Tendon impairment is a common injury associated with impairment of range of motion and pain. Currently, evidence has confirmed that natural herbs contribute to orthopedics and have shown excellent results in the clinical management of tendon impairment. Shujin Huoxue tablet (SHT) and its complex prescriptions are regularly used in tendon rupture therapy with positive results. This study aimed to discover the potential molecules that promote tendon healing. The Chinese traditional medicine system pharmacological database analysis platform (TCMSP) is the primary resource. The Traditional Chinese Medicine Integrated Database and Encyclopedia of Traditional Chinese Medicine database were used as secondary databases. The GeneCards database was used to search for reported tendinopathy-related genes by keywords. Functions of the targeted genes were analyzed using Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes. Protein–protein interaction information was extracted from the STRING database. Docking study, MTT assay, quantitative real-time PCR, and migration assays were performed to obtain a better understanding of the herbs according to cell function to test the basic pharmacological action in vitro. A total of 104 disease nodes, 496 target gene nodes, 35 ingredient nodes, and one drug node were extracted. According to the TCMSP database, 6-hydroxykaempferol, which reportedly promotes the proliferation of microvascular endothelial cells, is a molecule found in SHT. We found that it promoted the proliferation and migration of tendon fibroblasts and elevated tendon repair-related gene expression. Purified 6-hydroxykaempferol promoted the proliferation and migration of tendon fibroblasts and increased their mRNA expression in tendon proliferation.

PangenomeNet: a pan-genome-based network reveals functional modules on antimicrobial resistome for Escherichia coli strains

BackgroundDiscerning genes crucial to antimicrobial resistance (AMR) mechanisms is becoming more and more important to accurately and swiftly identify AMR pathogenic strains. Pangenome-wide association studies (e.g. Scoary) identified numerous putative AMR genes. However, only a tiny proportion of the putative resistance genes are annotated by AMR databases or Gene Ontology. In addition, many putative resistance genes are of unknown function (termed hypothetical proteins). An annotation tool is crucially needed in order to reveal the functional organization of the resistome and expand our knowledge of the AMR gene repertoire.ResultsWe developed an approach (PangenomeNet) for building co-functional networks from pan-genomes to infer functions for hypothetical genes. Using Escherichia coli as an example, we demonstrated that it is possible to build co-functional network from its pan-genome using co-inheritance, domain-sharing, and protein–protein-interaction information. The investigation of the network revealed that it fits the characteristics of biological networks and can be used for functional inferences. The subgraph consisting of putative meropenem resistance genes consists of clusters of stress response genes and resistance gene acquisition pathways. Resistome subgraphs also demonstrate drug-specific AMR genes such as beta-lactamase, as well as functional roles shared among multiple classes of drugs, mostly in the stress-related pathways.ConclusionsBy demonstrating the idea of pan-genome-based co-functional network on the E. coli species, we showed that the network can infer functional roles of the genes, including those without functional annotations, and provides holistic views on the putative antimicrobial resistomes. We hope that the pan-genome network idea can help formulate hypothesis for targeted experimental works.

A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks

BackgroundMolecular interaction networks summarize complex biological processes as graphs, whose structure is informative of biological function at multiple scales. Simultaneously, omics technologies measure the variation or activity of genes, proteins, or metabolites across individuals or experimental conditions. Integrating the complementary viewpoints of biological networks and omics data is an important task in bioinformatics, but existing methods treat networks as discrete structures, which are intrinsically difficult to integrate with continuous node features or activity measures. Graph neural networks map graph nodes into a low-dimensional vector space representation, and can be trained to preserve both the local graph structure and the similarity between node features.ResultsWe studied the representation of transcriptional, protein–protein and genetic interaction networks in E. coli and mouse using graph neural networks. We found that such representations explain a large proportion of variation in gene expression data, and that using gene expression data as node features improves the reconstruction of the graph from the embedding. We further proposed a new end-to-end Graph Feature Auto-Encoder framework for the prediction of node features utilizing the structure of the gene networks, which is trained on the feature prediction task, and showed that it performs better at predicting unobserved node features than regular MultiLayer Perceptrons. When applied to the problem of imputing missing data in single-cell RNAseq data, the Graph Feature Auto-Encoder utilizing our new graph convolution layer called FeatGraphConv outperformed a state-of-the-art imputation method that does not use protein interaction information, showing the benefit of integrating biological networks and omics data with our proposed approach.ConclusionOur proposed Graph Feature Auto-Encoder framework is a powerful approach for integrating and exploiting the close relation between molecular interaction networks and functional genomics data.

PhosIDN: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein-protein interaction information.

MotivationPhosphorylation is one of the most studied post-translational modifications, which plays a pivotal role in various cellular processes. Recently, deep learning methods have achieved great success in prediction of phosphorylation sites, but most of them are based on convolutional neural network that may not capture enough information about long-range dependencies between residues in a protein sequence. In addition, existing deep learning methods only make use of sequence information for predicting phosphorylation sites, and it is highly desirable to develop a deep learning architecture that can combine heterogeneous sequence and protein–protein interaction (PPI) information for more accurate phosphorylation site prediction. ResultsWe present a novel integrated deep neural network named PhosIDN, for phosphorylation site prediction by extracting and combining sequence and PPI information. In PhosIDN, a sequence feature encoding sub-network is proposed to capture not only local patterns but also long-range dependencies from protein sequences. Meanwhile, useful PPI features are also extracted in PhosIDN by a PPI feature encoding sub-network adopting a multi-layer deep neural network. Moreover, to effectively combine sequence and PPI information, a heterogeneous feature combination sub-network is introduced to fully exploit the complex associations between sequence and PPI features, and their combined features are used for final prediction. Comprehensive experiment results demonstrate that the proposed PhosIDN significantly improves the prediction performance of phosphorylation sites and compares favorably with existing general and kinase-specific phosphorylation site prediction methods.Availability and implementationPhosIDN is freely available at https://github.com/ustchangyuanyang/PhosIDN.Supplementary information Supplementary data are available at Bioinformatics online.

A neural network-based method for polypharmacy side effects prediction

BackgroundPolypharmacy is a type of treatment that involves the concurrent use of multiple medications. Drugs may interact when they are used simultaneously. So, understanding and mitigating polypharmacy side effects are critical for patient safety and health. Since the known polypharmacy side effects are rare and they are not detected in clinical trials, computational methods are developed to model polypharmacy side effects.ResultsWe propose a neural network-based method for polypharmacy side effects prediction (NNPS) by using novel feature vectors based on mono side effects, and drug–protein interaction information. The proposed method is fast and efficient which allows the investigation of large numbers of polypharmacy side effects. Our novelty is defining new feature vectors for drugs and combining them with a neural network architecture to apply for the context of polypharmacy side effects prediction. We compare NNPS on a benchmark dataset to predict 964 polypharmacy side effects against 5 well-established methods and show that NNPS achieves better results than the results of all 5 methods in terms of accuracy, complexity, and running time speed. NNPS outperforms about 9.2% in Area Under the Receiver-Operating Characteristic, 12.8% in Area Under the Precision–Recall Curve, 8.6% in F-score, 10.3% in Accuracy, and 18.7% in Matthews Correlation Coefficient with 5-fold cross-validation against the best algorithm among other well-established methods (Decagon method). Also, the running time of the Decagon method which is 15 days for one fold of cross-validation is reduced to 8 h by the NNPS method.ConclusionsThe performance of NNPS is benchmarked against 5 well-known methods, Decagon, Concatenated drug features, Deep Walk, DEDICOM, and RESCAL, for 964 polypharmacy side effects. We adopt the 5-fold cross-validation for 50 iterations and use the average of the results to assess the performance of the NNPS method. The evaluation of the NNPS against five well-known methods, in terms of accuracy, complexity, and running time speed shows the performance of the presented method for an essential and challenging problem in pharmacology. Datasets and code for NNPS algorithm are freely accessible at https://github.com/raziyehmasumshah/NNPS.

A New Sequential Forward Feature Selection (SFFS) Algorithm for Mining Best Topological and Biological Features to Predict Protein Complexes from Protein–Protein Interaction Networks (PPINs)

Protein-protein interaction plays an important role in the understanding of biological processes in the body. A network of dynamic protein complexes within a cell that regulates most biological processes is known as a protein-protein interaction network (PPIN). Complex prediction from PPINs is a challenging task. Most of the previous computation approaches mine cliques, stars, linear and hybrid structures as complexes from PPINs by considering topological features and fewer of them focus on important biological information contained within protein amino acid sequence. In this study, we have computed a wide variety of topological features and integrate them with biological features computed from protein amino acid sequence such as bag of words, physicochemical and spectral domain features. We propose a new Sequential Forward Feature Selection (SFFS) algorithm, i.e., random forest-based Boruta feature selection for selecting the best features from computed large feature set. Decision tree, linear discriminant analysis and gradient boosting classifiers are used as learners. We have conducted experiments by considering two reference protein complex datasets of yeast, i.e., CYC2008 and MIPS. Human and mouse complex information is taken from CORUM 3.0 dataset. Protein interaction information is extracted from the database of interacting proteins (DIP). Our proposed SFFS, i.e., random forest-based Brouta feature selection in combination with decision trees, linear discriminant analysis and Gradient Boosting Classifiers outperforms other state of art algorithms by achieving precision, recall and F-measure rates, i.e. 94.58%, 94.92% and 94.45% for MIPS, 96.31%, 93.55% and 96.02% for CYC2008, 98.84%, 98.00%, 98.87 % for CORUM humans and 96.60%, 96.70%, 96.32% for CORUM mouse dataset complexes, respectively.

MultiPredGO: Deep Multi-Modal Protein Function Prediction by Amalgamating Protein Structure, Sequence, and Interaction Information.

Protein is an essential macro-nutrient for perceiving a wide range of biochemical activities and biological regulations in living cells. In this work, we have presented a novel multi-modal approach, named MultiPredGO, for predicting protein functions by utilizing two different kinds of information, namely protein sequence and the protein secondary structure. Here, our contributions are threefold; firstly, along with the protein sequence, we learn the feature representation from the protein structure. Secondly, we develop two different deep learning models after considering the characteristics of the underlying data patterns of the protein sequence and protein 3D structures. Finally, along with these two modalities, we have also utilized protein interaction information for expediting the efficiency of the proposed model in predicting the protein functions. For extracting features from different modalities, we have utilized various variations of the convolutional neural network. As the protein function classes are dependent on each other, we have used a neuro-symbolic hierarchical classification model, which resembles the structure of Gene Ontology (GO), for effectively predicting the dependent protein functions. Finally, to validate the goodness of our proposed method (MultiPredGO), we have compared our results with various uni-modal along with two well-known multi-modal protein function prediction approaches, namely, INGA and DeepGO. Results show that the overall performance of the proposed approach in terms of accuracy, F-measure, precision, and recall metrics are better than those by the state-of-the-art methods. MultiPredGO attains an average 13.05% and 30.87% improvements over the best existing comparing approach (DeepGO) for cellular component and molecular functions, respectively.

P311 regulates distal lung development via its interaction with several binding proteins

Little is known about the molecular mechanisms underlying alveolar development. P311, a putative neuronal protein originally identified for its high expression during neuronal development, has once been reported to play a potential role in distal lung generation. However, the function of this protein has been poorly understood so far. Hence, we carried out a yeast two-hybrid screen, combining with other protein-protein interaction experiments, to isolate several binding partners of P311 during lung development, which may help us explore its function. We report 7 proteins here, including Gal-1, Loxl-1 and SPARC, etc, that can interact with it. Most of them have similar spatio-temporal expression patterns to P311. In addition, it was also found that P311 could stimulate their expression indirectly in L929 mouse fibroblast. Besides, computational methods were applied to construct a P311 centered protein-protein interaction network during alveolarization, using the 7 binding partners and their protein interaction information provided by public data resources. By analyzing the structure and function of this network, the effects of P311 on lung development were further clarified and all of the bioinformatic predictions from the network could be validated by real experiments. We have found here that P311 can control lung redox events, extracellular matrix and cell cycle progression, which are all crucial to pulmonary morphogenesis. This gives us a novel thought to explore the mechanisms controlling alveolarization.

Identification of crucial aberrantly methylated and differentially expressed genes related to cervical cancer using an integrated bioinformatics analysis.

Methylation functions in the pathogenesis of cervical cancer. In the present study, we applied an integrated bioinformatics analysis to identify the aberrantly methylated and differentially expressed genes (DEGS), and their related pathways in cervical cancer. Data of gene expression microarrays (GSE9750) and gene methylation microarrays (GSE46306) were gained from Gene Expression Omnibus (GEO) databases. Hub genes were identified by ‘limma’ packages and Venn diagram tool. Functional analysis was conducted by FunRich. Search Tool for the Retrieval of Interacting Genes Database (STRING) was used to analyze protein–protein interaction (PPI) information. Gene Expression Profiling Interactive Analysis (GEPIA), immunohistochemistry staining, and ROC curve analysis were conducted for validation. Gene Set Enrichment Analysis (GSEA) was also performed to identify potential functions.We retrieved two upregulated-hypomethylated oncogenes and eight downregulated-hypermethylated tumor suppressor genes (TSGs) for functional analysis. Hypomethylated and highly expressed genes (Hypo-HGs) were significantly enriched in cell cycle and autophagy, and hypermethylated and lowly expressed genes (Hyper-LGs) in estrogen receptor pathway and Wnt/β-catenin signaling pathway. Estrogen receptor 1 (ESR1), Erythrocyte membrane protein band 4.1 like 3 (EPB41L3), Endothelin receptor B (EDNRB), Inhibitor of DNA binding 4 (ID4) and placenta-specific 8 (PLAC8) were hub genes. Kaplan–Meier method was used to evaluate survival data of each identified gene. Lower expression levels of ESR1 and EPB41L3 were correlated with a shorter survival time. GSEA results showed that ‘cell adhesion molecules’ was the most enriched item. This research inferred the candidate genes and pathways that might be used in the diagnosis, treatment, and prognosis of cervical cancer.