Open source tool for prediction of genome wide protein-protein interaction network based on ortholog information

DSSGNN-PPI: A Protein-Protein Interactions prediction model based on Double Structure and Sequence graph neural networks

The process of experimentally confirming complex interaction networks among proteins is time-consuming and laborious. This study aims to address Protein-Protein Interactions (PPIs) prediction based on graph neural networks (GNN). A novel multilevel prediction model for PPIs named DSSGNN-PPI (Double Structure and Sequence GNN for PPIs) is designed. Initially, a distance graph between amino acid residues is constructed. Subsequently, the distance graph is fed into an underlying graph attention network module. This enables us to efficiently learn vector representations that encode the three-dimensional structure of proteins and simultaneously aggregate key local patterns and overall topological information to obtain graph embedding that adequately represent local and global structural features. In addition, the embedding representations that reflect sequence properties are obtained. Two features are fused to construct high-level protein complex networks, which are fed into the designed gated graph attention network to extract complex topological patterns. By combining heterogeneous multi-source information from downstream structure graph and upstream sequence models, the understanding of PPIs is comprehensively enhanced. A series of evaluation results validate the remarkable effectiveness of DSSGNN-PPI framework in enhancing the prediction of multi-type interactions among proteins. The multilevel representation learning and information fusion strategies provide a new effective solution paradigm for structural biology problems. The source code for DSSGNN-PPI has been hosted on GitHub and is available at https://github.com/cstudy1/DSSGNN-PPI.

Pangenome diversification and resistance gene characterization in Salmonella Typhi prioritized RfaJ as a significant therapeutic marker

BACKGROUND

Salmonella Typhi stands as the etiological agent responsible for the onset of human typhoid fever. The pressing demand for innovative therapeutic targets against S. Typhi is underscored by the escalating prevalence of this pathogen and the severe nature of its infections. Consequently, this study employs pangenome analysis to scrutinize 119 S. Typhi-resistant strains, aiming to identify the most promising therapeutic targets originating from its core genome.

RESULTS

Subtractive genomics was employed to systematically eliminate non-homologous (n=1147), essential (n=551), drug-like (n=80), and pathogenicity-related (n=18) proteins from the initial pool of 3351 core genome proteins. Consequently, lipopolysaccharide 1,2-glucosyltransferase RfaJ was designated as the optimal pharmacological target due to its potential versatility. Furthermore, a compendium of 9000 FDA-approved compounds was repurposed for evaluation against the RfaJ drug target, with the specific intent of prioritizing novel, high-potency therapeutic candidates for combating S. Typhi. Ultimately, four compounds, namely DB00549 (Zafirlukast), DB15637 (Fluzoparib), DB15688 (Zavegepant), and DB12411 (Bemcentinib), were singled out as potential inhibitors based on the ligand-protein binding affinity (indicated by the lowest anticipated binding energy) and the overall stability of these compounds. Notably, molecular dynamics simulations, conducted over a 50 nanosecond interval, convincingly demonstrated the stability of these compounds in the context of the RfaJ protein.

CONCLUSION

In summary, the present findings hold significant promise as an initial stride in the broader drug discovery endeavor against S. Typhi infections. However, the experimental validation of the identified drug target and drug candidate is further required to increase the effectiveness of the applied methodology.

Insights into molecular docking and dynamics to reveal therapeutic potential of natural compounds against P53 protein

P53 is eminent tumour suppressor protein that plays a prominent role in cell cycle arrest, DNA repair, senescence, differentiation and initiation of apoptosis. P53 is an attractive drug target and the high toxicity of some cancer chemotherapy drugs increase the demand for new anti-cancer drugs from natural products. In this current scenario, identification of promising anticancer compounds from natural sources by repurposing approach is still relevant for the early prevention and effective management of cancer. In present study, we docked natural compounds like podophyllotoxin, quercetin and rutin along standard drugs (MG-132 and Bay 61-3606) against p53 protein. ADME/T analysis predicted toxicity of phytochemicals and drugs. In silico docking analysis of podophyllotoxin, quercetin and rutin gave HDOCK docking scores of -187.87, -148. 97 and -143.85, whereas control drugs MG-132 and Bay 61-3606 showed docking scores of -159.59 and -140.71 against p53 respectively. AutoDock analysis of rutin and MG-132 showed highest binding affinity scores of -7.3 and -6.8 kcal/mol against p53. Molecular dynamic simulation for p53 protein displayed stable conformation and convergence. In this study, P53-rutin complex showed free binding energy score of 11.84 kcal/mol and P53-MG-132 complex reported free energy score of 16.3 kcal/mol. Protein contacts atlas gives non-covalent contacts framework by exploring interfaces of individual subunits and protein-ligand interactions. STRING tool predicts physical and functional interactions between proteins. The results of this study revealed that rutin and MG-132 could be promising inhibitors against targeted p53 protein and this could prove detrimental for molecular therapeutics and drug-designing strategies.Communicated by Ramaswamy H. Sarma.

Chromatin and non-chromatin immunoprecipitations to capture protein-protein and protein-nucleic acid interactions in living cells

Proteins are expressed from genes via sequential biological processes of transcription, mRNA processing, export and translation, and play their roles in maintaining cellular functions via interactions with proteins, DNAs or RNAs. Thus, it is important to study the protein interactions during biological processes in living cells towards understanding their mechanisms-of-action in real time. Methodologies have been developed over the years to study protein interactions in vivo. One state-of-the-art approach is formaldehyde crosslinking-based immuno- or chemi-precipitation to analyze selective as well as genome/proteome-wide interactions in living cells. It is a popular and widely used methodology for cellular analysis of the protein-protein and protein-nucleic acid interactions. Here, we describe this approach to analyze protein-protein/nucleic acid interactions in vivo.

In silico analysis and molecular docking studies of natural compounds of Withania somnifera against bovine NLRP9

CONTEXT

NLRP9 is a member of nucleotide-binding domain leucine-rich repeat-containing receptors and is found to be associated with many inflammatory diseases. In the current scenario, the identification of promising anti-inflammatory compounds from natural sources by repurposing approach is still relevant for the early prevention and effective management of the disease.

METHODS

In the present study, we docked bioactives of Ashwagandha (Withanoside IV, Withanoside V, Withanolide A, Withanolide B, and Sitoindoside IX) and two control drugs against bovine NLRP9 protein. ADME/T analysis was used to determine the physiochemical properties of compounds and standard drugs. Molecular modeling was used to evaluate the correctness and quality of protein structures. In silico docking analysis revealed Withanolide B had the highest binding affinity score of -10.5 kcal/mol, whereas, among control drugs, doxycycline hydrochloride was most effective (-10.3 kcal/mol). The results of this study revealed that bioactives of Withania somnifera could be promising inhibitors against bovine NLRP9. In the present study, molecular simulation was used to measure protein conformational changes over time. The Rg value was found to be 34.77A°. RMSD and B-factor were also estimated to provide insights into the flexibility and mobile regions of protein structure. A functional protein network interaction was constructed from information collected from non-curative sources as protein-protein interactions (PPI) that play an important role in determining the function of the target protein and the ability of the drug molecule. Thus, in the present situation, it is important to identify bioactives with the potential to combat inflammatory diseases and provide strength and immunity to the host. However, there is still a need to study in vitro and in vivo to further support these findings.

Graph-BERT and language model-based framework for protein-protein interaction identification

Identification of protein-protein interactions (PPI) is among the critical problems in the domain of bioinformatics. Previous studies have utilized different AI-based models for PPI classification with advances in artificial intelligence (AI) techniques. The input to these models is the features extracted from different sources of protein information, mainly sequence-derived features. In this work, we present an AI-based PPI identification model utilizing a PPI network and protein sequences. The PPI network is represented as a graph where each node is a protein pair, and an edge is defined between two nodes if there exists a common protein between these nodes. Each node in a graph has a feature vector. In this work, we have used the language model to extract feature vectors directly from protein sequences. The feature vectors for protein in pairs are concatenated and used as a node feature vector of a PPI network graph. Finally, we have used the Graph-BERT model to encode the PPI network graph with sequence-based features and learn the hidden representation of the feature vector for each node. The next step involves feeding the learned representations of nodes to the fully connected layer, the output of which is fed into the softmax layer to classify the protein interactions. To assess the efficacy of the proposed PPI model, we have performed experiments on several PPI datasets. The experimental results demonstrate that the proposed approach surpasses the existing PPI works and designed baselines in classifying PPI.

Computational Approaches for Identification of Potential Plant Bioactives as Novel G6PD Inhibitors Using Advanced Tools and Databases

In glucose metabolism, the pentose phosphate pathway (PPP) is the major metabolic pathway that plays a crucial role in cancer growth and metastasis. Although it has been pointed out that blockade of the PPP is a promising approach against cancer, in the clinical setting, effective anti-PPP agents are still not available. Dysfunction of the G6PD enzyme in this pathway leads to cancer development as this enzyme possesses oncogenic activity. In the present study, an attempt was made to identify bioactive compounds that can be developed as potential G6PD inhibitors. In the present study, 11 natural compounds and a controlled drug were taken. The physicochemical and toxicity properties of the compounds were determined via ADMET and ProTox-II analysis. In the present study, the findings of docking studies revealed that staurosporine was the most effective compound with the highest binding energy of −9.2 kcal/mol when docked against G6PD. Homology modeling revealed that 97.56% of the residues were occupied in the Ramachandran-favored region. The modeled protein gave a quality Z-score of −10.13 by ProSA tool. iMODS server provided significant insights into the mobility, stability and flexibility of the G6PD protein that described the collective functional protein motion. In the present study, the physical and functional interactions between proteins were determined by STRING. CASTp server determined the topological and geometric properties of the G6PD protein. The findings of the present study revealed that staurosporine could be developed as a potential G6PD inhibitor; however, further in vivo and in vitro studies are needed for further validation of these results.

Protein-protein interaction and interference of carcinogenesis by supramolecular modifications

Protein-protein interactions (PPIs) are essential in normal biological processes, but they can become disrupted or imbalanced in cancer. Various technological advancements have led to an increase in the number of PPI inhibitors, which target hubs in cancer cell's protein networks. However, it remains difficult to develop PPI inhibitors with desired potency and specificity. Supramolecular chemistry has only lately become recognized as a promising method to modify protein activities. In this review, we highlight recent advances in the use of supramolecular modification approaches in cancer therapy. We make special note of efforts to apply supramolecular modifications, such as molecular tweezers, to targeting the nuclear export signal (NES), which can be used to attenuate signaling processes in carcinogenesis. Finally, we discuss the strengths and weaknesses of using supramolecular approaches to targeting PPIs.

Recent developments of sequence-based prediction of protein-protein interactions

The identification of protein-protein interactions (PPIs) can lead to a better understanding of cellular functions and biological processes of proteins and contribute to the design of drugs to target disease-causing PPIs. In addition, targeting host-pathogen PPIs is useful for elucidating infection mechanisms. Although several experimental methods have been used to identify PPIs, these methods can yet to draw complete PPI networks. Hence, computational techniques are increasingly required for the prediction of potential PPIs, which have never been seen experimentally. Recent high-performance sequence-based methods have contributed to the construction of PPI networks and the elucidation of pathogenetic mechanisms in specific diseases. However, the usefulness of these methods depends on the quality and quantity of training data of PPIs. In this brief review, we introduce currently available PPI databases and recent sequence-based methods for predicting PPIs. Also, we discuss key issues in this field and present future perspectives of the sequence-based PPI predictions.

PPI-Miner: A Structure and Sequence Motif Co-Driven Protein-Protein Interaction Mining and Modeling Computational Method

Protein-protein interactions (PPIs) play important roles in biological processes of life, and predicting PPIs becomes a critical scientific issue of concern. Most PPIs occur through small domains or motifs (fragments), which are challenging and laborious to map by standard biochemical approaches because they generally require the cloning of several truncation mutants. Here, we present a computational method, named as PPI-Miner, to fish potential protein interacting partners utilizing protein motifs as queries. In brief, this work first developed a motif-matching algorithm designed to identify the proteins that contain sequential or structural similar motifs with the given query motif. Being aligned to the query motif, the binding mode of the discovered motif and its receptor protein will be initially determined to be used to build PPI complexes accordingly. Eventually, a PPI complex structure could be built and optimized with a designed automatic protocol. Besides discovering PPIs, PPI-Miner can also be applied to other areas, i.e., the rational design of molecular glues and protein vaccines. In this work, PPI-Miner was employed to mine the potential cereblon (CRBN) substrates from human proteome. As a result, 1,739 candidates were predicted, and 16 of them have been experimentally validated in previous studies. The source code of PPI-Miner can be obtained from the GitHub repository (https://github.com/Wang-Lin-boop/PPI-Miner), the webserver is freely available for users (https://bailab.siais.shanghaitech.edu.cn/services/ppi-miner), and the database of predicted CRBN substrates is accessible at https://bailab.siais.shanghaitech.edu.cn/services/crbn-subslib.

Integrated Bioinformatics-Based Subtractive Genomics Approach to Decipher the Therapeutic Drug Target and Its Possible Intervention against Brucellosis

Brucella suis, one of the causative agents of brucellosis, is Gram-negative intracellular bacteria that may be found all over the globe and it is a significant facultative zoonotic pathogen found in livestock. It may adapt to a phagocytic environment, reproduce, and develop resistance to harmful environments inside host cells, which is a crucial part of the Brucella life cycle making it a worldwide menace. The molecular underpinnings of Brucella pathogenicity have been substantially elucidated due to comprehensive methods such as proteomics. Therefore, we aim to explore the complete Brucella suis proteome to prioritize the novel proteins as drug targets via subtractive proteo-genomics analysis, an effort to conjecture the existence of distinct pathways in the development of brucellosis. Consequently, 38 unique metabolic pathways having 503 proteins were observed while among these 503 proteins, the non-homologs (n = 421), essential (n = 350), drug-like (n = 114), virulence (n = 45), resistance (n = 42), and unique to pathogen proteins were retrieved from Brucella suis. The applied subsequent hierarchical shortlisting resulted in a protein, i.e., isocitrate lyase, that may act as potential drug target, which was finalized after the extensive literature survey. The interacting partners for these shortlisted drug targets were identified through the STRING database. Moreover, structure-based studies were also performed on isocitrate lyase to further analyze its function. For that purpose, ~18,000 ZINC compounds were screened to identify new potent drug candidates against isocitrate lyase for brucellosis. It resulted in the shortlisting of six compounds, i.e., ZINC95543764, ZINC02688148, ZINC20115475, ZINC04232055, ZINC04231816, and ZINC04259566 that potentially inhibit isocitrate lyase. However, the ADMET profiling showed that all compounds fulfill ADMET properties except for ZINC20115475 showing positive Ames activity; whereas, ZINC02688148, ZINC04259566, ZINC04232055, and ZINC04231816 showed hepatoxicity while all compounds were observed to have no skin sensitization. In light of these parameters, we recommend ZINC95543764 compound for further experimental studies. According to the present research, which uses subtractive genomics, proteins that might serve as therapeutic targets and potential lead options for eradicating brucellosis have been narrowed down.

Protein-protein interactions: Methods, databases, and applications in virus-host study

Almost all the cellular processes in a living system are controlled by proteins: They regulate gene expression, catalyze chemical reactions, transport small molecules across membranes, and transmit signal across membranes. Even, a viral infection is often initiated through virus-host protein interactions. Protein-protein interactions (PPIs) are the physical contacts between two or more proteins and they represent complex biological functions. Nowadays, PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins. Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets. In this review, we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies. Here, we present a short but comprehensive review on PPIs, including the experimental and computational methods of finding PPIs, the databases dedicated to virus-host PPIs, and the associated various applications in protein interaction networks of some lethal viruses with their hosts.

Bioinformatics analysis of high frequency mutations in myelodysplastic syndrome-related patients

Background

Myelodysplastic syndrome (MDS) is a group of hematological malignancies that may progress to acute myeloid leukemia (AML). Bioinformatics-based analysis of high-frequency mutation genes in MDS-related patients is still relatively rare, so we conducted our research to explore whether high-frequency mutation genes in MDS-related patients can play a reference role in clinical guidance and prognosis.

Methods

Next generation sequencing (NGS) technology was used to detect 32 mutations in 64 MDS-related patients. We classified the patients' genes and analyzed them by Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, protein-protein interaction (PPI) analysis, and then calculated the gene survival curve of high-frequency mutations.

Results

We discovered 32 mutant genes such as ASXL1, DNMT3A, KRAS, NRAS, TP53, SF3B1, and SRSF2. The overall survival (OS) of these genes decreased significantly after DNMT3A, ASXL1, RUNX1, and U2AF1 occurred mutation. These genes play a significant role in biological processes, not only in MDS but also in the occurrence and development of other diseases. Through retrospective analysis, genes associated with MDS-related diseases were identified, and their effects on the disease were predicted.

Conclusions

Thirty-two mutant genes were determined in MDS and when mutations occur in DNMT3A, ASXL1, RUNX1, and U2AF1, their survival time decreases significantly. This results providing a theoretical basis for clinical and scientific research and broadening the scope of research on MDS.

Reconstruction of the Fas-Based Death-Inducing Signaling Complex (DISC) Using a Protein-Protein Docking Meta-Approach

The death-inducing signaling complex (DISC) is a fundamental multiprotein complex, which triggers the extrinsic apoptosis pathway through stimulation by death ligands. DISC consists of different death domain (DD) and death effector domain (DED) containing proteins such as the death receptor Fas (CD95) in complex with FADD, procaspase-8, and cFLIP. Despite many experimental and theoretical studies in this area, there is no global agreement neither on the DISC architecture nor on the mechanism of action of the involved species. In the current work, we have tried to reconstruct the DISC structure by identifying key protein interactions using a new protein-protein docking meta-approach. We combined the benefits of five of the most employed protein-protein docking engines, HADDOCK, ClusPro, HDOCK, GRAMM-X, and ZDOCK, in order to improve the accuracy of the predicted docking complexes. Free energy of binding and hot spot interacting residues were calculated and determined for each protein-protein interaction using molecular mechanics generalized Born surface area and alanine scanning techniques, respectively. In addition, a series of in-cellulo protein-fragment complementation assays were conducted to validate the protein-protein docking procedure. The results show that the DISC formation initiates by dimerization of adjacent Fas_DD trimers followed by recruitment of FADD through homotypic DD interactions with the oligomerized death receptor. Furthermore, the in-silico outcomes indicate that cFLIP cannot bind directly to FADD; instead, cFLIP recruitment to the DISC is a hierarchical and cooperative process where FADD initially recruits procaspase-8, which in turn recruits and heterodimerizes with cFLIP. Finally, a possible structure of the entire DISC is proposed based on the docking results.

Fluorescence resonance energy transfer in revealing protein-protein interactions in living cells

Genes are expressed to proteins for a wide variety of fundamental biological processes at the cellular and organismal levels. However, a protein rarely functions alone, but rather acts through interactions with other proteins to maintain normal cellular and organismal functions. Therefore, it is important to analyze the protein-protein interactions to determine functional mechanisms of proteins, which can also guide to develop therapeutic targets for treatment of diseases caused by altered protein-protein interactions leading to cellular/organismal dysfunctions. There is a large number of methodologies to study protein interactions in vitro, in vivo and in silico, which led to the development of many protein interaction databases, and thus, have enriched our knowledge about protein-protein interactions and functions. However, many of these interactions were identified in vitro, but need to be verified/validated in living cells. Furthermore, it is unclear whether these interactions are direct or mediated via other proteins. Moreover, these interactions are representative of cell- and time-average, but not a single cell in real time. Therefore, it is crucial to detect direct protein-protein interactions in a single cell during biological processes in vivo, towards understanding the functional mechanisms of proteins in living cells. Importantly, a fluorescence resonance energy transfer (FRET)-based methodology has emerged as a powerful technique to decipher direct protein-protein interactions at a single cell resolution in living cells, which is briefly described in a limited available space in this mini-review.

A survey on computational models for predicting protein-protein interactions

Proteins interact with each other to play critical roles in many biological processes in cells. Although promising, laboratory experiments usually suffer from the disadvantages of being time-consuming and labor-intensive. The results obtained are often not robust and considerably uncertain. Due recently to advances in high-throughput technologies, a large amount of proteomics data has been collected and this presents a significant opportunity and also a challenge to develop computational models to predict protein-protein interactions (PPIs) based on these data. In this paper, we present a comprehensive survey of the recent efforts that have been made towards the development of effective computational models for PPI prediction. The survey introduces the algorithms that can be used to learn computational models for predicting PPIs, and it classifies these models into different categories. To understand their relative merits, the paper discusses different validation schemes and metrics to evaluate the prediction performance. Biological databases that are commonly used in different experiments for performance comparison are also described and their use in a series of extensive experiments to compare different prediction models are discussed. Finally, we present some open issues in PPI prediction for future work. We explain how the performance of PPI prediction can be improved if these issues are effectively tackled.

Aqueous extract from Madhuca indica bark protects cells from oxidative stress caused by electron beam radiation: in vitro, in vivo and in silico approach

In an endeavor to find the novel natural radioprotector to secure normal cells surrounding cancerous cell during radiation exposure, Madhuca indica (M. indica) aqueous stem bark extract was evaluated for radioprotective activity using in vitro, in vivo, and in silico models. M. indica extract exhibited concentration dependent protective effect on electron beam radiation (EBR) induced damage to pBR322 DNA; the highest protection was achieved at 150 μg concentrations. Similarly, M. indica extract (400 mg/kg) administrated to mice prior to irradiation protected DNA from the radiation damage, which was confirmed by inhibiting comet parameters. The study showed a significant increase in the levels of glutathione and superoxide dismutase levels. The study also revealed that administration of M. Indica at the different dose to mice significantly reduced EBR induced MDA, sialic acid and nitric acid levels. Further extract prevented histophatological changes of skin and liver. In contrast, protein-protein interaction studies were performed to find the hub protein, involved in radiation-induced DNA damage. Among 437 proteins that are found expressed during radiation, p53 was found to be a master protein regulating the whole pathway. Molecular interaction between p53 and M. indica extract was predicted by quantitative structure-activity relationship and ADMET properties. Biomolecules such as quercetin, myricetin, and 7-hydroxyflavone were found to be promising inhibitors of p53 protein and may help in the protection of EBR induced DNA damage during cancer treatment.

The application of degree related clustering coefficient in estimating the link predictability and predicting missing links of networks

Though a lot of valuable algorithms of link prediction have been created, it is still difficult to improve the accuracy of link prediction for some networks. Such difficulties may be due to the intrinsic topological features of these networks. To reveal the correlation between the network topology and the link predictability, we generate a group of artificial networks by keeping some structural features of an initial seed network. Based on these artificial networks and some real networks, we find that five topological measures including clustering coefficient, structural consistency, random walk entropy, network diameter, and average path length significantly show their impact on the link predictability. Then, we define a topological score that combines these important topological features. Specifically, it is an integration of structural consistency with degree-related clustering coefficient defined in this work. This topological score exhibits high correlation with the link predictability. Finally, we propose an algorithm for link prediction based on this topological score. Our experiment on eight real networks verifies good performance of this algorithm in link prediction, which supports the reasonability of the new topological score. This work could be insightful for the study of the link predictability.

Generalizing biomedical relation classification with neural adversarial domain adaptation

Motivation

Creating large datasets for biomedical relation classification can be prohibitively expensive. While some datasets have been curated to extract protein-protein and drug-drug interactions (PPIs and DDIs) from text, we are also interested in other interactions including gene-disease and chemical-protein connections. Also, many biomedical researchers have begun to explore ternary relationships. Even when annotated data are available, many datasets used for relation classification are inherently biased. For example, issues such as sample selection bias typically prevent models from generalizing in the wild. To address the problem of cross-corpora generalization, we present a novel adversarial learning algorithm for unsupervised domain adaptation tasks where no labeled data are available in the target domain. Instead, our method takes advantage of unlabeled data to improve biased classifiers through learning domain-invariant features via an adversarial process. Finally, our method is built upon recent advances in neural network (NN) methods.

Results

We experiment by extracting PPIs and DDIs from text. In our experiments, we show domain invariant features can be learned in NNs such that classifiers trained for one interaction type (protein-protein) can be re-purposed to others (drug-drug). We also show that our method can adapt to different source and target pairs of PPI datasets. Compared to prior convolutional and recurrent NN-based relation classification methods without domain adaptation, we achieve improvements as high as 30% in F1-score. Likewise, we show improvements over state-of-the-art adversarial methods.

Availability and implementation

Experimental code is available at https://github.com/bionlproc/adversarial-relation-classification.

Supplementary information

Supplementary data are available at Bioinformatics online.

Fusion of expression values and protein interaction information using multi-objective optimization for improving gene clustering

One of the crucial problems in the field of functional genomics is to identify a set of genes which are responsible for a particular cellular mechanism. The current work explores the usage of a multi-objective optimization based genetic clustering technique to classify genes into groups with respect to their functional similarities and biological relevance. Our contribution is two-fold: firstly a new quality measure to compute the goodness of gene-clusters namely protein-protein interaction confidence score is developed. This utilizes the confidence scores of the protein-protein interaction networks to measure the similarity between genes of a particular cluster with respect to their biochemical protein products. Secondly, a multi-objective based clustering approach is developed which intelligently uses integrated information of expression values of microarray dataset and protein-protein interaction confidence scores to select both statistically and biologically relevant genes. For that very purpose, some biological cluster validity indices, viz. biological homogeneity index and protein-protein interaction confidence score, along with two traditional internal cluster validity indices, viz. fuzzy partition coefficient and Pakhira-Bandyopadhyay-Maulik-index, are simultaneously optimized during the clustering process. Experimental results on three real-life gene expression datasets show that the addition of new objective capturing protein-protein interaction information aids in clustering the genes as compared to the existing techniques. The observations are further supported by biological and statistical significance tests.

Evaluation of liver cirrhosis and hepatocellular carcinoma using Protein-Protein Interaction Networks

AIM

In the current study, we analysised only the articles that investigate serum proteome profile of cirrhosis patients or HCC patients versus healthy controls.

BACKGROUND

Increased understanding of cancer biology has enabled identification of molecular events that lead to the discovery of numerous potential biomarkers in diseases. Protein-protein interaction networks is one of aspect that could elevate the understanding level of molecular events and protein connections that lead to the identification of genes and proteins associated with diseases.

METHODS

Gene expression data, including 63 gene or protein names for hepatocellular carcinoma and 29 gene or protein names for cirrhosis, were extracted from a number of previous investigations. The networks of related differentially expressed genes were explored using Cytoscape and the PPI analysis methods such as MCODE and ClueGO. Centrality and cluster screening identified hub genes, including APOE, TTR, CLU, and APOA1 in cirrhosis.

RESULTS

CLU and APOE belong to the regulation of positive regulation of neurofibrillary tangle assembly. HP and APOE involved in cellular oxidant detoxification. C4B and C4BP belong to the complement activation, classical pathway and acute inflammation response pathway. Also, it was reported TTR, TFRC, VWF, CLU, A2M, APOA1, CKAP5, ZNF648, CASP8, and HSP27 as hubs in HCC. In HCC, these include A2M that are corresponding to platelet degranulation, humoral immune response, and negative regulation of immune effector process. CLU belong to the reverse cholesterol transport, platelet degranulation and human immune response. APOA1 corresponds to the reverse cholesterol transport, platelet degranulation and humoral immune response, as well as negative regulation of immune effector process pathway.

CONCLUSION

In conclusion, this study suggests that there is a common molecular relationship between cirrhosis and hepatocellular cancer that may help with identification of target molecules for early treatment that is essential in cancer therapy.

Comparative genomic analysis of novel Acinetobacter symbionts: A combined systems biology and genomics approach

The increasing trend of antibiotic resistance in Acinetobacter drastically limits the range of therapeutic agents required to treat multidrug resistant (MDR) infections. This study focused on analysis of novel Acinetobacter strains using a genomics and systems biology approach. Here we used a network theory method for pathogenic and non-pathogenic Acinetobacter spp. to identify the key regulatory proteins (hubs) in each strain. We identified nine key regulatory proteins, guaA, guaB, rpsB, rpsI, rpsL, rpsE, rpsC, rplM and trmD, which have functional roles as hubs in a hierarchical scale-free fractal protein-protein interaction network. Two key hubs (guaA and guaB) were important for insect-associated strains, and comparative analysis identified guaA as more important than guaB due to its role in effective module regulation. rpsI played a significant role in all the novel strains, while rplM was unique to sheep-associated strains. rpsM, rpsB and rpsI were involved in the regulation of overall network topology across all Acinetobacter strains analyzed in this study. Future analysis will investigate whether these hubs are useful as drug targets for treating Acinetobacter infections.

Rapid comparison and correlation analysis among massive number of microbial community samples based on MDV data model

The research in microbial communities would potentially impact a vast number of applications in “bio”-related disciplines. Large-scale analyses became a clear trend in microbial community studies, thus it is increasingly important to perform efficient and in-depth data mining for insightful biological principles from large number of samples. However, as microbial communities are from different sources and of different structures, comparison and data-mining from large number of samples become quite difficult. In this work, we have proposed a data model to represent large-scale comparison of microbial community samples, namely the “Multi-Dimensional View” data model (the MDV model) that should at least include 3 aspects: samples profile (S), taxa profile (T) and meta-data profile (V). We have also proposed a method for rapid data analysis based on the MDV model and applied it on the case studies with samples from various environmental conditions. Results have shown that though sampling environments usually define key variables, the analysis could detect bio-makers and even subtle variables based on large number of samples, which might be used to discover novel principles that drive the development of communities. The efficiency and effectiveness of data analysis method based on the MDV model have been validated by the results.

Protein-protein interaction detection: methods and analysis

Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid), TAP (tandem affinity purification), and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signaling pathways and protein complex identification in specific diseases.

Bio::Homology::InterologWalk--a Perl module to build putative protein-protein interaction networks through interolog mapping

BACKGROUND

Protein-protein interaction (PPI) data are widely used to generate network models that aim to describe the relationships between proteins in biological systems. The fidelity and completeness of such networks is primarily limited by the paucity of protein interaction information and by the restriction of most of these data to just a few widely studied experimental organisms. In order to extend the utility of existing PPIs, computational methods can be used that exploit functional conservation between orthologous proteins across taxa to predict putative PPIs or 'interologs'. To date most interolog prediction efforts have been restricted to specific biological domains with fixed underlying data sources and there are no software tools available that provide a generalised framework for 'on-the-fly' interolog prediction.

RESULTS

We introduce Bio::Homology::InterologWalk, a Perl module to retrieve, prioritise and visualise putative protein-protein interactions through an orthology-walk method. The module uses orthology and experimental interaction data to generate putative PPIs and optionally collates meta-data into an Interaction Prioritisation Index that can be used to help prioritise interologs for further analysis. We show the application of our interolog prediction method to the genomic interactome of the fruit fly, Drosophila melanogaster. We analyse the resulting interaction networks and show that the method proposes new interactome members and interactions that are candidates for future experimental investigation.

CONCLUSIONS

Our interolog prediction tool employs the Ensembl Perl API and PSICQUIC enabled protein interaction data sources to generate up to date interologs 'on-the-fly'. This represents a significant advance on previous methods for interolog prediction as it allows the use of the latest orthology and protein interaction data for all of the genomes in Ensembl. The module outputs simple text files, making it easy to customise the results by post-processing, allowing the putative PPI datasets to be easily integrated into existing analysis workflows. The Bio::Homology::InterologWalk module, sample scripts and full documentation are freely available from the Comprehensive Perl Archive Network (CPAN) under the GNU Public license.