DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes

Predicting essential proteins based on subcellular localization, orthology and PPI networks

Background

Essential proteins play an indispensable role in the cellular survival and development. There have been a series of biological experimental methods for finding essential proteins; however they are time-consuming, expensive and inefficient. In order to overcome the shortcomings of biological experimental methods, many computational methods have been proposed to predict essential proteins. The computational methods can be roughly divided into two categories, the topology-based methods and the sequence-based ones. The former use the topological features of protein-protein interaction (PPI) networks while the latter use the sequence features of proteins to predict essential proteins. Nevertheless, it is still challenging to improve the prediction accuracy of the computational methods.

Results

Comparing with nonessential proteins, essential proteins appear more frequently in certain subcellular locations and their evolution more conservative. By integrating the information of subcellular localization, orthologous proteins and PPI networks, we propose a novel essential protein prediction method, named SON, in this study. The experimental results on S.cerevisiae data show that the prediction accuracy of SON clearly exceeds that of nine competing methods: DC, BC, IC, CC, SC, EC, NC, PeC and ION.

Conclusions

We demonstrate that, by integrating the information of subcellular localization, orthologous proteins with PPI networks, the accuracy of predicting essential proteins can be improved. Our proposed method SON is effective for predicting essential proteins.

An ensemble framework for identifying essential proteins

Background

Many centrality measures have been proposed to mine and characterize the correlations between network topological properties and protein essentiality. However, most of them show limited prediction accuracy, and the number of common predicted essential proteins by different methods is very small.

Results

In this paper, an ensemble framework is proposed which integrates gene expression data and protein-protein interaction networks (PINs). It aims to improve the prediction accuracy of basic centrality measures. The idea behind this ensemble framework is that different protein-protein interactions (PPIs) may show different contributions to protein essentiality. Five standard centrality measures (degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and subgraph centrality) are integrated into the ensemble framework respectively. We evaluated the performance of the proposed ensemble framework using yeast PINs and gene expression data. The results show that it can considerably improve the prediction accuracy of the five centrality measures individually. It can also remarkably increase the number of common predicted essential proteins among those predicted by each centrality measure individually and enable each centrality measure to find more low-degree essential proteins.

Conclusions

This paper demonstrates that it is valuable to differentiate the contributions of different PPIs for identifying essential proteins based on network topological characteristics. The proposed ensemble framework is a successful paradigm to this end.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1166-7) contains supplementary material, which is available to authorized users.

Properties of Boolean dynamics by node classification using feedback loops in a network

Background

Biological networks keep their functions robust against perturbations. Many previous studies through simulations or experiments have shown that feedback loop (FBL) structures play an important role in controlling the network robustness without fully explaining how they do it. Hence, there is a pressing need to more rigorously analyze the influence of FBL structures on network robustness.

Results

In this paper, I propose a novel node classification notion based on the FBL structures involved. More specifically, I classify a node as a no-FBL-in-upstream (NFU) or no-FBL-in-downstream (NFD) node if no feedback loop is involved with any upstream or downstream path of the node, respectively. Based on those definitions, I first prove that every NFU node is eventually frozen in Boolean dynamics. Thus, NFU nodes converge to a fixed value determined by the upstream source nodes. Second, I prove that a network is robust against an arbitrary state perturbation subject to a non-source NFD node. This implies that a network state eventually sustains the attractor despite a perturbation subject to a non-source NFD node. Inspired by this result, I further propose a perturbation-sustainable probability that indicates how likely a perturbation effect is to be sustained through propagations. I show that genes with a high perturbation-sustainable probability are likely to be essential, disease, and drug-target genes in large human signaling networks.

Conclusion

Taken together, these results will promote understanding of the effects of FBL on network robustness in a more rigorous manner.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0322-z) contains supplementary material, which is available to authorized users.

A New Method for Identifying Essential Proteins Based on Network Topology Properties and Protein Complexes

Essential proteins are indispensable to the viability and reproduction of an organism. The identification of essential proteins is necessary not only for understanding the molecular mechanisms of cellular life but also for disease diagnosis, medical treatments and drug design. Many computational methods have been proposed for discovering essential proteins, but the precision of the prediction of essential proteins remains to be improved. In this paper, we propose a new method, LBCC, which is based on the combination of local density, betweenness centrality (BC) and in-degree centrality of complex (IDC). First, we introduce the common centrality measures; second, we propose the densities Den1(v) and Den2(v) of a node v to describe its local properties in the network; and finally, the combined strategy of Den1, Den2, BC and IDC is developed to improve the prediction precision. The experimental results demonstrate that LBCC outperforms traditional topological measures for predicting essential proteins, including degree centrality (DC), BC, subgraph centrality (SC), eigenvector centrality (EC), network centrality (NC), and the local average connectivity-based method (LAC). LBCC also improves the prediction precision by approximately 10 percent on the YMIPS and YMBD datasets compared to the most recently developed method, LIDC.

Comprehensive Identification of Meningococcal Genes and Small Noncoding RNAs Required for Host Cell Colonization

Neisseria meningitidis is a leading cause of bacterial meningitis and septicemia, affecting infants and adults worldwide. N. meningitidis is also a common inhabitant of the human nasopharynx and, as such, is highly adapted to its niche. During bacteremia, N. meningitidis gains access to the blood compartment, where it adheres to endothelial cells of blood vessels and causes dramatic vascular damage. Colonization of the nasopharyngeal niche and communication with the different human cell types is a major issue of the N. meningitidis life cycle that is poorly understood. Here, highly saturated random transposon insertion libraries of N. meningitidis were engineered, and the fitness of mutations during routine growth and that of colonization of endothelial and epithelial cells in a flow device were assessed in a transposon insertion site sequencing (Tn-seq) analysis. This allowed the identification of genes essential for bacterial growth and genes specifically required for host cell colonization. In addition, after having identified the small noncoding RNAs (sRNAs) located in intergenic regions, the phenotypes associated with mutations in those sRNAs were defined. A total of 383 genes and 8 intergenic regions containing sRNA candidates were identified to be essential for growth, while 288 genes and 33 intergenic regions containing sRNA candidates were found to be specifically required for host cell colonization.

Meningococcal meningitis is a common cause of meningitis in infants and adults. Neisseria meningitidis (meningococcus) is also a commensal bacterium of the nasopharynx and is carried by 3 to 30% of healthy humans. Under some unknown circumstances, N. meningitidis is able to invade the bloodstream and cause either meningitis or a fatal septicemia known as purpura fulminans. The onset of symptoms is sudden, and death can follow within hours. Although many meningococcal virulence factors have been identified, the mechanisms that allow the bacterium to switch from the commensal to pathogen state remain unknown. Therefore, we used a Tn-seq strategy coupled to high-throughput DNA sequencing technologies to find genes for proteins used by N. meningitidis to specifically colonize epithelial cells and primary brain endothelial cells. We identified 383 genes and 8 intergenic regions containing sRNAs essential for growth and 288 genes and 33 intergenic regions containing sRNAs required specifically for host cell colonization.

Essential protein discovery based on a combination of modularity and conservatism

Essential proteins are indispensable for the survival of a living organism and play important roles in the emerging field of synthetic biology. Many computational methods have been proposed to identify essential proteins by using the topological features of interactome networks. However, most of these methods ignored intrinsic biological meaning of proteins. Researches show that essentiality is tied not only to the protein or gene itself, but also to the molecular modules to which that protein belongs. The results of this study reveal the modularity of essential proteins. On the other hand, essential proteins are more evolutionarily conserved than nonessential proteins and frequently bind each other. That is to say, conservatism is another important feature of essential proteins. Multiple networks are constructed by integrating protein-protein interaction (PPI) networks, time course gene expression data and protein domain information. Based on these networks, a new essential protein identification method is proposed based on a combination of modularity and conservatism of proteins. Experimental results show that the proposed method outperforms other essential protein identification methods in terms of a number essential protein out of top ranked candidates.

Identification of lung-specific genes by meta-analysis of multiple tissue RNA-seq data

Lung-specific genes play critically important roles in lung development, lung physiology, and pathogenesis of lung-associated diseases. We performed a meta-analysis of multiple tissue RNA-seq data to identify lung-specific genes in order to better investigate their lung-specific functions and pathological roles. We identified 83 lung-specific genes consisting of 62 protein-coding genes, five pseudogenes and 16 noncoding RNA genes. About 49.4% of lung-specific genes were implicated in the pathogenesis of lung diseases and 21.7% were involved with lung development. The identification of genes with enriched expression in the lung will facilitate the elucidation of lung-specific functions and their roles in disease pathogenesis.

siRNAs with decreased off-target effect facilitate the identification of essential genes in cancer cells

Since the essential genes are crucial to the proliferation and survival of cancer cells, the interference of these genes is promising to be an option for cancer therapy to overcome heterogeneity. However, the essential genes are highly overestimated by RNA interference (RNAi) screenings, which is mainly caused by the pervasive off-target effect of small interference RNA (siRNA) and short hairpin RNA (shRNA). In the present study, we designed Match-Mismatch paired siRNAs to discriminate the on-target effect from off-target effect of siRNAs on cell viability. Only one of the 7 potential essential genes was validated as essential to cell viability, which demonstrates the high false positive rate in RNAi screenings. We modified the siRNA by introducing random nucleotides (N) into the guide strand to mitigate the off-target effect, without significantly compromising the on-target effect. The whole transcriptome profile analysis of cells transfected with siRNAs with or without Nindicates that siRNA-dN (with Ns on both the 2^nd and the 18^th bases of the guide strand) weakens the off-target effect by decreasing the unintended targets. The optimized siRNAs can be applied in the characterization of essential genes in cancer cells.

Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review

Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research.

Temperature Sensitivity Conferred by ligA Alleles from Psychrophilic Bacteria upon Substitution in Mesophilic Bacteria and a Yeast Species

We have assembled a collection of 13 psychrophilic ligA alleles that can serve as genetic elements for engineering mesophiles to a temperature-sensitive (TS) phenotype. When these ligA alleles were substituted into Francisella novicida, they conferred a TS phenotype with restrictive temperatures between 33 and 39°C. When the F. novicida ligA hybrid strains were plated above their restrictive temperatures, eight of them generated temperature-resistant variants. For two alleles, the mutations that led to temperature resistance clustered near the 5' end of the gene, and the mutations increased the predicted strength of the ribosome binding site at least 3-fold. Four F. novicida ligA hybrid strains generated no temperature-resistant variants at a detectable level. These results suggest that multiple mutations are needed to create temperature-resistant variants of these ligA gene products. One ligA allele was isolated from a Colwellia species that has a maximal growth temperature of 12°C, and this allele supported growth of F. novicida only as a hybrid between the psychrophilic and the F. novicida ligA genes. However, the full psychrophilic gene alone supported the growth of Salmonella enterica, imparting a restrictive temperature of 27°C. We also tested two ligA alleles from two Pseudoalteromonas strains for their ability to support the viability of a Saccharomyces cerevisiae strain that lacked its essential gene, CDC9, encoding an ATP-dependent DNA ligase. In both cases, the psychrophilic bacterial alleles supported yeast viability and their expression generated TS phenotypes. This collection of ligA alleles should be useful in engineering bacteria, and possibly eukaryotic microbes, to predictable TS phenotypes.

Identification of potential drug targets by subtractive genome analysis of Escherichia coli O157:H7: an in silico approach

Bacterial enteric infections resulting in diarrhea, dysentery, or enteric fever constitute a huge public health problem, with more than a billion episodes of disease annually in developing and developed countries. In this study, the deadly agent of hemorrhagic diarrhea and hemolytic uremic syndrome, Escherichia coli O157:H7 was investigated with extensive computational approaches aimed at identifying novel and broad-spectrum antibiotic targets. A systematic in silico workflow consisting of comparative genomics, metabolic pathways analysis, and additional drug prioritizing parameters was used to identify novel drug targets that were essential for the pathogen’s survival but absent in its human host. Comparative genomic analysis of Kyoto Encyclopedia of Genes and Genomes annotated metabolic pathways identified 350 putative target proteins in E. coli O157:H7 which showed no similarity to human proteins. Further bio-informatic approaches including prediction of subcellular localization, calculation of molecular weight, and web-based investigation of 3D structural characteristics greatly aided in filtering the potential drug targets from 350 to 120. Ultimately, 44 non-homologous essential proteins of E. coli O157:H7 were prioritized and proved to have the eligibility to become novel broad-spectrum antibiotic targets and DNA polymerase III alpha (dnaE) was the top-ranked among these targets. Moreover, druggability of each of the identified drug targets was evaluated by the DrugBank database. In addition, 3D structure of the dnaE was modeled and explored further for in silico docking with ligands having potential druggability. Finally, we confirmed that the compounds N-coeleneterazine and N-(1,4-dihydro-5H-tetrazol-5-ylidene)-9-oxo-9H-xanthene-2-sulfon-amide were the most suitable ligands of dnaE and hence proposed as the potential inhibitors of this target protein. The results of this study could facilitate the discovery and release of new and effective drugs against E. coli O157:H7 and other deadly human bacterial pathogens.

Gene Essentiality Is a Quantitative Property Linked to Cellular Evolvability

Gene essentiality is typically determined by assessing the viability of the corresponding mutant cells, but this definition fails to account for the ability of cells to adaptively evolve to genetic perturbations. Here, we performed a stringent screen to assess the degree to which Saccharomyces cerevisiae cells can survive the deletion of ~1,000 individual "essential" genes and found that ~9% of these genetic perturbations could in fact be overcome by adaptive evolution. Our analyses uncovered a genome-wide gradient of gene essentiality, with certain essential cellular functions being more "evolvable" than others. Ploidy changes were prevalent among the evolved mutant strains, and aneuploidy of a specific chromosome was adaptive for a class of evolvable nucleoporin mutants. These data justify a quantitative redefinition of gene essentiality that incorporates both viability and evolvability of the corresponding mutant cells and will enable selection of therapeutic targets associated with lower risk of emergence of drug resistance.

Comparative Genome and Network Centrality Analysis to Identify Drug Targets of Mycobacterium tuberculosis H37Rv

Potential drug targets of Mycobacterium tuberculosis H37Rv were identified through systematically integrated comparative genome and network centrality analysis. The comparative analysis of the complete genome of Mycobacterium tuberculosis H37Rv against Database of Essential Genes (DEG) yields a list of proteins which are essential for the growth and survival of the pathogen. Those proteins which are nonhomologous with human were selected. The resulting proteins were then prioritized by using the four network centrality measures: degree, closeness, betweenness, and eigenvector. Proteins whose centrality value is close to the centre of gravity of the interactome network were proposed as a final list of potential drug targets for the pathogen. The use of an integrated approach is believed to increase the success of the drug target identification process. For the purpose of validation, selective comparisons have been made among the proposed targets and previously identified drug targets by various other methods. About half of these proteins have been already reported as potential drug targets. We believe that the identified proteins will be an important input to experimental study which in the way could save considerable amount of time and cost of drug target discovery.

A New Type of YumC-Like Ferredoxin (Flavodoxin) Reductase Is Involved in Ribonucleotide Reduction

The trxB2 gene, which is annotated as a thioredoxin reductase, was found to be essential for growth of Lactococcus lactis in the presence of oxygen. The corresponding protein (TrxB2) showed a high similarity with Bacillus subtilis YumC (E value = 4.0E−88), and YumC was able to fully complement the ΔtrxB2 mutant phenotype. YumC represents a novel type of ferredoxin (flavodoxin) reductase (FdR) with hitherto-unknown biological function. We adaptively evolved the ΔtrxB2 mutant under aerobic conditions to find suppressor mutations that could help elucidate the involvement of TrxB2 in aerobic growth. Genome sequencing of two independent isolates, which were able to grow as well as the wild-type strain under aerated conditions, revealed the importance of mutations in nrdI, encoding a flavodoxin involved in aerobic ribonucleotide reduction. We suggest a role for TrxB2 in nucleotide metabolism, where the flavodoxin (NrdI) serves as its redox partner, and we support this hypothesis by showing the beneficial effect of deoxynucleosides on aerobic growth of the ΔtrxB2 mutant. Finally, we demonstrate, by heterologous expression, that the TrxB2 protein functionally can substitute for YumC in B. subtilis but that the addition of deoxynucleosides cannot compensate for the lethal phenotype displayed by the B. subtilisyumC knockout mutant.

Ferredoxin (flavodoxin) reductase (FdR) is involved in many important reactions in both eukaryotes and prokaryotes, such as photosynthesis, nitrate reduction, etc. The recently identified bacterial YumC-type FdR belongs to a novel type, the biological function of which still remains elusive. We found that the YumC-like FdR (TrxB2) is essential for aerobic growth of Lactococcus lactis. We suggest that the YumC-type FdR is involved in the ribonucleotide reduction by the class Ib ribonucleotide reductase, which represents the workhorse for the bioconversion of nucleotides to deoxynucleotides in many prokaryotes and eukaryotic pathogens under aerobic conditions. As the partner of the flavodoxin (NrdI), the key FdR is missing in the current model describing the class Ib system in Escherichia coli. With this study, we have established a role for this novel type of FdR and in addition found the missing link needed to explain how ribonucleotide reduction is carried out under aerobic conditions.

Treponema pallidum putative novel drug target identification and validation: rethinking syphilis therapeutics with plant-derived terpenoids

Syphilis, a slow progressive and the third most common sexually transmitted disease found worldwide, is caused by a spirochete gram negative bacteria Treponema pallidum. Emergence of antibiotic resistant T. pallidum has led to a search for novel drugs and their targets. Subtractive genomics analyses of pathogen T. pallidum and host Homo sapiens resulted in identification of 126 proteins essential for survival and viability of the pathogen. Metabolic pathway analyses of these essential proteins led to discovery of nineteen proteins distributed among six metabolic pathways unique to T. pallidum. One hundred plant-derived terpenoids, as potential therapeutic molecules against T. pallidum, were screened for their drug likeness and ADMET (absorption, distribution, metabolism, and toxicity) properties. Subsequently the resulting nine terpenoids were docked with five unique T. pallidum targets through molecular modeling approaches. Out of five targets analyzed, D-alanine:D-alanine ligase was found to be the most promising target, while terpenoid salvicine was the most potent inhibitor. A comparison of the inhibitory potential of the best docked readily available natural compound, namely pomiferin (flavonoid) with that of the best docked terpenoid salvicine, revealed that salvicine was a more potent inhibitor than that of pomiferin. To the best of our knowledge, this is the first report of a terpenoid as a potential therapeutic molecule against T. pallidum with D-alanine:D-alanine ligase as a novel target. Further studies are warranted to evaluate and explore the potential clinical ramifications of these findings in relation to syphilis that has public health importance worldwide.

An integrated machine-learning model to predict prokaryotic essential genes

Essential genes are indispensable for the target organism's survival. Large-scale identification and characterization of essential genes has shown to be beneficial in both fundamental biology and medicine fields. Current existing genome-scale experimental screenings of essential genes are time consuming and costly, also sometimes confer erroneous essential gene annotations. To circumvent these difficulties, many research groups turn to computational approaches as the alternative to identify essential genes. Here, we developed an integrative machine-learning based statistical framework to accurately predict essential genes in microorganisms. First we extracted a variety of relevant features derived from different aspects of an organism's genomic sequences. Then we selected a subset of features have high predictive power of gene essentiality through a carefully designed feature selection system. Using the selected features as input, we constructed an ensemble classifier and trained the model on a well-studied microorganism. After fine-tuning the model parameters in cross-validation, we tested the model on the other microorganism. We found that the tenfold cross-validation results within the same organism achieves a high predictive accuracy (AUC ~0.9), and cross-organism predictions between distant related organisms yield the AUC scores from 0.69 to 0.89, which significantly outperformed homology mapping.

Reconstruction and analysis of a genome-scale metabolic network of Corynebacterium glutamicum S9114

Corynebacterium glutamicum S9114 is commonly used for industrial glutamate production. Therefore, a comprehensive understanding of the physiological and metabolic characteristics of C. glutamicum is important for developing its potential for industrial production. A genome-scale metabolic model, iJM658, was reconstructed based on genome annotation and literature mining. The model consists of 658 genes, 984 metabolites and 1065 reactions. The model quantitatively predicted C. glutamicum growth on different carbon and nitrogen sources and determined 129 genes to be essential for cell growth. The iJM658 model predicted that C. glutamicum had two glutamate biosynthesis pathways and lacked eight key genes in biotin synthesis. Robustness analysis indicated a relative low oxygen level (1.21mmol/gDW/h) would improve glutamate production rate. Potential metabolic engineering targets for improving γ-aminobutyrate and isoleucine production rate were predicted by in silico deletion or overexpression of some genes. The iJM658 model is a useful tool for understanding and optimizing the metabolism of C. glutamicum and a valuable resource for future metabolic and physiological research.

Escherichia coli ASKA Clone Library Harboring tRNA-Specific Adenosine Deaminase (tadA) Reveals Resistance towards Xanthorrhizol

Xanthorrhizol is a potent antimicrobial compound isolated from the rhizome of Curcuma xanthorrhiza. However, the mechanism of xanthorrhizol action is unknown. To screen for probable target(s), we introduced the ASKA pooled-plasmid library into Escherichia coli W3110 imp4213 and enriched the library for resistant clones with increasing concentrations of xanthorrhizol. After three rounds of enrichment, we found nine genes that increased xanthorrhizol resistance. The resistant clones were able to grow in LB medium containing 256 µg/mL xanthorrhizol, representing a 16-fold increase in the minimum inhibitory concentration. Subsequent DNA sequence analysis revealed that overexpression of tadA, galU, fucU, ydeA, ydaC, soxS, nrdH, yiiD, and mltF genes conferred increased resistance towards xanthorrhizol. Among these nine genes, tadA is the only essential gene. tadA encodes a tRNA-specific adenosine deaminase. Overexpression of E. coli W3110 imp4213 (pCA24N-tadA) conferred resistance to xanthorrhizol up to 128 µg/mL. Moreover, overexpression of two tadA mutant enzymes (A143V and F149G) led to a twofold increase in the MIC. These results suggest that the targets of xanthorrhizol may include tadA, which has never before been explored as an antibiotic target.

Evolutionary conservation analysis between the essential and nonessential genes in bacterial genomes

Essential genes are thought to be critical for the survival of the organisms under certain circumstances, and the natural selection acting on essential genes is expected to be stricter than on nonessential ones. Up to now, essential genes have been identified in approximately thirty bacterial organisms by experimental methods. In this paper, we performed a comprehensive comparison between the essential and nonessential genes in the genomes of 23 bacterial species based on the Ka/Ks ratio, and found that essential genes are more evolutionarily conserved than nonessential genes in most of the bacteria examined. Furthermore, we also analyzed the conservation by functional clusters with the clusters of orthologous groups (COGs), and found that the essential genes in the functional categories of G (Carbohydrate transport and metabolism), H (Coenzyme transport and metabolism), I (Transcription), J (Translation, ribosomal structure and biogenesis), K (Lipid transport and metabolism) and L (Replication, recombination and repair) tend to be more evolutionarily conserved than the corresponding nonessential genes in bacteria. The results suggest that the essential genes in these subcategories are subject to stronger selective pressure than the nonessential genes, and therefore, provide more insights of the evolutionary conservation for the essential and nonessential genes in complex biological processes.

Comparative analysis of essential genes in prokaryotic genomic islands

Essential genes are thought to encode proteins that carry out the basic functions to sustain a cellular life, and genomic islands (GIs) usually contain clusters of horizontally transferred genes. It has been assumed that essential genes are not likely to be located in GIs, but systematical analysis of essential genes in GIs has not been explored before. Here, we have analyzed the essential genes in 28 prokaryotes by statistical method and reached a conclusion that essential genes in GIs are significantly fewer than those outside GIs. The function of 362 essential genes found in GIs has been explored further by BLAST against the Virulence Factor Database (VFDB) and the phage/prophage sequence database of PHAge Search Tool (PHAST). Consequently, 64 and 60 eligible essential genes are found to share the sequence similarity with the virulence factors and phage/prophages-related genes, respectively. Meanwhile, we find several toxin-related proteins and repressors encoded by these essential genes in GIs. The comparative analysis of essential genes in genomic islands will not only shed new light on the development of the prediction algorithm of essential genes, but also give a clue to detect the functionality of essential genes in genomic islands.

Identification of Essential Proteins Based on a New Combination of Local Interaction Density and Protein Complexes

Background

Computational approaches aided by computer science have been used to predict essential proteins and are faster than expensive, time-consuming, laborious experimental approaches. However, the performance of such approaches is still poor, making practical applications of computational approaches difficult in some fields. Hence, the development of more suitable and efficient computing methods is necessary for identification of essential proteins.

Method

In this paper, we propose a new method for predicting essential proteins in a protein interaction network, local interaction density combined with protein complexes (LIDC), based on statistical analyses of essential proteins and protein complexes. First, we introduce a new local topological centrality, local interaction density (LID), of the yeast PPI network; second, we discuss a new integration strategy for multiple bioinformatics. The LIDC method was then developed through a combination of LID and protein complex information based on our new integration strategy. The purpose of LIDC is discovery of important features of essential proteins with their neighbors in real protein complexes, thereby improving the efficiency of identification.

Results

Experimental results based on three different PPI(protein-protein interaction) networks of Saccharomyces cerevisiae and Escherichia coli showed that LIDC outperformed classical topological centrality measures and some recent combinational methods. Moreover, when predicting MIPS datasets, the better improvement of performance obtained by LIDC is over all nine reference methods (i.e., DC, BC, NC, LID, PeC, CoEWC, WDC, ION, and UC).

Conclusions

LIDC is more effective for the prediction of essential proteins than other recently developed methods.

Rechecking the Centrality-Lethality Rule in the Scope of Protein Subcellular Localization Interaction Networks

Essential proteins are indispensable for living organisms to maintain life activities and play important roles in the studies of pathology, synthetic biology, and drug design. Therefore, besides experiment methods, many computational methods are proposed to identify essential proteins. Based on the centrality-lethality rule, various centrality methods are employed to predict essential proteins in a Protein-protein Interaction Network (PIN). However, neglecting the temporal and spatial features of protein-protein interactions, the centrality scores calculated by centrality methods are not effective enough for measuring the essentiality of proteins in a PIN. Moreover, many methods, which overfit with the features of essential proteins for one species, may perform poor for other species. In this paper, we demonstrate that the centrality-lethality rule also exists in Protein Subcellular Localization Interaction Networks (PSLINs). To do this, a method based on Localization Specificity for Essential protein Detection (LSED), was proposed, which can be combined with any centrality method for calculating the improved centrality scores by taking into consideration PSLINs in which proteins play their roles. In this study, LSED was combined with eight centrality methods separately to calculate Localization-specific Centrality Scores (LCSs) for proteins based on the PSLINs of four species (Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Drosophila melanogaster). Compared to the proteins with high centrality scores measured from the global PINs, more proteins with high LCSs measured from PSLINs are essential. It indicates that proteins with high LCSs measured from PSLINs are more likely to be essential and the performance of centrality methods can be improved by LSED. Furthermore, LSED provides a wide applicable prediction model to identify essential proteins for different species.

Identifying putative drug targets and potential drug leads: starting points for virtual screening and docking

The availability of 3D models of both drug leads (small molecule ligands) and drug targets (proteins) is essential to molecular docking and computational drug discovery. This chapter describes a simple approach that can be used to identify both drug leads and drug targets using two popular Web-accessible databases: (1) DrugBank and (2) The Human Metabolome Database. First, it is illustrated how putative drug targets and drug leads for exogenous diseases (i.e., infectious diseases) can be readily identified and their 3D structures selected using only the genomic sequences from pathogenic bacteria or viruses as input. The second part illustrates how putative drug targets and drug leads for endogenous diseases (i.e., noninfectious diseases or chronic conditions) can be identified using similar databases and similar sequence input. This chapter is intended to illustrate how bioinformatics and cheminformatics can work synergistically to help provide the necessary inputs for computer-aided drug design.

Principles for the organization of gene-sets

A gene-set, an important concept in microarray expression analysis and systems biology, is a collection of genes and/or their products (i.e. proteins) that have some features in common. There are many different ways to construct gene-sets, but a systematic organization of these ways is lacking. Gene-sets are mainly organized ad hoc in current public-domain databases, with group header names often determined by practical reasons (such as the types of technology in obtaining the gene-sets or a balanced number of gene-sets under a header). Here we aim at providing a gene-set organization principle according to the level at which genes are connected: homology, physical map proximity, chemical interaction, biological, and phenotypic-medical levels. We also distinguish two types of connections between genes: actual connection versus sharing of a label. Actual connections denote direct biological interactions, whereas shared label connection denotes shared membership in a group. Some extensions of the framework are also addressed such as overlapping of gene-sets, modules, and the incorporation of other non-protein-coding entities such as microRNAs.

Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein-protein interaction network

Background

In spite of the implementations of several strategies, tuberculosis (TB) is overwhelmingly a serious global public health problem causing millions of infections and deaths every year. This is mainly due to the emergence of drug-resistance varieties of TB. The current treatment strategies for the drug-resistance TB are of longer duration, more expensive and have side effects. This highlights the importance of identification and prioritization of targets for new drugs. This study has been carried out to prioritize potential drug targets of Mycobacteriumtuberculosis H37Rv based on their flow to resistance genes.

Methods

The weighted proteome interaction network of the pathogen was constructed using a dataset from STRING database. Only a subset of the dataset with interactions that have a combined score value ≥770 was considered. Maximum flow approach has been used to prioritize potential drug targets. The potential drug targets were obtained through comparative genome and network centrality analysis. The curated set of resistance genes was retrieved from literatures. Detail literature review and additional assessment of the method were also carried out for validation.

Results

A list of 537 proteins which are essential to the pathogen and non-homologous with human was obtained from the comparative genome analysis. Through network centrality measures, 131 of them were found within the close neighborhood of the centre of gravity of the proteome network. These proteins were further prioritized based on their maximum flow value to resistance genes and they are proposed as reliable drug targets of the pathogen. Proteins which interact with the host were also identified in order to understand the infection mechanism.

Conclusion

Potential drug targets of Mycobacteriumtuberculosis H37Rv were successfully prioritized based on their flow to resistance genes of existing drugs which is believed to increase the druggability of the targets since inhibition of a protein that has a maximum flow to resistance genes is more likely to disrupt the communication to these genes. Purposely selected literature review of the top 14 proteins showed that many of them in this list were proposed as drug targets of the pathogen.

Electronic supplementary material

The online version of this article (doi:10.1186/s40169-015-0061-6) contains supplementary material, which is available to authorized users.

Biophysics of protein evolution and evolutionary protein biophysics

The study of molecular evolution at the level of protein-coding genes often entails comparing large datasets of sequences to infer their evolutionary relationships. Despite the importance of a protein's structure and conformational dynamics to its function and thus its fitness, common phylogenetic methods embody minimal biophysical knowledge of proteins. To underscore the biophysical constraints on natural selection, we survey effects of protein mutations, highlighting the physical basis for marginal stability of natural globular proteins and how requirement for kinetic stability and avoidance of misfolding and misinteractions might have affected protein evolution. The biophysical underpinnings of these effects have been addressed by models with an explicit coarse-grained spatial representation of the polypeptide chain. Sequence-structure mappings based on such models are powerful conceptual tools that rationalize mutational robustness, evolvability, epistasis, promiscuous function performed by 'hidden' conformational states, resolution of adaptive conflicts and conformational switches in the evolution from one protein fold to another. Recently, protein biophysics has been applied to derive more accurate evolutionary accounts of sequence data. Methods have also been developed to exploit sequence-based evolutionary information to predict biophysical behaviours of proteins. The success of these approaches demonstrates a deep synergy between the fields of protein biophysics and protein evolution.

Determining minimum set of driver nodes in protein-protein interaction networks

BACKGROUND

Recently, several studies have drawn attention to the determination of a minimum set of driver proteins that are important for the control of the underlying protein-protein interaction (PPI) networks. In general, the minimum dominating set (MDS) model is widely adopted. However, because the MDS model does not generate a unique MDS configuration, multiple different MDSs would be generated when using different optimization algorithms. Therefore, among these MDSs, it is difficult to find out the one that represents the true driver set of proteins.

RESULTS

To address this problem, we develop a centrality-corrected minimum dominating set (CC-MDS) model which includes heterogeneity in degree and betweenness centralities of proteins. Both the MDS model and the CC-MDS model are applied on three human PPI networks. Unlike the MDS model, the CC-MDS model generates almost the same sets of driver proteins when we implement it using different optimization algorithms. The CC-MDS model targets more high-degree and high-betweenness proteins than the uncorrected counterpart. The more central position allows CC-MDS proteins to be more important in maintaining the overall network connectivity than MDS proteins. To indicate the functional significance, we find that CC-MDS proteins are involved in, on average, more protein complexes and GO annotations than MDS proteins. We also find that more essential genes, aging genes, disease-associated genes and virus-targeted genes appear in CC-MDS proteins than in MDS proteins. As for the involvement in regulatory functions, the sets of CC-MDS proteins show much stronger enrichment of transcription factors and protein kinases. The results about topological and functional significance demonstrate that the CC-MDS model can capture more driver proteins than the MDS model.

CONCLUSIONS

Based on the results obtained, the CC-MDS model presents to be a powerful tool for the determination of driver proteins that can control the underlying PPI networks. The software described in this paper and the datasets used are available at https://github.com/Zhangxf-ccnu/CC-MDS .

Evolutionary Conservation of Bacterial Essential Metabolic Genes across All Bacterial Culture Media

One of the basic postulates of molecular evolution is that functionally important genes should evolve slower than genes of lesser significance. Essential genes, whose knockout leads to a lethal phenotype are considered of high functional importance, yet whether they are truly more conserved than nonessential genes has been the topic of much debate, fuelled by a host of contradictory findings. Here we conduct the first large-scale study utilizing genome-scale metabolic modeling and spanning many bacterial species, which aims to answer this question. Using the novel Media Variation Analysis, we examine the range of conservation of essential vs. nonessential metabolic genes in a given species across all possible media. We are thus able to obtain for the first time, exact upper and lower bounds on the levels of differential conservation of essential genes for each of the species studied. The results show that bacteria do exhibit an overall tendency for differential conservation of their essential genes vs. their non-essential ones, yet this tendency is highly variable across species. We show that the model bacterium E. coli K12 may or may not exhibit differential conservation of essential genes depending on its growth medium, shedding light on previous experimental studies showing opposite trends.

Systems biology perspectives on minimal and simpler cells

The concept of the minimal cell has fascinated scientists for a long time, from both fundamental and applied points of view. This broad concept encompasses extreme reductions of genomes, the last universal common ancestor (LUCA), the creation of semiartificial cells, and the design of protocells and chassis cells. Here we review these different areas of research and identify common and complementary aspects of each one. We focus on systems biology, a discipline that is greatly facilitating the classical top-down and bottom-up approaches toward minimal cells. In addition, we also review the so-called middle-out approach and its contributions to the field with mathematical and computational models. Owing to the advances in genomics technologies, much of the work in this area has been centered on minimal genomes, or rather minimal gene sets, required to sustain life. Nevertheless, a fundamental expansion has been taking place in the last few years wherein the minimal gene set is viewed as a backbone of a more complex system. Complementing genomics, progress is being made in understanding the system-wide properties at the levels of the transcriptome, proteome, and metabolome. Network modeling approaches are enabling the integration of these different omics data sets toward an understanding of the complex molecular pathways connecting genotype to phenotype. We review key concepts central to the mapping and modeling of this complexity, which is at the heart of research on minimal cells. Finally, we discuss the distinction between minimizing the number of cellular components and minimizing cellular complexity, toward an improved understanding and utilization of minimal and simpler cells.

Module organization and variance in protein-protein interaction networks

A module is a group of closely related proteins that act in concert to perform specific biological functions through protein–protein interactions (PPIs) that occur in time and space. However, the underlying module organization and variance remain unclear. In this study, we collected module templates to infer respective module families, including 58,041 homologous modules in 1,678 species, and PPI families using searches of complete genomic database. We then derived PPI evolution scores and interface evolution scores to describe the module elements, including core and ring components. Functions of core components were highly correlated with those of essential genes. In comparison with ring components, core proteins/PPIs were conserved across multiple species. Subsequently, protein/module variance of PPI networks confirmed that core components form dynamic network hubs and play key roles in various biological functions. Based on the analyses of gene essentiality, module variance, and gene co-expression, we summarize the observations of module organization and variance as follows: 1) a module consists of core and ring components; 2) core components perform major biological functions and collaborate with ring components to execute certain functions in some cases; 3) core components are more conserved and essential during organizational changes in different biological states or conditions.

A Topology Potential-Based Method for Identifying Essential Proteins from PPI Networks

Essential proteins are indispensable for cellular life. It is of great significance to identify essential proteins that can help us understand the minimal requirements for cellular life and is also very important for drug design. However, identification of essential proteins based on experimental approaches are typically time-consuming and expensive. With the development of high-throughput technology in the post-genomic era, more and more protein-protein interaction data can be obtained, which make it possible to study essential proteins from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. Most of these topology based essential protein discovery methods were to use network centralities. In this paper, we investigate the essential proteins' topological characters from a completely new perspective. To our knowledge it is the first time that topology potential is used to identify essential proteins from a protein-protein interaction (PPI) network. The basic idea is that each protein in the network can be viewed as a material particle which creates a potential field around itself and the interaction of all proteins forms a topological field over the network. By defining and computing the value of each protein's topology potential, we can obtain a more precise ranking which reflects the importance of proteins from the PPI network. The experimental results show that topology potential-based methods TP and TP-NC outperform traditional topology measures: degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), subgraph centrality (SC), eigenvector centrality (EC), information centrality (IC), and network centrality (NC) for predicting essential proteins. In addition, these centrality measures are improved on their performance for identifying essential proteins in biological network when controlled by topology potential.

UDoNC: An Algorithm for Identifying Essential Proteins Based on Protein Domains and Protein-Protein Interaction Networks

Prediction of essential proteins which are crucial to an organism's survival is important for disease analysis and drug design, as well as the understanding of cellular life. The majority of prediction methods infer the possibility of proteins to be essential by using the network topology. However, these methods are limited to the completeness of available protein-protein interaction (PPI) data and depend on the network accuracy. To overcome these limitations, some computational methods have been proposed. However, seldom of them solve this problem by taking consideration of protein domains. In this work, we first analyze the correlation between the essentiality of proteins and their domain features based on data of 13 species. We find that the proteins containing more protein domain types which rarely occur in other proteins tend to be essential. Accordingly, we propose a new prediction method, named UDoNC, by combining the domain features of proteins with their topological properties in PPI network. In UDoNC, the essentiality of proteins is decided by the number and the frequency of their protein domain types, as well as the essentiality of their adjacent edges measured by edge clustering coefficient. The experimental results on S. cerevisiae data show that UDoNC outperforms other existing methods in terms of area under the curve (AUC). Additionally, UDoNC can also perform well in predicting essential proteins on data of E. coli.

Characterization of protein complexes and subcomplexes in protein-protein interaction databases

The identification and characterization of protein complexes implicated in protein-protein interaction data are crucial to the understanding of the molecular events under normal and abnormal physiological conditions. This paper provides a novel characterization of subcomplexes in protein interaction databases, stressing definition and representation issues, quantification, biological validation, network metrics, motifs, modularity, and gene ontology (GO) terms. The paper introduces the concept of "nested group" as a way to represent subcomplexes and estimates that around 15% of those nested group with the higher Jaccard index may be a result of data artifacts in protein interaction databases, while a number of them can be found in biologically important modular structures or dynamic structures. We also found that network centralities, enrichment in essential proteins, GO terms related to regulation, imperfect 5-clique motifs, and higher GO homogeneity can be used to identify proteins in nested complexes.

Tetrahydroisoquinolines affect the whole-cell phenotype of Mycobacterium tuberculosis by inhibiting the ATP-dependent MurE ligase

Objectives

(S)-Leucoxine, isolated from the Colombian Lauraceae tree Rhodostemonodaphne crenaticupula Madriñan, was found to inhibit the growth of Mycobacterium tuberculosis H₃₇Rv. A biomimetic approach for the chemical synthesis of a wide array of 1-substituted tetrahydroisoquinolines was undertaken with the aim of elucidating a common pharmacophore for these compounds with novel mode(s) of anti-TB action.

Methods

Biomimetic Pictet–Spengler or Bischler–Napieralski synthetic routes were employed followed by an evaluation of the biological activity of the synthesized compounds.

Results

In this work, the synthesized tetrahydroisoquinolines were found to inhibit the growth of M. tuberculosis H₃₇Rv and affect its whole-cell phenotype as well as the activity of the ATP-dependent MurE ligase, a key enzyme involved in the early stage of cell wall peptidoglycan biosynthesis.

Conclusions

As the correlation between the MIC and the half-inhibitory enzymatic concentration was not particularly strong, there is a credible possibility that these compounds have pleiotropic mechanism(s) of action in M. tuberculosis.

Identifying essential proteins from active PPI networks constructed with dynamic gene expression

Essential proteins are vitally important for cellular survival and development, and identifying essential proteins is very meaningful research work in the post-genome era. Rapid increase of available protein-protein interaction (PPI) data has made it possible to detect protein essentiality at the network level. A series of centrality measures have been proposed to discover essential proteins based on the PPI networks. However, the PPI data obtained from large scale, high-throughput experiments generally contain false positives. It is insufficient to use original PPI data to identify essential proteins. How to improve the accuracy, has become the focus of identifying essential proteins. In this paper, we proposed a framework for identifying essential proteins from active PPI networks constructed with dynamic gene expression. Firstly, we process the dynamic gene expression profiles by using time-dependent model and time-independent model. Secondly, we construct an active PPI network based on co-expressed genes. Lastly, we apply six classical centrality measures in the active PPI network. For the purpose of comparison, other prediction methods are also performed to identify essential proteins based on the active PPI network. The experimental results on yeast network show that identifying essential proteins based on the active PPI network can improve the performance of centrality measures considerably in terms of the number of identified essential proteins and identification accuracy. At the same time, the results also indicate that most of essential proteins are active.

Pan-genome analysis of human gastric pathogen H. pylori: comparative genomics and pathogenomics approaches to identify regions associated with pathogenicity and prediction of potential core therapeutic targets

Helicobacter pylori is a human gastric pathogen implicated as the major cause of peptic ulcer and second leading cause of gastric cancer (~70%) around the world. Conversely, an increased resistance to antibiotics and hindrances in the development of vaccines against H. pylori are observed. Pan-genome analyses of the global representative H. pylori isolates consisting of 39 complete genomes are presented in this paper. Phylogenetic analyses have revealed close relationships among geographically diverse strains of H. pylori. The conservation among these genomes was further analyzed by pan-genome approach; the predicted conserved gene families (1,193) constitute ~77% of the average H. pylori genome and 45% of the global gene repertoire of the species. Reverse vaccinology strategies have been adopted to identify and narrow down the potential core-immunogenic candidates. Total of 28 nonhost homolog proteins were characterized as universal therapeutic targets against H. pylori based on their functional annotation and protein-protein interaction. Finally, pathogenomics and genome plasticity analysis revealed 3 highly conserved and 2 highly variable putative pathogenicity islands in all of the H. pylori genomes been analyzed.

A statistical framework for improving genomic annotations of transposon mutagenesis (TM) assigned essential genes

Whole-genome transposon mutagenesis (TM) experiment followed by sequence-based identification of insertion sites is the most popular genome-wise experiment to identify essential genes in Prokaryota. However, due to the limitation of high-throughput technique, this approach yields substantial systematic biases resulting in the incorrect assignments of many essential genes. To obtain unbiased and accurate annotations of essential genes from TM experiments, we developed a novel Poisson model based statistical framework to refine these TM assignments. In the model, first we identified and incorporated several potential factors such as gene length and TM insertion information which may cause the TM assignment biases into the basic Poisson model. Then we calculated the conditional probability of an essential gene given the observed TM insertion number. By factorizing this probability through introducing a latent variable the real insertion number, we formalized the statistical framework. Through iteratively updating and optimizing model parameters to maximize the goodness-of-fit of the model to the observed TM insertion data, we finalized the model. Using this model, we are able to assign the probability score of essentiality to each individual gene given its TM assignment, which subsequently correct the experimental biases. To enable our model widely useable, we established a user-friendly Web-server that is accessible to the public: http://research.cchmc.org/essentialgene/.

Three computational tools for predicting bacterial essential genes

Essential genes are those genes indispensable for the survival of any living cell. Bacterial essential genes constitute the cornerstones of synthetic biology and are often attractive targets in the development of antibiotics and vaccines. Because identification of essential genes with wet-lab ways often means expensive economic costs and tremendous labor, scientists changed to seek for alternative way of computational prediction. Aiming to help to solve this issue, our research group (CEFG: group of Computational, Comparative, Evolutionary and Functional Genomics, http://cefg.uestc.edu.cn) has constructed three online services to predict essential genes in bacterial genomes. These freely available tools are applicable for single gene sequences without annotated functions, single genes with definite names, and complete genomes of bacterial strains. To ensure reliable predictions, the investigated species should belong to the same family (for EGP) or phylum (for CEG_Match and Geptop) with one of the reference species, respectively. As the pilot software for the issue, predicting accuracies of them have been assessed and compared with existing algorithms, and note that all of other published algorithms have not any formed online services. We hope these services at CEFG will help scientists and researchers in the field of essential genes.

Irreversible covalent modification of type I dehydroquinase with a stable Schiff base

Structural and computational studies carried out with two epoxides provide insight into the irreversible inhibition of type I dehydroquinase.

In silico analysis and prioritization of drug targets in Fusarium solani

Mycotic keratitis has emerged as a major ophthalmic problem and a leading cause of blindness, since its recognition in 1879. Filamentous fungi are major causative of mycotic keratitis. In India, the main etiological organism responsible for mycotic keratitis is Aspergillus species followed by Fusarium species. In South India, Fusarium based keratitis scales up to 43%. Nearly one-third of mycotic keratitis treatment results in failure, as fungal infections are highly resistant to antibiotic therapies. Therefore, there is need to determine novel and specific targets to constrain Fusarium infections in human eye. In this study, we implemented subtractive proteomics coupled with in silico functional annotation to prioritize potential and specific drug targets which can be used to modulate the virulence of Fusarium solani subsp.pisi (Nectria haematococca MPVI). The results infer that Thiamine thiazole synthase (Thi4), an intracellular membrane bound protein as the potential target, which is a core protein in biological and metabolic process of this pathogen. Moreover, this protein occurs in the thiamine thiazole biosynthesis pathway which is unique to F.solani and devoid in human. Hence, we predicted a plausible structure for this protein and also performed ligand-binding cavity analysis which can be for a strong base for drug designing studies. This study will pave way in better understanding of potential drug targets in F.solani and also leading to therapeutic interventions of fungal keratitis.

Identification and analysis of potential targets in Streptococcus sanguinis using computer aided protein data analysis

PURPOSE

Streptococcus sanguinis is a Gram-positive, facultative aerobic bacterium that is a member of the viridans streptococcus group. It is found in human mouths in dental plaque, which accounts for both dental cavities and bacterial endocarditis, and which entails a mortality rate of 25%. Although a range of remedial mediators have been found to control this organism, the effectiveness of agents such as penicillin, amoxicillin, trimethoprim-sulfamethoxazole, and erythromycin, was observed. The emphasis of this investigation was on finding substitute and efficient remedial approaches for the total destruction of this bacterium.

MATERIALS AND METHODS

In this computational study, various databases and online software were used to ascertain some specific targets of S. sanguinis. Particularly, the Kyoto Encyclopedia of Genes and Genomes databases were applied to determine human nonhomologous proteins, as well as the metabolic pathways involved with those proteins. Different software such as Phyre2, CastP, DoGSiteScorer, the Protein Function Predictor server, and STRING were utilized to evaluate the probable active drug binding site with its known function and protein-protein interaction.

RESULTS

In this study, among 218 essential proteins of this pathogenic bacterium, 81 nonhomologous proteins were accrued, and 15 proteins that are unique in several metabolic pathways of S. sanguinis were isolated through metabolic pathway analysis. Furthermore, four essentially membrane-bound unique proteins that are involved in distinct metabolic pathways were revealed by this research. Active sites and druggable pockets of these selected proteins were investigated with bioinformatic techniques. In addition, this study also mentions the activity of those proteins, as well as their interactions with the other proteins.

CONCLUSION

Our findings helped to identify the type of protein to be considered as an efficient drug target. This study will pave the way for researchers to develop and discover more effective and specific therapeutic agents against S. sanguinis.

A computational analysis of antisense off-targets in prokaryotic organisms

The adoption of antisense gene silencing as a novel disinfectant for prokaryotic organisms is hindered by poor silencing efficiencies. Few studies have considered the effects of off-targets on silencing efficiencies, especially in prokaryotic organisms. In this computational study, a novel algorithm was developed that determined and sorted the number of off-targets as a function of alignment length in Escherichia coli K-12 MG1655 and Mycobacterium tuberculosis H37Rv. The mean number of off-targets per a single location was calculated to be 14.1 ± 13.3 and 36.1 ± 58.5 for the genomes of E. coli K-12 MG1655 and M. tuberculosis H37Rv, respectively. Furthermore, when the entire transcriptome was analyzed, it was found that there was no general gene location that could be targeted to minimize or maximize the number of off-targets. In an effort to determine the effects of off-targets on silencing efficiencies, previously published studies were used. Analyses with acpP, ino1, and marORAB revealed a statistically significant relationship between the number of short alignment length off-targets hybrids and the efficacy of the antisense gene silencing, suggesting that the minimization of off-targets may be beneficial for antisense gene silencing in prokaryotic organisms.

Organized Modularity in the Interactome: Evidence from the Analysis of Dynamic Organization in the Cell Cycle

The organization of global protein interaction networks (PINs) has been extensively studied and heatedly debated. We revisited this issue in the context of the analysis of dynamic organization of a PIN in the yeast cell cycle. Statistically significant bimodality was observed when analyzing the distribution of the differences in expression peak between periodically expressed partners. A close look at their behavior revealed that date and party hubs derived from this analysis have some distinct features. There are no significant differences between them in terms of protein essentiality, expression correlation and semantic similarity derived from gene ontology (GO) biological process hierarchy. However, date hubs exhibit significantly greater values than party hubs in terms of semantic similarity derived from both GO molecular function and cellular component hierarchies. Relating to three-dimensional structures, we found that both single- and multi-interface proteins could become date hubs coordinating multiple functions performed at different times while party hubs are mainly multi-interface proteins. Furthermore, we constructed and analyzed a PPI network specific to the human cell cycle and highlighted that the dynamic organization in human interactome is far more complex than the dichotomy of hubs observed in the yeast cell cycle.