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The current trend in biomedical research is on prioritizing infections based on multidrug resistance. Elizabethkingia meningoseptica, a nosocomial infection-causing organism emerging from Neonatal Intensive Care Units (NICUs), leads to neonatal meningitis and sepsis resulting in severe illness, and, in some cases, fatal. Finding a solution remains challenging due to limited prior work. Translational S12 ribosomal proteins play a crucial role in decoding the codon-anticodon helix, which is essential for the survival of E. meningoseptica. These proteins do not exhibit significant similarity with humans, making them potential drug targets. An in silico study aims to identify specific inhibitors for E. meningoseptica ribosomal proteins among known bioactive compounds targeting prokaryotic 30S ribosomal protein. A 3D model of the 7JIL_h protein from Flavobacterium johnsoniae, showing 90% sequence similarity with the target protein was generated using SWISS-MODEL software. The model was validated through Molprobity v4.4, VERIFY 3D, Errata, and ProSA analysis, confirming conserved residues of the target protein. Insilico screening of known bioactive compounds and their analogs identified potential ligands for the target protein. Molecular Docking and post-docking analysis assessed the stability of the protein-ligand complexes among the shortlisted compounds. The top two compounds with high Gold fitness scores and low predicted binding energy underwent MD simulation and further estimation of free binding energy using the MM_PBSA module. These computationally shortlisted compounds, namely chEMBL 1323619 and chEMBL 312490 may be considered for future in-vivo studies as potential inhibitors against the modeled 30S ribosomal protein S12 of E. meningoseptica.Communicated by Ramaswamy H. Sarma.

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Multiple rounds of DNA replication take place in various stages of the life cycle in the human malaria parasite Plasmodium falciparum. Previous bioinformatics analysis has shown the presence of putative Autonomously Replicating Sequence (ARS) like sequences in the Plasmodium genome. However, the actual sites and frequency of replication origins in the P. falciparum genome based on experimental data still remain elusive. Minichromosome maintenance (MCM) proteins are recruited by the Origin recognition complex (ORC) to the origins of replication in eukaryotes including P. falciparum. We used PfMCM6 for chromatin immunoprecipitation followed by sequencing (ChIP-seq) in the quest for identification of putative replication origins in the parasite. PfMCM6 DNA binding sites annotation revealed high enrichment at exon regions. This is contrary to higher eukaryotes that show an inclination of origin sites towards transcriptional start sites. ChIP-seq results were further validated by ChIP-qPCR results as well as nascent strand abundance assay at the selected PfMCM6 enriched sites that also showed preferential binding of PfORC1 suggesting potential of these sites as origin sites. Further, PfMCM6 ChIP-seq data showed a positive correlation with previously published histone H4K8Ac genome-wide binding sites but not with H3K9Ac sites suggesting epigenetic control of replication initiation sites in the parasites. Overall, our data show the genome-wide distribution of PfMCM6 binding sites with their potential as replication origins in this deadly human pathogen that not only broadens our knowledge of parasite DNA replication and its unique biology, it may help to find new avenues for intervention processes.

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Annotation of genome data with biological features is a challenging problem. One such problem deals with distinguishing lncRNA from mRNA. In this study, three groups of classification features, namely base periodicity, physicochemical property and nucleotide compositions were considered. We are attempting to propose a simple neural network model to obtain better results using judicious combination of the above said sequence features. Our approach uses balanced dataset, simple prediction model and use of limited features in distinguishing lncRNA and mRNA. Accordingly (a) two properties of base periodicity: peak power spectrum of the signal and noise-to-signal ratio (SNR) of this peak signal (b) three physicochemical properties: solvation, stacking and hydrogen-bonding energy and (c) all dinucleotides and trinucleotides compositions were used. Classification was performed by considering features independently followed by combining these properties for improvement. Classification metric was used to compare the result for seven eukaryotic organisms for various combinations of features. Nucleotide compositions combined with physicochemical property or base periodicity group of features becomes a strong classifier with more than 99 percentage accuracy. Base periodicity analysis with SNR can be used as discriminating feature of lncRNA from mRNA.

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The pathogenesis of human malarial parasite Plasmodium falciparum is interlinked with its timely control of gene expression during its complex life cycle. In this organism, gene expression is partially controlled through epigenetic mechanisms, the regulation of which is, hence, of paramount importance to the parasite. The P. falciparum (Pf)-GCN5 histone acetyltransferase (HAT), an essential enzyme, acetylates histone 3 and regulates global gene expression in the parasite. Here, we show the existence of a novel proteolytic processing for PfGCN5 that is crucial for its activity in vivo We find that a cysteine protease-like enzyme is required for the processing of PfGCN5 protein. Immunofluorescence and immuno-electron microscopy analysis suggest that the processing event occurs in the vicinity of the digestive vacuole of the parasite following its trafficking through the classical ER-Golgi secretory pathway, before it subsequently reaches the nucleus. Furthermore, blocking of PfGCN5 processing leads to the concomitant reduction of its occupancy at the gene promoters and a reduced H3K9 acetylation level at these promoters, highlighting the important correlation between the processing event and PfGCN5 activity. Altogether, our study reveals a unique processing event for a nuclear protein PfGCN5 with unforeseen role of a food vacuolar cysteine protease. This leads to a possibility of the development of new antimalarials against these targets.This article has an associated First Person interview with the first author of the paper.

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GenerationE of huge "omics" data necessitates the development and application of computational methods to annotate the data in terms of biological features. In the context of DNA sequence, it is important to unravel the hidden physicochemical signatures. For this purpose, we have considered various sequence elements such as promoter, ACS, LTRs, telomere, and retrotransposon of the model organism Saccharomyces cerevisiae. Contributions due to di-nucleotides play a major role in studying the DNA conformation profile. The physicochemical parameters used are hydrogen bonding energy, stacking energy and solvation energy per base pair. Our computational study shows that all sequence elements in this study have distinctive physicochemical signatures and the same can be exploited for prediction experiments. The order that we see in a DNA sequence is dictated by biological regions and hence, there exists role of dependency in the sequence makeup, keeping this in mind we are proposing two computational schemes (a) using a windowing block size procedure and (b) using di-nucleotide transitions. We obtained better discriminating profile when we analyzed the sequence data in windowing manner. In the second novel approach, we introduced the di-nucleotide transition probability matrix (DTPM) to study the hidden layer of information embedded in the sequences. DTPM has been used as weights for scanning and predictions. This proposed computational scheme incorporates the memory property which is more realistic to study the physicochemical properties embedded in DNA sequences. Our analysis shows that the DTPM scheme performs better than the existing method in this applied region. Characterization of these elements will be a key to genome editing applications and advanced machine learning approaches may also require such distinctive profiles as useful input features.

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Autonomous replication sequences (ARS) are essential for the replication of Saccharomyces cerevisiae genome. The content and context of ARS sites are distinct from other segments of the genome and these factors influence the conformation and thermodynamic profile of DNA that favor binding of the origin recognition complex proteins. Identification of ARS sites in the genome is a challenging task because of their organizational complexity and degeneracy present across the intergenic regions. We considered a few properties of DNA segments and divided them into multiple subsets (views) for computational prediction of ARS sequences. Our approach utilized these views for learning classification models in an ensemble manner and accordingly predictions were made. This approach maximized the prediction accuracy over the traditional way where all features are selected at once. Our study also revealed that major groove width and major groove depth are the most prominent properties that distinguished ARS from other segments of the genome. Our investigation also provides clue about the most suitable classifier for a given feature set, and this strategy may be useful for finding ARS in other closely related species.

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DNA replication is a fundamental process in genome maintenance, and initiates from several genomic sites (origins) in eukaryotes. In Saccharomyces cerevisiae, conserved sequences known as autonomously replicating sequences (ARSs) provide a landing pad for the origin recognition complex (ORC), leading to replication initiation. Although origins from higher eukaryotes share some common sequence features, the definitive genomic organization of these sites remains elusive. The human malaria parasite Plasmodium falciparum undergoes multiple rounds of DNA replication; therefore, control of initiation events is crucial to ensure proper replication. However, the sites of DNA replication initiation and the mechanism by which replication is initiated are poorly understood. Here, we have identified and characterized putative origins in P. falciparum by bioinformatics analyses and experimental approaches. An autocorrelation measure method was initially used to search for regions with marked fluctuation (dips) in the chromosome, which we hypothesized might contain potential origins. Indeed, S. cerevisiae ARS consensus sequences were found in dip regions. Several of these P. falciparum sequences were validated with chromatin immunoprecipitation-quantitative PCR, nascent strand abundance and a plasmid stability assay. Subsequently, the same sequences were used in yeast to confirm their potential as origins in vivo. Our results identify the presence of functional ARSs in P. falciparum and provide meaningful insights into replication origins in these deadly parasites. These data could be useful in designing transgenic vectors with improved stability for transfection in P. falciparum.

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Genome-wide experimental studies in Saccharomyces cerevisiae reveal that autonomous replicating sequence (ARS) requires an essential consensus sequence (ACS) for replication activity. Computational studies identified thousands of ACS like patterns in the genome. However, only a few hundreds of these sites act as replicating sites and the rest are considered as dormant or evolving sites. In a bid to understand the sequence makeup of replication sites, a content and context-based analysis was performed on a set of replicating ACS sequences that binds to origin-recognition complex (ORC) denoted as ORC-ACS and non-replicating ACS sequences (nrACS), that are not bound by ORC. In this study, DNA properties such as base composition, correlation, sequence dependent thermodynamic and DNA structural profiles, and their positions have been considered for characterizing ORC-ACS and nrACS. Analysis reveals that ORC-ACS depict marked differences in nucleotide composition and context features in its vicinity compared to nrACS. Interestingly, an A-rich motif was also discovered in ORC-ACS sequences within its nucleosome-free region. Profound changes in the conformational features, such as DNA helical twist, inclination angle and stacking energy between ORC-ACS and nrACS were observed. Distribution of ACS motifs in the non-coding segments points to the locations of ORC-ACS which are found far away from the adjacent gene start position compared to nrACS thereby enabling an accessible environment for ORC-proteins. Our attempt is novel in considering the contextual view of ACS and its flanking region along with nucleosome positioning in the S. cerevisiae genome and may be useful for any computational prediction scheme.

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We have carried out an analysis on 500 bacterial genomes and found that the de-facto GC skew method could predict the replication origin site only for 376 genomes. We also found that the auto-correlation and cross-correlation based methods have a similar prediction performance. In this paper, we propose a new measure called correlated entropy measure (CEM) which is able to predict the replication origin of all these 500 bacterial genomes. The proposed measure is context sensitive and thus a promising tool to identify functional sites. The process of identifying replication origins from the output of CEM and other methods has been automated to analyze a large number of genomes in a faster manner. We have also explored the applicability of SVM based classification of the workability of each of these methods on all the 500 bacterial genomes based on its length and GC content.

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Computational prediction of the origin of replication is a challenging problem and of immense interest to biologists. Several methods have been proposed for identifying the replicon site for various classes of organisms. However, these methods have limited applicability since the replication mechanism is different in different organisms. We propose a correlation measure and show that it is correctly able to predict the origin of replication in most of the bacterial genomes. When applied to Methanocaldococcus jannaschii, Plasmodium falciparum apicoplast and Nicotiana tabacum plastid, this correlation based method is able to correctly predict the origin of replication whereas the generally used GC skew measure fails. Thus, this correlation based measure is a novel and promising tool for predicting the origin of replication in a wide class of organisms. This could have important implications in not only gaining a deeper understanding of the replication machinery in higher organisms, but also for drug discovery.

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Genomes of almost all organisms have been found to exhibit several periodicities, the most prominent one is the three base periodicity. It is more pronounced in the gene coding regions and has been exploited to identify the segments of a genome that code for a protein. The reason for this three base periodicity in the gene-coding region has been attributed to inhomogeneous nucleotide compositions in the three codon positions. However, this reason cannot explain the three base periodicity present at the level of the whole genome where the codon concept is not applicable. Even though the distribution of each nucleotide is uniform at the positions 0(mod 3), 1(mod 3) and 2(mod 3) when the whole genome data is considered, our analysis reveals that the three base periodicity is arising because of higher correlations among the nucleotides separated by three bases.

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An accurate method for locating genes under tumor suppressor p53 control that is based on a well-established mathematical theory and built using naturally occurring, experimentally proven p53 sites is essential in understanding the complete p53 network. We used a molecular information theory approach to create a flexible model for p53 binding. By searching around transcription start sites in human chromosomes 1 and 2, we predicted 16 novel p53 binding sites and experimentally demonstrated that 15 of the 16 (94%) sites were bound by p53. Some were also bound by the related proteins p63 and p73. Thirteen of the adjacent genes were controlled by at least one of the proteins. Eleven of the 16 sites (69%) had not been identified previously. This molecular information theory approach can be extended to any genetic system to predict new sites for DNA-binding proteins.

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