Inferring modules of functionally interacting proteins using the Bond Energy Algorithm

<p>Abstract</p> <p>Background</p> <p>Non-homology based methods such as phylogenetic profiles are effective for predicting functional relationships between proteins with no considerable sequence or structure similarity. Those methods rely heavily on traditional similarity metrics defined on pairs of phylogenetic patterns. Proteins do not exclusively interact in pairs as the final biological function of a protein in the cellular context is often hold by a group of proteins. In order to accurately infer modules of functionally interacting proteins, the consideration of not only direct but also indirect relationships is required.</p> <p>In this paper, we used the Bond Energy Algorithm (<it>BEA</it>) to predict functionally related groups of proteins. With <it>BEA </it>we create clusters of phylogenetic profiles based on the associations of the surrounding elements of the analyzed data using a metric that considers linked relationships among elements in the data set.</p> <p>Results</p> <p>Using phylogenetic profiles obtained from the Cluster of Orthologous Groups of Proteins (<it>COG</it>) database, we conducted a series of clustering experiments using <it>BEA </it>to predict (upper level) relationships between profiles. We evaluated our results by comparing with <it>COG's </it>functional categories, And even more, with the experimentally determined functional relationships between proteins provided by the <it>DIP </it>and <it>ECOCYC </it>databases. Our results demonstrate that <it>BEA </it>is capable of predicting meaningful modules of functionally related proteins. <it>BEA </it>outperforms traditionally used clustering methods, such as <it>k</it>-means and hierarchical clustering by predicting functional relationships between proteins with higher accuracy.</p> <p>Conclusion</p> <p>This study shows that the linked relationships of phylogenetic profiles obtained by <it>BEA </it>is useful for detecting functional associations between profiles and extending functional modules not found by traditional methods. <it>BEA </it>is capable of detecting relationship among phylogenetic patterns by linking them through a common element shared in a group. Additionally, we discuss how the proposed method may become more powerful if other criteria to classify different levels of protein functional interactions, as gene neighborhood or protein fusion information, is provided.</p

Bioinformatics: new tools and applications in life science and personalized medicine

While we have a basic understanding of the functioning of the gene when coding sequences of specific proteins, we feel the lack of information on the role that DNA has on specific diseases or functions of thousands of proteins that are produced. Bioinformatics combines the methods used in the collection, storage, identification, analysis, and correlation of this huge and complex information. All this work produces an “ocean” of information that can only be “sailed” with the help of computerized methods. The goal is to provide scientists with the right means to explain normal biological processes, dysfunctions of these processes which give rise to disease, and approaches that allow the discovery of new medical cures. Recently, sequencing platforms, a large scale of genomes and transcriptomes, have created new challenges not only to the genomics but especially for bioinformatics. The intent of this article is to compile a list of tools and information resources used by scientists to treat information from the massive sequencing of recent platforms to new generations and the applications of this information in different areas of life sciences including medicine.The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) and FEDER under Programme PT2020 for financial support to CIMO (UID/AGR/00690/2019).info:eu-repo/semantics/publishedVersio

Using Sequence Similarity Networks for Visualization of Relationships Across Diverse Protein Superfamilies

The dramatic increase in heterogeneous types of biological data—in particular, the abundance of new protein sequences—requires fast and user-friendly methods for organizing this information in a way that enables functional inference. The most widely used strategy to link sequence or structure to function, homology-based function prediction, relies on the fundamental assumption that sequence or structural similarity implies functional similarity. New tools that extend this approach are still urgently needed to associate sequence data with biological information in ways that accommodate the real complexity of the problem, while being accessible to experimental as well as computational biologists. To address this, we have examined the application of sequence similarity networks for visualizing functional trends across protein superfamilies from the context of sequence similarity. Using three large groups of homologous proteins of varying types of structural and functional diversity—GPCRs and kinases from humans, and the crotonase superfamily of enzymes—we show that overlaying networks with orthogonal information is a powerful approach for observing functional themes and revealing outliers. In comparison to other primary methods, networks provide both a good representation of group-wise sequence similarity relationships and a strong visual and quantitative correlation with phylogenetic trees, while enabling analysis and visualization of much larger sets of sequences than trees or multiple sequence alignments can easily accommodate. We also define important limitations and caveats in the application of these networks. As a broadly accessible and effective tool for the exploration of protein superfamilies, sequence similarity networks show great potential for generating testable hypotheses about protein structure-function relationships

Evolutionary interplay between symbiotic relationships and patterns of signal peptide gain and loss

Can orthologous proteins differ in terms of their ability to be secreted? To answer this question, we investigated the distribution of signal peptides within the orthologous groups of Enterobacterales. Parsimony analysis and sequence comparisons revealed a large number of signal peptide gain and loss events, in which signal peptides emerge or disappear in the course of evolution. Signal peptide losses prevail over gains, an effect which is especially pronounced in the transition from the free-living or commensal to the endosymbiotic lifestyle. The disproportionate decline in the number of signal peptide-containing proteins in endosymbionts cannot be explained by the overall reduction of their genomes. Signal peptides can be gained and lost either by acquisition/elimination of the corresponding N-terminal regions or by gradual accumulation of mutations. The evolutionary dynamics of signal peptides in bacterial proteins represents a powerful mechanism of functional diversification

Generation and analysis of a mouse intestinal metatranscriptome through Illumina based RNA-sequencing

With the advent of high through-put sequencing (HTS), the emerging science of metagenomics is transforming our understanding of the relationships of microbial communities with their environments. While metagenomics aims to catalogue the genes present in a sample through assessing which genes are actively expressed, metatranscriptomics can provide a mechanistic understanding of community inter-relationships. To achieve these goals, several challenges need to be addressed from sample preparation to sequence processing, statistical analysis and functional annotation. Here we use an inbred non-obese diabetic (NOD) mouse model in which germ-free animals were colonized with a defined mixture of eight commensal bacteria, to explore methods of RNA extraction and to develop a pipeline for the generation and analysis of metatranscriptomic data. Applying the Illumina HTS platform, we sequenced 12 NOD cecal samples prepared using multiple RNA-extraction protocols. The absence of a complete set of reference genomes necessitated a peptide-based search strategy. Up to 16% of sequence reads could be matched to a known bacterial gene. Phylogenetic analysis of the mapped ORFs revealed a distribution consistent with ribosomal RNA, the majority from Bacteroides or Clostridium species. To place these HTS data within a systems context, we mapped the relative abundance of corresponding Escherichia coli homologs onto metabolic and protein-protein interaction networks. These maps identified bacterial processes with components that were well-represented in the datasets. In summary this study highlights the potential of exploiting the economy of HTS platforms for metatranscriptomics