Identifying Essential Proteins in Dynamic PPI Network with Improved FOA

Identification of essential proteins plays an important role for understanding the cellular life activity and development in postgenomic era. Identification of essential proteins from the protein-protein interaction (PPI) networks has become a hot topic in recent years. In this work, fruit fly optimization algorithm (FOA) is extended for identifying essential proteins, the extended algorithm is called EPFOA, which merges FOA with topological properties and biological information for essential proteins identification. The algorithm EPFOA has the advantage of identifying multiple essential proteins simultaneously rather than completely relying on ranking score identification individually. The performance of EPFOA is analyzed on dynamic PPI networks, which are constructed by combining the gene expression data. The experimental results demonstrate that EPFOA is more efficient in detecting essential proteins than the state-of-the-art essential proteins detection methods

Effective Identification of Conserved Pathways in Biological Networks Using Hidden Markov Models

The advent of various high-throughput experimental techniques for measuring molecular interactions has enabled the systematic study of biological interactions on a global scale. Since biological processes are carried out by elaborate collaborations of numerous molecules that give rise to a complex network of molecular interactions, comparative analysis of these biological networks can bring important insights into the functional organization and regulatory mechanisms of biological systems.In this paper, we present an effective framework for identifying common interaction patterns in the biological networks of different organisms based on hidden Markov models (HMMs). Given two or more networks, our method efficiently finds the top matching paths in the respective networks, where the matching paths may contain a flexible number of consecutive insertions and deletions.Based on several protein-protein interaction (PPI) networks obtained from the Database of Interacting Proteins (DIP) and other public databases, we demonstrate that our method is able to detect biologically significant pathways that are conserved across different organisms. Our algorithm has a polynomial complexity that grows linearly with the size of the aligned paths. This enables the search for very long paths with more than 10 nodes within a few minutes on a desktop computer. The software program that implements this algorithm is available upon request from the authors

Methods for protein complex prediction and their contributions towards understanding the organization, function and dynamics of complexes

Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organization of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight challenges faced by these methods, in particular detection of sparse and small or sub- complexes and discerning of overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.Comment: 1 Tabl

De novo assembly of the olive fruit fly (Bactrocera oleae) genome with linked-reads and long-read technologies minimizes gaps and provides exceptional Y chromosome assembly

Background: The olive fruit fly, Bactrocera oleae, is the most important pest in the olive fruit agribusiness industry. This is because female flies lay their eggs in the unripe fruits and upon hatching the larvae feed on the fruits thus destroying them. The lack of a high-quality genome and other genomic and transcriptomic data has hindered progress in understanding the fly’s biology and proposing alternative control methods to pesticide use. Results: Genomic DNA was sequenced from male and female Demokritos strain flies, maintained in the laboratory for over 45 years. We used short-, mate-pair-, and long-read sequencing technologies to generate a combined male-female genome assembly (GenBank accession GCA_001188975.2). Genomic DNA sequencing from male insects using 10x Genomics linked-reads technology followed by mate-pair and long-read scaffolding and gap-closing generated a highly contiguous 489 Mb genome with a scaffold N50 of 4.69 Mb and L50 of 30 scaffolds (GenBank accession GCA_001188975.4). RNA-seq data generated from 12 tissues and/or developmental stages allowed for genome annotation. Short reads from both males and females and the chromosome quotient method enabled identification of Y-chromosome scaffolds which were extensively validated by PCR. Conclusions: The high-quality genome generated represents a critical tool in olive fruit fly research. We provide an extensive RNA-seq data set, and genome annotation, critical towards gaining an insight into the biology of the olive fruit fly. In addition, elucidation of Y-chromosome sequences will advance our understanding of the Y-chromosome’s organization, function and evolution and is poised to provide avenues for sterile insect technique approaches

A single AKH neuropeptide activating three different fly AKH-receptors: an insecticide study via computational methods

Flies are a widely distributed pest insect that poses a significant threat to food security. Flight is essential for the dispersal of the adult flies to find new food sources and ideal breeding spots. The supply of metabolic fuel to power the flight muscles of insects is regulated by adipokinetic hormones (AKHs). The fruit fly, Drosophila melanogaster, the flesh fly, Sarcophaga crassipalpis, and the oriental fruit fly, Bactrocera dorsalis all have the same AKH that is present in the blowfly, Phormia terraenovae; this AKH has the code-name Phote-HrTH. Binding of the AKH to the extracellular binding site of a G protein-coupled receptor causes its activation. In this thesis, the structure of Phote-HrTH in SDS micelle solution was determined using NMR restrained molecular dynamics. The peptide was found to bind to the micelle and be reasonably rigid, with an S 2 order parameter of 0.96. The translated protein sequence of the AKH receptor from the fruit fly, Drosophila melanogaster, the flesh fly, Sarcophaga crassipalpis, and the oriental fruit fly, Bactrocera dorsalis were used to construct two models for each receptor: Drome-AKHR, Sarcr-AKHR, and Bacdo-AKHR. It is proposed that these two models represent the active and inactive state of the receptor. The models based on the crystal structure of the β-2 adrenergic receptor were found to bind Phote-HrTH with a predicted binding free energy of –107 kJ mol–1 for Drome-AKHR, –102 kJ mol–1 for Sarcr-AKHR and –102 kJ mol–1 for Bacdo-AKHR. Under molecular dynamics simulation, in a POPC membrane, the β-2AR receptor-like complexes transformed to rhodopsin-like. The identification and characterisation of the ligand-binding site of each receptor provide novel information on ligand-receptor interactions, which could lead to the development of species-specific control substances to use discriminately against these pest flies

Network Archaeology: Uncovering Ancient Networks from Present-day Interactions

Often questions arise about old or extinct networks. What proteins interacted in a long-extinct ancestor species of yeast? Who were the central players in the Last.fm social network 3 years ago? Our ability to answer such questions has been limited by the unavailability of past versions of networks. To overcome these limitations, we propose several algorithms for reconstructing a network's history of growth given only the network as it exists today and a generative model by which the network is believed to have evolved. Our likelihood-based method finds a probable previous state of the network by reversing the forward growth model. This approach retains node identities so that the history of individual nodes can be tracked. We apply these algorithms to uncover older, non-extant biological and social networks believed to have grown via several models, including duplication-mutation with complementarity, forest fire, and preferential attachment. Through experiments on both synthetic and real-world data, we find that our algorithms can estimate node arrival times, identify anchor nodes from which new nodes copy links, and can reveal significant features of networks that have long since disappeared.Comment: 16 pages, 10 figure

A Platform for Fast Detection of Let-7 Micro RNA Using Polyaniline Fluorescence and Image Analysis Techniques

The project describes a new strategy for transducing hybridization events through modulating intrinsic properties of the electroconductive polymer polyaniline (PANI). When DNA based probes electrostatically interact with PANI, its fluorescence properties are increased, a phenomenon that can be enhanced by UV irradiation. Hybridization of target nucleic acids results in dissociation of probes causing PANI fluorescence to return to basal levels. By monitoring restoration of base PANI fluorescence as little as 10-11 M (10 pM) of target oligonucleotides could be detected within 15 minutes of hybridization. Detection of complementary oligos was specific, with introduction of a single mismatch failing to form a target-probe duplex that would dissociate from PANI. Furthermore, this approach is robust and is capable of detecting specific RNAs in extracts from animals. This sensor system improves on previously reported strategies by transducing highly specific probe dissociation events through intrinsic properties of a conducting polymer without the need for additional labels. The change in fluorescence property of PANI by oligo immobilization and hybridization with mimic let-7 is measured by fluorescence microscope and the image analyzed by MATLAB. A heuristic algorithm determines color threshold of the fluorescent active image. This image segmentation helps to determine the average pixel intensity representing the active image foreground of PANI fluorescence triggered by DNA immobilization and hybridization process. This would help us to quantify response of PANI based biosensor for detecting micro RNA let-7

Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution

Directed evolution (DE) creates diversity in subsequent rounds of mutagenesis in the quest of increased protein stability, substrate binding, and catalysis. Although this technique does not require any structural/mechanistic knowledge of the system, the frequency of improved mutations is usually low. For this reason, computational tools are increasingly used to focus the search in sequence space, enhancing the efficiency of laboratory evolution. In particular, molecular modeling methods provide a unique tool to grasp the sequence/structure/function relationship of the protein to evolve, with the only condition that a structural model is provided. With this book chapter, we tried to guide the reader through the state of the art of molecular modeling, discussing their strengths, limitations, and directions. In addition, we suggest a possible future template for in silico directed evolution where we underline two main points: a hierarchical computational protocol combining several different techniques and a synergic effort between simulations and experimental validation.Peer ReviewedPostprint (author's final draft