246 research outputs found

    Computational prediction of the bioactivity potential of proteomes based on expert knowledge

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    Advances in the field of genome sequencing have enabled a comprehensive analysis and annotation of the dynamics of the protein inventory of cells. This has been proven particularly rewarding for microbial cells, for which the majority of proteins are already accessible to analysis through automatic metagenome annotation. The large-scale in silico screening of proteomes and metaproteomes is key to uncover bioactivities of translational, clinical and biotechnological interest, and to help assign functions to certain proteins, such as those predicted as hypothetical. This work introduces a new method for the prediction of the bioactivity potential of proteomes/metaproteomes, supporting the discovery of functionally relevant proteins based on prior knowledge. This methodology complements functional annotation enrichment methods by allowing the assignment of functions to proteins annotated as hypothetical/putative/uncharacterised, as well as and enabling the detection of specific bioactivities and the recovery of proteins from defined taxa. This work shows how the new method can be applied to screen proteome and metaproteome sets to obtain predictions of clinical or biotechnological interest based on reference datasets. Notably, with this methodology, the large information files obtained after DNA sequencing or protein identification experiments can be associated for translational purposes that, in cases such as antibiotic-resistance pathogens or foodborne diseases, may represent changes in how these important and global health burdens are approached in the clinical practice. Finally, the Sequence-based Expert-driven pRoteome bioactivity Prediction EnvironmENT, a public Web service implemented in Scala functional programming style, is introduced as means to ensure broad access to the method as well as to discuss main implementation issues, such as modularity, extensibility and interoperability.This work was supported by the Spanish “Programa Estatal de Investigación, Desarrollo e Inovación Orientada a los Retos de la Sociedad” (grant AGL2013-44039R); the Asociación Española Contra el Cancer (“Obtención de péptidos bioactivos contra el Cáncer Colo-Rectal a partir de secuencias genéticas de microbiomas intestinales”, grant PS2016). This study was also supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145- FEDER006684). SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from University of Vigo for hosting its IT infrastructure.info:eu-repo/semantics/publishedVersio

    From Mollusks to Medicine: A Venomics Approach for the Discovery and Characterization of Therapeutics from Terebridae Peptide Toxins

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    Animal venoms comprise a diversity of peptide toxins that manipulate molecular targets such as ion channels and receptors, making venom peptides attractive candidates for the development of therapeutics to benefit human health. However, identifying bioactive venom peptides remains a significant challenge. In this review we describe our particular venomics strategy for the discovery, characterization, and optimization of Terebridae venom peptides, teretoxins. Our strategy reflects the scientific path from mollusks to medicine in an integrative sequential approach with the following steps: (1) delimitation of venomous Terebridae lineages through taxonomic and phylogenetic analyses; (2) identification and classification of putative teretoxins through omics methodologies, including genomics, transcriptomics, and proteomics; (3) chemical and recombinant synthesis of promising peptide toxins; (4) structural characterization through experimental and computational methods; (5) determination of teretoxin bioactivity and molecular function through biological assays and computational modeling; (6) optimization of peptide toxin affinity and selectivity to molecular target; and (7) development of strategies for effective delivery of venom peptide therapeutics. While our research focuses on terebrids, the venomics approach outlined here can be applied to the discovery and characterization of peptide toxins from any venomous taxa

    Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

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    The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

    The Extended Nutrigenomics – Understanding the Interplay between the Genomes of Food, Gut Microbes, and Human Host

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    Comprehensive investigation of nutritional health effects at the molecular level requires the understanding of the interplay between three genomes, the food, the gut microbial, and the human host genome. Food genomes are researched for discovery and exploitation of macro- and micronutrients as well as specific bioactives, with those genes coding for bioactive proteins and peptides being of central interest. The human gut microbiota encompasses a complex ecosystem in the intestine with profound impact on host metabolism. It is being studied at genomic and, more recently, also at proteomic and metabonomic level. Humans are being characterized at the level of genetic pre-disposition and inter-individual variability in terms of (i) response to nutritional interventions and direction of health trajectories; (ii) epigenetic, metabolic programming at certain life stages with health consequences later in life and even for subsequent generations; and (iii) acute genomic expression as a holistic response to diet, monitored at gene transcript, protein and metabolite level. Modern nutrition science explores health-related aspects of bioactive food components, thereby promoting health, preventing, or delaying the onset of disease, optimizing performance and assessing benefits and risks in individuals and subpopulations. Personalized nutrition means adapting food to individual needs, depending on the human host’s life stage, -style, and -situation. Traditionally, nutrigenomics and nutri(epi)genetics are seen as the key sciences to understand human variability in preferences and requirements for diet as well as responses to nutrition. This article puts the three nutrition and health-relevant genomes into perspective, namely the food, the gut microbial and the human host’s genome, and calls for an “extended nutrigenomics” approach in order to build the future tools for personalized nutrition, health maintenance, and disease prevention. We discuss examples of these genomes, proteomes, transcriptomes, and metabolomes under the definition of genomics as the overarching term covering essentially all Omics rather than the sole study of DNA and RNA

    Binding site matching in rational drug design: Algorithms and applications

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    © 2018 The Author(s) 2018. Published by Oxford University Press. All rights reserved. Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning

    Predicting function from sequence in a large multifunctional toxin family

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    Venoms contain active substances with highly specific physiological effects and are increasingly being used as sources of novel diagnostic, research and treatment tools for human disease. Experimental characterisation of individual toxin activities is a severe rate-limiting step in the discovery process, and in-silico tools which allow function to be predicted from sequence information are essential. Toxins are typically members of large multifunctional families of structurally similar proteins that can have different biological activities, and minor sequence divergence can have significant consequences. Thus, existing predictive tools tend to have low accuracy. We investigated a classification model based on physico-chemical attributes that can easily be calculated from amino-acid sequences, using over 250 (mostly novel) viperid phospholipase A2 toxins. We also clustered proteins by sequence profiles, and carried out in-vitro tests for four major activities on a selection of isolated novel toxins, or crude venoms known to contain them. The majority of detected activities were consistent with predictions, in contrast to poor performance of a number of tested existing predictive methods. Our results provide a framework for comparison of active sites among different functional sub-groups of toxins that will allow a more targeted approach for identification of potential drug leads in the future

    Structure-function relationships of disulfide-rich peptides

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    This thesis focuses on applying the approach of downsizing disulfide-rich peptides for the development of potential drug leads, and providing insight into important structural features for bioactivity. Disulfide-rich peptides are widely distributed in nature and several hold promise for the development of novel therapeutics and diagnostic agents. This thesis explores the structure-function relationships of two disulfide rich peptides; the scorpion venom peptide chlorotoxin, and the parasitic liver fluke protein Ov-GRN-1. Chapter 2 focuses on chlorotoxin, a potent tumour-imaging agent that selectively binds to tumour cells. Interestingly, it has been shown that chlorotoxin can have biological effects without disulfide bonds stabilising the native fold. This finding suggests that smaller regions, the inter-cysteine loops, might be responsible for the bioactivity of chlorotoxin. To explore this hypothesis, four small fragments of chlorotoxin were chemically synthesised using Fmoc solid-phase peptide synthesis method. As expected for such small peptides, NMR analysis indicated that the peptides were unstructured in solution. The bioactivity of the fragments was assessed by cell migration and invasion assays, alongside cell surface binding and internalization assays. Our results indicate that a small, unstructured fragment from the C-terminal region plays a critical role in the bioactivity of chlorotoxin. This is an unusual finding as structure is often critical for bioactivity in disulfide-rich peptides. The remaining experimental chapters focus on the characterization of Ov-GRN-1, a protein isolated from the excretory/secretory (ES) products of the carcinogenic liver fluke Opisthorchis viverrini. Ov-GRN-1 belongs to the granulin family, which are growth factor proteins with a wide range of functions mainly involved in cell modulation. A recombinant version of Ov-GRN-1 causes proliferation of host (human) cells and can accelerate the repair of wounds in animals. However, recombinant expression of Ov-GRN-1 is challenging and leads to a low product yield, impeding its utility as a drug lead. Chapter 3 focuses on the design, structure and functional analysis of minimized analogues from the N-terminal region of Ov-GRN-1. A series of analogues from the N-terminal region of Ov-GRN-1 were chemically synthesised by solid-phase peptide synthesis, oxidized by air oxidation, purified by HPLC and characterized by mass spectroscopy. The structure of peptides was studied by NMR spectroscopy and the 3D structure was calculated by CYANA and visualized by MolMol. Cell proliferation and wound healing activity were assessed by an in vitro xCELLigence cell proliferation assay and an in vivo mouse-wounding model, respectively. The structural characterization of the Ov-GRN-1 N-terminal truncated peptides indicated that the introduction of a non-native disulfide bond appears to stabilize the fold and allow the peptide to form a β- hairpin structure. This analogue, which is called Ov-GRN₁₂₋₃₅_₃ₛ, induced cell proliferation and in vivo wound healing with similar potency to the full-length Ov-GRN-1. NMR analysis of Ov-GRN₁₂₋₃₅_₃ₛ indicated the presence of multiple conformations, most likely from proline cis/trans isomerisation. In Chapter 4, a series of analogues involving mutation of the proline residues was synthesised to investigate the role of proline residues in adopting the multiple confirmations by Ov-GRN₁₂₋₃₅_₃ₛ. Utilising the same techniques and methods used in Chapter 3, proline residues were shown to have a significant influence on the structure, activity and folding of Ov-GRN₁₂₋₃₅_₃ₛ. The results obtained for this chapter led to the development of a more potent analogue, GRN(P₄A), with improved folding yield. Chapter 5 further explores the structure-function relationships of granulin peptides through analysis of the N-terminal region of human granulin A, as well as the C-terminal region of Ov-GRN-1. The former peptide was designed to determine if the non-native disulfide bond present in Ov-GRN₁₂₋₃₅_₃ₛ could also be accommodated in a granulin from another species, whereas the latter represents the first truncation study of the C-terminal region of a granulin peptide. The same techniques and methods as Chapter 4 were used to synthesise and characterise the analogues. Bioactivity of analogues were assessed using an in vitro xCELLigence cell proliferation assay. The results indicated that accommodation of a non-native disuflide bond might be a general phenomenon in the granulin family, as the N-terminal half of the human granulin A protein also folds independently with three disulfide bonds, despite significant sequence differences to the Ov-GRN-1 peptide. We also show for the first time that the equivalent C-terminal half of Ov-GRN-1 does not fold into a well-defined structure, but still displays cell proliferative activity. Our results indicate that well-defined structures are not critical for granulin bioactivity. In summary, the results highlight the potential of the "downsizing" approach for elucidating bioactive sequences, providing insight into folding processes and the development of novel drug leads. One of the major findings from this thesis is the development of a truncated form of Ov-GRN-1 that is likely to have lower immunogenicity than the full-length protein because of its smaller size, is significantly easier to produce and more potent in a mouse wound healing assay. These features make it a more viable drug lead for wound healing applications, and it is currently being considered for commercial development
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