814 research outputs found

    The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases

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    One of the most intriguing groups of enzymes, the feruloyl esterases (FAEs), is ubiquitous in both simple and complex organisms. FAEs have gained importance in biofuel, medicine and food industries due to their capability of acting on a large range of substrates for cleaving ester bonds and synthesizing high-added value molecules through esterification and transesterification reactions. During the past two decades extensive studies have been carried out on the production and partial characterization of FAEs from fungi, while much less is known about FAEs of bacterial or plant origin. Initial classification studies on FAEs were restricted on sequence similarity and substrate specificity on just four model substrates and considered only a handful of FAEs belonging to the fungal kingdom. This study centers on the descriptor-based classification and structural analysis of experimentally verified and putative FAEs; nevertheless, the framework presented here is applicable to every poorly characterized enzyme family. 365 FAE-related sequences of fungal, bacterial and plantae origin were collected and they were clustered using Self Organizing Maps followed by k-means clustering into distinct groups based on amino acid composition and physico-chemical composition descriptors derived from the respective amino acid sequence. A Support Vector Machine model was subsequently constructed for the classification of new FAEs into the pre-assigned clusters. The model successfully recognized 98.2% of the training sequences and all the sequences of the blind test. The underlying functionality of the 12 proposed FAE families was validated against a combination of prediction tools and published experimental data. Another important aspect of the present work involves the development of pharmacophore models for the new FAE families, for which sufficient information on known substrates existed. Knowing the pharmacophoric features of a small molecule that are essential for binding to the members of a certain family opens a window of opportunities for tailored applications of FAEs

    Maize as production and delivery vehicle of edible vaccines against the enterotoxigenic Escherichia coli and the swine transmissible gastroenteritis (TGE)

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    Plants are becoming increasingly important as a production system for biopharmaceuticals and industrially important proteins. The work presented in this dissertation showed that maize can be used as a source and delivery vehicle for oral vaccines. Antigenic proteins from two economically important pathogens, enterotoxigenic Escherichia coli (E. coli ) and the swine transmissible gastroenteritis virus (TGEV) were expressed in transgenic maize.;This study showed that subunits of the E. coli heat labile enterotoxin (LT) can be synthesized in transgenic maize tissues, correctly processed and assembled in maize tissue. The role of regulatory sequences such as promoters, targeting and retention signals in accumulation of LT-B in transgenic maize kernels was studied. The seed specific 27 kDa gamma zein promoter achieved a significantly higher level of LT-B expression in kernels compared to the constitutive CaMV 35S promoter. The use of the endoplasmic reticulum retention motif SEKDEL significantly enhanced kernel accumulation of LT-B. The LT-13 gene was normally transmitted over three generations.;Maize generated LT-B had biochemical, biophysical, and immunogenic properties of the bacterial protein. Oral administration of transgenic maize expressing LT-B in BALB/c mice induced elevated titers of serum and mucosal antibodies, which protected the immunized animals from subsequent challenge with LT and Cholera toxin (CT).;Using two synthetic genes for the LT toxin subunits, LT-A and LT-B, a non-toxic derivative of the heat labile toxin, LTK63, was expressed in transgenic maize callus. This mutant toxin assembled in maize callus tissue, showing that complex folding of foreign antigens could be achieved in transgenic maize tissues. This mutant derivative was shown to be more immunogenic than the bacteria derived LT-B.;We fused an N-terminal domain of the spike (S) protein of the swine transmissible gastroenteritis virus to the A subunit of LT, and coexpressed this fusion with LT-B in transgenic maize callus. Expression of the fusion proteins and LT-B was observed in callus.;This work demonstrates that maize, a key ingredient in food and feed industry, can be used as a source and delivery vehicle of functional antigens for use as oral vaccines. Maize holds great potential for the generation of human and livestock vaccines, and this work lays the foundation for the development of vaccines against other pathogens in transgenic maize

    Automatic detection of exonic splicing enhancers (ESEs) using SVMs

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    <p>Abstract</p> <p>Background</p> <p>Exonic splicing enhancers (ESEs) activate nearby splice sites and promote the inclusion (vs. exclusion) of exons in which they reside, while being a binding site for SR proteins. To study the impact of ESEs on alternative splicing it would be useful to have a possibility to detect them in exons. Identifying SR protein-binding sites in human DNA sequences by machine learning techniques is a formidable task, since the exon sequences are also constrained by their functional role in coding for proteins.</p> <p>Results</p> <p>The choice of training examples needed for machine learning approaches is difficult since there are only few exact locations of human ESEs described in the literature which could be considered as positive examples. Additionally, it is unclear which sequences are suitable as negative examples. Therefore, we developed a motif-oriented data-extraction method that extracts exon sequences around experimentally or theoretically determined ESE patterns. Positive examples are restricted by heuristics based on known properties of ESEs, e.g. location in the vicinity of a splice site, whereas negative examples are taken in the same way from the middle of long exons. We show that a suitably chosen SVM using optimized sequence kernels (e.g., combined oligo kernel) can extract meaningful properties from these training examples. Once the classifier is trained, every potential ESE sequence can be passed to the SVM for verification. Using SVMs with the combined oligo kernel yields a high accuracy of about 90 percent and well interpretable parameters.</p> <p>Conclusion</p> <p>The motif-oriented data-extraction method seems to produce consistent training and test data leading to good classification rates and thus allows verification of potential ESE motifs. The best results were obtained using an SVM with the combined oligo kernel, while oligo kernels with oligomers of a certain length could be used to extract relevant features.</p

    Learning the Regulatory Code of Gene Expression

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    Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology

    GraphClust: alignment-free structural clustering of local RNA secondary structures

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    Motivation: Clustering according to sequence–structure similarity has now become a generally accepted scheme for ncRNA annotation. Its application to complete genomic sequences as well as whole transcriptomes is therefore desirable but hindered by extremely high computational costs

    Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio‑Cyber attacks

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    This article uses Deep Learning technologies to safeguard DNA sequencing against Bio-Cyber attacks. We consider a hybrid attack scenario where the payload is encoded into a DNA sequence to activate a Trojan malware implanted in a software tool used in the sequencing pipeline in order to allow the perpetrators to gain control over the resources used in that pipeline during sequence analysis. The scenario considered in the paper is based on perpetrators submitting synthetically engineered DNA samples that contain digitally encoded IP address and port number of the perpetrator’s machine in the DNA. Genetic analysis of the sample’s DNA will decode the address that is used by the software Trojan malware to activate and trigger a remote connection. This approach can open up to multiple perpetrators to create connections to hijack the DNA sequencing pipeline. As a way of hiding the data, the perpetrators can avoid detection by encoding the address to maximise similarity with genuine DNAs, which we showed previously. However, in this paper we show how Deep Learning can be used to successfully detect and identify the trigger encoded data, in order to protect a DNA sequencing pipeline from Trojan attacks. The result shows nearly up to 100% accuracy in detection in such a novel Trojan attack scenario even after applying fragmentation encryption and steganography on the encoded trigger data. In addition, feasibility of designing and synthesizing encoded DNA for such Trojan payloads is validated by a wet lab experiment

    Homology sequence analysis using GPU acceleration

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    A number of problems in bioinformatics, systems biology and computational biology field require abstracting physical entities to mathematical or computational models. In such studies, the computational paradigms often involve algorithms that can be solved by the Central Processing Unit (CPU). Historically, those algorithms benefit from the advancements of computing power in the serial processing capabilities of individual CPU cores. However, the growth has slowed down over recent years, as scaling out CPU has been shown to be both cost-prohibitive and insecure. To overcome this problem, parallel computing approaches that employ the Graphics Processing Unit (GPU) have gained attention as complementing or replacing traditional CPU approaches. The premise of this research is to investigate the applicability of various parallel computing platforms to several problems in the detection and analysis of homology in biological sequence. I hypothesize that by exploiting the sheer amount of computation power and sequencing data, it is possible to deduce information from raw sequences without supplying the underlying prior knowledge to come up with an answer. I have developed such tools to perform analysis at scales that are traditionally unattainable with general-purpose CPU platforms. I have developed a method to accelerate sequence alignment on the GPU, and I used the method to investigate whether the Operational Taxonomic Unit (OTU) classification problem can be improved with such sheer amount of computational power. I have developed a method to accelerate pairwise k-mer comparison on the GPU, and I used the method to further develop PolyHomology, a framework to scaffold shared sequence motifs across large numbers of genomes to illuminate the structure of the regulatory network in yeasts. The results suggest that such approach to heterogeneous computing could help to answer questions in biology and is a viable path to new discoveries in the present and the future.Includes bibliographical reference

    kernInt : A Kernel Framework for Integrating Supervised and Unsupervised Analyses in Spatio-Temporal Metagenomic Datasets

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    The advent of next-generation sequencing technologies allowed relative quantification of microbiome communities and their spatial and temporal variation. In recent years, supervised learning (i.e., prediction of a phenotype of interest) from taxonomic abundances has become increasingly common in the microbiome field. However, a gap exists between supervised and classical unsupervised analyses, based on computing ecological dissimilarities for visualization or clustering. Despite this, both approaches face common challenges, like the compositional nature of next-generation sequencing data or the integration of the spatial and temporal dimensions. Here we propose a kernel framework to place on a common ground the unsupervised and supervised microbiome analyses, including the retrieval of microbial signatures (taxa importances). We define two compositional kernels (Aitchison-RBF and compositional linear) and discuss how to transform non-compositional beta-dissimilarity measures into kernels. Spatial data is integrated with multiple kernel learning, while longitudinal data is evaluated by specific kernels. We illustrate our framework through a single point soil dataset, a human dataset with a spatial component, and a previously unpublished longitudinal dataset concerning pig production. The proposed framework and the case studies are freely available in the kernInt package at https://github.com/elies-ramon/kernInt
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