Now showing 1 - 10 of 64
  • Publication
    Potential utility of docking to identify protein-peptide binding regions
    (University College Dublin. School of Computer Science and Informatics, 2013-05) ; ; ; ;
    Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short regions involved in binding. We set out to determine if docking peptides to peptide binding domains would assist in these predictions. First, we investigated the docking of known short peptides to their native and non-native peptide binding domains. We then investigated the docking of overlapping peptides adjacent to the native peptide. We found only weak discrimination of docking scores between native peptide and adjacent peptides in this context with similar results for both ordered and disordered regions. Finally, we trained a bidirectional recurrent neural network using as input the peptide sequence, predicted secondary structure, Vina docking score and Pepsite score.We conclude that docking has only modest power to define the location of a peptide within a larger protein region known to contain it. However, this information can be used in training machine learning methods which may allow for the identification of peptide binding regions within a protein sequence.
      74
  • Publication
    Prediction of pathological response to neo‐adjuvant chemoradiotherapy for oesophageal cancer using vibrational spectroscopy
    In oesophageal cancer (OC) neo‐adjuvant chemoradiotherapy (neoCRT) is used to debulk tumour size prior to surgery, with a complete pathological response (pCR) observed in approximately ∼30% of patients. Presently no predictive quantitative methodology exists which can predict response, in particular a pCR or major response (MR), in patients prior to therapy. Raman and Fourier transform infrared imaging were performed on OC tissue specimens acquired from 50 patients prior to therapy, to develop a computational model linking spectral data to treatment outcome. Modelling sensitivities and specificities above 85% were achieved using this approach. Parallel in‐vitro studies using an isogenic model of radioresistant OC supplied further insight into OC cell spectral response to ionising radiation where a potential spectral biomarker of radioresistance was observed at 977 cm−1. This work demonstrates that chemical imaging may provide an option for triage of patients prior to neoCRT treatment allowing more precise prescription of treatment.
      196
  • Publication
    De-repression of myelin-regulating gene expression after status epilepticus in mice lacking the C/EBP homologous protein CHOP
    The C/EBP homologous protein CHOP is normally present at low levels in cells but increases rapidly after insults such as DNA damage or endoplasmatic reticulum stress where it contributes to cellular homeostasis and apoptosis. By forming heterodimers with other transcription factors, CHOP can either act as a dominant-negative regulator of gene expression or to induce the expression of target genes. Recent work demonstrated that seizure-induced hippocampal damage is significantly worse in mice lacking CHOP and these animals go on to develop an aggravated epileptic phenotype. To identify novel CHOP-controlled target genes which potentially influence the epileptic phenotype, we performed a bioinformatics analysis of tissue microarrays from chop-deficient mice after prolonged seizures. GO analysis revealed genes associated with biological membranes were prominent among those in the chop-deficient array dataset and we identified myelin-associated genes to be particularly de-repressed. These data suggest CHOP might act as an inhibitor of myelin-associated processes in the brain and could be targeted to influence axonal regeneration or reorganisation.
      132
  • Publication
    RNA sequencing of synaptic and cytoplasmic Upf1-bound transcripts supports contribution of nonsense-mediated decay to epileptogenesis
    The nonsense mediated decay (NMD) pathway is a critical surveillance mechanism for identifying aberrant mRNA transcripts. It is unknown, however, whether the NMD system is affected by seizures in vivo and whether changes confer beneficial or maladaptive responses that influence long-term outcomes such the network alterations that produce spontaneous recurrent seizures. Here we explored the responses of the NMD pathway to prolonged seizures (status epilepticus) and investigated the effects of NMD inhibition on epilepsy in mice. Status epilepticus led to increased protein levels of Up-frameshift suppressor 1 homolog (Upf1) within the mouse hippocampus. Upf1 protein levels were also higher in resected hippocampus from patients with intractable temporal lobe epilepsy. Immunoprecipitation of Upf1-bound RNA from the cytoplasmic and synaptosomal compartments followed by RNA sequencing identified unique populations of NMD-associated transcripts and altered levels after status epilepticus, including known substrates such as Arc as well as novel targets including Inhba and Npas4. Finally, long-term video-EEG recordings determined that pharmacologic interference in the NMD pathway after status epilepticus reduced the later occurrence of spontaneous seizures in mice. These findings suggest compartment-specific recruitment and differential loading of transcripts by NMD pathway components may contribute to the process of epileptogenesis.
      271Scopus© Citations 13
  • Publication
    Eosinophil peroxidase activates cells by HER2 receptor engagement and β1-integrin clustering with downstream MAPK cell signaling
    Eosinophils account for 1–3% of peripheral blood leukocytes and accumulate at sites of allergic inflammation, where they play a pathogenic role. Studies have shown that treatment with mepolizumab (an anti-IL-5 monoclonal antibody) is beneficial to patients with severe eosinophilic asthma, however, the mechanism of precisely how eosinophils mediate these pathogenic effects is uncertain. Eosinophils contain several cationic granule proteins, including Eosinophil Peroxidase (EPO). The main significance of this work is the discovery of EPO as a novel ligand for the HER2 receptor. Following HER2 activation, EPO induces activation of FAK and subsequent activation of β1-integrin, via inside-out signaling. This complex results in downstream activation of ERK1/2 and a sustained up regulation of both MUC4 and the HER2 receptor. These data identify a receptor for one of the eosinophil granule proteins and demonstrate a potential explanation of the proliferative effects of eosinophils.
    Scopus© Citations 6  261
  • Publication
    PaleAle 5.0: prediction of protein relative solvent accessibility by deep learning
    Predicting the three-dimensional structure of proteins is a long-standing challenge of computational biology, as the structure (or lack of a rigid structure) is well known to determine a protein's function. Predicting relative solvent accessibility (RSA) of amino acids within a protein is a significant step towards resolving the protein structure prediction challenge especially in cases in which structural information about a protein is not available by homology transfer. Today, arguably the core of the most powerful prediction methods for predicting RSA and other structural features of proteins is some form of deep learning, and all the state-of-the-art protein structure prediction tools rely on some machine learning algorithm. In this article we present a deep neural network architecture composed of stacks of bidirectional recurrent neural networks and convolutional layers which is capable of mining information from long-range interactions within a protein sequence and apply it to the prediction of protein RSA using a novel encoding method that we shall call "clipped". The final system we present, PaleAle 5.0, which is available as a public server, predicts RSA into two, three and four classes at an accuracy exceeding 80% in two classes, surpassing the performances of all the other predictors we have benchmarked.
      394Scopus© Citations 22
  • Publication
    Effective compiler error message enhancement for novice programming students
    Programming is an essential skill that many computing students are expected to master. However, programming can be difficult to learn. Successfully interpreting compiler error messages (CEMs) is crucial for correcting errors and progressing toward success in programming. Yet these messages are often difficult to understand and pose a barrier to progress for many novices, with struggling students often exhibiting high frequencies of errors, particularly repeated errors. This paper presents a control/intervention study on the effectiveness of enhancing Java CEMs. Results show that the intervention group experienced reductions in the number of overall errors, errors per student, and several repeated error metrics. These results are important as the effectiveness of CEM enhancement has been recently debated. Further, generalizing these results should be possible at least in part, as the control group is shown to be comparable to those in several studies using Java and other languages.
    Scopus© Citations 65  887
  • Publication
    SCLpred-EMS: Subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks
    Motivation: The subcellular location of a protein can provide useful information for protein function prediction and drug design. Experimentally determining the subcellular location of a protein is an expensive and time-consuming task. Therefore, various computer-based tools have been developed, mostly using machine learning algorithms, to predict the subcellular location of proteins. Results: Here, we present a neural network-based algorithm for protein subcellular location prediction. We introduce SCLpred-EMS a subcellular localization predictor powered by an ensemble of Deep N-to-1 Convolutional Neural Networks. SCLpred-EMS predicts the subcellular location of a protein into two classes, the endomembrane system and secretory pathway versus all others, with a Matthews correlation coefficient of 0.75-0.86 outperforming the other state-of-the-art web servers we tested. Contact: catherine.mooney@ucd.ie
    Scopus© Citations 18  401
  • Publication
    Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks
    Background: Prediction of protein structures from their sequences is still one of the open grand challenges of computational biology. Some approaches to protein structure prediction, especially ab initio ones, rely to some extent on the prediction of residue contact maps. Residue contact map predictions have been assessed at the CASP competition for several years now. Although it has been shown that exact contact maps generally yield correct three-dimensional structures, this is true only at a relatively low resolution (3–4 Å from the native structure). Another known weakness of contact maps is that they are generally predicted ab initio, that is not exploiting information about potential homologues of known structure. Results: We introduce a new class of distance restraints for protein structures: multi-class distance maps. We show that C trace reconstructions based on 4-class native maps are significantly better than those from residue contact maps. We then build two predictors of 4-class maps based on recursive neural networks: one ab initio, or relying on the sequence and on evolutionary information; one template-based, or in which homology information to known structures is provided as a further input. We show that virtually any level of sequence similarity to structural templates (down to less than 10%) yields more accurate 4-class maps than the ab initio predictor. We show that template-based predictions by recursive neural networks are consistently better than the best template and than a number of combinations of the best available templates. We also extract binary residue contact maps at an 8 Å threshold (as per CASP assessment) from the 4-class predictors and show that the template-based version is also more accurate than the best template and consistently better than the ab initio one, down to very low levels of sequence identity to structural templates. Furthermore, we test both ab-initio and template-based 8 Å predictions on the CASP7 targets using a pre-CASP7 PDB, and find that both predictors are state-of-the-art, with the template-based one far outperforming the best CASP7 systems if templates with sequence identity to the query of 10% or better are available. Although this is not the main focus of this paper we also report on reconstructions of C traces based on both ab initio and template-based 4-class map predictions, showing that the latter are generally more accurate even when homology is dubious. Conclusion: Accurate predictions of multi-class maps may provide valuable constraints for improved ab initio and template-based prediction of protein structures, naturally incorporate multiple templates, and yield state-of-the- art binary maps. Predictions of protein structures and 8 Å contact maps based on the multi-class distance map predictors described in this paper are freely available to academic users at the url http://distill.ucd.ie/.
    Scopus© Citations 42  442
  • Publication
    Investigating the Need for Pediatric-Specific Automatic Seizure Detection
    (IEEE, 2022-12-03) ;
    Approximately 1 in every 150 children is diagnosed with epilepsy during the first ten years of life [1]. These children experience seizures, which disrupt their lives and directly harm the developing brain. Electroencephalography (EEG) is the main tool used clinically to diagnose seizures and epilepsy. However, the interpretation of EEGs requires time-consuming expert analysis [2]. Automated detection systems are a powerful tool that can help address the issue by reducing expert annotation time. Research on the automatic detection of seizures in pediatric EEG has been limited. Most seizure detection methods have been developed and tested using larger numbers of adult EEG [3], [4]. However, research has shown that brain events in EEG change with ageing [5], [6]. Therefore, model trained on EEGs from adults may not be be suitable for children. To test this hypothesis, we trained a seizure detection model on adult EEG and tested on adult and pediatric EEG recordings.
      57Scopus© Citations 2