Nicolas Palopoli (Postdoctoral Research Fellow)

Nico Palopoli started training in Computational Biology with his undergraduate thesis project on the structural modeling and characterization of starch-synthase III from Arabidopsis thaliana, under the guidance of Dr. Gustavo Parisi at the Structural Bioinformatics Group from Universidad Nacional de Quilmes (Argentina). He stayed at the group to fulfill his PhD thesis on the validation of protein 3D models using a structurally constrained protein evolution model.

While still a PhD student, Nico was awarded an Erasmus Mundus Sandwich PhD fellowship to spend an 8-month stay at Dr. Rita Casadio's Biocomputing Group from University of Bologna (Italy) where he started to develop structurally constrained, evolutionary-simulated Hidden Markov Models of protein families. His first Postdoctoral fellowship was awarded to take part in PhasIbeAm, the common bean genome sequencing project. Most of his work was conducted at the Protein Physiology Lab from Universidad de Buenos Aires (Argentina), where he also collaborated with Dr. Ignacio Sanchez on the application of information theory-based methods to study protein interactions by linear motifs.

Nico moved to the University of Southampton in October 2012 to work as a Research Fellow in the Edwards Lab on the project 'Integrated in silico prediction of protein-protein interaction motifs'. He left the lab in late 2014.

Employment History

• 2012-2014: Research Fellow. Centre for Biological Sciences, University of Southampton (UoS), Southampton, UK.
• 2011-2012: Postdoctoral Research Fellow. Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (UBA), Buenos Aires, Argentina.

Summary of Academic Qualifications

• 2011: PhD, "Development of an evolutionary-based method for the validation of protein tertiary structure and its application to starch-synthase type III from Arabidopsis thaliana." Structural Bioinformatics Group, Universidad Nacional de Quilmes (UNQ), Bernal, Buenos Aires, Argentina.
• 2006: Licentiate in Biotechnology. UNQ.

Main Funding History

• 2012-2014: Research Fellowship, UoS
• 2011-2012: Postdoctoral Fellowship, National Agency of Scientific and Technological Promotion (ANPCyT)
• 2010: Sandwich PhD Fellowship, Erasmus Mundus External Cooperation Window Lot 16
• 2008-2010: PhD Fellowship Type II, National Council for Scientific and Technical Research (CONICET)
• 2006-2008: PhD Fellowship Type I, ANPCyT

SLiMPrints: conservation-based discovery of functional motif fingerprints in intrinsically disordered protein regions

Davey NE, Cowan JL, Shields DC, Gibson TJ, Coldwell MJ & Edwards RJ (2012): SLiMPrints: conservation-based discovery of functional motif fingerprints in intrinsically disordered protein regions. Nucleic Acids Research 40(21):10628-41.

Abstract

Large portions of higher eukaryotic proteomes are intrinsically disordered, and abundant evidence suggests that these unstructured regions of proteins are rich in regulatory interaction interfaces. A major class of disordered interaction interfaces are the compact and degenerate modules known as short linear motifs (SLiMs). As a result of the difficulties associated with the experimental identification and validation of SLiMs, our understanding of these modules is limited, advocating the use of computational methods to focus experimental discovery. This article evaluates the use of evolutionary conservation as a discriminatory technique for motif discovery. A statistical framework is introduced to assess the significance of relatively conserved residues, quantifying the likelihood a residue will have a particular level of conservation given the conservation of the surrounding residues. The framework is expanded to assess the significance of groupings of conserved residues, a metric that forms the basis of SLiMPrints (short linear motif fingerprints), a de novo motif discovery tool. SLiMPrints identifies relatively overconstrained proximal groupings of residues within intrinsically disordered regions, indicative of putatively functional motifs. Finally, the human proteome is analysed to create a set of highly conserved putative motif instances, including a novel site on translation initiation factor eIF2A that may regulate translation through binding of eIF4E.

PMID: 22977176

HSP-4 endoplasmic reticulum (ER) stress pathway is not activated in a C. elegans model of ethanol intoxication and withdrawal

Ient B, Edwards RJ, Mould R, Hannah M, Holden-Dye LM & O’Connor V (2012): HSP-4 endoplasmic reticulum (ER) stress pathway is not activated in a C. elegans model of ethanol intoxication and withdrawal. Invertebrate Neuroscience 12(2): 93-102.

Abstract

Acute and chronic exposure of Caenorhabditis elegans to concentrations of ethanol in the range 250-350 mM elicits distinct behaviours. Previous genetic analysis highlights specific neurobiological substrates for these effects. However, ethanol may also elicit cellular stress responses which may contribute to the repertoire of ethanol-induced behaviours. Here, we have studied the effect of ethanol on an important arm of the cellular stress pathways, which emanates from the endoplasmic reticulum (ER) in response to several conditions including heat shock and chemical or genetic perturbations that lead to protein misfolding. HSP-4 is a heat shock protein and homologue of mammalian BiP. It is a pivotal upstream component of the ER stress response. Therefore, we used a C. elegans heat shock protein mutant, hsp-4, and a strain carrying a transcriptional reporter, Phsp-4::gfp, to test the role of the ER following chronic ethanol conditioning. We found no evidence for an overt ER response during acute or prolonged exposure to concentrations of ethanol that lead to defined ethanol-induced behaviours. Furthermore, whilst hsp-4 was strongly induced by tunicamycin, pre-exposure of C. elegans to low doses of tunicamycin followed by ethanol was not sufficient to induce an additive ER stress response. Behavioural analysis of an hsp-4 mutant indicated no difference compared to wild type in susceptibility to ethanol intoxication and withdrawal. There is a clear precedent for a significance of ER stress pathways particularly in clinical conditions associated with toxic or pathological effects of high doses of alcohol consumption. The concentrations of ethanol used in this C. elegans study equate to the highest blood alcohol levels measured in patients with chronic alcohol dependency. Taken together, these observations imply that the classic ER stress pathway in C. elegans is relatively refractory to induction by ethanol.

PMID: 22661239

ELM--the database of eukaryotic linear motifs

Dinkel H, Michael S, Weatheritt RJ, Davey NE, Van Roey K, Altenberg B, Toedt G, Uyar B, Seiler M, Budd A, Jödicke L, Dammert MA, Schroeter C, Hammer M, Schmidt T, Jehl P, McGuigan C, Dymecka M, Chica C, Luck K, Via A, Chatr-Aryamontri A, Haslam N, Grebnev G, Edwards RJ, Steinmetz MO, Meiselbach H, Diella F & Gibson TJ (2012): ELM—the database of eukaryotic linear motifs. Nucleic Acids Research 40(D1): D242-D251.

Abstract

Linear motifs are short, evolutionarily plastic components of regulatory proteins and provide low-affinity interaction interfaces. These compact modules play central roles in mediating every aspect of the regulatory functionality of the cell. They are particularly prominent in mediating cell signaling, controlling protein turnover and directing protein localization. Given their importance, our understanding of motifs is surprisingly limited, largely as a result of the difficulty of discovery, both experimentally and computationally. The Eukaryotic Linear Motif (ELM) resource at http://elm.eu.org provides the biological community with a comprehensive database of known experimentally validated motifs, and an exploratory tool to discover putative linear motifs in user-submitted protein sequences. The current update of the ELM database comprises 1800 annotated motif instances representing 170 distinct functional classes, including approximately 500 novel instances and 24 novel classes. Several older motif class entries have been also revisited, improving annotation and adding novel instances. Furthermore, addition of full-text search capabilities, an enhanced interface and simplified batch download has improved the overall accessibility of the ELM data. The motif discovery portion of the ELM resource has added conservation, and structural attributes have been incorporated to aid users to discriminate biologically relevant motifs from stochastically occurring non-functional instances.

PMID: 22110040

Interactome-wide prediction of short, disordered protein interaction motifs in humans

Edwards RJ, Davey NE, O’Brien K & Shields DC (2012): Interactome-wide prediction of short, disordered protein interaction motifs in humans. Molecular Biosystems 8: 282-95.

Abstract

Many of the specific functions of intrinsically disordered protein segments are mediated by Short Linear Motifs (SLiMs) interacting with other proteins. Well known examples include SLiMs that interact with 14-3-3, PDZ, SH2, SH3, and WW domains but the true extent and diversity of SLiM-mediated interactions is largely unknown. Here, we attempt to expand our knowledge of human SLiMs by applying in silico SLiM prediction to the human interactome. Combining data from seven different interaction databases, we analysed approximately 6000 protein-centred and 1600 domain-centred human interaction datasets of 3+ unrelated proteins that interact with a common partner. Results were placed in context through comparison to randomised datasets of similar size and composition. The search returned thousands of evolutionarily conserved, intrinsically disordered occurrences of hundreds of significantly enriched recurring motifs, including many that have never been previously identified (). In addition to True Positive results for at least 25 different known SLiMs, a striking number of “off-target” proteins/domains also returned significantly enriched known motifs. Often, this was due to the non-independence of the datasets, with many proteins sharing interaction partners or contributing interactions to multiple domain datasets. The majority of these motif classes, however, were also found to be significantly enriched in one or more randomised datasets. This highlights the need for care when interpreting motif predictions of this nature but also raises the possibility that SLiM occurrences may be successfully identified independently of interaction data. Although not as compositionally biased as previous studies, patterns matching known SLiMs tended to cluster into a few large groups of similar sequence, while novel predictions tended to be more distinctive and less abundant. Whether this is due to ascertainment bias or a true functional composition bias of SLiMs is not clear and warrants further investigation.

PMID: 21879107