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Castel Guelfo di Bologna, Italy

Martelli P.L.,Biocomputing Group
BMC genomics | Year: 2012

Various computational methods are presently available to classify whether a protein variation is disease-associated or not. However data derived from recent technological advancements make it feasible to extend the annotation of disease-associated variations in order to include specific phenotypes. Here we tackle the problem of distinguishing between genetic variations associated to cancer and variations associated to other genetic diseases. We implement a new method based on Support Vector Machines that takes as input the protein variant and the protein function, as described by its associated Gene Ontology terms. Our approach succeeds in discriminating between germline variants that are likely to be cancer-associated from those that are related to other genetic disorders. The method performs with values of 90% accuracy and 0.61 Matthews correlation coefficient on a set comprising 6478 germline variations (16% are cancer-associated) in 592 proteins. The sensitivity and the specificity on the cancer class are 69% and 66%, respectively. Furthermore the method is capable of correctly excluding some 96% of 3392 somatic cancer-associated variations in 1983 proteins not included in the training/testing set. Here we prove feasible that a large set of cancer associated germline protein variations can be successfully discriminated from those associated to other genetic disorders. This is a step further in the process of protein variant annotation. Scoring largely improves when protein function as encoded by Gene Ontology terms is considered, corroborating the role of protein function as a key feature for a correct annotation of its variations.

Abruzzo P.M.,University of Bologna | Marini M.,University of Bologna | Bolotta A.,University of Bologna | Malisardi G.,University of Ferrara | And 5 more authors.
BioMed Research International | Year: 2013

Friedreich's ataxia (FRDA) is caused by deficient expression of the mitochondrial protein frataxin involved in the formation of iron-sulphur complexes and by consequent oxidative stress. We analysed low-dose tocotrienol supplementation effects on the expression of the three splice variant isoforms (FXN-1, FXN-2, and FXN-3) in mononuclear blood cells of FRDA patients and healthy subjects. In FRDA patients, tocotrienol leads to a specific and significant increase of FXN-3 expression while not affecting FXN-1 and FXN-2 expression. Since no structural and functional details were available for FNX-2 and FXN-3, 3D models were built. FXN-1, the canonical isoform, was then docked on the human iron-sulphur complex, and functional interactions were computed; when FXN-1 was replaced by FXN-2 or FNX-3, we found that the interactions were maintained, thus suggesting a possible biological role for both isoforms in human cells. Finally, in order to evaluate whether tocotrienol enhancement of FXN-3 was mediated by an increase in peroxisome proliferator-activated receptor-γ (PPARG), PPARG expression was evaluated. At a low dose of tocotrienol, the increase of FXN-3 expression appeared to be independent of PPARG expression. Our data show that it is possible to modulate the mRNA expression of the minor frataxin isoforms and that they may have a functional role. © 2013 Provvidenza Maria Abruzzo et al.

John M.,University of Rostock | Lhoussaine C.,Lille University of Science and Technology | Lhoussaine C.,Biocomputing Group | Niehren J.,French Institute for Research in Computer Science and Automation | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

We present the attributed π-calculus for modeling concurrent systems with interaction constraints depending on the values of attributes of processes. The λ-calculus serves as a constraint language underlying the π-calculus. Interaction constraints subsume priorities, by which to express global aspects of populations. We present a non-deterministic and a stochastic semantics for the attributed π-calculus. We show how to encode the π-calculus with priorities and polyadic synchronization π-@ and thus dynamic compartments, as well as the stochastic π-calculus with concurrent objects spico. We illustrate the usefulness of the attributed π-calculus for modeling biological systems at two particular examples: Euglena's spatial movement in phototaxis, and cooperative protein binding in gene regulation of bacteriophage lambda. Furthermore, population-based model is supported beside individual-based modeling. A stochastic simulation algorithm for the attributed π-calculus is derived from its stochastic semantics. We have implemented a simulator and present experimental results, that confirm the practical relevance of our approach. © 2010 Springer.

Trivella A.,CNRS Strasbourg Institute of Chemistry | Khoury Y.E.,CNRS Strasbourg Institute of Chemistry | Gaillard T.,Biocomputing Group | Stote R.H.,Biocomputing Group | And 3 more authors.
AIP Conference Proceedings | Year: 2010

The basic motions and the conformational flexibility of a protein have a strong impact on its molecular recognition properties and ultimately on its function. In the far infrared (or THz) spectral range the breathing of the hydrogen bonds can be monitored, providing essential information on local dynamics and mechanism. The use of this spectral range is rapidly evolving and a number of IR synchrotron beamlines are available for this research. Here we present a study on the I-domain of the integrin LFA-1, an allosteric receptor that transmits signals across the plasma membrane in a bidirectional way. The I-domain contains the principal binding site for extracellular ligands and thus crucial for the signaling and the integrin-mediated cell adhesion. We measured the temperature dependence of the conformational dynamics of the I-domain bound to four different divalent metal ions (Mg 2+, Ca 2+, Mn 2+ and Fe 2+) in the range 10-300 K. The H-bonding vibrations show distinct temperature dependences for the different samples. © 2010 American Institute of Physics.

Pierleoni A.,Biocomputing Group | Pierleoni A.,Externautics | Martelli P.L.,Biocomputing Group | Casadio R.,Biocomputing Group
Bioinformatics | Year: 2011

Motivation: Subcellular localization is a key feature in the process of functional annotation of both globular and membrane proteins. In the absence of experimental data, protein localization is inferred on the basis of annotation transfer upon sequence similarity search. However, predictive tools are necessary when the localization of homologs is not known. This is so particularly for membrane proteins. Furthermore, most of the available predictors of subcellular localization are specifically trained on globular proteins and poorly perform on membrane proteins. Results: Here we develop MemLoci, a new support vector machinebased tool that discriminates three membrane protein localizations: plasma, internal and organelle membrane. When tested on an independent set, MemLoci outperforms existing methods, reaching an overall accuracy of 70% on predicting the location in the three membrane types, with a generalized correlation coefficient as high as 0.50. © The Author 2011. Published by Oxford University Press. All rights reserved.

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