High-Accuracy ncRNA Function Prediction via Deep Learning Using Global and Local Sequence Information.

The prediction of the biological function of non-coding ribonucleic acid (ncRNA) is an important step towards understanding the regulatory mechanisms underlying many diseases. Since non-coding RNAs are present in great abundance in human cells and are functionally diverse, developing functional prediction tools is necessary. With recent advances in non-coding RNA biology and the availability of complete genome sequences for a large number of species, we now have a window of opportunity for studying non-coding RNA biology.

A Drug Discovery Approach for an Effective Pain Therapy through Selective Inhibition of Nav1.7.

Chronic pain is a widespread disorder affecting millions of people and is insufficiently addressed by current classes of analgesics due to significant long-term or high dosage side effects. A promising approach that was recently proposed involves the systemic inhibition of the voltage-gated sodium channel Nav1.7, capable of cancelling pain perception completely.

Speeding Up the Identification of Cystic Fibrosis Transmembrane Conductance Regulator-Targeted Drugs: An Approach Based on Bioinformatics Strategies and Surface Plasmon Resonance.

Cystic fibrosis (CF) is mainly caused by the deletion of Phe 508 (ΔF508) in the cystic fibrosis transmembrane conductance regulator (CFTR) protein that is thus withheld in the endoplasmic reticulum and rapidly degraded by the ubiquitin/proteasome system. New drugs able to rescue ΔF508-CFTR trafficking are eagerly awaited. An integrated bioinformatics and surface plasmon resonance (SPR) approach was here applied to investigate the rescue mechanism(s) of a series of CFTR-ligands including VX809, VX770 and some aminoarylthiazole derivatives (AAT).

Further Insights in the Binding Mode of Selective Inhibitors to Human PDE4D Enzyme Combining Docking and Molecular Dynamics.

Alzheimer's disease has recently emerged as a possible field of application for PDE4D inhibitors (PDE4DIs). The great structure similarity among the various PDE4 isoforms and, furthermore, the lack of the full length crystal structure of the enzyme, impaired the rational design of new selective PDE4DIs. In this paper, with the aim of exploring new insights into the PDE4D binding, we tackled the problem by performing a computational study based on docking simulations combined with molecular dynamics (D-MD).

Molecular dynamics and docking simulation of a natural variant of Activated Protein C with impaired protease activity: implications for integrin-mediated antiseptic function.

Activated Protein C (APC) is a multifunctional serine protease, primarily known for its anticoagulant function in the coagulation system. Several studies have already elucidated its role in counteracting apoptosis and inflammation in cells, while significant effort is still ongoing for defining its involvement in sepsis. Earlier literature has shown that the antiseptic function of APC is mediated by its binding to leukocyte integrins, which is due to the presence of the integrin binding motif Arg-Gly-Asp at the N-terminus of the APC catalytic chain.

Fine mapping of AHI1 as a schizophrenia susceptibility gene: from association to evolutionary evidence.

In previous studies, we identified a locus for schizophrenia on 6q23.3 and proposed the Abelson helper integration site 1 (AHI1) as the candidate gene. AHI1 is expressed in the brain and plays a key role in neurodevelopment, is involved in Joubert syndrome, and has been recently associated with autism.

BGBlast: a BLAST grid implementation with database self-updating and adaptive replication.

BLAST is probably the most used application in bioinformatics teams. BLAST complexity tends to be a concern when the query sequence sets and reference databases are large. Here we present BGBlast: an approach for handling the computational complexity of large BLAST executions by porting BLAST to the Grid platform, leveraging the power of the thousands of CPUs which compose the EGEE infrastructure.

Data handling strategies for high throughput pyrosequencers.

Background: New high throughput pyrosequencers such as the 454 Life Sciences GS 20 are capable of massively parallelizing DNA sequencing providing an unprecedented rate of output data as well as potentially reducing costs. However, these new pyrosequencers bear a different error profile and provide shorter reads than those of a more traditional Sanger sequencer. These facts pose new challenges regarding how the data are handled and analyzed, in addition, the steep increase in the sequencers throughput calls for much computation power at a low cost.