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. However, the computational methods used to predict the non-coding RNA functions are mostly either scarcely accurate, when based on sequence information alone, or prohibitively expensive in terms of computational burden when a secondary structure prediction is needed. We propose a novel computational method to predict the biological function of non-coding RNA genes that is based on a collection of deep network architectures utilizing solely ncRNA sequence information and which does not rely on or require expensive secondary ncRNA structure information. The approach presented in this work exhibits comparable or superior accuracy to methods that employ both sequence and structural features, at a much lower computational cost.

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. Notwithstanding numerous attempts, currently no drugs have been approved for the inhibition of Nav1.7. The task is complicated by the difficulty of creating a selective drug for Nav1.7, and avoiding binding to the many human paralogs performing fundamental physiological functions. In our work, we obtained a promising set of ligands with up to 5-40-fold selectivity and reaching 5.2 nanomolar binding affinity by employing a proper treatment of the problem and an innovative differential in silico screening procedure to discriminate for affinity and selectivity against the Nav paralogs. The absorption, distribution, metabolism, and excretion (ADME) properties of our top-scoring ligands were also evaluated, with good to excellent results. Additionally, our study revealed that the top-scoring ligand is a stereoisomer of an already-approved drug. These facts could reduce the time required to bring a new effective and selective Nav1.7 inhibitor to the market.

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). Computational studies tentatively identified a large binding pocket in the ΔF508-CFTR nucleotide binding domain-1 (NBD1) and predicted all the tested compounds to bind to three sub-regions of this main pocket. Noticeably, the known CFTR chaperone keratin-8 (K8) seems to interact with some residues located in one of these sub-pockets, potentially interfering with the binding of some ligands. SPR results corroborated all these computational findings. Moreover, for all the considered ligands, a statistically significant correlation was determined between their binding capability to ΔF508-NBD1 measured by SPR and the pockets availability measured by computational studies. Taken together, these results demonstrate a strong agreement between the in silico prediction and the SPR-generated binding data, suggesting a path to speed up the identification of new drugs for the treatment of cystic fibrosis.

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). Our work uniquely identified the binding mode and the key residues involved in the interaction with a number of in-house catechol iminoether derivatives, acting as PDE4DIs. Moreover, the new binding mode was tested using a series of analogues previously reported by us and it was used to confirm their key structural features to allow PDE4D inhibition. The binding model disclosed within the current computational study may prove to be useful to further advance the design and synthesis of novel, more potent and selective, PDE4D inhibitors.

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. Many natural mutants have been identified in patients with Protein C deficiency diagnosis including a variant of specificity pocket (Gly216Asp). In this work, we present a molecular model of the complex of APC with αVß3 integrin obtained by protein-protein docking approach. A computational analysis of this variant is hereby presented, based on molecular dynamics and docking simulations, aiming at investigating the effects of the Gly216Asp mutation on the protein conformation and inferring its functional implications. Our study shows that such mutation is likely to impair the protease activity while preserving the overall protein fold. Moreover, superposition of the integrin binding motifs in wild-type and mutant forms suggests that the interaction with integrin can still occur and thus the mutant is likely to retain its antiseptic function related to the neutrophyl integrin binding. Therapeutic applications could result in this APC mutant which retains antiseptic function without anticoagulant side effects.

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. The neurodevelopmental role of AHI1 fits with etiological hypotheses of schizophrenia. To definitively confirm our hypothesis, we searched for associations using a dense map of the region. Our strongest findings lay within the AHI1 gene: single-nucleotide polymorphisms rs11154801 and rs7759971 showed significant associations (P=6.23E-06; P=0.84E-06) and haplotypes gave P values in the 10E-8 to 10E-10 range. The second highest significant region maps close to AHI1 and includes the intergenic region between BC040979 and PDE7B (rs2038549 at P=9.70E-06 and rs1475069 at P=6.97E-06), and PDE7B and MAP7. Using a sample of Palestinian Arab families to confirm these findings, we found isolated signals. While these results did not retain their significance after correction for multiple testing, the joint analysis across the 2 samples supports the role of AHI1, despite the presence of heterogeneity. Given the hypothesis of positive selection of schizophrenia genes, we resequenced a 11 kb region within AHI1 in ethnically defined populations and found evidence for a selective sweep. Network analysis indicates 2 haplotype clades, with schizophrenia-susceptibility haplotypes clustering within the major clade. In conclusion, our data support the role of AHI1 as a susceptibility gene for schizophrenia and confirm it has been subjected to positive selection, also shedding light on new possible candidate genes, MAP7 and PDE7B.

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. BGBlast provides innovative features for efficiently managing BLAST databases in the distributed Grid environment. The system (1) keeps the databases constantly up to date while still allowing the user to regress to earlier versions, (2) stores the older versions of databases on the Grid with a time and space efficient delta encoding and (3) manages the number of replicas for each database over the Grid with an adaptive algorithm, dynamically balancing between execution parallelism and storage costs.

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. RESULTS: To address these challenges, we created an automated multi-step computation pipeline integrated with a database storage system. This allowed us to store, handle, index and search (1) the output data from the GS20 sequencer (2) analysis projects, possibly multiple on every dataset (3) final results of analysis computations (4) intermediate results of computations (these allow hand-made comparisons and hence further searches by the biologists). Repeatability of computations was also a requirement. In order to access the needed computation power, we ported the pipeline to the European Grid: a large community of clusters, load balanced as a whole. In order to better achieve this Grid port we created Vnas: an innovative Grid job submission, virtual sandbox manager and job callback framework. After some runs of the pipeline aimed at tuning the parameters and thresholds for optimal results, we successfully analyzed 273 sequenced amplicons from a cancerous human sample and correctly found punctual mutations confirmed by either Sanger resequencing or NCBI dbSNP. The sequencing was performed with our 454 Life Sciences GS 20 pyrosequencer. CONCLUSION: We handled the steep increase in throughput from the new pyrosequencer by building an automated computation pipeline associated with database storage, and by leveraging the computing power of the European Grid. The Grid platform offers a very cost effective choice for uneven workloads, typical in many scientific research fields, provided its peculiarities can be accepted (these are discussed). The mentioned infrastructure was used to analyze human amplicons for mutations. More analyses will be performed in the future.