Background: Employing methods to assess the quality of modeled protein structures is now standard practice in bioinformatics. In a broad sense, the techniques can be divided into methods relying on consensus prediction on the one hand, and single-model methods on the other. Consensus methods frequently perform very well when there is a clear consensus, but this is not always the case. In particular, they frequently fail in selecting the best possible model in the hard cases (lacking consensus) or in the easy cases where models are very similar. In contrast, single-model methods do not suffer from these drawbacks and could potentially be applied on any protein of interest to assess quality or as a scoring function for sampling-based refinement. Results: Here, we present a new single-model method, ProQ2, based on ideas from its predecessor, ProQ. ProQ2 is a model quality assessment algorithm that uses support vector machines to predict local as well as global quality of protein models. Improved performance is obtained by combining previously used features with updated structural and predicted features. The most important contribution can be attributed to the use of profile weighting of the residue specific features and the use features averaged over the whole model even though the prediction is still local. Conclusions: ProQ2 is significantly better than its predecessors at detecting high quality models, improving the sum of Z-scores for the selected first-ranked models by 20% and 32% compared to the second-best single-model method in CASP8 and CASP9, respectively. The absolute quality assessment of the models at both local and global level is also improved. The Pearson’s correlation between the correct and local predicted score is improved from 0.59 to 0.70 on CASP8 and from 0.62 to 0.68 on CASP9; for global score to the correct GDT_TS from 0.75 to 0.80 and from 0.77 to 0.80 again compared to the second-best single methods in CASP8 and CASP9, respectively. ProQ2 is available at http://proq2.wallnerlab.org.

Motivation: Learning-based model quality assessment programs have been quite successful at discriminating between high- and low-quality protein structures. Here, we show that it is possible to improve this performance significantly by restricting the learning space to a specific context, in this case membrane proteins. Since these are among the most important structures from a pharmaceutical point-of-view, it is particularly interesting to resolve local model quality for regions corresponding, e.g. to binding sites. Results: Our new ProQM method uses a support vector machine with a combination of general and membrane protein-specific features. For the transmembrane region, ProQM clearly outperforms all methods developed for generic proteins, and it does so while maintaining performance for extra-membrane domains; in this region it is only matched by ProQres. The predictor is shown to accurately predict quality both on the global and local level when applied to GPCR models, and clearly outperforms consensus-based scoring. Finally, the combination of ProQM and the Rosetta low-resolution energy function achieve a 7-fold enrichment in selection of near-native structural models, at very limited computational cost. Availability: ProQM is available as a server at +proqm.cbr.su.se+. Contact:bjorn@cbr.su.se Supplementary information: Supplementary data are available at Bioinformatics online.

Editing in microRNAs, particularly in seed can significantly alter the choice of their target genes. We show that out of 13 different human tissues, different regions of brain showed higher adenosine to inosine (A-to-I) editing in mature miRNAs. These events were enriched in seed sequence (73.33%), which was not observed for cytosine to uracil (17.86%) editing. More than half of the edited miRNAs showed increased stability, 72.7% of which had ΔΔG values less than −6.0 Kcal/mole and for all of them the edited adenosines mis-paired with cytosines on the pre-miRNA structure. A seed-editing event in hsa-miR-411 (with A – C mismatch) lead to increased expression of the mature form compared to the unedited version in cell culture experiments. Further, small RNA sequencing of GBM patients identified significant miRNA hypoediting which correlated with downregulation of ADAR2 both in metadata and qRT-PCR based validation. Twenty-two significant (11 novel) A-to-I hypoediting events were identified in GBM samples. This study highlights the importance of specific sequence and structural requirements of pre-miRNA for editing along with a suggestive crucial role for ADAR2. Enrichment of A-to-I editing in seed sequence highlights this as an important layer for genomic regulation in health and disease, especially in human brain.

MiRNAs are nodal regulators of gene expression and deregulation of miRNAs is causally associated with different diseases, including cancer. Modulation of miRNA expression is thus of therapeutic importance. Small molecules are currently being explored for their potential to downregulate miRNAs. Peptides have shown to have better potency and selectivity toward their targets but their potential in targeting and modulating miRNAs remain unexplored. Herein, using phage display we found a very selective peptide against pre-miR-21. Interestingly, the peptide has the potential to downregulate miR-21, by binding to pre-miR-21 and hindering Dicer processing. It is selective towards miR-21 inside the cell. By antagonising miR-21 function, the peptide is able to increase the expression of its target proteins and thereby increase apoptosis and suppress cell proliferation, invasion and migration. This peptide can further be explored for its anti-cancer activity in vivo and may be even extended to clinical studies.

MiRNAs are important components of regulatory networks that control gene expression and have implications in various diseases including cancer. Targeting oncogenic miRNAs with small molecules is currently being explored to develop cancer therapeutics. Here, we report the development of dual binding neomycin–bisbenzimidazole conjugates that target oncogenic miR-27a with high affinity (K_a = 1.2 to 7.4 × 10⁸ M⁻¹). These conjugates bring significant reduction (∼65% at 5 μM) in mature miRNA levels and penetrate easily in the cells where they localise both in the cytoplasm and the nucleus. Cell cycle analysis showed significant increase in the G0/G1 phase (∼15%) and decrease in the S phase (∼7%) upon treatment with neomycin–bisbenzimidazole conjugates, suggesting inhibition of cell proliferation. Using the conjugation approach, we show that moderately binding ligands can be covalently combined into high affinity binders. This study also highlights the role of linker optimization in designing high affinity ligands for miR-27a targeting.