Search

The Database of Aligned Structural Homologs (DASH) is a tool for efficiently navigating the Protein Data Bank (PDB) by means of pre-computed pairwise structural alignments. We recently showed that, by integrating DASH structural alignments with the multiple sequence alignment (MSA) software MAFFT, we were able to significantly improve MSA accuracy without dramatically increasing manual or computational complexity. In the latest DASH update, such queries are not limited to PDB entries but can also be launched from user-provided protein coordinates. Here, we describe a further extension of DASH that retrieves intermolecular interactions of all structurally similar domains in the PDB to a query domain of interest. We illustrate these new features using a model of the NYN domain of the ribonuclease N4BP1 as an example. We show that the protein-nucleotide interactions returned are distributed on the surface of the NYN domain in an asymmetric manner, roughly centered on the known nuclease active site.

Fibrosis is an incurable disorder of unknown etiology. Segregated-nucleus-containing atypical monocytes (SatMs) are critical for the development of fibrosis. Here we examined the mechanisms that recruit SatMs to pre-fibrotic areas. A screen based on cytokine expression in the fibrotic lung revealed that the chemokine Cxcl12, which is produced by apoptotic nonhematopoietic cells, was essential for SatM recruitment. Analyses of lung tissues at fibrosis onset showed increased expression of Rbm7, a component of the nuclear exosome targeting complex. Rbm7 deletion suppressed bleomycin-induced fibrosis and at a cellular level, suppressed apoptosis of nonhematopoietic cells. Mechanistically, Rbm7 bound to noncoding (nc)RNAs that form subnuclear bodies, including Neat1 speckles. Dysregulated expression of Rbm7 resulted in the nuclear degradation of Neat1 speckles, the dispersion of the DNA repair protein BRCA1, and the triggering of apoptosis. Thus, Rbm7 in epithelial cells plays a critical role in the development of fibrosis by regulating ncRNA decay and thereby the production of chemokines that recruit SatMs., Competing Interests: Declaration of Interests The authors declare competing financial interests: details are available in the online version of the paper. Readers are welcome to comment on the online version of the paper. Correspondence and requests for materials should be addressed to S.A. (sakira@biken.osaka-u.ac.jp) and T.S. (sohsatoh@biken.osaka-u.ac.jp)., (Copyright © 2020 Elsevier Inc. All rights reserved.)

RNA-modulating factors not only regulate multiple steps of cellular RNA metabolism, but also emerge as key effectors of the immune response against invading viral pathogens including human immunodeficiency virus type-1 (HIV-1). However, the cellular RNA-binding proteins involved in the establishment and maintenance of latent HIV-1 reservoirs have not been extensively studied. Here, we screened a panel of 62 cellular RNA-binding proteins and identified NEDD4-binding protein 1 (N4BP1) as a potent interferon-inducible inhibitor of HIV-1 in primary T cells and macrophages. N4BP1 harbours a prototypical PilT N terminus-like RNase domain and inhibits HIV-1 replication by interacting with and degrading viral mRNA species. Following activation of CD4 + T cells, however, N4BP1 undergoes rapid cleavage at Arg 509 by the paracaspase named mucosa-associated lymphoid tissue lymphoma translocation 1 (MALT1). Mutational analyses and knockout studies revealed that MALT1-mediated inactivation of N4BP1 facilitates the reactivation of latent HIV-1 proviruses. Taken together, our findings demonstrate that the RNase N4BP1 is an efficient restriction factor of HIV-1 and suggest that inactivation of N4BP1 by induction of MALT1 activation might facilitate elimination of latent HIV-1 reservoirs.

RNA-LIM is a procedure that can analyze various pseudo-potentials describing the affinity between single-stranded RNA (ssRNA) ribonucleotides and surface amino acids to produce a coarse-grained estimate of the structure of the ssRNA at the protein interface. The search algorithm works by evolving an ssRNA chain, of known sequence, as a series of walks between fixed sites on a protein surface. Optimal routes are found by application of a set of minimal "limiting" restraints derived jointly from (i) selective sampling of the ribonucleotide amino acid affinity pseudo-potential data, (ii) limited surface path exploration by prior determination of surface arc lengths, and (iii) RNA structural specification obtained from a statistical potential gathered from a library of experimentally determined ssRNA structures. We describe the general approach using a NAST (Nucleic Acid Simulation Tool)-like approximation of the ssRNA chain and a generalized pseudo-potential reflecting the location of nucleic acid binding residues. Minimum and maximum performance indicators of the methodology are established using both synthetic data, for which the pseudo-potential defining nucleic acid binding affinity is systematically degraded, and a representative real case, where the RNA binding sites are predicted by the amplified antisense RNA (aaRNA) method. Some potential uses and extensions of the routine are discussed. RNA-LIM analysis programs along with detailed instructions for their use are available on request from the authors., (Crown Copyright © 2014. Published by Elsevier Inc. All rights reserved.)

Increasing awareness of the importance of protein-RNA interactions has motivated many approaches to predict residue-level RNA binding sites in proteins based on sequence or structural characteristics. Sequence-based predictors are usually high in sensitivity but low in specificity; conversely structure-based predictors tend to have high specificity, but lower sensitivity. Here we quantified the contribution of both sequence- and structure-based features as indicators of RNA-binding propensity using a machine-learning approach. In order to capture structural information for proteins without a known structure, we used homology modeling to extract the relevant structural features. Several novel and modified features enhanced the accuracy of residue-level RNA-binding propensity beyond what has been reported previously, including by meta-prediction servers. These features include: hidden Markov model-based evolutionary conservation, surface deformations based on the Laplacian norm formalism, and relative solvent accessibility partitioned into backbone and side chain contributions. We constructed a web server called aaRNA that implements the proposed method and demonstrate its use in identifying putative RNA binding sites., (© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Background: Regulation of gene expression, protein synthesis, replication and assembly of many viruses involve RNA-protein interactions. Although some successful computational tools have been reported to recognize RNA binding sites in proteins, the problem of specificity remains poorly investigated. After the nucleotide base composition, the dinucleotide is the smallest unit of RNA sequence information and many RNA-binding proteins simply bind to regions enriched in one dinucleotide. Interaction preferences of protein subsequences and dinucleotides can be inferred from protein-RNA complex structures, enabling a training-based prediction approach., Results: We analyzed basic statistics of amino acid-dinucleotide contacts in protein-RNA complexes and found their pairing preferences could be identified. Using a standard approach to represent protein subsequences by their evolutionary profile, we trained neural networks to predict multiclass target vectors corresponding to 16 possible contacting dinucleotide subsequences. In the cross-validation experiments, the accuracies of the optimum network, measured as areas under the curve (AUC) of the receiver operating characteristic (ROC) graphs, were in the range of 65-80%., Conclusions: Dinucleotide-specific contact predictions have also been extended to the prediction of interacting protein and RNA fragment pairs, which shows the applicability of this method to predict targets of RNA-binding proteins. A web server predicting the 16-dimensional contact probability matrix directly from a user-defined protein sequence was implemented and made available at: http://tardis.nibio.go.jp/netasa/srcpred.