Songling Li

Here, we describe a web server that integrates structural alignments with the MAFFT multiple sequence alignment (MSA) tool. For this purpose, we have prepared a web-based Database of Aligned Structural Homologs (DASH), which provides structural alignments at the domain and chain levels for all proteins in the Protein Data Bank (PDB), and can be queried interactively or by a simple REST-like API. MAFFT-DASH integration can be invoked with a single flag on either the web (https://mafft.cbrc.jp/alignment/server/) or command-line versions of MAFFT. In our benchmarks using 878 cases from the BAliBase, HomFam, OXFam, Mattbench and SISYPHUS datasets, MAFFT-DASH showed 10-20% improvement over standard MAFFT for MSA problems with weak similarity, in terms of Sum-of-Pairs (SP), a measure of how well a program succeeds at aligning input sequences in comparison to a reference alignment. When MAFFT alignments were supplemented with homologous sequences, further improvement was observed. Potential applications of DASH beyond MSA enrichment include functional annotation through detection of remote homology and assembly of template libraries for homology modeling.

The AT-rich interactive domain-containing protein 5a (Arid5a) plays a critical role in autoimmunity by regulating the half-life of Interleukin-6 (IL-6) mRNA. However, the signaling pathways underlying Arid5a-mediated regulation of IL-6 mRNA stability are largely uncharacterized. Here, we found that during the early phase of lipopolysaccharide (LPS) stimulation, NF-κB and an NF-κB-triggered IL-6-positive feedback loop activate Arid5a gene expression, increasing IL-6 expression via stabilization of the IL-6 mRNA. Subsequently, mitogen-activated protein kinase (MAPK) phosphatase-1 (MKP-1) promotes translocation of AU-rich element RNA-binding protein 1 (AUF-1) from the nucleus to the cytoplasm, where it destabilizes Arid5a mRNA by binding to AU-rich elements in the 3΄ UTR. This results in downregulation of IL-6 mRNA expression. During the late phase of LPS stimulation, p38 MAPK phosphorylates Arid5a and recruits the WW domain containing E3 ubiquitin protein ligase 1 (WWP1) to its complex, which in turn ubiquitinates Arid5a in a K48-linked manner, leading to its degradation. Inhibition of Arid5a phosphorylation and degradation increases production of IL-6 mRNA. Thus, our data demonstrate that LPS-induced NF-κB and MAPK signaling are required to control the regulation of the IL-6 mRNA stabilizing molecule Arid5a. This study therefore substantially increases our understanding of the mechanisms by which IL-6 is regulated.

Reversible acetylation on lysine residues, a crucial post-translational modification (PTM) for both histone and non-histone proteins, governs many central cellular processes. Due to limited data and lack of a clear acetylation consensus sequence, little research has focused on prediction of lysine acetylation sites. Incorporating almost all currently available lysine acetylation information, and using the support vector machine (SVM) method along with coding schema for protein sequence coupling patterns, we propose here a novel lysine acetylation prediction algorithm: LysAcet. When compared with other methods or existing tools, LysAcet is the best predictor of lysine acetylation, with K-fold (5- and 10-) and jackknife cross-validation accuracies of 75.89%, 76.73%, and 77.16%, respectively. LysAcet's superior predictive accuracy is attributed primarily to the use of sequence coupling patterns, which describe the relative position of two amino acids. LysAcet contributes to the limited PTM prediction research on lysine epsilon-acetylation, and may serve as a complementary in-silicon approach for exploring acetylation on proteomes. An online web server is freely available at http://www.biosino.org/LysAcet/.

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.