Identification and Functional Characterization of Divergent 3’-Phosphate tRNA Ligase From Entamoeba histolytica

HSPC117/RtcB, 3’-phosphate tRNA ligase, is a critical enzyme involved in tRNA splicing and maturation. HSPC117/RtcB is also involved in mRNA splicing of some protein-coding genes including XBP-1. Entamoeba histolytica, a protozoan parasite responsible for human amebiasis, possesses two RtcB proteins (EhRtcB1 and 2), but their biological functions remain unknown. Both RtcBs show kinship with mammalian/archaeal type, and all amino acid residues present in the active sites are highly conserved, as suggested by protein alignment and phylogenetic analyses. EhRtcB1 was demonstrated to be localized to the nucleus, while EhRtcB2 was in the cytosol. EhRtcB1, but not EhRtcB2, was required for optimal growth of E. histolytica trophozoites. Both EhRtcB1 (in cooperation with EhArchease) and EhRtcB2 showed RNA ligation activity in vitro. The predominant role of EhRtcB1 in tRNAIle(UAU) processing in vivo was demonstrated in EhRtcB1- and 2-gene silenced strains. Taken together, we have demonstrated the conservation of tRNA splicing and functional diversification of RtcBs in this amoebozoan lineage.

An Application of the Eigenproblem for Biochemical Similarity

Protein alignment finds its application in refining results of sequence alignment and understanding protein function. A previous study aligned single molecules, making use of the minimization of sums of the squares of eigenvalues, obtained for the antisymmetric Cartesian coordinate distance matrices Dx and Dy. This is used in our program to search for similarities between amino acids by comparing the sums of the squares of eigenvalues associated with the Dx, Dy, and Dz distance matrices. These matrices are obtained by removing atoms that could lead to low similarity. Candidates are aligned, and trilateration is used to attach all previously striped atoms. A TM-score is the scoring function that chooses the best alignment from supplied candidates. Twenty essential amino acids that take many forms in nature are selected for comparison. The correct alignment is taken into account most of the time by the alignment algorithm. It was numerically detected by the TM-score 70% of the time, on average, and 15% more cases with close scores can be easily distinguished by human observation.

A Novel IRF6 Frameshift Mutation in a Large Chinese Pedigree With Van der Woude syndrome

Aims: Van der Woude syndrome (VWS) is one of the most common craniofacial anomalies, causing significant functional and psychological burden to the patients. This study aimed to identify the genetic cause of VWS in a Chinese family. Methods: Whole genome sequencing (WGS) was performed to screen for pathogenic mutations. Various Bioinformatics tools were used to assess the pathogenicity of the variants. Cosegregation analysis of the candidate variant was carried out. Interpretation of variants was performed according to the American College of Medical Genetics and Genomics guidelines. Results: A novel frameshift duplication c.373_374dupAA (p.Asn125Lys fs*43) was identified in exon 4 of the interferon regulatory factor 6 (IRF6) gene in all 3 affected members, which were not found in unaffected family members. The novel mutation leads to a frameshift and a premature stop codon which caused putative truncated protein. Protein alignment indicated high evolutionary conservation of the p.N125 residue, and this mutation was predicted by online tools to be damaging and deleterious. Conclusions: This study demonstrates that the novel mutation c.373_374dupAA (p.Asn125Lysfs*43) in the IRF6 gene corresponds to the VWS in this family. The discovery of this pathogenic variant enriches the genotypic spectrum of IRF6 gene and contributes to genetic diagnosis and counseling of families with VWS.

In Vitro Efficacy Comparison of Linezolid, Tedizolid, Sutezolid and Delpazolid Against Rapid Growing Mycobacteria Isolated in Beijing, China

The natural resistance of rapid growth Mycobacterium (RGM) against multiple antibiotics renders the treatment of caused infections less successful and time consuming. Therefore, new effective agents are urgently needed. The aim of this study was to evaluate the in vitro susceptibility of 115 isolates, constituting different RGM species, to four oxazolidinones i.e. delpazolid, sutezolid, tedizolid and linezolid. Additionally, 32 reference strains of different RGM species were also tested. The four oxazolidinones exhibited potent in vitro activity against the recruited RGM reference strains, 24 out of 32 RGM species had MICs ≤ 8 µg/mL against all four oxazolidinones whereas tedizolid and delpazolid generally presented lower MICs than linezolid or sutezolid. Tedizolid showed the strongest activity against clinical isolates of M. abscessus with MIC50 = 1 µg/mL and MIC90 = 2 µg/mL. MIC values for tedizolid were usually 4- to 8-fold less than the MICs of linezolid for M. abscessus subsp. abscessus. The MIC distributions of sutezolid and linezolid were similar, while delpazolid showed 2-fold lower MIC as compared with linezolid. Linezolid was not active against most of the tested M. fortuitum isolates, since 22 out of the 25 M. fortuitum were resistant against linezolid. However, delpazolid exhibited better antimicrobial activity against these isolates with 4-fold lower MIC values, in contrast with linezolid. In addition, the protein alignment of RplC and RplD and structure based analysis showed that there may be no correlation between oxazolidinones resistance and mutations in rplC ,rplD and 23srRNA genes in tested RGM. This study showed tedizolid harbors the strongest inhibitory activity against M. abscessus in vitro, while delpazolid presented the best activity against M. fortuitum, which provided important insights on the potential clinical application of oxazolidinones to treat RGM infections.

ProALIGN: Directly learning alignments for protein structure prediction via exploiting context-specific alignment motifs

Template-based modeling (TBM), including homology modeling and protein threading, is one of the most reliable techniques for protein structure prediction. It predicts protein structure by building an alignment between the query sequence under prediction and the templates with solved structures. However, it is still very challenging to build the optimal sequence-template alignment, especially when only distantly-related templates are available.Here we report a novel deep learning approach ProALIGN that can predict much more accurate sequence-template alignment. Like protein sequences consisting of sequence motifs, protein alignments are also composed of frequently-occurring alignment motifs with characteristic patterns. Alignment motifs are context-specific as their characteristic patterns are tightly related to sequence contexts of the aligned regions. Inspired by this observation, we represent a protein alignment as a binary matrix (in which 1 denotes an aligned residue pair) and then use a deep convolutional neural network to predict the optimal alignment from the query protein and its template. The trained neural network implicitly but effectively encodes an alignment scoring function, which reduces inaccuracies in the handcrafted scoring functions widely used by the current threading approaches. For a query protein and a template, we apply the neural network to directly infer likelihoods of all possible residue pairs in their entirety, which could effectively consider the correlations among multiple residues. We further construct the alignment with maximum likelihood, and finally build structure model according to the alignment.Tested on three independent datasets with in total 6,688 protein alignment targets and 80 CASP13 TBM targets, our method achieved much better alignments and 3D structure models than the existing methods including HHpred, CNFpred, CEthreader and DeepThreader. These results clearly demonstrate the effectiveness of exploiting the context-specific alignment motifs by deep learning for protein threading.

The role of extracellular matrix phosphorylation on energy dissipation in bone

Protein phosphorylation, critical for cellular regulatory mechanisms, is implicated in various diseases. However, it remains unknown whether heterogeneity in phosphorylation of key structural proteins alters tissue integrity and organ function. Here, osteopontin phosphorylation level declined in hypo- and hyper- phosphatemia mouse models exhibiting skeletal deformities. Phosphorylation increased cohesion between osteopontin polymers, and adhesion of osteopontin to hydroxyapatite, enhancing energy dissipation. Fracture toughness, a measure of bone’s mechanical competence, increased with ex-vivo phosphorylation of wildtype mouse bones and declined with ex-vivo dephosphorylation. In osteopontin-deficient mice, global matrix phosphorylation level was not associated with toughness. Our findings suggest that phosphorylated osteopontin promotes fracture toughness in a dose-dependent manner through increased interfacial bond formation. In the absence of osteopontin, phosphorylation increases electrostatic repulsion, and likely protein alignment and interfilament distance leading to decreased fracture resistance. These mechanisms may be of importance in other connective tissues, and the key to unraveling cell–matrix interactions in diseases.

dagLogo: An R/Bioconductor package for identifying and visualizing differential amino acid group usage in proteomics data

Sequence logos have been widely used as graphical representations of conserved nucleic acid and protein motifs. Due to the complexity of the amino acid (AA) alphabet, rich post-translational modification, and diverse subcellular localization of proteins, few versatile tools are available for effective identification and visualization of protein motifs. In addition, various reduced AA alphabets based on physicochemical, structural, or functional properties have been valuable in the study of protein alignment, folding, structure prediction, and evolution. However, there is lack of tools for applying reduced AA alphabets to the identification and visualization of statistically significant motifs. To fill this gap, we developed an R/Bioconductor package dagLogo, which has several advantages over existing tools. First, dagLogo allows various formats for input sets and provides comprehensive options to build optimal background models. It implements different reduced AA alphabets to group AAs of similar properties. Furthermore, dagLogo provides statistical and visual solutions for differential AA (or AA group) usage analysis of both large and small data sets. Case studies showed that dagLogo can better identify and visualize conserved protein sequence patterns from different types of inputs and can potentially reveal the biological patterns that could be missed by other logo generators.

dagLogo: an R/Bioconductor Package for Identifying and Visualizing Differential Amino Acid Group Usage in Proteomics data

Sequence logos have been widely used as graphical representations of conserved nucleic acid and protein motifs. Due to the complexity of the amino acid (AA) alphabet, rich post-translational modification, and diverse subcellular localization of proteins, few versatile tools are available for effective identification and visualization of protein motifs. In addition, various reduced AA alphabets based on physicochemical, structural, or functional properties have been valuable in the study of protein alignment, folding, structure prediction, and evolution. However, there is lack of tools for applying reduced AA alphabets to the identification and visualization of statistically significant motifs. To fill this gap, we developed an R/Bioconductor package dagLogo, which has several advantages over existing tools. First, dagLogo allows various formats for input sets and provides comprehensive options to build optimal background models. It implements different reduced AA alphabets to group AAs of similar properties. Furthermore, dagLogo provides statistical and visual solutions for differential AA (or AA group) usage analysis of both large and small data sets. Case studies showed that dagLogo can better identify and visualize conserved protein sequence patterns from different types of inputs and can potentially reveal the biological patterns that could be missed by other logo generators.