No abstract

Protein sequence alignment is essential for template-based protein structure prediction and function annotation. We collect 20 sequence alignment algorithms, 10 published and 10 newly developed, which cover all representative sequence- and profile-based alignment approaches. These algorithms are benchmarked on 538 non-redundant proteins for protein fold-recognition on a uniform template library. Results demonstrate dominant advantage of profile-profile based methods, which generate models with average TM-score 26.5% higher than sequence-profile methods and 49.8% higher than sequence-sequence alignment methods. There is no obvious difference in results between methods with profiles generated from PSI-BLAST PSSM matrix and hidden Markov models. Accuracy of profile-profile alignments can be further improved by 9.6% or 21.4% when predicted or native structure features are incorporated. Nevertheless, TM-scores from profile-profile methods including experimental structural features are still 37.1% lower than that from TM-align, demonstrating that the fold-recognition problem cannot be solved solely by improving accuracy of structure feature predictions.

A novel esterase gene (estSL3) was cloned from the Alkalibacterium sp. SL3, which was isolated from the sediment of soda lake Dabusu. The 636-bp full-length gene encodes a polypeptide of 211 amino acid residues that is closely related with putative GDSL family lipases from Alkalibacterium and Enterococcus. The gene was successfully expressed in E. coli, and the recombinant protein (rEstSL3) was purified to electrophoretic homogeneity and characterized. rEstSL3 exhibited the highest activity towards pNP-acetate and had no activity towards pNP-esters with acyl chains longer than C8. The enzyme was highly cold-adapted, showing an apparent temperature optimum of 30 °C and remaining approximately 70% of the activity at 0 °C. It was active and stable over the pH range from 7 to 10, and highly salt-tolerant up to 5 M NaCl. Moreover, rEstSL3 was strongly resistant to most tested metal ions, chemical reagents, detergents and organic solvents. Amino acid composition analysis indicated that EstSL3 had fewer proline residues, hydrogen bonds and salt bridges than mesophilic and thermophilic counterparts, but more acidic amino acids and less hydrophobic amino acids when compared with other salt-tolerant esterases. The cold active, salt-tolerant and chemical-resistant properties make it a promising enzyme for basic research and industrial applications.

We curated a reliable dataset of mA sites in Arabidopsis thaliana, built competitive models for predicting mA sites, extracted predominant rules from the prediction models and analyzed the most important features. In biological RNA, approximately 150 chemical modifications have been discovered, of which N-methyladenine (mA) is the most prevalent and abundant. This modification plays an essential role in a myriad of biological mechanisms and regulates RNA localization, nuclear export, translation, stability, alternative splicing, and other processes. However, mA-seq and other wet-lab techniques do not easily facilitate accurate and complete determination of mA sites across the transcriptome. Therefore, the use of computational methods to establish accurate models for predicting mA sites is essential. In this work, we manually curated a reliable dataset of mA sites and non-mA sites and developed a new tool called RFAthM6A for predicting mA sites in Arabidopsis thaliana. Briefly, RFAthM6A consists of four independent models named RFPSNSP, RFPSDSP, RFKSNPF and RFKNF and strict benchmarks show that the AUC values of the four models reached 0.894, 0.914, 0.920 and 0.926, respectively in a fivefold cross validation and the prediction performance of RFPSDSP, RFKSNPF and RFKNF exceeded that of three previously reported models (AthMethPre, M6ATH and RAM-NPPS). Linear combination of the prediction scores of RFPSDSP, RFKSNPF and RFKNF improved the prediction performance. We also extracted several predominant rules that underlie the mA site identification from the trained models. Furthermore, the most important features of the predictors for the mA site identification were also analyzed in depth. To facilitate use of our proposed models by interested researchers, all the source codes and datasets are publicly deposited at https://github.com/nongdaxiaofeng/RFAthM6A .

A novel glycosyl hydrolase family 11 xylanase gene, xynMF13A, was cloned from Phoma sp. MF13, a xylanase-producing fungus isolated from mangrove sediment. xynMF13A was heterologously expressed in Pichia pastoris, and the recombinant XynMF13A (rXynMF13A) was purified by Ni-affinity chromatography. The temperature and pH optima of purified rXynMF13A were 45 °C and pH 5.0, respectively. rXynMF13A showed a high level of salt tolerance, with maximal enzyme activity being seen at 0.5 M NaCl and as much as 53% of maximal activity at 4 M NaCl. The major rXynMF13A hydrolysis products from corncob xylan were xylobiose, xylotriose, xylotetraose, and xylopentaose, but no xylose was found. These hydrolysis products suggest an important potential for XynMF13A in the production of xylooligosaccharides (XOs). Furthermore, rXynMF13A had beneficial effects on Chinese steamed bread production, by increasing specific volume and elasticity while decreasing hardness and chewiness. These results demonstrate XynMF13A to be a novel xylanase with potentially significant applications in baking, XOs production, and seafood processing.