RF-SVM: Identification of DNA-binding proteins based on comprehensive feature representation methods and support vector machine.

Protein-DNA interactions play an important role in biological progress, such as DNA replication, repair, and modification processes. In order to have a better understanding of its functions, the one of the most important steps is the identification of DNA-binding proteins. We propose a DNA-binding protein predictor, namely, RF-SVM, which contains four types features, that is, pseudo amino acid composition (PseAAC), amino acid distribution (AAD), adjacent amino acid composition frequency (ACF) and Local-DPP. Random Forest algorithm is utilized for selecting top 174 features, which are established the predictor model with the support vector machine (SVM) on training dataset UniSwiss-Tr. Finally, RF-SVM method is compared with other existing methods on test dataset UniSwiss-Tst. The experimental results demonstrated that RF-SVM has accuracy of 84.25%. Meanwhile, we discover that the physicochemical properties of amino acids for OOBM770101(H), CIDH920104(H), MIYS990104(H), NISK860101(H), VINM940103(H), and SNEP660101(A) have contribution to predict DNA-binding proteins. The main code and datasets can gain in https://github.com/NiJianWei996/RF-SVM.

Differences and Correlations of Morphological Characteristics and Fatty Acid Profiles of Seeds of Toona sinensis.

Toona sinensis (A.Juss.) M.Roem., a multi-purpose tree of Meliaceae, is widely distributed and intensively cultivated in Asia, yet its high yielding, lipid-rich seeds are rarely exploited. The present study systematically analyzed the differences and correlations of seed morphological characteristics and fatty acid (FA) profiles of 62 representative T. sinensis germplasms distributed across northern to southern China. T. sinensis seeds were rich in total FAs (TFA, 107.03-176.18 mg/g). Additionally, linoleic acid (54.69-100.59 mg/g), α-linolenic acid (ALA, 22.47-45.02 mg/g), oleic acid (OA, 5.12-23.94 mg/g), palmitic acid (6.87-14.14 mg/g), stearic acid (SA, 3.13-6.57 mg/g) and elaidic acid (1.70-2.88 mg/g) were the major FAs measured by GC/MS analysis. Size (average width of 3.94±0.01 mm and length of 5.79±0.02 mm) and mass (average thousand-seed weight of 10.52±0.17 g) were greater in T. sinensis seeds collected south than north of 30° latitude. These traits were also positively correlated with unsaturated FA content and negatively related to SA and saturated FA contents (P<0.05). Significant positive correlations were found between seed length and polyunsaturated FA (R2 =0.370) and ALA levels (R2 =0.296), as well as between thousand-seed weight and monounsaturated FAs (R2 =0.309) and OA levels (R2 =0.297) (P<0.05). Seventeen T. sinensis germplasms gathered by cluster analysis as cluster IV were determined as desirable for oil processing due to their higher TFA and ALA contents and greater seed size and mass than others. Generally, the wider, heavier, and especially longer seeds of T. sinensis contain much higher levels of FAs, especially ALA, and are the more promising sources for breeding and the oil processing industry.

Non-Interpenetrated Single-Crystal Covalent Organic Frameworks.

Growth of covalent organic frameworks (COFs) as single crystals is extremely challenging. Inaccessibility of open-structured single-crystal COFs prevents the exploration of structure-oriented applications. Herein we report for the first time a non-interpenetrated single-crystal COF, LZU-306, which possesses the open structure constructed exclusively via covalent assembly. With a high void volume of 80 %, LZU-306 was applied to investigate the intrinsic dynamics of reticulated tetraphenylethylene (TPE) as the individual aggregation-induced-emission moiety. Solid-state 2 H NMR investigation has determined that the rotation of benzene rings in TPE, being the freest among the reported cases, is as fast as 1.0×104  Hz at 203 K to 1.5×107  Hz at 293 K. This research not only explores a new paradigm for single-crystal growth of open frameworks, but also provides a unique matrix-isolation platform to reticulate functional moieties into a well-defined and isolated state.

DBP-PSSM: Combination of Evolutionary Profiles with the XGBoost Algorithm to Improve the Identification of DNA-binding Proteins.

BACKGROUND AND OBJECTIVE: DNA-binding proteins play important roles in a variety of biological processes, such as gene transcription and regulation, DNA replication and repair, DNA recombination and packaging, and the formation of chromatin and ribosomes. Therefore, it is urgent to develop a computational method to improve the recognition efficiency of DNA-binding proteins. METHODS: We proposed a novel method, DBP-PSSM, which constructed the features from amino acid composition and evolutionary information of protein sequences. The maximum relevance, minimum redundancy (mRMR) was employed to select the optimal features for establishing the XGBoost classifier, therefore, the novel model of prediction DNA-binding proteins, DBP-PSSM, was established with 5-fold cross-validation on the training dataset. RESULTS: DBP-PSSM achieved an accuracy of 81.18% and MCC of 0.657 in a test dataset, which outperformed the many existing methods. These results demonstrated that our method can effectively predict DNA-binding proteins. CONCLUSION: The data and source code are provided at https://github.com/784221489/DNA-binding.

FermatS: A Novel Numerical Representation for Protein Sequence Comparison and DNA-binding Protein Identification.

AIMS: Based on protein sequence information, a simple and effective method was used to analyze protein sequence similarity and predict DNA-binding protein. BACKGROUND: It is absolutely necessary that we generate computational methods of low complexity to accurate infer protein structure, function, and evolution in the rapidly growing number of molecular biology data available. OBJECTIVE: It is important to generate novel computational algorithms for analyzing and comparing protein sequences with the rapidly growing number of molecular biology data available. METHODS: Based on global and local position representation with the curves of Fermat spiral and normalized moments of inertia of the curve of Fermat spiral, respectively, moreover, composition of 20 amino acids to get the numerical characteristics of protein sequences. RESULTS: It has been applied to analyze the similarity/dissimilarity of nine ND5 proteins, the analysis results are consistent with the biological evolution theory. Furthermore, we employ the Logistic regression with 5-fold cross-validation to establish the prediction of DNA-binding proteins model, which outperformed the DNAbinder, iDNA-prot, DNA-prot and gDNA-prot by 0.0069-0.609 in terms of F-measure, 0.293-0.898 in terms of MCC in unbalanced dataset. CONCLUSION: These results show that our method, namely FermatS, is effective to compare, recognition and prediction the protein sequences.

Distinct physiological and transcriptional responses of leaves of paper mulberry (Broussonetia kazinoki × B. papyrifera) under different nitrogen supply levels.

Paper mulberry, a vigorous pioneer species used for ecological reclamation and a high-protein forage plant for economic development, has been widely planted in China. To further develop its potential value, it is necessary to explore the regulatory mechanism of nitrogen metabolism for rational nitrogen utilization. In this study, we investigated the morphology, physiology and transcriptome of a paper mulberry hybrid (Broussonetia kazinoki × B. papyrifera) in response to different nitrogen concentrations. Moderate nitrogen promoted plant growth and biomass accumulation. Photosynthetic characteristics, concentration of nitrogenous compounds and activities of enzymes were stimulated under nitrogen treatment. However, these enhancements were slightly or severely inhibited under excessive nitrogen supply. Nitrite reductase and glutamate synthase were more sensitive than nitrate reductase and glutamine synthetase and more likely to be inhibited under high nitrogen concentrations. Transcriptome analysis of the leaf transcriptome identified 161,961 unigenes. The differentially expressed genes associated with metabolism of nitrogen, alanine, aspartate, glutamate and glycerophospholipid showed high transcript abundances after nitrogen application, whereas those associated with glycerophospholipid, glycerolipid, amino sugar and nucleotide sugar metabolism were down-regulated. Combined with weighted gene coexpression network analysis, we uncovered 16 modules according to similarity in expression patterns. Asparagine synthetase and inorganic pyrophosphatase were considered two hub genes in two modules, which were associated with nitrogen metabolism and phosphorus metabolism, respectively. The expression characteristics of these genes may explain the regulation of morphological, physiological and other related metabolic strategies harmoniously. This multifaceted study provides valuable insights to further understand the mechanism of nitrogen metabolism and to guide utilization of paper mulberry.

Comparative and Phylogenetic Analyses of the Complete Chloroplast Genomes of Six Almond Species (Prunus spp. L.).

As a source of genetic variation, almond germplasm resources are of great significance in breeding. To better reveal the mutation characteristics and evolution patterns of the almond chloroplast (cp) genome, the complete cp genomes from six almond species were analyzed. The lengths of the chloroplast genome of the six almond species ranged from 157,783 bp to 158,073 bp. For repeat sequence analysis, 53 pairs of repeats (30 bp or longer) were identified. A total of 117 SSR loci were observed, including 96 polymorphic SSR loci. Nine highly variable regions with a nucleotide variability (Pi) higher than 0.08, including rps16, rps16-psbK, atpF-atpH, rpoB, ycf3-rps4, rps4-ndhJ, accD-psaI and rps7-orf42 (two highly variable regions) were located. Based on the chloroplast genome evolution analysis, three species (P. tenella, P. pedunculata and P. triloba) and wild cherry (P. tomentosa) were grouped into clade I. Clade II consisted of two species (P. mongolica and P. tangutica) and wild peach (P. davidiana). Clade III included the common almond (P. dulcis), cultivated peach (P. persica) and GanSu peach (P. kansuensis). This result expands the researchers' vision of almond plant diversity and promotes an understanding of the evolutionary relationship among almond species. In brief, this study provides abundant resources for the study of the almond chloroplast genome, and has an important reference value for study of the evolution and species identification of almond.