Qian-Zhong Li

Using pseudo amino acid composition to predict protein structural class: Approached by incorporating 400 dipeptide components

The proteins structure can be mainly classified into four classes: all‐α, all‐β, α/β, and α + β protein according to their chain fold topologies. For the purpose of predicting the protein structural class, a new predicting algorithm, in which the increment of diversity combines with Quadratic Discriminant analysis, is presented to study and predict protein structural class. On the basis of the concept of the pseudo amino acid composition (Chou, Proteins: Struct Funct Genet 2001, 43, 246; Erratum: Proteins Struct Funct Genet 2001, 44, 60), 400 dipeptide components and 20 amino acid composition are, respectively, selected as parameters of diversity source. Total of 204 nonhomologous proteins constructed by Chou (Chou, Biochem Biophys Res Commun 1999, 264, 216) are used for training and testing the predictive model. The predicted results by using the pseudo amino acids approach as proposed in this paper can remarkably improve the success rates, and hence the current method may play a complementary role to other existing methods for predicting protein structural classification.

Prediction of Subcellular Localization of Apoptosis Protein Using Chou’s Pseudo Amino Acid Composition

Apoptosis proteins play an essential role in regulating a balance between cell proliferation and death. The successful prediction of subcellular localization of apoptosis proteins directly from primary sequence is much benefited to understand programmed cell death and drug discovery. In this paper, by use of Chou’s pseudo amino acid composition (PseAAC), a total of 317 apoptosis proteins are predicted by support vector machine (SVM). The jackknife cross-validation is applied to test predictive capability of proposed method. The predictive results show that overall prediction accuracy is 91.1% which is higher than previous methods. Furthermore, another dataset containing 98 apoptosis proteins is examined by proposed method. The overall predicted successful rate is 92.9%.

Using support vector machine to predict β- and γ-turns in proteins

By using the composite vector with increment of diversity, position conservation scoring function, and predictive secondary structures to express the information of sequence, a support vector machine (SVM) algorithm for predicting β‐ and γ‐turns in the proteins is proposed. The 426 and 320 nonhomologous protein chains described by Guruprasad and Rajkumar (Guruprasad and Rajkumar J. Biosci 2000, 25,143) are used for training and testing the predictive model of the β‐ and γ‐turns, respectively. The overall prediction accuracy and the Matthews correlation coefficient in 7‐fold cross‐validation are 79.8% and 0.47, respectively, for the β‐turns. The overall prediction accuracy in 5‐fold cross‐validation is 61.0% for the γ‐turns. These results are significantly higher than the other algorithms in the prediction of β‐ and γ‐turns using the same datasets. In addition, the 547 and 823 nonhomologous protein chains described by Fuchs and Alix (Fuchs and Alix Proteins: Struct Funct Bioinform 2005, 59, 828) are used for training and testing the predictive model of the β‐ and γ‐turns, and better results are obtained. This algorithm may be helpful to improve the performance of protein turns prediction. To ensure the ability of the SVM method to correctly classify β‐turn and non‐β‐turn (γ‐turn and non‐γ‐turn), the receiver operating characteristic threshold independent measure curves are provided.

Identification of TATA and TATA-less promoters in plant genomes by integrating diversity measure, GC-Skew and DNA geometric flexibility.

Accurate identification of core promoters is important for gaining more insight about the understanding of the eukaryotic transcription regulation. In this study, the authors focused on the biologically realistic promoter prediction of plant genomes. By analyzing the correlative conservation, GC-compositional bias and specific structural patterns of TATA and TATA-less promoters in PlantPromDB, a hybrid multi-feature approach based on support vector machine (SVM) for predicting the two types of promoters were developed by integrating local word content, GC-Skew and DNA geometric flexibility. Compared with the TSSP-TCM program on the same test dataset, better prediction results were obtained. Especially for the TATA-less promoter, the accuracy is 10% higher than the result of TSSP-TCM program. The good performance of the hybrid promoters and the experimental data also indicate that our method has the ability to locate the promoter region of the plant genome.

Eukaryotic and prokaryotic promoter prediction using hybrid approach

Promoters are modular DNA structures containing complex regulatory elements required for gene transcription initiation. Hence, the identification of promoters using machine learning approach is very important for improving genome annotation and understanding transcriptional regulation. In recent years, many methods have been proposed for the prediction of eukaryotic and prokaryotic promoters. However, the performances of these methods are still far from being satisfactory. In this article, we develop a hybrid approach (called IPMD) that combines position correlation score function and increment of diversity with modified Mahalanobis Discriminant to predict eukaryotic and prokaryotic promoters. By applying the proposed method to Drosophila melanogaster, Homo sapiens, Caenorhabditis elegans, Escherichia coli, and Bacillus subtilis promoter sequences, we achieve the sensitivities and specificities of 90.6 and 97.4% for D. melanogaster, 88.1 and 94.1% for H. sapiens, 83.3 and 95.2% for C. elegans, 84.9 and 91.4% for E. coli, as well as 80.4 and 91.3% for B. subtilis. The high accuracies indicate that the IPMD is an efficient method for the identification of eukaryotic and prokaryotic promoters. This approach can also be extended to predict other species promoters.

Large-scale transcriptome comparison of sunflower genes responsive to Verticillium dahliae.

BackgroundSunflower Verticillium wilt (SVW) is a vascular disease caused by root infection with Verticillium dahliae (V. dahlia). It is a serious threat to the yield and quality of sunflower. However, chemical and agronomic measures for controlling this disease are not effective. The selection of more resistant genotypes is a desirable strategy to reduce contamination. A deeper knowledge of the molecular mechanisms and genetic basis underlying sunflower Verticillium wilt is necessary to accelerate breeding progress.ResultsAn RNA-Seq approach was used to perform global transcriptome profiling on the roots of resistant (S18) and susceptible (P77) sunflower genotypes infected with V. dahlia. Different pairwise transcriptome comparisons were examined over a time course (6, 12 and 24xa0h, and 2, 3, 5 and 10 d post inoculation). In RD, SD and D datasets, 1231 genes were associated with SVW resistance in a genotype-common transcriptional pattern. Moreover, 759 and 511 genes were directly related to SVW resistance in the resistant and susceptible genotypes, respectively, in a genotype-specific transcriptional pattern. Most of the genes were demonstrated to participate in plant defense responses; these genes included peroxidase (POD), glutathione peroxidase, aquaporin PIP, chitinase, L-ascorbate oxidase, and LRR receptors. For the up-regulated genotype-specific differentially expressed genes (DEGs) in the resistant genotype, higher average fold-changes were observed in the resistant genotype compared to those in the susceptible genotype. An inverse effect was observed in the down-regulated genotype-specific DEGs in the resistant genotype. KEGG analyses showed that 98, 112 and 52 genes were classified into plant hormone signal transduction, plant-pathogen interaction and flavonoid biosynthesis categories, respectively. Many of these genes, such as CNGC, RBOH, FLS2, JAZ, MYC2 NPR1 and TGA, regulate crucial points in defense-related pathway and may contribute to V. dahliae resistance in sunflower.ConclusionsThe transcriptome profiling results provided a clearer understanding of the transcripts associated with the crosstalk between sunflower and V. dahliae. The results identified several differentially expressed unigenes involved in the hyper sensitive response (HR) and the salicylic acid (SA)/jasmonic acid (JA)-mediated signal transduction pathway for resistance against V. dahliae. These results are useful for screening resistant sunflower genotypes.

One parameter to describe the mechanism of splice sites competition

The choice of a splice site is not only related to its own intrinsic strength, but also is influenced by its flanking competitors. Splice site competition is an important mechanism for splice site prediction, especially, it is a new insight for alternative splice site prediction. In this paper, the position weight matrix scoring function is used to represent splice site strength, and the mechanism of splice site competition is described by only one parameter: scoring function subtraction. While applying on the alternative splice site prediction, based on the only one parameter, 68.22% of donor sites and 70.86% of acceptor sites are correctly classified into alternative and constitutive. The prediction abilities are approximately equal to the recent method which is based on the mechanism of splice site competition. The results reveal that the scoring function subtraction is the best parameter to describe the mechanism of splice sites competition.

The effect of regions flanking target site on siRNA potency

For a successful RNA interference (RNAi) experiment, selecting the small interference RNA (siRNA) candidates which maximize the knock down effect of the given gene is the critical step. Although various computational approaches have been attempted, the design of efficient siRNA candidates is far from satisfactory yet. In this study, we proposed a novel feature selection algorithm of combined random forest and support vector machine to predict active siRNAs. Using a publically available dataset, we demonstrated that the predictive accuracy would be markedly improved when the context sequence features outside the target site were included. The Pearson correlation coefficient for regression is as high as 0.721, compared to 0.671, 0.668, 0.680, and 0.645, for Biopredsi, i-score, ThermoComposition21 and DSIR, respectively. It revealed that siRNA-target interaction requires appropriate sequence context not only in the target site but also in a broad region flanking the target site.

Irregular transcriptome reprogramming probably causes thec developmental failure of embryos produced by interspecies somatic cell nuclear transfer between the Przewalski’s gazelle and the bovine

BackgroundInterspecies somatic cell nuclear transfer (iSCNT) has been regarded as a potential alternative for rescuing highly endangered species and can be used as a model for studying nuclear–cytoplasmic interactions. However, iSCNT embryos often fail to produce viable offspring. The alterations in normal molecular mechanisms contributing to extremely poor development are for the most part unknown.ResultsPrzewalski’s gazelle–bovine iSCNT embryos (PBNT) were produced by transferring Przewalski’s gazelle fibroblast nuclei into enucleated bovine oocytes. The percentages of PBNT embryos that developed to morula/blastocyst stages were extremely low even with the use of various treatments that included different SCNT protocols and treatment of embryos with small molecules. Transcriptional microarray analyses of the cloned embryos showed that the upregulation of reprogramming-associated genes in bovine–bovine SCNT (BBNT) embryos was significantly higher than those observed in PBNT embryos (1527:643). In all, 139 transcripts related to various transcription regulation factors (TFs) were unsuccessfully activated in the iSCNT embryos. Maternal degradation profiles showed that 1515 genes were uniquely downregulated in the BBNT embryos, while 343 genes were downregulated in the PBNT embryos. Incompatibilities between mitochondrial DNA (mtDNA) and nuclear DNA revealed that the TOMM (translocase of outer mitochondrial membrane)/TIMM (translocase of inner mitochondrial membrane) complex-associated genes in BBNT embryos had the highest expression levels, while the PBNT embryos exhibited much lower expression rates.ConclusionsImproper degradation of maternal transcripts, incomplete activation of TFs and abnormal expression of genes associated with mitochondrial function in PBNT embryos likely contributed to incomplete reprogramming of the donor cell nuclei and therefore led to the developmental failure of these cloned embryos.

Sequence-specific flexibility organization of splicing flanking sequence and prediction of splice sites in the human genome

More and more reported results of nucleosome positioning and histone modifications showed that DNA structure play a well-established role in splicing. In this study, a set of DNA geometric flexibility parameters originated from molecular dynamics (MD) simulations were introduced to discuss the structure organization around splice sites at the DNA level. The obtained profiles of specific flexibility/stiffness around splice sites indicated that the DNA physical-geometry deformation could be used as an alternative way to describe the splicing junction region. In combination with structural flexibility as discriminatory parameter, we developed a hybrid computational model for predicting potential splicing sites. And the better prediction performance was achieved when the benchmark dataset evaluated. Our results showed that the mechanical deformability character of a splice junction is closely correlated with both the splice site strength and structural information in its flanking sequences.