A model of local-minima distribution on conformational space and its application to protein structure prediction

Molecular traits and functional analysis of Rapid Alkalinization Factors (RALFs) in four Gossypium species

Rapid Alkalinization Factors (RALFs) are plant-secreted, cysteine-rich polypeptides which are known to play essential roles in plant developmental processes and in several defense mechanisms. So far, RALF polypeptides have not been investigated in the Gossypium genus. In this study, 42, 38, 104 and 120 RALFs were identified from diploid G. arboreum and G. raimondi and tetraploid G. hirsutum and G. barbadense, respectively. These were further divided into four groups. Protein characteristics, sequence alignment, gene structure, conserved motifs, chromosomal location and cis-element identification were comprehensively analyzed. Whole genome duplication (WGD) /segmental duplication may be the reason why the number of RALF genes doubled in tetraploid Gossypium species. Expression patterns analysis showed that GhRALFs had different transcript accumulation patterns in the tested tissues and were differentially expressed in response to various abiotic stresses. Furthermore, GhRALF41-3 over-expressing (OE) plants showed reduction in root length and developed later with short stems and small rosettes than that of the wild type. The GhRALF14-8 and GhRALF27-8 OE plants, especially the latter, showed increase in seed abortion. Both transgenic Arabidopsis and VIGS cotton demonstrate that three GhRALFs are negative regulators in response to salt stress. Our systematic analyses provided insights into the characterization of RALF genes in Gossypium, which forms genetic basis for further exploration in their potential applications in cotton production.

Identification and characterization analysis of sulfotransferases (SOTs) gene family in cotton (Gossypium) and its involvement in fiber development

BACKGROUND

Sulfotransferases (SOTs) (EC 2.8.2.-) play a crucial role in the sulphate conjugation reaction involved in plant growth, vigor, stress resistance and pathogen infection. SOTs in Arabidopsis have been carried out and divided into 8 groups. However, the systematic analysis and functional information of SOT family genes in cotton have rarely been reported.

RESULTS

According to the results of BLASTP and HMMER, we isolated 46, 46, 76 and 77 SOT genes in the genome G. arboreum, G. raimondii, G. barbadense and G. hirsutum, respectively. A total of 170 in 245 SOTs were further classified into four groups based on the orthologous relationships comparing with Arabidopsis, and tandem replication primarily contributed to the expansion of SOT gene family in G. hirsutum. Expression profiles of the GhSOT showed that most genes exhibited a high level of expression in the stem, leaf, and the initial stage of fiber development. The localization analysis indicated that GhSOT67 expressed in cytoplasm and located in stem and leaf tissue. Additionally, the expression of GhSOT67 were induced and the length of stem and leaf hairs were shortened after gene silencing mediated by Agrobacterium, compared with the blank and negative control plants.

CONCLUSIONS

Our findings indicated that SOT genes might be associated with fiber development in cotton and provided valuable information for further studies of SOT genes in Gossypium.

Entropy-accelerated exact clustering of protein decoys

MOTIVATION

Clustering is commonly used to identify the best decoy among many generated in protein structure prediction when using energy alone is insufficient. Calculation of the pairwise distance matrix for a large decoy set is computationally expensive. Typically, only a reduced set of decoys using energy filtering is subjected to clustering analysis. A fast clustering method for a large decoy set would be beneficial to protein structure prediction and this still poses a challenge.

RESULTS

We propose a method using propagation of geometric constraints to accelerate exact clustering, without compromising the distance measure. Our method can be used with any metric distance. Metrics that are expensive to compute and have known cheap lower and upper bounds will benefit most from the method. We compared our method's accuracy against published results from the SPICKER clustering software on 40 large decoy sets from the I-TASSER protein folding engine. We also performed some additional speed comparisons on six targets from the 'semfold' decoy set. In our tests, our method chose a better decoy than the energy criterion in 25 out of 40 cases versus 20 for SPICKER. Our method also was shown to be consistently faster than another fast software performing exact clustering named Calibur. In some cases, our approach can even outperform the speed of an approximate method.

AVAILABILITY

Our C++ software is released under the GNU General Public License. It can be downloaded from http://www.riken.jp/zhangiru/software/durandal_released.tgz.

Prediction of flexible/rigid regions from protein sequences using k-spaced amino acid pairs

Background

Traditionally, it is believed that the native structure of a protein corresponds to a global minimum of its free energy. However, with the growing number of known tertiary (3D) protein structures, researchers have discovered that some proteins can alter their structures in response to a change in their surroundings or with the help of other proteins or ligands. Such structural shifts play a crucial role with respect to the protein function. To this end, we propose a machine learning method for the prediction of the flexible/rigid regions of proteins (referred to as FlexRP); the method is based on a novel sequence representation and feature selection. Knowledge of the flexible/rigid regions may provide insights into the protein folding process and the 3D structure prediction.

Results

The flexible/rigid regions were defined based on a dataset, which includes protein sequences that have multiple experimental structures, and which was previously used to study the structural conservation of proteins. Sequences drawn from this dataset were represented based on feature sets that were proposed in prior research, such as PSI-BLAST profiles, composition vector and binary sequence encoding, and a newly proposed representation based on frequencies of k-spaced amino acid pairs. These representations were processed by feature selection to reduce the dimensionality. Several machine learning methods for the prediction of flexible/rigid regions and two recently proposed methods for the prediction of conformational changes and unstructured regions were compared with the proposed method. The FlexRP method, which applies Logistic Regression and collocation-based representation with 95 features, obtained 79.5% accuracy. The two runner-up methods, which apply the same sequence representation and Support Vector Machines (SVM) and Naïve Bayes classifiers, obtained 79.2% and 78.4% accuracy, respectively. The remaining considered methods are characterized by accuracies below 70%. Finally, the Naïve Bayes method is shown to provide the highest sensitivity for the prediction of flexible regions, while FlexRP and SVM give the highest sensitivity for rigid regions.

Conclusion

A new sequence representation that uses k-spaced amino acid pairs is shown to be the most efficient in the prediction of the flexible/rigid regions of protein sequences. The proposed FlexRP method provides the highest prediction accuracy of about 80%. The experimental tests show that the FlexRP and SVM methods achieved high overall accuracy and the highest sensitivity for rigid regions, while the best quality of the predictions for flexible regions is achieved by the Naïve Bayes method.