Engineering a Z-Scheme Heterostructure on ZnIn2S4@NH2-MIL-125 Composites for Boosting the Photocatalytic Performance.

Constructing a Z-scheme heterostructure on a metal-organic framework (MOF) composite with an explicit charge transfer mechanism at the interface is considered to be an effective strategy for improving the photocatalytic performance of MOFs. Herein, an internal electric field (IEF)-induced Z-scheme heterostructure on the ZnIn2S4@NH2-MIL-125 composite is designed and fabricated by a facile electrostatic self-assembly process. Systematic investigations reveal that close interfacial contact and difference in work function between NH2-MIL-125 and ZnIn2S4 enable the formation of the IEF, which drives the Z-scheme charge transfer as revealed by the in situ irradiated X-ray photoelectron spectroscopy (ISI-XPS), photoirradiated Kelvin probe force microscope (KPFM) measurement, electron paramagnetic resonance (EPR) radical trapping experiment, as well as density functional theory (DFT) calculation; meanwhile, directions of the interfacial IEFs are determined. Benefiting from the unique merit of IEF-induced Z-scheme charge transfer, the optimized ZnIn2S4@NH2-MIL-125 composite exhibits significantly enhanced photocatalytic activity for the photoreduction of 4-nitroaniline (4-NA) to p-phenylenediamine (PPD) under visible light irradiation. This work not only provides in-depth insights for charge transfer in the IEF-induced Z scheme heterostructure but also affords useful inspirations on designing the Z-scheme MOF composite to boost the photocatalytic performance.

Pro54DB: a database for experimentally verified sigma-54 promoters.

Summary: In prokaryotes, the σ54 promoters are unique regulatory elements and have attracted much attention because they are in charge of the transcription of carbon and nitrogen-related genes and participate in numerous ancillary processes and environmental responses. All findings on σ54 promoters are favorable for a better understanding of their regulatory mechanisms in gene transcription and an accurate discovery of genes missed by the wet experimental evidences. In order to provide an up-to-date, interactive and extensible database for σ54 promoter, a free and easy accessed database called Pro54DB (σ54 promoter database) was built to collect information of σ54 promoter. In the current version, it has stored 210 experimental-confirmed σ54 promoters with 297 regulated genes in 43 species manually extracted from 133 publications, which is helpful for researchers in fields of bioinformatics and molecular biology. Availability and Implementation: Pro54DB is freely available on the web at http://lin.uestc.edu.cn/database/pro54db with all major browsers supported. Contacts: greatchen@ncst.edu.cn or hlin@uestc.edu.cn

Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition.

Mycobacterium tuberculosis is a bacterium that causes tuberculosis, one of the most prevalent infectious diseases. Predicting the subcellular localization of mycobacterial proteins in this bacterium may provide vital clues for the prediction of protein function as well as for drug discovery and design. Therefore, a computational method that can predict the subcellular localization of mycobacterial proteins with high precision is highly desirable. We propose a computational method to predict the subcellular localization of mycobacterial proteins. An objective and strict benchmark dataset was constructed after collecting 272 non-redundant proteins from the universal protein resource (the UniProt database). Subsequently, a novel feature selection strategy based on binomial distribution was used to optimize the feature vector. Finally, a subset containing 219 chosen tripeptide features was imported into a support vector machine-based method to estimate the performance of the dataset in accurately and sensitively identifying these proteins. We found that the proposed method gave a maximum overall accuracy of 89.71% with an average accuracy of 81.12% in the jackknife cross-validation. The results indicate that our prediction method gave an efficient and powerful performance when compared with other published methods. We made the proposed method available on a purpose built Web server called MycoSub that is freely accessible at . We anticipate that MycoSub will become a useful tool for studying the functions of mycobacterial proteins and for designing and developing anti-mycobacterium drugs.

Sequence analysis of origins of replication in the Saccharomyces cerevisiae genomes.

DNA replication is a highly precise process that is initiated from origins of replication (ORIs) and is regulated by a set of regulatory proteins. The mining of DNA sequence information will be not only beneficial for understanding the regulatory mechanism of replication initiation but also for accurately identifying ORIs. In this study, the GC profile and GC skew were calculated to analyze the compositional bias in the Saccharomyces cerevisiae genome. We found that the GC profile in the region of ORIs is significantly lower than that in the flanking regions. By calculating the information redundancy, an estimation of the correlation of nucleotides, we found that the intensity of adjoining correlation in ORIs is dramatically higher than that in flanking regions. Furthermore, the relationships between ORIs and nucleosomes as well as transcription start sites were investigated. Results showed that ORIs are usually not occupied by nucleosomes. Finally, we calculated the distribution of ORIs in yeast chromosomes and found that most ORIs are in transcription terminal regions. We hope that these results will contribute to the identification of ORIs and the study of DNA replication mechanisms.

iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition.

The σ(54) promoters are unique in prokaryotic genome and responsible for transcripting carbon and nitrogen-related genes. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapidly and effectively identifying the σ(54) promoters. Here, a predictor called 'iPro54-PseKNC' was developed. In the predictor, the samples of DNA sequences were formulated by a novel feature vector called 'pseudo k-tuple nucleotide composition', which was further optimized by the incremental feature selection procedure. The performance of iPro54-PseKNC was examined by the rigorous jackknife cross-validation tests on a stringent benchmark data set. As a user-friendly web-server, iPro54-PseKNC is freely accessible at http://lin.uestc.edu.cn/server/iPro54-PseKNC. For the convenience of the vast majority of experimental scientists, a step-by-step protocol guide was provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics that were presented in this paper just for its integrity. Meanwhile, we also discovered through an in-depth statistical analysis that the distribution of distances between the transcription start sites and the translation initiation sites were governed by the gamma distribution, which may provide a fundamental physical principle for studying the σ(54) promoters.

Identifying the subfamilies of voltage-gated potassium channels using feature selection technique.

Voltage-gated K+ channel (VKC) plays important roles in biology procession, especially in nervous system. Different subfamilies of VKCs have different biological functions. Thus, knowing VKCs' subfamilies has become a meaningful job because it can guide the direction for the disease diagnosis and drug design. However, the traditional wet-experimental methods were costly and time-consuming. It is highly desirable to develop an effective and powerful computational tool for identifying different subfamilies of VKCs. In this study, a predictor, called iVKC-OTC, has been developed by incorporating the optimized tripeptide composition (OTC) generated by feature selection technique into the general form of pseudo-amino acid composition to identify six subfamilies of VKCs. One of the remarkable advantages of introducing the optimized tripeptide composition is being able to avoid the notorious dimension disaster or over fitting problems in statistical predictions. It was observed on a benchmark dataset, by using a jackknife test, that the overall accuracy achieved by iVKC-OTC reaches to 96.77% in identifying the six subfamilies of VKCs, indicating that the new predictor is promising or at least may become a complementary tool to the existing methods in this area. It has not escaped our notice that the optimized tripeptide composition can also be used to investigate other protein classification problems.

iCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels.

Conotoxins are small disulfide-rich neurotoxic peptides, which can bind to ion channels with very high specificity and modulate their activities. Over the last few decades, conotoxins have been the drug candidates for treating chronic pain, epilepsy, spasticity, and cardiovascular diseases. According to their functions and targets, conotoxins are generally categorized into three types: potassium-channel type, sodium-channel type, and calcium-channel types. With the avalanche of peptide sequences generated in the postgenomic age, it is urgent and challenging to develop an automated method for rapidly and accurately identifying the types of conotoxins based on their sequence information alone. To address this challenge, a new predictor, called iCTX-Type, was developed by incorporating the dipeptide occurrence frequencies of a conotoxin sequence into a 400-D (dimensional) general pseudoamino acid composition, followed by the feature optimization procedure to reduce the sample representation from 400-D to 50-D vector. The overall success rate achieved by iCTX-Type via a rigorous cross-validation was over 91%, outperforming its counterpart (RBF network). Besides, iCTX-Type is so far the only predictor in this area with its web-server available, and hence is particularly useful for most experimental scientists to get their desired results without the need to follow the complicated mathematics involved.

iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition.

Translation is a key process for gene expression. Timely identification of the translation initiation site (TIS) is very important for conducting in-depth genome analysis. With the avalanche of genome sequences generated in the postgenomic age, it is highly desirable to develop automated methods for rapidly and effectively identifying TIS. Although some computational methods were proposed in this regard, none of them considered the global or long-range sequence-order effects of DNA, and hence their prediction quality was limited. To count this kind of effects, a new predictor, called "iTIS-PseTNC," was developed by incorporating the physicochemical properties into the pseudo trinucleotide composition, quite similar to the PseAAC (pseudo amino acid composition) approach widely used in computational proteomics. It was observed by the rigorous cross-validation test on the benchmark dataset that the overall success rate achieved by the new predictor in identifying TIS locations was over 97%. As a web server, iTIS-PseTNC is freely accessible at http://lin.uestc.edu.cn/server/iTIS-PseTNC. To maximize the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web server to obtain the desired results without the need to go through detailed mathematical equations, which are presented in this paper just for the integrity of the new prection method.

iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition.

MOTIVATION: Nucleosome positioning participates in many cellular activities and plays significant roles in regulating cellular processes. With the avalanche of genome sequences generated in the post-genomic age, it is highly desired to develop automated methods for rapidly and effectively identifying nucleosome positioning. Although some computational methods were proposed, most of them were species specific and neglected the intrinsic local structural properties that might play important roles in determining the nucleosome positioning on a DNA sequence. RESULTS: Here a predictor called 'iNuc-PseKNC' was developed for predicting nucleosome positioning in Homo sapiens, Caenorhabditis elegans and Drosophila melanogaster genomes, respectively. In the new predictor, the samples of DNA sequences were formulated by a novel feature-vector called 'pseudo k-tuple nucleotide composition', into which six DNA local structural properties were incorporated. It was observed by the rigorous cross-validation tests on the three stringent benchmark datasets that the overall success rates achieved by iNuc-PseKNC in predicting the nucleosome positioning of the aforementioned three genomes were 86.27%, 86.90% and 79.97%, respectively. Meanwhile, the results obtained by iNuc-PseKNC on various benchmark datasets used by the previous investigators for different genomes also indicated that the current predictor remarkably outperformed its counterparts. AVAILABILITY: A user-friendly web-server, iNuc-PseKNC is freely accessible at http://lin.uestc.edu.cn/server/iNuc-PseKNC.