MOTIVATION: Although high-throughput sequencing methods have been proposed to identify splicing branch points in the human genome, these methods can only detect a small fraction of the branch points subject to the sequencing depth, experimental cost and the expression level of the mRNA. An accurate computational model for branch point prediction is therefore an ongoing objective in human genome research. RESULTS: We here propose a novel branch point prediction algorithm that utilizes information on the branch point sequence and the polypyrimidine tract. Using experimentally validated data, we demonstrate that our proposed method outperforms existing methods. Availability and implementation: https://github.com/zhqingit/BPP. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Pre-mRNA splicing is the removal of introns from precursor mRNAs (pre-mRNAs) and the concurrent ligation of the flanking exons to generate mature mRNA. This process is catalyzed by the spliceosome, where the splicing factor 1 (SF1) specifically recognizes the seven-nucleotide branch point sequence (BPS) and the U2 snRNP later displaces the SF1 and binds to the BPS. In mammals, the degeneracy of BPS motifs together with the lack of a large set of experimentally verified BPSs complicates the task of BPS prediction in silico. RESULTS: In this paper, we develop a simple and yet efficient heuristic model for human BPS prediction based on a novel scoring scheme, which quantifies the splicing strength of putative BPSs. The candidate BPS is restricted exclusively within a defined BPS search region to avoid the influences of other elements in the intron and therefore the prediction accuracy is improved. Moreover, using two types of relative frequencies for human BPS prediction, we demonstrate our model outperformed other current implementations on experimentally verified human introns. CONCLUSION: We propose that the binding energy contributes to the molecular recognition involved in human pre-mRNA splicing. In addition, a genome-wide human BPS prediction is carried out. The characteristics of predicted BPSs are in accordance with experimentally verified human BPSs, and branch site positions relative to the 3'ss and the 5'end of the shortened AGEZ are consistent with the results of published papers. Meanwhile, a webserver for BPS predictor is freely available at http://biocomputer.bio.cuhk.edu.hk/BPS .

BACKGROUND: Reactive oxygen species can modify the structure and function of proteins and may also act as important signaling molecules in various cellular processes. Cysteine thiol groups of proteins are particularly susceptible to oxidation. Meanwhile, their reversible oxidation is of critical roles for redox regulation and signaling. Recently, several computational tools have been developed for predicting redox-sensitive cysteines; however, those methods either only focus on catalytic redox-sensitive cysteines in thiol oxidoreductases, or heavily depend on protein structural data, thus cannot be widely used. RESULTS: In this study, we analyzed various sequence-based features potentially related to cysteine redox-sensitivity, and identified three types of features for efficient computational prediction of redox-sensitive cysteines. These features are: sequential distance to the nearby cysteines, PSSM profile and predicted secondary structure of flanking residues. After further feature selection using SVM-RFE, we developed Redox-Sensitive Cysteine Predictor (RSCP), a SVM based classifier for redox-sensitive cysteine prediction using primary sequence only. Using 10-fold cross-validation on RSC758 dataset, the accuracy, sensitivity, specificity, MCC and AUC were estimated as 0.679, 0.602, 0.756, 0.362 and 0.727, respectively. When evaluated using 10-fold cross-validation with BALOSCTdb dataset which has structure information, the model achieved performance comparable to current structure-based method. Further validation using an independent dataset indicates it is robust and of relatively better accuracy for predicting redox-sensitive cysteines from non-enzyme proteins. CONCLUSIONS: In this study, we developed a sequence-based classifier for predicting redox-sensitive cysteines. The major advantage of this method is that it does not rely on protein structure data, which ensures more extensive application compared to other current implementations. Accurate prediction of redox-sensitive cysteines not only enhances our understanding about the redox sensitivity of cysteine, it may also complement the proteomics approach and facilitate further experimental investigation of important redox-sensitive cysteines.

SUMMARY: Redox regulation and signaling, which are involved in various cellular processes, have become one of the research focuses in the past decade. Cysteine thiol groups are particularly susceptible to post-translational modification, and their reversible oxidation is of critical role in redox regulation and signaling. With the tremendous improvement of techniques, hundreds of redox proteins along with their redox-sensitive cysteines have been reported, and the number is still fast growing. However, until now there is no database to accommodate the rapid accumulation of information on protein oxidative modification. Here we present RedoxDB-a manually curated database for experimentally validated redox proteins. RedoxDB (version 1.0) consists of two datasets (A and B, for proteins with or without verified modified cysteines, respectively) and includes 2157 redox proteins containing 2203 cysteine residues with oxidative modification. For each modified cysteine, the exact position, modification type and flanking sequence are provided. Additional information, including gene name, organism, sequence, literature references and links to UniProt and PDB, is also supplied. The database supports several functions including data search, blast and browsing. Bulk download of the entire dataset is also available. We expect that RedoxDB will be useful for both experimental studies and computational analyses of protein oxidative modification. AVAILABILITY: The database is freely available at: http://biocomputer.bio.cuhk.edu.hk/RedoxDB. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Online.

BACKGROUND: Type III Secretion System (T3SS), which plays important roles in pathogenesis or symbiosis, is widely expressed in a variety of gram negative bacteria. However, lack of unique nomenclature for T3SS genes has hindered T3SS related research. It is necessary to set up a knowledgebase integrating T3SS-related research data to facilitate the communication between different research groups interested in different bacteria. DESCRIPTION: A T3SS-related Database (T3DB) was developed. T3DB serves as an integrated platform for sequence collection, function annotation, and ortholog classification for T3SS related apparatus, effector, chaperone and regulatory genes. The collection of T3SS-containing bacteria, T3SS-related genes, function annotation, and the ortholog information were all manually curated from literature. BPBAac, a highly efficient T3SS effector prediction tool, was also implemented. CONCLUSIONS: T3DB is the first systematic platform integrating well-annotated T3SS-related gene and protein information to facilitate T3SS and bacterial pathogenecity related research. The newly constructed T3 ortholog clusters may faciliate effective communication between different research groups and will promote de novo discoveries. Besides, the manually-curated high-quality effector and chaperone data are useful for feature analysis and evolutionary studies of these important proteins.

MOTIVATION: Bacterial type III secreted (T3S) effectors are delivered into host cells specifically via type III secretion systems (T3SSs), which play important roles in the interaction between bacteria and their hosts. Previous computational methods for T3S protein prediction have only achieved limited accuracy, and distinct features for effective T3S protein prediction remain to be identified. RESULTS: In this work, a distinctive N-terminal position-specific amino acid composition (Aac) feature was identified for T3S proteins. A large portion (∼50%) of T3S proteins exhibit distinct position-specific Aac features that can tolerate position shift. A classifier, BPBAac, was developed and trained using Support Vector Machine (SVM) based on the Aac feature extracted using a Bi-profile Bayes model. We demonstrated that the BPBAac model outperformed other implementations in classification of T3S and non-T3S proteins, giving an average sensitivity of ∼90.97% and an average selectivity of ∼97.42% in a 5-fold cross-validation evaluation. The model was also robust when a small-size training dataset was used. The fact that the position-specific Aac feature is commonly found in T3S proteins across different bacterial species gives this model wide application. To demonstrate the model's application, a genome-wide prediction of T3S effector proteins was performed for Ralstonia solanacearum, an important plant pathogenic bacterium, and a number of putative candidates were identified using this model. AVAILABILITY: An R package of BPBAac tool is freely downloadable from: http://biocomputer.bio.cuhk.edu.hk/softwares/BPBAac.