Predicting protein folding pathways at the mesoscopic level based on native interactions between secondary structure elements

Background  

Since experimental determination of protein folding pathways remains difficult, computational techniques are often used to simulate protein folding. Most current techniques to predict protein folding pathways are computationally intensive and are suitable only for small proteins.     

Predicting biological functions of compounds based on chemical-chemical interactions

Given a compound, how can we effectively predict its biological function? It is a fundamentally important problem because the information thus obtained may benefit the understanding of many basic biological processes and provide useful clues for drug design. In this study, based on the information of chemical-chemical interactions, a novel method was developed that can be used to identify which of the following eleven metabolic pathway classes a query compound may be involved with: (1) Carbohydrate Metabolism, (2) Energy Metabolism, (3) Lipid Metabolism, (4) Nucleotide Metabolism, (5) Amino Acid Metabolism, (6) Metabolism of Other Amino Acids, (7) Glycan Biosynthesis and Metabolism, (8) Metabolism of Cofactors and Vitamins, (9) Metabolism of Terpenoids and Polyketides, (10) Biosynthesis of Other Secondary Metabolites, (11) Xenobiotics Biodegradation and Metabolism. It was observed that the overall success rate obtained by the method via the 5-fold cross-validation test on a benchmark dataset consisting of 3,137 compounds was 77.97%, which is much higher than 10.45%, the corresponding success rate obtained by the random guesses. Besides, to deal with the situation that some compounds may be involved with more than one metabolic pathway class, the method presented here is featured by the capacity able to provide a series of potential metabolic pathway classes ranked according to the descending order of their likelihood for each of the query compounds concerned. Furthermore, our method was also applied to predict 5,549 compounds whose metabolic pathway classes are unknown. Interestingly, the results thus obtained are quite consistent with the deductions from the reports by other investigators. It is anticipated that, with the continuous increase of the chemical-chemical interaction data, the current method will be further enhanced in its power and accuracy, so as to become a useful complementary vehicle in annotating uncharacterized compounds for their biological functions.     

Predicting demographic group structures based on DNA sequence data

The ability to infer relationships between groups of sequences, either by searching for their evolutionary history or by comparing their sequence similarity, can be a crucial step in hypothesis testing. Interpreting relationships of human immunodeficiency virus type 1 (HIV-1) sequences can be challenging because of their rapidly evolving genomes, but it may also lead to a better understanding of the underlying biology. Several studies have focused on the evolution of HIV-1, but there is little information to link sequence similarities and evolutionary histories of HIV-1 to the epidemiological information of the infected individual. Our goal was to correlate patterns of HIV-1 genetic diversity with epidemiological information, including risk and demographic factors. These correlations were then used to predict epidemiological information through analyzing short stretches of HIV-1 sequence. Using standard phylogenetic and phenetic techniques on 100 HIV-1 subtype B sequences, we were able to show some correlation between the viral sequences and the geographic area of infection and the risk of men who engage in sex with men. To help identify more subtle relationships between the viral sequences, the method of multidimensional scaling (MDS) was performed. That method identified statistically significant correlations between the viral sequences and the risk factors of men who engage in sex with men and individuals who engage in sex with injection drug users or use injection drugs themselves. Using tree construction, MDS, and newly developed likelihood assignment methods on the original 100 samples we sequenced, and also on a set of blinded samples, we were able to predict demographic/risk group membership at a rate statistically better than by chance alone. Such methods may make it possible to identify viral variants belonging to specific demographic groups by examining only a small portion of the HIV-1 genome. Such predictions of demographic epidemiology based on sequence information may become valuable in assigning different treatment regimens to infected individuals.     

Next-generation SELEX identifies sequence and structural determinants of splicing factor binding in human pre-mRNA sequence

Many splicing factors interact with both mRNA and pre-mRNA. The identification of these interactions has been greatly improved by the development of in vivo cross-linking immunoprecipitation. However, the output carries a strong sampling bias in favor of RNPs that form on more abundant RNA species like mRNA. We have developed a novel in vitro approach for surveying binding on pre-mRNA, without cross-linking or sampling bias. Briefly, this approach entails specifically designed oligonucleotide pools that tile through a pre-mRNA sequence. The pool is then partitioned into bound and unbound fractions, which are quantified by a two-color microarray. We applied this approach to locating splicing factor binding sites in and around ∼4000 exons. We also quantified the effect of secondary structure on binding. The method is validated by the finding that U1snRNP binds at the 5′ splice site (5′ss) with a specificity that is nearly identical to the splice donor motif. In agreement with prior reports, we also show that U1snRNP appears to have some affinity for intronic G triplets that are proximal to the 5′ss. Both U1snRNP and the polypyrimidine tract binding protein (PTB) avoid exonic binding, and the PTB binding map shows increased enrichment at the polypyrimidine tract. For PTB, we confirm polypyrimidine specificity and are also able to identify structural determinants of PTB binding. We detect multiple binding motifs enriched in the PTB bound fraction of oligonucleotides. These motif combinations augment binding in vitro and are also enriched in the vicinity of exons that have been determined to be in vivo targets of PTB.     

Predicting subcellular localization of proteins based on their N-terminal amino acid sequence

A neural network-based tool, TargetP, for large-scale subcellular location prediction of newly identified proteins has been developed. Using N-terminal sequence information only, it discriminates between proteins destined for the mitochondrion, the chloroplast, the secretory pathway, and other localizations with a success rate of 85% (plant) or 90% (non-plant) on redundancy-reduced test sets. From a TargetP analysis of the recently sequenced Arabidopsis thaliana chromosomes 2 and 4 and the Ensembl Homo sapiens protein set, we estimate that 10% of all plant proteins are mitochondrial and 14% chloroplastic, and that the abundance of secretory proteins, in both Arabidopsis and Homo, is around 10%. TargetP also predicts cleavage sites with levels of correctly predicted sites ranging from approximately 40% to 50% (chloroplastic and mitochondrial presequences) to above 70% (secretory signal peptides). TargetP is available as a web-server at http://www.cbs.dtu.dk/services/TargetP/.     

Identification of tissue-specific cis-regulatory modules based on interactions between transcription factors

Background  

Evolutionary conservation has been used successfully to help identify cis-acting DNA regions that are important in regulating tissue-specific gene expression. Motivated by increasing evidence that some DNA regulatory regions are not evolutionary conserved, we have developed an approach for cis-regulatory region identification that does not rely upon evolutionary sequence conservation.