Hua-Sheng Chiu

Protein subcellular localization prediction based on compartment-specific features and structure conservation

BackgroundProtein subcellular localization is crucial for genome annotation, protein function prediction, and drug discovery. Determination of subcellular localization using experimental approaches is time-consuming; thus, computational approaches become highly desirable. Extensive studies of localization prediction have led to the development of several methods including composition-based and homology-based methods. However, their performance might be significantly degraded if homologous sequences are not detected. Moreover, methods that integrate various features could suffer from the problem of low coverage in high-throughput proteomic analyses due to the lack of information to characterize unknown proteins.ResultsWe propose a hybrid prediction method for Gram-negative bacteria that combines a one-versus-one support vector machines (SVM) model and a structural homology approach. The SVM model comprises a number of binary classifiers, in which biological features derived from Gram-negative bacteria translocation pathways are incorporated. In the structural homology approach, we employ secondary structure alignment for structural similarity comparison and assign the known localization of the top-ranked protein as the predicted localization of a query protein. The hybrid method achieves overall accuracy of 93.7% and 93.2% using ten-fold cross-validation on the benchmark data sets. In the assessment of the evaluation data sets, our method also attains accurate prediction accuracy of 84.0%, especially when testing on sequences with a low level of homology to the training data. A three-way data split procedure is also incorporated to prevent overestimation of the predictive performance. In addition, we show that the prediction accuracy should be approximately 85% for non-redundant data sets of sequence identity less than 30%.ConclusionOur results demonstrate that biological features derived from Gram-negative bacteria translocation pathways yield a significant improvement. The biological features are interpretable and can be applied in advanced analyses and experimental designs. Moreover, the overall accuracy of combining the structural homology approach is further improved, which suggests that structural conservation could be a useful indicator for inferring localization in addition to sequence homology. The proposed method can be used in large-scale analyses of proteomes.

Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks

We introduce a method for simultaneous prediction of microRNA-target interactions and their mediated competitive endogenous RNA (ceRNA) interactions. Using high-throughput validation assays in breast cancer cell lines, we show that our integrative approach significantly improves on microRNA-target prediction accuracy as assessed by both mRNA and protein level measurements. Our biochemical assays support nearly 500 microRNA-target interactions with evidence for regulation in breast cancer tumors. Moreover, these assays constitute the most extensive validation platform for computationally inferred networks of microRNA-target interactions in breast cancer tumors, providing a useful benchmark to ascertain future improvements.

PSLDoc: Protein subcellular localization prediction based on gapped-dipeptides and probabilistic latent semantic analysis

Prediction of protein subcellular localization (PSL) is important for genome annotation, protein function prediction, and drug discovery. Many computational approaches for PSL prediction based on protein sequences have been proposed in recent years for Gram‐negative bacteria. We present PSLDoc, a method based on gapped‐dipeptides and probabilistic latent semantic analysis (PLSA) to solve this problem. A protein is considered as a term string composed by gapped‐dipeptides, which are defined as any two residues separated by one or more positions. The weighting scheme of gapped‐dipeptides is calculated according to a position specific score matrix, which includes sequence evolutionary information. Then, PLSA is applied for feature reduction, and reduced vectors are input to five one‐versus‐rest support vector machine classifiers. The localization site with the highest probability is assigned as the final prediction. It has been reported that there is a strong correlation between sequence homology and subcellular localization (Nair and Rost, Protein Sci 2002;11:2836–2847; Yu et al., Proteins 2006;64:643–651). To properly evaluate the performance of PSLDoc, a target protein can be classified into low‐ or high‐homology data sets. PSLDocs overall accuracy of low‐ and high‐homology data sets reaches 86.84% and 98.21%, respectively, and it compares favorably with that of CELLO II (Yu et al., Proteins 2006;64:643–651). In addition, we set a confidence threshold to achieve a high precision at specified levels of recall rates. When the confidence threshold is set at 0.7, PSLDoc achieves 97.89% in precision which is considerably better than that of PSORTb v.2.0 (Gardy et al., Bioinformatics 2005;21:617–623). Our approach demonstrates that the specific feature representation for proteins can be successfully applied to the prediction of protein subcellular localization and improves prediction accuracy. Besides, because of the generality of the representation, our method can be extended to eukaryotic proteomes in the future. The web server of PSLDoc is publicly available at http://bio‐cluster.iis.sinica.edu.tw/∼bioapp/PSLDoc/. Proteins 2008.

High-throughput validation of ceRNA regulatory networks

BackgroundMicroRNAs (miRNAs) play multiple roles in tumor biology. Interestingly, reports from multiple groups suggest that miRNA targets may be coupled through competitive stoichiometric sequestration. Specifically, computational models predicted and experimental assays confirmed that miRNA activity is dependent on miRNA target abundance, and consequently, changes in the abundance of some miRNA targets lead to changes to the regulation and abundance of their other targets. The resulting indirect regulatory influence between miRNA targets resembles competition and has been dubbed competitive endogenous RNA (ceRNA). Recent studies have questioned the physiological relevance of ceRNA interactions, our ability to accurately predict these interactions, and the number of genes that are impacted by ceRNA interactions in specific cellular contexts.ResultsTo address these concerns, we reverse engineered ceRNA networks (ceRNETs) in breast and prostate adenocarcinomas using context-specific TCGA profiles, and tested whether ceRNA interactions can predict the effects of RNAi-mediated gene silencing perturbations in PC3 and MCF7 cells._ENREF_22 Our results, based on tests of thousands of inferred ceRNA interactions that are predicted to alter hundreds of cancer genes in each of the two tumor contexts, confirmed statistically significant effects for half of the predicted targets.ConclusionsOur results suggest that the expression of a significant fraction of cancer genes may be regulated by ceRNA interactions in each of the two tumor contexts.

Transmembrane helix and topology prediction using hierarchical SVM classifiers and an alternating geometric scoring function.

MOTIVATION A key class of membrane proteins contains one or more transmembrane (TM) helices, traversing the membrane lipid bilayer. Various properties such as the length, arrangement and topology or orientation of TM helices, are closely related to a proteins functions. Although a range of methods have been developed to predict TM helices and their topologies, no single method consistently outperforms the others. In addition, topology prediction has much lower accuracy than helix prediction, and thus requires continuous improvements. RESULTS We develop a method based on support vector machines (SVM) in a hierarchical framework to predict TM helices first, followed by their topology. By partitioning the prediction problem into two steps, specific input features can be selected and integrated in each step. We also propose a novel scoring function for topology models based on membrane protein folding process. When benchmarked against other methods in terms of performance, our approach achieves the highest scores at 86% in helix prediction (Q(2)) and 91% in topology prediction (TOPO) for the high-resolution data set, resulting in an improvement of 6% and 14% in their respective categories over the second best method. Furthermore, we demonstrate the ability of our method to discriminate between membrane and non-membrane proteins, with higher than 99% in accuracy. When tested on a small set of newly solved structures of membrane proteins, our method overcomes some of the difficulties in predicting TM helices by incorporating multiple biological input features.

Protein subcellular localization prediction based on compartment-specific biological features.

Prediction of subcellular localization of proteins is important for genome annotation, protein function prediction, and drug discovery. We present a prediction method for Gram-negative bacteria that uses ten one-versus-one support vector machine (SVM) classifiers, where compartment-specific biological features are selected as input to each SVM classifier. The final prediction of localization sites is determined by integrating the results from ten binary classifiers using a combination of majority votes and a probabilistic method. The overall accuracy reaches 91.4%, which is 1.6% better than the state-of-the-art system, in a ten-fold cross-validation evaluation on a benchmark data set. We demonstrate that feature selection guided by biological knowledge and insights in one-versus-one SVM classifiers can lead to a significant improvement in the prediction performance. Our model is also used to produce highly accurate prediction of 92.8% overall accuracy for proteins of dual localizations.

The number of titrated microRNA species dictates ceRNA regulation

Abstract microRNAs (miRNAs) play key roles in cancer, but their propensity to couple their targets as competing endogenous RNAs (ceRNAs) has only recently emerged. Multiple models have studied ceRNA regulation, but these models did not account for the effects of co-regulation by miRNAs with many targets. We modeled ceRNA and simulated its effects using established parameters for miRNA/mRNA interaction kinetics while accounting for co-regulation by multiple miRNAs with many targets. Our simulations suggested that co-regulation by many miRNA species is more likely to produce physiologically relevant context-independent couplings. To test this, we studied the overlap of inferred ceRNA networks from four tumor contexts—our proposed pan-cancer ceRNA interactome (PCI). PCI was composed of interactions between genes that were co-regulated by nearly three-times as many miRNAs as other inferred ceRNA interactions. Evidence from expression-profiling datasets suggested that PCI interactions are predictive of gene expression in 12 independent tumor- and non-tumor contexts. Biochemical assays confirmed ceRNA couplings for two PCI subnetworks, including oncogenes CCND1, HIF1A and HMGA2, and tumor suppressors PTEN, RB1 and TP53. Our results suggest that PCI is enriched for context-independent interactions that are coupled by many miRNA species and are more likely to be context independent.

A Two-stage Classifier for Protein B-turn Prediction Using Support Vector Machines

β-turns play an important role in protein structures not only because of their sheer abundance, which is estimated to be approximately 25% of all protein residues, but also because of their significance in high-order structures of proteins. In this study, we introduce a new method of β-turn prediction that uses a two-stage classification scheme and an integrated framework for input features. Ten-fold cross validation based on a benchmark dataset of 426 non-homologue protein chains is used to evaluate our methods performance. The experimental results demon- strate that it achieves substantial improvements over BetaTurn, the current best method. The prediction accuracy, Qtotal, and the Matthews correlation coefficient (MCC) of our approach are 79% and 0.47 respectively, compared to 77% and 0.45 respec- tively for BetaTurn.

Abstract IA14: Genomic alterations dysregulate cancer genes by modulating microRNA activity

microRNAs play key roles in cancer etiology, and recent evidence suggests that targets of the same microRNA programs are stoichiometrically coupled via a competitive endogenous RNA (ceRNA) mechanism. Consequently, aberrant expression of multiple targets of the same microRNA program dysregulate coupled ceRNA- oncogenes and tumor suppressors. We computationally inferred and experimentally validated that coordinated copy number and methylation alterations contribute to physiologically significant in-trans dysregulation of hundreds of coupled oncogenes and tumor suppressors in multiple tumor contexts. Both high-throughput perturbation assays and low-throughput mutational assays confirmed significant ceRNA-mediated regulation of cancer genes in eight tumor contexts, including ESR1 and APC in breast and colon cancer, respectively. Our analysis infers roles for previously uncharacterized genomic alterations and it suggests that ceRNA-mediated interactions account for a substantial fraction of cancer9s missing genomic variability. Citation Format: Hua-Sheng Chiu, Xuerui Yang, Andrea Califano, Pavel Sumazin. Genomic alterations dysregulate cancer genes by modulating microRNA activity. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr IA14.

Abstract 5231: Highly conserved ceRNA regulatory interactions cooperate with genomic variability to modulate drivers of tumorigenesis.

MicroRNAs (miRs) have been repeatedly linked to tumorigenesis and tumor progression, suggesting their potential value as biomarkers and targets for therapeutic intervention. Recent evidence suggests that mRNAs compete for binding and regulation by a finite pool of miRs, thus regulating each other as competitive endogenous RNAs (ceRNAs). The extent of this new regulatory layer and its potential to alter the expression of cancer genes has been shown in several tumor types, but the actual impact of ceRNA interactions on disease remains unclear. In this study, using TCGA (The Cancer Genome Atlas) tumor datasets, we provide evidence that ceRNA play an important role in tumor etiology. Leveraging millions of shRNA-mediated perturbations from LINCS (Library of Integrated Network-based Cellular Signatures), we extensively validated ceRNA interactions in breast and prostate cancer. Moreover, we identified over 160,000 ceRNA interactions that are conserved across tumor types despite being implemented by miR programs with tumor type-specific expression. We present evidence that these ultraconserved interactions are mechanistically responsible for the dysregulation of the majority of oncogenes and tumor suppressors through TCGA-profiled genomic alteration of their cognate ceRNA regulators. Using targeted biochemical validation in cell lines, we verified the potential of ceRNA regulators to target ESR1, an established oncogene and drug target in breast cancer, and APC, an established tumor suppressor in colon adenocarcinoma. Our analysis suggests that ceRNA interactions are implemented through competition for multiple miRNAs, rendering them virtually independent of single miRNA kinetics and availability, and more likely to be conserved across contexts. Using TCGA samples, we demonstrated that conserved ceRNA interactions enable systematic identification of genomic alterations that contribute to aberrant expression of hundreds of cancer genes in each of eight distinct tumor contexts. We submit this network as a resource for analyzing ceRNA regulation in datasets where reverse engineering context-specific networks is infeasible due to low sample number or lack of miRNA expression profiles. Citation Format: Hua-Sheng Chiu, Xuerui Yang, Maria Rodriguez Martinez, Archana Iyer, Pavel Sumazin, Andrea Califano. Highly conserved ceRNA regulatory interactions cooperate with genomic variability to modulate drivers of tumorigenesis. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5231. doi:10.1158/1538-7445.AM2013-5231