Computational approaches to protein structure prediction

One of the most promising problems in bioinformatics is still the protein folding problem which tries to predict the native 3D fold (shape) of a protein from its amino acid sequence. The native fold information of proteins provide to understand their functions in the cell. In order to determine the 3D structure of the huge amount of protein sequence, the development of efficient computational techniques is needed. The thesis studies the computational approaches to provide new solutions for the secondary structure prediction of proteins. The 3D structure of a protein is composed of the secondary structure elements: α-helices, β-sheets, β-turns, and loops. The secondary structures of proteins have a high impact on the formation of their 3D structures. Two subproblems within secondary structure prediction have been studied in this thesis. The first study is for identifying the structural classes (all-α, all-β, α/β, α+β) of proteins from their primary sequences. The structural class information could provide a rough description of a protein’s 3D structure due to the high effects of the secondary structures on the formation of 3D structure. This approach assembles the statistical classification technique, Support Vector Machines (SVM), and the variations of amino acid composition information. The performance results demonstrate that the utilization of neighborhood information between amino acids and the high classification ability of the SVM provides a significant improvement for the structural classification of proteins. The second study in thesis is for predicting one of the secondary structure element, β-turns, through primary sequence. The formation of β-turns has been thought to have critical roles as much as other secondary structures in the protein folding pathway. Hence, Hidden Markov Models (HMM) and Artificial Neural Networks (ANN) have been developed to predict the location and type of β-turns from its amino acid sequence. The neighborhood information between β-turns and other secondary structures has been introduced by designing the suitable HMM topologies. One of the amino acid similarity matrices is used to give the evolutionary information between proteins. Although applying HMMs and usage of amino acid similarity matrix is a new approach to predict β-turns through its protein sequence, the initial results for the prediction of β-turns and type classification are promising

A signal transduction score flow algorithm for cyclic cellular pathway analysis, which combines transcriptome and ChIP-seq data

Determination of cell signalling behaviour is crucial for understanding the physiological response to a specific stimulus or drug treatment. Current approaches for large-scale data analysis do not effectively incorporate critical topological information provided by the signalling network. We herein describe a novel model- and data-driven hybrid approach, or signal transduction score flow algorithm, which allows quantitative visualization of cyclic cell signalling pathways that lead to ultimate cell responses such as survival, migration or death. This score flow algorithm translates signalling pathways as a directed graph and maps experimental data, including negative and positive feedbacks, onto gene nodes as scores, which then computationally traverse the signalling pathway until a pre-defined biological target response is attained. Initially, experimental data-driven enrichment scores of the genes were computed in a pathway, then a heuristic approach was applied using the gene score partition as a solution for protein node stoichiometry during dynamic scoring of the pathway of interest. Incorporation of a score partition during the signal flow and cyclic feedback loops in the signalling pathway significantly improves the usefulness of this model, as compared to other approaches. Evaluation of the score flow algorithm using both transcriptome and ChIP-seq data-generated signalling pathways showed good correlation with expected cellular behaviour on both KEGG and manually generated pathways. Implementation of the algorithm as a Cytoscape plug-in allows interactive visualization and analysis of KEGG pathways as well as user-generated and curated Cytoscape pathways. Moreover, the algorithm accurately predicts gene-level and global impacts of single or multiple in silico gene knockouts.Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich

Integrative Biological Network Analysis to Identify Shared Genes in Metabolic Disorders

Identification of common molecular mechanisms in interrelated diseases is essential for better prognoses and targeted therapies. However, complexity of metabolic pathways makes it difficult to discover common disease genes underlying metabolic disorders; and it requires more sophisticated bioinformatics models that combine different types of biological data and computational methods. Accordingly, we built an integrative network analysis model to identify shared disease genes in metabolic syndrome (MS), type 2 diabetes (T2D), and coronary artery disease (CAD). We constructed weighted gene co-expression networks by combining gene expression, protein-protein interaction, and gene ontology data from multiple sources. For 90 different configurations of disease networks, we detected the significant modules by using MCL, SPICi, and Linkcomm graph clustering algorithms. We also performed a comparative evaluation on disease modules to determine the best method providing the highest biological validity. By overlapping the disease modules, we identified 22 shared genes for MS-CAD and T2D-CAD. Moreover, 19 out of these genes were directly or indirectly associated with relevant diseases in the previous medical studies. This study does not only demonstrate the performance of different biological data sources and computational methods in disease-gene discovery, but also offers potential insights into common genetic mechanisms of the metabolic disorders

Compound Target Identification in Tissue-Specific Interaction Networks

Characterizing all possible side effects of compounds is not a trivial task due to unknown off-target proteins that might eventually lead lethal reactions. There is still a tremendous need of computational methods to identify protein targets of a new compound. We have performed a comprehensive analysis for identification of protein targets by integrating tissue-specific protein-protein interaction (PPI) networks and compound induced transcriptome data. Several network centrality metrics are computed to suggest the most probable off-targets of a given compound. The effects of interaction types between proteins and tissue-specific PPI networks are evaluated from multiple perspectives. Usage of network centrality metrics on a tissue-specific PPI network enhances the correct prediction rate of known targets of a given compound. The detailed analysis of successfully identified known targets indicated that degree and local radiality metrics are more practical for determining different types of target protein families, such as GPCR, chemokines, proteasome, and protein kinase families. Therefore, the proposed computational pipeline is applicable while investigating proteins from these families that can be especially targeted for treatment of complex diseases. To the best of our knowledge, this study for the first time presents how tissue-specific transcriptome changes alter the topological structure of PPI networks and the relative effects of tissue-specific networks in target identification. Overall, the proposed computational methods are practical tools for choosing more accurate protein targets for later computational analysis and wet-lab experiments

Evaluation of Signaling Cascades Based on the Weights from Microarray and ChIP-seq Data

In this study, we combined the ChIP-seq and the transcriptome data and integrated these data into signaling cascades. Integration was realized through a framework based on data- and model-driven hybrid approach. An enrichment model was constructed to evaluate signaling cascades which resulted in specific cellular processes. We used ChIP-seq and microarray data from public databases which were obtained from HeLa cells under oxidative stress having similar experimental setups. Both ChIP-seq and array data were analyzed by percentile ranking for the sake of simultaneous data integration on specific genes. Signaling cascades from KEGG pathway database were subsequently scored by taking sum of the individual scores of the genes involved within the cascade. This scoring information is then transferred to en route of the signaling cascade to form the final score.Signaling cascade model based framework that we describe in this study is a novel approach which calculates scores for the target process of the analyzed signaling cascade, rather than assigning scores to gene product node

Protein structural class determination using support vector machines

Abstract. Proteins can be classified into four structural classes (all-α, all-β, α/β, α+β) according to their secondary structure composition. In this paper, we predict the structural class of a protein from its Amino Acid Composition (AAC) using Support Vector Machines (SVM). A protein can be represented by a 20 dimensional vector according to its AAC. In addition to the AAC, we have used another feature set, called the Trio Amino Acid Composition (Trio AAC) which takes into account the amino acid neighborhood information. We have tried both of these features, the AAC and the Trio AAC, in each case using a SVM as the classification tool, in predicting the structural class of a protein. According to the Jackknife test results, Trio AAC feature set shows better classification performance than the AAC feature.

Predicting drug synergy for precision medicine using network biology and machine learning.

Identification of effective drug combinations for patients is an expensive and time-consuming procedure, especially fo

Fetal urinary ascites in a neonate without detectable obstructive uropathy or neurogenic bladder etiology

Fetal urinary ascites is usually secondary to an obstructive uropathy or neurogenic bladder. We present such a case in the absence of these conditions, but the presence of ipsilateral vesicoureteral reflux with Hutch diverticula. The patient was a 5-day-old boy presenting with distension of the abdomen and impairment of renal function. Tests revealed urinary ascites and renal insufficiency which spontaneously resolved after transurethral urinary drainage was established. This rare complication should be considered in neonates with high intrapelviureteric and intrarenal pressure as a result of high-grade vesicoureteral reflux with paraureteral diverticula. (C) 2008 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved

Effects of Sevoflurane and Desflurane on Oxidant/Antioxidant Status of Young Versus Old Rat Liver Tissues

Nurlu Ayan, Nilhan/0000-0002-0844-5050WOS: 000260428600002Anesthetic agents modulate on oxidant/antioxidant activity. This study aimed to evaluate the effects of desflurane and sevoflurane anesthesia on the oxidant/antioxidant activity in the liver of young and aged rats. The study involved 60 male Wistar Albino Rats. Rats, which 5-6 months of age deemed as young (Group Y, n = 30) and 10-11 months of age deemed as old (Group O, n = 30). The weight range of the rats was 270-350 g. The groups of rats were randomly divided into 3 groups as the control group [Group Y, (Young Control 100% O-2; n = 10) and Group O-c (Old Control 100% 0,; n = 10)], desflurane group [Group Y-D, (Young Desflurane; 6% Desflurane in 100% O-2; n = 10)] and Group O-2, (Old Desfluranee; 6% Desflurane in 100% O-2; n = 10)] and sevoflurane group [Group Y-S (Young Sevoflurane; 2% Sevoflurane in 100% O-2; n = 10) and Group O-S (Old Sevoflurane; 2% Sevoflurane in 100% 02; n = 10)]. The rats placed into a transparent plastic cage. The rats were exposed to different anesthetic agents or oxygen for 2 h by the use of half open Anesthesia System (AMS, Senior 425), while rats' simultaneous normal breathing was maintained. At the end of the exposure, they were administered a high dose of ketamine and the livers of the animals were sugically removed. SOD, GST and NOS activities were determined and levels of oxidative stress was monitored by measuring TBARS via levels of MDA in the liver. Desflurane induce oxidative stress in both young and old rats, with higher levels in old rats. However, sevoflurane did not cause oxidative stress in young rats. Sevoflurane increased the oxidative stress in the old rats based on SOD and TBARS levels, while it maintained GST activity and decreased NOS activity. However, further studies are needed

In silico identification of novel biomarkers for key players in transition from normal colon tissue to adenomatous polyps.

Adenomatous polyps of the colon are the most common neoplastic polyps. Although most of adenomatous polyps do not show malign transformation, majority of colorectal carcinomas originate from neoplastic polyps. Therefore, understanding of this transformation process would help in both preventive therapies and evaluation of malignancy risks. This study uncovers alterations in gene expressions as potential biomarkers that are revealed by integration of several network-based approaches. In silico analysis performed on a unified microarray cohort, which is covering 150 normal colon and adenomatous polyp samples. Significant gene modules were obtained by a weighted gene co-expression network analysis. Gene modules with similar profiles were mapped to a colon tissue specific functional interaction network. Several clustering algorithms run on the colon-specific network and the most significant sub-modules between the clusters were identified. The biomarkers were selected by filtering differentially expressed genes which also involve in significant biological processes and pathways. Biomarkers were also validated on two independent datasets based on their differential gene expressions. To the best of our knowledge, such a cascaded network analysis pipeline was implemented for the first time on a large collection of normal colon and polyp samples. We identified significant increases in TLR4 and MSX1 expressions as well as decrease in chemokine profiles with mostly pro-tumoral activities. These biomarkers might appear as both preventive targets and biomarkers for risk evaluation. As a result, this research proposes novel molecular markers that might be alternative to endoscopic approaches for diagnosis of adenomatous polyps