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Predicting helix-helix interactions from residue contacts in membrane proteins

bioinformatics 2009年第8期25卷 996-1003页

作者： Lo, Allan Chiu, Yi-Yuan Rødland, Einar Andreas Lyu, Ping-Chiang Sung, Ting-Yi Hsu, Wen-Lian Bioinformatics Program Taiwan International Graduate Program Academia Sinica Taipei Taiwan Institute of Bioinformatics and Structural Biology Department of Life Sciences National Tsing Hua University Hsinchu Taiwan Bioinformatics Laboratory Institute of Information Science Academia Sinica Taipei Taiwan Centre for Cancer Biomedicine University of Oslo NO-0027 Oslo Norway SAMBA Norwegian Computing Center Blindern NO-0314 Oslo P.O. Box 114 Norway

Motivation: Helix-helix interactions play a critical role in the structure assembly, stability and function of membrane proteins. On the molecular level, the interactions are mediated by one or more residue contacts. Although previous studies focused on helix-packing patterns and sequence motifs, few of them developed methods specifically for contact prediction. Results: We present a new hierarchical framework for contact prediction, with an application in membrane proteins. The hierarchical scheme consists of two levels: in the first level, contact residues are predicted from the sequence and their pairing relationships are further predicted in the second level. Statistical analyses on contact propensities are combined with other sequence and structural information for training the support vector machine classifiers. Evaluated on 52 protein chains using leave-one-out cross validation (LOOCV) and an independent test set of 14 protein chains, the two-level approach consistently improves the conventional direct approach in prediction accuracy, with 80% reduction of input for prediction. Furthermore, the predicted contacts are then used to infer interactions between pairs of helices. When at least three predicted contacts are required for an inferred interaction, the accuracy, sensitivity and specificity are 56%, 40% and 89%, respectively. Our results demonstrate that a hierarchical framework can be applied to eliminate false positives (FP) while reducing computational complexity in predicting contacts. Together with the estimated contact propensities, this method can be used to gain insights into helix-packing in membrane proteins. © 2009 The Author(s).

关键词： Computational Biology/methods* Databases, Protein Databases, Protein Membrane Proteins/chemistry* Membrane Proteins/metabolism Models, Biological Protein Structure, Secondary Reproducibility of Results

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U1 snRNP regulates alternative promoter activity by inhibiting premature polyadenylation

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Molecular Cell 2025年第10期85卷 1968-1981.e7页

作者： Kim, GyeungYun Carroll, Christine L. Wakefield, Zachary Peters Tuncay, Mustafa Fiszbein, Ana Biology Department Boston University Boston 02215 United States Graduate Program in Molecular Biology Cell Biology & Biochemistry Boston University Boston 02215 United States Graduate Program in Cell and Molecular Biology Boston University Boston 02215 United States Bioinformatics Program Faculty of Computing and Data Science Boston University Boston 02215 United States Center for Computing & Data Sciences Boston University Boston 02215 United States

Emerging evidence indicates that splicing factors mediate the close link between transcription and splicing. However, the mechanisms underlying this coupling remain unclear. U1 small nuclear ribonucleoprotein particle (U1 snRNP) not only initiates splicing but also plays a crucial role in preventing premature cleavage and polyadenylation, facilitating long-distance transcriptional elongation. Here, we show that U1 snRNP regulates alternative promoter activity in human cells by inhibiting premature polyadenylation. In genes carrying premature polyadenylation sites between two promoters, U1 snRNP inhibition with antisense oligonucleotides leads to a significant decrease in downstream promoter activity. Conversely, restoring U1 snRNP activity or inhibiting premature polyadenylation rescues downstream promoter activity. Mechanistically, U1 snRNP inhibition correlates with reduced chromatin accessibility, decreased RNA polymerase II serine 5 phosphorylation, and increased promoter-proximal pause at downstream promoters. Our findings support a model in which U1 snRNP favors productive elongation from upstream promoters, triggering downstream promoter activation by destabilizing nucleosomes and promoting promoter escape. © 2025 The Author(s)

关键词： alternative promoters dowsntream promoters promoter-proximal pause splicing factors splicing transcription transcription cooperativity transcription initiation transcription interfierence transcription start sites U1 snRNP

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Refinement of NMR-determined protein structures with database derived distance constraints

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Journal of bioinformatics and Computational Biology 2005年第6期3卷 1315-1329页

作者： Cui, Feng Jernigan, Robert Wu, Zhijun Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011 United States Department of Mathematics Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States

The protein structures determined by NMR (Nuclear Magnetic Resonance Spectroscopy) are not as detailed and accurate as those by X-ray crystallography and are often underdetermined due to the inadequate distance data available from NMR experiments. The uses of NMR-determined structures in such important applications as homology modeling and rational drug design have thus been severely limited. Here we show that with the increasing numbers of high quality protein structures being determined, a computational approach to enhancing the accuracy of the NMR-determined structures becomes possible by deriving additional distance constraints from the distributions of the distances in databases of known protein structures. We show through a survey on 462 NMR structures that, in fact, many inter-atomic distances in these structures deviate considerably from their database distributions and based on the refinement results on 10 selected NMR structures that these structures can actually be improved significantly when a selected set of distances are constrained within their high probability ranges in their database distributions. © Imperial College Press.

关键词： NMR protein structure refinement Protein structure database Structural bioinformatics

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Finding function in the unknown

Finding function in the unknown

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IEEE International Conference on bioinformatics and Biomedicine, BIBM 2015

作者： Boyd, Kelly Highland, Emma Misch, Amanda Hu, Amber Reddy, Sushma Putonti, Catherine Department of Biology Bioinformatics Program Loyola University Chicago ChicagoIL60660 United States Stevenson High School LincolnshireIL60069 United States Departments of Biology and Computer Science Bioinformatics Program Loyola University Chicago ChicagoIL60660 United States

ISBN: (纸本)9781467367981

Through high-throughput RNA sequencing (RNAseq), transcriptomes for a single cell, tissue, or organism(s) can be ascertained at a high resolution. While a number of bioinformatic tools have been developed for transcriptome analyses, significant challenges exist for studies of non-model organisms. Without a reference sequence available, raw reads must first be assembled de novo followed by the tedious task of BLAST searches and data mining for functional information. We have created a pipeline, PyRanger, to automate this process. The pipeline includes functionality to assess a single transcriptome and also facilitate comparative transcriptomic studies. © 2015 IEEE.

关键词： Gene expression

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Constructing a Boolean implication network to study the interactions between environmental factors and OTUs

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Frontiers of Electrical and Electronic Engineering in China 2014年第4期9卷 127-141页

作者： Congmin Zhu Rui Jiang Ting Chen MOE Key Laboratory of Bioinformatics and Bioinformatics Division Center for Synthetic & Systems Biology TNLIST/ Department of Automation Tsinghua University Beijing 100084 China Computational and Molecular Biology Program University of Southern California Loa Angles CA 90089 USA

Mining relationships between microbes and the environment they live in are crucial to understand the intrinsic mechanisms that govern cycles of carbon, nitrogen and energy in a microbial community. Building upon next- generation sequencing technology, the selective capture of 16S rRNA genes has enabled the study of co-occurrence patterns of microbial species from the viewpoint of complex networks, yielding successful descriptions of phenomena exhibited in a microbial community. However, since the effects of such environmental factors as temperature or soil conditions on microbes are complex, reliance on the analysis of co-occurrence networks alone cannot elucidate such complicated effects underlying microbial communities. In this study, we apply a statistical method, which is called Boolean implications for metagenomic studies （BIMS） for extracting Boolean implications （IF-THEN relationships） to capture the effects of environmental factors on microbial species based on 16S rRNA sequencing data. We first demonstrate the power and effectiveness of BIMS through comprehensive simulation studies and then apply it to a 16S rRNA sequencing dataset of real marine microbes. Based on a total of 6,514 pairwise relationships identified at a low false discovery rate （FDR） of 0.01, we construct a Boolean implication network between operational taxonomic units （OTUs） and environmental factors. Relationships in this network are supported by literature, and, most importantly, they bring biological insights into the effects of environmental factors on microbes. We next apply BIMS to detect three-way relationships and show the possibility of using this strategy to explain more complex relationships within a microbial community.

关键词： Boolean implication metagenome marine OTUs environmental factors

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Investigation of Aframomum melegueta compounds as ERK5 inhibitor related to breast cancer via molecular docking and dynamic simulation

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In silico pharmacology 2025年第1期13卷 18页

作者： Paul Olamide Ottu Olorunfemi Oyewole Babalola Cecilia Oluwamodupe Ayodeji Folasade Oluwatobiloba Idayat Oyinkansola Kehinde Olufemi Adebisi Akinola Sulyman Olalekan Ibrahim Olusola Olalekan Elekofehinti Teady Bioscience Research Laboratory Ilara Mokin Ondo State Nigeria. Phytomedicine Molecular Biology and Bioinformatics lab Department of Chemical Science (Biochemistry program) Olusegun Agagu University of Science and Technology Okitipupa Ondo State Nigeria. Bioinformatics and Molecular Biology Lab Department of Biochemistry Federal University of Technology Akure Akure Ondo State Nigeria. Department of Industrial Chemistry University of Ilorin Ilorin Kwara State Nigeria.

Breast cancer remains a global health challenge, with rising cases predicted in the coming decades. The complexity of breast cancer treatment arises from its complex nature, often involving multiple therapeutic strategies. One promising approach is targeting the ERK5 pathway, a key regulator in cancer cell proliferation and survival. In this study, we explored the anticancer potential of bioactive compounds from Aframomum melegueta, a plant traditionally used in West African medicine. The 3D structure of ERK5 (PDB ID: 4B99) was prepared and optimized using the Schrödinger Protein Preparation Wizard. Six phytochemicals from Aframomum melegueta were screened for their binding affinities to ERK5 using GlideXP docking. Dihydrogingerenone A,1-(3,4-dihydroxy-5-methoxyphenyl)-7-(3,4-dihydroxyphenyl) heptane-3,5-diyldiacetate and Dihydrogingerenone C emerged as the lead compound, demonstrating a high docking score of − 9.659 kcal/mol, − 9.383 kcal/mol, and − 8.264 kcal/mol compared to standard anticancer drugs like Docetaxel (− 4.175 kcal/mol) and Temozolomide (− 5.443 kcal/mol). Post-docking analyses using MM-GBSA free energy calculations confirmed the compound’s high binding stability, with van der Waals interactions and hydrogen bonding at critical residues such as Met140 playing a significant role. Pharmacokinetic profiling using ADME analysis showed that our compounds exhibited favorable drug-likeness properties, adhering to Lipinski’s Rule of Five without violations. QSAR modeling and molecular dynamics (MD) simulations further validated its pharmacological potential. These findings suggest that Aframomum melegueta contains bioactive compounds with strong potential as ERK5 inhibitors, offering a novel approach to breast cancer treatment.

关键词： Aframomum melegueta Breast cancer Dihydrogingerenone A ERK5 Molecular dynamic simulation

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A kernel method for MicroRNA target prediction using sensible data and position-based features

A kernel method for MicroRNA target prediction using sensibl...

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2005 IEEE Symposium on Computational Intelligence in bioinformatics and Computational Biology, CIBCB '05

作者： Kim, Sung-Kyu Nam, Jin-Wu Lee, Wha-Jin Zhang, Byoung-Tak Graduate Program in Bioinformatics School of Computer Science and Engineering Seoul National University Seoul 151-744 Korea Republic of

ISBN: (纸本)0780393872

MicroRNAs (miRNAs) are small endogenous RNAs of ∼22nt that act as direct post-transcriptional regulators in animals and plants. MicroRNAs generally perform a function by binding to the complementary site on the 3' untranslated region of Its target gene and especially the Smers on the 5' part of miRNA seems important as a seed. Computational methods for miRNA target prediction have been focusing on this seed region, but recent researches revealed that the specificity of the seed region may be sharply decreased even by a point mutation. In this paper, we present a kernel method for miRNA target prediction in animals, which improves the prediction performance with biologically sensible data and position-based features reflecting the way of miRNA:mRNA pairing mechanism. In building a training dataset, we choose experimentally verified data only to improve the quality of dataset by excluding randomly synthesized one and consequently to make the result of learning valid. We use sensitivity, specificity, and area under ROC curve as performance measures of our algorithm and compare the results of various dataset configurations. The overall results were 92.1% in sensitivity, 83.3% in specificity, and 0.931 in area under ROC curve. With position-based features, an increase of 3.3% in sensitivity and 1.6% in specificity were observed. In the feature selection experiment to investigate the role of individual position-based features, the result suggests that pairing at positions 4, 5, 6 of the seed part is of greater importance than the other positions together with the general stability of the seed part. © 2005 IEEE.

关键词： RNA

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Training finite state classifiers to improve PCR primer design

Training finite state classifiers to improve PCR primer desi...

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Collection of Technical Papers - 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

作者： Ashlock, Daniel A. Bryden, K.M. Corns, Steven M. Schnable, Patrick S. Wen, T. Iowa State University Ames IA 50011 Mathematics Department Bioinformatics Program Department of Mechanical Engineering Agronomy Department

ISBN: (纸本)1563477165

We present results on training finite state machines as classifiers for polymerase chain reaction primers. The goal is to decrease the number of primers that fail to amplify correctly. Finite state classifiers are trained with a novel evolutionary algorithm that uses an incremental fitness reward system and multi-population hybridization. The system presented here creates a post-production add-on to a standard primer picking program intended to compensate for organism- and lab-specific factors. Copyright © 2004 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

关键词： Enzymes

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Incorporating gene annotations as node metadata to improve network centrality measures for better node ranking 13

Incorporating gene annotations as node metadata to improve n...

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2013 4th ACM Conference on bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013

作者： Mistry, Divya Dickerson, Julie Department of Electrical and Computer Engineering Bioinformatics and Computational Biology Program Iowa State University Ames IA United States

ISBN: (纸本)9781450324342

Network centrality measures allow ranking of nodes and edges based on their importance to the network topology. Closeness centrality [1] and shortest path betweenness centrality [2] are two of the most popular and well-utilized centrality measures that have provided good results [3,4,5,6]. Both of these centralities rely exclusively on topological features of the network [7] to calculate node importance. We propose an improvement to these path length based centrality measures that incorporate nodespecific metadata to provide biologically relevant node ranking. We choose gene annotations and gene ontology (GO) evidences as our metadata to highlight the new approach. Application of the newly proposed centrality measures to synthetic networks, and pathogen infected barley's gene co-expression networks resulted in a significantly better prioritization of the nodes. We compared our results against unmodified centrality measures applied to the same networks. Our proposed improvements provide a new avenue for tailoring centrality measures for biological networks, and hold great potential for further improvement of random walk based [8] and motif-based centrality [9] measures. Copyright © 2007 by the Association for Computing Machinery.

关键词： Metadata

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Evolving hypernetwork classifiers for microRNA expression profile analysis

Evolving hypernetwork classifiers for microRNA expression pr...

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2007 IEEE Congress on Evolutionary Computation, CEC 2007

作者： Kim, Sun Kim, Soo-Jin Zhang, Byoung-Tak Center for Bioinformatics Technology Graduate Program in Bioinformatics Seoul National University Seoul 151-742 Korea Republic of School of Computer Science and Engineering Seoul National University Seoul 151-744 Korea Republic of

ISBN: (纸本)1424413400

High-throughput microarrays inform us on different outlooks of the molecular mechanisms underlying the function of cells and organisms. While computational analysis for the microarrays show good performance, it is still difficult to infer modules of multiple co-regulated genes. Here, we present a novel classification method to identify the gene modules associated with cancers from microarray data. The proposed approach is based on 'hypernetworks', a hypergraph model consisting of vertices and weighted hyperedges. The hypernetwork model is inspired by biological networks and its learning process is suitable for identifying interacting gene modules. Applied to the analysis of microRNA (miRNA) expression profiles on multiple human cancers, the hypernetwork classifiers identified cancer-related miRNA modules. The results show that our method performs better than decision trees and naive Bayes. The biological meaning of the discovered miRNA modules has been examined by literature search. © 2007 IEEE.

关键词： Microarrays

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