448 research outputs found
Proteome scanning to predict PDZ domain interactions using support vector machines
<p>Abstract</p> <p>Background</p> <p>PDZ domains mediate protein-protein interactions involved in important biological processes through the recognition of short linear motifs in their target proteins. Two recent independent studies have used protein microarray or phage display technology to detect PDZ domain interactions with peptide ligands on a large scale. Several computational predictors of PDZ domain interactions have been developed, however they are trained using only protein microarray data and focus on limited subsets of PDZ domains. An accurate predictor of genomic PDZ domain interactions would allow the proteomes of organisms to be scanned for potential binders. Such an application would require an accurate and precise predictor to avoid generating too many false positive hits given the large amount of possible interactors in a given proteome. Once validated these predictions will help to increase the coverage of current PDZ domain interaction networks and further our understanding of the roles that PDZ domains play in a variety of biological processes.</p> <p>Results</p> <p>We developed a PDZ domain interaction predictor using a support vector machine (SVM) trained with both protein microarray and phage display data. In order to use the phage display data for training, which only contains positive interactions, we developed a method to generate artificial negative interactions. Using cross-validation and a series of independent tests, we showed that our SVM successfully predicts interactions in different organisms. We then used the SVM to scan the proteomes of human, worm and fly to predict binders for several PDZ domains. Predictions were validated using known genomic interactions and published protein microarray experiments. Based on our results, new protein interactions potentially associated with Usher and Bardet-Biedl syndromes were predicted. A comparison of performance measures (F1 measure and FPR) for the SVM and published predictors demonstrated our SVM's improved accuracy and precision at proteome scanning.</p> <p>Conclusions</p> <p>We built an SVM using mouse and human experimental training data to predict PDZ domain interactions. We showed that it correctly predicts known interactions from proteomes of different organisms and is more accurate and precise at proteome scanning compared with published state-of-the-art predictors.</p
State of Alaska Amount: $1,045,362.00
Dear Mr. Bader: The Exxon Valdez Trustee Council has unanimously detennined to expend 3,794,162.00 will come from the Research Investment Sub-Account. The Research Investment Sub-Account is currently held by the State of Alaska in the Exxon Valdez Oil Spill Investment Fund and invested by the Treasury Division, Alaska Department of Revenue. Under the terms of the Reimbursable Services Agreemen
An automated method for finding molecular complexes in large protein interaction networks
BACKGROUND: Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery. RESULTS: This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. CONCLUSION: Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from
Predicting PDZ domain mediated protein interactions from structure
BACKGROUND: PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors. RESULTS: We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on trainingātesting domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling. CONCLUSIONS: We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on trainingātesting domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at http://webservice.baderlab.org/domains/POW
Pathguide: a Pathway Resource List
Pathguide: the Pathway Resource List () is a meta-database that provides an overview of more than 190 web-accessible biological pathway and network databases. These include databases on metabolic pathways, signaling pathways, transcription factor targets, gene regulatory networks, genetic interactions, proteinācompound interactions, and proteināprotein interactions. The listed databases are maintained by diverse groups in different locations and the information in them is derived either from the scientific literature or from systematic experiments. Pathguide is useful as a starting point for biological pathway analysis and for content aggregation in integrated biological information systems
Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learning
Histology imaging is an important tool in medical diagnosis and research,
enabling the examination of tissue structure and composition at the microscopic
level. Understanding the underlying molecular mechanisms of tissue architecture
is critical in uncovering disease mechanisms and developing effective
treatments. Gene expression profiling provides insight into the molecular
processes underlying tissue architecture, but the process can be time-consuming
and expensive. In this study, we present BLEEP (Bi-modaL Embedding for
Expression Prediction), a bi-modal embedding framework capable of generating
spatially resolved gene expression profiles of whole-slide Hematoxylin and
eosin (H&E) stained histology images. BLEEP uses a contrastive learning
framework to construct a low-dimensional joint embedding space from a reference
dataset using paired image and expression profiles at micrometer resolution.
With this framework, the gene expression of any query image patch can be
imputed using the expression profiles from the reference dataset. We
demonstrate BLEEP's effectiveness in gene expression prediction by benchmarking
its performance on a human liver tissue dataset captured via the 10x Visium
platform, where it achieves significant improvements over existing methods. Our
results demonstrate the potential of BLEEP to provide insights into the
molecular mechanisms underlying tissue architecture, with important
implications in diagnosis and research of various diseases. The proposed
framework can significantly reduce the time and cost associated with gene
expression profiling, opening up new avenues for high-throughput analysis of
histology images for both research and clinical applications
Biological Network Exploration with Cytoscape 3
Cytoscape is one of the most popular openāsource software tools for the visual exploration of biomedical networks composed of protein, gene, and other types of interactions. It offers researchers a versatile and interactive visualization interface for exploring complex biological interconnections supported by diverse annotation and experimental data, thereby facilitating research tasks such as predicting gene function and constructing pathways. Cytoscape provides core functionality to load, visualize, search, filter, and save networks, and hundreds of Apps extend this functionality to address specific research needs. The latest generation of Cytoscape (version 3.0 and later) has substantial improvements in function, user interface, and performance relative to previous versions. This protocol aims to jumpāstart new users with specific protocols for basic Cytoscape functions, such as installing Cytoscape and Cytoscape Apps, loading data, visualizing and navigating the networks, visualizing network associated data (attributes), and identifying clusters. It also highlights new features that benefit experienced users. Curr. Protoc. Bioinform. 47:8.13.1ā8.13.24. Ā© 2014 by John Wiley & Sons, Inc.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143619/1/cpbi0813.pd
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