scholarly journals Sites Inferred by Metabolic Background Assertion Labeling (SIMBAL): adapting the Partial Phylogenetic Profiling algorithm to scan sequences for signatures that predict protein function

2010 ◽  
Vol 11 (1) ◽  
Author(s):  
Jeremy D Selengut ◽  
Douglas B Rusch ◽  
Daniel H Haft
Keyword(s):  
Protein Function ◽  
Predict Protein Function ◽  
Phylogenetic Profiling

Spatial localization of unknown proteins in the endoplasmic reticulum predicts functions

2007 ◽  
Vol 30 (4) ◽  
pp. 84
Author(s):  
Michael D. Jain ◽  
Hisao Nagaya ◽  
Annalyn Gilchrist ◽  
Miroslaw Cygler ◽  
John J.M. Bergeron
Keyword(s):  
Endoplasmic Reticulum ◽  
Protein Folding ◽  
Protein Synthesis ◽  
Protein Function ◽  
Protein Translocation ◽  
Spatial Localization ◽  
Predict Protein Function ◽  
Insight Into ◽  
Soluble Fractions ◽  
Er Membrane

Protein synthesis, folding and degradation functions are spatially segregated in the endoplasmic reticulum (ER) with respect to the membrane and the ribosome (rough and smooth ER). Interrogation of a proteomics resource characterizing rough and smooth ER membranes subfractionated into cytosolic, membrane, and soluble fractions gives a spatial map of known proteins involved in ER function. The spatial localization of 224 identified unknown proteins in the ER is predicted to give insight into their function. Here we provide evidence that the proteomics resource accurately predicts the function of new proteins involved in protein synthesis (nudilin), protein translocation across the ER membrane (nicalin), co-translational protein folding (stexin), and distal protein folding in the lumen of the ER (erlin-1, TMX2). Proteomics provides the spatial localization of proteins and can be used to accurately predict protein function.

scholarly journals Practical and theoretical advances in predicting the function of a protein by its phylogenetic distribution

2007 ◽  
Vol 5 (19) ◽  
pp. 151-170 ◽  
Author(s):  
Philip R Kensche ◽  
Vera van Noort ◽  
Bas E Dutilh ◽  
Martijn A Huynen
Keyword(s):  
Protein Function ◽  
Similarity Measures ◽  
Phylogenetic Distribution ◽  
Genomic Context ◽  
Profile Similarity ◽  
Phylogenetic Profiles ◽  
Fungal Genomes ◽  
Phylogenetic Profiling ◽  
Genome Information ◽  
Theoretical Considerations

The gap between the amount of genome information released by genome sequencing projects and our knowledge about the proteins' functions is rapidly increasing. To fill this gap, various ‘genomic-context’ methods have been proposed that exploit sequenced genomes to predict the functions of the encoded proteins. One class of methods, phylogenetic profiling, predicts protein function by correlating the phylogenetic distribution of genes with that of other genes or phenotypic characteristics. The functions of a number of proteins, including ones of medical relevance, have thus been predicted and subsequently confirmed experimentally. Additionally, various approaches to measure the similarity of phylogenetic profiles and to account for the phylogenetic bias in the data have been proposed. We review the successful applications of phylogenetic profiling and analyse the performance of various profile similarity measures with a set of one microsporidial and 25 fungal genomes. In the fungi, phylogenetic profiling yields high-confidence predictions for the highest and only the highest scoring gene pairs illustrating both the power and the limitations of the approach. Both practical examples and theoretical considerations suggest that in order to get a reliable and specific picture of a protein's function, results from phylogenetic profiling have to be combined with other sources of evidence.

scholarly journals The Protein Interactome of Streptococcus pneumoniae and Bacterial Meta-interactomes Improve Function Predictions

mSystems ◽  
2017 ◽  
Vol 2 (3) ◽  
Author(s):  
S. Wuchty ◽  
S. V. Rajagopala ◽  
S. M. Blazie ◽  
J. R. Parrish ◽  
S. Khuri ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Experimental Approach ◽  
Bacterial Species ◽  
Human Pathogen ◽  
Content Type ◽  
Predict Protein Function ◽  
Cellular Pathways ◽  
The Individual ◽  
New Protein

ABSTRACT Identification of protein interactions in bacterial species can help define the individual roles that proteins play in cellular pathways and pathogenesis. Very few protein interactions have been identified for the important human pathogen S. pneumoniae. We used an experimental approach to identify over 2,000 new protein interactions for S. pneumoniae, the most extensive interactome data for this bacterium to date. To predict protein function, we used our interactome data augmented with interactions from other closely related bacteria. The combination of the experimental data and meta-interactome data significantly improved the prediction results, allowing us to assign possible functions to a large number of poorly characterized proteins. The functions of roughly a third of all proteins in Streptococcus pneumoniae, a significant human-pathogenic bacterium, are unknown. Using a yeast two-hybrid approach, we have determined more than 2,000 novel protein interactions in this organism. We augmented this network with meta-interactome data that we defined as the pool of all interactions between evolutionarily conserved proteins in other bacteria. We found that such interactions significantly improved our ability to predict a protein’s function, allowing us to provide functional predictions for 299 S. pneumoniae proteins with previously unknown functions. IMPORTANCE Identification of protein interactions in bacterial species can help define the individual roles that proteins play in cellular pathways and pathogenesis. Very few protein interactions have been identified for the important human pathogen S. pneumoniae. We used an experimental approach to identify over 2,000 new protein interactions for S. pneumoniae, the most extensive interactome data for this bacterium to date. To predict protein function, we used our interactome data augmented with interactions from other closely related bacteria. The combination of the experimental data and meta-interactome data significantly improved the prediction results, allowing us to assign possible functions to a large number of poorly characterized proteins.

Predict Interactions between Protein Residues for Developing Antibody Therapeutics

2011 ◽  
Vol 383-390 ◽  
pp. 4003-4006
Author(s):  
Wen Lung Shu ◽  
Chia Hsuan Lee
Keyword(s):  
Mathematical Model ◽  
Secondary Structure ◽  
Protein Sequence ◽  
Protein Function ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Protein Residues ◽  
Antibody Therapeutics ◽  
Predict Protein Function ◽  
Data Files

Recently, protein-antibody therapeutics becomes a hot search topic. In this paper, all protein interaction data files are collected from INTERPARE. Protein sequence and its secondary structure both are used to build HMM mathematical model. We randomly take 80% data to train positive and negative HMM models and 20% data to test. The accuracy of this approach can reach to 79.80%. This model can further be used to predict protein function sites and predict if a protein interacts with other proteins.

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