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About 10,000 papers for “Protein function prediction”

Protein Function Prediction

Leonardo M. Cruz, Sheyla Trefflich, Vinícius Almir Weiss et al. · Methods in molecular biology · 2017

Cited by 62

Proteomics protein function prediction.

Emily C. A. Goodall (9582758), Camila Azevedo Antunes (15361417), Jens Möller (427295) et al. · Figshare · 2023

<div><p>Diphtheria is a respiratory disease caused by <i>Corynebacterium diphtheriae</i>. While the toxin-based vaccine has helped control outbreaks of the disease since the mid-20<sup>th</sup> century there has been an increase in cases in recent years, including systemic infections caused by non-toxigenic <i>C</i>. <i>diphtheriae</i> strains. Here we describe the first study of gene essentiality in <i>C</i>. <i>diphtheriae</i>, providing the most-dense <u>Tra</u>nsposon <u>D</u>irected <u>I</u>nsertion <u>S</u>equencing (TraDIS) library in the phylum Actinobacteriota. This high-density library has allowed the identification of conserved genes across the genus and phylum with essential function and enabled the elucidation of essential domains within the resulting proteins including those involved in cell envelope biogenesis. Validation of these data through protein mass spectrometry identified hypothetical and uncharacterized proteins in the proteome which are also represented in the vaccine. These data are an important benchmark and useful resource for the <i>Corynebacterium</i>, <i>Mycobacterium</i>, <i>Nocardia</i> and <i>Rhodococcus</i> research community. It enables the identification of novel antimicrobial and vaccine targets and provides a basis for future studies of Actinobacterial biology.</p></div>

Cited by 0Open access

Protein function prediction using . . .

Hon Nian Chua, et al. · 2011

Proteins perform biological functions by participating in a large number of interactions, ranging from transient interactions in signaling pathways to permanent interactions within stable complexes. Studies have shown that the immediate interaction neighborhood of a protein can be used to infer its functions. While using only such direct interactions limits prediction coverage, extending the interaction neighborhood to include indirect interaction partners reduces precision significantly, making functional inference unviable. In a series of studies, we find that the extent of partner-sharing between two non-interacting proteins makes a good estimator for their co-participation in similar function. This allows us to include indirect interactions in network-based functional inference with little compromise in precision. We also extend this idea to the related problems of protein complex prediction and interaction data

Cited by 0

Protein Molecular Function Prediction

Bayesian Phylogenomics, Barbara E. Engelhardt, Michael I. Jordan et al. · 2005

We present a statistical graphical model to infer specific molecular function for unannotated protein sequences using homology. Based on phylogenomic principles, SIFTER (Statistical Inference of Function Through Evolutionary Relationships) accurately predicts molecular function for members of a protein family given a reconciled phylogeny and available function annotations, even when the data are sparse or noisy. Our method produced specific and consistent molecular function predictions across 100 Pfam families in comparison to the Gene Ontology annotation database, BLAST, GOtcha, and Orthostrapper. We performed a more detailed exploration of functional predictions on the adenosine-59-monophosphate/adenosine deaminase family and the lactate/malate dehydrogenase family, in the former case comparing the predictions against a gold standard set of published functional characterizations. Given function annotations for 3% of the proteins in the deaminase family, SIFTER achieves 96% accuracy in predicting molecular function for experimentally characterized proteins as reported in the literature. The accuracy of SIFTER on this dataset is a significant improvement over other currently available methods such as BLAST (75%), GeneQuiz (64%), GOtcha (89%), and Orthostrapper (11%). We also experimentally characterized the adenosine deaminase from Plasmodium falciparum, confirming SIFTER’s prediction. The results illustrate the predictive power of exploiting a statistical model of function evolution in phylogenomic problems. A software implementation of SIFTER is available from the authors.

Cited by 0

Protein Molecular Function Prediction

Bayesian Phylogenomics Barbara, Barbara E. Engelhardt, Michael I. Jordan et al. · 2005

this report, we use only GO IDA- and IMP-derived annotations as observations for SIFTER, because of the high error rate and contradictions in the non-experimental annotations (i.e., all annotation types besides IDA and IMP). However, SIFTER can also incorporate other types of annotations, weighted according to their reliability

Cited by 0

Protein Function Prediction Using Function Associations in Protein–Protein Interaction Network

Pingping Sun, Tan Xian, Sijia Guo et al. · IEEE Access · 2018

In recent years, the rapid development of high-throughput technology has led to huge amounts of protein–protein interaction (PPI) data and unannotated protein sequences. Many approaches for protein function prediction have been developed which use PPI networks information. Traditional methods usually use the dependencies among interacting proteins for each same function only. However, the functions which are barely linked with the same function are more difficult to predict. In multi-label settings, the dependencies among related instances with multiple labels are more complex; rationally using these associations can make up for the shortcomings of traditional methods. In this paper, an iterative algorithm is applied to predict protein function based on the new network. The proposed method is able to capture the dependencies among functions based on proteins and interactions for protein function prediction. The test results show that the algorithm performs better than most of existing network based PPI algorithms; adding sequence similarity edges and spread function information can really improve the prediction performance. In addition, the dependencies among functions based on proteins and interactions can be effectively applied to the prediction of protein function.

Cited by 23Open access

Sequenced-based protein function prediction

Brett Poulin, Te E, Brett Poulin et al. · 2012

copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatever without the author’s prior written permission.

Cited by 0

Multi-species protein function prediction

Paolo Perlasca, Giorgio Valentini, Marco Frasca et al. · 2016

The visualization and analysis of big bio-molecular networks is a key feature for the investigation and prediction of protein functions in a multi-species context. In this paper we present the design of a system that integrates data management, machine learning and visualization facilities to make effective the visual analysis of big networks by means of web-based interfaces.

Cited by 2

Group Gene/protein Function Prediction

Ishita Khan, Reda Rawi · Qatar Foundation Annual Research Conference Proceedings Volume 2014 Issue 1 · 2014

Functional annotation of genes and their protein products is an essential step in the course of genome analysis. Experimental functional analysis techniques such as microarray or yeast two-hybrid systems simply can not handle the quantity of sequences made available by next-generation sequence technologies, and thus annotation of gene products is primarily predicted applying computational tools. A variety of computational methods are now available applying different methodologies, amongst others, homology-, sequence-, structure- or network-based methods. Nonetheless, so far there is no method that predicts the function of a group of genes and their products; for instance genes that are expressed during the course of a disease or cellular stress. We developed a computational pipeline that fuzes different network data sources, namely protein-protein interaction, gene ontology, phylogenetic, gene expression and pathway information, in order to predict the group function(s) of genes. The main steps of the pipeline are, first, the network integration of the different input sources, second, the clustering of the involved genes according to their similarity, and, third, the (re-)assignment of genes/proteins with unknown function. These steps are repeated until the algorithm converges into one or more final clusters/groups, which are additionally mapped onto KEGG pathways in order to biologically identify and interpret higher-level systemic/organismic functions. We successfully applied the pipeline to different groups of genes over-expressed in diseases of major interest in Qatar, such as type-2 diabetes, breast cancer and pancreatic cancer.

Cited by 0

Prediction of Protein Function Using Protein-Protein Interaction Data

Minghua Deng Kui, Kui Zhang, Shipra Mehta et al. · 2002

Assigning functions to novel proteins is one of the most important problems in the post-genomic era. Several approaches have been applied to this problem, including analyzing gene expression patterns, phylogenetic profiles, protein fusions and protein-protein interactions. We develop a novel approach that applies the theory of Markov random fields to infer a protein's functions using protein-protein interaction data and the functional annotations of its interaction protein partners. For each function of interest and a protein, we predict the probability that the protein has that function using Bayesian approaches. Unlike in other available approaches for protein annotation where a protein has or does not have a function of interest, we give a probability for having the function. This probability indicates how confident we are about the prediction. We apply our method to predict cellular functions (43 categories including a category "others") for yeast proteins defined in the Yeast Proteome Database(YPD), using the protein-protein interaction data from the Munich Information Center for Protein Sequences (MIPS, http://mips.gsf.de). We show that our approach outperforms other available methods for function prediction based on protein interaction data.

Cited by 0

Prediction of protein function using protein-protein interaction data

Minghua Deng, Kui Zhang, Shipra Mehta et al. · 2003

Assigning functions to novel proteins is one of the most important problems in the post-genomic era. Several approaches have been applied to this problem, including analyzing gene expression patterns, phylogenetic profiles, protein fusions and protein-protein interactions. We develop a novel approach that applies the theory of Markov random fields to infer a protein's functions using protein-protein interaction data and the functional annotations of its interaction protein partners. For each function of interest and a protein, we predict the probability that the protein has that function using Bayesian approaches. Unlike in other available approaches for protein annotation where a protein has or does not have a function of interest, we give a probability for having the function. This probability indicates how confident we are about the prediction. We apply our method to predict cellular functions (43 categories including a category "others") for yeast proteins defined in the Yeast Proteome Database (YPD), using the protein-protein interaction data from the Munich Information Center for Protein Sequences (MIPS, http://mips.gsf.de). We show that our approach outperforms other available methods for function prediction based on protein interaction data.

Cited by 72

Prediction of Protein Function Using Protein–Protein Interaction Data

Minghua Deng, Kui Zhang, Shipra Mehta et al. · Journal of Computational Biology · 2003

Assigning functions to novel proteins is one of the most important problems in the postgenomic era. Several approaches have been applied to this problem, including the analysis of gene expression patterns, phylogenetic profiles, protein fusions, and protein-protein interactions. In this paper, we develop a novel approach that employs the theory of Markov random fields to infer a protein's functions using protein-protein interaction data and the functional annotations of protein's interaction partners. For each function of interest and protein, we predict the probability that the protein has such function using Bayesian approaches. Unlike other available approaches for protein annotation in which a protein has or does not have a function of interest, we give a probability for having the function. This probability indicates how confident we are about the prediction. We employ our method to predict protein functions based on "biochemical function," "subcellular location," and "cellular role" for yeast proteins defined in the Yeast Proteome Database (YPD, www.incyte.com), using the protein-protein interaction data from the Munich Information Center for Protein Sequences (MIPS, mips.gsf.de). We show that our approach outperforms other available methods for function prediction based on protein interaction data. The supplementary data is available at www-hto.usc.edu/~msms/ProteinFunction.

Cited by 320

Protein Structure Prediction to Function Prediction Using AlphaFold

Kentaro Tomii · Seibutsu Butsuri · 2024

大量の蛋白質配列・構造データに基づく深層学習モデルであるAlphaFoldによって，多くの蛋白質の高精度な構造予測モデルが利用可能となっている．本稿では，AlphaFold/AlphaFold-Multimerを用いた蛋白質構造／複合体構造予測の背景や効果，利用の際の注意点などについて概説する．

Cited by 1Open access