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Results for “Protein function prediction”

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At least 19 recordsLinked to original sources

PubMed2005

Protein function prediction using the Protein Link EXplorer (PLEX).

UNLABELLED: We introduce the Protein Link EXplorer (PLEX), a web-based environment that allows the construction of a phylogenetic profile for any given amino acid sequence, and its comparison with profiles of approximately 350,000 predicted genes from 89 genomes, as a means of interactively identifying functionally linked genes and predicting protein function. PLEX can be searched iteratively and also enables searches for chromosomal gene neighbors and Rosetta Stone linkages. PLEX search results are accompanied by quantitative estimates of linkage confidence, enabling users to take advantage of coinheritance, operon and gene fusion-based methods for inferring gene function and reconstructing cellular systems and pathways. AVAILABILITY: http://bioinformatics.icmb.utexas.edu/plex

Algorithms↗

PubMed2003

Assessment of the reliability of protein-protein interactions and protein function prediction.

As more and more high-throughput protein-protein interaction data are collected, the task of estimating the reliability of different data sets becomes increasingly important. In this paper, we present our study of two groups of protein-protein interaction data, the physical interaction data and the protein complex data, and estimate the reliability of these data sets using three different measurements: (1) the distribution of gene expression correlation coefficients, (2) the reliability based on gene expression correlation coefficients, and (3) the accuracy of protein function predictions. We develop a maximum likelihood method to estimate the reliability of protein interaction data sets according to the distribution of correlation coefficients of gene expression profiles of putative interacting protein pairs. The results of the three measurements are consistent with each other. The MIPS protein complex data have the highest mean gene expression correlation coefficients (0.256) and the highest accuracy in predicting protein functions (70% sensitivity and specificity), while Ito's Yeast two-hybrid data have the lowest mean (0.041) and the lowest accuracy (15% sensitivity and specificity). Uetz's data are more reliable than Ito's data in all three measurements, and the TAP protein complex data are more reliable than the HMS-PCI data in all three measurements as well. The complex data sets generally perform better in function predictions than do the physical interaction data sets. Proteins in complexes are shown to be more highly correlated in gene expression. The results confirm that the components of a protein complex can be assigned to functions that the complex carries out within a cell. There are three interaction data sets different from the above two groups: the genetic interaction data, the in-silico data and the syn-express data. Their capability of predicting protein functions generally falls between that of the Y2H data and that of the MIPS protein complex data. The supplementary information is available at the following Web site: http://www-hto.usc.edu/-msms/AssessInteraction/.

Computational Biology↗

PubMed2006

Diffusion kernel-based logistic regression models for protein function prediction.

Assigning functions to unknown proteins is one of the most important problems in proteomics. Several approaches have used protein-protein interaction data to predict protein functions. We previously developed a Markov random field (MRF) based method to infer a protein's functions using protein-protein interaction data and the functional annotations of its protein interaction partners. In the original model, only direct interactions were considered and each function was considered separately. In this study, we develop a new model which extends direct interactions to all neighboring proteins, and one function to multiple functions. The goal is to understand a protein's function based on information on all the neighboring proteins in the interaction network. We first developed a novel kernel logistic regression (KLR) method based on diffusion kernels for protein interaction networks. The diffusion kernels provide means to incorporate all neighbors of proteins in the network. Second, we identified a set of functions that are highly correlated with the function of interest, referred to as the correlated functions, using the chi-square test. Third, the correlated functions were incorporated into our new KLR model. Fourth, we extended our model by incorporating multiple biological data sources such as protein domains, protein complexes, and gene expressions by converting them into networks. We showed that the KLR approach of incorporating all protein neighbors significantly improved the accuracy of protein function predictions over the MRF model. The incorporation of multiple data sets also improved prediction accuracy. The prediction accuracy is comparable to another protein function classifier based on the support vector machine (SVM), using a diffusion kernel. The advantages of the KLR model include its simplicity as well as its ability to explore the contribution of neighbors to the functions of proteins of interest.

Databases, Protein↗

PubMed2004

An integrated probabilistic model for functional prediction of proteins.

We develop an integrated probabilistic model to combine protein physical interactions, genetic interactions, highly correlated gene expression networks, protein complex data, and domain structures of individual proteins to predict protein functions. The model is an extension of our previous model for protein function prediction based on Markovian random field theory. The model is flexible in that other protein pairwise relationship information and features of individual proteins can be easily incorporated. Two features distinguish the integrated approach from other available methods for protein function prediction. One is that the integrated approach uses all available sources of information with different weights for different sources of data. It is a global approach that takes the whole network into consideration. The second feature is that the posterior probability that a protein has the function of interest is assigned. The posterior probability indicates how confident we are about assigning the function to the protein. We apply our integrated approach to predict functions of yeast proteins based upon MIPS protein function classifications and upon the interaction networks based on MIPS physical and genetic interactions, gene expression profiles, tandem affinity purification (TAP) protein complex data, and protein domain information. We study the recall and precision of the integrated approach using different sources of information by the leave-one-out approach. In contrast to using MIPS physical interactions only, the integrated approach combining all of the information increases the recall from 57% to 87% when the precision is set at 57%-an increase of 30%.

Computational Biology↗

PubMed2006

Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions.

MOTIVATION: Most approaches in predicting protein function from protein-protein interaction data utilize the observation that a protein often share functions with proteins that interacts with it (its level-1 neighbours). However, proteins that interact with the same proteins (i.e. level-2 neighbours) may also have a greater likelihood of sharing similar physical or biochemical characteristics. We speculate that functional similarity between a protein and its neighbours from the two different levels arise from two distinct forms of functional association, and a protein is likely to share functions with its level-1 and/or level-2 neighbours. We are interested in finding out how significant is functional association between level-2 neighbours and how they can be exploited for protein function prediction. RESULTS: We made a statistical study on recent interaction data and observed that functional association between level-2 neighbours is clearly observable. A substantial number of proteins are observed to share functions with level-2 neighbours but not with level-1 neighbours. We develop an algorithm that predicts the functions of a protein in two steps: (1) assign a weight to each of its level-1 and level-2 neighbours by estimating its functional similarity with the protein using the local topology of the interaction network as well as the reliability of experimental sources and (2) scoring each function based on its weighted frequency in these neighbours. Using leave-one-out cross validation, we compare the performance of our method against that of several other existing approaches and show that our method performs relatively well.

Algorithms↗

PubMed2005

Protein function prediction via graph kernels.

MOTIVATION: Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid motifs, interaction partners or phylogenetic profiles. We present a new approach that combines sequential, structural and chemical information into one graph model of proteins. We predict functional class membership of enzymes and non-enzymes using graph kernels and support vector machine classification on these protein graphs. RESULTS: Our graph model, derivable from protein sequence and structure only, is competitive with vector models that require additional protein information, such as the size of surface pockets. If we include this extra information into our graph model, our classifier yields significantly higher accuracy levels than the vector models. Hyperkernels allow us to select and to optimally combine the most relevant node attributes in our protein graphs. We have laid the foundation for a protein function prediction system that integrates protein information from various sources efficiently and effectively. AVAILABILITY: More information available via www.dbs.ifi.lmu.de/Mitarbeiter/borgwardt.html.

Algorithms↗

PubMed2006

Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity.

Protein sequence contains clues to its function. Functional prediction from sequence presents a challenge particularly for proteins that have low or no sequence similarity to proteins of known function. Recently, machine learning methods have been explored for predicting functional class of proteins from sequence-derived properties independent of sequence similarity, which showed promising potential for low- and non-homologous proteins. These methods can thus be explored as potential tools to complement alignment- and clustering-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying difficulties in using machine learning methods for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented, which need to be interpreted with caution as they are dependent on such factors as datasets used and choice of parameters.

Algorithms↗

PubMed2003

Global protein function prediction from protein-protein interaction networks.

Determining protein function is one of the most challenging problems of the post-genomic era. The availability of entire genome sequences and of high-throughput capabilities to determine gene coexpression patterns has shifted the research focus from the study of single proteins or small complexes to that of the entire proteome. In this context, the search for reliable methods for assigning protein function is of primary importance. There are various approaches available for deducing the function of proteins of unknown function using information derived from sequence similarity or clustering patterns of co-regulated genes, phylogenetic profiles, protein-protein interactions (refs. 5-8 and Samanta, M.P. and Liang, S., unpublished data), and protein complexes. Here we propose the assignment of proteins to functional classes on the basis of their network of physical interactions as determined by minimizing the number of protein interactions among different functional categories. Function assignment is proteome-wide and is determined by the global connectivity pattern of the protein network. The approach results in multiple functional assignments, a consequence of the existence of multiple equivalent solutions. We apply the method to analyze the yeast Saccharomyces cerevisiae protein-protein interaction network. The robustness of the approach is tested in a system containing a high percentage of unclassified proteins and also in cases of deletion and insertion of specific protein interactions.

Algorithms↗

PubMed2005

Pattern recognition methods for protein functional site prediction.

Protein functional site prediction is closely related to drug design, hence to public health. In order to save the cost and the time spent on identifying the functional sites in sequenced proteins in biology laboratory, computer programs have been widely used for decades. Many of them are implemented using the state-of-the-art pattern recognition algorithms, including decision trees, neural networks and support vector machines. Although the success of this effort has been obvious, advanced and new algorithms are still under development for addressing some difficult issues. This review will go through the major stages in developing pattern recognition algorithms for protein functional site prediction and outline the future research directions in this important area.

Algorithms↗

PubMed2005

FrankSum: new feature selection method for protein function prediction.

In the study of in silico functional genomics, improving the performance of protein function prediction is the ultimate goal for identifying proteins associated with defined cellular functions. The classical prediction approach is to employ pairwise sequence alignments. However this method often faces difficulties when no statistically significant homologous sequences are identified. An alternative way is to predict protein function from sequence-derived features using machine learning. In this case the choice of possible features which can be derived from the sequence is of vital importance to ensure adequate discrimination to predict function. In this paper we have successfully selected biologically significant features for protein function prediction. This was performed using a new feature selection method (FrankSum) that avoids data distribution assumptions, uses a data independent measurement (p-value) within the feature, identifies redundancy between features and uses an appropriate ranking criterion for feature selection. We have shown that classifiers generated from features selected by FrankSum outperforms classifiers generated from full feature sets, randomly selected features and features selected from the Wrapper method. We have also shown the features are concordant across all species and top ranking features are biologically informative. We conclude that feature selection is vital for successful protein function prediction and FrankSum is one of the feature selection methods that can be applied successfully to such a domain.

Amino Acid Sequence↗

PubMed2005

Feature selection and the class imbalance problem in predicting protein function from sequence.

When the standard approach to predict protein function by sequence homology fails, other alternative methods can be used that require only the amino acid sequence for predicting function. One such approach uses machine learning to predict protein function directly from amino acid sequence features. However, there are two issues to consider before successful functional prediction can take place: identifying discriminatory features, and overcoming the challenge of a large imbalance in the training data. We show that by applying feature subset selection followed by undersampling of the majority class, significantly better support vector machine (SVM) classifiers are generated compared with standard machine learning approaches. As well as revealing that the features selected could have the potential to advance our understanding of the relationship between sequence and function, we also show that undersampling to produce fully balanced data significantly improves performance. The best discriminating ability is achieved using SVMs together with feature selection and full undersampling; this approach strongly outperforms other competitive learning algorithms. We conclude that this combined approach can generate powerful machine learning classifiers for predicting protein function directly from sequence.

Algorithms↗

PubMed2006

A traveling salesman approach for predicting protein functions.

BACKGROUND: Protein-protein interaction information can be used to predict unknown protein functions and to help study biological pathways. RESULTS: Here we present a new approach utilizing the classic Traveling Salesman Problem to study the protein-protein interactions and to predict protein functions in budding yeast Saccharomyces cerevisiae. We apply the global optimization tool from combinatorial optimization algorithms to cluster the yeast proteins based on the global protein interaction information. We then use this clustering information to help us predict protein functions. We use our algorithm together with the direct neighbor algorithm 1 on characterized proteins and compare the prediction accuracy of the two methods. We show our algorithm can produce better predictions than the direct neighbor algorithm, which only considers the immediate neighbors of the query protein. CONCLUSION: Our method is a promising one to be used as a general tool to predict functions of uncharacterized proteins and a successful sample of using computer science knowledge and algorithms to study biological problems.

Journal Article↗

PubMed2026

ProtPen Combines Sequence- and Structure-based Approaches to Facilitate Protein Function Predictions on a Proteome-wide Scale.

Proteins of unknown function represent a significant gap in our understanding of biological processes, encompassing large portions of the proteomes of many organisms, especially prokaryotes. Addressing this gap is critical to understanding the biology and pathogenicity of such organisms. We introduce ProtPen, an open-source pipeline that facilitates protein function prediction by combining eggNOG-mapper for sequence-based annotation with Foldseek for rapid structural similarity searches using AlphaFold-predicted protein structures. Annotation results from both tools are merged and enriched with UniProt metadata to produce a comprehensive output suitable for downstream analysis. The pipeline requires only a FASTA input file with UniProt identifiers, and is designed to analyze data sets on the scale of whole proteomes. Benchmarking on a curated data set of well-characterized Pseudomonas aeruginosa proteins demonstrated an annotation accuracy of >90%, and highlighted the complementarity of sequence- and structure-based methods. Further evaluation of ProtPen included its application to biologically relevant data sets, comprising proteins of unknown function that exhibited significant differential abundances in a proteomics data set of P. aeruginosa, and uncharacterized glycoproteins from Haloferax volcanii. ProtPen is readily extensible to incorporate additional protein function prediction tools. In summary, this pipeline facilitates the systemwide annotation of proteins of unknown function from proteomic data sets and whole proteomes.

Pseudomonas aeruginosa↗

PubMed2002

Future directions in protein function prediction.

New directions in computational methods for the prediction of protein function are discussed. THEMATICS, a method for the location and characterization of the active sites of enzymes, is featured. THEMATICS, for Theoretical Microscopic Titration Curves, is based on well-established finite-difference Poisson-Boltzmann methods for computing the electric field function of a protein. THEMATICS requires only the structure of the subject protein and thus may be applied to proteins that bear no similarity in structure or sequence to any previously characterized protein. The unique features of catalytic sites in proteins are discussed. Discussion of the chemical basis for the predictive powers of THEMATICS is featured in this paper. Some results are given for three illustrative examples: HIV-1 protease, human apurinic/apyrimidinic endonuclease, and human adenosine kinase.

Adenosine Kinase↗

PubMed2005

Beyond annotation transfer by homology: novel protein-function prediction methods to assist drug discovery.

Every entirely sequenced genome reveals 100 s to 1000 s of protein sequences for which the only annotation available is 'hypothetical protein'. Thus, in the human genome and in the genomes of pathogenic agents there could be 1000 s of potential, unexplored drug targets. Computational prediction of protein function can play a role in studying these targets. We shall review the challenges, research approaches and recently developed tools in the field of computational function-prediction and we will discuss the ways these issues can change the process of drug discovery.

Computational Biology↗

PubMed2006

Faster and more accurate global protein function assignment from protein interaction networks using the MFGO algorithm.

MOTIVATION: Predicting protein function accurately is an important issue in the post-genomic era. To achieve this goal, several approaches have been proposed deduce the function of unclassified proteins through sequence similarity, co-expression profiles, and other information. Among these methods, the global optimization method (GOM) is an interesting and powerful tool that assigns functions to unclassified proteins based on their positions in a physical interactions network [Vazquez, A., Flammini, A., Maritan, A. and Vespignani, A. (2003) Global protein function prediction from protein-protein interaction networks, Nat. Biotechnol., 21, 697-700]. To boost both the accuracy and speed of GOM, a new prediction method, MFGO (modified and faster global optimization) is presented in this paper, which employs local optimal repetition method to reduce calculation time, and takes account of topological structure information to achieve a more accurate prediction. CONCLUSION: On four proteins interaction datasets, including Vazquez dataset, YP dataset, DIP-core dataset, and SPK dataset, MFGO was tested and compared with the popular MR (majority rule) and GOM methods. Experimental results confirm MFGO's improvement on both speed and accuracy. Especially, MFGO method has a distinctive advantage in accurately predicting functions for proteins with few neighbors. Moreover, the robustness of the approach was validated both in a dataset containing a high percentage of unknown proteins and a disturbed dataset through random insertion and deletion. The analysis shows that a moderate amount of misplaced interactions do not preclude a reliable function assignment.

Algorithms↗

PubMed2004

Kernel-based data fusion and its application to protein function prediction in yeast.

Kernel methods provide a principled framework in which to represent many types of data, including vectors, strings, trees and graphs. As such, these methods are useful for drawing inferences about biological phenomena. We describe a method for combining multiple kernel representations in an optimal fashion, by formulating the problem as a convex optimization problem that can be solved using semidefinite programming techniques. The method is applied to the problem of predicting yeast protein functional classifications using a support vector machine (SVM) trained on five types of data. For this problem, the new method performs better than a previously-described Markov random field method, and better than the SVM trained on any single type of data.

Algorithms↗

PubMed2005

Protein function prediction using local 3D templates.

The prediction of a protein's function from its 3D structure is becoming more and more important as the worldwide structural genomics initiatives gather pace and continue to solve 3D structures, many of which are of proteins of unknown function. Here, we present a methodology for predicting function from structure that shows great promise. It is based on 3D templates that are defined as specific 3D conformations of small numbers of residues. We use four types of template, covering enzyme active sites, ligand-binding residues, DNA-binding residues and reverse templates. The latter are templates generated from the target structure itself and scanned against a representative subset of all known protein structures. Together, the templates provide a fairly thorough coverage of the known structures and ensure that if there is a match to a known structure it is unlikely to be missed. A new scoring scheme provides a highly sensitive means of discriminating between true positive and false positive template matches. In all, the methodology provides a powerful new tool for function prediction to complement those already in use.

Models, Molecular↗