SEARCH · PubMed Health

Results for “Protein function”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources

PubMed2002

Hydration, slaving and protein function.

Protein dynamics is crucial for protein function. Proteins in living systems are not isolated, but operate in networks and in a carefully regulated environment. Understanding the external control of protein dynamics is consequently important. Hydration and solvent viscosity are among the salient properties of the environment. Dehydrated proteins and proteins in a rigid environment do not function properly. It is consequently important to understand the effect of hydration and solvent viscosity in detail. We discuss experiments that separate the two effects. These experiments have predominantly been performed with wild-type horse and sperm whale myoglobin, using the binding of carbon monoxide over a broad range of temperatures as a tool. The experiments demonstrate that data taken only in the physiological temperature range are not sufficient to understand the effect of hydration and solvent on protein relaxation and function. While the actual data come from myoglobin, it is expected that the results apply to most or all globular proteins.

Animals↗

PubMed2000

Molecular genetic approaches to microtubule-associated protein function.

Protein function in vivo can be studied by deleting (knock-out) the gene that encodes it, and search for the consequences. This procedure involves different technologies, including recombinant DNA procedures, cell biology methods and histological and immunocytochemical analysis. In this work we have reviewed these procedures when they have been applied to ascertain the function of several microtubule-associated proteins. These proteins have been previously involved, through in vitro experiments, in having a role in the microtubule stabilization. Here, we will summarize the generation and characterization of different microtubule-associated protein knock-out mice. Special attention will be paid to MAP1B knock-out mice. Amongst the different MAPs knock-out mice these show the strongest phenotype, the most likely for being MAP1B, the MAP that is expressed earliest in neurogenesis. Molecular genetics could be considered as a valid and useful procedure to truly establish the in vivo functions of a protein, although it is necessary to be aware of possible artifacts such as the generation of some kinds of RNA alternative splicing. To avoid this the best strategy to be used must consider the deletion of the exon that contains the functional domains of the protein.

Animals↗

PubMed2003

Inference of protein function and protein linkages in Mycobacterium tuberculosis based on prokaryotic genome organization: a combined computational approach.

The genome of Mycobacterium tuberculosis was analyzed using recently developed computational approaches to infer protein function and protein linkages. We evaluated and employed a method to infer genes likely to belong to the same operon, as judged by the nucleotide distance between genes in the same genomic orientation, and combined this method with those of the Rosetta Stone, Phylogenetic Profile and conserved Gene Neighbor computational methods for the inference of protein function.

Bacterial Proteins↗

PubMed2002

Intramolecular interactions at protein surfaces and their impact on protein function.

Protein surfaces play a key role in the biological function of proteins. Consequently, structural features of protein surfaces are the basis for predicting function from structure. A well-established principle of binding by proteins is that ligands must compete with water and other small molecules to form interactions with protein surfaces. A less obvious issue, and the emphasis of this article, is that ligands must also compete with interactions among residues at protein surfaces. Results from structural surveys, a variety of experimental studies and computations suggest that intramolecular interactions are present at protein surfaces and that the energetics of these interactions can change when proteins bind to other molecules.

Animals↗

PubMed2005

Regulation of protein compartmentalization expands the diversity of protein function.

Proteins destined for the secretory pathway are translocated into the endoplasmic reticulum (ER) by signal sequences that vary widely in their functional properties. We have investigated whether differences in signal sequence function have been exploited for cellular benefit. A cytosolic form of the ER chaperone calreticulin was found to arise by an aborted translocation mechanism dependent on its signal sequence and factors in the ER lumen and membrane. A signal sequence that functions independently of these accessory translocation factors selectively eliminated cytosolic calreticulin. In vivo replacement of endogenous calreticulin with a constitutively translocated form influenced glucocorticoid receptor-mediated gene activation without compromising chaperone activity in the ER. Thus, in addition to its well-established ER lumenal functions, calreticulin has an independent role in the cytosol that depends critically on its inefficient compartmentalization. We propose that regulation of protein translocation represents a potentially general mechanism for generating diversity of protein function.

Amino Acid Sequence↗

PubMed2001

Assessment of prediction accuracy of protein function from protein--protein interaction data.

Functional prediction of open reading frames coded in the genome is one of the most important tasks in yeast genomics. Among a number of large-scale experiments for assigning certain functional classes to proteins, experiments determining protein-protein interaction are especially important because interacting proteins usually have the same function. Thus, it seems possible to predict the function of a protein when the function of its interacting partner is known. However, in vitro experiments often suffer from artifacts and a protein can often have multiple binding partners with different functions. We developed an objective prediction method that can systematically include the information of indirect interaction. Our method can predict the subcellular localization, the cellular role and the biochemical function of yeast proteins with accuracies of 72.7%, 63.6% and 52.7%, respectively. The prediction accuracy rises for proteins with more than three binding partners and thus we present the open prediction results for 16 such proteins.

Fungal Proteins↗

PubMed2004

Metal-assembled modular proteins: toward functional protein design.

Metal-assembled parallel helix-bundle proteins have been used to investigate electron transfer through alpha-helical structures. Fermi Golden Rule distance dependence of electron transfer rates was established in a family of designed metalloproteins, and the contribution of intrahelical hydrogen bonding to the matrix tunneling element was explored. The first steps toward the design of functional proteins using dynamic combinatorial assembly of alpha-helical structural elements are described.

Amino Acid Sequence↗

PubMed2003

Promoters can contribute to the elucidation of protein function.

Protein interaction maps are valuable tools in the understanding of the actions of genes and proteins in a real-life context. However, many proteins and genes interact functionally - without physical contact - via transcriptional coregulation of the genes, which is partially manifested in functional promoter structures called promoter modules. Here, the example of interleukin 9 (IL-9) induction by IL-1 and tumour necrosis factor (TNF) is used to demonstrate how comparative promoter analysis can identify a shared promoter module and thus indicate the crucial partners. This strategy can be used for genes from a small initial set of coregulated genes, such as can be derived from expression array data. The combination of expression and promoter analysis with proteomics should be considered as an important link in functional genomics.

Animals↗

PubMed2003

Prediction of protein function from protein sequence and structure.

The sequence of a genome contains the plans of the possible life of an organism, but implementation of genetic information depends on the functions of the proteins and nucleic acids that it encodes. Many individual proteins of known sequence and structure present challenges to the understanding of their function. In particular, a number of genes responsible for diseases have been identified but their specific functions are unknown. Whole-genome sequencing projects are a major source of proteins of unknown function. Annotation of a genome involves assignment of functions to gene products, in most cases on the basis of amino-acid sequence alone. 3D structure can aid the assignment of function, motivating the challenge of structural genomics projects to make structural information available for novel uncharacterized proteins. Structure-based identification of homologues often succeeds where sequence-alone-based methods fail, because in many cases evolution retains the folding pattern long after sequence similarity becomes undetectable. Nevertheless, prediction of protein function from sequence and structure is a difficult problem, because homologous proteins often have different functions. Many methods of function prediction rely on identifying similarity in sequence and/or structure between a protein of unknown function and one or more well-understood proteins. Alternative methods include inferring conservation patterns in members of a functionally uncharacterized family for which many sequences and structures are known. However, these inferences are tenuous. Such methods provide reasonable guesses at function, but are far from foolproof. It is therefore fortunate that the development of whole-organism approaches and comparative genomics permits other approaches to function prediction when the data are available. These include the use of protein-protein interaction patterns, and correlations between occurrences of related proteins in different organisms, as indicators of functional properties. Even if it is possible to ascribe a particular function to a gene product, the protein may have multiple functions. A fundamental problem is that function is in many cases an ill-defined concept. In this article we review the state of the art in function prediction and describe some of the underlying difficulties and successes.

Amino Acid Sequence↗

PubMed2005

Inference of protein function from protein structure.

Structural genomics has brought us three-dimensional structures of proteins with unknown functions. To shed light on such structures, we have developed ProKnow (http://www.doe-mbi.ucla.edu/Services/ProKnow/), which annotates proteins with Gene Ontology functional terms. The method extracts features from the protein such as 3D fold, sequence, motif, and functional linkages and relates them to function via the ProKnow knowledgebase of features, which links features to annotated functions via annotation profiles. Bayes' theorem is used to compute weights of the functions assigned, using likelihoods based on the extracted features. The description level of the assigned function is quantified by the ontology depth (from 1 = general to 9 = specific). Jackknife tests show approximately 89% correct assignments at ontology depth 1 and 40% at depth 9, with 93% coverage of 1507 distinct folded proteins. Overall, about 70% of the assignments were inferred correctly. This level of performance suggests that ProKnow is a useful resource in functional assessments of novel proteins.

Amino Acid Motifs↗

PubMed2005

Global analysis of protein function using protein microarrays.

Protein microarrays containing thousands of proteins arrayed at high density can be prepared and probed for a wide variety of activities, thereby allowing the large scale analysis of many proteins simultaneously. In addition to identifying the activities of many previously uncharacterized proteins, protein microarrays can reveal new activities of well-characterized proteins, thus providing new insights about the functions of these proteins. Below, we describe the construction and use of protein microarrays and their applications using yeast as a model system.

Animals↗

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↗

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↗

PubMed1999

ERG30, a VAP-33-related protein, functions in protein transport mediated by COPI vesicles.

Intracellular transport of newly synthesized and mature proteins via vesicles is controlled by a large group of proteins. Here we describe a ubiquitous rat protein-endoplasmic reticulum (ER) and Golgi 30-kD protein (ERG30)-which shares structural characteristics with VAP-33, a 33-kD protein from Aplysia californica which was shown to interact with the synaptic protein VAMP. The transmembrane topology of the 30-kD ERG30 corresponds to a type II integral membrane protein, whose cytoplasmic NH(2) terminus contains a predicted coiled-coil motif. We localized ERG30 to the ER and to pre-Golgi intermediates by biochemical and immunocytochemical methods. Consistent with a role in vesicular transport, anti-ERG30 antibodies specifically inhibit intra-Golgi transport in vitro, leading to significant accumulation of COPI-coated vesicles. It appears that ERG30 functions early in the secretory pathway, probably within the Golgi and between the Golgi and the ER.

Amino Acid Sequence↗

PubMed2003

Prediction of protein function using protein-protein interaction data.

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.

Bayes Theorem↗

PubMed2002

Prediction of protein function using protein-protein interaction data.

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.

Amino Acid Sequence↗

PubMed2003

A microscope-based screening platform for large-scale functional protein analysis in intact cells.

A modular microscope-based screening platform, with applications in large-scale analysis of protein function in intact cells is described. It includes automated sample preparation, image acquisition, data management and analysis, and the genome-wide automated retrieval of bioinformatic information. The modular nature of the system ensures that it is rapidly adaptable to new biological questions or sets of proteins. Two automated functional assays addressing protein secretion and the integrity of the Golgi complex were developed and tested. This shows the potential of the system in large-scale, cell-based functional proteomic projects.

Automation↗

PubMed2005

Functional protein microarrays: just how functional are they?

Arrays of immobilized proteins have been developed for the discovery and characterization of protein functions ranging from molecular recognition to enzymatic activity. The success of these applications is highly dependent upon the maintenance of protein structure and function while in an immobilized state - a largely untested hypothesis. However, the immobilization of functional proteins is not without precedent. Active enzymes have been successfully immobilized for industrial applications for several decades. Furthermore, a survey of recent protein microarray literature reveals that an even wider range of proteins can maintain 'proper' function while immobilized. These reports help to validate the functionality of so-called functional protein microarrays.

DNA-Binding Proteins↗