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Results for “functional inference”

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At least 19 recordsLinked to original sources

PubMed2004

EFICAz: a comprehensive approach for accurate genome-scale enzyme function inference.

EFICAz (Enzyme Function Inference by Combined Approach) is an automatic engine for large-scale enzyme function inference that combines predictions from four different methods developed and optimized to achieve high prediction accuracy: (i) recognition of functionally discriminating residues (FDRs) in enzyme families obtained by a Conservation-controlled HMM Iterative procedure for Enzyme Family classification (CHIEFc), (ii) pairwise sequence comparison using a family specific Sequence Identity Threshold, (iii) recognition of FDRs in Multiple Pfam enzyme families, and (iv) recognition of multiple Prosite patterns of high specificity. For FDR (i.e. conserved positions in an enzyme family that discriminate between true and false members of the family) identification, we have developed an Evolutionary Footprinting method that uses evolutionary information from homofunctional and heterofunctional multiple sequence alignments associated with an enzyme family. The FDRs show a significant correlation with annotated active site residues. In a jackknife test, EFICAz shows high accuracy (92%) and sensitivity (82%) for predicting four EC digits in testing sequences that are <40% identical to any member of the corresponding training set. Applied to Escherichia coli genome, EFICAz assigns more detailed enzymatic function than KEGG, and generates numerous novel predictions.

Amino Acid Sequence↗

PubMed2003

Structure-based functional inference in structural genomics.

The dramatically increasing number of new protein sequences arising from genomics and proteomics requires the need for methods to rapidly and reliably infer the molecular and cellular functions of these proteins. One such approach, structural genomics, aims to delineate the total repertoire of protein folds in nature, thereby providing three-dimensional folding patterns for all proteins and to infer molecular functions of the proteins based on the combined information of structures and sequences. The goal of obtaining protein structures on a genomic scale has motivated the development of high throughput technologies and protocols for macromolecular structure determination that have begun to produce structures at a greater rate than previously possible. These new structures have revealed many unexpected functional inferences and evolutionary relationships that were hidden at the sequence level. Here, we present samples of structures determined at Berkeley Structural Genomics Center and collaborators' laboratories to illustrate how structural information provides and complements sequence information to deduce the functional inferences of proteins with unknown molecular functions. Two of the major premises of structural genomics are to discover a complete repertoire of protein folds in nature and to find molecular functions of the proteins whose functions are not predicted from sequence comparison alone. To achieve these objectives on a genomic scale, new methods, protocols, and technologies need to be developed by multi-institutional collaborations worldwide. As part of this effort, the Protein Structure Initiative has been launched in the United States (PSI; www.nigms.nih.gov/funding/psi.html). Although infrastructure building and technology development are still the main focus of structural genomics programs, a considerable number of protein structures have already been produced, some of them coming directly out of semiautomated structure determination pipelines. The Berkeley Structural Genomics Center (BSGC) has focused on the proteins of Mycoplasma or their homologues from other organisms as its structural genomics targets because of the minimal genome size of the Mycoplasmas as well as their relevance to human and animal pathogenicity (http://www.strgen.org). Here we present several protein examples encompassing a spectrum of functional inferences obtainable from their three-dimensional structures in five situations, where the inferences are new and testable, and are not predictable from protein sequence information alone.

Adenosine Triphosphate↗

PubMed2004

Inferred functions of "novel" genes identified in fibroblasts chondroinduced by demineralized bone.

Little is known about the cellular mechanisms that control postnatal chondrocyte differentiation. As a first step towards identifying those mechanisms, gene expression shifts were characterized in an in vitro model of chondroinduction. In previous studies, several functional classes of genes (cytoskeletal and matrix elements, cell adhesion proteins, peptide growth factors, and signal transduction proteins) were found to be altered in human dermal fibroblasts (hDFs) cultured in porous collagen sponges with chondroinductive demineralized bone powder (DBP) for 3 days. In addition, a number of "novel" sequences were identified. In this study, molecular techniques were combined with computational methods to characterize those sequences. Gene expression of all 10 novel sequences tested was found in hDFs by RT-PCR. The sequences were compared to the human genome, and their cellular functions were inferred from genes that mapped to the same chromosomal coordinates. Only one of the novel sequences contained a protein-coding region (kinesin superfamily protein 26B). The others contained 3' untranslated (osteonectin, alpha-V integrin, RAP2B) or other untranslated regions (PTPN21, GAS6) of mRNAs. The cellular functions of the DBP-regulated genes described in this study fall into similar categories as those previously identified. These results provide new details on the cellular response of hDFs exposed to DBP.

3' Untranslated Regions↗

PubMed2006

Inferring functional linkages between proteins from evolutionary scenarios.

Identifying potential protein interactions is of great importance in understanding the topologies of cellular networks, which is much needed and valued in current systematic biological studies. The development of our computational methods to predict protein-protein interactions have been spurred on by the massive sequencing efforts of the genomic revolution. Among these methods is phylogenetic profiling, which assumes that proteins under similar evolutionary pressures with similar phylogenetic profiles might be functionally related. Here, we introduce a method for inferring functional linkages between proteins from their evolutionary scenarios. The term evolutionary scenario refers to a series of events that occurred in speciation over time, which can be reconstructed given a phylogenetic profile and a species tree. Common evolutionary pressures on two proteins can then be inferred by comparing their evolutionary scenarios, which is a direct indication of their functional linkage. This scenario method has proven to have better performance compared with the classical phylogenetic profile method, when applied to the same test set. In addition, predicted results of the two methods are found to be fairly different, suggesting the possibility of merging them in order to achieve a better performance. We analyzed the influence of the topology of the phylogenetic tree on the performance of this method, and found it to be robust to perturbations in the topology of the tree. However, if a completely random tree is incorporated, performance will decline significantly. The evolutionary scenario method was used for inferring functional linkages in 67 species, and 40,006 linkages were predicted. We examine our prediction for budding yeast and find that almost all predicted linkages are supported by further evidence.

Computational Biology↗

PubMed2006

LC-MS/MS based proteomic analysis and functional inference of hypothetical proteins in Desulfovibrio vulgaris.

High efficiency capillary liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to examine the proteins extracted from Desulfovibrio vulgaris cells across six treatment conditions. While our previous study provided a proteomic overview of the cellular metabolism based on proteins with known functions [W. Zhang, M.A. Gritsenko, R.J. Moore, D.E. Culley, L. Nie, K. Petritis, E.F. Strittmatter, D.G. Camp II, R.D. Smith, F.J. Brockman, A proteomic view of the metabolism in Desulfovibrio vulgaris determined by liquid chromatography coupled with tandem mass spectrometry, Proteomics 6 (2006) 4286-4299], this study describes the global detection and functional inference for hypothetical D. vulgaris proteins. Using criteria that a given peptide of a protein is identified from at least two out of three independent LC-MS/MS measurements and that for any protein at least two different peptides are identified among the three measurements, 129 open reading frames (ORFs) originally annotated as hypothetical proteins were found to encode expressed proteins. Functional inference for the conserved hypothetical proteins was performed by a combination of several non-homology based methods: genomic context analysis, phylogenomic profiling, and analysis of a combination of experimental information, including peptide detection in cells grown under specific culture conditions and cellular location of the proteins. Using this approach we were able to assign possible functions to 20 conserved hypothetical proteins. This study demonstrated that a combination of proteomics and bioinformatics methodologies can provide verification of the expression of hypothetical proteins and improve genome annotation.

Amino Acid Sequence↗

PubMed2006

Leveraging enzyme structure-function relationships for functional inference and experimental design: the structure-function linkage database.

The study of mechanistically diverse enzyme superfamilies-collections of enzymes that perform different overall reactions but share both a common fold and a distinct mechanistic step performed by key conserved residues-helps elucidate the structure-function relationships of enzymes. We have developed a resource, the structure-function linkage database (SFLD), to analyze these structure-function relationships. Unique to the SFLD is its hierarchical classification scheme based on linking the specific partial reactions (or other chemical capabilities) that are conserved at the superfamily, subgroup, and family levels with the conserved structural elements that mediate them. We present the results of analyses using the SFLD in correcting misannotations, guiding protein engineering experiments, and elucidating the function of recently solved enzyme structures from the structural genomics initiative. The SFLD is freely accessible at http://sfld.rbvi.ucsf.edu.

Animals↗

PubMed2003

Inferring functional relationships of proteins from local sequence and spatial surface patterns.

We describe a novel approach for inferring functional relationship of proteins by detecting sequence and spatial patterns of protein surfaces. Well-formed concave surface regions in the form of pockets and voids are examined to identify similarity relationship that might be directly related to protein function. We first exhaustively identify and measure analytically all 910,379 surface pockets and interior voids on 12,177 protein structures from the Protein Data Bank. The similarity of patterns of residues forming pockets and voids are then assessed in sequence, in spatial arrangement, and in orientational arrangement. Statistical significance in the form of E and p-values is then estimated for each of the three types of similarity measurements. Our method is fully automated without human intervention and can be used without input of query patterns. It does not assume any prior knowledge of functional residues of a protein, and can detect similarity based on surface patterns small and large. It also tolerates, to some extent, conformational flexibility of functional sites. We show with examples that this method can detect functional relationship with specificity for members of the same protein family and superfamily, as well as remotely related functional surfaces from proteins of different fold structures. We envision that this method can be used for discovering novel functional relationship of protein surfaces, for functional annotation of protein structures with unknown biological roles, and for further inquiries on evolutionary origins of structural elements important for protein function.

Amino Acid Sequence↗

PubMed2006

Functional inference of the methylenetetrahydrofolate reductase 677C > T and 1298A > C polymorphisms from a large-scale epidemiological study.

Two functional single nucleotide polymorphisms, 677C > T and 1298A > C have been described for the methylenetetrahydrofolate (MTHFR) gene. Both are associated with reduced enzyme activity in vitro. For the 677T, but not the 1298C allele, significantly lower serum folate and higher plasma total homocysteine (tHcy) have been reported. We genotyped 10,034 middle-aged (50-64 years old) subjects and measured serum folate and tHcy. Within strata of 677 genotypes, 1,298 genotypes had significantly different serum folate and tHcy (P < or = 0.03 for all comparisons). Each additional 1298C allele reduced mean serum folate and increased mean tHcy, by (on average) 4.5 and 3.0%, respectively. In comparison, within strata of 1,298 genotypes, the increase from no, to one 677T-allele reduced serum folate and increased tHcy by, 7.1 and 6.3%, respectively. Lowest serum folate and highest tHcy level was found for the 677TT/1298AA genotype. The difference in tHcy was significantly larger at low folate than at high folate when genotypes 677TT/1298AA and 677CT/1298AA, 677CT/1298AC and 677CC/1298AC, and genotypes 677CT/1298AC and 677CT/1298AA were compared. We interpreted these data in the context of a model of the MTHFR enzyme that describes the enzyme as a dimer that mainly exist in six different configurations. The model reconciled the observed phenotypic effects of the 677/1,298 combination genotypes with previous in vitro measurements, and identified enzyme configurations that are sensitive to low folate levels. In conclusion, this report demonstrates functional inference of the MTHFR 677 C > T and 1,298 A > C polymorphisms from a large-scale epidemiological study.

Adenine Nucleotides↗

PubMed2025

Exact model-free function inference using uniform marginal counts for null population.

MOTIVATION: Recognizing cause-effect relationships is a fundamental inquiry in science. However, current causal inference methods often focus on directionality but not statistical significance. A ramification is chance patterns of uneven marginal distributions achieving a perfect directionality score. RESULTS: To overcome such issues, we design the uniform exact function test with continuity correction (UEFTC) to detect functional dependency between two discrete random variables. The null hypothesis is two variables being statistically independent. Unique from related tests whose null populations use observed marginals, we define the null population by an embedded uniform square. We also present a fast algorithm to accomplish the test. On datasets with ground truth, the UEFTC exhibits accurate directionality, low biases, and robust statistical behavior over alternatives. We found nonmonotonic response by gene TCB2 to beta-estradiol dosage in engineered yeast strains. In the human duodenum with environmental enteric dysfunction, we discovered pathology-dependent anti-co-methylated CpG sites in the vicinity of genes POU2AF1 and LSP1; such activity represents orchestrated methylation and demethylation along the same gene, unreported previously. The UEFTC has much improved effectiveness in exact model-free function inference for data-driven knowledge discovery. AVAILABILITY AND IMPLEMENTATION: An open-source R package "UniExactFunTest" implementing the presented uniform exact function tests is available via CRAN at doi: 10.32614/CRAN.package.UniExactFunTest. Computer code to reproduce figures can be found in supplementary file "UEFTC-main.zip."

Algorithms↗

PubMed2001

Exploiting big biology: integrating large-scale biological data for function inference.

The amount of data produced by molecular biologists is growing at an exponential rate. Some of the fastest growing sets of data are measurements of gene expression, comparable in quantity only to gene sequences and the vast biological literature. Both gene expression data and sequence data offer hints as to the functions of thousands of newly discovered genes, but neither give complete answers. Therefore, much effort is being focused on integrating these large data sets and combining them with all available functional data to draw inferences about the functions of uncharacterised genes. This review discusses the most pertinent functional data for genome-wide functional inference and describes several methods by which these disparate data types are being integrated.

Data Collection↗

PubMed2003

ELISA: structure-function inferences based on statistically significant and evolutionarily inspired observations.

UNLABELLED: The problem of functional annotation based on homology modeling is primary to current bioinformatics research. Researchers have noted regularities in sequence, structure and even chromosome organization that allow valid functional cross-annotation. However, these methods provide a lot of false negatives due to limited specificity inherent in the system. We want to create an evolutionarily inspired organization of data that would approach the issue of structure-function correlation from a new, probabilistic perspective. Such organization has possible applications in phylogeny, modeling of functional evolution and structural determination. ELISA (Evolutionary Lineage Inferred from Structural Analysis, http://romi.bu.edu/elisa) is an online database that combines functional annotation with structure and sequence homology modeling to place proteins into sequence-structure-function "neighborhoods". The atomic unit of the database is a set of sequences and structural templates that those sequences encode. A graph that is built from the structural comparison of these templates is called PDUG (protein domain universe graph). We introduce a method of functional inference through a probabilistic calculation done on an arbitrary set of PDUG nodes. Further, all PDUG structures are mapped onto all fully sequenced proteomes allowing an easy interface for evolutionary analysis and research into comparative proteomics. ELISA is the first database with applicability to evolutionary structural genomics explicitly in mind. AVAILABILITY: The database is available at http://romi.bu.edu/elisa.

Amino Acid Sequence↗

PubMed2004

Ancestral sequence reconstruction in primate mitochondrial DNA: compositional bias and effect on functional inference.

Reconstruction of ancestral DNA and amino acid sequences is an important means of inferring information about past evolutionary events. Such reconstructions suggest changes in molecular function and evolutionary processes over the course of evolution and are used to infer adaptation and convergence. Maximum likelihood (ML) is generally thought to provide relatively accurate reconstructed sequences compared to parsimony, but both methods lead to the inference of multiple directional changes in nucleotide frequencies in primate mitochondrial DNA (mtDNA). To better understand this surprising result, as well as to better understand how parsimony and ML differ, we constructed a series of computationally simple "conditional pathway" methods that differed in the number of substitutions allowed per site along each branch, and we also evaluated the entire Bayesian posterior frequency distribution of reconstructed ancestral states. We analyzed primate mitochondrial cytochrome b (Cyt-b) and cytochrome oxidase subunit I (COI) genes and found that ML reconstructs ancestral frequencies that are often more different from tip sequences than are parsimony reconstructions. In contrast, frequency reconstructions based on the posterior ensemble more closely resemble extant nucleotide frequencies. Simulations indicate that these differences in ancestral sequence inference are probably due to deterministic bias caused by high uncertainty in the optimization-based ancestral reconstruction methods (parsimony, ML, Bayesian maximum a posteriori). In contrast, ancestral nucleotide frequencies based on an average of the Bayesian set of credible ancestral sequences are much less biased. The methods involving simpler conditional pathway calculations have slightly reduced likelihood values compared to full likelihood calculations, but they can provide fairly unbiased nucleotide reconstructions and may be useful in more complex phylogenetic analyses than considered here due to their speed and flexibility. To determine whether biased reconstructions using optimization methods might affect inferences of functional properties, ancestral primate mitochondrial tRNA sequences were inferred and helix-forming propensities for conserved pairs were evaluated in silico. For ambiguously reconstructed nucleotides at sites with high base composition variability, ancestral tRNA sequences from Bayesian analyses were more compatible with canonical base pairing than were those inferred by other methods. Thus, nucleotide bias in reconstructed sequences apparently can lead to serious bias and inaccuracies in functional predictions.

Animals↗

PubMed2006

Structure-based function inference using protein family-specific fingerprints.

We describe a method to assign a protein structure to a functional family using family-specific fingerprints. Fingerprints represent amino acid packing patterns that occur in most members of a family but are rare in the background, a nonredundant subset of PDB; their information is additional to sequence alignments, sequence patterns, structural superposition, and active-site templates. Fingerprints were derived for 120 families in SCOP using Frequent Subgraph Mining. For a new structure, all occurrences of these family-specific fingerprints may be found by a fast algorithm for subgraph isomorphism; the structure can then be assigned to a family with a confidence value derived from the number of fingerprints found and their distribution in background proteins. In validation experiments, we infer the function of new members added to SCOP families and we discriminate between structurally similar, but functionally divergent TIM barrel families. We then apply our method to predict function for several structural genomics proteins, including orphan structures. Some predictions have been corroborated by other computational methods and some validated by subsequent functional characterization.

Bacterial Proteins↗

PubMed2000

Functional inferences from reconstructed evolutionary biology involving rectified databases--an evolutionarily grounded approach to functional genomics.

If bioinformatics tools are constructed to reproduce the natural, evolutionary history of the biosphere, they offer powerful approaches to some of the most difficult tasks in genomics, including the organization and retrieval of sequence data, the updating of massive genomic databases, the detection of database error, the assignment of introns, the prediction of protein conformation from protein sequences, the detection of distant homologs, the assignment of function to open reading frames, the identification of biochemical pathways from genomic data, and the construction of a comprehensive model correlating the history of biomolecules with the history of planet Earth.

Amino Acid Sequence↗

PubMed2001

Functional inferences vary with the method of analysis in fMRI.

Neuroanatomic substrates of specific cognitive functions have been inferred from anatomic distributions of activated pixels during fMRI studies. With declarative memory tasks, interest has focused on the extent to which various medial temporal lobe anatomic structures are activated while subjects encode new information. The aim of this project was to examine how commonly used variations in fMRI data processing methods affect the distribution of activation in anatomically defined medial temporal lobe regions of interest (ROIs) during a complex scene-encoding task. ROIs were drawn on an MRI anatomic template formed from 3D SPGR scans of eight subjects combined in Talairach space. Separate ROIs were drawn for the posterior and anterior hippocampal formation, parahippocampal gyrus, and entorhinal cortex. Twelve different activation maps were created for each subject by using four correlation coefficients and three cluster volumes. Friedman's two-way ANOVA by ranks was used to test the hypothesis that the distribution of activated pixels among defined anatomic ROIs varied as a function of the data processing method. By simply varying the combination of correlation coefficient and cluster volume, significantly different distributions of activation within named medial temporal lobe structures were obtained from the same fMRI datasets (P < 0.015; P < 0.001). The number of subjects studied (n = 8) is in a range commonly found in the literature yet this clearly resulted in spurious associations between processing parameter variations and activation distribution. Using data processing methods that are independent of the arbitrary selection of cutoff values for thresholding activation maps may reduce the likelihood of obtaining spurious results.

Adult↗

PubMed2004

Using human brain lesions to infer function: a relic from a past era in the fMRI age?

Recent technological advances, such as functional imaging techniques, allow neuroscientists to measure and localize brain activity in healthy individuals. These techniques avoid many of the limitations of the traditional method for inferring brain function, which relies on examining patients with brain lesions. This has fueled the zeitgeist that the classical lesion method is an inferior and perhaps obsolescent technique. However, although the lesion method has important weaknesses, we argue that it complements the newer activation methods (and their weaknesses). Furthermore, recent developments can address many of the criticisms of the lesion method. Patients with brain lesions provide a unique window into brain function, and this approach will fill an important niche in future research.

Brain↗

PubMed2003

Inferring functional constraints and divergence in protein families using 3D mapping of phylogenetic information.

Comparative sequence analysis has been used to study specific questions about the structure and function of proteins for many years. Here we propose a knowledge-based framework in which the maximum likelihood rate of evolution is used to quantify the level of constraint on the identity of a site. We demonstrate that site-rate mapping on 3D structures using datasets of rhodopsin-like G-protein receptors and alpha- and beta-tubulins provides an excellent tool for pinpointing the functional features shared between orthologous and paralogous proteins. In addition, functional divergence within protein families can be inferred by examining the differences in the site rates, the differences in the chemical properties of the side chains or amino acid usage between aligned sites. Two novel analytical methods are introduced to characterize rate- independent functional divergence. These are tested using a dataset of two classes of HMG-CoA reductases for which only one class can perform both the forward and reverse reaction. We show that functionally divergent sites occur in a cluster of sites interacting with the catalytic residues and that this information should facilitate the design of experimental strategies to directly test functional properties of residues.

Animals↗

PubMed2005

Estimation of amino acid residue substitution rates at local spatial regions and application in protein function inference: a Bayesian Monte Carlo approach.

The amino acid sequences of proteins provide rich information for inferring distant phylogenetic relationships and for predicting protein functions. Estimating the rate matrix of residue substitutions from amino acid sequences is also important because the rate matrix can be used to develop scoring matrices for sequence alignment. Here we use a continuous time Markov process to model the substitution rates of residues and develop a Bayesian Markov chain Monte Carlo method for rate estimation. We validate our method using simulated artificial protein sequences. Because different local regions such as binding surfaces and the protein interior core experience different selection pressures due to functional or stability constraints, we use our method to estimate the substitution rates of local regions. Our results show that the substitution rates are very different for residues in the buried core and residues on the solvent-exposed surfaces. In addition, the rest of the proteins on the binding surfaces also have very different substitution rates from residues. Based on these findings, we further develop a method for protein function prediction by surface matching using scoring matrices derived from estimated substitution rates for residues located on the binding surfaces. We show with examples that our method is effective in identifying functionally related proteins that have overall low sequence identity, a task known to be very challenging.

Amino Acid Substitution↗