Dataset Information

Evolution-Based Functional Decomposition of Proteins.

ABSTRACT: The essential biological properties of proteins-folding, biochemical activities, and the capacity to adapt-arise from the global pattern of interactions between amino acid residues. The statistical coupling analysis (SCA) is an approach to defining this pattern that involves the study of amino acid coevolution in an ensemble of sequences comprising a protein family. This approach indicates a functional architecture within proteins in which the basic units are coupled networks of amino acids termed sectors. This evolution-based decomposition has potential for new understandings of the structural basis for protein function. To facilitate its usage, we present here the principles and practice of the SCA and introduce new methods for sector analysis in a python-based software package (pySCA). We show that the pattern of amino acid interactions within sectors is linked to the divergence of functional lineages in a multiple sequence alignment-a model for how sector properties might be differentially tuned in members of a protein family. This work provides new tools for studying proteins and for generally testing the concept of sectors as the principal units of function and adaptive variation.

SUBMITTER: Rivoire O

PROVIDER: S-EPMC4890866 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evolution-Based Functional Decomposition of Proteins.

Rivoire Olivier O Reynolds Kimberly A KA Ranganathan Rama R

PLoS computational biology 20160602 6

The essential biological properties of proteins-folding, biochemical activities, and the capacity to adapt-arise from the global pattern of interactions between amino acid residues. The statistical coupling analysis (SCA) is an approach to defining this pattern that involves the study of amino acid coevolution in an ensemble of sequences comprising a protein family. This approach indicates a functional architecture within proteins in which the basic units are coupled networks of amino acids term ...[more]

PMID: 27254668

Similar Datasets

Project description:Hill numbers (or the "effective number of species") are increasingly used to characterize species diversity of an assemblage. This work extends Hill numbers to incorporate species pairwise functional distances calculated from species traits. We derive a parametric class of functional Hill numbers, which quantify "the effective number of equally abundant and (functionally) equally distinct species" in an assemblage. We also propose a class of mean functional diversity (per species), which quantifies the effective sum of functional distances between a fixed species to all other species. The product of the functional Hill number and the mean functional diversity thus quantifies the (total) functional diversity, i.e., the effective total distance between species of the assemblage. The three measures (functional Hill numbers, mean functional diversity and total functional diversity) quantify different aspects of species trait space, and all are based on species abundance and species pairwise functional distances. When all species are equally distinct, our functional Hill numbers reduce to ordinary Hill numbers. When species abundances are not considered or species are equally abundant, our total functional diversity reduces to the sum of all pairwise distances between species of an assemblage. The functional Hill numbers and the mean functional diversity both satisfy a replication principle, implying the total functional diversity satisfies a quadratic replication principle. When there are multiple assemblages defined by the investigator, each of the three measures of the pooled assemblage (gamma) can be multiplicatively decomposed into alpha and beta components, and the two components are independent. The resulting beta component measures pure functional differentiation among assemblages and can be further transformed to obtain several classes of normalized functional similarity (or differentiation) measures, including N-assemblage functional generalizations of the classic Jaccard, Sørensen, Horn and Morisita-Horn similarity indices. The proposed measures are applied to artificial and real data for illustration.

Project description:Organellar gene expression (OGE) is crucial for plant development, photosynthesis, and respiration, but our understanding of the mechanisms that control it is still relatively poor. Thus, OGE requires various nucleus-encoded proteins that promote transcription, splicing, trimming, and editing of organellar RNAs, and regulate translation. In metazoans, proteins of the mitochondrial Transcription tERmination Factor (mTERF) family interact with the mitochondrial chromosome and regulate transcriptional initiation and termination. Sequencing of the Arabidopsis thaliana genome led to the identification of a diversified MTERF gene family but, in contrast to mammalian mTERFs, knowledge about the function of these proteins in photosynthetic organisms is scarce. In this hypothesis article, I show that tandem duplications and one block duplication contributed to the large number of MTERF genes in A. thaliana, and propose that the expansion of the family is related to the evolution of land plants. The MTERF genes-especially the duplicated genes-display a number of distinct mRNA accumulation patterns, suggesting functional diversification of mTERF proteins to increase adaptability to environmental changes. Indeed, hypothetical functions for the different mTERF proteins can be predicted using co-expression analysis and gene ontology (GO) annotations. On this basis, mTERF proteins can be sorted into five groups. Members of the "chloroplast" and "chloroplast-associated" clusters are principally involved in chloroplast gene expression, embryogenesis, and protein catabolism, while representatives of the "mitochondrial" cluster seem to participate in DNA and RNA metabolism in that organelle. Moreover, members of the "mitochondrion-associated" cluster and the "low expression" group may act in the nucleus and/or the cytosol. As proteins involved in OGE and presumably nuclear gene expression (NGE), mTERFs are ideal candidates for the coordination of the expression of organelle and nuclear genomes.

Project description:The rise of neuroimaging in research and clinical practice, together with the development of new machine learning techniques has strongly encouraged the Computer Aided Diagnosis (CAD) of different diseases and disorders. However, these algorithms are often tested in proprietary datasets to which the access is limited and, therefore, a direct comparison between CAD procedures is not possible. Furthermore, the sample size is often small for developing accurate machine learning methods. Multi-center initiatives are currently a very useful, although limited, tool in the recruitment of large populations and standardization of CAD evaluation. Conversely, we propose a brain image synthesis procedure intended to generate a new image set that share characteristics with an original one. Our system focuses on nuclear imaging modalities such as PET or SPECT brain images. We analyze the dataset by applying PCA to the original dataset, and then model the distribution of samples in the projected eigenbrain space using a Probability Density Function (PDF) estimator. Once the model has been built, we can generate new coordinates on the eigenbrain space belonging to the same class, which can be then projected back to the image space. The system has been evaluated on different functional neuroimaging datasets assessing the: resemblance of the synthetic images with the original ones, the differences between them, their generalization ability and the independence of the synthetic dataset with respect to the original. The synthetic images maintain the differences between groups found at the original dataset, with no significant differences when comparing them to real-world samples. Furthermore, they featured a similar performance and generalization capability to that of the original dataset. These results prove that these images are suitable for standardizing the evaluation of CAD pipelines, and providing data augmentation in machine learning systems -e.g. in deep learning-, or even to train future professionals at medical school.

Dataset Information

Evolution-Based Functional Decomposition of Proteins.

Publications

Evolution-Based Functional Decomposition of Proteins.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets