Large-scale prediction of protein-protein interactions from structures

Background  

The prediction of protein-protein interactions is an important step toward the elucidation of protein functions and the understanding of the molecular mechanisms inside the cell. While experimental methods for identifying these interactions remain costly and often noisy, the increasing quantity of solved 3D protein structures suggests that in silico methods to predict interactions between two protein structures will play an increasingly important role in screening candidate interacting pairs. Approaches using the knowledge of the structure are presumably more accurate than those based on sequence only. Approaches based on docking protein structures solve a variant of this problem, but these methods remain very computationally intensive and will not scale in the near future to the detection of interactions at the level of an interactome, involving millions of candidate pairs of proteins.     

Structural assembly of two-domain proteins by rigid-body docking

Background  

Modelling proteins with multiple domains is one of the central challenges in Structural Biology. Although homology modelling has successfully been applied for prediction of protein structures, very often domain-domain interactions cannot be inferred from the structures of homologues and their prediction requiresab initiomethods. Here we present a new structural prediction approach for modelling two-domain proteins based on rigid-body domain-domain docking.     

Structure-function evolution of the Transforming acidic coiled coil genes revealed by analysis of phylogenetically diverse organisms

Background  

Examination of ancient gene families can provide an insight into how the evolution of gene structure can relate to function. Functional homologs of the evolutionarily conserved transforming acidic coiled coil (TACC) gene family are present in organisms from yeast to man. However, correlations between functional interactions and the evolution of these proteins have yet to be determined.     

Predicting direct protein interactions from affinity purification mass spectrometry data

Background  

Affinity purification followed by mass spectrometry identification (AP-MS) is an increasingly popular approach to observe protein-protein interactions (PPI) in vivo. One drawback of AP-MS, however, is that it is prone to detecting indirect interactions mixed with direct physical interactions. Therefore, the ability to distinguish direct interactions from indirect ones is of much interest.     

Reuse of structural domain–domain interactions in protein networks

Background  

Protein interactions are thought to be largely mediated by interactions between structural domains. Databases such as iPfam relate interactions in protein structures to known domain families. Here, we investigate how the domain interactions from the iPfam database are distributed in protein interactions taken from the HPRD, MPact, BioGRID, DIP and IntAct databases.     

A candidate gene association study on muscat flavor in grapevine (<Emphasis Type="Italic">Vitis vinifera</Emphasis>L.)

Background  

The sweet, floral flavor typical of Muscat varieties (Muscats), due to high levels of monoterpenoids (geraniol, linalool and nerol), is highly distinct and has been greatly appreciated both in table grapes and in wine since ancient times. Muscat flavor determination in grape (Vitis vinifera L.) has up to now been studied by evaluating monoterpenoid levels through QTL analysis. These studies have revealed co-localization of 1-deoxy-D-xylulose 5-phosphate synthase (VvDXS) with the major QTL positioned on chromosome 5.     

Fast dynamics perturbation analysis for prediction of protein functional sites

Background  

We present a fast version of the dynamics perturbation analysis (DPA) algorithm to predict functional sites in protein structures. The original DPA algorithm finds regions in proteins where interactions cause a large change in the protein conformational distribution, as measured using the relative entropy D _x . Such regions are associated with functional sites.     

<Emphasis Type="Italic">Filifactor alocis</Emphasis> - involvement in periodontal biofilms

Background  

Bacteria in periodontal pockets develop complex sessile communities that attach to the tooth surface. These highly dynamic microfloral environments challenge both clinicians and researchers alike. The exploration of structural organisation and bacterial interactions within these biofilms is critically important for a thorough understanding of periodontal disease. In recent years, Filifactor alocis, a fastidious, Gram-positive, obligately anaerobic rod was repeatedly identified in periodontal lesions using DNA-based methods. It has been suggested to be a marker for periodontal deterioration. The present study investigated the epidemiology of F. alocis in periodontal pockets and analysed the spatial arrangement and architectural role of the organism in in vivo grown subgingival biofilms.     

Implications for domain fusion protein-protein interactions based on structural information

Background  

Several in silico methods exist that were developed to predict protein interactions from the copious amount of genomic and proteomic data. One of these methods is Domain Fusion, which has proven to be effective in predicting functional links between proteins.     

The origin and evolution of the ribosome

Background  

The origin and early evolution of the active site of the ribosome can be elucidated through an analysis of the ribosomal proteins' taxonomic block structures and their RNA interactions. Comparison between the two subunits, exploiting the detailed three-dimensional structures of the bacterial and archaeal ribosomes, is especially informative.     

Prediction of indirect interactions in proteins

Background  

Both direct and indirect interactions determine molecular recognition of ligands by proteins. Indirect interactions can be defined as effects on recognition controlled from distant sites in the proteins, e.g. by changes in protein conformation and mobility, whereas direct interactions occur in close proximity of the protein's amino acids and the ligand. Molecular recognition is traditionally studied using three-dimensional methods, but with such techniques it is difficult to predict the effects caused by mutational changes of amino acids located far away from the ligand-binding site. We recently developed an approach, proteochemometrics, to the study of molecular recognition that models the chemical effects involved in the recognition of ligands by proteins using statistical sampling and mathematical modelling.     

Biotic interactions influence the projected distribution of a specialist mammal under climate change

Aim

To measure the effects of including biotic interactions on climate‐based species distribution models (SDMs) used to predict distribution shifts under climate change. We evaluated the performance of distribution models for an endangered marsupial, the northern bettong (Bettongia tropica), comparing models that used only climate variables with models that also took into account biotic interactions.

Location

North‐east Queensland, Australia.

Methods

We developed separate climate‐based distribution models for the northern bettong, its two main resources and a competitor species. We then constructed models for the northern bettong by including climate suitability estimates for the resources and competitor as additional predictor variables to make climate + resource and climate + resource + competition models. We projected these models onto seven future climate scenarios and compared predictions of northern bettong distribution made by these differently structured models, using a ‘global’ metric, the I similarity statistic, to measure overlap in distribution and a ‘local’ metric to identify where predictions differed significantly.

Results

Inclusion of food resource biotic interactions improved model performance. Over moderate climate changes, up to 3.0 °C of warming, the climate‐only model for the northern bettong gave similar predictions of distribution to the more complex models including interactions, with differences only at the margins of predicted distributions. For climate changes beyond 3.0 °C, model predictions diverged significantly. The interactive model predicted less contraction of distribution than the simpler climate‐only model.

Main conclusions

Distribution models that account for interactions with other species, in particular direct resources, improve model predictions in the present‐day climate. For larger climate changes, shifts in distribution of interacting species cause predictions of interactive models to diverge from climate‐only models. Incorporating interactions with other species in SDMs may be needed for long‐term prediction of changes in distribution of species under climate change, particularly for specialized species strongly dependent on a small number of biotic interactions.     

Stability of gene contributions and identification of outliers in multivariate analysis of microarray data

Background  

Multivariate ordination methods are powerful tools for the exploration of complex data structures present in microarray data. These methods have several advantages compared to common gene-by-gene approaches. However, due to their exploratory nature, multivariate ordination methods do not allow direct statistical testing of the stability of genes.     

Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine

Background  

MicroRNAs (miRNAs) are a group of short (~22 nt) non-coding RNAs that play important regulatory roles. MiRNA precursors (pre-miRNAs) are characterized by their hairpin structures. However, a large amount of similar hairpins can be folded in many genomes. Almost all current methods for computational prediction of miRNAs use comparative genomic approaches to identify putative pre-miRNAs from candidate hairpins. Ab initio method for distinguishing pre-miRNAs from sequence segments with pre-miRNA-like hairpin structures is lacking. Being able to classify real vs. pseudo pre-miRNAs is important both for understanding of the nature of miRNAs and for developing ab initio prediction methods that can discovery new miRNAs without known homology.     

Modeling of cell signaling pathways in macrophages by semantic networks

Background  

Substantial amounts of data on cell signaling, metabolic, gene regulatory and other biological pathways have been accumulated in literature and electronic databases. Conventionally, this information is stored in the form of pathway diagrams and can be characterized as highly "compartmental" (i.e. individual pathways are not connected into more general networks). Current approaches for representing pathways are limited in their capacity to model molecular interactions in their spatial and temporal context. Moreover, the critical knowledge of cause-effect relationships among signaling events is not reflected by most conventional approaches for manipulating pathways.     

MAPPER: a search engine for the computational identification of putative transcription factor binding sites in multiple genomes

Background  

Cis-regulatory modules are combinations of regulatory elements occurring in close proximity to each other that control the spatial and temporal expression of genes. The ability to identify them in a genome-wide manner depends on the availability of accurate models and of search methods able to detect putative regulatory elements with enhanced sensitivity and specificity.     

Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset

Background  

Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN) that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge.