58,896 research outputs found
Methods for protein complex prediction and their contributions towards understanding the organization, function and dynamics of complexes
Complexes of physically interacting proteins constitute fundamental
functional units responsible for driving biological processes within cells. A
faithful reconstruction of the entire set of complexes is therefore essential
to understand the functional organization of cells. In this review, we discuss
the key contributions of computational methods developed till date
(approximately between 2003 and 2015) for identifying complexes from the
network of interacting proteins (PPI network). We evaluate in depth the
performance of these methods on PPI datasets from yeast, and highlight
challenges faced by these methods, in particular detection of sparse and small
or sub- complexes and discerning of overlapping complexes. We describe methods
for integrating diverse information including expression profiles and 3D
structures of proteins with PPI networks to understand the dynamics of complex
formation, for instance, of time-based assembly of complex subunits and
formation of fuzzy complexes from intrinsically disordered proteins. Finally,
we discuss methods for identifying dysfunctional complexes in human diseases,
an application that is proving invaluable to understand disease mechanisms and
to discover novel therapeutic targets. We hope this review aptly commemorates a
decade of research on computational prediction of complexes and constitutes a
valuable reference for further advancements in this exciting area.Comment: 1 Tabl
Global Functional Atlas of \u3cem\u3eEscherichia coli\u3c/em\u3e Encompassing Previously Uncharacterized Proteins
One-third of the 4,225 protein-coding genes of Escherichia coli K-12 remain functionally unannotated (orphans). Many map to distant clades such as Archaea, suggesting involvement in basic prokaryotic traits, whereas others appear restricted to E. coli, including pathogenic strains. To elucidate the orphansâ biological roles, we performed an extensive proteomic survey using affinity-tagged E. coli strains and generated comprehensive genomic context inferences to derive a high-confidence compendium for virtually the entire proteome consisting of 5,993 putative physical interactions and 74,776 putative functional associations, most of which are novel. Clustering of the respective probabilistic networks revealed putative orphan membership in discrete multiprotein complexes and functional modules together with annotated gene products, whereas a machine-learning strategy based on network integration implicated the orphans in specific biological processes. We provide additional experimental evidence supporting orphan participation in protein synthesis, amino acid metabolism, biofilm formation, motility, and assembly of the bacterial cell envelope. This resource provides a âsystems-wideâ functional blueprint of a model microbe, with insights into the biological and evolutionary significance of previously uncharacterized proteins
Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks
The idea of 'date' and 'party' hubs has been influential in the study of
protein-protein interaction networks. Date hubs display low co-expression with
their partners, whilst party hubs have high co-expression. It was proposed that
party hubs are local coordinators whereas date hubs are global connectors. Here
we show that the reported importance of date hubs to network connectivity can
in fact be attributed to a tiny subset of them. Crucially, these few, extremely
central, hubs do not display particularly low expression correlation,
undermining the idea of a link between this quantity and hub function. The
date/party distinction was originally motivated by an approximately bimodal
distribution of hub co-expression; we show that this feature is not always
robust to methodological changes. Additionally, topological properties of hubs
do not in general correlate with co-expression. Thus, we suggest that a
date/party dichotomy is not meaningful and it might be more useful to conceive
of roles for protein-protein interactions rather than individual proteins. We
find significant correlations between interaction centrality and the functional
similarity of the interacting proteins.Comment: 27 pages, 5 main figures, 4 supplementary figure
Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes
We propose a novel stochastic global optimization algorithm with applications to the refinement stage of protein docking prediction methods. Our approach can process conformations sampled from multiple clusters, each roughly corresponding to a different binding energy funnel. These clusters are obtained using a density-based clustering method. In each cluster, we identify a smooth âpermissiveâ subspace which avoids high-energy barriers and then underestimate the binding energy function using general convex polynomials in this subspace. We use the underestimator to bias sampling towards its global minimum. Sampling and subspace underestimation are repeated several times and the conformations sampled at the last iteration form a refined ensemble. We report computational results on a comprehensive benchmark of 224 protein complexes, establishing that our refined ensemble significantly improves the quality of the conformations of the original set given to the algorithm. We also devise a method to enhance the ensemble from which near-native models are selected.Published versio
Uncovering the overlapping community structure of complex networks in nature and society
Many complex systems in nature and society can be described in terms of
networks capturing the intricate web of connections among the units they are
made of. A key question is how to interpret the global organization of such
networks as the coexistence of their structural subunits (communities)
associated with more highly interconnected parts. Identifying these a priori
unknown building blocks (such as functionally related proteins, industrial
sectors and groups of people) is crucial to the understanding of the structural
and functional properties of networks. The existing deterministic methods used
for large networks find separated communities, whereas most of the actual
networks are made of highly overlapping cohesive groups of nodes. Here we
introduce an approach to analysing the main statistical features of the
interwoven sets of overlapping communities that makes a step towards uncovering
the modular structure of complex systems. After defining a set of new
characteristic quantities for the statistics of communities, we apply an
efficient technique for exploring overlapping communities on a large scale. We
find that overlaps are significant, and the distributions we introduce reveal
universal features of networks. Our studies of collaboration, word-association
and protein interaction graphs show that the web of communities has non-trivial
correlations and specific scaling properties.Comment: The free academic research software, CFinder, used for the
publication is available at the website of the publication:
http://angel.elte.hu/clusterin
Bridging topological and functional information in protein interaction networks by short loops profiling
Protein-protein interaction networks (PPINs) have been employed to identify potential novel interconnections between proteins as well as crucial cellular functions. In this study we identify fundamental principles of PPIN topologies by analysing network motifs of short loops, which are small cyclic interactions of between 3 and 6 proteins. We compared 30 PPINs with corresponding randomised null models and examined the occurrence of common biological functions in loops extracted from a cross-validated high-confidence dataset of 622 human protein complexes. We demonstrate that loops are an intrinsic feature of PPINs and that specific cell functions are predominantly performed by loops of different lengths. Topologically, we find that loops are strongly related to the accuracy of PPINs and define a core of interactions with high resilience. The identification of this core and the analysis of loop composition are promising tools to assess PPIN quality and to uncover possible biases from experimental detection methods. More than 96% of loops share at least one biological function, with enrichment of cellular functions related to mRNA metabolic processing and the cell cycle. Our analyses suggest that these motifs can be used in the design of targeted experiments for functional phenotype detection.This research was supported by the Biotechnology and Biological Sciences Research Council (BB/H018409/1 to AP, ACCC and FF, and BB/J016284/1 to NSBT) and by the Leukaemia & Lymphoma Research (to NSBT and FF). SSC is funded by a Leukaemia & Lymphoma Research Gordon Piller PhD Studentship
Biophysical and electrochemical studies of protein-nucleic acid interactions
This review is devoted to biophysical and electrochemical methods used for studying protein-nucleic acid (NA) interactions. The importance of NA structure and protein-NA recognition for essential cellular processes, such as replication or transcription, is discussed to provide background for description of a range of biophysical chemistry methods that are applied to study a wide scope of protein-DNA and protein-RNA complexes. These techniques employ different detection principles with specific advantages and limitations and are often combined as mutually complementary approaches to provide a complete description of the interactions. Electrochemical methods have proven to be of great utility in such studies because they provide sensitive measurements and can be combined with other approaches that facilitate the protein-NA interactions. Recent applications of electrochemical methods in studies of protein-NA interactions are discussed in detail
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