43,318 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 orphans’ 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 “systems-wide” functional blueprint of a model microbe, with insights into the biological and evolutionary significance of previously uncharacterized proteins
Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems
A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a
predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the
Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in
Computational Biology.Peer ReviewedPostprint (author's final draft
Quantitative methods for reconstructing protein-protein interaction histories
Protein-protein interactions (PPIs) are vital for the function of a cell and the
evolution of these interactions produce much of the evolution of phenotype of an
organism. However, as the evolutionary process cannot be observed, methods are
required to infer evolution from existing data. An understanding of the resulting
evolutionary relationships between species can then provide information for PPI
prediction and function assignment.
This thesis further develops and applies the interaction tree method for modelling
PPI evolution within and between protein families. In this approach, a
phylogeny of the protein family/ies of interest is used to explicitly construct a history
of duplication and specification events. Given a model relating sequence change
in this phylogeny to the probability of a rewiring event occurring, this method can
then infer probabilities of interaction between the ancestral proteins described in
the phylogeny.
It is shown that the method can be adapted to infer the evolution of PPIs
within obligate protein complexes, using a large set of such complexes to validate
this application. This approach is then applied to reconstruct the history of the
proteasome complex, using x-ray crystallography structures of the complex as
input, with validation to show its utility in predicting present day complexes for
which we have no structural data.
The methodology is then adapted for application to transient PPIs. It is shown
that the approach used in the previous chapter is inadequate here and a new scoring
system is described based on a likelihood score of interaction. The predictive ability
of this score is shown in predicting known two component systems in bacteria and
its use in an interaction tree setting is demonstrated through inference of the
interaction history between the histidine kinase and response regulator proteins
responsible for sporulation onset in a set of bacteria.
This thesis demonstrates that with suitable modifications the interaction tree
approach is widely applicable to modelling PPI evolution and also, importantly,
predicting existing PPIs. This demonstrates the need to incorporate phylogenetic
data in to methods of predicting PPIs and gives some measure of the benefit in
doing so
Specialized dynamical properties of promiscuous residues revealed by simulated conformational ensembles
The ability to interact with different partners is one of the most important features in proteins. Proteins that bind a large number of partners (hubs) have been often associated with intrinsic disorder. However, many examples exist of hubs with an ordered structure, and evidence of a general mechanism promoting promiscuity in ordered proteins is still elusive. An intriguing hypothesis is that promiscuous binding sites have specific dynamical properties, distinct from the rest of the interface and pre-existing in the protein isolated state. Here, we present the first comprehensive study of the intrinsic dynamics of promiscuous residues in a large protein data set. Different computational methods, from coarse-grained elastic models to geometry-based sampling methods and to full-atom Molecular Dynamics simulations, were used to generate conformational ensembles for the isolated proteins. The flexibility and dynamic correlations of interface residues with a different degree of binding promiscuity were calculated and compared considering side chain and backbone motions, the latter both on a local and on a global scale. The study revealed that (a) promiscuous residues tend to be more flexible than nonpromiscuous ones, (b) this additional flexibility has a higher degree of organization, and (c) evolutionary conservation and binding promiscuity have opposite effects on intrinsic dynamics. Findings on simulated ensembles were also validated on ensembles of experimental structures extracted from the Protein Data Bank (PDB). Additionally, the low occurrence of single nucleotide polymorphisms observed for promiscuous residues indicated a tendency to preserve binding diversity at these positions. A case study on two ubiquitin-like proteins exemplifies how binding promiscuity in evolutionary related proteins can be modulated by the fine-tuning of the interface dynamics. The interplay between promiscuity and flexibility highlighted here can inspire new directions in protein-protein interaction prediction and design methods. © 2013 American Chemical Society
Inter-protein sequence co-evolution predicts known physical interactions in bacterial ribosomes and the trp operon
Interaction between proteins is a fundamental mechanism that underlies
virtually all biological processes. Many important interactions are conserved
across a large variety of species. The need to maintain interaction leads to a
high degree of co-evolution between residues in the interface between partner
proteins. The inference of protein-protein interaction networks from the
rapidly growing sequence databases is one of the most formidable tasks in
systems biology today. We propose here a novel approach based on the
Direct-Coupling Analysis of the co-evolution between inter-protein residue
pairs. We use ribosomal and trp operon proteins as test cases: For the small
resp. large ribosomal subunit our approach predicts protein-interaction
partners at a true-positive rate of 70% resp. 90% within the first 10
predictions, with areas of 0.69 resp. 0.81 under the ROC curves for all
predictions. In the trp operon, it assigns the two largest interaction scores
to the only two interactions experimentally known. On the level of residue
interactions we show that for both the small and the large ribosomal subunit
our approach predicts interacting residues in the system with a true positive
rate of 60% and 85% in the first 20 predictions. We use artificial data to show
that the performance of our approach depends crucially on the size of the joint
multiple sequence alignments and analyze how many sequences would be necessary
for a perfect prediction if the sequences were sampled from the same model that
we use for prediction. Given the performance of our approach on the test data
we speculate that it can be used to detect new interactions, especially in the
light of the rapid growth of available sequence data
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