Dataset Information

Conservation weighting functions enable covariance analyses to detect functionally important amino acids.

ABSTRACT: The explosive growth in the number of protein sequences gives rise to the possibility of using the natural variation in sequences of homologous proteins to find residues that control different protein phenotypes. Because in many cases different phenotypes are each controlled by a group of residues, the mutations that separate one version of a phenotype from another will be correlated. Here we incorporate biological knowledge about protein phenotypes and their variability in the sequence alignment of interest into algorithms that detect correlated mutations, improving their ability to detect the residues that control those phenotypes. We demonstrate the power of this approach using simulations and recent experimental data. Applying these principles to the protein families encoded by Dscam and Protocadherin allows us to make testable predictions about the residues that dictate the specificity of molecular interactions.

SUBMITTER: Colwell LJ

PROVIDER: S-EPMC4224327 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Conservation weighting functions enable covariance analyses to detect functionally important amino acids.

Colwell Lucy J LJ Brenner Michael P MP Murray Andrew W AW

PloS one 20141107 11

The explosive growth in the number of protein sequences gives rise to the possibility of using the natural variation in sequences of homologous proteins to find residues that control different protein phenotypes. Because in many cases different phenotypes are each controlled by a group of residues, the mutations that separate one version of a phenotype from another will be correlated. Here we incorporate biological knowledge about protein phenotypes and their variability in the sequence alignmen ...[more]

PMID: 25379728

Similar Datasets

Project description:BackgroundWell-performing automated protein function recognition approaches usually comprise several complementary techniques. Beside constructing better consensus, their predictive power can be improved by either adding or refining independent modules that explore orthogonal features of proteins. In this work, we demonstrated how the exploration of global atomic distributions can be used to indicate functionally important residues.ResultsUsing a set of carefully selected globular proteins, we parametrized continuous probability density functions describing preferred central distances of individual protein atoms. Relative preferred burials were estimated using mixture models of radial density functions dependent on the amino acid composition of a protein under consideration. The unexpectedness of extraordinary locations of atoms was evaluated in the information-theoretic manner and used directly for the identification of key amino acids. In the validation study, we tested capabilities of a tool built upon our approach, called SurpResi, by searching for binding sites interacting with ligands. The tool indicated multiple candidate sites achieving success rates comparable to several geometric methods. We also showed that the unexpectedness is a property of regions involved in protein-protein interactions, and thus can be used for the ranking of protein docking predictions. The computational approach implemented in this work is freely available via a Web interface at http://www.bioinformatics.org/surpresi.ConclusionsProbabilistic analysis of atomic central distances in globular proteins is capable of capturing distinct orientational preferences of amino acids as resulting from different sizes, charges and hydrophobic characters of their side chains. When idealized spatial preferences can be inferred from the sole amino acid composition of a protein, residues located in hydrophobically unfavorable environments can be easily detected. Such residues turn out to be often directly involved in binding ligands or interfacing with other proteins.

Project description:The H1N1 subtype of influenza A virus has caused two of the four documented pandemics and is responsible for seasonal epidemic outbreaks, presenting a continuous threat to public health. Co-circulating antigenically divergent influenza strains significantly complicates vaccine development and use. Here, by combining evolutionary, structural, functional, and population information about the H1N1 proteome, we seek to answer two questions: (1) do residues on the protein surfaces evolve faster than the protein core residues consistently across all proteins that constitute the influenza proteome? and (2) in spite of the rapid evolution of surface residues in influenza proteins, are there any protein regions on the protein surface that do not evolve? To answer these questions, we first built phylogenetically-aware models of the patterns of surface and interior substitutions. Employing these models, we found a single coherent pattern of faster evolution on the protein surfaces that characterizes all influenza proteins. The pattern is consistent with the events of inter-species reassortment, the worldwide introduction of the flu vaccine in the early 80's, as well as the differences caused by the geographic origins of the virus. Next, we developed an automated computational pipeline to comprehensively detect regions of the protein surface residues that were 100% conserved over multiple years and in multiple host species. We identified conserved regions on the surface of 10 influenza proteins spread across all avian, swine, and human strains; with the exception of a small group of isolated strains that affected the conservation of three proteins. Surprisingly, these regions were also unaffected by genetic variation in the pandemic 2009 H1N1 viral population data obtained from deep sequencing experiments. Finally, the conserved regions were intrinsically related to the intra-viral macromolecular interaction interfaces. Our study may provide further insights towards the identification of novel protein targets for influenza antivirals.

Project description:Response regulators undergo regulated phosphorylation and dephosphorylation at conserved aspartic acid residues in bacterial signal transduction systems. OmpR is a winged helix-turnhelix DNA-binding protein that functions as a global regulator in bacteria and is also important in pathogenesis. A detailed mechanistic picture of how OmpR binds to DNA and activates transcription is lacking. We used NMR spectroscopy to solve the solution structure of the C-terminal domain of OmpR (OmpR(C)) and to analyze the chemical shift changes that occur upon DNA binding. There is little overlap in the interaction surface with residues of PhoB that were reportedly involved in protein/protein interactions in its head-to-tail dimer. Multiple factors account for the lack of overlap. One is that the spacing between the OmpR half-sites is shorter than observed with PhoB, requiring the arrangement of the two OmpR molecules to be different from that of the PhoB dimer on DNA. A second is the demonstration herein that OmpR can bind to its high affinity site as a monomer. As a result, OmpR(C) appears to be capable of adopting alternative orientations depending on the precise base composition of the binding site, which also contributes to the lack of overlap. In the presence of DNA, chemical shift changes occur in OmpR in the recognition alpha-helix 3, the loop between beta-strand 4 and alpha-helix 1, and the loop between beta-strands 5 and 6. DNA contact residues are Val(203) (T), Arg(207) (G), and Arg(209) (phosphate backbone). Our results suggest that OmpR binds to DNA as a monomer and then forms a symmetric or asymmetric dimer, depending on the binding site. We propose that during activation OmpR binds to DNA and undergoes a conformational change that promotes phosphorylation of the N-terminal receiver domain, the receiver domains dimerize, and then the second monomer binds to DNA. The flexible linker of OmpR enables the second monomer to bind in multiple orientations (head-to-tail and head-to-head), depending on the specific DNA contacts.

Dataset Information

Conservation weighting functions enable covariance analyses to detect functionally important amino acids.

Publications

Conservation weighting functions enable covariance analyses to detect functionally important amino acids.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets