Dataset Information

Coevolution in defining the functional specificity.

ABSTRACT: Covariation between sites can arise due to a common evolutionary history. At the same time, structure and function of proteins play significant role in evolvability of different sites that are not directly connected with the common ancestry. The nature of forces which cause residues to coevolve is still not thoroughly understood, it is especially not clear how coevolutionary processes are related to functional diversification within protein families. We analyzed both functional and structural factors that might cause covariation of specificity determinants and showed that they more often participate in coevolutionary relationships with each other and other sites compared with functional sites and those sites that are not under strong functional constraints. We also found that protein sites with higher number of coevolutionary connections with other sites have a tendency to evolve slower. Our results indicate that in some cases coevolutionary connections exist between specificity sites that are located far away in space but are under similar functional constraints. Such correlated changes and compensations can be realized through the stepwise coevolutionary processes which in turn can shed light on the mechanisms of functional diversification.

SUBMITTER: Chakrabarti S

PROVIDER: S-EPMC2649964 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Coevolution in defining the functional specificity.

Chakrabarti Saikat S Panchenko Anna R AR

Proteins 20090401 1

Covariation between sites can arise due to a common evolutionary history. At the same time, structure and function of proteins play significant role in evolvability of different sites that are not directly connected with the common ancestry. The nature of forces which cause residues to coevolve is still not thoroughly understood, it is especially not clear how coevolutionary processes are related to functional diversification within protein families. We analyzed both functional and structural fa ...[more]

PMID: 18831050

Similar Datasets

Project description:Protein-carbohydrate interactions are significant in a wide range of biological processes, disruption of which has been implicated in many different diseases. The capability of glycan-binding proteins (GBPs) to specifically bind to the corresponding glycans allows GBPs to be utilized in glycan biomarker detection or conversely to serve as targets for therapeutic intervention. However, understanding the structural origins of GBP specificity has proven to be challenging due to their typically low binding affinities (mM) and their potential to display broad or complex specificities. Here we perform molecular dynamics (MD) simulations and post-MD energy analyses with the Poisson-Boltzmann and generalized Born solvent models (MM-PB/GBSA) of the Erythrina cristagalli lectin (ECL) with its known ligands, and with new cocrystal structures reported herein. While each MM-PB/GBSA parametrization resulted in different estimates of the desolvation free energy, general trends emerged that permit us to define GBP binding preferences in terms of ligand substructure specificity. Additionally, we have further decomposed the theoretical interaction energies into contributions made between chemically relevant functional groups. Based on these contributions, the functional groups in each ligand can be assembled into a pharmacophore comprised of groups that are either critical for binding, or enhance binding, or are noninteracting. It is revealed that the pharmacophore for ECL consists of the galactopyranose (Gal) ring atoms along with C6 and the O3 and O4 hydroxyl groups. This approach provides a convenient method for identifying and quantifying the glycan pharmacophore and provides a novel method for interpreting glycan specificity that is independent of residue-level glycan nomenclature. A pharmacophore approach to defining specificity is readily transferable to molecular design software and, therefore, may be particularly useful in designing therapeutics (glycomimetics) that target GBPs.

Project description:BackgroundA large panel of methods exists that aim to identify residues with critical impact on protein function based on evolutionary signals, sequence and structure information. However, it is not clear to what extent these different methods overlap, and if any of the methods have higher predictive potential compared to others when it comes to, in particular, the identification of catalytic residues (CR) in proteins. Using a large set of enzymatic protein families and measures based on different evolutionary signals, we sought to break up the different components of the information content within a multiple sequence alignment to investigate their predictive potential and degree of overlap.ResultsOur results demonstrate that the different methods included in the benchmark in general can be divided into three groups with a limited mutual overlap. One group containing real-value Evolutionary Trace (rvET) methods and conservation, another containing mutual information (MI) methods, and the last containing methods designed explicitly for the identification of specificity determining positions (SDPs): integer-value Evolutionary Trace (ivET), SDPfox, and XDET. In terms of prediction of CR, we find using a proximity score integrating structural information (as the sum of the scores of residues located within a given distance of the residue in question) that only the methods from the first two groups displayed a reliable performance. Next, we investigated to what degree proximity scores for conservation, rvET and cumulative MI (cMI) provide complementary information capable of improving the performance for CR identification. We found that integrating conservation with proximity scores for rvET and cMI achieved the highest performance. The proximity conservation score contained no complementary information when integrated with proximity rvET. Moreover, the signal from rvET provided only a limited gain in predictive performance when integrated with mutual information and conservation proximity scores. Combined, these observations demonstrate that the rvET and cMI scores add complementary information to the prediction system.ConclusionsThis work contributes to the understanding of the different signals of evolution and also shows that it is possible to improve the detection of catalytic residues by integrating structural and higher order sequence evolutionary information with sequence conservation.

Dataset Information

Coevolution in defining the functional specificity.

Publications

Coevolution in defining the functional specificity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets